diff --git a/NiBabel.ipynb b/NiBabel.ipynb new file mode 100644 index 00000000..844f8759 --- /dev/null +++ b/NiBabel.ipynb @@ -0,0 +1,3029 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# NiBabel\n", + "\n", + "
\n", + "\n", + "## Neuroimaging data and file structures in Python\n", + "\n", + "###### Christopher J Markiewicz\n", + "\n", + "###### NeuroHackademy 2020" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "The goal of this presentation is to familiarize you with some broad classes of neuroimaging data that can be interacted with in Python, using the NiBabel library.\n", + "\n", + "This document is intended to be viewed as a [RISE](https://rise.readthedocs.io/) presentation. It works fine as a notebook, but blocks with images may look strange because they are formatted to work as slides." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# NiBabel\n", + "\n", + "NiBabel is a low-level Python library that gives access to a variety of imaging formats, with a particular focus on providing a common interface to the various volumetric formats produced by scanners and used in common neuroimaging toolkits:\n", + "\n", + "| | | |\n", + "|:---: |:---: |:---:|\n", + "| NIfTI-1 | NIfTI-2 | MGH |\n", + "| MINC 1.0 | MINC 2.0 | AFNI BRIK/HEAD |\n", + "| ANALYZE | SPM99 ANALYZE | SPM2 ANALYZE |\n", + "| DICOM | PAR/REC | ECAT | \n", + "\n", + "It also supports surface file formats:\n", + "\n", + "| | |\n", + "|:--:|:--:|\n", + "| GIFTI | FreeSurfer (FS) geometry |\n", + "|FS labels | FS annotations |\n", + "\n", + "Tractography files:\n", + "\n", + "| | |\n", + "|:--:|:--:|\n", + "| TrackVis (TRK) | MRtrix (TCK) |\n", + "\n", + "As well as the CIFTI-2 format for composite volume/surface data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Installation\n", + "\n", + "NiBabel is available on [PyPI](https://pypi.org/project/nibabel/):\n", + "\n", + "```Shell\n", + "pip install nibabel\n", + "```\n", + "\n", + "And [conda-forge](https://anaconda.org/conda-forge/nibabel):\n", + "\n", + "```Shell\n", + "conda install -c conda-forge nibabel\n", + "```\n", + "\n", + "*Note*: This notebook assumes NiBabel 3+, which requires a minimum Python version of 3.5." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.1.1\n" + ] + } + ], + "source": [ + "import nibabel as nb\n", + "print(nb.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "# Some additional, useful imports\n", + "from pathlib import Path # Combine path elements with /\n", + "from pprint import pprint # Pretty-printing\n", + "\n", + "import numpy as np # Numeric Python\n", + "from matplotlib import pyplot as plt # Matlab-ish plotting commands\n", + "from nilearn import plotting as nlp # Nice neuroimage plotting\n", + "import transforms3d # Work with affine algebra\n", + "from scipy import ndimage as ndi # Operate on N-dimensional images\n", + "import nibabel.testing # For fetching test data\n", + "\n", + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Assume we're on the NeuroHackademy hub.\n", + "data_dir = Path('/home/jovyan/data')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Learning objectives\n", + "\n", + "1. Be able to load and save different types of files in NiBabel\n", + "1. Become familiar with the `SpatialImage` API and identify its components\n", + "1. Understand the differences between array and proxy images\n", + "1. Acquire a passing familiarity with the structures of surface images, CIFTI-2 files, and tractograms" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Basic I/O\n", + "\n", + "### Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "t1w = nb.load(data_dir / 'openneuro/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz')\n", + "bold = nb.load(data_dir / 'openneuro/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "data shape (256, 156, 256)\n", + "affine: \n", + "[[ 9.99131918e-01 -5.16291820e-02 1.25127016e-02 -1.25263863e+02]\n", + " [ 4.07721959e-02 1.29202044e+00 -9.81179178e-02 -7.31330109e+01]\n", + " [-8.54506902e-03 1.28044292e-01 9.95096147e-01 -1.77554291e+02]\n", + " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n", + "metadata:\n", + " object, endian='<'\n", + "sizeof_hdr : 348\n", + "data_type : b''\n", + "db_name : b''\n", + "extents : 0\n", + "session_error : 0\n", + "regular : b'r'\n", + "dim_info : 0\n", + "dim : [ 3 256 156 256 1 1 1 1]\n", + "intent_p1 : 0.0\n", + "intent_p2 : 0.0\n", + "intent_p3 : 0.0\n", + "intent_code : none\n", + "datatype : float32\n", + "bitpix : 32\n", + "slice_start : 0\n", + "pixdim : [1. 1. 1.2993759 1. 0.009668 0. 0.\n", + " 0. ]\n", + "vox_offset : 0.0\n", + "scl_slope : nan\n", + "scl_inter : nan\n", + "slice_end : 0\n", + "slice_code : unknown\n", + "xyzt_units : 10\n", + "cal_max : 0.0\n", + "cal_min : 0.0\n", + "slice_duration : 0.0\n", + "toffset : 0.0\n", + "glmax : 0\n", + "glmin : 0\n", + "descrip : b'FSL5.0'\n", + "aux_file : b''\n", + "qform_code : scanner\n", + "sform_code : scanner\n", + "quatern_b : 0.049235623\n", + "quatern_c : 0.005271982\n", + "quatern_d : 0.020155331\n", + "qoffset_x : -125.26386\n", + "qoffset_y : -73.13301\n", + "qoffset_z : -177.55429\n", + "srow_x : [ 9.9913192e-01 -5.1629182e-02 1.2512702e-02 -1.2526386e+02]\n", + "srow_y : [ 4.0772196e-02 1.2920204e+00 -9.8117918e-02 -7.3133011e+01]\n", + "srow_z : [-8.5450690e-03 1.2804429e-01 9.9509615e-01 -1.7755429e+02]\n", + "intent_name : b''\n", + "magic : b'n+1'\n" + ] + } + ], + "source": [ + "print(t1w)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Saving\n", + "\n", + "All NiBabel images have a `.to_filename()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "t1w.to_filename('/tmp/img.nii.gz')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "`nibabel.save` will attempt to convert to a reasonable image type, if the extension doesn't match:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "nb.save(t1w, '/tmp/img.mgz')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Some image types separate header and data into two separate images. Saving to either filename will generate both." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "data shape (256, 156, 256)\n", + "affine: \n", + "[[ 9.99131918e-01 -5.16291820e-02 1.25127016e-02 -1.25263863e+02]\n", + " [ 4.07721959e-02 1.29202044e+00 -9.81179178e-02 -7.31330109e+01]\n", + " [-8.54506902e-03 1.28044292e-01 9.95096147e-01 -1.77554291e+02]\n", + " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n", + "metadata:\n", + " object, endian='<'\n", + "sizeof_hdr : 348\n", + "data_type : b''\n", + "db_name : b''\n", + "extents : 0\n", + "session_error : 0\n", + "regular : b'r'\n", + "dim_info : 0\n", + "dim : [ 3 256 156 256 1 1 1 1]\n", + "intent_p1 : 0.0\n", + "intent_p2 : 0.0\n", + "intent_p3 : 0.0\n", + "intent_code : none\n", + "datatype : float32\n", + "bitpix : 32\n", + "slice_start : 0\n", + "pixdim : [1. 1. 1.2993759 1. 1. 1. 1.\n", + " 1. ]\n", + "vox_offset : 0.0\n", + "scl_slope : nan\n", + "scl_inter : nan\n", + "slice_end : 0\n", + "slice_code : unknown\n", + "xyzt_units : 10\n", + "cal_max : 0.0\n", + "cal_min : 0.0\n", + "slice_duration : 0.0\n", + "toffset : 0.0\n", + "glmax : 0\n", + "glmin : 0\n", + "descrip : b'FSL5.0'\n", + "aux_file : b''\n", + "qform_code : scanner\n", + "sform_code : scanner\n", + "quatern_b : 0.049235623\n", + "quatern_c : 0.005271982\n", + "quatern_d : 0.020155331\n", + "qoffset_x : -125.26386\n", + "qoffset_y : -73.13301\n", + "qoffset_z : -177.55429\n", + "srow_x : [ 9.9913192e-01 -5.1629182e-02 1.2512702e-02 -1.2526386e+02]\n", + "srow_y : [ 4.0772196e-02 1.2920204e+00 -9.8117918e-02 -7.3133011e+01]\n", + "srow_z : [-8.5450690e-03 1.2804429e-01 9.9509615e-01 -1.7755429e+02]\n", + "intent_name : b''\n", + "magic : b'ni1'\n" + ] + } + ], + "source": [ + "nb.save(t1w, '/tmp/img.img')\n", + "print(nb.load('/tmp/img.hdr'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Serialization\n", + "\n", + "Some NiBabel images can be serialized to and deserialized from byte strings, for performing stream-based I/O." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'\\\\\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00r\\x00\\x03\\x00\\x00\\x01\\x9c\\x00\\x00\\x01\\x01\\x00\\x01\\x00\\x01\\x00\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x10\\x00 \\x00\\x00\\x00\\x00\\x00\\x80?\\x00\\x00\\x80?\\xf3Q\\xa6?\\x00\\x00\\x80?\\x88f\\x1e<\\x00\\x00\\x00\\x00'\n" + ] + } + ], + "source": [ + "bstr = t1w.to_bytes()\n", + "print(bstr[:100])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "new_t1w = nb.Nifti1Image.from_bytes(bstr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Images that save to single files can generally be serialized. NIfTI-1/2, GIFTI and MGH are currently supported." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Spatial Images\n", + "\n", + "For MRI studies, neuroimaging data is typically acquired as one or more *volumes*, a regular grid of values. NiBabel represents these data as *spatial images*, a structure containing three things:\n", + "\n", + "1. The image *data* array: a 3D or 4D array of image data\n", + "1. An *affine* matrix: 4x4 array relating voxel coordinates and \"world\" coordinates\n", + "1. Image *metadata*: usually a format-specific header\n", + "\n", + "Many file types encode this basic structure. NiBabel will read any of ANALYZE (plain, SPM99, SPM2 and later), NIfTI-1, NIfTI-2, MINC 1.0, MINC 2.0, AFNI BRIK/HEAD, MGH, ECAT, DICOM and Philips PAR/REC, and expose a simple interface:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data = t1w.get_fdata()\n", + "affine = t1w.affine\n", + "header = t1w.header" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Spatial images have some properties that should be familiar from NumPy arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "(256, 156, 256)\n" + ] + } + ], + "source": [ + "print(t1w.ndim)\n", + "print(t1w.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### The data array\n", + "\n", + "The data is a simple NumPy array. It can be accessed, sliced and generally manipulated as you would any array:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(256, 156, 256)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(data.shape)\n", + "i, j, k = np.array(data.shape) // 2\n", + "fig, axes = plt.subplots(1, 3)\n", + "axes[0].imshow(data[i,:,:].T, cmap='Greys_r', origin='lower')\n", + "axes[1].imshow(data[:,j,:].T, cmap='Greys_r', origin='lower')\n", + "_ = axes[2].imshow(data[:,:,k].T, cmap='Greys_r', origin='lower')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each location in the image data array is a *voxel* (pixel with a volume), and can be referred to with *voxel coordinates* (array indices).\n", + "\n", + "This is a natural way to describe a block of data, but is practically meaningless with regard to anatomy." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "NiBabel has a basic viewer that scales voxels to reflect their size and labels orientations." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+H1BrskLqJLL92mZez51r2KcESRoN0LlEQ9LsE1eDxesSUPGztofn4m84d28UneAcryDKZXz4XFXzqcw52aV6vW7n02dwtdumBT8MZ2low9WBbDT5cxC4rIOrheHLqgNbvTfNbgC86eZAEzzRdEJj2IM6FLbT9QafTCRbLpcRjUbXvXxPds/ufWhKPu9D71t/Ty8QaIan3Bo8/LvrabHNLv3sUtbuedgulw7X3xL4Ksvhpokro+O+1Pos+f+qHVCBM/VXrr7BDSm4/eDqsNgmNWpBGAKJxWKe40ulkgcMsU9d8KSLhgInFTjTC+f3Le/uqTMyAnyvyYLweXA+4vvGhY9/59jmgucykzyvevlkN13gwHAHRc0ESsr+6NgGmg6bZmwp8CDg4pxGoOMyJBrq0d8q+FJ9kLIeypRspGuxOaMO0ywq68r7YHgK8Kbw61xJsMm5gNfXMBePV5ZNQQiPZUhK59aNfkdTR4/PRsOlHA9cA3kuN1zP82u9Mp0D+Kzr9YacQec/BbtXq21a8OOK/YCN9SsKKpQaJvJ1mR2lMTXrZqOBrd69elL8Pc/FQcrFkB6aG+pQ5kdfWk4AvEYoFEIkEvEUT9SXx03pV+BEwKh6HZcmDYVCKNWbIFGFvBqCUeGdnoumsWWgycwRVDBGrSCBfeLqVXTB4Llcr25tbc1ABCdZd1J1J0L2v/5L0/4vlUqeQpEKQDih8L5Um8Dz6hhV1k4nL32WOhaUmUskEp4sRbbT1bnptRT4sq2At6p0y76/pqEjBctcEBnG4rzExYyMDZ8j32X+PxdkBfPKQCj40bRvLsjaJsAbbnYBBsERf6vvBxdxOlb6Pc+rITc3bEyWZXV11QPQyEjo3KMMDedFXn8jphvwZv7y3VQ21mVrdJ5X0KIhL2WW9L1WJl/1NG7/cT50Q3b6PDjXF4tFm890jGwEypRdJquj7LTP5zMJgjs3KLi62m3Tgh8O9Fgs5ol1K0hwTYEQwyRKAwJN9kG9NP5WBbP64gPNmirK1rjmMg0K4FwgxZeEbXJFfqpfcdkVoLmw6USqv9WFTycMDZmxX9i/9EL0GWifss3Kcqg2SQEB74v9qtdjOxXUBYNBrK6uevqM2Us8l3vfG2VJ8aVWoKSxeH5Xr68Xr7senT5rBRUKoNkm/r9687wvnWw1zZftVlCpoQIer21xWUD3OSvAd8dMy75/poujG/bhe6XOmbLUQPO5a/hIGWG+W5zXOPbVqeG/XIiDwaAtihrSJuNCxkEZWQUwLLap4FzHOu/RfW84X6nTxPtznR7OEQRDGu7S/7R9Pp8PdTTBBo9RPZyeh21QVsrV3ChgU6dLs/h4j+ps6/2wnxhG1H7Qd1J/Uy6Xba1w5wK9X2VrFEipM85nzhAe+5VzKceXEgpXs21a8NOylrWsZT8opuFa1evogqNiV9W2AF5RL7+no8dFnc4XGRIusAqmgWaCxMrKiuecbCdDtspWuSFkfiZYiEQinlCaq0tRtoQMhoIeshm8F4ILZbZ4jwpEKEfQPnL7XNlf1dQQ7GwUnmPfM5Sk7LeyLdoXrhPK81Nnx/PRSeK5VYIANMNTBKkEy8oQ8fxst27Ro+waz0VwoyBaWT2g6fTp/V7NtmnBD+uVaNjFUL9MNPrZDdHovxQWKruijIiic37mb2j60irD4db7oY6D53OF2WQRlFFR0aumUNI2ipnzXKptcl9y0q/aZ7wXHu9WJyZ7wvi+skYamuLEpCEbjbe7jIlOaq6min3mUu0a3uOLrW1V1kn/dal3YL3mSIsaKvOm1L77O17DZc80zKghCo5hTZOPRCIePYcuTq5GS38DwBY0/p33ynNpuG6j8EDLvj/GhZFjRdkRXYR1HOiYdkPhKozWeU/Py3NqYoSOf55br6miXl2kVZukqetu2E11l/qeBwIBu45mJur9uFogzZrS0hxuyEznBL/fb3oftlvHv76XCgS0f3g9DVUrI+KWQdHSIypcV4ZYw4/6vJTJ4zzP8L+CTp0DeB1lcziG9D75N2V83SiAhgxV89OyTQx+NMarC5tqSPjyayiLxpin7nEFrF8odDLZqGKwu6jpi6kASycZntudlBR4uJOP0rtumMxdqPVa1WoVkUjEgAp/r4upvsjsM50QVMviLuQKwnifOsFon7jhNoo7tSK3UreubkABIvtfgZcWj1Svl/3igiGXqlawoM9DvWxgvVaGfahjR8edu+jofbJfaa43pvS79ps7pl3qH4BpBXjMysqKlYNo2VNrfK9cp0rHOZ0FFeOqM6HhCgWu9Ng1dVsZCg23ayiH281oureyBCo21v80WYQLPXVoqh0BvDXAyEzpws93RYEA5xGCATporvOjizSP9/l8VqOM7VUHQgGPAkNd/N05R4XXmoavYbyNnGoeSzClzhmfJ7+nw7OysuIJY3EMaPhOgQ+vpd/ruNI+okyAbVYQ+2SO99Vsmxb8KFugf9NFSwVstO+m03AfusZR+SLSw+YkxsGlynu2S4EVKU5+3ki4py+OLtz8ztWv6GKrC62+YC5rpGI6oMGoRCIRj/C7Wq2iVl+/QSvvQ/cUe7K4OY9XcONOYJwgFJTqy6ovr1Ll+lnpaO0j3qdSyapNUEYLgOfZsR2ugFn7VNvtTnwEfBtlTLhsE0MMeg6XgWL7AVhWhkv1UyvCcch7ou6K98TCckBL8PxUmi7WqvOgA1CvN9LL9dm7Amb9f4Y5XHCv8xvfRYIdjnGdCzTkwnlGt6HQuYpzEucdBVV6X/yb3icZCA3nadYRAGOVlAXSd4z36zpQytDo3MY2sc+UESG4q9frnrDSRgwN26/3p7odZXOVsQOa0QCdP9RZ17ZvpG1y1zntOz6rSqVi4nnNLNPjOZ/pPao+TPvNdTqvVtu04Ec9eT5ojSfzs3pJ+mIXi8V1L5fGtTfy0PVcbhzdjdtqpthGQIsvDE2zwziANTTiAgGNdSt9zLRpVyypL48rrGR72Y5AIADZ3cKzMGsKvxuDprmAVE03miXQ0slAr1kul61QJBcJrQmk3tpGBRZd0OKGKd10VxrHEK/l1nBif7ribP1enz8nSp5bw1Y6mWlbtA/VM6PH7jKMG4n8uahquJTH81m07Kkxzk2aNk0AQ0aZcxAXJA1j8BzKNqi2h+BJdSTKHOui7qa0K0ugbdDzs2yC3gPvw2V/XRCmpoyzy1S4jCffb3Wa9Dg3/GXzqQ/woZm1pe+crhcqDWAfKehSkMQ2A17xOgGLssduqHqj+yOjToeESS1k79QBdsuy8LoaJqQMhM4PwY6bYaj9r1mrBHLunHk126YFPzT3gaqegoNLX0yGf1zwoSgfaC6muqi4i5ZbL0fFbMokqCcDNEVzuii5i6UOQncxjEQidgzb7tLBCvjckI5S66rn4fGVSsUqPNdqNU+Kt77U7B/NuNK2EEDwPlV0x7YqU6Xt5sKtjJYey75WQMjj+L0+Y7/f7wGFzHLhsaqz4cRPtoXsmHppLjMUi8WsrXyeLEWg48bdykI9Un5WJkj7RO9b+8Ed49onbjjMnZBb9tSY1iQj+6LOjbI5GvZQZkJBC98HBSxqGz1b1QjRODdoSMoFRO477fM1hcdPBoT4dx1vnHvopLhaEwIrthXw6tZcTQv/c7UwJZQ874OGhQh0aAqQXIDgOmV6LXUsmKKvaeguW8c1gH3F+V5lFRwnCkx1buCzcit5s136DABYyJtjRwEkgRHvi+sXn02r+vsmBj988BvFyPnwWTtDXyg3lJRIJOxYXSiI/tVb1lR319snOt9o4eGAUiGwInkONpc90raql8OFW5kHBRScaPVe1Ht0KVh9waxP680FWhdu1e1wklNA6E6uPCeP1+/0d/xeJwHAKxant8TvdfLgxKrPSycgbsWh4T2d5HRiYx+wXe7WGRr/p+k+QZxYFKCwnWyDgk2XmdPNTnnfCqSU9eOzdatZs+80JObqoNwQXMu+v6bMDeAV2CrI1XcK8G7xovo//Y2KbnlOZQHU0XPD1brIu1o0nkuZFgUiLtvBNjGcq98/GRukwEPnSrZfxyxNQZkCmGCwsSmztlfBg/7niqcJSMgwa6FD7WPeK9cXrb7t6jjZFwQ+vCcydWSF1dlTEMi/89x0TjVEpiEsbnLM9YzzkEYTlM1j/ytgVUfvarZNC36UMlXvQhctjffyN+5D5cKg2QRAExRoWEXBjfvyuB6S21ZldjSllb/V0AgXeReZazFABQL6AquHw3a6gMPVqyjL4TJibn9uxCQ8mYBW7wloAkQFIPpMFJC4n91wEK+rGQp6H1z4FfiqTkoBCMMFPBez3xRQbgQadVwxS4OftR+13dxWQz8rcOE5XL2HXlsF7LoQAg1wpZXDtR28X2XyWvbUGBdNHce60akCEM4j6snr7xhCYzhU2Rq+U1yY3Wvy/XdZDZ2L+C6xHRsxGbwnF4zxnSFzoMwQ3xNtr2pl1HlVRov35Orc9H752UK5V6YpHqu/dbWgZELI4BB8KLvPa7ihL94PmWPek6uTCgaDiEajCAQCyOVyBk55LJ8XgQdBn7JWmiWsDhLvj/Md1yUdN4wWAE2WSR10Zch0jr6abdP2QLlctoHJxYMFoXRB1PCTDng+YC4UbkVLDl4320sXX9Vi8OXUwaQbx+kE5zILrgfunlt1MXrMRqCO59bF0C2IqAX+6AUo87C2tuYJez1Zu91r80XWduveXdp2oJk9Fo/HAcAj+HQLCyp7wnYpq8f+0ElXgZouIvzMfmcMXbVMOgG4wJYLgruAaIFFVw+mwFX7lOfltTip6bjiQgk0AAszQrStCiB1vOu/zO5xMxxb9v03LvIEvuo48X13QYEuRloVWLfo0efuhme0ergex2P4d2V33MxDmjICGsrWeYXtV10a30NlxN0tG5Td4LWq1eYWExzj/I/9yevzHsh6++BD3bee1VKWSBkyGoGRsqmaWaUMtf6rz4/riR6j86GGtJSlZt8rs6vaPo4ZF+TquqPzmV6Djh5ZQG6NxH3h2E6dS1us8CYGP4yvqjBQvSugKepyFxb+vzspuN68yxy4gEMnHw5y3UBOmQM3FKUDzA31cKJzX3SXCldxtb4kClBcapceigsa1tHKaAIaDU8pA0Vz26l9rKJa3pMu5GoKwlzhHbcH0QlDt/Hg5MPwkDJnep809Xp4Xa3C7KbgqwCa4Qc9v9LIq6ur69gabYe2TYG73sdGi5r2Na/l7rOmoI3jn0BXy0C07Kk3Fv1zq3grgNAFWkGzhkc4n3DMcu7RhZznpZPo1swhG6BiWp7HFRiznWQeXN2RhrU4Vjnv8PwbMbw6l9I49hX08N54DV5H2XgCI5+vUd0Z9aYDyvZtdC1+1krNDH2xXzRUyXMom0SgpptmK2vDe2Cf8L41+UND9sFg0Bx7BUQKjmnK2LFv3bApWUJqygjCFAjqnLZRmPFqtE0NflwKl4NdQzq60Oj/u4uT+yKqFwOsz7DSmCuAdQNbvRTSlO6A1Haura1ZZhP1KXqvbujEXVjVu1fvzRUe8l/3vhTEBYNBlFDyHMdzaR8w/KaTrjJe4XAYxWJx3QRO74j3r2Ea1xNTTYOyNfyb1kBRZs4FalwIVHelmX8a53afvRv752SkWYV8hvy9y+wpUFVmh2116Wd9XvSSeSwpej4DbUut1kxt1wmPnxVgtrK9nlpTZsXNPuKYocPAd4WLlZtQwXdanUCOeV0kVReiISAN5fCz6xBoSD8UCqFQKNg5NTTE9rvhsVqtZvXUOF9qmI/vjf5OHVnXsXSdAN6/hsYIaHxoAigCOp07FLjpO8e5ludRJttNMOG7xN9z7VGniOEm7Uu2n89Xf6+f9f1UzZA+ZxWg8/zK9vDZhEKhddqjjSIGbG+LFd7E4Icvkb6kGhfmZ134NbzAl0cXZvdY9wVU44BzF34FFeqBq1fE9rrMh96Hhm04KfD3Go5xr6v0MY/Vv7meA3d/1vusVqsWM3dT8JWupbnX1snCfYm1Ro3bds1g4/WU4nfDd7wfmmbf0WtWRkzvWxeitrY2j+BdPULtE46VjUJN7sTKkvb8XttVqTRT7jkJamFODd3y3rW20kYZiMpaKahWr1fZLfZvy546c/UpdM64gG7EDmioRBcl/objkiFNZrhyTOr45zgJBAImjNUQKtuowKRer1uoVtkMPS/gdUY5Jl2w5o59l7VU1lMZD96ntkEXdJ1zarVaY+66Mi3rlh+us6shM9XIKLhwyxFoezVcFY1G15UY0UwrvtMuuOJYUDDMvuWWIQRvHAecD/h7zqFcYxRUazYo71P1jsooueG3q902LfjRAesunu4ipzSjy3A8mVfMRUjPpaheX2Z+dkXKulAriODLqAueu6hpiEfj0Py9TiJ6bjIB+gIpQOBkoOdSIKCLvnvP9Nz0fO59KsPFmLmG2DQsyVCWZiPphMhnwutoeroey7a52iXVYKmwUtsCNMNUyiK5om+X9dN/ebwCTC5qfAaqBXAnFk72vBbg1YG5ixiBoJ5bM/Dc56N0uj7fFvPz1JmCCi5oymhUKhV7d3UsuUBcWRwFCPr+uowJv9MFT9kGoLl5MtDMNuN8xrlHF0men+fT8IsyDuoEqkhY70edCTckpuw526F9pgy8ghkfmskYKkTW67qiZJ5D703DhS5zpWwZQaLq6Wq1GorFogdY6BoAYN18qkYnSp+/Ou2VSsVTQoFrSjQaxerqqkdszr7XeUJDfurMKzC/mueITQt++ODo8QDrRbFKJwNe1sNlU1xq0w3nbEQLqxFNK3jShVgHMMNB6qG72V48J9ukYRfV7NA4obgeDillDcHoueg96WTzZOCRE6Srm1IwoABQj9N2EghonJuf2We6KSLQFLjrZE1vZ6M+JrDSEJsby9aJVcNcvK5OVAoi3MmK7eD31FhouFVNaeqN2DQFWu44dVlB3pt+r56phsSY1eaOr5Y9NUaATeDuCnrJAJIt1blJhcPqrfN7fU81m8nnawhcqf9yw1nq9JlmRkJRCgo2AgEaMtP5j78lKAiHw3Z/OqY3YsT1HnlNsvbKonJ8Kxiq1+uAD6jDq0PS/3SO5nkAb6iP90XHaCMmREXbhUJhnfPFftK/K2PDMJmuX+xnBXqcc1XvRWfbnbt5P0z80FA/n1epVFrHPPHZ8zo8Rwv8bEJzF3OgqadwgYYuxhyE0WjUoxHhb1yBsy50LhhyPTQFDrowczJymQPVcQDwoHIuVPxe28Jr8t+Nwjt6X8pquBOny/Qkk0nk8/l1ExLbpccrAAXWa5HouegiqxMk26MeiPaRAgLXc3LbrucBYHvY6H0rS6XsF+9NJ1/tM7bHDYXppKMeHCcnZYZc0O1qk/Raei+qr2Bfa9/w3Po8XUpbyzVoKnwL/Dx1VigUkEqlbHGjQJ5CW4ICshnK8ASDQVuQlCmhZ052gYupgnoCbS58GnLhtfR4Am+XQXVZcV2g9Z3kedQZdNkeBfYue0JGQ+cwHkPwxDbz3CwXYWtB3dsWNdc5JTtEJoj9w7XBdUrZhxrC5HkJbBW86nzGfmT/8TfaRp171CHT58i2qODbDVlpSNB91mwj50BlG922XM22aWfHjWrc8AFrOMkNyzCtulgserQWHDQuNaq/3YgJcicE3RdHwYkbJ3eBlTIq/L16IRpOIojT7CIXxOh5tA/4QqqGZKNK1aX6eo2Jz+fzAC0eyz7YSEOiv6fnoloo7WPVunAi0z7UhZ1ekobkFFgp1Qysr3TtanQ0VEgWxwXY2ocartPr6LNzPVS2U6/NPnJ1BAo+3eenz56TmNsP/E61CoA34+tqn9yeauNz5iagBCu66bDOMcoK8T1Th0mfs+7bRNBLcOHWFQO86dBc7LkQc+xTAMt3SUG2agk5f3Cs6lwWDoctCUPfF4IZBfKqWeE7pEzTk7E+ymYrGNAtOTSDltdU8O+CRII9DQepA8U+4XrBeU5BDsEc5ynOveqQsr+UuWV73DWA90VnyAWD1HtVq1UTeCuD5F5fQZCCTp1nr2bbtOCHg1B1IVyMOWC4QKn3r3ta1evNyrnqeQFN2lLP7S6+jNHzfAoEXBZIF0dNceS59V+XpYhEIutoVzdEpFoZBRxsm3p3yoBpvBxoZj7RSM/ynlSEzImRfegyCZyUlMFSgEkBs3vfbKcCPk5gGsJxJ3R6OOwflxHR+1a2JRKJIJ/PW0o4j2O7KpWKheF4bmUYtayB3rtOjC77qGCF98t2uuFTXpOfXaZI71PPyUlew5b6bFsT3FNrCiKAZih3o6QEXTgBeMaLLr66WHGe4TtL4MMQibIABAUE0Tq/aG0wzqEa2t6IWWYIZmVlxdqm7+5GCzDfEZbf0EVZxzSvSQDDY9X5Ul1bBY3/Z3iHgMBluVz2SvtWmdqNWFn9j/eo96rzs/5O30ce5zLHGk7TUJ2GP3lOndO0v5WR0j7Ta/DabgFfZcWuZtu04Gd1dRWRSMQjJtWKqUATIOkeTurt6uSvAwpYD3Y4eN0XUkNWLsukTI1egzT2k+2OzklCXyw32yAQaKaMx2IxD4hzF14ez37TODtfDLcej/aLMkyaccX71eN1Wwf+VsGNTrgbXY/35Aq1FTDx3Ap01evl9/QageZk5xZP5PPRc/Gz24cbAVu9LwVmGhZTIMrjXOpfReE8DmjuBaYTk06sriZBxyA9cP6WnrCCo5Y9daYshQIC9zkou0CHg+OAf3NDVLqIEbCorkbnDgIjfQ8JyFllmG1lG/n/ykzyvHTmdEHl+8L5gnOvMkQAbD7w+/2eIqcuGOGxyobpe8N+8/kadX7UmeB86oaTNZzGTCyt7eWGqzW8RICgQEXXAJ1f1KFT5k5BiZuMoHMZ0HSYtT/YJvY5Q6mcf9ySCarVcsGWyyq6UZCr0TYt+HE1G4BXrwNsPLm7qZeusFQZEfVwiLAVUevvXRpUBbAcyGQW1GMD4MkMUlOP3dXeUOkPNClstku3TFA62+0D/r8CGDuvREQIOLSgmrbF9Vi0D/UF4ourgmcKk9lPeh+uN+KGnnQS5WSoz0dBAZ8fF/6VlRWPR6ceORcC3jcXC2WGFOxw0XGvrW1T0eFGfajAVydBTs46absLpk5kqnHTiY6/VXbTZata9v03XfD4rLnQuOOKCyqz+zTtWlkBvic6F3E+IthnuFNZF/6nwIBOFhdyrRkFeLOu+HfWyNI9rnivGobiOZS14X3zPtUBoW5Pw2B8l9xyDrxvZUa0LVwv2G8KBnkfBJAugNNjlM3nHBQIBExUrhl7+iwUOPL3GoLivM3fapiL4IjPiHMsw3EEaRwXyjzxGavDy3HjgktllvRer1bb1OCH8XMdkIBXva9pwC7tp/oILghc8GKx2IYeur7AOqB0geNnNwSjXg0XSFq1Wl0XCtEJR4/nRKDgSLVGCnYCAW9KuEvjskaIqy+qhBttT6fTHlBQr9c9ads+n8+jWXBBhGZc8aXW3dI1rq0LP/vOBRw09XD4WfuE4TitzVOv11EsFq2d2id6TbZbJwtOInw+GkLihKfPW8GQS+Er/e6ygAoAtU+fTI/EyVL7zWXiaPQe9b5a9tSZ6zxouIXzBcceQyQKjnRvJ45JN4ytiynHI5mc1dVVz8LoOnQE+co8Ac1Qm85DCjB4Tt6HOzfq+NTfca7h9wrsS6WSx5HSxVxTshly4290TOt7x9/xum4mKgGpOnAEg+l02vqPv4vFYsaYuyCG5+Mz1vOqc6xAlyyNPgN9jm7YDvCGtFxm3GWUNGymjKM73yjT7GYUX222acEPH46+WETDXJRWVlY8KNzd00oXKI0rA/AslEBjsHPyANZX6uWkpQNGhaUKfhjSUcbDVfKrCNllmfg3vujRaNTCXuVyGclk0r5zgVQkEkF/f7/dWyaTQSqVMoCSSCTg8/lwf9v9AIBbb73VI2ReXV3FwsKCnbtWq2F6etr66Pz589YnxWLRw0qwTzTc5xr7gAuAAiM1aoAUvCr44YuuTI8LIHnPChy1HQqQ9DvqjXQiVwE0waduh6HAShkr/p5Gj1dDhxuF1LRdmiWnY5LtUmdAWb6rOY316TBlOLi4uzoTV8NSr9eRz+c9401ZXc5hXCg5B/FvZDD5G4Jtt6q6stacjxRYqA7E1aFxQSUDpCw678sFfWwfmWm+uwQ2LMKoIT4Cl3q9ufu5vu/qSPFvgLcis7Jc+g66TKwCPM6tbtiNjIuCIPcedU5n/7nOJPtagQp/o8fxHshgkynS5+PqVGkqeqf+ywXNPBcjES3ws0lNJ3Q+ZA3P6Hca6lKKVF94InwNVbmhDR1MbuiKL6uyN65n7YZwaG58V2O0QANw6EtTq9UQi8U8BbUSiYT9tq2tDZlMBgDQ0dHhWUwDgQC6u7tRKBQAAPF43FPltVKpIB6Pw+9rvhTMkOMLxs/d3d1YXFxEd3c3gMaknE6n7T4uXrzoiePT0yA45QvMtkQiEU8Wn76AnADYJ0rP8reuHklBh+oR+L1qHjiJabs2Aly8Nu+H51KAR+2EO/nyPjY6p4JUl8rX8UHwoyJ+Nf1MZ0AZQp2QNwKfLfv+2Ubhn42K8K2srNjfqPfhfOUu+HpuV++ojgffB+r2VNOhDBIXQbbVLR2iYS/VhjCcrVlVmjigjiXvRdvJ941sjYbOtY/4HZnetbU1T6iqWq1ahWdl610NH6/N//R+lW3ieRWwAfCw2ZyXNAwGNEt/uOwM4HVSXAaQbdJQv4Y5yb4TLNbrddtGhPfHZ8FSIwyHKzDl+fibjVjlq9k2Lfgh60OkStOdakmp6kvnMjMaWuIxwPowSqlUQjKZtIWaHjoByOrqKpLJ5DoalefUhffJwmDaFnfrDBdo+f1+JJNJAA22Zu/evQBgAI59Eo/Hkc1mPeeampry3Ec+n/cUxfL7/SiuNJih8+fPezIp8vm8AZypqSksLy+jo6PD+mhgYMBe8q1btyKXy9nv4/E4xsbG7D6npqZQKpXs2pw4eS4XVCj43EiUp//v8/k8RbrYx653y/tSZo0TLvuQabr8TKZNmR435LbR/ms8lwtAFMDznt2yBzqm3X7Q8K2rm3DDkPr5avfsnmqjVsV1dHRMkK3U56SOnjt2NLSjzhXDy3T+NIREU8BP8EPnhEwmmRm2k++cMg4ENlpMkQ4EF+Z6vW5hbg25cLzqNh9kq3if4XAYsVjMw+DwPmOxGEKhEFZWVrCysoJ6vY7F4CKCwSB6e3uxuLjo6V8ybwSYfN8IMBX46T3oMyGooCmg5W9V/KzMl/aZAh32MceA9g/7Xs+nEQcFK/qZ961zqIIpDcXzeP371T4/bFrwo2EvhrJyudw62tDV3vCFSiQSnrBWIBBAoVBYB17UM3cnD/WIAHjCYnyh9TgXhLmiPWUK1DvitXm+vr4+hEIh7Ny5EwDQ09Nj3y0vL2NxcdHaUS6XMTMzY0wQACwtLXkWW7/fb5uqdnZ2NqqV+hsTzNDQEObm5qz9yWTS+nN8fBwrKyvmBeXzeeTzebsvnnNwcBBAYxLbunUrtm7das/r9OnTHnqZv11eXsbExIQxWgAMsLEtGgNXIAk0tzpxAZHG4l3Aq0BXvaJKpWITO6+li47f7/eAI96Pai50wnbHjYIkl5LXFGXety5kOrG559J+4rEKtFxtUcu+v6YLkXrfWiQUwLqxoiFcLn4aygew7nsuZApYdBHnGGCqPffvUnCiYVNlR3TMaZYUi6O6Gh62j4u5WzeH12WIjSxIIpFAOp1GZ2cnkskkgsGgscOVSsUcp0AggGKxaPXO7k/fj1AohNtuuw0jIyOWkFAulxGPx1GtVjE5OYlisQifz+fR8gDeTCx9Hzk3abjPdS4IbthHfP+U9eO5NCSoz0ydKQWlbJs6O7wunyGPUY2RgjZl2XgdkgQqpKYDrXuVXW22acGPVqrN5/MAmsiXE0I8Hvc8PF3w3BCAxsmBJgNCwMKUemVvVldXPeJhrcqsi44ulEBTr8Jru5SoTnpAA6jF43H09fUBALZv345EIoHOzk4AwOLiIqampgAAly9f9rwc3CFeQ1eHDx+2a0WjUYTDYQtdsebGhdgFAMCznvUsAzEjIyMYHh7G5cuXATRYoXA4bDHxWq2GhYUF6/+FhQVks1lrG+9leXnZ2hKJRIxJisfjBnAOHTqEiYkJnDx5EkADKGmoj89FxdaaLsoXW0GClgtIJBI2btgPyqiod8dxxfvULBRgfYgBWC9yVg2Weq/MriOAJ3OpQnt6kuwzv9/v0bUpa6Vt0MVMf6tMXMueOuMCpfoOdXTohJD15DNWRoKMZSAQsHGoCxmPU1G+ggU9HmiyzGTQyRJxfnSvoWyIzpk6vjUkxvZSa6SLO68Ri8VQr9eRSCTQ1dWFrq4uhMNhRKNRtLW12V5VOt8y3KO11sgwBQNB+H1+Y+e56Wg4HEYmk4HP50N7ezvm5uasvQsLC7ZrPd9JOlAqlmbblbWhaehK33nVGSlrow6cO3e4OicdL0y+4Hjw+XwGXrmWqGPNY3Tt4rhiFEITSjYCWFejbVrwo/S/CxxonCR0EXM1IzS3YJ+GYHi87vytaYkbXcsVvymTQxqTL4fGZgHYRMAFb+/evchkMgZ+KpUKlpaWMD8/D6DB5GiRw+7ubo8GqLu727NwHzlyxCNwdoWCbW1t9vvdu3dbWCsWi2Hv3r32efv27R5QEI1Gce7cOSwtLdl9zc/PG1hKJpPIZrP2uVgsoqOjw0BIrVYzcDM5OYn29nYcOHDA2qWTH8/D0N/U1BR8Pp99LhQKHkDjhhA0E0w9Lj5btod9qiwSn50CFA1bcly4qfT8jkBMzZ38eO56ve7RH5muQUz1ZVpAkxMlv+NixHZd7ZPbU20cNwyzuM9RnTGaLo4uc0PwQfaGzAfPz0VRF0kNv6i5ehitAE3mgWCKzhvbp+F96nM41/J7NyMqHo8jmUwawBscHMTu3bvR2dmJarWK2dlZTE1NYWlpCfV63RhmsknZbNbmDYIbgqFisYhAMIBz584hl8vB52vsbxYKhbC4uIiOjg6kUikLKYXDYUsC4bu0srKCxcVFzM3NoVAoWPiPeht1LPifslucD1Rzp/O/6rwILpVZ5jn1magekEBSQ/90lPRcG7HMBDtM2uFxOgbcbYmuRvO5nbdZLJ1O14H18VbXNLtIRWEcpMoEKQjgC+yKAnXScCv7qifAeDTQZISebCsGvgQ68fT29uLo0aMAGmBkYWHBwnRzc3OYn583tqCtrc0mkeuuuw5ra2tob28H0BQRE5D4fD4cPXrU+iEcDmPnzp3IZrMAGmGuZDKJZ+FZAICv4qt2f+6iXy6XceHCBWvH8vIyTp06hdnZWetjoMnOhMNhzM/PY3x83P5eLpft2rlczsOAqHaJ90Fw09nZienpaTt+amoK5XIZjz76qF1Lxwbg3WpD2RSfz+cBGG7Y0aW9NUuHpt4b+0i1aHx2LpPD87lhLPXOdFy5GSFuxiMXPqCZgs9zsg/Z31eewQ9tvrvP59tUkxcdCmUBCC7IugDNyvTUK3JRdbMGAW9pBBXEAt5qw3pdzn08TtuhWiFlAhiSIviORCIGuDSzi5oazb5k4USOw6GhIWzdutUcPII3vp/FYhFTU1OYnJxEuVxGNBpFOp22c3NezefzKBaL8Pv91re5XA6P/3+PIxgM4ub/cjOWl5fN0eAeaMxwJaCKx+PGwiQSCSQSCRMNU0vEd2pqagpTU1MGJglUdO3Q0JECGL/fb7onzidcn1xpBteDQCDgSWzREJs6N8oGERCTuXLDpTrfcG3iM1N2KRAIeNjxHzSr1+v/prlt00I/eiE6OEjJKoOini+rG/M7XRw5CN3BpYsRRbRAMzyhx6vnRo8dWL/pqS6s/D4Wi9kLfPjwYWzdutXaOjo6ipmZGWNYAoEAenp6sH37dgCNyYSD+cCBA0YXA7BwGftkZmYGe/bs+Vf1uesJhMNh7Nu3z/O3w4cPm0aI2iNOHMvLyxgbG7NU+Wq1irm5OQNLoVAIMzMzABo6prNnz9rnUqlk3hjQmPgKhYKFbrq6uhCPxy2EFg6HMTY2Zv185swZD+B0Q5wKRoDGM+KzdoWDKojkfbietIbFtAglz6deoIJ2V2TICUuPcRnGarVqIFBDs9pGnktDrK6GrWXfX2PIhCBGFyyg+Ty0aCYdDn1WnEvUGdEwiWrbOFZVU6LFErnAqvaEx5TLZUsaUCfCDSnrHKqhHLI/gUAAiUQCu3fvxuDgINLpNOr1OpaWlpDP51GpVLCysoJisWjvWjAYxP79+zEwMIBkMmlhMN5LKpWybWny+bz1Sblcxkh6BLV6Dbt37/aw/eVyGYuLi1haWjLQsra2ZnMM741MEp2FtrY2pFIppNNpbN261YBZPp9HLpczhodtYwaasixAs4Ya55J63bs5KZ+X/sc5X5k0AlquYQSkqslS7ZTqjRQMqxNFJlETfdw58WqzTQt+WtaylrXsB8kU7CigUSeNoDgaja5jD5QJ4t/Vk1fxLRc6npM1htSJc7OeaKp31OxBV9fD3+l5V1ZWLAklkUggk8mgo6MD+/fvt9DS/Pw8pqenMTc3Z2Dd7/cjlUqht7cX0WgUPT09OHLkCIaHh7GysoJcLodIJGL6oFgshkwmg0qlgqmpKSwsLDSBVjKBWq2GQ4cOIRQKmZNcq9UwPz+Ps2fPYm5uzgBEqVRCqVRCLpdDNptFpVLB6uqqR1cUCoXQ39+PtrY2dHR0oK2tDaVSCdls1jSXmqWmQnFeR8N2fB4aclcQxGem4XI9F0EO/8b92MgYEVSp48TjGA5VvSvBJa/llna5Gm3Thr3a29vrnDT4kCKRiCeLikyP0sBEs2tra0gkEp5qxS5NqeEG9a6A5rYROjGod65F7+gNaUhNNSGpVAqBQACve93r7NoLCws4d+4cgAZjUqlUjBnatm0b+vr6LL19//79dq7u7m7LpvpebHp6GseOHUNPTw+ABqMyMjKCd971TgDAf/vyf/PobBKJhHky5XIZPT091kcdHR04fPiwJ1S1kWnRsEuXLtkzCAaD+PrXv279OTk5ac+rWCxicXHRCiouLCx4NFo+X6PW0cDAAABgeHgYU1NTSKVSAIBHHnkE4+PjFvLRCZ/ieS1w6eovNOTA62m4gfQ7gHX/qr5DU4F5La0mTd0GJyKGI3RsaQjVFZyqWJqTKcMELPSoda8KhUIr7PUUGZ8D4GX8XM9fw0gaAgGatWN0QdMwCwELz8tzKDOkLCUXTDIEblq4y1JreIzzI1kCshPUJw4MDKCzs9OKH66trWFpaQkzMzNYWlpCMBhEMplEZ2cnMpkM4vG4sSypVAo7duzAvn37kEwmLbOL7/NGxrZsFLIvl8uYm5vD2NgYTp8+jYmJCdTrdUuxX1lZwezsLLLZrGmXWINscXER2WzWss2oIYpGowgEAojH46jVGsVeJyYmUCwWsba2ZoUs2S73mbEPlTkmMK1Wq55wF/ua4Sw3BMaQWjQaXbfWcP7RsCTvkWNPIxcaytMQ+Q+a/VvDXpsW/PT09NQZ9sjlcgC88VJg/eaXrrZCv6O5WTOqA3E1JBo3d/tJj2V5eU1FDIfDJhzesmULnv/859uCd99998Hv92NiYgIA0N/f7xE89/T0NHQ5z3oWgAYY+peM53rwwQdx7tw53HfffQAak8Ljjz9uWprZ2VnkcjkUP9fQqLTd0+ZJMee9aJ8Q5GQyGfT391s7d+7ciS1bthgY27lzJ7q7u+2+v5vNzs7i4sWLdu2RkRH4fD489NBDABr6ifHxcUvhP3v2rGWRAY16R6VSCVu2bAHQ1BMxJEcxJf+fXiXQBCAahnDTwvV7Lgh8flzAOCaUPt4os0O1Z8zeUjG2qxdTAOOGxFw9kAvYdSsTn8+HfD7fAj9PkSUSCXs+ZDsIGqjrcZ0pN4QFNEOdrm4QWL+hrwqWuegS5GhIXzeF5rEqqmabVbgMNMH32toa0uk09uzZg+HhYaRSKWMjFhYWLLTd2dmJtrY2dHZ2YmBgwPRA0WgUuVwOwWAQhw4dwvXXX/+v7ueNwM9GNj8/j7m5OSwvL2NmZgYTExPI5XIol8soFoumxWGIbmJiAoVCAaurqyiVSgiHw1aDKJlMWjh6eXnZwvksRshnGwwGzTnkXECmTZlAfaZac4yfVcejrNFGjjafqTJ5HD8q0FaQphpCnf9/kOyHVvNTKBRMV7MRAgaa5f3dxQCAUY46SehCoZMJ0MyUURW9S1Nq+r27OHKiYzsjkQhuueUWAA1QUKlU8K1vfQtAQyvT3t6OI0eOAGgwKlu3bkV/fz+Ahq6HHtOTGRfLz3/+8/j85z+Pb3/72wCAsbExFItFY5GoD6KwLZlMIhaLYTIyCQA4ePCgfccaEVrcr1gs2suxvLxstX94fCgUQldXl+c+mMHV1dWF6667Dvv37wfQKIrI/uzu7rb0e55rbGzMnlc8Hsf9999v4CeRSCCVSllqPCdcei6RSASDg4MGzPbv34/R0VEAjcyy6elp0yJFo1GPzoFAVjP7GJbg9wpg2F79f2UBdXJxM/80tAHAhKa6Ka6CeHr9mn2jY1rHZCwW8xTPvNpp7afauEhRXKqaQS1voWyestYKXAB4gArQFPRrUgfDHDwHxcrKHDDEQpbHDc9x/vL7/Z5Chhw/iUQCO3fuNI1OvV5HLpczB4P32d/fj4MHD2Lbtm3o7u5GPB5HPp9HIBCwWj7/kiM3PT1toIXObzQaRT6fx/LyciNT7FmzqFVr+It//At0dXVhx44d6O/vt/mT1tnZ6ZlD8/k8FhYWMDIyYnpDv9+P9vZ2rKysIJ1O27s0Pj5uSQzZbBbLy8smnA6FQhgeHkZHRwcmJiawsLBgwEfT/MnyBAIBiyS422RoMUUVVCsQ5fuvlbB5HWXnNFTmMoqAF1S7IPpqtE3L/CSTyborJGYtHt0iQScHPVazwIDmnkuuGMzNDqNxEiLroUibptdl9hHb+dznPhc7duwA0GA1lpeX7fz0Ig4dOgSgUdRw//79Bhp0kXXtq1/9Kj7wgQ/g7NmzAIDTp09jbm7OrkUvj6BhYGDAYt1AIwRXr9fx6HsfBQC88e/eaNlZ8XjcXlCgAUAjkYjV8SFNTHaFmSDsIwoTCZbo0TDkNjQ0ZJPfS1/6Utx1110m3N7Ijh8/bvc5Pj5uqfVAA1idOXPGGK9Lly6hWq0aoOrp6TGQRtr6iSeeANColcRqsdrfbiq8fq/gh+OOx2sBMk46LrhWZkfHWSDgrSflCqt5DgXauveTZqEQaPF5rK2tIZfLtZifp9AYtnE1GwTQKlgFmhmluiC5rDQ/67jTOi0EL/x9LBYzhlHZWwJ6fqeOoIZwNZOro6MD1113HXbv3g2fz2dAIJ/PW2X5oaEhAyBkhDo7OzE0NITu7m5jnV379re/jc997nNWFV7Tz2dmZjyV6ak1CgaDyH06hzrqSL6kkUrPYon9/f0YGBjA4OAgdu3aheHhYWzduhXbtm1bN6eWSiUcP34cuVzOQM/Zs2eRzWYRDocxMzNjIfilpSVMTU2ZNgloOHJdXV3I5/OYmJjAzMwMstmsaYKU1VMmkJ8ZqtKsY7J4fCYa5dDf6TYWvI5GKdysM4Jfps1rGj6f6Q+i/dCGvdrb2+tM5WUbtb4CzV0sNLauTA7BkGot1AiMdNFSRbzW0wBg3hXQ3DeM373yla9Ed3c3Tpw4AaABIug9AQ1Asrq6imuuuQYA8KIXvei79sXjjz+O97///QCAf/iHf8D09LQBjHQ6jWg0auwKhXe81uDgIEZGRnDmzBkAjbT5arWK039xGgDwCx/9BVuYE4kEtmzZYqBheXkZyWTSGJOpqSkkk0lcvHgRQINlGhsbQ29vL4DGJLG8vLyObldBJ0NR5XIZe/bsMfbr9ttvx/Oe9zwLYz1ZP5w+3Wh3PB7H1NQUxsbGADT2GZuamrLJSSsyJxIJ9PX1WTvy+Twee+wxC6OVy2UkEgn7bbFY9LAznDBc0E3Aoh6WskBAsyaQZm8BzXG6EUujFLkb/lCmR0MS+lm9veXl5Rb4eQqtra3No8UBmuFz1beRsdGyCpohxPCJZpTqv/obLeiq+hHOVxyn3P8JaI43asYoAvb7/Ray37Jli4W58vk8zp8/j7m5OdTrdXR0dKC3txeZTAadnZ2WndXT04Ndu3Y9acbp/Pw8PvCBD+BTn/oUTp48ieXlZdNnaqXn1dVVLC4uolwuIxaLIZ1O2/x//q/OAz6g7yf6rOo0Q1la36yvrw/d3d2Wej88PIwjR47gpptuMv3o8vIyIpEIkskkVldXMT4+jvHxcYyMjGB6etqY74WFBQNmBEjt7e2Ix+MIBBq7B4yNjWF8fNyiFW44U43PSCu6q96Kv1cHmwUpCQQ1OsH70bkCaGaXKjhSsKxlUn7Q7Ic27MWXVkGKu4AQ3Cg65gTjxq7JQqhXtVGhJ/XudQC78Vq9VjweRzwex+233w6gUbTwxIkT1l56RAQJe/bswZYtWwz8PJl95StfAQC8+93vxje+8Q0ADc9SQ0b05nQfsAsXLqwraMXv2X4yFWNjY8YSFQoFxGIxY1fW1tYwPj5u6edTU1Nob2+3MBmrshJE1Ot1ZDIZT6FJxvr5mXqgUCiEc+fOGav00Y9+FLt378Yzn/lMAI1w3HOe8xxL9+ffDh48aNf68pe/7IlXDwwMGFNUKBRMh1Aul1EqlYxl6u3ttUwRoKHBmpiYME1QKpXybIVCMKJgiPfD73WSIQhhO9XB0AWRbYtEIh76W8cdJziajjs3BV+zdIBWkcOnwwj4GaLQBYqfyWC4YSzAK8anFo2/0bAVxc9Akw1U7Y4KcckIVioVC7+o2JmghwBj69atOHz4MDo7O1Gv1zExMYHz589jdXUVQ0NDGBwcRGdnJ3p7ew3E9fb24pprrjGWV21ychKf/exn8Y1vfAMPPvggTp8+jWq1ip6eHuzbt8+0Kul02pjQy5cvY3l5GalUysLUDJ9NxCcQCATwjGc8wyofl8tl5PN5K4I4PT2N6elpTE5O4vjx4zZ/p9NpHD58GHfeeScOHjxoWVA33ngj4vE4du7caXXRRkZGMDExgcnJSasQvbCwgPHxcUxMTGBiYgKhUAhtbW0Ih8MYGBhAKpXC7OwslpaWPOyysrdA09mu15vZZsr2kOWh0722toZYLLZOPM8xRBaJDLXLLrJkjG7Rw+OvVtu0zE8qlaoz9KThpJWVFXuAfMhK8ytF7BaqUzGopgvys6JmTgyataMeOQADFIFAAPv378eP/uiPAgCeeOIJZLNZW0x7enpwzTXXWKbSLbfcsuGg4wJ44cIFfP3rX8ff/d3fAWjQw7wW69xwIa9Wq0gmk6Z16e7uxunTpw1k7Nmzx2LOQEMrUy6X8blf/xwA4Jm/80yPGDcajRogSafTWF5eNoCxsrKCHTt2GPPDmhn0HJLJpE1ENKanaluBBnBYWFiwCXx6ehqxWMyzcHd1deElL3kJAOBHfuRHcNddd63rMx7/8MMPY35+HhcuNLbtKJVKBoSWlpYwOjpqMXxOqASQsVgMk5OTnq02AHgKgCWTSbsvTjYqktZxps/WzRrkQqX6oo0yzRQ86fcusFKAwwxEXv9KiKPF/DyFlkwm12USKqDRUBQXOXfHcw3lawiF8xmfsW5XoeGzjeZAtoFOHeDd/qBarSKdTuOGG27A9ddfD5/Ph/HxceRyOXNyhoeHcfToUWzbts1EwO3t7U+affrwww/jwx/+MO69914DT11dXabF6enpMR1NItFIX5+dncWlS5dw/vx5LC8vI5FIoL293QBdd3c3Lv71RQSDQbzyz15p1e5ZsJD9s7i4aMkd2WzWUv6Z9RUKhbB9+3YTZ99xxx148YtfbAy6mqbPz87OYnR0FGNjY5icnMTs7CwqlQpisRg6OzsRj8exsrKCyclJXL58Gdls1gM06aBo1p27FxrHB511ZZrdceRqgICm7lDrSWn4lWOD12yFvTaZpdPpukvhcbdt/ZuyN65A2V1UlEamKNHVC7maHvX2lYliPQoAeP7zn4/9+/fbtS9duuRZXJ/73Odi586dJgzeyI4fP45PfepTABqTxvHjx02wG4/HraIz+4EsUqFQwM6dO+2+enp6MD8/b0K/eDyOjo4OC10tLCxgbGwM9/1BIxts209vs1AgK2TzZejo6MDc3JwHzHR0dFjhwVQqhYsXL1qflMtl0wHxGfh8PmOW2tvbjY3h30lTz8zMIBwOG4vE/mb/J5NJ3HXXXcb8vPrVr94wRMYwGEXO/P/Tp09jZGQEQCMtnlVnAeDIkSPo6emxtt1///0YHx+3fllZWbEJjvep2gzN/KI3phTzRtla/K0WxuN96xikhodMnY59XcT0O/19sVhsgZ+n0HQ/NS5YCjwIVLiouQJoLbjJv3OxY4o0sL64IdlDV8dGUSudPd2vi4twIpHAtm3bsH//fuzatQvhcBhnzpzBxYsXkUqlsG/fPuzcuRN9fX3o6OjAjh07njQ8/cgjj+DrX/86vvGNb+C+++6zMHhvby927NiBzs5O24uQcwnDWoVCAadPn8bp06cxPj5uv02lUjavxGIxzH5sFoFAAM/7/efhmmuuQXd3N0qlkm1smkgksLa2hvn5eSwsLBh409T2ubk5TxFGapSuv/56PPvZz8azn/3sdXrEfD6Pixcv4uLFi7h06RKmpqYwPj6O+fl5K3xYr9dtn7FSqYTz589jdnbWI5nQSssERWTudB82DZ3yWaqgmXMNQaxGOzguCJwIkAB4xiPZwB9E+6EFPxQ868KhiwawcYqyLipAcyFwU9ypw9DwkHpc9Jp06wYdQKFQCM+6kop+4MABjI6O2kAdHBxEKBTCS1/6UgCwBds1goyHHnoIf/mXf2kZWyMjI0in0wYaNJV6z549yGQytjCPjIwglUoZq9HZ2YlsNmv3MTMzg1qtZqEsZh5Nf7QBDO78rTs9wslCoYBLly7Z9bgfDn+7urpqnt7Q0BCOHz9uLw/7U6lV7qsDwJNuns/nzVvjtZPJpLFOLJ5GwMLS9RSUDg4O4vWvfz3e+MY32rldI0M1MjKCXC5nrNBDDz2EWq1mDM/ly5exZcsW00lls1nMz8/jwQcfBNAYS9QmAI2xE4vFPMyQhiDcf3Xc8G/ss3A47NGa8fnwWurRs091LOs7QWazVefn6bNUKuUBKJpiTm2K6n/4fHX7HX5mppCr21Dv3dUV0aHTzxyDKoDlb/r7+3Ho0CHs2rULQLNqO8tB7NmzxyrSDw8Pe5wwtePHj+Mv/uIv8PnPfx4TExOoVCoYHBzEgQMHsHPnToTDYdPkMBs2FovZop3JZGxvwMnJSYyPj2NhYcEAwtLSErLZLCKRCCb/9yQCgQBue9ttNhdyHuns7ER3d7fVw9HElmKxaH2Wz+cNAE1MTGBkZASlUgmpVAp9fX3o7e3FgQMHNmScFxYW8OCDD2JkZAQrKytYWFjAhQsXcObMGSwsLCAWi6G/vx89PT0olUpWg4z3yvfbfZd1bWL5C92eAvCyvXzGygwRWBPoKiAieOZnhtLoGP+g2Q8t+Glra6srQga8dXyA5kKgehxOPBSHKRPEmgxAM7Wd5oIoompXiEpAsm3bNjz3uc8F0KixMzY2Zp7CoUOHcMstt+COO+7Y8N7q9ToeffRRK/j3N3/zNzh58qR5jaFQCD09PTZBau2IZz/72ZiYmLBU9snJSaOUaczmoA0ODhrrRHBx8n82QjwH3tzcWJReEhd1bvFBwEJmjKGrwcFBXLp0yVgJvlg8vlgsIp1O233lcjm7J9Y1YkiNpeQ5Hg8fPozDhw8bYGFRSBX31mo1o6lf9KIX4RWveAWGhobW9Xc2m8Xx48ctrLW6uuop3vjJT34SjzzyiIG6trY27N+/37LavvGNbyCbzRrgZHqvMi5uthbHDbVmbuaOC35oBD/6vY57FTSTLteUZO5czXa1sr2eWuPWDrpAqbeuIlcCEgAewKqghuBIyyWQBdJFlEwQQ9EEUJpuz+tWKhWk02ns2rUL+/fvR3t7O0qlEsbGxpDNZrFz504cOHDA3tHdu3djeHh43b2ura3hC1/4Aj7+8Y/jW9/6FiYnJ9Hd3Y2uri5LwrjuuuuQTCZx+fJlnDx5EouLix4Zw9raGhYXFxGLxbB161bEYjGUy2UDYIuLi1heXkYul7N5afbjDebn4JsP2nzFLWy4TQW3wunu7rZCjBQiU1vEvb+Wl5dN2zM/P2+MUaVSwdGjR/GGN7xhnf4QaGxLtLy8jNOnT+ORRx7B2NiYh01ilepSqYSLFy9ibGzMHGxqldysUF3PXCeJ40dD4Moyc06MRqNYXV31jBk37KVMkdZP+0GyH1rwk0ql6hwcbgaW1sEgyAG84EjpXgBW0ErFYgqWmPGgg0MXGp+vUfWTIOKnfuqnLEwyOTmJdDptxz772c/GK17xinX3xAnxzW9+M3K5HL75zW8CaLAzPp/PwmT0isiYxONxYzxqtRouXLhggGJ5edmzCWowGER7e7sdT0+LfXbhwoVGCfpPNJigxEsSBlaYButmNWmGibIU1CuwD5UpY7tXVlY8zJGeKxqNmjaJwkPqdPbv349nPetZtjhEIhGMjo5aavvc3BxOnjxpk0MkEsG1116L1772tQAaYTGtuLu6ump1lh599FEEAgHTSbW3t+Ozn/0sHnvsMQCNGP+BAwfs+2q1ik9/+tOeopQzMzMe4TGNY1I1PjpB0bsniCMrpCErDb/SI+Tz1awzd2NTZoLpmG0VOXxqLZVKeTabVE0N4H3+OsdQ+M45yc3cc7dCUDE0WWrqyVirR0P2ZAJYqHRgYABbt241jV8+n0cwGERvby+uv/567N27F9FoFFu2bDGtIm1lZQXvec978MUvfhGXL1/G9PQ0uru7sX37dtx6663YsmWLvfOrq6uYn5+3ENHo6CgKhQK6urrQ3d2NXC6Hubk5CxXl83lUq1UrybG8vIxisYhyuWy1z07/xWlUa1UMv37Y+ikWi5kwmuF33u/g4KCBsq6uLtPvcd8uZnaydtj4+LjdF7VQ11xzDZ73vOfhjW984zr5wrFjx3D8+HGbixcXFzEzM4PJyUnU643MuEAggNnZWVy+fBm5XM5CjqrZ4bvO/6jdYYiUQJhrFyUfBEFMfiFrp1ogMmA8nyZxXK1FDjct+IlEInXSeFwAmSKq3j8Az6SgC7MCJZfJ0Vg60Fg4QqGQeRfuruHd3d2o1WoWZgmFQhZWGRgYQDQatbDJXXfdtY4ePnPmDH7t134NQENTojqPzs5O5PN5z07tw8PD9pJls1mjJqenpz0iZIqduThGIhHMz8/buaPRKKanpw2ULC4uYnV11So8J16c8PSJq0/RcBJT+nkuTvLs41AoZPQzPysgVXDEzAW2m9olLuz8Db2t66+/HqFQyHQ81WoVZ8+e9dQBongSaOh4SFe/8Y1v9NzHE088gc9+9rP2/O655x7s3r0b//zP/wygUSWbrBwA3HnnnSgWi1Yu4NFHH0Umk7ENExneABqAJJlMWp8yK4efOdlxzGrf8XvW8gCauhFlP12xtAJKZsXQstlsC/w8haZVnpWZ5lzDBU/nKjecxUWJBTFV9Mrfcm5kaQU3u5WsEhfMQqGATCaDG264AVu3bjWWqFAoYHR0FF1dXbj11luxbds2ZDIZq4/j2sc+9jG8733vw/3332/3u2/fPlx77bXYvXu3VXXmhqKnTp3C6OioLbLnzp3D8vIy2traEI1GrWZQKpWyzYvZN9VqFcViEcViEYFAwAqdPvo/HkWlUkH7y9st2YLZtExZJ2NEBysej2N4eBiHDx9GV1cXVlZWkM1mPe8jmRbqgubn5zE6OorFxUV71575zGfiDW94g7H+tDNnzmB6ehoXLlzA1NQUlpeXcenSJdNtcosjhtQXFhaM6WYUQrOv9Dn7fD7P5t5A0yninM3n6fP5zAHS+Vfrt7nawBb42WRG5mej3bhVtFytVo0F0aqkCpKAJkDSAnGcpIAm3ahFDwOBgJ07Go3i1a9+tQGSy5cvG7MwODiII0eO4OabbwawXn9y7NgxvOUtbzFmgQXHVLRcr9dx66232n3EYjFjGpaWlkyHE4lEkE6nrR2hUAizs7Me0MDMBrZ7amrKBni5XMbKygpK/6fxMsVfHPfEmjlZAjCgwsU0mUyiWCyuY+J4LWqPnizFWid4to39z/OyT7QyKtAIkx08eNCudejQIStZDzQYsKmpKZw/fx5AwztlGPK2227Drbfeire+9a2e9nz6058GAPzzP/8z7rnnHuv/y5cv44//+I9Ng5VKpTA4OIjBwUEADfDz4IMP2r0o4NMxxf4mqwjA48Fpn7ip8htpfABv+rqbqqqMAK0Ffp5aSyQSnpRjLqp+f3OTUn1nVIvDxYzPEYAtYgA8dVw0XEYvXvUc9PpV07h7924cOnTIBPShUAi5XA65XA67d+/G0aNHTavihnjOnDmDP/3TP8WnPvUp20pm79692LVrF3bv3o2tW7eagzg2Nobp6WnMzs5iZGQE8/Pzpu+5cOECxsbGbK893asqGAzavl8M6xcKBduYk/d94s9OoFqpoutVXaYDYj+w1AadvGq1ivb2doTDYaRSKRw4cADbtm2zSvdakRlolu+gcHp0dBRnz57F7OwsJicnsby8jC1btuCee+7BG9/4xnX9dOrUKZw8eRIjIyO4ePGi7TdITWYikbDEDvYNWT1NqtHQJ8cH71+1XUBz/0AAFk4jGOIaqgBc2cWrOdtr0yb5c4FghgQAj8cDNAGKqtW1IrMuQvw9Fw9NJ+S5dHGmQIyp5a961avQ2dlpmhnGjoHGBHTnnXeuuwdmb73lLW/BysqKhXjy+Tw6OzsNzbe1tdmOwgBw/vx5S6UE4KkOfeDAARPvAY09smKxmIW5CoWCJ119cXERS0tLnpcjmUwCDcIFmUzGs1gODAwYuFlcXLRdmoHmxKC7AyeTSbt2R0cHisWisVS5XG5dGE11C6xCyj4MBAIGZjgxqb7lO9/5jomUZ2ZmTJwINLLctm3bZnVGzpw5Y33w5S9/GQ899BAeffRRAMDdd9+N17zmNSZI7+jowPHjx+15HDlyBG9605sMDH32s5/F8ePHDaBcd911iEaj+NznGuUCEomE3VehUPDE1pl+roykhsl4r8qmuQ6Jsj2u6F9ZJGWbAG/KfcueGuN7xueiIXoCE44Bhi14rLJETGMHms9fnz0ZpGg0amFWN1ym4bCenh7ceOONqFarGB0dhc/nw5YtW7Br1y57j7iZMnWNtE996lP47//9v2NqagrZbBbbtm3D4cOHMTw8jP7+fpsHKSheWFjAzMyMhZBmZmY8cxT1JnQ2o9GoATHdEJWmwIeAplJt3H9bW5v95orGzTYlJQuilefj8TiWlpYQj8et3hozdykwrlQqiMfj6Ovrw9raGhYWFtDR0YG9e/dienoaY2Nj+MAHPoAnnngCP/dzP2flOABg3759BiqoMc3n8zaf5/N5pNNpxGIxy6ybnJxcB4qVGdTyCOqcu+FUFyjzWM2I5r+uBvFqtE07O/KF14XCRcRE/AQ/uiM59SWqGdF9j+gZ8bfqlQENViIWixm9uXXrVpw9e9bOp8JaFuZTu++++/D617/eztXZ2WmTBLMKNHNJs4tYOp733tfXZ8CIxbPINuTzeZs4AJh4jeEh3hcH+aFDh5BIJPBw7GEAjYWe54pGo7YVBABLMdWihpq5FI/HsWvXLgOczLpgW3O5HJ544gkPONXnwQkcaLyMDD0CMC0RPTJutcHnd+rUKU/GVSAQwHXXXYd9+/ZZn5ItO336NNbW1vCZz3wGQGOLkAsXLuA1r3kNgEZ16dtvvx1/+Id/CKCxieq+ffssA+aWW25BKpUy5m5mZgaHDx+2bL9jx44Zs0ZPkn2iGTf8/N3Ciy74oQenQn4t3+CKoVWj5Yr4W/b9N45XBaCaQapaHIbWdWywjIIuZlzkNAFEs8I0C9EN9a+urmLv3r247bbbMDAwgIsXL1qdoHq9jt7eXtx4443Yv3+/Z3NNoBFu/0//6T/hU5/6lDGKL3zhC3H48GGbVzhPsro7qyZzH8BLly5hcXERwWBjx3QtAsqtihS4VSoVzM3NGSukxSBXV1cxOzuLeq0Ov6/JdMXjcayurlobmVhBZ4bbIjGTjGAom80im81ahWr+R6eaYfn+/n57Z7ds2YLdu3fjvvvuw9e//nWMjo7isccew2tf+1pL/9+zZw86OjqQSqVw7tw5C49Xq1UUCgXTMfX39+Oaa65BOBzGpUuXPNel8br6Xmu4SzVlmiHK56tlDgietf7P1WybNuyVTCbrSuXSNHOGmTSqI+FCTq2QCktJjQJeb5rnZZYB0GADtm3bhuc85zkAYHUk+EIdOnQIN954IwDYFg200dFRHD161CYhFvWigHl2dtboZ6CRLXby5ElbyDs6OpoMzZW2sm7P5cuXEQwGjapk7Fc1JPSA2A+9vb2WyTQwMNDI0viNLwAAXvKel3j2/eIkAzQz4hjfn5ub8+wddejQIQNwvLYCznA4jO985zvWpwRqQAM4FQoFm3D5kvLanKBV60KKHIAJCQm0YrGY5/POnTvtWjMzM7hw4YKBy/n5eQwNDRlL9MY3vhE//dM/bffxyU9+EiMjI/b8MpkMdu7ciQceeAAA8JnPfMYodKDBkN13X6NuEusMcZyFQiGrMaJ9qimuXOAA2D5f6tHp8T5fc8sVhkt1yxYNu1Wr1Va211NssVjMU7eHz4TPkQCGoMjVbrihTGrq+NwV+FIPVq/XLSVaF7xQKITbbrsN11xzjTEQdLy6urosm+uZz3ymOWY0ahQvXryIubk5DA0N4fnPfz7uvPNOxONxTExMYHZ21mqQFQoFTE5OYmxsDKdOnbLsKS7KKvrXTTrp8Cj7w9B5IpFAT08PksmkpZTncjnk/ymPcDiMa3/5WtPuzM/Pe9gSsqncFJQOIOcPXjOVSqGzsxPbt2/HgQMHbL8usm9kuqPRKC5fvozV1VX09vZibm4OJ06cwOLiIvL5PIaHh/ETP/ET5lDRzp07h7Nnz+Lxxx/HzMwMVlZWcObMGYyOjiKTySCTyRgo4n5iXJtUO0Z9JdBkETkvcF7UDb91DAJNMK7sEQG2luz4QbIf2rAXEa0CH/WaaKVSyRZjhhwAWLqwLsQKfjTzCGju56Tez8te9jKcO3fOPvf29lrK56FDh9aBHtrNN99sHgXQ3ACUdDJfeDIT09PTnq0f+C/FfLFYzAa2K1orlUoe748Dn/dN74IgJRqNYmFhwcPAaMVsBYzU5BA4TU1NIRwOG+uVTCYtowKATTRsY7VaRX9/v6cPGSN36y6l02nk83kL9fG+NIypmUx+vx+zs7MWYtu6davVBeH5Kdg8cOAAwuGwnWt0dBRTU1Mmlv6t3/otlMtlvOlNbwIAvPzlL8fi4iK+9rWvAWhoei5cuICbbroJQDM77KGHHgLQCIOR+v7c5z6Hubk5A5+q7eC/SlezngeN417HJrOB+P+uZ6iiadX8/KAWL/tBNj5P1mbRd5NhfNVqaNKGZvxobReCIw15cOELBoOW9ccFs1gsoq2tDXfddRduuukmKx5YKBRw8OBB3Hzzzdi7dy9SqZSlhKt97nOfwx/8wR/g4sWL2L9/P+644w7s3bsXt956K7q7uzE1NYV4PI6enh5Uq1Xk83ksLS0hl8thfHwcZ8+eRS6Xs1IZCgQJhkKhEAYGBtDR0YFgMIhMJoNUKoWFhQWcPXsWU1NTFrphBedIJIJIJIJavOEwbNu2DdVqFfPz86jX6555jYwSNaJ0Mqi3ZDmAxcVF27oCAHbt2mWMGsFpJpNBe3s72trarOJ1R0cHrr/+etRqNZw8eRLf+ta3cOLECZw/fx6/8Ru/YXPqrl27sGXLFgQCAVy+fNmuVa/XTZgdCARsb7RwOIyJiQnUajVbswjEOCe4O8Mrs89+pqk+k2upguSrmR3etOCHcVcNRbHeDScTaincrAmgWXNGM2NUQ6JeAtAMg/H3b3rTmzysU7lcRkdHB1796lcDwIbVmnXfqXQ67dlOoV6v2+7pzHpi3Zmenh50d3cba0VGS9PbCSBY9IyCwEwmg7m5OdPl0AjSrrnmGrS3t3vuOxKJIOBvgh+eixOMgpe1tTWPCLlWqxkY4qRFtoUVVvn76elp9PX1mY4nGo1auxYWFiw1FWhmfxEwUrSt+i6ldvV+gEZBQ60+ffLkSeuzw4cPo7u728DqM57xDHzzm9+0jWenpqbwrne9yzQ8//W//lfcdNNNeNnLXgagwRT99V//tWWHXX/99Xj5y1+Oz3/+8wCAr33ta6YPev7zn49/+Id/MDaGC4WCbsC72aEugkpn6/eaCeSm1mvtIE7cgLc4ZsueGtNnyUWXbDW9dy68DHGpGJVzEMGOW+5DgY+mxnN8xONxbN++HXv27MGePXswOzuL6elp1Go1bNu2DXv27MGOHTuwe/fuDdv/9re/Hf/0T/+EpaUlHDx4EHfffTd27tyJHTt2YHh4GNPT0xgfH0exWLSaPCdOnMC5c+dQr9ctuSKfzyORSHiAuzI//f392Lt3L+LxuGXxckNVzhHcJZ0i3q6uLkQiEWQjWdRRN0DT0dFhoJK/WV5e9oSLGApi8gJZ39nZWWNvHn30UczOzqKjowOZTAYDAwMWZp6bmzONI2sQLSwsIBKJYNeuXcjn8zh27Bje9773YXR0FHfddZdt8BqPx3HNNdegv78f4+PjyGQyph26fPky8vk8lpeXbd2Ix+OYm5vD8vKyx3lS7Zc6Udz1gOsd10wCYrKHBEl0oNzox9Vmmxb8sAiVTiZcfDnZMyyirAeN5cU13EC0rb9VzUkymcQNN9wAANiyZQvOnj1rCL6/vx8HDx580i0qXvnKVxqT0NXV5RFTE3RxMe7p6cHq6qqFtdrb2zE9PW2eXi6XM/ocaIS6aIlEAul02n7LcB1rDtXrdU9IJh6Pe7IB+Bua3+/3iKV1e4t0Ou1h2rhzvdYUSqVSRpsS7JEJWlhYMOqa11J9ViQS8bTLvVY2m/UUUGTaLwDzKDUMViwWrS2FQsHu67777sNNN91kIG7v3r2eGkA9PT0YHR01pufEiRN429veZpqtzs5OvPWtb8Xv/d7vAQA+8pGP4KUvfanVFPrkJz9pNYRuvvlmvPKVr8QHP/hBAI0wo5Ye4ATFvqLeh+PQDV254mUF9NRJsA9Z8E7HdMueWnOF7cpg08Hi35ldyRo8XKDd2lAETm5mF98N1vUJh8M4cuSIgYfJyUnMzc0hEonghhtuwLXXXmv1fVw7c+YMfud3fgdf+9rXkMlkcMcdd2D//v0YGhpCf38/ent7rRbO8vKybXszPT2NBx98EGfOnLE5j4yty1Kz7V1dXejv70csFkMoFDKdITWFPT098Pv9th8fBdPRaBRtbW2N8/lgO8CrM8yQmc4NyroyiysajSKRSKCtrQ1zc3M4d+4cxsfHMT09jYGBAezZswe9vb3o7u5GpVLB7Ows8vm8sVYEa0zT37p1qyV4PPLII3jooYesfMCLX/xiHD16FFu2bMG1116Lxx9/HMePH7f7nZmZwejoKFZWVpBMJs2hnJqawsTEhDFQ1EGSsSErpCJ73R6FnykhILgmaCW7frXapgU/zAJgwSag6eXyYTMM4go+gWZRQxo1I7ow6ELB9MoXvOAFABrFALnnDADccMMNG26sCQB//ud/jv/zf/6PMShuVV9OXGzP9PQ0+vv7LbxFL4oLIj0nfuZLATTEz4FAcwsJZsApRb5//347N9ug+1KxOizgDbsEAgEsLy9buG1oaAihUMgo4b6+Po8miGCCwIsTA9kbgiTdAFZ3TndDAlppNB6PY21tzcCMz+fzMFhtbW04d+6cxzPSNPy1tTUPlX38+HHTXNXrdfT19eH6668H0BCJ79y508JYs7Oz+N3f/V0DQ//lv/wX7NmzB7/zO78DAHjPe96Dv//7v7fx8Ja3vAV//ud/DgD4yle+ghe/+MV44QtfCKCRKdbW1obFxUUAzdCUgj7NWHQnbGUFaAr+qSPheVxWqGVPrWm4y52X6LFTcKqAlsdwQeQmv8yCJBuoGiEawyDUIS4uLmJsbAzBYBB9fX3Yv38/brjhBmzfvt3eAbV/+Id/wJ/92Z9hamoKz3zmM63uVb1eRzabxdraGmZnZ1EoFDA7Owu/34+FhQUcP34ck5OTtgVOvV5Hd3e3ZZDOz89jYmLCHJV6vY62tjZ0d3fbDvLpdBqJRMLq63CBTyaTiMfjNh/F43FLeKijjqC/wTpT78TyIBT38lgyyrVaoyo2a4GRMWFyQldXF3K5HBYWFnD58mVPlvHQ0BDa29uxsrKCpaUlFItFY6joIF6+fNmKK66trWF8fBwnTpzApUuX4Pf7ceTIEXvn9+zZg7m5OeTzeWQyGXME5+bm7L/29nYTWrNOkkYryBq66ewE1xxvrHytpQ9YCJJjjuvs1WabFvxwgGpNFNLA7lYC7iTC7zjgAXg8JaAZE9bqw/fcc48nlTQSiVgoyy1qpfb+97/fQnRAg8mJx+MWiuIWBmREuru70d/fbwWwdANNoAF+dDAqumexL9X8aJiERQ/ZZ6wxo3V/6vU64Gv2g2ZMKdXqhga7uro8zA8FmAQZa2tr6OzstN93dnZidnbW0tEDgYABo0AggKWlJU86b7FYtD5gGrw+246ODju+p6cHKysrFlIj9a+pnARWly5dQiQSsdT3crmMgYEBA2/pdNr2TAMa2WEXL17EF77QEIU/9thj+Ku/+iscPnwYAPBrv/ZryGaz+F//63/ZfZIFKhQKuPfeey0D8BWveAXe//732zhUoSfQGGek9XmfqnVj1oqOea1zpccyrHI1e3NPt/EZMDyh4xBoCtq5SGkGl9Z14cLEcD3nQy5eFOP6fD4kk0lcc801OHTokBUu5PYSBw8exPbt27F169YNWes//MM/xCc+8QnEYjG89rWvxXOe8xzs37/fCorynaxUKlhcXMTo6ChmZmZw6dIl2+mcesUDBw6gp6fH3tGBgQF0d3fj3LlzmJ+fN8aE7EN7e7uFsjgHhUIhZLNZm3/JijJc6JrP57ONUlklmn1DUEC5hOrl6EByTWHdH855Y2Njlqp/4403Yvv27Ugmkwa26DhHIhHbnZ6aJc6ha2trmJycxMc+9jEsLS3hta99La6//nqEw2E861nPwvDwMM6fP49Lly6hq6sLly5dsorYi4uLaG9vx8DAAOLxOCYnJ5HL5Txhf+paFQwzmsF7pRiajhFBjzKTTMq42mzTgh8KbzXjhx6EWyBMQwYECXy4uqgAzZCPxpkB4NZbb0V7e7ttpLl9+3aUy2UrbLeR/cf/+B8BwOhZMj8dHR2IxWKm8RkYGEA6nTYWqVqtYnx83FOXZXp62sJNGy1qZHIikQgymYxNMIxvc0Hcs2ePJyuE6fwbpUHye1owGDS2CWjWBKKOhqJjggTW3SBgKRaLKJVKxhz19PRYmj/PR+ZHQQ/QyARjmX22xShuNMKOFB/yWt3d3QYgSbPzJWaRRACW5koGihsSkrEaGBhAIpGwcAD7koL0Y8eO4ed+7ufwS7/0SwCAn/mZn8E73vEOK/v/V3/1V/jxH/9xAMBv/uZv4l3vepexRm94wxtw8803W6aZ6jrYhwpu9G/sH3dy060ueE5+p2Gxq7mGx9NluVzOU/dJ69koS62Ah4sRCw/y/+m88TMXbC3umkwmcdttt5mDwW1X9u7dizvvvBNbtmwxMbFrv/Ebv4GPf/zjGBwcxOte9zocPXoU8XjcdEJkHAjcL1++jPvvvx/z8/O23cTa2hqWl5cRj8eRyWQQi8Vs/662tjZLjuBcATS1mgxlAc2wNkPCBEjsDyaJ8LMyGQQ3q6ur6OvrM7YjEonYuRKJhIFO/obnowNCRo5ggbrM+fl57N69G52dnRgcHMS+ffvQ2dmJ5eVl23KHu93v2rULU1NTSCQSGBwcxOLiIk6ePImPfOQjeOSRR/Arv/IrVl9s+/btaG9vt3Cez9eo5EzHOZfLoaOjw5y0fD5v5REYSdBsLs0K5Xd0tjQJhsbwujq4V5NtWvBDQRaAdQumhmyAprBzZWXFMxh0awwObF1EVPdx/fXXe0TD8XgcAwMDOHTo0Ibte9/73oePfOQjdm6fz2cLczAYNDEc25LNZj3iaa1PxFRN3kckErFN9wBYpgEA27+FgGR6ehrT09P2ub293cN4aTYI+yiRSMCHZgo5F0m/34+5ubkn7UOGWDhhqXfL+/D5fJbp1N3dbSXp9RkAsH11eO7l5WWsrq7aM0kkEujs7LTPZJSoH2KWBL/nou+2jffIGDnQmMRrtZrV32BFV9rw8LDVJAEaIbaTJ0+a5ufYsWN473vfi1/4hV8A0ABPrBF0zTXX4Hd/93fxtre9DQDwpS99CXfeeaeBtNXVVSwtLXm29VCROJ+DMpi8B37HdvFvbnhL2cyWPfVGUEPmRhceOmxk6NwUd/5G6/twPPC7SqWCZDKJaDSK22+/Hdu2bcP8/DxmZmbQ39+Pa6+91gTK7r5cAPDQQw/hbW97G06dOoWbbroJhw4dwtDQEAKBgDEXzEyKxWLw+XwYGRnB+fPnsby8bGLroaEhbNu2zTZE5R6DbW1t9v5wW42Ojg5MTU2tc0iVtVYmIhqN2vxDLRH/7vc1ND8qJifry/0Ri8WiAbdwOGzbaVDrQ6eQYXxWutb20AnN5/N4+OGHEYlEsHv3boTDYfT19WHXrl3Gbi8uLmJlZQVbt27F8PCwZ6ud4eFhHDt2DPPz8/iDP/gDfPOb38RP/uRP4rrrrkMmk8F1112Hzs5OE1mfPn0aJ06cwPz8vDlt6XQavb291ocK5Ch45njSzGZlfTguOaYIojRUfjXZpgU/mjJNZoFI1QVBZDXcfb+UPVExHtCklhmeSCaTmJiYsJj41NQU7rzzznVo+f/+3/8LoBHqIsORSDQ2B1UwNT09bUwD28n7YAVT7lieyWTMUwEaDMnKyop5/27FVe6xBTQBBye5dDrt2Ymdkw0ZHWV2+L0KK5eXlz0VmbWiMycRt1wAj6c+S7U7KjLnPj0APIXGAFi2CIsUlstl9PT0GDsTj8c9GXB8jmTERkdHzQMEmsJh9rtugFur1TA6Oup5Pro/WmdnJ/bv32+MFScMFo7867/+a5RKJdP5vPSlL7VJ933vex9++Zd/Gb/+678OAPid3/kdnDx50ry997///R76njS6FnfU0vXsZwWgG6XP81wtkfPTb8Fg0Db0VdGzhhu4MHEB5xgjAOLcxTnQTViIRqN4/vOfj66uLts4FIAxPm1tbRsCn6985Sv4zd/8TZw/fx4vfvGL8YpXvMLGFjcQBRpzWqlUQjabxenTp3H+/Hmbt5iWTQ0SN9PkHlx9fX2m2eM8zFo2uVzOwKAuxgwT8XwU6SYSCesPNwNSdVV08Lhj++Liol2L80YqlbLwmTqYhUIBS0tLJiEgKGPtMKCR8VkqlTA6OoqvfOUrOHv2rIUaDx48iLm5OVy6dAkTExPIZDJIJpPGUh06dAgDAwM4deoU7r//fnzwgx/EY489hpe//OV46Utfit7eXuzfvx/79+9HLpfDiRMnkMlkLIuOGWhkv0dGRjwZhRxbuvO71vMBmtnMFEGzPphqm64227TgR2k7Fe1STAzAUyEU8O6uzfRGAg4VhwENkFAoFHD06FEAMPEZ4+KDg4OeKs40Vgk+deqUhbmq1cb+MZygKNCjubFqxrV1M1LN/mJdIBrF2uwDTWdmhg/r51B7pAs9AAMZ2WzWFmq2TcNDfr/f+qxSqWBpackDAlgIEWhMOFqKgJO1aoBqtZqFslh6nlapVHD69Glrt9/vt81dyewQEKbTacvI47UXFxdtciqXy5iZmfF4kWzn0NAQlpaWPJl+qrOpVCoYHR01VmlmZgZ+v98qPLMgI7P1vvzlL+ODH/yg3dcHP/hBPOMZzwDQAE7/+T//Z/z2b/82AOA//af/hHe84x0GZO+44w588YtftPtSlg1YX2yTEyhNwY8WR2T/66aoV3Ma69NpBN5kI7g4MbxJ0MNMP1fQTm+cbIemNq+urmLXrl246aab0NPTYxuDknHYv38/duzY4dkegvb7v//7+NCHPoRYLIbXve51eOlLX4p9+/ZZcb1sNmugpFgs4uzZs7hw4QKWlpY84COdTqO7u9uqFZNRKRaLVsMGaIxPhsIA2HkYutMK16xjozuyA833WIFZrV5DwBcwNtfv93sADcPKWhFe6/uwcjT1WVpPhwCvq6sL6XQanZ2dCIfDxu5QzPzYY4/hc5/7HPbv348XvehFOHToEPr7+7GysmIFB9fW1hCNRpFMJi0zbWVlBWtra7jvvvvw2GOP4YEHHsAv/uIvWhmOVCple0TS4Tt16pSFwAYHB1EqlbCwsGDvv4a6OFbcemBaf4qsOAGShtyvJtu0d82BqPoIZgepCJYeAoB1A0HZmNXVVU/4x+fz4XnPe54tniMjI9i3bx/2798PoLFIufbqV78ajzzyiLWFC/Xq6ira29sNYFB4p4yNbjZaq9U8AGlxcRGFQsEWX6JzFe8SWBHsKY3e3d1tjEilUkFnZ6enIFq5XLaFenFxcR0YU/Co4ZdCoYB0Om0Lt+oXgMbkRm+P962hK+5jQ4ZMhZ+5XA7T09Oe3Yq3bt1qx/T09HhCZslkEpFIxMJ/QMMbIwDt6emxTBEaAWFnZyf6+vpw/Phxawfpdd7n+fPnrWAiPTyCwGuuuQbXXXcd9uzZA6ABQB988EF89rOfBQD8yq/8Cv7oj/4IQENz9frXv97E0L/6q7+KN7zhDXjHO94BoFESYWJiwoTaq6urSCQSNnbUQ+Mz4Xhgn7uifrew50Z6rpY9dUaWmQs7gQxDs7rokOlw67Jo3R/9vGPHDtx1111oa2szQfLg4CCuv/567Ny5E4ODgxsCn7e+9a349Kc/jZtvvhk/9VM/hWuvvdbqjeVyOczNzVnYeHp6Gg899BDuu+8+ZLNZS01nlmgsFjPxbzab9RTkA5qFVlniQ8P3Pp/P9I/xeNxYISaFUMxNBpvMsaZl12o1BPwBOz4YDJrTx3Nxby+uCalUCm1tbfZuMRuKx7EkSDwex+7du9Hb24t6vY5EImH6U7I509PTBtLOnTuHP/uzP8ORI0ds01T2VaVSMcZpcXERkUgE1157LZLJJL72ta/h4sWL+Pu//3s89NBDuOOOO3DPPffgjjvuQDAYxMGDBxEOh5FIJBCPx/HYY4+hWCwilUqhv7/fWDmGtLhesk/cZCBXA6v1wq5WJ2nTzo5ahEnFoZo94S7G9FqA5u7mbloww0WhUAjPfOYzTffBaqdPpvF5z3veg3vvvdezsCirtLS0ZJMO6xFx4Z6cnLRFGoBlM/DarKSs2Wjq7XPiBJq1XVRQybpBvK9wOGzhJZ6Teiael/1SKBQ8TEKhUDCAMT4+bqmi/I2mVlNkrCG2+fl5Ew5TVK5FEwlA/P7GFhzso97eXmQyGWtfb2+vaQd4HwoKYrGYp4AiU2b5ubOz08BprVZDd3e3iULn5uY8Qm2COIbrODFou5/xjGdYxWiOP26U+uEPf9i2vvjkJz+Jl73sZRau+x//43/gbW97m4VXH3jgAbzoRS/C3/zN3wBoVKYeGRmxccktQFS3oxl2mtHIscD2cHHiWLlahYxPt2mRQmYs6Ya3DBdx0eICze857zEMxkV9YGDANtVl2Li7uxv79u3Djh07rHSD2ic+8Ql85CMfwbFjx3D33Xfjx3/8x3HkyBEsLS3h0qVLCAYbW+XMzMzYJsjz8/M4ffo0RkZGLNTd3d1t8y3nt2AwaOwKK6grqwN4Q7PpdNrAQGdnp2Uilctlm6sJpMLhMFZWVjzhMa3QT3N1LMyEJSvEv6dSKatvw2uRmdYCgMlkEh0dHTZfR6NRFAoF1Go1tLW1GRhJJpNYWFgw8DU3N4cvfvGLyGQyOHr0KA4fPoyenh6kUinkcjnbLqe7uxvhcNhKX5BRnp6exj/90z/hta99LX72Z38WW7ZswfXXX2/PY3FxEWfPnsXq6ipSqRQymQxyuZwHUAPNauBaV4x9R8eKcgZ+3wp7taxlLWtZy/7N5mZx8W8uk6siVaCZOKGsJPV7kUgE1113Hdra2jA+Pm7VkPft24c777zTWE7aQw89hHe84x14+OGHEQ6HcffddxvTzX23ZmdnkUqlMD8/j8cff9wjyuc+Vj09Pejt7bXNQ8mqEGiwRhkFysVi0TYWJaBLpVKoVqtWSy0WixmoIbPFMAydWTon1Lvx3MFgsCF4RnN3eIaZCErIGBPgUEBOeQFBJQEWs1QJ4sigxGIxY8dYWJYALRQKIZVKIZ1Oo6enB8ViEWNjY5iensaXv/xlZLNZ3HrrrQgEApiensbFixctElGrNapVHz58GPPz87h8+TImJiZQqVTwoQ99CD09PfjFX/xFAI3q9KxfVq/XMTk5iXA4jM7OTszPz9t37Esyv5qlx+/5bHl/vI+rNSt004IfImr1bJlZpFk8GhZTLQwRsArDWEodAF73utchGo3algXXXXcdhoaGPGEVtXe+853o6OgwxK6pm1T8MyVxeXkZAwMDxhywoJemdbe1tXnCQFpMkNSyhqBcKpvfUYNDpM+CYa4oliGd3bt3N1iwujeLCICl67Ndk5OTaGtrsz7J5/MbaoS0fID2A1NIyaiwjhD/3+/3207I6XTaUyCRKfZsCzVSzCQLh8MYGBiwcB4ZKLalra3NxgX1QmRXFhYWrFo10CjeODs7a3164cIF7Nixw9ibBx98EN3d3aYBCwaDOHz4sLFW3/72t/HEE08AAF7zmtfgE5/4BN74xjcCaIQbvvjFL+Ltb387AODnf/7ncf/999vk9t73vtdqMfHcnLT5/EjTA81xzT50tSKa5Xa1TmpPtzFUqenr8XjcI4JmjRiOcS5aymwwbJLNZnHjjTdicHDQQjHpdBp79+7Fy172Mgvd0/72b/8W73rXu3Dq1CnccsstuPnmm3Httdeira3NUuYZijl58iROnz6N8fFxBAIBC0f19vZ6Cq2SyVJtWigUsv2uWAeIIbRIJIJUKmX9wHdTGU6CEoatKGmgzoesjRui4dYW1EKylIdWPF5bW/OE6/lvPB731LbhO8O/aR0ibo4KNPU3PCfnNobj4vE4tmzZAr+/UZn64Ycfhs/nw7XXXotMJoPh4WFj2NbW1oxBYsHI3t5eFItFLCws4H/+z/+JarWKN7/5zejs7MRLXvISpFIpKyOyuLiIcDiMnp4eXLx40Z6FsmPUNQKNtYVZe5SKEPSUSiVPxf+ryTYt+CGo0bolnDBUO1MsFj2TvXpXfNFp0WjUdBu7d+/GxYsXTZfj8/lw++23r2sHN7PkzsJ6Pv6WdS64MLNqp1aPnpub84TYMpmMtZWZBPzM+Ldm+KjIW2u9dHR0GBUKNF5STaNPJBKeOkDMhtAsCb4kBHZcXNva2pDNZj3hPU6cQEO3Q8+I3xcKBbuPcrmMVCrl0fxcc801AIAnnnjCU+yPbeW5WeuGbRodHUVHR4dpY3p7e9HR0WHgaWlpCfl83q7Nnav53LkhIdAo1jg6OmrPNhKJeM594cIFXLx4ETt27AAATExM4Ktf/SpuvPFGAI0Ufu53xD79yle+AqCRtXbPPffgk5/8JADgj//4j/G2t70NZ86cAdCorfLLv/zLNlbuuusuPPTQQ579z5i6CjRDWxqrV4qb7wi/4284jlr21BtZDa3FpanGfG4MRegz5UJfKpVMCzY0NGTzViQSQX9/P7Zt24Yf/dEf9WSBnT59Gu9973vx4IMPYmpqCrfeeite8YpXYMuWLRbW6erqQiqVwsjICM6cOYNHH30U0WgUHR0dyOfziEQiGBgYQGdnJ4LBoFVY5v6E+XwesVgM8XjcNkXt6OgwAELRM3ePZ+Yqs4sYZiLQ4wKsm6Byv0HqbIrFoic8WK/XUUMz/K6icDI9BFfUwNAxVn0jw18EPtQFURCt4UmgCfgINAgyAoGAiZprtZplkT3wwAN44okncOedd+KGG25AOp3GyMgIqtWq6YdYbXl5eRlTU1MYGRnB5cuX8e53vxu5XA6/8Ru/AaChO5ycnMSFCxcwNTUFn69R8X5+fh7Ly8sGbjTcx5IndNR1jGlo7Gp1kjYt+AFg6NT1hjgY6U2pyJmLH/+uVZdrtZptOzA7O4uVlRWbVNxaKQDwJ3/yJzh//jwAYOfOnTh//rwxE7Ozs6YpKRQKGB4etgWtq6vLvDagAY5U8MyN+Dhx6YQINBgUUpYAPIJlFgEjQGHFZp6bQIYeWywWw9TUlF0rlUp5drtfWVkxYMRsBnfrBYKGZDLp0VXRu1Amgs8NaG7zQc90ZmbGs2nh+Pi4PVseq0C3Wq160ubHxsY81+7s7DRGjGXmCXhCoZBpj0ZHR1EqlayAZSaTwdatW9fVMyKzMzs7a1kdbEs6nTaNz6FDh+D3+y3UcPDgQQNpx44dwyOPPGJFD7/whS/gbW97mwmif/qnfxrvf//7rWDij/zIj+Cb3/ymid1Z70l1btoHrqlOyn0OrV3dnx7TsBffay6y6qjx71xEV1dXbXHXQobXXXcdhoeHUSgU4PP5MDw8jB/7sR/zXPP3f//38b//9//G7OwsisUi9uzZg7vvvhu33XYbkskkZmZmkMlkbMPlxx57DI8++qjV/WI4ihoeFunz+XwWGmI21fLysml3IpGIVUfW7YPoVJHFtK0proCGRCJh2bi5XA75fN4WbP5LTRHPR42gDz7THjGESMCo2iHOvxQAs3yIvh8Eobwew3V8NloIkcLr2dlZLC0tWRsymQw6Ozs9UYlCoYALFy7g4YcfxuTkJEZHR23ua29vRzqdRrVaNYH6+Pg48vm8gZdyuYy//du/RXt7O37hF34B3d3deOYzn2kMGrfNIGjVemB0mHQeJ8j2+/2eDXfr9bo5t1ebbWrww9CXZhGoYl2L5gHNzS15rNLI4XAYd955pwGWY8eOYevWrSbW3Si767d/+7dNJDs9PY3BwUFjb/r7+622RjqdRj6fNyagUCh4av5Eo1HPRqVLS0tGzwLNasTuouXuXk9jXB1ohGyYAgs090QjCJyensbly5ftxWMWgopi+duJiQmPmDqRSKC9vd3Ef6pT4LU05ObzNUrtK1hVZi6TyRhIGB4extjYmAmxu7q6kM1mPXV82tvbLRSYTCZx8eJFAzesLMvPyWQS/f39lkG3srJi7BjpbRV5c3sMoLGJLSuqAo0yB6dOnfKA1cnJSeuXiYkJ3H333XZfoVDIwMstt9yCUqlkFZ1f9KIX4XOf+xze8IY3AGikyf/kT/4kXvOa1wAAvvnNb+Kee+7BRz/6UQAwNmxubg7fi2ktD518W/b0maYaq/C0UqkYe6AsBFluznVM085ms7jmmmuwdetW06zs3bvXgDXtuc99Lu6//350dXVhZWUF+/fvx913342jR4/aJqHc/bxQKOChhx7Ct771LSwsLBhDnkqlLGuUYyidTiOXy6FUKqFYLFoVYmaIUUQci8XM4SPwIBDh3EfHRjOwgMb4JUMDNOd0t+QD9+pbW1sDfM0QFo2MEoELQ40KvDSMRidaS2JwfeG/BBXU06ytrVlZgEqlYtfnfbnPkyHKWq2GL3zhC4jH4zhy5Aie9axnoaury5IbGIrjvV++fNnqt33kIx/BgQMHcMcdd6C/vx9Hjx7F3NwclpaWrN6bZj9rAUQK6jWtnfM+HT7qt1rbW2wiUy9BK4ISGfOz6yXTCH5Y04ZhLd1PKx6P26LFf2nvfOc712kqSN8C3lAUkT+vXyqV1qWT6+LLmC0Bkupm+Jkb1wEwr0j7YGxsDEBj4dVy+pycuLA/+uijWF1dNZaiXC43Xshqs8YNwz1klNiWnTt3AoABEj4Ljb+zOBk/d3R02GemiRLMcXNAnuvQoUOmuSKrRADCjBLdroQlAoBG6ImaI6DBrmlNEe6zA8D2xqEXyX9Z1oDVo3luv7+x8zSBLrMjHn/8cQCNkBvpawDYsWOHTR779+/3VPO+//778frXvx5/+7d/CwD4yZ/8SavwCjTAz7Zt26yvs9msXed7MV1gXZ3X1UpnP93msjy60HOxVeZH5zc6H6VSyfbLisViuHTpEvbs2YNXv/rVnmv90i/9Es6ePWvXHRoawg033IDnPOc5GB4eto02mdF4+fJlPPDAAzh27JgVz2Mtm3g87vl/jvu5uTlks1m0tbWZTqVardo4TyQSyGQyFsLhvlpueQbeNwEQnVWtt0VdFDMXGQoLBpuboPrgZdZUxMw6aMzWJCDisdQsMst1dXUVq6urnm00+P4wHMdnSNaL52HKvjJYCwsLWFxcxNzcnM11rG/EzWDb2trMyWEYs7e318p51Ot1nD9/HoVCAePj4/j2t79tznk2m7WMtbW1NbS1tSEWi1npFNXAckwwvMo+IEB0N1O+2mzTgh9O5AoaKAbVCsQAPJM/Jxx6TwwDvepVr0KpVLLw0YEDBxAMBjdkfICGVqOzs9PYnaGhISwsLFgF6AsXLtiCxT3AdPfzYrFo7VtbW8Pi4qKnbRoG44vtLvwEEbrTeiaT8dDLFy5cMPEcAEuDJTiam5vDwMCAhYe4Pwz7VPuEAkICJXprWrRseXnZPjMbgmwbY9l8qVjinQCG3gbQAJL0HIFGSOzAgQP2vDo6OiyVk9fmtiE0rf3BLAb2YVtbm11rx44dmJ+fNxavWq1i+/btlrrO7QIUnDIMADS0TeppViqNXZz5vDW0l8vlPOUSQqEQvvjFL+Itb3kLgEYo9ejRo3att771rXjnO99pTNB/+2//zUKs/5Ixrdet3aF1r1r21BuBjaauk+3ggg40w8Oa/k5GYWVlBQMDA9i2bZttpMnid7S3ve1t+PrXv458Po94PI6+vj4cOHAAd955J3bu3IlQKISlpSXUao1CoydPnsTx48cxNjaGYrFoGwtzDy0yJPF4HIlEAjt37jR9EHd358JPJkQZlHq9btoX3hcXW3XOaPyeGh3NuiqXyyiVSgZOOLdbBfdqzVhkhg655Qd1P+znjZJi+BsCLs59BD8aeuR8PT09bWxZJpNBd3c3urq64Pf7TeuUzWaxtLSEubk5FAoFJBIJe76sJXTffffh8uXL2LFjB7Zv346enh7bKyyVSqGnpweFQgGzs7OYm5vDo48+irm5OXR1ddlWIW1tbbh8+bKl5mezWRMvM2ympQjYz24pBd0S6mqzTQt+yOpojFbrQPAYrWtCgRfQAESaqbR3717Mz8+bxmfr1q0meFVjFg49Di6YzBBS4KU1aug9ADChoMZh4/G4JzMJgGk5KM519S4qHCY7k0qlEAgETM8yMjLiKW9PypkgoL29HXv27LGJgIUIme1VqzX3uAoEAtiyZYuFBknRqyeksWROEGS5mI3AUBRfSDcEyHucm5sz0DA9PW17AQFNMTVBH8NpBEcsfkYQ6Pc3tubQkBzHQmdnJ/bs2WP3FQqFTM/Az9ykkediBVX2ETUDbMvZs2ft9w888ADuueceAA0Q19PTg1tuucXOHQgE8Nd//dcAGvqgN73pTVbZevv27XjBC16Ae++9FwDwEz/xE/j85z+P78U43tkuetdXe/Gyp9u01phqWBQEAM2sMIpmNVzCmjOsw3PzzTfj1ltvtd9++MMfxne+8x2srKwgHo+jvb0d27dvt32iuLdgqVSyCuZf+9rXcPnyZUQiERw6dAh9fX0YHR3FxYsXrcBfuVxGNBpFX1+f1d7q7u62zUwZ6mYGkTI0BAUU8nIxZno353S+SwQL/E+/J+vC/qPmhuLner2OhYWFdYU8tYo/tVQMJxIAaL/r+sGiggQ9FF9TZrC4uIhqtYpMJmPCaJ/PZ98XCgXL2FpaWkI4HEZ7ezsCgQASiQR6e3tRrVYNRLGGTzAYxJ49e1CtVm1Pw8nJSWMPWQn6hS98IXp7e3HjjTdaIsrMzAxisRjS6bTpXFXSoOJyRlPcrMKrNVS+acEPFw2ND3MyIZ3qpvSyzgHQYCWWl5dNGFgqlTAxMWFp2P39/ZZ2qfaxj30MQGOBm56eNq3M+Pg4enp6cPnyZQAN8ETP4+DBg1bHAWiGSajbiMVi6O3t9ZRsd0Gb3g83DyQwyGaztq8UU1F1E1V6cUBj0S8UCibMpg6GCzvT8nGFGZ2ZmTHwk0wm7WUF4InTA7DsCwJAehIECZ2dnRgfHzdGRcuwA43FmOAnkUh4Ng9lnRCee25uzvOMCoWCeXW8ti4kpHYJnsrlsoGfer1uNTn4PDR8x3ZQ20Q2jEwPGScezwmYzySRSFiqe39/P0KhkPUBGS5qm373d38XgUAAP/uzPwugCZz++Z//2fqQOi4A35UBWllZsfMD8LALvO+WPfWmOgvNWCUo0GJ91PrpIpXP59HV1YVdu3bZOH7uc5/ruQY1b6xy3tnZiR07dqC7u9tCQQy1nD9/HlNTU5apSTlAb28vQqEQHn/8cduTqq+vz1jocDhsdXOoFeT+fBT7co5gqIwAjowSwzi6uzgFytlsFvl83oTRqq3hfmHMlNMkCqCxxQU3JGaYn/MRdUgM4SsgIhPN0BFLEVCbxPmN68jq6qox736/H319fejv77fjKLTO5XIG6FhWpaOjA+3t7Va0kaG1TCaDQCCAfD6PixcvolZrFHrs6+tDX18farUa5ufnMTs7i0KhgImJCXzgAx/AgQMHMDQ0hO3bt+Omm27C9PQ0HnjgAbtvhgzZxwxzaSYc+5lr0NVsmxb8ME6taaDAeoGnMiRcAIHGAlcqlXDDDTcAAO69917LXABgKddq1113nb1kTA28dOmSnU8FeKQngQZrUavVPBWCC4WChWhY1IuAhS+NgqHZ2Vm7Dy6ymnrOc5EB4iQQj8fR1tZmrAYHOYEYmQGtE6OZZefOnbN2sR6OmoYdgUbYjBMDS9zzPrZs2YLZ2VkDU+l0GvPz8wZCSJkDDbZseHjYA35WV1ctrDg9PY1oNGr3RQ+J7S4UCh7WA4CnlpLqnqif0E1PWTANaO4AzftkaJD0PScQfl8sFm0bAN43K1cvLi5a2ABogJl0Om1A+MSJE3j3u9+Nl73sZQAapRS+8IUv4FWvehUA4KMf/She+MIX4g/+4A/wvZqCfxWsX+2T29NlXHTIOgBNvY8KexnqYuiB30WjUezevRt9fX2Ym5vDnj17rKQD7dSpU7bDdzqdxp49e7Bt2zYDI9SVjI6O4v777wfQ0DW2t7cbMKlWq+jq6kJbWxsuXrxotXG2b99u45cF/hh2pniZgIaiZgIarbXGkAv35dKihppVpVleFECvrq4in88biIvH48YI1eo1oN7cjoehMTqUDHmTmVFdIq/Lsh0EnQRJ1Gty3eE9MpGE4Ifvmt/vt4xZtpklBTo6Ojyi4sXFRQOzFJbncjnr+yNHjmBoaAh9fX3WB8ePH8fS0hIefvhhfOxjH8PP//zP224E6XTatEdbtmwxNpyCey2tQHPX00CgsX8ioxJXk21a8EOvWx8e6/xw8VXhHODdy8jv9+PWW2/1LIb/4T/8BwwODm54vfe///2Ym5szpufxxx9HJpOxBZQTkYoXGXq6cOGCR7i3urqKpaUlz27z09PTBjLIcHDX5ZGREY8wmIu8Fqnivc3OzhqNCjQHr4pcXbG17ruTzWYbns9a41rlbNmjZdEaM7wnt1iYhpo0w6itrQ2ZTMZAID1QZW8IyugR877oNfL7TCaDcDhs4TxOfjye9UbYx8xEYZiShdfYfxRCsv1aj4hlCbSeB0EYj4/H49YPzMZiP7MaLvskHo9bH2zZsgXBYBDPfvaz7T6+8pWv4CUveQmARoHEo0eP4rHHHgPQYI6i0aiFZGdnZ/9FAbRqybiw8b5a9tQbQ/NkeDQUoSEXOm9uRk4mk8HQ0JCxFG54/hvf+AYeeughy3bcs2eP7T1HrQf1eRMTE5ifn7fxOTg4aMzDwsKCVUWORCJYWlrCxYsXbRsKAhOmwjPbSgtxEtREIhHLYiOQU2EtGRmWzigUCibGdis6K5Dw+/1WXTmfz2N0dBTlUtnmxFwuZ9pAOnUMxWufahkUOrHUJOr+gmRhmN7ObLFkMgmfz4dEImHnphOVy+VQLBYxNzeHqakpz5xIZ5MZckDjfY1GozaHB4NBTE1NWYbo0NCQMXOJRAK5XA7ZbBanTp3CpUuXsG/fPosuLCwsoFKpmPRicnLSHHeGHPmcgOYcrkJoajavNtu04IdUnmZckTHhINbUUH5PW1pawm233WbMzT333GNF6jay3/zN3/TUBWKFYU3THhkZMbZn27ZtFmpaXV1FNpv1ZGfRU+C90AMCGguz1vEZGhqyvV8Ab0VSGvuALBPDd2SZeG73t6yfw9/7fD5P5dakP+nZeJD3A3jF0ADMQ2UG3UYVn3X3ZU4mvPbCwoIBvqWlJU/IhqCLYSvd5Z73o5WOXUaKf2Of66ayFCQrE6QlE6hNYKiwWq1iYmLCM1mqmJ4Li9ZS4lgAGiCPNYV27dqF4eFhe9433XQTyuUyPvWpTwFo1Pn5zGc+Y5ln3/nOdzA9PY3bbrsNAPCnf/qn+JdMRd4a+r1asziebiOoIbuj7wj33ePfGTahMwA0Q5+aGq02MzNjgCYYDGJwcBBDQ0Nob2+3Qp/FYhEXL17E2NiY1dyhXqWzs9O2qkgmk1bhfG5uDsvLyxgfH0dnZ6cna0oz08jQaDYuxcgMqyhzwjmITlqxWEQ2m7VQEf/j3EEmR/cNZP/Nzs5itbSKOhoFF/kf29bd3Y329nZP//M+mBnM+1HnmYUK29vbkUwmLftTdaQM9el8l81mbS6hw9XR0eHJLPX5fB5QyhCh7srOQqwTExPmMFHLMz8/j4WFBZw4cQIPPPCAFVm99dZbMTk5iccff9z0mAxBss8ICDXUpanx7BOCy6vJNi344UtGDwloMg6cJIjKOcmrpiSdTqOvrw8nTpwAANx888148MEHbbHdu3cvAFh4oVQqYc+ePZ4QztzcnNHNFDyT7WHZeaCx+JAeBWCTGl/cbDaLZLIJMphqqYUKu7q6TBdCL4WLmCv0nZ6etpBYW1ubUbZAo14OMxeABthZXl62vkskErh06RLqtaYnyuwkghUNLS0tLdmLzI38eB+ZTMY8RACYmppaF9ripMtnypAY6+xoxpuK2VdXV7G4uGiCdda9cMcIw4H0HPl7MjBspxb8Y/orX3ZOiAR/nZ2d9kzZhyrcJuvHsgnKYM3NzWFhYcFAci6X8wCtW2+9FUNDQ3jOc54DoAF2PvnJT+LlL385gEZmmrJvL37xi/F3f/d3+G6mgvJgMOgZhy176i2fz1t4iewA0AA+ZEYYnlYxNNB4Zr29vVa8lPOUGpMcyK7wvOPj46hUKhgaGoLP58P8/DyWlpYQjUYN9DCzi4kkkUgEnZ2d2LVrF3w+H6anpzE7O4tcLod0Om1gRevnkE0hw6FhVoIggiMCC2ZRkYWgIJr/r4kKdHw5dwHNOZ+LeLFYxLFjx4xFAZpSBc6/1PqRkdJQJJ9NNBq1bTji8TgikYj1PZkp1glTTZCG5aghYlYor8lnyuOonVJdKpkvhtxYjT+TyaCrq8vqyRWLRVy6dAn33nsvdu3ahTvvvBO33347YrEYPvGJT+Dxxx+3kCfBtLJPKhHhth0EhZyDrzbbtOCHE4TqeHw+n+3JAjSZID7kcrlsi85NN92EYDDo0Yz09fWti51//OMfB9BYNDRNmwI0gqVTp05h165dJkLO5/PmcXPhZTuoD1Lmhy8M0Nwmgovz3NycifSAZrhIw0ya8q20KkNxPDabzdo+NLwWJ0qgmSXFPtV78vl8HgaLOzoTeGnFVKABhubm5uz4bDaLRCJhoSfGpjVcxBR8rYUDNNkLaq6SyaTtiMw+XF1dNSCWz+cxNDRkTBHj7mwLN1cEGmBmcXHRQlm8d94HKWhlzbq7u23iJeOleqhqtWrAbHFx0Z4t2SlqfILBIB566CEcOHAAQCOcumvXLuvL6elp/Mmf/ImBn1e+8pWo1+u2S/ztt9+Or3zlK7b9xUbG+yRVr4xVy54eI1Ag66shLs5tFORqReFYLIa+vj7bQoKFU2n5fB5f+tKXsLy8bLuPUxPI0BKBB8Mw1KqwGjjFvWQruC8Vwcni4iJGRkbQ19dnGWMEDsw25btA5kffPWpp9P6oxysUCiYgZt00AppCoYBCoWB1zQgM6ZCRPa9Wq/D7GtmdpVLJsuIIJLhGVKtVtLe3I5VKIRqNmiOmpSC6u7sNDFF3yGdGIMUQElPMWeWZ+3TVajUrfeH3+7G0tGQhy+XlZczNzcHn81lpEIb5OMeVSiVks1msrq5ibGwMFy5cwKFDh5DJZNDT04OOjg4Ui0Wsrq7izJkz+PrXv45t27ZhaGgIBw4cwIkTJzA1NYXFxUXP+CIwZWiS90Q5A6UWGvG4mmzTgh+ticGXimBIQQEHFdDc2gFoVBAul8uWdZNIJNYBn7/4i7+wAmH79+9HKpWyLQ2y2SyOHDmCb3/72wAaWo3+/n5b5Mrlsi1IZFYYiuLxKuZ1w2JdXV3GBjDTSVOvufkdr0VvXtM1AViZeRqzMbhwEwxpFVNdxNvb243Bam9vR3d3t4EShnvIMhGscVGdmprC8vKyZ58xBXHFYtHqEgENsMQ0eC4EPBe1BqpNYIgAaABMFYUvLCwgm82anoj3xbZybx8ANqly3BCcsZ30hgkimFXGz8rI8V/qiIBmij+NzxtohDTn5+ct/MpnfeTIEQDApUuX8Pd///f4zd/8TQDA7/3e7+FVr3qVpcKPjY3hOc95Dr70pS8BaGw265rq2lg8Dbh6U1g3g/HdJnhgeEVZa7IoBD7UlAwODqKtrQ1bt25dN2d97GMfw+nTpxGLxZDJZLBr1y5s2bLFWFgu/KdOncLx48cxOTlptXfIWsTjcXR3d6OtrQ2zs7OW3k2n68yZMxgZGUE6ncbu3bs97HIgEPBUq+dirqFuTeYgA0KAxBA9s95Y22dubs5C+nRyGUYj4GE6fslfsi0u/H6/sUn6zvJ3+XweqVTKNiXlu0shMuUNXDvYTs7HBLEEjGzr7Oys7ao+Pz+PbDaLWq1mThTvrVAooF6vG+OjMgRqPzkPMtPvscceQyQSsYrbHR0dmJiYQLXa2O7n2LFj+NjHPoYf+7EfQ09Pjzm71G7pFh+1Ws3E6JotTbCoz/Fqs017x5qKpzVqdC8pUngqLKZAtrOzE0tLSzh8+DAAWOE+tb/8y780JoGonIsW98PhS3348GHP9xShATCWR3U8ulM3d1BWMJRMJj07lCtw8/l8VkcCaDAPfKnz+bzRuUAj5V6zoDKZjKdUeSQSsewBAEZ3L9WXADRSqQnaKPTluSlK5AvC+DXvmx4qQQWfh37WtuZyOWPieF9KAQeDQQNY1WrVU0iQ2gECwlqthieeeMKYvq6uLkxOTlqf0uPhPSuw7e7uthRePh/S8OyHUChkzA7BJvuQYmd9JjRObgQkJ0+exNGjRw1kb9myBdPT0wbA9u7dizvuuAMf/vCHATTADwC8+c1vBgC8/e1vx65du/C1r30NT2bsI3q4HAutVPenz8gAUouhNXz0+VBTwvmOoalAILDhnHX//fdjdHQUg4OD2LFjBw4cOICtW7eiv78fW7ZsscXz7NmzCIVCdg5Wd+b8yfAPxz7D9MPDwwiFQrh8+bIlYgwMDCCdTnvCvgxv8d4IGjS1muwNGcmVlRWPTmd6etoqIedyOSQSCZRKJaunRq0Sw3PMmPP7/Bay0a0cAHgYKRYx5ILPhBkCJrJJnHu0+B/DQiq/4IalxWLRmOiZmRkUCgWEw2F0d3fbhrAUdQeDQZvLy+WysUIsUMp+ZB0zOpunT582zVQoFEI6ncbU1BQKhQIWFxfx8MMP48CBA+jr67NzpdNpZDIZVCoVKyHAfgNgwJLOsOoxde26WmzTgp/CPxWAKxIPgp1c/crCduWLsq+MFd+KbdUQiUZsQfrLrX+JQrGATFsGAJCGt6bP7OwsTv/JacTijRf6Yd/DjRLwKw2mIRqNetiCY5ljjWyEatP7r1auaFnqNZQiJeQDXsGYioxLpZLV1gmFQljyL9lxWnGUxwcDQfgDTa0Tz1UuNTyExXCD4cgFc55Fzufzwe/zN39bqWLJv2R9Bt+V8+1snG/kAyOYDDXYhEgkgoC/qZuq1hoTcmWtWVfJH/BjrdwsBlhek33H6ld2DPaLNqcOBENBz2fgSqXVSnNvtmq1ipVAMyV/AQuoVqpYq6zZT0PBEEr+kvVZrpbDfL3BytDbCgSv7DOGZhsWa4uYi83Z2PhO5DuNSdDXpOlrtZoVftR7AYBKteIJPfL+mTGXz+Wtv4PBIAq+gk04K5UVfDP2Tbuvj4c/jnRbs1hm5GgEaz+6hrELjXBg1+NdOHjwIJBpNGPpt5eQy+aw+sorgHYaT2o55OAP+D3Po2VPjzGlWRkELX5IT5yaRoLmTCZjwtvdu3d7zrm0tIQzZ854WF/WpKEjsbi4aCLnrq4uc2xYM4ZO29zcHNLptEfjQlaH4GtmZga5XM5+m06nLUyktb4AeAAKhcEMqVCHQpbk8uXLGB8fx8LCgoXteHwymcSWLVvMqV1aWjIgQgZXNZ56LQIw3YeMfcPj6EBTLK4MPVkelwXR2mI2D1/p61KphJ6eHvT391u/UcQeiUQ8m8UCMADFtlAiwfmCNdsmJiaQSqUM2DA8xsrztVrNKm+zjAeLNJKFprNKVpzrCMONfDbqKF5NtmnBD3xoTN6+5kJW52zOub1eh9/XXGyr1aplE1WrVQQDwSetcjs2OuYJAQUDQZTXyt6tM+pAIp6wz9Va1QZJvV5vLpb1xsKsVqvXPOnnqMuu28GAh9ny+/yoo5nSHwwGm/eKJqDhvddqNQT8zUXe5/d5FnsFHz6fD/Va3f5GobO1JRDw7PNV99dtUQ/4r+zdVWmCnSCkT31Xasv4mt6W3e+VPvIFmm3RY01YfeVUVX8V/oDfJnZ6W+wHv8+PYChobQ2GgggiiNJqUwgeDAYRCnt3MgYaE05pteTRwvjgM6BUq9ea/Xulj3z+ZgkFZmRYMcqAH6jDMwnzXAwNcDxUaw1xZjTSAJTFlaInYyTgD6Dmr3m2x1DLZDKePYb+RZNxVkMr2+vpMiYscIsBVt+lZo+siAIPn8+HaDSKzs5OdHd3r/PGP/WpT2FkZMRTiBSAFQy8ePEiTp8+jUceeQTFYtGYADKcBGGVSsVYFYbauKAyEYMgh04ZQ1as4MwxzEWb8yYZL60rxkW3UChgZmYGIyMjtr8hw04M+7B2DVPq+/r6LNxDZmYttAYffEin0wa2CC4JHrq7u1GpVDA/P2+sDPua+iWCEi0KqKnxfCbUaAYCAQulsdBpW1sb+vv70dXVZWE13XuyVqsZC2Nz1JWQObOJGTbU0gjc3oJlBnjv0WgU/f39VrJlamrKjlleXraikgxHUnzO9YbPTkOtZP6uNvNtVmo8lUrV3VownCRopFcZSonH43jXu94FoLFh5OHDh/HKV77Sc95Pf/rTAICf+qmfQk9Pj1G51PfcfffdABr08urqqn1+4oknsLCwYBk+WqV1586dngKBpVLJFkEAVq2Tk9nw8LCnDk293ijVzrBLe3s7fD6fpXqHw2FD81NTUwiFQti1axeApgick00mk/EMZBf88SX6ym9/BQBww6/e0Kj4jIY+iBQ3AEsPp86EGVHMeGO9IRXc1mo108iwAisnRk35DQYbW1loH6hnNTc3h8XFRY/OhoJEoOHVcKdntiUcDnt2hded7qempswLpkfNdsfjcXR1dVk7WfaeIIILCdsyMTGBbDaLRx55xJ43x+CWLVs8GR2hUAhnzpyxUCHQyHTj8/v5n/95nD171jRaf/RHf4Sf+Zmfwdvf/nY7/ktf+hI+8YlPAGhofj7zmc/ge7V6vf5DW+nQ91Xf5py8AAPDdOAIqNVJqaPJBjEE09He2Gg0GAqiPdPedPRqdVy4cAHjE+OIhCOIxhrF7ZKJZMOZqlRRKpdQyBeM6QhHGunloWDjvaQTEwwEDYTV6ldSoRteJuq1BiNbWaugWms4gNbuK84O5zafz9dkF33N2l31eh31Wt0cQDoOBA7lUuPdCIfDDYfmCliik0eHJxAIwAcfqrXmZrB+vx+rexvvdeyMt3YXGVn4YHWHfPAhGAqa8Nrvu8KM8jHQn/b5UEej3dVas/YS75HO0OpKc68xPudQMGROKe+7WmmeA2g6w8FgEKFgg10KhoLwwWcZbmuVhmCbjlUddSTiCfj8PqwUG1EHAq729nbEojFEY1HUa40MYGbOca6p1WvmwCqJwGdl4/DK8/+BAkDP/rfPbZuW+SGSp0iLf9MKlRSvcaF53eteZyLkRCJh9WjU3vnOdwJopMKz/DjQeFmGhoZscfb7/Th69KgtSouLi5iZmfHsY8X/379/v+19AzQWfV3oM5kM5ufnbbGlKJWLM3eY5+BjBoN6T0zprtfr6Ovr8xRb1IV+I92Nz9fc9oHXCgYajz4QCFh9G/UKgQZjkkwmPRuTqoiWuheCJZZX52d6GwrAVMfV3d1t16JGQIscTkxMmJ6GEx8F06VSCTMzM/b8WLFZNzPk8+jo6EAwGDTgSpZF+6hWq5m4NJlMeoTc1CHpdigEdkBDr8SK4YODg7YJIm3Pnj02LkdHRzE+Pm7nevzxxzE8PGzP/vbbb8df/uVfesDPoUOHTHjf1tb2/wR+WvY0GRm4K2FmMq+1Wm0dm23vSL3BKtbRBCO0au3K5qFXmNtgoMFcRCIR1FFHabWEYqFoIQ1/wI+AP4CAPwB/oBGuDgaDjWsz84csMHxWNZkLdCAQ8AAYMqMEJvVaHfDDwAHZbd470GSseYyVtQgFG20j83oFNNR9V9h0X3PeMmAEXzPL7EpUgKx3wB9oyASo5anVDXwAMFARiUQQDoU9ma+eR3aFDUG9wTRTW1RHHX747TkFggFvP6LeuOcr/VCpNnevr6MRnQj6GzKGUDCEUDjULEyKJuun7D8B32ppFZFwpDF+qlf2blwtobJWQSgd8mzFUa01QI5mFlqtr/qVZ+cDfPUrrPYVYI46UN+8fsT3zTYt+KEnz20MaIpOWdqdGT7Dw8NWXO7aa6/Ftdde6znn5z//eZw6dQoAsG/fPnR3dzf0FYDtiswqmz09PZ7U7NnZWdsmAWiwINzVnempvHalUsHS0pItcBTyEWQUCgVPij7TsjVsxJefn7W2Tltbmw1qZoxwoZ6dnbVy9ABMlMg+Im3N49vb222h7urqsrg6j9X4PtDcPgNoAEat4KzFzHjfFA/zean4mRQsAAsLEFAwBMD7ZrE1giPursxsMAoH2RamdLJdmUzGMq4ee+wxJJNJEzRzOwCtu5TNZj17g/Ge2S99fX2eyqgU1rPveB/JZBJjY2MGrE6ePInOzk5jx7785S/jda97nZ3/xhtvxD/+4z9accM3v/nN6O3tNXD19a9/3TbnPXPmDK5qe/bT3YAnt3CsKQbWrR0AeN5dOnDU/vz4T/w4rrnmGhw5csRTlPXUuVP4vd/7PXzoQx9C33AfnvGMZ+B5z3ueMYjHjx/HvffeizNnziCVSmFoaMi2SmAqONkQrU5MNlOruJNtp1aRIR8WH2RbGcajw6T1i5hKTTHxxMQEHnnkEczNzdm8QGEx26HlLHp7e5FMJk2fQxamVqvh2B8fg9/nxw2/cYNnj7+1tTXLdiUDxXAaN37dtm2bbcsDNJksDT0xNAXA9uqi07i2tmabmFLHxIKn9Xrd/kahNxNh0um0hTPb2tqwsrJiWWJAs8Ds5OSklRApFouIxWLo7+9HPp/HpUuXUKvVsGvXLtxxxx245557cOTIEeRyOXzoEx/CN7/5TaytreHkyZOWaUankuuA9iX1SQSYmihzNdimBT9E8cr8ECErkxCLxQyQ8HdAowKz/h0Afuu3fssACBkKLuzbt2/HsWPHLCPoGc94Bi5evOjZUC8ajXrCS0NDQwCaO4jrBp6aJUX2iIupz+fD6uqqZyHXisacJAn0lpaWPDvIa0ydxbeUsdKtFzo7Oy2dEmiADC2Kp+Eeht44IXIfHwVtrIcENIu2MQ2fk5iCCBUKUwjIdhYKBTtXNBrF8vKyJzOBxR+BBtPCuDl/z81nAViKPgEN4+zsI118qMWgcX80Ph+38Fq5XPYwdawftW/fPgDNIolAgyHM5/PG1DH1lWOD2V6M2Y+OjuLRRx+1GkTlchk33XSTVYBm1tedd94JoLFH3TOf+UwALfCzmU03lqQ2hgs1F36CBS7Q3NIgnU57ymYAjZIHzBKMx+PYtm0bfD4flpaWEAwGTRjc3t6Ozs5OpFIpJJNJAx9cBFV4re8EdSH8f4IZamK03AbBEIEDz83v/H6/bTRKFikajaKjo8MDVsjmUuNDUXJ7ezu2bduGRCKB+fl5zM/PG5jhpqrRSBQHDhywRb5UKlmdHU2vpwaHZVLYHs4zWpuIIFTLEhAs8nyhUAjd3d3o7u42LdLY2JhHWM1MX2aMUsTe0dGBdDqNQqGAsbExTE1NIZvNWiZgV1cXMpmMzakEcrVaYysO7bdCoYDJyUls374d9XrdyhhQw0XGXDPvNLWdERStbdYCP5vEWM9GC2ox7KL1cXw+H17wghcAaCzOBAlMgaa9+93vxsLCgnnNzIZQ1uMb3/iGbYvAujI8X71ex9DQkA2Qrq4uD9ghSgca3j71MABsgHOgJRIJqwMBNIDU+Pi4LaCJRMLDNhSLRVss4/E4isWiLdQUTvLaZIXYR9y6QdmX1dVVEzWvrKxYX/F3PDfFkiqOVtaICz01Q5oeCjRDT0ovs53sD/Y3gZJmj3APHQDGEpFZ4URK4Eb6VwXTuj+cerk7duxAOBw2b6unpwednZ12rcuXL1umCdAs1qipvtFo1BYO3cy1UCjg9OnTnqyQzs5Ou6+9e/dibm7O2j05OYkHH3wQv/qrvwqgUfH55ptvxoc+9CGo8RkdPHjwuxY8bNnmMYpmucgzvKpbLxBEcH+5np4e7Ny503R1AHD69GmMj49b7R2KkbPZLAKBAHK5HE6dOoXV1VWrDJxMJhGNRi00TRCmLIdW/uW+WrpTOXU2Fu65wuRQP0NwR0DH++X3vEalUkEymbSNV+fm5uw9VrARj8fR29uLvr4+c7hWV1cxPz/v3afuynTS2dmJwcFBlMtlS5/PZrMW+iOTRJad+3MBzZAXARGBqNZR43ynkQf2CbO4eExbW5sVXeTvQ6EQOjs70dXVZSCEBRInJycxMzNjWwTF43Hr37a2NnOICVjI+FNszXYwBV/T9Nvb27G4uGhsOq/LbDyCHs1E3Kza3++nbVrwo4NUMwo8cUw0KuQePXoUAHD27Flcd911AODZmBIA/uRP/gT79u3zFNkj/Qo0wgmTk5O22/YjjzziESWvrKxg69atnqJQKpjVbSC4Y6+GnlQ7Q5DGKsBdXV2Ix+MehiQUChk4ikQixmiQytTqxCydDsB2a1b9UTweNyBgouIrY10LkhEEaKEw/ZcvCbUzuVzOAA+wvqgef6eVrRWsVCoV6wMyWKqzicfjFq4D4MlyYZqoei7VatXOl0qlPDopTrYAzCPWsCQ3owUaeyf19PTYtXkN1TJls1k7//j4uHnFs7OzmJiYsOKa9BYJfur1ukcsvWfPHpw+fdqzN9uhQ4fsmXzgAx/A6173Omv7c57zHAuJ3XrrrfjWt76Flm0+IwjnQkOnTQvOATBgQLDAxVvt/PnzOHv2LGZmZtDV1WV1XDgfTU5OWuG9UqmETCaD9vZ2T1YPjYCdCyudJwIXshBkrriZaSQSQSaTsdRwppVzjzCG37ngEgQxq4xMO5meqakpC+34fD6kUin09/dj165dSKfTttUDiyACsPbV6jWU18qYm5tDb2+vZW0lk0n09PQY6MnlcvYOs04RqyszvEeHj3WDgKZmhs+P8xuP5bNkcUXOVb29vXZtPncWVGSdI+6XyPOw/AAdvc7OTosisLAkna3Ozk6USiUsLCxYGYJisWhbdDCrkGsP09/JMupG2Tonc1xebbZpwY8ONM32shRpNAb04OCgLRT9/f245ZZbPOd5z3veAwBW3IkL2NLSEmZmZky388lPftKAEdCovMsJgKYgQjU63CtFX1Jd8FZWVqxiMdAIySSTSWtLPp83/QwA29SP51NAQsaExRwZy9f9g0h1s8/oVbFtAKwuTSwW89yjVovm1hUM27HQoF67UqnYuRni0YwrQISOUoyRz0ABkgJI9p+WoqcOAGgwP/F43LN/lYayVHs0MjKCtbU1z7Nva2uztHJS6RxHLPFPwMT6GWzr8vIypqenje1R1i6bzaKvr8/ATyqV8ui3CoUCOjo6PFWZC4UCPve5zwFojOldu3YZ0/fHf/zHHvCzf/9+/Pqv/zoA4JprrsHc3Fwr/LUJjWFbLjzU0dCZI1inRobvRalUMj0Y7dKlS7hw4YKxoZVKBT09Pdi9ezemp6cxMjKC0dFRLC4uWgiJ52VKOasyc/7kIs0tIQhWgOZ7S9aD4S2gGS7hbxmyo6aEzJbWlgGacwAduUKhgLm5OavA3NHRgYGBAXR0dFiYjeErfTctC7MOzxzJtUEBB/uLzme1WkUul7MwNhd9DY8RyBHg8dwEiXR6Nb2e56Fmsa2tzdpNNooAk1sh1Wo1ZDIZS47IZrNob283uYTWGyKQIwO3tLSES5cumQP1vOc9D8PDw+jo6FgnXdCtLTSBSFPgr0bgA2xi8EPkzIfEvymoCIVCuPHGGw3dHzx4cJ3O58/+7M8ANBad8+fP20J98eJF7NmzB+fOnQPQeDlvvfVWHDt2zH4bi8VMxEz0rou37t2ladfZbBbRaNTCQfTGCG5Y2IrZaExfJ2vBCU4LY/ElTyaTWFpassV0enoaPT09HsDhZntRk0TTGkPctBCA0eH8vLa25tm9HGgs/AxVkd4mK0XQxe8Z92afuZv9zc7OesoDTE1NebwsiisB2KTFCamtrc32JGKfcUdm/p5sGAXhWktHs/Ooa2BYa3p6GpcuXfLsDD84OGjjbmpqCvPz8zbuuCs10AgV7t+/38BLIpHAwsKC9TcnZo2v7927F9/5zncAAK94xStQKBSMwbz33nuhppqNX/zFX2wBn01qqisBmmyuuz0PHSxunJtOpz0OCNB456gBSaVStqt3LBYzFoC7uRPw0Jligbz29nYPKGDozbKSrgAi1e8wrMLQNx2pWq1mCyzFz5yvqYvkOai1sYymK8d1dnbavJRMJjE4OGg7yXMzYBYCpMaFCQ8+nw/+gB9tbW3WNoIT3TYkGo0aEGEfaxaoMtVuZjH7Ulm6YDBoDI0WcySI4Lyqzo6ySzyec3IymcTQ0JBpDCn25m/YnkqlYnpIvv/lchnj4+NYWlrCnXfeiV27dqG3txcXL1608aUhS61ZRvCjbWyFvTaRMYWdDxBohjYIYMbHx3HTTTfZgCUjQXvf+95n3vmePXtw6dIlT2ZAJBKxRYfVTpndxV3HlRU5ffq0MUtajI5ZBgyDnD59GoODg9Yeakz40mm8lfdaKpU8KeV8SYAG8if7wt2iVdirLyhjxAzhZDIZT7aWMSVXxjp3LAYaYGZxcdEj9OWO9Dx2aWkJN9xwAwBYeXzVH6lOhwBEd7N3w1oaYstkMvYSEpRp3R9mmwAwWpzCvnQ6jf7+fs/mpRwnu3btss0F9b547mQyadoCoAF+FKzm83mPOJsF31QTxAVraGjIs0XIysoKlpaWLKtjaWnJExLl9iME4exPgp+vfvWrOH78OA4dOgTX/vzP/3zd31q2OUwFtsoQcAyqiJaLGRdx1xMfHh627VF8Ph+2bt2KRCJhG+p2d3fbbuxkgBYWFrBlyxak02kP6xKJRCwcVS6XrUo0HQ0W36MGiOCHYIALK5keFQkrqOP9kSVixhHBTzqdtlARNS1krZiQQGcMaDpmWkyRfcqMMAVjZF6oieE2Iyoa5r8ER6o/okMLNNl2oDGHkTVjeI/toxaHrBGvxYwqOvMMHXK+TKVSmJ+fx/LysoGzXC5nawJD/PF43PY/y2QylnXKenXcvHZ1ddUTzmNbOA7IXKkD1gI/m8w4QDmIOJDpwUejUWQyGQMZGtsGGvskseIzs5R4bCKRwKlTpwywPOtZz8LDDz9sCzf1JXfddReAxiJ0/vx58+g7OjqM2eGCrtlA3FsHaLAUboVev99vAIrZFVysgUaoi+fLZrPGvvDF4WDt7e3F8vKyvaj0FggKlpeXMTAwYItvW1ubR+eje44Fg0Hk83ljoHw+H7Zt22aZJ7VaDQMDA8a2qFCR56KXqt9rtp5qk6iN4ndkvIBmrSPeR09Pj4WngAaI4K7VQANkaD8NDQ0Zc5PJZEyjoM/LfR4EyslkEsPDw9an9GR1D69isWj33d/fb9fq6elBe3u79eHk5KQBV6BR8HJmZsb6oVAoWDYLj69Wq54Mu9///d+3vb/YHrb5uuuuw6OPPrruflr29JqbWaW1VzT8RIdOGUHOSbSXvvSlOHv2rDHF+Xwe8/PzCAQCxhgFg0ED+EyYWF5e9mQtcVGnY0nAQueFYRsyKPxe2QOyQQz1q/CZ963VhDW7ilt+MBzI+2Y/EMjwfeOmoG1tbRaeI+hCvTEHsKJzNpu1JAbqiti3+izodFEXqZmaDHkRGCpYAhrvHbPY+D1Fz9y3S+diVs7WUFy9XkcikUB7ezvy+bzVhON2SgxRcv8wDdtREJ3NZm0D3Be84AVob2+3HeaDwaCJp7VkAZ8hk0I0NKm62qvJNjX4aVnLWtayH2RTVlazMHXB56LKdGYFywBsr69kMmn7Y01NTcHv9+PMmTOYnJxENBrF7t274fP5sGPHDgANZ4yZWNTHcYHntehIEVRwoaRQlnoXttO2mLjCflAPxO81BZ6CXWZb6gaqFH4zZEwHkHXGuAcW9TTlctkyoLgNz+joKNbW1pDP5xEKhdDf32/hHIIuv99viRMLCwsWuqITyZIWZIyUbfL7/dYmflZdlOpoisUistksZmdnkcvlrBwGK9zTsSNbFI1GDeBQn7i0tITFxUXrfy2XEIlEPKEzpvVTW0j2izpNOo4Mf3K8ERBybBKMq+b0arFNC364yZ0yJqQxiVKf8YxnmLcOAC9+8Yvt/0dGRlCr1WxwtLW1oa2tDSdOnADQYApOnDhh21fU640S4VrF9+DBg8YcRSIR/OM//qNl11x//fXrBMukQOkV0XsHvDobUuL09jXTAGjqi8jIRKNRY1O0zDuP1T6hh0CN0OTkpCeEEwgEPNtK6D2zxgYZjVQqhZ07d3o0CJx8ed/cjRlohB3H/3/2/jxG9vw870Of6qWqu2vtWnpfzumzz8plRkNqKFK0bFIUdEUzNgQpln1hGNC9wIUdBMh1bCDGDRLcAAkSJddArhPBRnKTyLKFyJStWJREiaLEnZz9zJw5S5/uc3rvrn2v3qruH8XPW++vh9TCWJrmTH8BgtOnq6t+9Vu+3+f7vM/zvNvbtvPB1grTMjIyEtAHkWvB9fEUPfV8X1I7PDy034+Pj2tqasqOvVAoqFQq6d69e3aOveMqnU4b08YOk+Nmh4X1nXPg3XzYRyVZZ2Z24tTupb5zD2El53d4eNhKZtJgUpX67jCfvVQqlZRIJHT79m1J/d3jV7/6VfnBffT3/t7f08bGxgXzcw6HCXMl6+gNcwELATCg/ITFHQbW3zMImBuNhq5du2bamPX1dWvkOT8/r1QqpXQ6bXME1neeaa9ZodyGBIByiC8Zc6wwCcw7LJxeiMzwbiiOe39/X1tbW+p2u9aeIZfLWV8qSaZlPDk5sSBBtH+lUsnA0/HRsXojPXOihcNhzczMaHFxUfF43GzonU5H5XJZhUJBDx8+1Pb2tjmqlpeX7VzDyhwfHweyxCjzI1AeHh5WOp22oFjKecRi4HRDTE1OD7orHy4I4INJ8yDx5OTEnHEejHlQWqlULGJjaWnJyv7ca7QdIi8Phi4cDgdCIL0xxjPt74dxbsEPF8e7idrttqLRqGXz4Hb42Mc+JinoVPrZn/1Z1Wo1W2x/5Ed+JJDjk8/nNT8/bwvkW2+9pXg8biCBvAkW8sXFRX3iE5/QK6+8Ikn62te+pieffFJSv+wBFSwNGmECfmhkR+mJSYHXF4tFm4AkWRM/FvaJiQnrr8Xi6OvJyWQyIN7zPbIWFxd17dq1QL230+lobWxNUj90j3M0PT2thYWFgA0XV5zUB1K1Ws3AT61WC+wWcG/5cmUkErHXh8NhPX782H4nDezj09PTmpqaMsCB7R5A0u12zbEhybodA6xmZ2d17949+6ytra2Avmh0dNTuD/QS3FfsonzZsdPpWHru4uKijo+PLQCzWq2q2Wza+2ezWZvA4/G4jo+PrTxaLpfN+cM57fV6du0Rp3LO9vf3lU6nDaR/rxIdKeUPHz4MdO72LTUuxrs7vFuIa46w3tvfz8ZaZLNZzc3NBYBPvV4PWMgJZ0VHg36NRpk4G6enp5XNZu35xgzBouwdRfw3It+zAmDAzNnMrsPDQ7XbbcsTOmudD4VCevTokb75zW9qbW3NFntyf27cuGERJGxA6vW6Jicnjf0pl8v2fA0PD+trE18z1gMWC01MPB7X8PCwyuWyVldXdf/+fTMoHB8fa2JiwgIjT09PLXsIEAOzRRmrUqkYyCIjDYDD3wBQABmAH64F50+SpT/7648AmbBKSqKwPoA+GDzWxWazaWV/jCdEdDDPIwjn2vr7kHvUm4reT+Pcgh9JFq1+1mHFwplMJrW5uWkgRBrobgqFQmACgXW4fPmypP5NeOnSJduhkwHBQzY9Pa1QKGS7+93dXd28edNukldffdVEiKOjo4pGo/Ze9Xpdx8fHgSRkb9fkeLwVnklBCooheS2/a7VampqaMoTuuyJLMtfHjRs3JPUbtvqmq+hwvh7tM1gvvviiaahCoZBqtZoBlEePHgUi2Pf39wPfg4eXBdoHukl9AOvZGnZ1vBZdgiRrGuv1R9ls1s7/0NCQ5ubmjGFhQocRy2QyunXrlh379va2tbOYnp7W4uKiBQWSK8I5q1ar6na7dv2gxj0rdffuXbsPG42G9vf3DSwRsiYNXGcAEoIg+d4+cZbh4wTu3r1rUfa899DQkP7ZP/tnkqS/83f+joHkj3zkI1pdXbU2Lhfg5/yMarVq2hvuG+8Cwi2Eq4k8nHw+b61MGOVy2ZyRuJ1gjAjbJI4BQTM5L5KMzfD6u9HRUcvtYQE/62YltweGAI2MN2pQuuI5h82QBvNBPp+355IcslqtpjfeeMNs/JcuXdLc3Jzm5uaUy+U0OTlpTEitVlOv1zPB9puJN9XtdXXt2jUDbTBr7XZb5XJZt2/f1ttvv21/ixYKULCzs6OpqSlNTU0FHGk4cQEoIyMjarVayufzBkYAb7BfgBFSvL1LDCcYYbMcJ7lvaI84z95pSygj5TYCJnlftEcMv574UiQbLX5G/Mya55Of30/j3IIfarBSUMgcjUZNcNvpdHT9+nX7WZL+h//hf5AkS91k4S0UClpZWTFw8+1vf1vDw8MBG3en0zFXTTgcVjKZNACTyWS0t7enD33oQ5L6jAu789u3b1tmgzRo0umZKO+qYlLg2FhofbnIf39SgiW9g5b0gkGpf+OvrKxY8GM0Gg20c/AiRqkv3Oa/33jjDd25c0cPHz6UNMi/8UyQBzBMBt5aefZnWmBIg7Rpzp+JFzUQgPv4gEqlYu67oaEhvf3225qZmZHUB3VPP/20fdbR0ZGVKBnEFIRCIc3MzNjvodcRJfPdqO2zC+W919bWdHBwYMCaNh/8vlarGTvGBMt7MTHyt5xTvwj5772zs2NaB+6FarWq//l//p8l9cEPAP4f/aN/pL/9t//2O773xTgf46zFmxILYISec1L//ob1qFQqdp9LfXa0Xq8rEomY2xW2CNCEO3JyctLC7fiMo6Mj09yQ3ZNKpQLZP41Gw9glQBrPu3dz8T/AGxsANCcs+l5k6//Gf/dwOKxisahCoaA33nhD2WxW169f140bNzQ/P28tGyYmJuxYTk9PrRmsFzW3Wi0dHBxob29PGxsb2tvbM3Eyx+LLWt1uVwcHB2o2m5bhxbxLOV8aGEyw2rMxZWMOm+KfZ85HrVZTpVIxETfgtNfrqdFoKBqNGhD0II1zy89olGKxmHUHODo6CmSRkRtHPzSuI+U17h0vwGfdGBkZCWQDvV/GuQU/fmd8NnEYq3Wz2dSnP/3pwN/97//7/y6pD3aeeuopK6uMjIwok8nYIgWaBhSUSiXFYrFA7y/fWT2dTgeU/Ldu3bJjfOmllyykShrYmX0+kV9MvbtD6pdVIpGILcYABJ95A3pnl8B70erCO5mw1kv9XaOnX9k90tF5f3/fso3eeuutAEjjQfdgx0cNkEHBICPIa5sIBuP7eKtpJBIJ0MJMJtKAGeIckIQK+MMVxS6ZHCDPYgFGC4WC1tfX7bihfJngxsbGjNXy553/39jYULvdtrYSIyMjWlhYMGCczWYN6EIhU34jmh8ND/EJsGksil4vRuQ956Hdbts1wq3D+J/+p/9Jv/ALvyCpnxeE5ulivPsDISn3MWwNDAlzRLvdVjabtb5dRCgwiKsAiGA9B2wQ+1AqlTQxMaFoNGqlMcBUpVJRo9EwfdH8/Ly2trbs33xpxZfsIpGIfZ6kAGDi97AYvgTk2V70kLy/TxzG8t1sNtXpdLS5uan79+/r+vXrunLliiU4o08ygBUasu9+cHCgu3fv6sGDB4GASEAnw2/OYIAoVaEt8v0buYb059rf37cyNv/uLeSEM3Y6HQs39OeAaIBut2uAB30n38/HksDsYaMfGxuziICjoyPl83nt7++rWq0qmUzaPbS5uWnMTr1et3Pvc/I4N2zOPDB6v4xzC378g8KiRM4BWgxuWEan07HQN8S7LIbQkSxg1IoRnhYKBS0tLdnDEovFApqSbrer2dlZA0/tdtv0Qjs7O0omk/bzK6+8omq1apMY2TwsWhzzWfqYzyKTwu8mOE5pkFAsDVgIPuvatWtKJpPGaI2PjwdAB03z9N37/NGjR3rzzTcl9an6s0DprEOF78I58IBlfHw8wGjx0PG9PJghW8QHqvEa/t9nUZx1JJyenuq3fuu3DBQuLS2ZjkHq75bp4xYKhbS/v2+s3/z8vDk0+KytrS27V3BoAJ6wufP6TCaj6elpyxSiY7M00CJxjlqtlra3t602z6QEiPO7bKkPxM4ufqR4S9IXvvAF/czP/Ezg9wDfq1evXoCfczS8s8brOxC3suhLA41QJBIJNB6W+kYCkoHj8bgxPSxi3j5OI8zh4X6jyq2tLW1vbxtz4XU/p6enyufztuhSLkPbEgqFTEAdjUYVj8fNmUZZjTIX1na+M+UUn+lTr9ftPABAmBNgJjqdjjE39+/f1wc/+EFduXLFctiGh4c1PDSsnnomZL57964eP34cKIGxmQBwMP/70hfBkdj++V8kErGk6Xa7HWCVS6VSoOUOpSzWInLXsPVHIhEDpLi+er2eDg4OrOej3xx6dxnd4E9PT7Wzs2PAEv1ouVw2w8MnPvEJE7MzV8ZiMZVKJft3Y87ceuCvBfP4+2WcW/Dj6+Ts0Ml68eF0PhPjn//zf2478CeffFJXr1611+bzeesSLA1KG2T1NJtNQ/LSoFzEQwT1yHENDQ3ZQ3Dt2jUtLi7awvvcc8/pi1/8or13JpNRo9F4B7L2ZTJ2HZICuyaOzS/67N44Lv++y8vLVt/mPHr2hfo8i+3e3p59R3RIvBabKbsRJjSO72yHeVxl/P2DBw8MEHHO+FuoYhZ1djY+cyMUCgV6XgFYpT5ArNfr+p3f+R1J0uc+9zlNT0/bNcENJg1KaOhqzvZ9QzDK6wHOHDeMHxNgLpfT7OysHUs+n7fzmc1mAwLCVqulhw8f2jlmUfGlRJ/4jCbAM5LRaNTA72/8xm+8A/zAOJ5NBr4Y7+6ABaXk4ku+AFqch5IMHCCWZ+zv76tSqZgmByAei8UMPFPK4h6o1+vWf65WqxnY9g4gwlNxlgG6AAY4wHzLGY6X7Bkf6OcZeoDe0FC/W/3i4qK2t7dNDAxA8+GDmEaYszY3N5XP5/XUU0/p0qVLSiaT5mztqaf19XW99tprNqejSfKMuTTYSOHCRVB8enqqUqlkAnPOJYw3QIUqAZujvb09a0nBJhnWB4MOzBu/Y45GT4gsgwHQQmCNI5fNFK08fPmPOfD27dv6kR/5EaXTaaXTaQuxpMwKwAIAsqmljAcjeTYn770+zi348ayBL8PQyFPqLzReHPh//B//hwGOj3/84xoaGrKf2XFTWpqamlIkErHFMhqNWssEXr+1tWULXrfbVaFQsKaevhxEXZgFLpFI6OMf/7iVKrxbQRoAFi9+9EBLkgntJAVsrwAQAOH169dVLpdtwmQBP1vO86ACXYkky/jwx8VkzGehP5iamlI6nbZJLhwOB/pxjY2NBUo2rVZL9XrdXs/uk9d6kR2TH5/Nw8n3kAaTm9QHvu122zRBf/iHf6jPfe5zAcEgx0WXa8Amuye+NztZFo7Dw8OAg25oaEjpdNq0ZViJEbyXy2X721qtplwuZxMJmR++6W0ikbDjZiHkeyNW5HvEYrGAxfgrX/mKzo7/8X/8HyVJf+Nv/A1NT09fCJ/PyYABRPtDWUgaACMfVMj8c7ax6fj4uDX9hVHOZDK2oFUqFfV6PU1PT5uGg9Y0gG0ABwF4LLTlctnmWcpfGDYojcB8AsphfFngSXpHfE1JD2YnHA5rYWHBsoV4T8Dc2dILCzaalGKxqJGREb399tv9dPa/UdNoeNRK9IAsxON8Pv/G+fesG4AE5gfRuDQI0wUsACLGx8eVTqd1cnKi7e1tm4NzuZzNnaxbZ8XpAJZGo2GlazY1uHNHR0fNnEK7pHw+H2hUCxjFVUdjWe6RlZUVra2t2TwMWKLc7q8L5UmO0a8/74dxbsEPtXJfPx4ZGdHKykqgPIT7R5K+/vWvm8vpp3/6p/XSSy9ZmWtqasqCsiTpx37sxwJpxEtLS5qcnLT33t/f187Oju2qT09P9fDhQ3s/SSZWpCbNzuD09FTT09MGnHZ2drS4uGgPVzKZDDBWTIie7kQcKA0WX0m2m+NnQNLNmzclDWhYnxuEwJDx+uuvBxgVvwNBGyX1AeHy8rL9TEYEgIRJ2DNHfgf47LPPand31xb6s/3FPMuEs4SfAascNzsqzhEPP6Dg/v37unv3rq5duyZJgZIaIXE4TrygUBpoA84K1nnvK1euWMaH1AdPuFT4XoBbdlDsosky4bhhr3Cxvf3224FyHZQ1xxKLxUyUyTn/fmFkpVJJN2/evAA/52QghvU6OHbdgH2/UOZyOeVyucCzK8mY3sPDQ0tRLxaLlidF2QhhM3280M159mZ4eFilUsk0QMSHeNGudxix8HugIA3Ks8PD/UbGiIaHh4etpQ1tZFigs9ms9vb27PsDUAAllOoAK7lcTkdHR5qfn1er1dKDBw/6ZajGkSJjEXtWIpGIARHe86yjkkUenSFACWkFFQXaCvlWHKOjo4rFYiZgBlCR5TY3N2fHHw6H1Ww2dXp6ao2eeQ+ca/7Y2GDCepF8T+eCcrlsZU6YpsnJSQOZxWIxENGB5T8Wi5kWi422T/n2ydve/v5+GucW/EC79nq9gMB2amrKQMfZ7se9Xk8//dM/LUm6efOmCoWCZaLMzc3ppZdeMgF0KpXS7du3bVGZm5uzSUDqU5ugcqk/kR0cHFjpxLMpNCP0TMP29ra5cqCXffnHW9lxRAEiAD480DMzMwHBsw/Yy+fzajQagY70OIh4b1+K2tjY0P37920B3dvbM4CHAwQAmEgkND4+bhoU2keczUrywm5vvxweHtb09LSBjsnJScsrghr3ImMmas631xP5ciO/57vy7/fu3TPwA33OOSD8jNciOpdkvwNs4rzj3pidnTWRotTPENrc3LT3Z8LhWiWTSSsz3Lhxw3owcbye5Ts9PQ1cD5ob+gatHiROTEzod3/3d/W9BkLsi3E+BgCH54MSEfe916XQhHRqauod17DRaJibiuez1WpZ3lQ0GlUmkzGbdqPRMJaFxZfw05OTk4B+DcYGwICuBgMBOsB4PG7MBc85rqKhoSETRQMa2u22Go2GAazDw0PF43EVCgXbsAFIYPNhSHu9nml8xsbGlMlktLOzYyF9x6Fjy//JZrPK5/P23VkvcIgxHwG2fOnIhxnixuKZJxEb5s67UylBdTodpVIpK00NDw+bvoeWFb6c1Ov1+5zh/qIEJg2akHLfZLNZTU5Oan9/X61WywwXMzMzBmxarZZ2dnZ07949ff3rX9eP/uiPmnCadSIajQa0izBfvprCRvDC7XVOBjeqz/kBrbMAAmR+6Zd+yX7/cz/3c5IG8eMshoeHh7p9+7aWl5cl9Zmdo6MjW/jPinXJXYCtyefztvhJCvSloru5z97x3eWfeOIJbWxsGIg4ODgIhCJ6fYkkmzB84jDMweTkpCW9SoPEZ96Lc8QN72PrOS/VatUamz5+/DjAMoyOjpqo+/Hjx4HSE9ZWXw7yOROwTvweHQ/MUq/Xs8mFyZ5xeHhojQ2lQVmA1zBBeivp2XAuz4j43TZ0vXdS8HmSzEHjxamNRsMYL7JQYFTYzfJdvEgV7QTvRXI1x9JqtVQoFOx7zs7OanV11RbCSCQSKHNCmXOeR0dHA32+/PjN3/xN/a2/9be+5+8uxl/8OGsYkIJ6PnQZsBHYnc+OQqFgwlTmP4AV92q1WrUFGxDPe3mrOfkxnj3g/w8PDw0U1Ot1cwoNDw9rbm5OmUzGSi0wO34+gCEBLMAKAWKuXr2qo6MjPX78WCcnJ9bFnlISFnM2Qp1OR5/85CfV7XZNA2Pnp9c/L4uLi7p69aoePnwYMIFQSiRJHpfa4eGhCoWCubYQPTM3sd5w7bzhIhwOB5ovo9OEQfIsTywWs2sByPMOXCQAPtIE5hxWfHR0VPPz8wZCYf2SyaTGxsaMRapUKrp3755+9Ed/1Bg4QODo6KiJnrn/KI3CYPlN6PtpnFvw40VrXLh6va5kMmlAANfXv/7X/1pSv5RF2evg4EDFYtFYii996Us6ODjQCy+8IEm6c+eO1bal/mJYqVQCYYKeTuSB8ouSF7FOTU3ZcbEww8ZgR0d/tLu7q+3tbTs2HlImRuhy/p5djCTLnOCzFhcXlc1mDQxRmgIYAuC882x8fFzHQwNGCzaGkowv/3gWYmpqKuCQg8qF8UILwHnjAeN7bm1t2QTFztMLzElWZdDzhvvgbDmPY+Czfcmg0+mYDscLEnkv4gf4GW0N52xnZ8eu7/z8vI6Pj00fhv7BL1Re14SoVJIF2PE96RDPOYT18602/I5wb28vAGbHxsb0+7//+/p+g3vsYrz7g1ISjAqbAxZX7h+aWuJyWlxctPd4/fXXtbOzo3a7baXew8NDJRIJc1FhefZNMdkEpVIpEx8DZnAysXGi9MMCWyqVLPXc37exWMxYUt97q1QqKRwOK5PJ2H06Pj5uIX0s5tPT07py5Yq1ZaD05jODAATz8/O6evWqhoeH9ejRowD46amn0FDIUpovX75srHKlUjH2SRoIjAF+ONgQbmN3R8ztM8qI14BB8SWxWCxmpXW0OAzeA7BKg1a0oeFwWKVSyVpZ+A2czy2KRCLGzAHQ6E8Gg0NkChILnzyNRtbrfLzQ2ZcFvcni/TLO9bf1ojVp0IMJwAI9TFnlr/yVv2J/OzU1pWw2azbub3/72/ZvkrS+vq6TkxMDCeQl+BLP3NycLZitVstuRKlffmIhp8bKYGHzpQtqr1J/UqRuLw0sjT6leXp62tijdrttQuZXX3010N388PBQ09PTdtzUyT3VeXp6aj8zadbUZ7FwX0gDIOLdXeQd8e9nHxgv3K7X6wqFBn2BmBB8fy4edCZOPssnXHN+0Q9IwUaC0qALvAeB165dC7A/fjcD9c2/Dw0NBb5XIpEwxousDmzjfkKS+oDEHysLkjQIUOR7waSh8WKHC0jBYejjADwln0gkbPcmyer+0vcGOpyPi/HuDxYbLOTS4Hk7q6+p1+sBIwLj2Wef1e3bt5XP53V8fGyZUrBFlFyxrTcaDQ0N9Xt6zczMmEVdkumAJAXAhGeRKFHRewt7OywOMRCwKOVy2QwElO9gPtDwEMSIsPrSpUuanJzU3t6eDg4O1Gq17BiuXbtmeUf379/XnTt3VK1Wbc4Lh8Pq9DrGnG1sbOjo6EhPPPGEnn32Wfse3oLf7XYN2PlMI2lgDKlUKjb/TkxMBHJ5fINRxNVY5nkPD3i4tgiZscBzLWZmZpRIJNRsNk10znEhkQAQMk+ygTo56fc9SyQSKhaLxpbxmfT4Wl9ft/nBi7AZgHCYKknv2Fi+18e5Bj/QhT43BqAgyUpagKGnnnoq8PfXrl2zEkGlUtGLL75o6cWNRkOXLl0y7Uy9XlexWLS2Aoj4PMUZj8cDvaZoYTA/Px/YNWAP92xMs9m0309OTurWrVt6++23JfUF0aOjowE7KlSw1J8gcanl8/nAZzebTbNDS9Lm5qYWFhZsoeaBZXGlFk3Ioc8fot2Hz0Ly4AggBZhBRHc21JDXe3Gk1J9UPI3s83B81o00aOnhS4sk0XIv1Ot1A4gvvPCCZmdnDVDG43Gb2DqdTiDnh10T9w0uNT6fWAI/udVqNXt9q9XS0tJSIIjQd4JuNBrGdtFwlfuK1wJmSOn1JQrKDny2v3e8+473/Nmf/VlJ0q/92q+9wyZ9Md69wcbA78SlQfwEpSjcfrVa7Xv2cpuZmQks1tFoVOPj46pWq7ZJ4B5HLJxMJnVwcGAleK9LkWRaGCzvlMNDoZCmpqYM/BD6BwvMM0XpBPCGoxLW02+66NJOBg7z6sjIiM2/ABJA0/379/XNb37TQA7zQ7fblXoD+/rw8LCxSDRKnpub09TUlAXLhsNhE00TeTE0NKRisWiMMPMlwYIcXyqVsuuDVofX9Xo9c40CUMgFgtHBAQeY4v/ZEFP6gx1CkzQ0NGTrXLvdto0fn4Pt369bnU5HyWRSMzMzWlhYUK1Ws36E3H+UuZhf+R6w9O+ncW7BD5Se191Uq1WlUqmAC0oaINYPfOADgfeo1+v6xje+IUkmyGPB+MhHPmLdkaU+83NwcGCLR7PZDAAvqb+g+qwXJoKxsTFbrKTBQs4D5XUsHG80GrWeZH/wB3+gnZ0d+57Ly8uKx+N2bCcnJ3rppZfsPTzTMD8/r0qlYuwXicIwXAgVOVePHj3S3t6evbdPRR4ZGVE+nw/sBE5PTwMMFzoFXu+jB6CC/eh2u/b+S0tL5pCiNu1LbL6MhDjUW/IRcHLOT09Prcx56dIlKw1IA62TNOivRTkBuptyH84NQB0TF+D6LDAbHR3V2NiY7aix/EoKTHKSrDs19wDHwnelQ7wXdA8NDRmDA9vmGTAm4M997nP6/Oc/r1/7tV+zY/vkJz9pAPD7CaMvxl/MwJLsDQA4WLEzs/DACH6vBQhth9eJeMcrCy7CZnp2IX72uh5pkOcFeOK5mpiYMEMJgmnv+hobG7PyfKfTUa/Xs5YL/A2fw/3uXVYACJgKAABlaPRw9Xpdu7u7VvrxUQ+np/32Fuop8B7Hx8fa2tpSPp9XqVTSzMyMMpmMiYM5NjR4qVTK2DQAI5stP3dIg7wgND1+k8dmplKpqFQq2XcDqHBuAGJoq4ji4NjS6bS50Kanp21DRGmNOdmX5olJoNn0o0ePdPPmTTuudDqtWCxm4bCsA8zf3nXmN+/vl3FuwY+/6VlIUNF74fHBwYGxMY8fP7aU5Xv37uk3fuM3jPp74oknVK1WTSfE7oAdOH1RuDFWV1dVKBRsBw97Qkknl8sZ4CEingfGI39pkN3i9UU8SNKgsSXAa25uTt1u18ow6+vrNtmR6AxoaLfbSqVSxgy1Wi01Gg2LAFhYWLCsDEn66le/2j+u71avJicnbWGFVeK4qI8j/GWn49kwf87YTQBYqHdz7EtLSwG7Oa/nvZiA+NmLwtmR+RLolStXDKCkUint7u5aSSgajRr43NraUiqVMhBXKBRssvKDCWZiYkLZbNZKjdVqNVD2wtEBEJ6amrJrOzk5qWq1amAIN4hnifL5vAGn/f19u385h74HGo43nw7+7W9/W99v/OIv/qJ+/Md//Pv+/mL8xQ122V7jA7gFEEgDp2O5XNbjx4917do1e+YkmTUdoBSPxwNlJakfpMqzSr8+Sk6SrB3CycmJdUwndVzqP+s3btywkhOACKadHBuvGYFNoAeiLw/B0gASKGtjHmBBHx4eNj0L+rdwOKxcLvcObSFM9JGOpNBAIMzfzszMKJfL2evQv9RqNWNtAHmTk5OKx+OWlIwtv1AoWHkP1xrOKYAa35vSX61W0/7+vvb3920DBpBio8XaQXyHd8j5Zqg85ycnJ5bT5HVIADFMLrj+6C948+ZNZbNZY/Rgx7wOkc8nzZv3e7+Jns8t+PF2ZBZE9A8+/t07Zf7lv/yX5sj58pe/rJdfftkASCwWs+7evBdCY0lGUfqd1927d+3vKbl455lvdOkF0Nx4/C2CVR5mgslgQbweR+qzGtvb23r55ZclBQMVeQhZbNfX17W0tGR/ix2dktrq6qpGRgbtL8rlcn+SGen/fTQatUmQkC2fCZRIJOyz0S749hecOz/8OaJu7V/v/8afI/+3vN67Y+htI/VLAcvLy3Y9oYoBDcVi0cDKycmJZmdn7b3L5bLVyxmnp6f23ouLi1paWgpogCqVir0f9yDOwUwmE4gl8GneY2NjWlhYMPA5Ojqqhw8f2rEwyfq4ADKN/PfypUU//qv/6r/S3//7fz/wb1/+8pd1Mc7HoLzFws4C7LVyzB2tVkt3797VzZs3A+Dn4ODABPpYrgEoiHgp+bBw+1IVbkHPLOBaogEo8yyZQCyOY2NjikajZrpoNBqB7zAxMaFUKmXHgvX99PTUmKqzcgDPhnGcPo8oHA4bQKNsA1MM6BgeGtbU1JQ1HMZxSRjgWT0i7JJnbnBvAVLL5bK2t7dNU+W1PBwvpTyAbLPZNL0orYOIJpBkYDSZTAZYewTonCvuDdY6NFLefcxn4HLlfqB3G/PTrVu39NJLL+nRo0cBsMScQz+xs4nOFwnP52Qw6TebTQMo8XhcyWQyoKxfW1uzi3r79m3L1tnc3FShULBWBjMzM1paWjKWY2pqKrDooPFgkUokEoEa/OjoaMCl40MIOVYWU6ynXgTXaDQMBFSrVb355psGIrCo+tLG6upqICmZRd1HAEj9h3F9fd30TvSPIY+Ih9GDp/HxcTVDA2ead9Z5sXQymQyAtrm5OYuj57h9q4zDw0ObbPmZ4+V7eW0MXZulPiDxIYhMAjyQuVzOxHxSf1Lx2Ua1Wk31et0+y+tkZmZmNDIyYmGLTBQ+bTeRSBiYmZqaUj6ft7LY0dGRDg4O7HuNj48rlUoFguP4HnRxhnGs1Wp2fRlXrlyx8qt3BHHOfAkQ5pN76yz4+c53vqOLcT6HdwTC7gBkmXsAGcPDw6rVaqpWqza/lctl/eqv/qrefPNNC85jI0Q5ySci40pKp9O20LKwIkqm+/vExISuXr2q2dlZ7e/v22YNdigej1tZBfs5wAPXmjRo5OvLZ7AjvAaNHSyQZ4V8Fg8ADY2Mb82QyWRMHxcKhaSQDCyRawZrQ5o7wmvKWJTO+Tz0j+l02hxhu7u7evjwoQ4PD5VOpy37rNVqmZjbN5I+Pj62uYT38KJq9EVcHzRG3BsI4vl32B3PAvlwS5xsgErY9XK5rN3dXZtHEomEsVuwdD6yxA/K7hean3MyKIt4ao56s79IhUIhkDvD79hlwAwsLi5qeHg4wPQADHg9okBp4MhizMzMBMS9rVbrHc4a2JV2ux1ofseNxb8xEV65ckXSAIBQ/lhbWws4ebzWBVrVu4NGRkZsh8Suz58jf7NbwunJwBLOIk6pkbLi4uKi8vl8wAVG3V/qg6P9/X0rNflIfD7XZ/Vg9+R3TzzxhJ1/6vVn6+uUqmZnZy35VOqXlyghSQONkBdZ+15KhULBzhETPd8jEono0qVLxvyUSiVtbGzY9adbNmBrfn5es7OzBl739/ftOwNyKe9hEfauNnbtHDf6DM6h10KxcPhATdK87969a+1WJOk/+o/+I/3X//V/rYtxPgY6F+4TrjnPoCRb4H3TXgD+9va2ZX6hseFeZn4in4bwQxxF4+PjttgRSAgzQYaPNHCkwtLs7e2pVCrZZoMyG5ofnmVfSqGFBq/DcUaJHE0d5R3fH5Dn3Wt/AFQAQbRRmBS63a563X6PM6I9KI0dHh4aA+Y3dbBG3t4Ow4MeKJPJmL19Y2NDpVJJU1NTNvdT7vNgKplMqtPpqFKpBJKapYF2kDm10WgEEu0BPwRLAqK63a7FFTDnjoyM2JyHgQbwzOZ6d3dXOzs79j1h7igPMqd68TxZRDBw8Xg8oF99L49zC35Y1L1FmcXWJ6CWSiW7yaenp23RoPS0srIiqQ+cfHmGTAgudCgUCljbqV2z+8c2ygJJuqo0yKjxi7wX/bFj4saPRqP64Ac/aBMP3wFwtL6+HigvsWNh4BaQFNhNSf1JzAePlcvlgJKfiajbG1hteS8ccLAnp6f9+Hyvs5mamgo8PF63czbThuvC92i32+ZSy+VygSRkb6+XZJMJ1xrhohePAmAlWZgif0+dX+prwXxiNrstn2wdjUbN5UZJDDA7Pj6uhYUFc6ZcunTJqGPOOSMUCqlYLJqe6Pj4OKDxobs14+joSJlMJkA5e7vsWbDf7XYNAEoyrZcki3W4GOdjsJP3IMTHIHgB6ujoqGq1ms1Lk5OT1tCzXC7rwYMHlktWq9U0NDRkomDvbAQgYB2nTM7zEo/HlclkzPWEzRzmGudTPp+39GjcTyzkMA5nQRubnbMbHm87p6TGe1ECg/XBzVQsFk3f2Ov1rLnzycmJQgqp2+sa0yzJAMPOzo4ikYi1M0I7eHp6anZ8jouGxpTtxsfHtbS0ZJ3VOT+9Xk8LCwuKRqMBBh0tHqUzz85SWmM+xLzBtQHQcSwIvqX+HMixoaXCTYcjrFgsBpg1ynk0Wu10Onbsfo7mvgT4wAzCoHEM74dxbsGP9M7sl1QqpXK5bC4pqW8T54ak67vUr5OPjY0Zu0JmjNeaVKtVs7775nBS/wZeXFy0HXwsFrPdkCQ9fPjQFihv/ZYGk56vsVKy47N44KVBk887d+5IGmiAGKFQyP6t1+sZQ8PvPFBit8HCnkwmVS6XDWidnp4qHo+rqUHZi+MPh8Nqt9v2HQEXfE8i6n0dPBaL2UMPBctCDwXM4l2tVk2I/eSTTyqfz9tC0Ol0tLi4aN+T3RBMEeJqL1D3TW9PT08D5T2fmVKv161UIMlyUHx3dwSLfJZnxFZWVrS0tBQAYh540cCQ6+PbiTBpA6R4DdeepFyv6fExCbBCUOCJRMIApDQAlhzHxTg/g0wm2FAWRQSnvkSEXXpjY0Pf+c53rHyPVjEUCqnRaFhIKW5SgDyAw4t9pYHmCMs6AIWgPrQkJERzv6HBIUjxLKMLa8HxI9JHb8Pv/SLPgKFlw+iBgdSf/wqFgjqdjj3/JDYfHR31Qw4VMlE3wOrkpJ92vL6+LknWIgNWiWoCQBBWe3Z21uZkdIV8l0ajoe3tbdsce4YKoIbWibRn78CDpee7MnfimvXOK1x1MOT8LV3gOfesVTBNfjMMiIPxQVQNyEG36bPf/Hz0fhrnFvxwsT2rwUPIDdNoNEzwJvUXb3b71WrVUjl5H/IUeO3IyIgteEdHR5qbm7OdAU09eXDHxsaUy+UCIMP3HPOaHr9TkQYd5L1Ox2ttwuGwNjc3jRFgt+9pch+c6G3WW1tbgewdNDiwSNevXzfHiTTomeZtjZzPmZkZS2uV+vQ74FAa2OzPgp2z9nPfxwqNAeeMBZoH0pfveA+pzwx5DRC7Ie+wo5bNOfNd45vNZkCgzATF9Ugmk/b+AB9vkx8bGwvoxYgf4Hr7c+w1PWQw8dntdtsmP6nP1NBhmXPqW4qwC/OgsNfrGSDNZDJ2HC+88EKgd9rnP/95XYzzNVigAD++BM1uHcaDe3xjY8NeQx8pAMP4+Lg9V37nz2LXbDattMqCixiY8D5AEDKCdDptLlGvm5uYmLDSF05MHz8BIwS7wKIKI0J4o2d0ACn8GyUkyulHR0fWzNODRUBAp9NRq9eyc0cSNcc2PNxv3IrFG9DG+cU9ie3+5ORE+/v79nuCbGlWPDw8rL29Pd2/f1/dbtfWCM4h55jSEiAWlso7gAGrMF6+WsDxS4PNMpsoXzb3gYWwZpQJ6/W69vf3tby8rFQqpUQiEQjE9brRs0D8/QiAzjX44ebx+gc/+UsKKOsRvkkDNOsXoXQ6Hag1e0R9cHCgdrttN8jCwoISiYSJkOPxuOLxuDEPPBjSoNO2F/o+fvzY8m0QT3PcsC1MIrVaTTs7OwEm6eTkJGCz58ak9IY4t1gs6vDw0GzZW1tbgZyKRqOhRCJh5RE6IFP2SiaTAT3KjRs3zP0FAEQojHCY44Sh8EzVxMSEnXNEknxP71orFAoBpobh6XvvcgqHwyYelAahk4COUqlkVK+kd+w4fbsRQiABviSq+v5cS0tLdv3YEfPejUZDOzs79rO/9qlUSgsLC3Y9mCi5luvr60okEsYEMYF78TUNLP3147uMj48bgB8eHta/+Tf/Rn/7b/9tSf3F6J/8k3+ii3F+Bt3YYXl8BgxloNHRUXuGmLNor4MWkcX/5OQk4ByEafS6wv39fVuYsVjDfPPMoBMKh8OWRVOtVpVIJOy5A/C02+3AHAzbgFaGZxXm2Lsuz2qe+FuYWDSMzIfoVpgzvAW7XC4biOz2+o2GfVmYuWZoaEjlctlK/IQVwpqh/ZycnDQg0Wq1dHx8bA1dydZiI1SpVLS1tWWbYAaMmtclYeYARAEyeG+asuKc49gBadKgRyMC9pGRERUKBWOTfCmc80o1A+YInZKfYzleL23gWvs+ju+HcW6/KVa/VCplAlxcOZRO6GzLjXB6emqiVmhKbiYmFG4EKE5292tra1am4b2i0ah9FloXX77gvY+OjgJBdtvb2+p2u5Y55Bd///98r/39fTWbzUDjzbGxMVus0+m0vvnNb9p3TiQSVkID+LBQ03STQRKyt4yPjIwo9N2gH2/xTqfTWlhYsMmm2WyadVSS2TX9iMViBqwQ2DHq9brRs1J/4fbxAIVCwV5PppBnjbxjxIsWJdmE7JOrfT6O/yyi6Jm0otGoubJ4bSgUMsCSy+UsRVnqAzEP1A4ODrS/v2/fq16v27WkJOXZNK4D5zAajRp7hnDV7wAJXpPe2Sy2UqnYQgRwJ/fngx/8oC7G+RoAY0rV6DC8BduXlHwKMUw3GxSADqw2TEutVlOtVjMHIiCAOZTFuVAo2DOaTqdtM+bLT5TD2HDBprCZ9K4ljp/hrfzSwMLv2R4Wbp5nH1ibz+e1ubkZsNLzmTwT3W5XCkkhDZxlsFkeECASh3mD6eUcemMFzJPZ6B3DTYVgYmJCjUZD+/v7xvT6UiaAErcsbBzv5ft/we76thRem+XZJM4zBhuEzN4wMTIyYsfpYw1CoZCtj2iFvOPL34OwYmdLlO/lcW7BD5MCF1TqL1KVSsVuCHY67MDz+bzu378vqc9wLC8v2wMRj8dtVyLpHSh3eHhY29vbFlyXz+ct+JDP2t3dtQXVa3a63a52dnbssw4PD/WhD33IwEu9Xg802ztbFqtUKgEV/kc+8hE9fvzYFsjnnnvOLM2tVisgzIYSBgxdunRJm5ub9rdMYD5l2gOYaDRq+gJcBL6NQygUMq0LTJkXcPpMGylYAoL+BlyxU+QceT0Rf8c5wXXgRcU+myIcDgecfrlcLiDGxqbPfeOB09HRUaAkGg6HrTkt7wV9z/WCLpcGJU8PhP3utNVq2Tm7dOlSQM+wsrKira0tOw/j4+OWvcT7eNspixIgfWNjw9xeTz/9tJ599lkrk/xv/9v/potxvgbPG4F57O5ZaA8PDwONJmmwfHBwYI5CbxzwzIkkK3c8fvxYe3t7AW0ICyjsA6Cl2+2qWCxa5g6hfKRD+4RnFni0LtjT+RlnIgs+9zWCX9xdnrHwbAPzXq1Ws+cJ8IM+MZ1O23npdDpST1JIZsXnPFMq84GMlABzuZxyuZwajYaFGvq/Yz1AyA3Y43t1u10LHTw5ObFARUpOMEQ+rBWmrtVqmQsMpokeiwAk/oZzxjlC4E7vMa9f4jp7DShtkSqVigFYzgvXnvfudruBNH+Yq/fLOLfghywFwqqk/uK7t7dnrhxsiSz8PERSf/HLZrO2KJ1NREUYjEPm5OTEtBNSf6JZWVmxhwv1vVfDUwJrNBpqNBoWTJZOpzU7O2sPMzsTPrtUKun09NRs2vV63SYfqb8YLy8vG9tTr9eNtTg4ONCTTz4ZEAZDz0pSNptVuVy28t/+/n6g/QI6mdNuf3FdXl42QIH40VPck5OT9oDw8DLBQZX6cMezvYl42Hk9gAN61u98cOhxrcnO4Huzg5QGzUd9+ilxBlLQIeftrXzW2NiYicKxHrMTho3hs6PRaEDLhMDU65d4baVSCejBSqVSQOOTTqd17949e30kEtHY2FjgegKAuF64SaQ+KARUT09P67XXXtNv/uZv6mKcz4EImQWH58GDEzQzNG2uVCpW2qIFA88t9z0lHXb6pVLJ5hjuNVpPAErQBSH85RgkmcWcjdTly5eNTeZZ8mCFZ+no6EgbGxsqFovKZDKanZ21cguAh/dmccdQgE6vXq+rVCpZuw3mBXK9MpmMhoaGtLW1ZXP0aXfQNgOwxHPJuYbN6Xb7nedJoiYwkdFutw2gSAMpxfHxsZrNps1Z5Ok0m01Lrocp9xk9Z12w4XA4EA2A3gdtHwCx3W6rVCqpVCrp6OhIkUjEgI0XSFcqFdVqNfV6PRNo836ZTEaHh4cqlUra29uzdkeA67OxC9695jfJ74dxbsHPxbgYF+Ni/LAPtC1ktgA6ACEAfJhhcng8M8HrR0ZGrFRCKGI4HNbU1JRGRkase3ir1bJFkfYzlJ1gFVjkvLi+1WoZKzI7OxvIteG78F61Ws1aQlDmp58W+VuUlxBMU7LL5/MqFovmdPSSBC8NwJkk9TcY2WxWnU5Hj0KPNDoyqpmZGTsGhL0IigF9bFyKxaL19CIawJeJ+K6lUsmkA5QB2VxyjjlXxWIxECgpDaz9nC+Yb5gXynmU7GhNQQ9AIlS8Xisej1v5G5DEOeI7Em/CsXMvsfHisz2r7HOGuP/eT0GH5xb8+BouomNKYeyq8/m86YKkfumEBxVxtN+9j4yMBMIDt7a2bJK5deuW2u12QNfR6XTss9n9wPbs7u7aBJJOp5XNZq00wW7Ou7l8OjRaGK9P6vV6xvwUi0Vls1ljJlZXV223T1nFt8Lw+RWUorxy338vT2NLfTE2wl/q0DAgo6OjSiaT7xAseso1nU7bLhXhIMdGDdyzMd7izb9JsuwKrg/njN0Tx+Kpcf/3dKPn9X5nx8PPZ3rxsvTONhx8hk9Y3d3dDQi7T09PA9EFfN7m5qaazaa9B1oDjov8Il6P7oH7DNG9P/5oNGqJ0HNzcxbf4J13jF/8xV/UL//yL+tinI/BYsyidza7TBqUHKRBzILXwqDL428BEs1mU/Pz81peXjb2kr/3n4cLlsWYVGJAGcABkEX4H2wD7AN6mUKhYPNQo9HQ8PCwzbWU8/f29lSv122u6XQ6xmj47uiU+mBqOCZv6WbeiMfjunr1qvLR/hxB+ODQUL834tramomIz+qVOHaAUjgcNsYEYApQof0HgAEGhjn28PDQXKzlctnmN5x4gD2uPSJjD4TJUKtUKsYkYWRgvmBeh/nD5YdI3INFwNz+/r5plTg+wn7RkzHncD8gQYA1e7+Mcwt+WLB8CN/JyYmmpqYsuPDb3/62lV4kBRZmrH5MImNjY7bbkPplrLW1NX34wx+WJD311FPK5/O2aBWLRa2trQWoS9/KYW9vz3Qdc3Nz7wgv42Hmu4yOjtpCNTIyEhDQUlP2pY1Go2FNWPf393X9+nVJ0ttvv63j42MDGEyEUNSPHj3SwcGBPajUof3uLRwOa3is/9D4XabvzSMN8ou8A8s76uLxeOABlAY6IakPCpkcpf7Ezt9Ce/tspM3NTStbsjvjWOLxeKAExw7Wl4d8sCSTDsc0OTkZ0E54qzrBYCQ64yTjXmi1Wrp//779vT8OzrHvrXZ0dGThmLlcTtFo1L730dGRJicn7b5klwa4JbHZg7qRkRET3ieTyYAl31unOdaLcX7G2RRfdBs+A4yEXhbio6MjFQoFlUolPX782PKCKDsNDw+r2WxaqB1lWlgWylt+J8/iSEYQOiBS7kdGRszdOj09baDDW/HpUXhwcKC9vT1bPAEK6+vr1g+Rhf7se0SjUdPf4FyDoQDoEUC6vLysbDZrpb6hoX4z0NGRUTsezB8rKyvK5XL62te+Zs4tXHRsonwZDFE3jArnByYOt5YkY1woT+L+BJShu4FRg43xLBDzFaUlGBw2Q8R+9Ho9K1Vls1lFo9HARpF5Ex0jcySbQ9aWUqmkt99+W61WS/Pz85qcnLR5a2NjI2DD570p5TM3vdfHuwp+QqHQqaTb3z2OtyX9X3u9XksauHt8ewsoXhZMdDg+aflsBoXvfn7v3j274N/5zneUzWbNITM0NKRLly7Z4sFDfO/ePUmy8D7cO0899ZTpcHAL+awdz7ycnp5qYmLCFtuRkRHduXMnUG8nn0LqA5R0Om1sgDQAItzgDLq6c07a7bZZWzmPXlsj9YFEbahm5xBg1Gg0NDs7GxAVA3g4Tg9u+JlJAtu7Z0iwlUoK9DvzEfeSbLcLeNnY2NDc3Nw76GRYPo7Ds1D+GkAJSwONjwdh1OA532SkcK49UMvn8yoUCvb3nr7mu/gJA6u81AcvrVbL4gMKhYJOTk7s2vL9cMzBXgK8MpmMTWiS9Oyzzxo7Fo1G9bu/+7vy4+7du/rjRigU+quSPi/pVq/X++NffA5GKBRq9Hq92Lt9HD/o8Bo1Fnjs0F40zOKDhgXLOmAaZqLX69mzw3OLtiYajWpyctLeBwaR0g62b5gR31MKJsT31EKY7dmjQqGgtbU17e7uKplManZ2VicnJxY14U0BOI6w6dN01c9NgA1KYLjJLl26pEuXLhmIgqFoNBqBuBJYrMnJSc3NzWlqakqbm5uBAERAVbVaNVB3fHxsYADdEYCU9/caKeYmQEoulzOm3muhmPuYj5h7+X78zjPUfC7v4UXkJDyzYSNAEpaGc8vnMK88ePBAjx8/1tzcnGZmZlSpVKwcGIvF7PviRCOG5Oxm6odlnMES65L+Zq/Xq/xxf/NuMz/tXq/3AUkKhUK/Iun/LumXpMGC5B9yFjZuMMAPizUPjiRrhcDv9vb21Gg0LLRva2tLf/kv/2W7CcfGxtRoNAK9pBKJhAESEoFxBJ0VtPmbxqcJSwN2hsWyWq0GhNrY3D3weu655+y7eJskWUU+aDCbzdokSxkKyzW0KhNOJBLR/Py8qr1B3zLeC7smDyO7I74nD4cvB4XDYWNr1tfXtbGxYYCSeriPBIAhgUHiHFSrVWWz2QAzRMNFqd9Pq9lsBs6JD39kgvFlT28X9/Z/JnImo6Ojo0CTVCYY36jWu9wQnPIz96nULyMeHx9bafbg4EAPHjwI5GoQHicNLPt8L7QPfnKcnJw0lpF+Q9LA4s74j//j/1j/5X/5X+pPGD8v6avf/f//15/04ovxf24gaAWQwJSSN4MDDCaGRbtWq2l4uN+5nM2VXwR9KYX3w0Hk3VvePQgQAjAMDw9bI1A+G0DBvMpryuWyXnrpJa2ururg4MAYzOHhQU8qABoampGREaXTaWUymYAuhSah0gDAwNQCAGh/Q1kJxorXeis4GxHKaXxnnmNYGUl2nuv1uvb29ix/h00J+XK8jy8rcW3Y8HlAyhxHE1JYJfLhzpap/HfhvRGGT05OGniCpcPVBwhD/A4AY55ut9va2NjQ2tqaIpGIrly5ovn5edOIvfHGGyamlgZuYBxyP8Ssj8cS/z9J/w9J/+8/7g/ebfDjx1ckPcMP5B6crY2jspcGHYW5oehrJcnKQCwq7HpefvllSdKLL75oyaZSP+enUChYd/SFhYXA7v+szZuHShp0N/eOHe9ykvpMBgvz2tqaZmZm9IEPfECS9Nu//duBnlilUkkvvfSSfYd4PK4bN27Y96JOL8lSp32Y4+LiojFUIyMjgSarExMTikajgZwIJiISsAEkZ3cBsECACmjtt99+W1JfB9Xtdu2cERsA61EsFu38AejYqYTDYc3PzxvgYFJi3Lp1S/l8PpCkTNCaJNNCwN4wUfFaXwKIRqOq1WrvKF35PCb/90xC3irv23iQQyINtEy3b9+2c3J8fGzX8vj4WPPz8xbJkM1mVSwWjR2jMS3vl8vlNDExYXEEq6ur9hk+fVuSXnrpJf1xIxQKxSR9TNInJf2mLsDPn/ugHA64AHxQ/qIMQ+sBn9Yr9a8/5ZpEImGL6tbWVkCLR0lEGrRMYTODLhANCZKCoaEhS3727KVnpwgX/c53vqOvfe1rGhkZ0dzcnJXQcKfh9IQtxpU4PT1tgMz3OjvLqMA0+SDU0dFRY0IRNp/NGPI2/FqtZgnXMGGU8omqIOyW8h36K2/59yVD9JKALd4L0bKXZfR6PVUqFW1sbNj5R2DN31Eyg3n2JUFp4IjFAXp8fKzx8XHrNQgwQx/F5jKVSml5eVkHBwd66aWXtLm5qbm5OV29etVKgjS0RRckDdauWq0WkBn8kI9vyGGJ7zfOBfgJhUIjkj4j6bfdv1ldl4eSBY4bxXfPZbD4QQWyuI6Pj+vx48c2YVy5csWoYKmvpdnf3zfRMswPQYUgdT7T91/yAYL+tSz0q6urJlTje8zMzNiCuLKyogcPHgQyF4ial/qlD0TFaFG4SWERHjx4IKm/2D733HP2PZeWlgITZTgctveS+mUZ3gv9AcdNPduH6vmspOPjYxWLRRNMAzKIImCiw56Ks4HrMT4+bt/x5s2bWl5eDmiXHj58aKJwMj48W+PF1fQp8i1Czto4uV60vjgbyAiIhr2B9UNE6vONaLTKd+G919fXdXh4aOckmUwqnU4HGp2Oj49b6YoJkPuWSHrPRN26dcs+e2JiwsAkImjGn6Kx6Wcl/Xav17sfCoWKoVDow71e7+U/6Y8uxg8+WFDPJuv6jRSsJYs5i3ej0TBwj6bj8PBQsVhMu7u7JmhmoQZckAsTiUSMefH5P17ACwjxkRO8Dv3H6uqq9vb2ND8/r8uXL1sPRR8iSCo5ZRrKK7ARvB72Kp/Pq1wu21wE+EOcvLe3p1wuZ8dXKBRsc3VycqKe+kCj0WioUCjY58Cwwc4SeXJycqKtrS1tbW0ZQwxopJyGuNubEfifd74BIv3GGiCCld8n2dfrdWOIEomEcrmcJicnlUgk7PiZX2CR+X8AC6DIH0skElEqlbLS18jIiLa3t3VwcKBwOGyJ/Pfu3dPa2pqWlpZ06dIlra6uWhsjzjdg+Gzi/g/bCIVCw5J+QtI/+5Ne+26Dn/FQKPTad//7KzpzwEwGTBLcfCyA0IT+9fyOrt4AEtxeLHjesij1F8+tra2AZmJ4eNjKR75VhtRnM+jHNDk5GSiz8CDQYO/u3bt65plnBhkV392VIG7NZDKq1WrG0IRCIS0tLRmzsLOzY4zV008/HSjheMEkx5JOp/XGG29I6mtEPKtB7fv4pA+Gtra2tLi42L8Y4+MBQS3n1NfYeQ+pz1DR+sJfDy8ax6HC9wIExONx1Wq1QF7I6empidlHR0e1vLysr3/965L64IQcIqkPKjqdjk2eZ1lB3zJAUuBeoHbPsfg0WT4Ldofj9hNDLBbT+Pi4nbd2u23X7tGjR8rlcnbf5PN5K2FIfcaLgDk+m0lM6rv5mLClPthJp9MWZHh6evoOavoXfuEXJP2pQg5/XtL/57v//S+++/MF+PlzHCwulGT4mU0G1xJWgZJRq9XSwcGBpTbDqrCwUgry7jHYn2q1anMMQXqAJFxAACUvSpYGbWF4jtAcXbt2zRgM8tdGR0eVyWQ0NzdnwIYWF81m0/JyOF6+I/3LDg4OrCQI6OO1u7u7isfjZlUH0Fk4bGjQv9Hn5qTTaVUqFTsfmUxGuVzOjhtwwOaLzXWtVgv0MmODRzkPgEq7iWw2q5GREbPpDw31W1pMTk5qenpa+Xze1iNfgkNSwDmenJw0cOcdYeFwWOl02kTVtFNC43p0dGQNTHGqVioVm4eQHNy9e1ebm5uanJy0sEfkIb7sSqXihzjkECwxr75++It/0h+82+DH6nRnByI1adCQjR0G/16tVk0LIsmoO6mPuH3oYa/XUzqdfodgltJVPB5XKpWyckWn0wl0DaeXFuAIYCMNmqb6HlkHBwdWDkomkwFhNospx1qv1/Xss8/qK1/5iiRZsCNAYGRkxHb5+Xxe09PTxq6k02k9fPjQgNlzzz1nE5HUn8wWFxdtkkUgbRT3yZCVyJhgOWdQq57WJWRLku7du2fp1Zwzr3fie/hEboZ3Xkl90LC9va1r165JkhYXF/XUU0/pxRdftN8/fPjQju3k5ES5XM4YFGy0PrKfCR3RJT8jmuScxeNxdbuDAMtAlL5kmgjfOuPo6MhYprt37wbcO+jN+J5Q75JMJ8C9EIvFAqDNC0alPniNRqMGnJeXl835Rwjma6+9pj9phEKhtKS/JOnpUCjUkzQsqRcKhf6fvR/iGe+8DxZ1FmdKqD65GVCCxuTo6Eh7e3tWxsfGDnN0dv6ghOZLJrFYTHNzc8ZS+PvTl66xUsNwsAiGQiFjn2GOCoWC9d2CqSEBmWedRGIvEIZRwQ3FYuvF115cDSvLwo+bcmJiwkBCt9dncnO5nFKplOXw1Go1lUol5fN55XI5e9ZY+H0yf6lUshY+GEzI5AmHw1ae4rqxJsRiMZ2enurg4MCeXYTjnHds5TByPuQQQIkjeXJy0iJKAEpEDrBZPTk5sfehNOqdfBMTE0qlUtra2lKz2VQsFlMymdTOzo4qlYpisZji8bjm5uYCbYr8HPnDLHjWd7FEKBSakPQ76mt+/vEf9wfvNvj5voO6OLsFqb+40rNF6i/kKysrBkiIAJf6i6WnjY+Pj5VKpaysUi6XA5ZxHgDKFS+99JKVwKT+AhkKhWzxpT8Lx8UOROozNa+99poBkNnZWbsJJZmdkfen2SC5PsTbw2pMT0+bDmd7e1uzs7NWfvNWf97r0aNHxhz0ej1r6CfJmgEy+eVyuYB2xdOe7FS9Tmdvb8+0JtVq1YACr4etkwZaBD+8eJrdKH8LxS71Ld+lUsmOjZKXZ598/g2TJ+AWYSLX1gvUsfPz2d4OK/XBjU9lZjLkXhkZGbEkVknWRVrqg5m9vb0A2yUNyglMtlwPyga4vSi54SSEGodJisfj+qf/9J8Gzqln3/6Y8dcl/a+9Xu//xj+EQqE/lPRjkv7oT/MGF+PPPigpsMh7nQ5RD+TokOcCMGeuI/gOZseXKqQ+gCGZmbwdNIrepg0YoXxCuxmYBJ4LnltAO27Dra0tK0PzPPm+gTBIAAHmBZhpNIMwUX5T5TdMqVRKly5d0szMjLE6PsE5NBTSUG/IMn3QNXE+0um0sa2kOQOopqenrdHs0NCQisWiQqGQ0ul0oFEs54NUe0mBFhb5fF7b29s6OTmxFGo25qOj/QDGUCikcrn8jk2h11fxGVNTU4rH42q1Wsbc8V6w3JxrHy3COWPTxaaNeRPb+8jIiFZXV02fxd8BfnH0IQb/YR29Xq8VCoX+nqTfCIVC/99er/d963jnFvwMDQ1ZjozXWnhh3/Lysqanp3Xnzh1JMupS6gOI7e1tPfnkk/ae4XDYsnPu3Lmj/f19zc/PS+prY1DWS33ws729bQvewcGBksmkARTfU4xkVH7e3t5Wq9XSs88+a7/nBpT6O7WDgwN7/dNPP61isRgoAbHrkfo3KeLhUChk/b2kPkigk7g0KOdxHhBi+3DAUqlkE8XMzIwdRyKRsJ0Wf+sD+qgpAwA5pxwnwk2f0hqNRgPlwrN2Us43DyDH3Wg09Nprr9kkgVOE34+NjWl3d9dABXk4AIGJiQk7F4A0XFInJydaWFgw9gXnGceG3sFrsLxGiEmKcXR0ZGVHymP8LZOsLzvSnFbqA7NXXnnFzgcptpwXtE446P7T//Q/lR+f+cxn9IUvfEF/ivHzks5awX79u/9+nsHPRCgU8ujul3q93i+9a0fzAww2BLAI7O6J8oBV4P4GMBcKBT18+FDlclmhUMhK98wTACpKImhXaHdBKZiNgc+ISSaTWlhYsJwg5llYIUAScRa0dfBOKq91BPAAxGCxms2mSqWSlbETiYT29va0vb1tOhfADe+FLZ65BcYoHA4rEomo2W2qp56xQ4ACzBzZbFYbGxumlaH5Mi5MQODMzIxtLvL5vKrVqrG8lCRhzjg/iM4BEbi7CiAGMQABAABJREFUfEQBwubZ2VlFIhFLzaZsBSAFaOK4813nASCwT6VSyUqE3AO+hNlsNtVut1UoFKzhc71et7yjfD6v3/u939PY2Ji50bzAe2hoyFhF+ir+sI5er/dqKBR6Q/257X/9fq97V8HPH5ffwcMkDRb0SCRi9k1JtjNgh9FsNg3svPnmm3rw4IE1gWw2m0qlUqbTePToke7du2cMSSgU0uXLl620kUgk9Nu//dsWVlcsFpVKpWzH7oPq9vf3Tb/z3e+llZUVWwAfPnxogjpJRtHy/U5PT7Wzs2M/Ly0taWNjwz47EokYu5XJZAKN7BDGwTz4ej3f22f8UOP2jAy/Z1fJ3zPB+fMbiUSsHHc2HPDNN9+0ZomSbEcDIPGTnCTLF+EYer1eoDToWSjoeCZ+JiwWCiZljqVcLtt90mq1dHR0ZOwZ18LHB7Tb7UDwoGfB8vm8YrGYMV63bt2y9Fqup89s8uVSnBi899TUlGZnZwPOHNwx/D27RqnP6ly5cuUdoEeS/u7f/bv6t//2377j37/X6PV6n/we//bH0sLnYfR6vR9aHl4alJm8eFYaMIJobrhn0H2QIlyr1Uxc63U/PCsAJhY1Sv9oZ3ykA+97cnJi5Z2FhYUAmKFcjIuK8hVMKuwNCz/lHF++kmTiZxb58fFxhcNhbW5uqlAo2Pdl/vIWcwAIzxhi3FQqpbm5OZVDZWOuvGMTcDk9Pa1sNmtBkVi9PeNDVQA32vr6umn9eBaZNwBhrVZLhULBbPfeng+wpPkpguOZmRmFw2Hb/PZ6PSu3nRWcp1Ipu1+kgcaVkhhid5++PTk5qWg0amCRzKJ4PG4EAsDMb9K5bsxTXHNccT9s4yyW6PV6/5c/6W/OLfNDoJPX/vCg+7YPjx8/toWl1WqZVf3ll1/WvXv39MlP9ud8GBB23C+++KK+9rWvGY07Pz9vCFzqMwsf/ehH9eqrr0rqs0qhUMgWUF87LhaLAaZheXlZCwsL5sqBCYAtmJiYsAZ7kqx7OaBjcXFRGxsbBhpwLUh9AHLlyhUT85LrwzlgQmESGhsb09HRkTEmnmrm/31+DfVkqQ+UEO7xtysrK3YsPDC+14wPb/TiSK4f5wyLLX+LZZ9SEpZeP5mGw+GAS8pPfJQCuFdgrbgvjo6OAuWhQqEQYKygzqWB/Rzx4Fl3IWmp/rzA/OCQ4fwuLi6qVCrZYgeDxL0CSGOnVavVdPnyZfvsS5cuWTuLs+PNN98MdJa/GOdv1Go1axHDrh7NDyMajdr8wDODQJrGxbiI/POECBeGAyAFmIGxQJfD8yANNguSTKjMMQGAsJoTpTExMWGsL4wVmxZKw955BGtCTtHW1pYFnYbDYT377LPa29tTPp8PHE82mzXAAeDzLUF66imkUEArxKJNOZDy88HBgSYnJ5VKpYwhSiaTAfG4t+2TlXRycmLnOxKJ2Pwj9edYemixnpDYj4OYaxmLxQI5bABMrq/P2eG/sdf7OADfa2xiYsLKiMhDADeETY6MjNj6srOzo0ajYXMOIIzzxmdR+v8h1v38mca5BT/sCLjokoz+Y7GWZPHs0sByKPUByN7engmYb926pUajYah+YWFBi4uLBm7Gx8fVbDZtget2u7p27ZoxEdCK/B56UBpY2ymhLS4uql6v2wKG6M2LkKEtJZmIjpt1bGxMly9fNjfZzs6OlT12d3fV6XQMgHiqkvMRj8cNVBQKBZvEpD5L1e12FVLIzqlPHvWOOdgRjnN0dNTEwZwDdFiSdP36dTUaDQOU7F69uNmX0DqdjkUJfOITn9DLL78cuNY83JxDn/Z91gkIM3Q2/ZXPHRoaMjBKCY1riZPMnyPPprHzBGh4K7/UZ8B8uTGTydg5IXUcbRK7PVi9Vqul8fFxW1TC4bDq9boB/MXFRdP7nB0HBwcGFi/G+R0sKJRbcHux085kMmZZlgaBrFtbW5YFBttD6SSRSGhqasqMFLAklEQAP8RNTExMmCiZ1hHT09P2/PtNA04twFq327Xgva2tLWu/gF0fgOaZilarZc8oGiLYklQqpatXryoUCtlzwGuwkvsQR+YBhNa9bs+AFwCQTZy3qodCIVWrVW1sbGhmZiYQD0KfQNyTAM1sNqtYLGaZOhgbvJWfPCbMILDrXGfmS5hoz3Z7nRdABxYM5qndbtv8x8/086LMKcnKb91uV48ePdLh4aFu374daLHB+ec8SjKg6MEu/+/dve/1cW7BDzcJqFeSOQ7Y/X/4wx/WtWvXDMAcHh5a2ePq1ataW1vT7/3e70nql7F8ueHo6Ejz8/P2t1/+8pf14Q9/2BbAXC6n09NTK5t95zvf0d7eni2I0sAmf3x8rIWFBRNTkx7KzXZ4eKh0Om27hL29PWteymft7OwYOKLu/cILL0iSvvGNb9gix6LI4grL4B84hHZSn1mYmpoyIbd3c0iDhZ3zy4QjDZrD+hh9auwcp29T0W63deXKFXPCITb0rgQvOo5EIvrsZz8rSbpx44YODw+tLQS7PYDtzs6ODg8PA+UhdshcX0mBie9s/hJ/y6TlQwy5DlIfzD58+NB2yuy8+fnx48cmJuX9mEDQVOBaW19f17Vr1wzIRqPRQB5Rq9WynSVjf3/fGEtJ7wA4/8F/8B9I0p+65HUx3t1BMjnMpGcn6KPl+1Fls1mtra1pfX3dNne0OkilUrYhwyXE32KRRrwqyZgC2l0kk0nNzMwolUqZ1bnRaJiepN1uK5/Pa39/3xiRsbExTU1NmZkA/RAOM2zjhAiy2Wg0GmbaQIydTqcNtNE8utfrWW8znluYfxZynjHrz9UbtD7ivAH4KBFSHtrc3NTe3p41i2YzyPGzeUTjwzw8MTGhqakpFQoF02g2Gg0lEgkTGI+OjhqDxNxJhWJ0dNT0NaOjoyae5jtw3n38CppJNDjValX5fF7NZtMAJueYLKVYLKZWq6Wvfe1revTokTly2cBRmuO6eacgTkSu6w97zs+fZZxr8CMpkL/S6XQ0MzNjD//KyoqeeeYZff7zn5fUL9OwyD/xxBOWhixJX//61/XpT3/6HcwCbM3bb7+txcVFAzdQjjzw6XRaW1tbtqhVq1Vb5FOpVMASjh0TUDEyMqJkMmnszVtvvaW7d++affmJJ54IlICgWzmWmzdvGmsBwOJ3ExMT5gzge7VaLXuAl5aWtL6+bgs3u4eeBrsAD5z8LgXHCO+NQM5rCLx2xtfLuR6+sal/L8SWJFfTyJXdCOI93u/OnTvK5XJ2fQENnqIFMEvB/jmSAtcD1x6Ao9lsKpFI2PVYXFwMlECh0wFHlUpFqVQqEBbpk6n5PP87r6saHR2161Gv1zUzMxPIpzo5ObHzsr29bd+Z4QMTL8YPx/DBdX5x9c8LYlXvLN3b27OSEkJaadAkGAA/NTVlzzZtadDFwAAlEglduXJFMzMzarfb5vz09nNCSynjwKZ0Oh3Nz88rmUwG4iRIXm40Gmo2mwZ82u22tra2LLsrk8loe3s7oBmi1U6tVrPQRhgK5rRQKGRlM4wAoVBIoV7I9EBY4QFLMGos7JTwsHszH2GoqdVqFoXhtVlDQ0NKJpNmMkEWwKYMtsdnLjUaDTvfsF+wRZh1YL0PDw+NnWEDi2QB0XOpVDKtEiVMmCFAXjQaVbPZtE1Zt9vVzs6OZmZmdOPGDd26dUulUkmVSsXuQT4TtorSmWeI3uvj3IIfGAzvHiIPh4uTz+f15JNPBug7FoqlpSU9/fTTFg5YKBT0rW99yxawq1evKhKJGJOytLQUaPL5+PFjQ8tSH+D4dgL1et1AWDqdDjTllPoLvXce5XI5o3jn5uYC/Z62traUyWSsNAJi94GA7A6Ghoasl440EA176zQPhdRfPHd3dwPCtuHhYSt7IS6UZCJJz275UD1Yo7MA0of/+TBBH54l9Sdszvfc3Jxu3rxpJSRcKoCC/f19NZtNY/nq9bqmp6eN8qXvmi97tVotu35+kWHx8E0HWWA4Z2i6JJkbju/l+4fxfnNzc/a9fDm13W6rVCoZ8zY3N6f9/X07p5TOYKrQAPkE7uXlZQPl/vMZsJuAtYtxvgesDwsyCyu6kp/5mZ/RCy+8YG1t7ty5o8ePH2t3d1dvvvmmnn76acu0gmFgAWaxx+ZMmjJzJgv0+Pi40um0iWK5p7xOThowsrwXcwLaNMopPNedTsfybihPdbtdC21NJBLKZrPGZMBC8SyhYYKFwt3E3M9nRiIRTUxMmKNM6s+7hPfRpoFKAWzO0dGRLl26pJWVFU1OThp4abfbAQ2U1H+uSLAnY6dWq5nofGpqyti2SqWiYrGoZrOpk5MTLS4uGuPGtQVcnJycKJ/PW24S5435k+8DUwVAYo0BvPV6PbuerF2dTsdiMrg2gNGDgwMtLy/rxo0bqlareumll0z/5ckFX5Lk/d8P49x+S3YtqOCl/uL86NEj+3l+fl4LCwtWmlpdXTVAcv/+fX3qU5+yi7y2tqY7d+6YVqbVamllZcVuvFgsZrkX0kAHwqhUKtrb2zOAsrS0ZADjrCsNdsSnhGazWesQ/8QTT2h+ft4W9nv37unJJ58MiKmZgHg/bz9Pp9O2AEoKhFaxEwNUrK+vBwL6mIQ4tkQiYQsyuwDOb7VaDUyOACHvbPLln/HxcdvB8XofcgjVL/X1Kt5JVq1W1e12Td/CzhPASV0bkNdoNAK92WgO6gGoB21eHI02gr8NhUJKJBLKZDKS+k5ATwdjewWwsBPknLbbbQN16BwA6Ht7e5qeng6cgwcPHhi4KZfLisfj9j0TiYQWFxfte2QyGX3qU5+yY//5n//5H2ob6vtxsNhwb6AvOT09VSaT0RNPPKHFxUUzSty6dUtf+MIX9K1vfUuvv/66LeyAAR+SyIYDFxdanUwmY89tPB7XwsKCpqen1el0DDTjxmo0GqZDiUaj9n4sirg06QtFvy9KKAAyQAFle0rmzA+ECNL+Bbs4hgOagJ4NRIT5QEAdjvSNELOzs0qlUlYCh71gQ8gcTYaOj7Kg7E0qdCqVUj6fD7QROjw8NHYN3RRsMz33aB1UKBSUSCTUbretPCX154pKpWJzLOU75tpSqRQIVOU+8YGD3Df+XPM96FU2MjKivb09K4FGo9F+LECzqWw2qw984APa2dkJtFEC6EAwUDa9YH7e5eGZChb+ZrOpzc1Ny2v5xCc+oddee82ye+ihJfUbiZbLZXPKoB9iQmB3RcNIEDa7AfIZvHUa8Ri/9+FX1Wo1wCScTcvM5/OWzTM8PKzLly9bCatUKqnb7VrSMqDIl6B4b3ZHAKXx8XHL4pCkBw8eaH9/P7CweyeFHXNkoOPxzjAWeqkP5iirMXxp7KymRpJRyJwjHzRJaKXUZ+IqlYrtWtDvACLoYeUt4gcHB7bwh8Nh7e7u2jmNRCKW9cOx+fA1b6vn9z4As91uB3QSfH9/jmF3ms2mtre3jYWSBvcrjKW3y+7s7BjYSafTevDggQGt0dFRXbp0ybKqhoeHLaNKkt2fjHq9HshZuhjnf3hXoBcAMye9+eabeu211yx7Jh6Pa2VlRffu3dPq6qot4N7W3mw2dXp6ahogWCQvcOYzcJd2u11Vq9VATIM0CJT1ImmcRdKgXyJifIS8ZGdh5SY5PZ1Om9Ua91MymbR5m40NfcfQpLDRQkzsjwWdDi0pet1+nyxMDsxvfC8YLOZpBMyelcZ1xeYSBye2f8AmJg+AAWAR7Q/fe2hoyFgXHFUEB56cnNjfk/nD8XpTD2CEjSPzGYw64JnvIPXnRqQSh4eH5pKNx+MaHR3V48ePde3aNT311FPWa4zyGptZXIJ+4/ZeH+cW/HhGgV3w7u6uMpmMPYyFQkF7e3sBB5avib/66qt6/vnnJfXFrE888YTtevb391Wr1WzRGRsbC2hM6HHlm3pGIpGAywqgxMLn++F4IVur1dK9e/csH6dSqWhhYcEmg3K5rLGxMRNA02LCh+zxELBb4zi8C4D3glLl9SS4SjLLaCwSs2Pz5R3q0Xyud035HRk/e5dUs9lUtVq1Y2MXxYTjbZTz8/PqdDqmZcJ2yd9QhmICphGfd3N5jRe7H7/QMGh6CpDCdsxDzg4JDdfCwoLW1tYCYGhmZsbOCx2XGbOzswFxuy/VktgME/f888+bAFPqT9KvvPKKnaOxsTHdvXtXTz/9tKR3gp9IJPKnTXS+GOdksKiw2PhGu3t7e9rc3DSXJAwQmxaADY5C7N+UTrjfWCh9MOHo6Kh1FT86OlK5XLZNHBsdPgc3FcfIwr67u6uDgwP1er3vOd8dHx9re3tbpVJJqVRK09PTJgRm3oHdARDE43EzgKRSKSsNwRz5fmBnc3yYJ7qhAcMKM+VL8gzYas6NN0Kw6AM2CGWl/IXY2jvKYNfQItGygvcGxMEWIVSmpMRc7pkebwjhdQTu8pmUvmiPw7Vj7ue6HR8fK5PJGHOdTCZ17949c9jdvn1b1WrV2EjmeEqynKv3wzi339LThr5lxVNPPWULx4MHD7SysmKOrh/7sR/Tt771LUn9Rb5YLFp/rVu3bikejwdYCOrfUn9H7dslpFIpiy6X+pTtlStXAmUYmJujoyNNTU3Zwu1tp7y2UqlY2Qthm8/LkWQ7et+dWFKgIzyWUxgTKcgQXbp0KQCGRkdHdf/+ffs99fah4YEYm9cSYsiEQ93Zl5L8g+FzNqQ+2KlUKsZqIHbmQY/H48akTU1NBUIK2Q35/CG/Q4L698yONJiIsaJyDn15jmP0E6nfLfIz90IymQyUEnu9niXT8vvR0VG7/l4f1Ov1AowVugm+J2GbHAtCUa7v0VG/+eJbb70lSfpH/+gfyY9f//Vff0e8/cU4/wMAROlG6t9z9XpdDx8+VKfTMRHzzs6OARwcSQCak5MTEwDjKqWsnkwmA4v55OSkcrmcxsbGAinNbBZhZYjhgHXAmQQA8S1jAB+dTsd0JbSSAPTwPWnMyXNGQjPuMN6Tz2GBh4kCCLBhs/u+FzynnmH3Oj+YNmnQegfQQEnNgyZKa8wtkUjEyml8tjd5oEPyXQj4vpwnaVBV4D17vZ5pb1gn+N58H84jDlkPItkUIo2gKzwb7uHhYQOW169f1+joqAEerx3yYA2myc/X7/VxbsHP0dGRPSA8PMVi0R46qS/4/IVf+AVrCNpsNk3gjLPI7+Zv3rxpZbB4PK69vT0rXdy/f99qy9IAkSNSTiaTgSwOnzQ9OTmpfD6vBw8eSJIJ33xasRe2hcNhK/vw+ps3bxrwQkPCgugfru3t7cB701PGBxNSbpJkdC5lFBofnmhgafRlRc/0AG5gPHzNXBo0f+VhyefzNnH5700JjqAxqa9zKhQKAev7WTDi3WFexCm9s+8YO2KfvcT7kabKBMQEwO+ZfHmvUqmkTCZjwLnX6xn4kmTdpfledHGW+gxjtVq14xgfH9fOzo7dy2dBWLFY1I0bN0wgTZPSf/Ev/oW+33i/5HC8VwalDbNpu8WlUqnopZdektSfYyYmJmxeaLfbJh5Ggzg+Pm5tLvxOfWxszABLKBQyW7ck656Ok1Dqz0HoWihHe1BWKBRUq9VUrVaVyWRsbkSHCePsm4FSEuL+9kAnGo1akCgbsEajYc81bis+g5IZTDNA4PT01JyqABqG1ycyj8GWwAABevhbSlsAqUQiYTk4lLMAOZLMTRcKhez4qVJgPPGOMcAE18WDDn++KYlRtmL9gUXme3L9ZmZmrK0JrCAaovHxcS0uLurmzZv6qZ/6Ka2vr+sLX/iCMYxerkCJj/Xp/RJwKJ1j8ONruV443Ov1AuWhXq+nj370o5KkP/qjP7J8lN///d83dC1Jt2/f1tzcnJ544gn727W1tYBuA2AhDZgjbjpcPHTUzmQygUaluMOkvhgaF4Q0yFLgWKjPw/zQeJPFlN0hLMn8/HzAcYSgTZJNDD57h7o4x4adVOozInNzc9rQhr2f3wn6CQU7qmfhoHP5Wx+Rns/nNT4+bsALGz3nBeqY88lOUxrog5g4z9afmfy8Y+t7OSW4NyqVil1Lfsf5ZsIDpPnJUOrrxXxuUyaTUbFYtGNrtVo6PT01vRFlTqkvcPZ5UgsLCyoUCnZc3W5XxWLRQLXUzzjiGqysrKherxuz936pv7+XBwsTg0XaPz8ABdgG2IJoNBpgRqRgOnMkElEikbBytQc+rVbLWEhyeGBz2Xh4t+bh4aEJcCmL5fN5Az70nQMQDA0NaXFxUaOjo9aMmRKU1J/nsPMD/BBSe0AAEPDBe4A7WNuzz6t6QV2oZ4xZN5gffId4L+D2/4ZmB/3So0ePVK/XNT4+rlQqZWGHWN5h2Ohgj2CYudvLCTgOjtHnGXE9AaEwfd+rfM9ncD5xhs7MzGhsbEwbGxtWKlxaWtJP/uRPanp6Wo8ePbL1wKd5s8H06dJ+U/heH+cW/EDpeTTPzeoTLvf3980hdHp6GlhkvONqfX1dr776quWnxONxfehDHzKAcvXqVaXTafuZ5E1+JmacRSmdThtjsra2FgBhnk6U+qAAqpjjjEajthhPTU0Fen+B9mEPyBiSZEwLDwUTm0+LZlLl98lk0lxWh4eHyuVy2uj1wQ8ToDRwQXAc9PbxNnsPYHhgAGkI6bzrjQlc6jMqPk3UNz2F2vYPu3ce4Azzfce4J7heyWTSjp2MIv72bOaP73kGE8TodDra398PhBxubGzowx/+sKR+qWpnZ8fA76NHjwLHNTs7ayAN2hox+7e+9S1VKhX9+I//uKR+uCYLhyT7/4vx3hlnw0MlBXb97N5JeqaXnE8Yh1EdGhpSNBo1MMP8yHzFfVgoFLS5ualyuWwAiueSNggAAWnQuoXSd6PRsHYV2Nwpx2cyGWt7AQOEwJjyCwtrOp225qmeZWLOYs7BjIJZBFDG/AMbc3JyYmUvBMA+34dz6sEQmx1AB4CIxZ6NFSCpUqloZ2fHnuFqtWr6K0TFvkzoIy9ooYR8AAMIxw6AARD50hy/I8dHUmDDDNNNppDfVLbbbTWbTRNYP/HEE8rlcvrqV7+qL37xi9YChffiPLCWeMDoxfDv5XFuwQ+WvnA4bAtkPB5XuVy2BYLO6SxSN2/e1Ouvvy5pYBeHMRkZGTH2R+oLT+fn523hXllZCdjP9/f3A2WVTqej7e1tK01NTEwEBMxXr161zurFYtEU+NJggvMpzX6iajQaqlarNjkyYQGOsI/yXgT8SYMUZd5rZGRExWLRJk4Wel+CO6t9AQR4MR3v5SftszuDsbExnZ6eBvpxUauXBjscX24iZ+ng4CAQq++1Dfytt5MT8uVpbe9Ioekt+gTvStvb27NcIK6lb0Z6tqEruzeo7mq1GtBdSf0FiWa2TPhSn+mh74800FVwr1y+fFlf/vKX7T6S+s7D90ud/f04vP7DL8osQGh/RkdHNTU1ZaDaC2mlQfBqIpGw/k5EPOAwhIWhlxMlGT4DRslvNGCTI5GIsQ4wIvTQ87oQ/5no+mjRc3JyYvIEAF0ymVS73Q5ohthk0UuQz2KT6NkxL/IOhUL6bkSZMT5n2R6efYTHALGz7Sd8uxHe2wvIeR9E4sx/6G1oi4Emh/MKICKvjYwx/g3wgZyBzaoHaQAT7hPmUlps0HoE80U+n1en09HS0pIuX76so6Mj/fqv/7q+/e1va29vT5cvXzawyufx/t5ZBiv3fhjnFvxw4/uLEYlEVK1WA5bJfD5vqctPPvmkubcymYx2dnbsbxGm/e7v/q59hqeiI5GIZmZmDDRUq1XLcpD67E2tVjMQsbq6ap87MzMT2NWRw8NC/cQTT2hnZ8d0HalUSlNTU7bbwlmAYHp6ejoAGnzPMn5mkY9EIgGKFRcUky3ULJTx5ORk/3ffnUA80kdj43MkxsfHDSCeTRRmsvPtFzxYZZdIGcxnFzEh+TKXd0HwwHtnCUFo/J5MEr53tVq1UhWvkfqLRjwet1LT7OysNTnks4eHhwM6KazBUh8IR6NRcwrOzc2p3W4HWEbei+Njkdvb21M4HLb76ld/9VfVaDQCDUnfL5PN+3n4e82zDWg7ENv63oKUqcjtSSQStvGDYfVxDQhzEcfCZlQqlYBx4fT01DYu3lXlS2sAAJ9PROCgT5+nZAPYQdd3fHysarWqvb09K6Fx3DAmvd4g7dk7JM9umnxG1vDwsDE//vv4tQJBMGYZvh+fAfAAyHgw0mg0LKUZgODDCM+KjWG3uX7JZNIE5vV63TSWONgAGLyfL0F5AML5wSLP+ZucnLRzH41GdfPmTdVqNb355psGjJeWlvTo0SM9evRIu7u7WlhYUCaT0dbWlhlBvBCcOZh74f2i+zm34Icdi6fg0Hz4Eo+fAKjTSv0Fir4q0sDxw6L0pS99Sd1u18pglUolUP44PT3VtWvXbMEcGhrS9vZ2oJUDAud0Oh0QCp+enmpjY8MW7tnZWVWrVWNrYFg8M0R2hyQDd5SLcIH41/p+ML7hKjZ4z6hA0/KeExMTNoH4z2USALygQ/I6KN/1fWhoSMVi0cCR32Xy916ns7S0ZMyaP18cN7ZRvqen5dllebGed2Ewudy9e1eSAhM5eSCwTtevX9crr7wS6AvmXTAjIyNmrednFhXuFahnqc9AMiHx33yPN954Q9lsVv/tf/vf2jm+GO+v4RdygDZMAotnOBy2vm/SIO9lfHzcyt8AeJ5PdDCVSsUchzAcvV7P2lfQdoLPBuiQBwNQ4nnz2hoWadxotIfg2NloHR8fW8NPwAGMxNjYmHK5nB0DGh6/yMLKeKEvoNCXZwAtPfXs94ATf259mcufewZAiTnn6OhIhUJBpVLJ7OJkF3GtOF6ADs8/IHF0dFSJRMI6xkciEbPyIzTm3PAdOSehUMjKg2h/YIIAdrlcTrOzs3a8mUxG8Xhc9Xpd3W4/DJisokePHimfz2tmZkbXr19XJpPR2tqaXWsG14Hz68/Xe32cW/DDhcfex2CHIck0ICzePgk5l8vp6OgoUAbxdc9Wq6WvfvWrBkCq1aoeP35sguZoNGqR8pJ07do1y+vh+NbW1iRJzzzzjHK5nFmhYWr87gh7tDRoCOrD6kjklPqLKyFhfBaLJt+ZG7Rer6ter1uJzGtw+N64J6QB8Or2unYePFjxdDhUta8/+wdnZGREjx49CriwmASkPtOTz+ctGTkWi1neTSjUzxoB1LHL8pZR/7PfZTGGh4cNwFAv5/rX6/VAAnepVDL3S61W09LSkrGE2OR5bxgkz5YBrqR+WXN5edl24YBvjrtWq2ljY8P+zccSXIz33/BuKhYaH+IHAxAKhezZBcwAUIrFoi5duqTh4WFrc4FYutlsamxszBheStsAGxZXSjlnWxnAuHow4ZOE2ZgAztA+Ev4Hg8viCdvRbrdVqVRsLvX5WL6ch/4HwNdqtQIsOyU2f57OGiG8AeL09DTQeNVvBAEOnU7Hgg9h4KrVqvXbgjEDFHJ8IyMjyuVyisfjajQaAfYKZyshs4lEwjboOE6ZU7yWieHZQVxwHqDlcjnFYjErkQ4PD2tvb097e3saHR214NydnR3t7OxocXHRWnLAFLI2UUbEyu+ZyIucn4txMS7GxbgY/04GizXshU8b979rtVrWERydCH2oZmdnNTY2pq9//ev2+kKhYIsXmhrEwwAsgBSsDyyJ17DwWpKEvZOShVYaiK9LpZLpKj3D4rV5xJKMjIxobm7OenxJg5I0gEsalO7QtvhyGNoUz5x6zYp3BGNmAKwAKhqNhmUSkVYNkOC7c/y07+h2+22W0um0gRzAwcTEhOmE+LnVaqlSqRgLdXJyonK5bOCI44Uh6/V6lv0DKAPseSYctzDvg2tvf39fh4eHmpqa0ujoqOr1usrlsi5duqRoNKpwOKxsNmvvDaPmAQ/XwjvvEonEez5J/tyCH+8e8Lv9QqFgO/qtrS2Fw2HT0nz0ox+1ndPe3p4+97nP2UTx7/17/56+8IUvmK6DeHayXOr1uvL5vD0oyWRSlUol0OTzqaeeMiaIurA0YHq8dsZ3P5YUQPlMGL6WTQ6INHB7caykvEqDHlYwJuVyWa1Wy3YU6XQ64EAgN4Kf/THx93xH6Fp/HBMTE3ZOsUT6BGecEHyWpMDDxN/xPXxiti97+fo7v/c09VlnBecSlonzQknAf2cmCz777HWlBo4OZ25uToeHh3auarWaZmdnTdsUi8WUy+UCaeEImn0Z8GJcDEnGVjC3eBej732FcYFFk67jBwcHVk556qmnJEl3797Vzs6OSqWSgRXP4PqcK9gcz+ACkABX3vYOe8MCSXYYOiAvDIYV9YCLgYZwd3dXY2NjunHjhjUuRVODdZxnFSYaFt+LxXFbnXZPFVLIytG4vcbGxnRycmLlcMTM6BDX19e1vb2tdDqtlZUVY1JKpZJ1YCffh7Y8uVzOwN/Y2Jiq1arK5bJOT08tVBJTCZIMmp+Ojo6qXC5b6cznDjFveQ0SbJtPloaJZ83AmMFcXKlUbI1sNpsqFovK5XLWX2x+fl6np6fa2trS9va2HQNxBWdLgh4Uv9fHuQU/0oAG5MHwOhepf6GazaYuXbokSXrppZfsolUqFXN0SX29ybPPPmsL2O7urra2tqw9QjKZ1MnJiZVC2GUhat3c3FQ0GjU9zsbGhh3f7u6uZmZmLECxWCwGxL3U3Bk05PRZPeVy2SZBbl7AxNHRkZVsdnZ2bHcgKSCk45xwniRZsislNSZUhq+7+xIYg92BP+8c5+3bt98xqfvR6XQ0NTUVADx8HqUA7zzzGi9EjP57MUnwsxdIn30vdnac/2eeecaufaVSCVjhCZfz+qFKpWJurlQqpcuXLxvoe/nllzU5OWn3jvTOe/NiXAxGtVo13Zm3rAPwEREjJCbjywP7drutBw8eqFaraWJiwtyk29vbarVagR5NBAWi3wNM4JySZMCC58xn6iBQZrPhn0lyetAjSQMwxX+zePp8HRqizs7OWr9AWvUUCgWNj48HGgf7AVthGikNxLqwGDBVMFx+g3l0dKTHjx9rfX3dABEbzMPDQysX0q6C94vH48pms8YQeUFyo9Gw8wVQA+T4RGxKZ7yW7+Hdf97m73WpzHcAV64fNvput6vd3V3VarVAt4F2u61MJqNsNmvyjNu3b2tnZycgbUBH6Y0h3IsX4OddHNChUIDSAOx4YXCz2TSL+T/+x//YHsgPfehDevvtt/XX/tpfk9QHL1evXrVWD9PT0xoZGbFWDK+++mrALTQyMqLl5WUDLTQTBbAsLS3Ze+XzeRUKBWuFwd+weM7Ozmpvb8+A0/j4uOr1umlFaIHg6VTfyqHRaBgIIzuC78yD4MW35AhJfdaiVCrZ660G/t17m74z0gCQeJG4zxRiwiM1m+Pm996twft58XWj0TAQJinwt0xcjLP6I/7NgzVPQTNZ8zM7NV5Xq9WMITw9PVWj0Qg0h5UGfbRWV1cDndMrlYo2NjYCoPKll16y3J+XX37Z7lEWgYtxMfzwVnJp0H6BexYGxC/YPNeJRMKSwml9QUJzNpu1PoCIlQEEzKFeOMwCzKINUPGiVwALZRCfIk3pBuaHMp0kY785bliNWCymaDRq7Gq1WtX4+Liy2awxMo1GwwS7lHCk/hxEuYak5aGhIXV7XQMMvlTHHIbuCPPCxsaGjo+PlUqlTHtYqVQsu2hyclKZTEbRaFS7u7sGeIrFoo6Pjy3clRT5drutWq1m14f5lnmn0+moVCpZGwzmBa6BZ6aYB/l35kEYvKOjI/veAOHT034TXMImT05OtL29rd3dXX3qU59SLpfTycmJBVo+fvzY7inWGubYk5MTA7lomN4PxoxzC364oJS+pEHeDT/ncjkDEpL0sY99zMIA//Jf/sv6L/6L/8LaBLz44osaHR3VrVu3JPX7gs3PzxtgCYVCKhQKgbTT1dVVA0fT09NKp9OWE9TpdAwEhEIhax4n9R8A0k75LrVazT777A1Gg0JKMQjTPJOCuJYyFjs4bmLKPeyqmDxqtVog8ZlSFoOmn9IgEwgwAHsFmKODNELvs2mkPjtC6rNMBKVJfWDmmZyz7UQ8sGFX5ydW/xoP0Ph/ygtSv4zJ9SBE0AuP6ZTNOR0eHjY3mG+4yvC5PAxaqXzoQx9639hDL8YPPljsvA6F58UH9YXDYXuOiWnAHdrtdi0HLB6Pa2ZmRo1GQ4VCwVhYv7j50gk/w5J61gSwAsPqWVcWZUrVlKm83Z75BVEypT3KToQ2ElzIZ1PuKZfLKpVK1lAVZhaAA0A7Pj7Wabd/7gBXiMI55mg0avMrrHqv19Pk5KTl3RwcHFipi0w15i9CJJmHiQXgMzg2D7wAtj5wENDJPM/xekbM67243gBVGDyMOwixh4eHLYzy6tWrunPnjvb39zU0NKQXX3zRND6cD7LxfGPo75V0zee+X7J+zjX44WEGBNTrdeVyORNinZ6eWmd3qR9U+JM/+ZOS+ov+c889p+985zuS+inMiURCV69eldRfEL0A79KlS3bTSn1QsLq6agt/PB632rvUbzlBKapYLOrk5MSA1/Xr1zUxMWGvffz4cSCd2NvBpT4g8e4k3xHYf7bUZ08WFhYCgXvDw8MGnOjXAwhoNptaWloK2PBHR0ct5weKWBq03eCBZALluKLRqN5880079mg0ag+ZNGgmyjmdn59Xq9Uy7ZKPKfAJy9IgidXXwhH58d5+QmaH6TvKn7XOAoozmYyGh4e1vLwsqQ9adnd3Ay0mPvaxj9n3ptfSn3a88sor7xuHxMX4wYZ3KPkSk7dpoxuJxWKamZnR6uqqlZSKxaK9tlwuq1ar2cLPPLazsxOwevP/nkmQFAi749hYeFn4AGr8HQskmy8vNiZqwzNb/A0xHYigCemDDWFhJxcIYDIxMRFgU3weWa/XU0ihQAQH84fvr0XeEZpIH3w4MTGhubk5EwJTapQGDVi73a6xVjBQZ88l2UDeKUooIjpDDybYMLbb7UBjaMqVfnOH/soDounpaS0uLmp1ddUA0NHRke7cuaOVlRXTYxGCWK/XzcUGgATk8Z0AqN45611o79VxbmdsHwXu827GxsZMa3Hr1i09evTIyhXSQHcyNDSkn/zJn9TP/dzPSZJ+8Rd/UZlMxliNGzduqNlsmg4kk8lodHRUv//7vy+pv7Bfu3bNcmOSyWSgFh2Px+1zw+Gw9vb2DPwkEgldv37dHqZWqxWwefIwevEuN780aHQH+AmHwwb4stmsNbKTBgDFZ9KEQiE7R0waniny7Mzh4WEgf4g6vCTL6OD39+7dU7vdNsYEmt6HkR0eHhooXF5etn5BkmwHx3GcdW6wu/TX37NSvm+Y777Mz51OxwAndLbUZ20+/OEP2/n8XiMejxt79oOM9wNNfDF+8OEzVHgmvcV8ZKSfzD47O6v5+XmlUimtra3ZJgE9UCQS0ebmpsUzUMYBkNCWxSceS7JnGAaV3/G80YMPMMPz5/O4vBuI70B6Mbo5aaB7i8fjSiQS1pQ1n89bU1aCWWOxmMbGxjQ9Pa1IJKJSqaRSqWTNQ2F+JA1Kd6EhKRRsx+PnZhgdvm+r1bIIivv372tsbEyZTEaZTMacWGgsK5WKtQNiM4h5gpYYsDuE0wJYESFTivLsnjeBHB0dBbKaYJakgb7JO8vGx8cNKEajUS0uLkrqz2uVSkWJREKLi4uanZ218iKGGcqMRCP4tQBwzGcjgPZs/nt5nFvww0PrLw49UggX/Imf+AkLdJL6u3e6Yn/gAx8IpP1+7nOf0y//8i9bKWRyclInJydWConH43rhhRf0oQ99SJL0n/wn/4mefvppK3vdvn1b6XTa3nNoaMjcQKRssgA+fPjQdiBSnwHZ3d213zcaDY2PjwfyiZjYpAEDw0KP5VPq61I8cAD583vqx3wvau78TAnubEqqNKhX8288OGhjGo1GIDup0WgYbc31GRoasvIedLhvHuvbbFCq4rh9mavValmNW5JR4b6slcvlzKGVTqcDDUV9n698Ph/Y6X6vUa1WzRl4MS7Gv+vhmUF23n5Eo1FVKhXVajUNDfXbWqTTaStn8HxQAqPjOqGl8XjcBM4wLQAiNlfo4gA/HAcskU94P8sgYVnnNb1ez3J/WMB9mfz0tN9nMZPJqNlsGsjodrva29szl1o0GlU2m1UqlbJS1eHhoT3f6JhYuA1IfBfr+Owk7/ziuHFfni0zssA3Go0AY83cl0gkVK1WLUqAZqJkrtF8lk2Zd/DBUnNuOU7CIM8CDO+yg72iNMq55TWw+tlsVo1GwwDS9evXlcvlDDBS6jo4ODChM2JmrivHxbwKQ+VNLO/lcW7BD2ieG1vqX6iDgwMDJJ7ulGQdiKW+1Z1mkpL01//6X9ev/uqv2uJbLpdVKBTsIq+vryuVSunv//2/L6lvo//v/rv/Tp/97Gcl9RtSvvXWW3r66aclSYuLiwYCyFlgIT46OtK3v/1t3bx5U5LMBs3CTssDBLjQtDAkRMTDBG1tbdl3RtcCwwG1CXAieAvglE6nA5ogqQ8sQkODnRLnwNsupf4Ozve0QnjJZ2GR9buu6elpa/tBI1N0UmtrawH7eTweD4Avn3p9VkPDxMA5IWmWkhzNFmGdpqenTZv0iU98Qnt7e++w+ftxAXwuxp/n8AF8/j5m0WFRLJVKarfbikajikajAVdQq9WyzczBwYEJX9HfTExMaGlpSQ8fPrSFURrEPvhyFlk3LJ64hDhOX4qBXYGRYONGCQWNkmewvYkCoOT1JD4exAcOSrKMIEAA5SYza4SkkAabp5OTE2OSKBFNTEyo2+2qVqtZDAZBsmdLP4AZROSARH5PaazRaATY+Gg0ahqrSqVirBq6IO8Oo0RfLpfN9crGEZ2UH7D/vp0P14/rNjQ0ZPdEMpm04+Sc5fN5PX78WLVaLaDBAiTynsz7MGtng4Xfq+Pcgh+QO4ueJKNzWXxfeeUV3bx507J3vvGNb+hnf/ZnJUn/6l/9K3384x+3XIzx8XH9+//+v6/f+q3fktQvyVSrVXNRlctlvfXWW/pX/+pfSZL+wT/4B2o2m/ryl78sqQ8i1tfXDeA8/fTTBq7y+byuXLli7i8mHgTR3W5Xs7OzNpGRbAwbw4KNrodeYTwQXsODhdunR/t0aGhq/nZmZkaFQsGAF9SszyTywWO+3xaTE78nSZrj5r1gdiKRiF544YVAQ9CZmRmbCN94442AfX9iYsJ0OeyeAG18Jt8D6hcWamJiIiBgrlarNnlxPTmvkUhE9Xr9+zqxVlZWtLS0ZOfkD/7gDwIajYtxMf7PDmztlDPYZLAjR1NzcHCgzc1NjY+PW3kG9xRJvsyDkkzE2mq11Gq1rA8YERh8Hs+yL79xXH5+iUajgSA8D5K8LbvZbL4jugIgMjQ0pHQ6rXg8rmazaZ3NYYZTqVRAiwQD489Ls9m0rB0WbIAFA+AC28P/sKzjSJNkJTrfvBngB9Dke3JdJiYmrDQJ681n4vKSBmw2wNKbcgBQ3yuoEfcq0gPYbZh8H+DojT9cB3qqoVeicW0ymbT1AAbdlzt9zAL3ABUDyp/vB9fquQU/Humf1YEAQF555RUtLi7qrbfektTv1M7vPvjBDxrwYfzET/yEgR/Ed17gfHp6qs9//vOS+s1I//P//D83AfXi4qIymYyJqzc3N43Kpo6N9fn1119XKDSIqn/48KEKhYLlEcEY8WAimGYiOTg4sCwgSYEAM/JAfPS7p2uhZmGKDg8PdXBwYJ/d7fbj0snK8DZMkD9sCkFpfoLjgZP6AGVyctIAzKVLl0wPJA1aWACOzlrZI5GIiZBHR0dVKpXss8rlsk5OTrS0tCRJJvAEcE5NTRllLfVZKt/LjYVD6oPToaEhvfnmm/peY3JyUpOTk/a9nnvuuT+z6PliXIw/brDh8M8TO3m/saGFztjYmNmvJycnzTXJAg5AYaFikTw+7vf263Q6lq3Dgi0NXLRoe/gbFkvmWp+ZAwPBHNPtdpVIJOz40f7NzMzY3HS2dQOLO8eKwxXQgZ3dByz6UhsanHq9rl63p9DwoK2FZ044RvKDWq2Wlcz4n+/PCMBBh9NsNk2GAAADVPmSGefGX7fj42PNzMwolUqpVCqpVqtZCYyIFsAnZSwMJz7fxzPwMDa47I6OjrS+vq5yuayNjQ2rEBC6y/y3uLhom1AIBMTWvD8AC2NJs9k08HZR9noXB7Skt+dRt2axjcVi+s53vmP9uCqVigGhz33uc+94z1QqZaGHiGNhZ7Av8mD88i//sn7pl35J//Af/kNJ/ZLNwcGB/vAP/1BS32bPcXGjcrNls1lNTU1Z2QV3xiuvvCKpnxG0sLBgx12r1bS5uWmgo1wuD7Q56oMrSkfNZjPAhlEPPtsQkcluZ2cn8L18M1BJJqTjb/1DzeTIoOTGA0afGSbvhYWFQMhiNpsNCLkJkpT62UdEFUh9wDc9Pa3p6WlJfWbn3r17xt7kcjk1Gg1jdqDPfb+0TCZj52x8fNzcXN/85jffcS9IfYArDYCXZ6EuxsX4dzl4jgJhfd99jrFXs9krlUqWHj4xMWHsBmAHLQmbk+HhYWOK6OAei8W0v79vjJK3vPtMl263376BhRCmwM8JPumZJs6+SzysLN+BMhYDrQrnAaaBZzWZTFrpDM3T8fGxms2m9aWiBEZZP6RQYFMMO8KGa2hoSM1mU/V63Vgp1g/+R7mLlGScrZSNGo2GisWinTeSrgEuPhMtFAppYmLCGK6JiQkr7bFOeN0X1xxgCsDxpSmfl0amEHpOwBVi83A4rBs3blhZbW5uzpq0ekCJzgqwefYePetmey+Pc/sNz1ob+X9fsul0Otrd3TV24MGDB1YWIQyMspbUf0jp4n737l17CKRg6JjU3zn8+q//uoUkJhIJ/cqv/Eogi4dFnq7pMDVjY2NaWFiwn0dHR63zstTX/DQaDXOLodRnwtvZ2bHvymcDCsrlciBF2E+InCMEkQwABN+z/x+Dc+0fMhJO/XXg9+Ra+F1Wp9MxxiuVSimVStn1KBQKajQaBsxu3rxpO8xUKmXZHlKf/RobG7PcJanfTJZzNj4+brHyUj9V23erR5/0/didP27QgBFn38W4GP+uB2CDXTcMkNfGsONmQUQftLOzo+PjY2t2DGgBOAGkAO10FJ+cnLTO7j5f66zrDMYXkMUGijkIFuf4+NhEyDR4pp0D+hOCYz0jS08tAArghPIQ5hBKXOiJyuWypqen7XwATBSSeuoZUADYIY72TrVeb9A7y4MbLOrFYtGcc8QIABYowUsyjczZtGU2vmihcFixAeWawXZ5vSTnyLN/sPx+XqO8RnkO1jyVSikejyuVSunKlSuam5vT66+/btlQ3t2KnZ3vDeiFXcIKD1P4fhjnGvyMj48HMnF8EBevSaVSdkNNTk7aBW+328by+PHcc89J6peifK6MNBDnMf7oj/7InEvZbFbT09NWSnnllVesQ/ny8nIAkCQSCUUiEQNKRNoXi0VJA+sjpZVbt27phRdeMGH0r/3arwXyc3wSMpMAwAqQAxA7q1WZmppSKpWyf2NCpezlmTUs+QASb8Pls3mwpL7OZn5+3oBZLBYLCMEBE5zjaDRqbrlisaitrS1jZ7xFVOoDwCtXrhhwazabqlar9j12dnZUq9WMEavX63r48KH+tOOpp56y74VD8GJcjD+v4Z9JL/o/2+MO1qPdbgd6NsF++LRhyilERKCHTKVSOj09NccS86g/Bg+CvA4IoTCpzjApsPCk3G9tbZkwm7LP5uamaWvS6bQF0OK0xPXls8UoNwGKABDomEhsRoxNyb7XHYjEvWiXHB90SbBZqVTKAA69uyiTw5wUi0XTxRCoyzxOcj4lNpgpzjv2d5+vJMnYIETQUn/zS5nMN43lfBNmSclxYmJCqVTKkq9xcAF4qTpEIhElEgljs/b39y0CxF9zzhslSO8E82DyvT7OLfgh4+VsbVVSgJIdHR21xfjy5ctWuqBEw8TCAozGJBqNBnpu0biPG5TJ5wtf+IKkfonk537u5wx0LC4u6r//7/97O55Wq2XHF4lEVKvVDLGXy2WNjY0ZK3V6eqpKpWJamLfeekuNRkOf+cxnJPUZj/X1dbsBASFSH+xkMpmAVdE3We12u6pWq3aOLl26ZO0y+H0oFFJPwZYWnHNvk/e7J77X6OiogbihoSEtLy/b36dSKQvckvo5FD6sjI7DUp+5KRQKdr2gZYktaLfbevXVV/Xss89Kkrkw0FxR6uN7+l30n2b0ej0DbRfjYvx5j7NsBAsRix+7f8/iAhBGRkY0OTlpZS2aO7Owk6cj9Z+DbDarTqdjoYde3OpLNtJg7mLR9joU314jmUxaEjp9o+7evWtMBexKtVpVt9s1YDQ0NKTx8XFjK3j+2dCdnJy8IwgQMBUKhVSv180sAvjoqadet2dCYc9UAFgODw8DUSLoYmDM6vW6er2eMpmMwuGwSqWSBf4hgeh0OorFYlpeXtbp6amV3CSZgcLrnvgcAAXNaAGOZJPFYjHbtML2odfh/HOd0DtNTk7apnJ7e1v3799XJBJRPB7XxsaGer2ebt26ZTltq6urpiX1YAamkGuPnsvfH+8Xo8e5BT+UXzyTQW3X75Q6nU5AQAxrwaLqmR1eIw0Shv2FxhrJOD091fb2tqR+aWtlZcWYi+eee85YIcIAfcYMQV1SP1Dx8PBQGxsbkvoMy+TkpIEMrOi/93u/J6nfLoESmNSfBAFdgBuAFjs1JgBq04Al8ozQI6EP6HUH35uyFJ9DvX5oaEiJRMLoX+LVOZbZ2VklEgk7/6VSSfv7+wZgksmkWq2WndN2u23hi7u7u4EEZ2rN3pLf6/Us0+nGjRs6OTkxIOXt+VIQxP1pRr1ef9/Quxfj3R/s6r3jhlITwJ/dNwslzw/g5uTkxDYNLKqtVkvZbDbQMHR0dNRSfVmYYRJ8yrPvNg9A8hvN2dlZOy7aP+As9WCrVqspl8vZJgtGot1uq1gsGiiAlfKWfiz0tVrN2BGAYDgctggSX8KTBou0Z8RhyiKRiJrNprE1SBIAQ4A/4jFKpZLpCdH7SP25dWFhQYuLiyYQ55zmcjkTdxMi6FOtvdsLYIj+iOsB4+JbTpB1BjMDICYvLRwOm3aVe4mSVafTUS6X0/HxsR49emTzNueTtdTrijgOb/u/CDl8l4cvIwFQuCA8wFNTU5qdnTXmx7uFyK04K15l58HF9/QrNybDx41HIhG98cYbJlIeHh62QMRf+ZVfsZtR6peD/E2N/ZSFHcYJJqharWpvby/QNf3WrVuBcozv3eUXenZt3to4OjpqKaChUD/4DCDmb3bOJQ9JLBZTPp+34yCbAnaFFGQA4KVLl4z+lfpZSfv7+/bZ0WhUDx8+tOvnwQ+WT39t/XExmQDEyuVyIDiSY/xePbf+NGNjY8Mm8ItxMf68ByUbhMG+Ezm/9+nraEz8jp0edT6bp9PpqFarqVAoaGlpSdlsVqFQSPF4XNFo1JgYgA8Lry9/kFnjAxApIxFeGIvFzBmFy3NqasqaqhaLRXs2o9GoEomEKpWKsSjY11mgp6am7PnnGGBjYHP8Mx6LxQwAhkIh9UK9gGgbaQFlQtyiAEu+jy8DSf15hdZCOH4BeZlMxlxTbJY4N2Secf7JWmq1WioWi8rn8xZaGY/HA90FaFIK6wfjRBAiydexWMyuHbpPQCv3UCQS0RNPPKFLly4pl8spkUjo5Zdf1tbWlvWA9MGU/p7yFni0ZtyHZ3PW3ovj3IIfFkHvTGKX4muks7OzVtJptVrWvTwUClmSsh+AiBs3buj+/fvGBHED8t5e1Cb1O33funXLmKCDgwNjfnK5nPb3902syw3vM216vZ6BnWg0GihVQYXy+pdfflkf/OAHjfXw6cTxeNx2hxy3NGC4er2eisWiWcLZdXmGxbNpPuOD7+XPt9fZSP0HF5dULBbT1taWsWz379/X/Py8sUz7+/uqVCp2TovFYqBk6bMk/CQmKVDWk/pg5cqVK/bzn0Xf8/3GDyKOvhgX4wcZlIJ84CAxFeTR4MSamJhQtVpVs9k0gID1m40PwITFHu0c5Zder6epqSljJihroSMhx4fjoXw1Pj5u5Znh4WFjWpkjEPJOT0/r6aef1ltvvaXd3V2NjIyoWq0awIOJODo60vj4uCVYS30jxPz8vLLZrPL5vIESyno0RIaJ2N/fVyqV0uzsbIDtgTlD5AwAIUPNgz6AJZsuWG76iaEJIidoeXnZnGaHh4dqtVpm6afdkO/4TjPUSqViLBLvh5ZqenranHFeb4OulTXHu/eQS6AdOjk5UbVatXP9sY99TFeuXJEkzc3N2bFR4qN0iYjcA0Y2mAAifi9duL3e1ZHNZu3G9K4qkKk0cAjxUNVqNVuYU6lUADgxYCmmp6d1//59Kz0BkvgsBov1xsaG3nzzTWN+vv71r+unfuqnJPUThL/whS8YcJiamlK9XjdGikmA945EItrf3zfGBcE01vjDw0PVajXTpKytrRnoWllZ0cTEhIE4QBalq6OjI1WrVRNmUzMGNNhN/V28w45UktlkvX6o0+kE4gG83bzb7QbYths3bigcDlvJ7mwLkbMCcyZp/tv3O4M542EsFApmnf9BxlNPPXUBdi7Guza8i8aXOXz+jAf82M0BRSyYo6OjJuSFaWCxbLfbOjg4MEAQiUQ0Pj4eKOUDsIgSYQFEz5JMJi15PZVKKZ1Oq1Kp2CYJoJbNZrWwsGBhirhZMVRUKhVjczOZjIl+ydza3Nw0J5Qvt6TTaXtPyjMnJyfa29szHVL3dMBc+YDWSCRiCz2C4lAoZHMJOlLKa4BR9DmEL0ajUcXjccsVOjo6skiPmZkZzc/Pq1araWNjw/SVgKNSqaRIJKIPfehDSiQSlsQNA8WcOjY2Zt8TmQLfJ51Om6YrmUwqk8kEpA3tdlvValVPPvmk5ufnLX+t1+upVCppY2MjEHnCvAtj6FuXcF4AYoDIXq+nWCwWcLy918a5BT+JREK9Xk+FQsH0IFj1YBYQ1bG4xmIxW5i5gfwoFApWdlldXbVdj/S9OweDzvnsjY0NK3UlEgm9/vrrkvqg4IknntCXvvQlSf1F3wOUaDRq1KgkS2KlBLe/vx/YZQ0PD2tra8s60G9tbRmwGh4e1tLSkoEddmkwQNjLAX7Q3D7kzAdY+Rr36uqqie94bTqdtpIZVnM+u1AoWGNF6XszRZ6l8qJwGo9yvukL5AGrvx5S3z32zDPP6AcZF8DnYrybgwXY93bCZswCxPNAl3CACrZzBuAIZpoARHQ7tVrNkn8J7AuFQoGEYp4zL4aNRqO6fPmywuGwHj58qFarpWQyaYsmn9lut1UqlaxEtLS0pMnJSTOJINRlIV1eXrbvUa/XjSkpl8u28KN3oZwESwwbgX4HO3y3N3Ck+XIOLjHE2YA2eoyFQv32IHwf2Ld4PK54PG4gg4BaNocw80QOXLlyRZcuXdLa2pp2d3fte+VyOa2srOjWrVsqFouma6rVatrf3zcDCKyQT6cmRymdThvDNzk5aQn1+Xze8siazaZSqZTC4bCq1arZ1ldXV7W+vm5ibECOL315cT3fkWvLPfN+aHFxbsEPuxUWRWlQivKdiBGHSX0g4BOGzzI/vvzz8ssv68aNGybWpS7sxW5MVlKfYfEaop/6qZ8yhuOf/tN/qs985jOWb/P222/rR3/0R42tYVKizNNsNhWPx61Et76+bjZSqQ8yqtWq/T6RSFgLD3J++J44r0Do0N4AROr43MgIlhE8M1lIsoaIvrWEB3FMJFjY6S7POdne3jZ3iSQTa/JzoVAIuAqYkKUBC+SFzDywnP96va433nhDF+Ni/LCNYrGoTCYTaG+A2wutB4JdSaalSyaT6na75vCEAaENAos17A1ZN5TSk8lkgEViV0+Zn+cONoQcnwcPHmhvb89yYSi78EwSIDgyMqKFhQUDa2iTarWams2mbUj9HPj48WNblJnfKeN43ZE00F3SvoNMoW5vENqIZgibOH2+fAmHY69UKjZvMd/EYjFzpHFtfF80yk+1Wk3f+MY3tL6+rk9+8pO6fPmyrly5ounpaQss7Ha7WllZUbvd1v3791WpVJROp82ZSgI/JUxKl+Pj48rlciZybzQaymazxhgVi0Xt7e0pn8/r+PjYMn8AmwDGO3fuKJ/Pm/6JNRMtKCVUyoA4/KSB9AB27L2u+zm34KdcLls9EsDCAs5NvbCwEKA+6Q8lDfqA+drl5OSkNRuF3vQ2bG4Mfi8pwJIcHByYDufTn/60ldj29/cVi8X08z//85Kk/+w/+8+0trZmx8JxwJiwC+IzpqenLWlaGgSeAcRmZ2cDfaza7bb9XK1WbRf5vY6bc8hkgn30tDv4XoA0PxlJssZ9AFFq514gHQ6HrZTIDtVnD0WjUQNPRADw3961R/NBjvusa+1iXIwf9oH2jwWJhdA/m2zYhoaG1G63tbi4aGUNGqKyM4cZoeTBAAzR4POs5dubDQBDzJPlclnz8/O6fPmysTEsxj7QkMwcQBUbrmg0GrBywwCdnp6aUBdGJpFIKJPJmE4GVhqGRhoIwU9PT3VwcKBsNquh4SENacjyi5i3OCfM4YDLw8NDlUola3NBWyOSpwGIgJzDw0Pt7Oyo3W4rHo+rWq2qXC7bcbJJTSaTGhkZUTab1czMjEqlks3LBwcHWl9ft1w2PlsayAdoVYLdn82xN+NwrbnOnKOFhQVz4kYiEZMbwNix5nBveC0rrJe31OOyg5n8s0aH/DCOcwt+vPjV60I8Gp2enjaLJMMLe8/a3Hd3d62U9IEPfMAE05IC4jU+/6zNXpLd3J1Ox1xPf/Nv/k194xvf0I//+I/ba375l3/ZPjcWiymVSr3j2GBtwuFwQExNKjKfNTs7a6CLRFRExmf7zKDY9xOANNA04c7g30dHRy07B4qZ0mEikbCgMc5tNpsN5Ppgq+UzWq2WsU705iF+gImH4Utap6enOjw8tL+FtvctRC7GxfhhH+yuKV35co23w8MOHB8fKxaLaX19Xffv39fo6KgFf/qgvG63a+5LGCD0kmNjY/ZvuDeZMzgOEqFxVWUyGYVCIU1PT2tlZUXb29vvSIGmkzg281arpXq9bg06JVneDn9Xr9cNfDE/A5b8e8OIwM6QZ9ZsNvsBrW46YLFGFA6Q4tx6fQ3zJA4nvj/Jz6RRl8tly/wB9MzNzen69et6+umn9cwzz2h8fNwE29VqVXfu3LHsof39feXzefs+nAMvakfUPD09rbGxMXMB0x6DnKBwOGzuuWq1qkgkokuXLlk5c3p6WpFIRFtbW9re3rbqCCU7vjfvDfhE9uFLq5zzC/DzLg6Efb7s4vtTSTI6lAUzk8kYk1MsFgOtEqTgzur555/XP/kn/0Qf//jHJfXLXF7cBcgC9HBTsxgXi0UDKz5FVZL+w//wP1StVtPv/u7vSupbNTudjqURVyoVJRIJs1pvbGxoZmbGRMqlUkmHh4dW6iKoUBqAAr4HOhoPds6W+9iZ8b0sJv67n+Vt8pFIxEpq1Wo1EBTJ33hwIynQ8qNWqwVSmff29gIgFqBK9gbXlB0pg595AH3+z7+rAQt40dbiYvxFDhbds81OKZuwoSGFWBrETvB8squv1Wra3d3VycmJnnjiicDmkPJ6Mpm0XlwAD1hcXyabnZ3V/Py8hQQWi0Xlcjl99KMf1eHhoR49eqT9/X3t7e1pd3fX5ltaUzSbTWMvcDWxoPd6vYAdnYBGjAy0u0FoixbRMxHHx8fa39/vzychmZMUcFOv1y1zhxIa1nAWf+Y6L/al07vfcC4tLWl0dFSFQkGVSkXT09P6+Mc/rps3b2p0dHRwHBr0ESyXy9bceXV1VVJ/swk7hcW91+tpfn5eqVRKiURCt27dUjab1euvv67XX3/dkqNJjcb9RrRBLpezvoiER+bzea2vr5tw3VcD0HkBrNkgcywejHMf+o3/e3WcW/ADTertd34XIMmoQX6+evVqIDej1+vZ5DE5OWn2b6kfUvj6668b+EGUxsPR6XRM9yPJHg4Az/7+vu1uqPNC9dbrdf2Vv/JXDBT84R/+od566y0TWx8eHurhw4dmlSfwENAyNzcXyLipVCrWlR1XAQyK//4MaExJRpVTcqNMxTmlzQfnnAeUc8ADxjnAlcXnMIFKgwmF12ezWe3s7AR0PJzf4+PjwPkFtDF5UhLwAul/1+MC9FyMv8jBs8gmygf1AUZ87hXuofHxcbNd5/N5FQoFJRIJ2+i1222NjY1pY2PDUvHp1wQ7BAvA53thNcLZSCRizlQaj96+fVvDw8O6deuWWc2Pjo60v79vUgEaJyNRoB0DYANtH13mWZgpq3c6HcsnIvwQkTMbPd67Uqn0559Qfw6Ox+OW3ozZwwf2wZycFWIzlyEv8KXBxcVFRSIR3b9/X48fPzbg2Ww2tb29bQwXpbuFhQWFw2EtLy9renrarPaJRELxeNwACW6ySqWiTCajK1euWLuQZrNpGq5Go2Gb3Lm5OQOS6Kf4G+bearWq1dVVbW1tBXKcADeI6hHIn7Wxo3Xy+T8+Cfq9Os7tt4Mh8F3IoXi5KDSZO5sJIw1AgQ+0kmT9uObm5vTX/tpfs7+Jx+MBYTBKfBZ62A4YCA8KUqmUrl27ZmWs1dVVdTodAzekotLL6/Lly1pbWzO32FNPPaV4PG7gaHx8XLFYzJB7Pp831gh3ByCBHeBZl5S30/pgr3a7bROipICWaHx8XJcvX7Z/47j5zkzMnLNYLKaxsTFzorE74bPJzuBYPQMlDahn3pudB797P4juLsb7Z1CekgYsqC818G+4cVqtlra3t60nHs8WCyPC56tXr2p4eFjb29uqVqtaWlpSIpF4R64LbRZYOLG093o9S2cuFouKx+NKp9OqVqsqFotmMlhYWNDMzIxisZhmZmYCCdKUm7wjSpKVnZizKJ2RDwbgw16Otdyz28zBCKaPjo80FOqDCMTN3knmzy+C3pOTEyUSCcViMWOhiAqgZJdOpzU1NaVOp6M7d+7o7bffViwWUzabVaPR0N27d9Xtdi1eZGNjw4Ibn3nmGR0eHurtt99WPp/XgwcPNDExYRlwRAEAQGq1mpWpOp2OxQlwzrrdfs+xubk5DQ8PW080tD+dTsdcaITHEi9ArAJsmdfN+mgTHyXCppRyGSDqvTzOLfjxtVkGSJ2Fu1arKZVKWb8uaD2pf5ErlUqgi+7u7m6Ayvv0pz+tl19+WVIf3Pgmn6Bm706iG7Eku/mkAUvBzTI/P287K6mf4/OZz3zGbu5kMqmlpSV985vflNS3YX/sYx+zB/f4+FhPPvmkvZ8PY1xYWDA1P8fV7XYDycfetcDOhmNlErDO7Y7ZpAbtwwdHRkb0/PPPS+ozWuPj46bxicViFrXPOfaOOaywPrjQg0ufbXI2wJJ/43tcjIvxwz6YW3B1wXb6EjsCX8q+9Xpd9+/fVyaTMSaBBZD0ZvQ30oCd7XQ6Ojw81MzMjC5duqRoNKoHDx5Yto3X00hBYwRzHTkvOM/QqaTTaV26dEmnp/1+V61WSwcHB7p37552dnasnA2LAUuVTCatOWe1WlWtVjPQsru7q3Q6bZZ5384H0S/nrNft6TR0am0k2PzxM+cTsTPfL5FImNuV751MJo35CofD2tra0p07d9RoNBSNRjU3N6fp6WnbdDabTRUKBT148ECFQkEjIyO6ceOGjo6OtLGxocePH5uTK51Oq9vtql6vq1KpqNfr2fnLZDKWLo0JpdvtKpFImG0d8Mq19kz40dGR4vG4pqamtLa2pr29PQONsDjeRUhq9fHxcUAEzZoGYwhI9hKF9+o4t+BHemdbCyYEFshSqaSZmRnTqEC1Sv2bY3t723J5er2eVlZWAu0QUqmUNjc37edqtWqTiCQTG0oKTAxSH5Dw2pWVFQ0PD1uZKpFIaGpqyo5rY2ND6XTaHrh/+2//rV588UVjhv7Nv/k3evz4sbLZrCRZt2FAHW4DqV9KOjg4sAec7+ut7JICWhooXWlg+zw57oOKZDRpE8309LSxT7ynFy/OzMwE+qdNTExYTZpz5Huc4RTxgJRr56l/XisNNFY+9+RiXIz3wmAzgJiXxUbSO+51b/nG0YNGkH55uC19vk6v1+8h5d09mUzG8rgePHhgGTCAjGQyaUGo6HawbsdiMQMRzDWNRsOyzKR+2X56elqjo6OWX0MeGMxOpVLR6OioaRvRBZKpA9vgWX7vdPKZNP3/kGl66KYOcPQBjszbyWTScuNofTQ6OqobN26Y42xzc1OPHz9WpVLR5OSkFhYW7HvGYjHV63W99tpr2tvbs4Ts69ev69KlS2o2m9rc3DR7P264UqlkMgOYMtx0lNF8CCVMVq/XUz6f18bGhjKZjAnZk8mkFhYWrM9it9vV48ePtb+//w53M/ePFzV7oTvnhn+HcYIdeq+z7uca/FyMi3ExLsZ7ZbCJ8Am73nklDTZZXpR6fHwcADzY5MmBQUjsSyfZbNaAxcOHD5XNZpXNZtVsNs2FRHmE9hNocXySc6/XU7vd1t7entbW1szYQbm9VCpZ1s+LL76oZ555RqVSSVtbW9ra2jK2goWf5qRk46B16XQ6Ac0hdvRQKGSMshfi+owwfsY2DmvCuSE8EHNIJpNRqVQyNqpcLuvRo0d2HTKZjIE034+MDCOAJvqpTqej/f19bW5uKhKJKJfLqdVqaX9/30wkiUTCAGe73TYheKvVUrlcVrPZNGeeJHOI5fN5pVKpAKgdGxvT5OSkwuGwHj16pNdff12lUinA+Phzxc++USr3l/8b7k2iCd7r49yCH1gWX5fkgnFh6KaO1sazHLARZ51PsDNYxxG93rx5U+Fw2FgQ6FFffqN2zfvCcGxuburKlStmfV9dXVU0GrXQQ77PBz/4QfvsjY0NfepTn5LUdxb85m/+pr32xRdf1N27d628NDc3pzt37thx0axOGmii2JWdFYGfbU6K+I+cn+HhYdMTUarzydi9Xs/6BkmyOjd/S52e1yaTSZuAfWS7JGtYyGvRN0j9h53JS5L93Xs5Xv1ivP8GbA/6RW8WkBR4JnxwIM06h4aGbC6Ym5uzHmDoQJifWOSOj49VLBaNecFyje0ZpgBzA9obSZaUDEBDe0i43urqqt5++211Oh099dRT+tSnPqUPfvCDarfbWl1d1dHRkekah4eHlc1mjb2fmZnRycmJpSPXajXTHtEY9fT01JgTvlsoNLC5o9+BMfJp1YiwR0dHNTs7a/rQ559/XtevX9ebb76pR48e6e233zaX2KVLl+y4MpmMwuGwNjc3VSgULJQWQbV3TnlrOeU8gAdrBBl0xAIgzl5fX7fG00g0AEHobgBJsFUnJyem17p9+7YePHhgr6XsCbvnWR+uNUARQOrlCGelHu/lcW7BjySj3rgw9EFhseVBRnDbarVs4a5Wq8rlcgYKAAtQx9/61rc0PT0dWMgXFhYsbNDbtyUZBez1K0xS3/zmN/Xyyy/rs5/9rCRZ3xhAQ7PZVLvdtjTpX/iFX9CXvvQlfeMb35DUd5795E/+pAGymZkZZbNZ6/WVzWZNdJhIJDQzMxPICPLKfB/aJSkQnc/32N3dNa3P4eGhrl27JmkADP3ug47HUh+0EdEuyaz4gB0cI94x52vH7G4kWe8hfsdDzt8ijrwYF+O9Mry9mMUaN9RZhw7ghWBEMmxweEn957VQKFjTSqn/HLGw4nA9PT1VPp9XPp830TAuIDZ81WrVNnhsOEdHRxWJRNRoNGyhD4fDOj4+1sOHD/X1r3/d+iuura2p0+no53/+57WwsKBcLmci7cePH1ubBko6k5OT9v2np6dt40V7hdPTU6VSKUWjUe3t7Vk/s6GhIR2O9BkMHHDks/mwR5x1mUzGtEupVEqpVMp0o/l83rQ0dK7PZrPGVhHuSM/DhYUFs+FTGrx586Zu3rypWq2mzc1Nra6uWm9FWCU6A1BuovRJ93bKhljbAVS8dnh4WK1WK6B/2tvb08OHD/Xyyy/bdeX1XuDs7ztpAKgBtdIgwoRrTJ6e6ULfo+Ncgx8mA2/B85a8RCKhN954Qz/6oz8qqd9W4i/9pb8kqe/qymaz1oKCcMOVlRVJfaD06NEj64i7tbWlRCJh781Dyk7qrKPq3r17lsvz/PPP65d+6ZdMdPz888+bkE+S1ZvRyuRyOf3Vv/pX9d/8N/+NJOkb3/iGFhYWzHb/xS9+UR/72MdMM2T2TvUBxcLCgoEfgJbP0kkmk/Y9uMn5Hr1eL9C5fWpqykBZu902myWvpXYt9UMiFxcXAw46GBtJFk1APR/g5MGQF2KftV1CwzKOjo4sq8mzT1K/iWooFLLFAOH6xbgY53V4nYVvZUG2j3d/Mc+xmPlWFgys00NDQ8pmszo8PDSmgucqHo/bBsQ7emAPYFNwtrIJgUWArWDuLRaLKpVKevPNN1Uqlew9arWavv3tbysej2tlZUX1el2PHj1SOBy2EMN79+4pFovZJrLdbqteryudTiuZTGpzc9OYfVo6UOKBiT85OZFCUmio3ymdXl/1et1+j7YK7Q89CSXprbfe0quvvqqtrS0zxMzOzhrI4d8ODw9VKBRMvH18fKxyuWzxHicn/c728Xhcjx8/VrvdNm0QrjzyjyiRsZ7BxqdSKc3MzGhsbEzlcllbW1um42GunJiYsMwfvkev1zNh9fb2dkAIDavH2uUNJwAqwCV6LH5PBAB6zLNVk/faOLfgh4eQCyUNensBMgAFtJnY29szMIOynQmmXq8rGo0aAp6bm9Pa2ppl/7z++usBGpjoc39Ttdttez2iZEl64YUX9JGPfERf+9rXJPWFw7OzswHx8czMjGUEvfXWW/rwhz+sH/mRH5HU79q+trZmoKFQKOib3/ymuawODg7sOG7fvq3nn3/eQML3KnvFYrGA4LHZbAZAnBdAT05O2t8Wi0Wzf0p9QfPIyIjFA3i3CufE21pPTk4s/l6STaI+bIvr4XsKSQOxNK/N5XI6PDy0cwLYAYiRVgvA/PSnP61yuWzM3tbWln3HPynP5+rVqxZKdjEuxp/XYGFHUErPJc/++DgJwJIvs/jd+Pj4uBYXFxWLxUw/Mzo6qjt37tiGqFQqWbozicAswizE8XjcgAMlkmazacxsLBYzjQ75NuT1oF0hTuMP/uAP9JWvfMUyemCKOAY6ku/t7anX6ymdTltoXyqV0r1791Sr1WwePj4+tsgMnFwhhSw0kN5nsBa4ljjfuKlgbFgzEEdTYgKgEL+BBZ5r1Wg0tLW1pdHRUS0uLloW0Z07d6zFEPIC2BNYHQCTD1is1WqamZnR3NycJUrT8kKSgb7Z2VkDp+FwWL1eT6urq9b6ApAKY4jYHLDKeaDy4edPfua+4/18avh7eZxb8DM6Omo3KRcMRM0i3Gg0FI/HrXw0Pz+v3/qt35Ik/a2/9bfUbrfNQUXde3FxUVJ/RxSJRIw5wPHAYlupVFQulwPWQl+KuXLliqn0i8WiPvvZz+p/+V/+F0nSV77yFf3Mz/yM3exMEICGVCql1dVVPfXUU5L6AGR9fV1f//rXJfUfji996Us20W1vbxuQQgfDzcyOgxuVMphvbIoDQpLlRfBzPB43UFYqlZROp9+h0TkbfoXzDDYM11ulUtHy8rJplRDY8T08ECIewH9WOBw2tmtoaEhra2sGZuPxuNn4pT7r55kkdqk+Mp9rv7y8rM3NTQN13vEn6QL4XIy/kIGY1JcW6LXke9r5MjbPCGyNZ11xp+L2isViunbtmkKhkO7du2d/7xdFAAgLpC95oBuhBQKZNIuLi7p165ZZrtlg0GEcxgOrOaAjHA7bgspnnJycWCo/7SLoU4XO5c6dO8acwJIR2DgyMqLQUMj0hhMTEzbHwfrAeuBYk2TWfbRLfl4kj4dRqVTMqUY69ubmphKJhG7evKn5+XljpgAtlKMODw+NbUHcjJvKbx6LxaLS6bROTk60sbFhwAqX3OzsrG7evKmlpSVrvcRa6GNNAJhcax86S8sggIxPtGbDzP3mgY53fr2Xx7kFP1xYj0B5gD3LAdUr9Rc8dvx37tzR5cuX7UYhGt5rgDKZTMCmjXVUki2k3LB092UBXVxctPLRv/7X/1rz8/PWhf3x48c6PT210lS9Xtfdu3ctPPDHfuzHAu+9vLysVCpl7/3Nb35T1WpVr732miSZFZRzsLu7ayBhd3c3UOOlVgvgODw81NjYmP19oVDoM2JDITuHnD/KTgi3eXgAcdVqVRMTE+8IH/NWTXroSIOMDt+agu98tmcXFLZPj/7ABz5g4NR3lub31WrV3ptsDcpj/lrTMRnG6yz4uRgX4y9i+KwaP2CZJQUSkH27AX72GVw4vMjnwtGVSqXUbre1sbGhXq9ngYccA8wNcxDPomctfKlteXlZi4uLxijNzMwolUpZACJ9rCKRiJ544gktLS1pdXVV+XzerOI4usgoy+VyunLliq5cuaLx8XEVCgXbrC4uLqpQKNh3xxEmDfKIWPR9mcznJMGMSAoEC/peY2hvotGoHeNZxofzwvszDg8PA6n5HA9lSpxarAGsWz4PrtPpWJCk1G/PxLxGenQqlbL5n8BZr53inmLd8toxz5TD8Hh2C/D9vYTi74eYkXMLfjwi9d3RuVjSIPGTC+xzIl566SUtLCwYCzEzM2O2T6kfFhiPx00L9OUvfznwXuFwWNevXw+kLp8V1Pld0xe/+EXTzjz//PM6OjoyhmRtbU1vvvmmCZg3Nzf10z/90/ZeJCV/5CMfkdQXYx8fH9uxn5ycaH5+3v62VCoZI3JwcGA6AY7TJ1sfHx8rkUiY3qharQaSlJngJCmdTlsNX5KltqJtogzG36ZSKU1MTJiuih2O7xDtGxiSxyENaNezydWeGRoZGbEJBsGinzjYpfL7UChk15fj5TsPDw9bwvbHP/5xjY6O6vd///d1MS7GX9SAHfBtYihb+80Lz7MXHqPR8BsJNgP0ecKKHYlEdOXKFT1+/FjlctnAEbt/r9M7m6ODlRqRL4Cq2+3q4OBAxWJRc3NzunHjhuX3PHjwwFgJNDTtdlv5fN5CAKPRqHZ3d20DGQqFtLu7q8nJSV2+fFmRSEQHBwfmtKK9BqyWZzhCoZCGQkMBdsKDO16HAYYspFKpZCU4gA/iZTRPoVDIDCNs/HDAxeNxa3FULBYDzthOp2M292g0qnA4bMJzadBY2pf5YYB86CXMYK/XU61Ws/kd+zwOPAAKTBfOLQ/aPFj2JVRYKKoE3GcAIv77or3FuzR44D1DcrZuiRLe71x4iNvttiqVigGSer2uUqlkbi4mA8pcmUwm0JSzXq9rfn7eAEg4HFa5XLbFmhYUUr8EU6vV7KGjqy+jWCyqWCwa87Ozs6NEIqEPfOADkga7GQDNysqK7t+/bw/O1NRUAKB4QSM3rgeE0M3+nAB+cDZ0hweTBoAjm80GnAG+aaDUB5C+gWs6ndbc3JwBLSYXrgF1ft9+xMesz83N2Tnc29sz9wPXh8aGfE+/+4Lh4l7A+sk5zWQydlxosRh/9Ed/pItxMf6ix9mYh6GhoUB8BuUoNn1nM1m8QWBxcVHFYtHmwFqtZgLhK1euaGJiQh/4wAd09+5dVSoV64EFQAGISYPWBuhM0um0ZQYlk0mdnp5qfX3dsmQKhYK1vnn66actk0fqP6c0KGUBp08iGyOSj1utlu7cuaNQKKSlpSXNzMxYfyrmDR9067OQFBq0x0G745u1AgYAmuPj4zo5OTGtZjKZVDwetw7qnA+0PzBFzWbTrhWNZD3L5EuS5C2hkUqlUtbny29QET+3Wi1jxGgRwjUeHR1VpVKx+wNdlxcw+xBDP2/z7569OT09tQgUzwKxflI94Ht5kuG9Os4t+IFhOJt6CgCSZHVLJo9EImG/Hx8f197eni5fviypz0LMzMxYqerk5ESbm5tWJsvlcsrn8/Ze1JlZjL1FlePjs/b29sz2yN8WCgUrH8XjcVWr1YB9/tVXXzVr+fLysur1uoG4K1euKBqN2rGOj4+bsHd+ft52A9LA7eXbcvgHBDEfn8XkdzR0ZK/3u76z9sahoSH7W/rMnLXV+vPvJ2gmRSZXAsYYU1NTVqZqtVpKpVIGWLC9+syharVqx3d6ehoQbl+7di0Qbf87v/M7uhgX4zwNv1HxGWaUubwIFW2GL7v75r7Y3AuFgkZHR21Rjsfj6na7unz5slZWVjQ2NqbXX3/dhKxo9PgsyhzSgG0NhUJm/R4dHdXjx49t80Su15tvvmnlnvn5eQspDIVCqlQqlo5PVk+n01GtVgvonE5OTlQqlfSd73xH+/v7Gh8fV6VSsbYdzF1sdNg89Xo9nZ6cBmJKcLN5ZxrnHFABAJiYmND09LQFBbIRrtfrNm9WKhVjrhCkE/zI3wFcYKUODw+1vb2tUqlkwYaERA4PD5vdHfDh/xa2CGEz9wSskAc2HhRi2z9LBHAfce689gow5/Vl3pHmN5gI6d+L49yCn7N0rDQI7vKtEHxQmO/K7puSSrLcCNxad+7c0f7+vt5++21JfXBEZ3epL1Ku1+t2I6AhYQLCKir1wU8ikbBQQ/IyfCmqXq/b78llIACMTsqAnVu3bmllZcVYKu968poXqT8p1ut1+948RLA10WjUEkQ5h5I0FOo/TIgIpQF1zGccHByYQFLqs0j1et1eV6vVAiwUuwkYrna7HdAAzc7OBpifUqlkND6aK0Den1WXc/PmzYB24mJcjPM2MGgAbthts0v3ZXy/C/eCVOY6Av4AFB4kUIaemZnR9PS0gQi0PoAWFlIYC8TI5MBIgyy1ZrNpIAOgAyvOe6E9gX2emJgw44jUn1dolUFoYTabVa1Ws+BGQCEiaqzskuzYD3Sgk9O+NobzJw1a90gDppjjJc8nnU5raWnJQgw5p2NjY9bDS+p3jK9Wq5qZmdGNGzc0Pj6u3d1dK83RE4ueXLAylAvpDE/Z0reLAHx4mYCPHOB/XPeJiQnNzMxod3fX5Be+3RLXHybHx4l4cgDQ7f/H9YNphMXy7/9eHecW/DAh+KacUh8I+Frk0dGRaTuazab9jjKXd01UKpWA/W9xcdFKItVqdZAg+t3P8Y1Ra7VaYFJgsZb6FDRiOj5rdnbWwEuj0bAMHUn2sABIisWi8vm8TWyHh4cWEib1y2QEEYLgYUgAiJ4h87tERJGe4vYpqVDn/M7nj8C8MPHGYjEVi0V7b+rFfG+6TFPyY6eFaHliYsKCESORSODhwpnxg9aZf+M3fuMH+ruLcTH+IodnaAE3gB7AkGeGJFnH9aGhoUBmljQIrfP29cPDQ62trWlqasqs8MyFiH+HhoYsONAzvzAcjUbD4j3YSMKijI+PKxKJ2O8IDmSzGIvFzExSLpdVqVSslQQsNY5Tyk6IfNFK8n3ZROIUGx8ft5/RL7JY8xk4xHq9nlncaaIK8PA6GdgfWGlY51AopFwup2effVbj4+OKx+NmlwdUAGr4Lt1uV6VSyZpuU3bzzWQZiM894+KF7bBABESWSiVrNcK6h5AasOSDMgFWnikaHR21pGjuMY6fkiEmo4ucn3dpkA/hNT9YQlmMyX7w2TG89vj4OABI6MjL71944QVJ0q/92q/Ze3khMNoWnxPka6A+AHF2dlavv/66sRa0oEB8m81mNTk5GUhlRs0vyeyQ6IkqlYoymYw++tGPSupb5wFSH/zgBy2xVAr2AmIwwUmyLBx+j3399KR/HkqlkrFK8XjcJj6OOxwOmxW+0+moVCrZ945Gozo6OjJgxeTha/NkcUhBTRZ1eM43AlDe64UXXtC3vvWtd9wXF+Ni/DAP33QTpoZFy1vg0aucZQEAPX5u8oYQyltERcD+oC+RZEyxtz/7jR0LIYsz2pGzjiAWdZ8TVKvVtL+/r0KhYOzU2SRhGBpAjDQIbKTZqjdsEDNiGW89Sb0+SPHHB0vC/Dc2NmZMG4xJtVrVgwcPzBWHEzafz2toaMi0UYlEwgAceUmUtHCipdNp29gBYnHJFYtFTU1NWcWAFhb+mnJ+0WD5zSa/h00D1Pj0ZSQI/LvfPJ6tinAuOf9cSx9o6MX2vP69LHo+t9/MuyJgSLzVUJLRfAzCqfhv+lhJg/IPr0+n0zo4OLAsmJ2dHU1OTtpn+ZuWz/JlscPDQ5ssPJBhfOMb3zBG5ZlnnlGz2dQXvvAFSQOamL9vtVrmiJD6jq6hoSELORwZGdFXv/pVO858Pm/Jx9yg3KQ+nl4adE0+W9o6+f+z9+fBlt5ndTC69jzPw5mH7la31BqslmVJSDKSbWK7wHZ9kFAQvuAqEggJ+XJD6uaPEC73Atd1E4r68uH4UqmKgWJIAt9H+RpwAraZbMsu21iSLUuy1HOfPuM+++x5nt/7x9Z6zrOPhI0Ndnef86wqlfqcs4d32Pv9rXc961nPeCTvfe7cOXkf7isAiY/XZa7hcCgkx+fzoVwuC7kJBAKSOMr3puGcoCxNrxLVLT1GBMCMYdxgOC7Q4YW644aLFbt4tPJBY7++3ml1FsDrVNNQKITd3V00Gg2ZK8XnUflh+Qs4TF+nCpJMJuW6cjSbix4WqgftdltCDYPBIOr1Ovb394W0Me0+Ho+L8szt16nHVCu4XxznoWNPhsMhRuMRJuNDDxNv/ur1ujyfYyKY2cPZZTz29Xod9XpdQhZ5DNh1Sk9npVLBSy+9hL29PRwcHEiQIteWaDQqr6k9Qfl8XshXNBoVhUc37WjFT1c3WFYcDAZiv2AOEq0f7ITV12fe/DOlWac98ziTNAUCgZkcPbbNc//5OeVrH0fctuRH1y15AsiS+TfWObnY0jUPHHYWaRbrdrul5bBareLg4EBKZgBmuolY69Ypzbr1WhuU2+02UqmU/Ly7u4tarYa3vOUtAKaKyNvf/nYhMHt7ezOkYH5+Hj6fT7wy/X4f1WpVfn7iiSdEjfn85z+P9fV1kb83NjZm5FReoLhf3W5X7oKAw3IiSUYmlZG/sbNB1835pQAgsfR87aN3P8lkEhsbG/JF5gVRK3fcj3g8jmazKceT5+cowTUYjhv0kE7t5yEJ0mZdZpsd9V8cNb0Ch23evHEbjUa4ePEiFhYWEAgEEAqFZm4QNZh0HIvF5HEsm9C7pzNjWGrioM94PI6zZ88iHA7jxo0b2N7eFmJQKBRkOnkmk5HnjcdjRCIRmSjPG8JsNitNI9lsVq5po9EIzWYTzuTQA8SSEVUx7ttkMhG7AdcJXTLjQs/rYzqdhsvlEtKkAwp5zDhDze/3z7Su06hM4nD27FkxRTOYtdVqCVljuZOqDgmmbhZh6GSr1UKz2UQ+n5djoW9ktWKoP09UAakq6YBLqnEsr/HYs9SlCflxvh7ftuSHRjadjMkTrHN9WHsGILIpAHHasxTFKHg9ikEbmkmiuNBrExkfz1RhADOjL6LRqEweBqbE6M1vfrN8EX/3d38X6+vrMzNUAAgBmpubw7Vr18QYmEwmsbW1Ja/3tre9TdSWhx56CIPBQIagMlqex4SkiP8vl8tyNwdMCV42m0XZmZbgEomEbBcvnpSpPR7PTOQ63f/a4KyN3vwC88LAu0edwkxQ6qf6VavVEI1GZSHQAxwNhuMELja8MTjaWsz2bfo6tEKgc8ZIgPh4j8cjKonjOAgGgzIvi/ldXBBpTO50OjPeFb4G/UB8fRqmw+GwEDfeLFKV4feepIMZQQzuI2GiqsKFmgSD/+50OuJRmpubk6YOBrSOx2O4PW7pbGN5iCUcqt+cds8SeygUknKe7gArFouyTjCLTN84j0ajme4ttozrTjKO0Xj44YeRzWZxcHAwE+TKhGodd8Aqg/ZiUbXh2kdCxxtxnkOtyPB88zPBY6lN1Lq7kK/NdUEraFQXWZ7T1+zjhtuW/NBTw1ovcJj9oyVf7fNhezoAMYNpQlGr1aRc1Gq1sLGxMVN6yuVy8iWIx+PCoIHDOVYkQ/F4XJ6rpVdgSjzm5+fxp3/6pwCmvqLz588LAYnH4/L6wHRIKuvafD3gcOwCW+WBaRu81+uV9GfeSbD0xC8SX2t/f38mX4eJz9wv5ulwH2OxmJAfx3HQarXkgttsNjE3NyelP4Yzkmhdv35djjv3MxQKzag9WoHi84DDFk7+3bq2DMcRvMHQZXkuOvzu8u6b6oT25WjjKhc8diuRBOhW5sFggO3tbUQiEcTjcVnwfD6fJCeznER1Q7fYs2mBNzEkOpxWzryaUqmEGzduiBI0GAxQKpWQSqWQyWRksaa6rIMWR6PpwNRisSjJyZ1OB+l0WkYD0UTs8Xjgck9DDtlwQnMwO594E8XxGxxbFIlExJhN9Yfri54eQCLHayubYXg8dDmSJb3xeIy1tTXcddddcBxHSOfS0hJyudzMcGoSSBJClhe1X4cEhuoU9037g0gmgcP1kp8TKmVUunS3Gc8tiSGDHrVhntugo0aOG25b8qMvBkdZrq5zH8Xc3ByAQ08JP7Q+nw/Xrl0TklAoFLCzsyMLM+fkaBVES8vAYQoxQRKQTCbh9/slDycajaJarcpr3X333TL7hvvg8XhkYCgHiupgQn45AGBzc1NI2ebmJp5++umZ4wRA9osdDToPR4+oYDsmwTtGPlanLLNerJUbXRbj72jGLhQKcvfF52vyqgkeL866JZ81aWBKpNbW1qRt12A4DjiqdOiyMgkGVZWjfj4+n/+nOs7vkc4GYpszAEmL53eXCxwVI3ph9Nggr9cr3hNuA8MLqbZzVAU7vKgmMblaZ+XozBoAMq283W6jVCrh4OBA9oHKymAwwI0bN2RfAoEAYrEYyt6y+GR4jQIOhyPzxpLl/nQ6LSGLLD9RwdYhtuzYItjtBhy26dM8rQlLKpXCmTNnsLi4KGU/XvfYgba7uyteKZ5r3niSzPAmmn5SJk1zFJPMNlNt6FoN5LWTniquX9oYzePFG2CWzvRnSXcaHs19O064bckPAGHzul1dy8NHa+a8EwAO82toRI7FYiiXy9K5tLOzM0N22DnGhb5Wq0nAFQD5kPJDwam8wKE3hojH4zKOAQDe+9734vLlyzJeIZPJzARHnTlzRlpLgcNkZH5RtL+oWq1ic3NzJsxKt8Dyw8r/0+jHx3s8nqka9lqru9frle0kSWK5jneJvGDF43F0u10hWjRSM59oMpnInR2PC5Niecz0XYfOpOC26p+N+BiOG7jI6OuYbjsGDvNYdOmfixevM/S48BrBGw2qvDq9dzQaSfmMCjb9K1xw2fWkv3/6xoXvyfLb+vo6zp07h2q1ikajgTNnziCRSOCVV17B9va2EDGOoWFOEG9ymL1TKBRQrVbF/6LLPjQqA1M1PZ1Oz4Tf6jEM7PLSCz9VaRqOm82mXMfD4fDM2A+3ezolPpVKwePxoNPpyMBSAKIa8RrJY8WU6yeeeALD4RCXL18WokPFhQq44zgysJvkl2sM06p1NYOkJJlMIh6Pi2WDnXv6ONC2QeXw6OscXaN0KYzvy+sxy10kmBZy+B0GT6SWelmv1Zk20v6IacovTcJk/HxusVhEPp+XLzc9NCQkNN3p0D1dYqP0rEs62gszmUxEdWLAlzYd7+zsiFK0urqK3d1deTzLQVSh+IUnyeh0OuKB8Xq9M+3m7XYb8Xh8pozlcrmk3EffkvZJud1uCTnU3QLJZBLFYnHmoqzbJbXBGzjMBTlqiuMFIxwOo1QqzXSo6HZZSrM8j5FI5FjLrAaD9nzoxZt39Lwz5+JFlUgv6sDhNUfHgWiPDgmWvobxffl3PZeKJTYu0CQvNOEynZ1NJefOncPDDz8sNziRSASNRgP1el1mVpHgcYHlgt/r9ST/p1arYTKZIJPJiAJFBSYWi8n1xev1IhqNihLjcXskdweYXr8ZnsgbK71//F0ikZBrD0uOVIWY2EwFCzj0gjJvTl+vut0u2u02Njc3kc1m0el0cOXKFezt7cm2dDodIbf8P2ebcb+Aw+G2NKbz71TLut2urB/hcHjmRlFnQ3GbdVo/u7e4dvJzR4VN5x/pz8jRysdxw21LfvhF0YYr1sv1CfF6vXjggQcAYGb0QrVanRp7X8vWqVQqePOb3yxKT7vdxt7ennyZ+IVgeSmZTEoSM1+bUiRwmHEDHA5YJUGh2Y5qzJUrV9BoNESF4jA7fjlrtZrU24Hphzsej6NQKMg+kuAVi8WZOxJuuy4NDgYDiaPnMdEdHpFIBD3PYUga95kGZ26XDl0DDgfHcjspL+tOM92Vwi+Qlup5/LUBT28nSd3KyopMmzcYjgtYzuFdu27x1nk1/DcXZhIW3iCR3OgSCIkDgJnvFhdPj8czY3wOBoPyfdaLKW96uGjqJgR6aTY3N5FIJBCPx8WTUyqVJAyWqgoNuLy5PDg4kA6mwWAgiz3nbOmgPipBJIQkgh6PB27P4bWCvydo9qVviubecDiMQCAgk93r9bocV5IyGoupPOlsMq5JNC6zQebVV19FrVabMYFzOwCIWsY5iSQ0JITcRk16CUYLkGhR0eI5orKn1yl+PgjtDeLrk1jxxpjqkUShvBZTcJy9l7ct+WEkOz/AwGwMNwDpYqDCUigUkM/nAUAMczQNP/DAAyiXy/LY/f19vPDCC9jc3AQwLT2RWQOHigk/wKyD8+JSqVTkC8caMOdUjcdjIV3AYQlKz/ai9Mtt1V8ySq/68frD7DiOPJfbwA+ty+WakSm5vdyPdDqNaDSKunta62ZWBDAljLp0SClYlxkpj3K/joZMauM35Ww+Xpf2AoEAPB6PnA/uEwmjER/DcYQOq9OqqSYwJEa6TMXfEbwr1yZp/l4TFp0LA0xv6pguz7/p0gkXb5Ifne6cyWSk0WNjY0M6NMfjsczFokqSTCYlWZoKBweDMh8nlUqJIqK9hrw2UnXi9rP8R+LAkURsy6ZCRRWDN3G8wQ2Hw0LIgEP1huUrKm1HfUdUpHTgJMknCWSlUpmxHzAShNdTvXa4XC7xU1Gp1+VQntdoNCrRA1ohOqoS6nIZzyMJEc8v1SBGFuiMJx6/Tqcjf9PeseOK25b88ARqAqJb+YDpArq2tiaeE+BwJlQgEEA2mxVz73g8ltEKwFQK3N3dFdMxh4vyA8EWUMLlckntGJh+oajG7O7uYnFxUV6fd2j80u3v789c6JLJpMzk4baUSiUJ20omkzIdmMdCe3aAQ0LFtkQd9qhr9UdLVrFYbNpK756SC323SCOg7v7Sc8M4e4tErNPpzARH8uJHBYw5P1oJ4jFlcqo+vroMZjAcR3Ch0tcxfVeuw0n1Dc/Rxo+jHWJMfNYhhiQzvNHgNUjnprEhQv+dijs9Kewk0wGyVA7YWEI1hASO20RVXGfm5PN5LC0tYTgcyvWZhAA4LNOwJMM4D+2JGo+nA1PT6bQ8PhwOy80f94NlOZb2gMO1hQSBx5Y+Fx533pDxeOi5kbr7l9dPfQx152yn05FWeG1U9vl8YvzWbfVUgxxnOp4jmUzK43i+uG8kSm8Um6D/TdWKnz9NbHms9I0t/Ur6Gn3ccNuSH3Y66EndvEPgCUmlUsJYgUNJGQAefPBBeDweSS9uNBpTxeM1d/9oNMLBwYEkPAOYMU9rMyCAGaYNYCZbh6Fd/PLOz8+jXC4LESsUClhYWBDJmsY/gjOx+OVmHZqP9/l8Yko+ODiYIWbD4fB1PplWqzXT3aXJE+983J7pMU0kErLdsVhsJldpMBigUCjMfDGbzaaoNbyoHJWFeQxpIuc50V9yLe3y2GvPlcFwHEGCctTHyBsDbVLV6g4w293KGwVd0uBjtDpC9Yivz2sK35uKAcdscGHkNYPeIMaOlMtlURtIiKjQcBGlIZjl+UAgIN2vc3NzWFpamhlfdLRcxAWbpIZEQx9Dqiz8P7dVB6xqvxSJID2hND6zs4qkJB6PC3nj8WH3bSwWE8Nyq9WSEiVbxkl8eIMYjUYxGo1Qq9WkZEWSRQKkS4tUdbQCxs/D0cgB7Q3Tx+So9+uNymIklTp3iWQHmPWFBQKBmc/MccJtS34oReovPCVNnsh0Oi3D7YDpSVtbWwMwLWM1m02cOXMGwLQzqVqtysL9wgsvoN/vS0AfgJn0aH44eeLZ3knfDuvoACQfiEqQ4zi4cuWKfNGA2YGs4/FY7oqAw3ITFSyfzydmP2B6kSJZ4hdFm5KPtsPSwMa/8yIDHM4dO2pe5vO8Xq90FVQqFZTLZfm5VCpJKyv3KRaLCXnSd1Q8RrqLRXd38QupVSLeRQLA+vr6Nz3Z3WC43cH2a73g8SZLL1okAFyESZIIXR7TigS/91zQuCDSNxiPx+XGqtlsIpFIyA0Qy9L01/AapVUkDiKtVquoVCooFotyg8XrcCwWk2upy+WSmyCfzyfGZJIH3vSxXR44LMPQl6RLWwDgwjTnhzddPE707LDVnsoUm1v4Pq1WC61WSzqxOPtwNBpJzlEsFkMsFpvZNra+cwAr1wi+NzC9yR6NpsNel5aWkEwmZ6JMdCcsVSdej0m2+LqpVAq5XE58mTQs65BIguRHe4f054pEiorP0c6woyoYCaVWG48bblvyo2u8GroO6ff7Ua1WZQGNRqNCdihZbm9vA5jWezlHCoC0QOouKE1QeNeiJ5in02lRWbhY87kul0vUma997Wu4fPmy/N3tdmN/f1+IE+8ONKNmYBgAuUvQH25+ubrd7kz57ahBjl98XigYFMYLi5Y6AcgAQr5vKBSSQaeFQmHGl9NqtZDJZEQ9492hvvvQxkNK1fz7YDCQY8CWel2e0/4hK38ZjiOoePDGgF4PKgfA4U0DFzrdxk5w0aWKoHN/+DdeK/n68XgcCwsLSKVSuHHjBvb39xEKhWSAMf0vemI8W+Rpip6fn0cul8ONGzdwcHDwuiR2fQNERYkjI+g5YQmKHhxuH8tWnJGlu+C0qkyC2G635eZPqz707OibMBJFlqF4I6sHoXJsBbdVk6tarYbx+HA0CMkClbzhcDpIO5FI4K677pLjBEyvg4lEYsYbpbuItT9HN4WMx2MkEglZp1ju0mSQZEnbC97I/MxjyFIdPyN8PX2MSIyoHB1X3LbkR5efeAKYV6Fd9GwRBKYdQvwy0rRHE/K9996LbDaLL33pSwAgtXDtzNflJD1/5+j2AJjJAOp0OshkMvJam5ubItsCh2nG/FDzboj17lgshkajIV+WQqEgqZ4AZgISI5EIMpmMmLNZO+YxYOeWTk8dj8dCcHhHp0uJ+vgyQp6vxbsCvmYoFBK1iF9cLbvruxvuM7eN5T1udyKRwMsvv/yG558xAAbDcYNWdbgYaaMybyqks0mVMwguWNpLyOfxtfWMJi5uwWBQRkbwBrNerwspIdGhz8bn86Hb7Yoq5PP5kEgkMD8/L4OPmZ/DEht/1jeK7XYbwWBQlBGSF23YjkajUjJjSQg4VLJF4YcDuCBDUvXNnO6iAg6vfySBLNGlUqmZnDem8kej0dd107LJhseX89PYkENTcjAYxN13340HH3wQsVgM3W5XlHOG1jIxnyU7duryppTqDxUqbrvumNV+H63acC3g49lEo71IPDa87vN4a9+YLsEe55vQ25b88ITpL702CANTz4le+LWTn+RFf4g//elP4/nnnwcASSylJ6hUKr1hKzbfi9ImtyUQCAjRCAaDWFhYkFC+3d1dJBIJee+lpSUEg0FpXU+lUmg2mzNeHQ4NBaZlMF4cgGkJiNuxsLAwU8M96vfRgWHA4awaPf6i3W5Lzo8uLbKmzOPJvA1NXvTwPd1RwnPGXAsAcrfKv/MuFcAMAXsjHGe51XByQcVDlyeAQ78cgBkCpMtaGlRI9HePP+uSh25lJrHp9/tYWFhAMBjElStXcOPGDfHAUEkhtMLAElW9XofP50MqlZKOKa0UUJHSpmJ2belWb85H5DWMN3S8TlDlPnotIBHhMGlel2ngZr4OSQpv0rgv4XAY+XweHo8HW1tbmEwmSCaTQmh0iY3qVCKRmDFAU9Xq9XqIxWKIx+PweqeJ0Tdu3MDS0pIcR5YB6/W6nJOjJmceYyr5PGa8PrMsyZEkJL6aNJPgaOVQP043pmhfGKEVIa0ykRAeN9y25MdgMBiOG9jmzUWGpS2SFEZPcIF3HEdKLUcHA2sfCW8StVmZxla2t9Obw84jqi3M19ELMgApz1B5Yejh5uYmIpHI65RzkoxgMChDjZl0fNSHqIP3mC3GGy0u2OVyGYPBQF5LBnBOpsoP3w84DHelQqTzavR700tKBbter8vfdTMMy2A8xvQsMYyWJGQymaBcLksm0Pb2NsrlMtxuN/L5vBDcTqcjGW3aowUctvbrpGoA0uV1dGyIjjfRHdH6RlMfA37OaN7WXjO+hm6Vp09UE/LjiNuW/GhWTAbNLwv/xmDBo7kzwGGWDk3I169fx5UrV0RdKZVKGAwG4m+5evXqTFcUv4AEy0U0+zKYEADy+Tyq1SpefPFFeW0qNPx7pVIR9cXv96NcLmNlZQXA9I7H7/fLnc9dd90FAHj11VcBzHZ56M4J4DCpWvtqtHOfd1m8u+IFRLeNat8NJxcD0/Kbvjt1u90yXJD7cTRgTJe9GGnP99ZmaF4s/zqwXGkwHDew3KMVFq2S6jILcBhnoUsQLFVwEePv+Dpc5AOBgMyI8vv9yOVy0tVJhVhne+m4CV7vdNdWo9FAtVpFOp2W9GOSBxINtq1nMhkxOLOjSl+H9OKv1aJer4dcLofJZIJWqyXbzuPjwAEczHhCeS3RHbn0FHId0Is6S2UsSfn9fnQ6HVF69P54vV65RnNwc71eh8vlkhlhJGJUSkKhkPiLuF30K/H/RzuKgcOIA93hq8kOt02DfkmSGX5+NCHWpU8AM+ndusOQr82OY1YO6H09TrhtyY/G0fIKF3bWwukPIYPmv1utlvhqAoEA7r//fly/fh3AdLbX/Py8fDhbrRai0ahcSCiX6g4LXfKh6x+YtotvbGxIMB8vLvzAlEolXL9+Hevr6wCmRIwBYQAk7pxE7PTp0zPdYBxsB0Ba+7UZDzg0RLOWq5Ne9f9lVIbKriKh63Q6M2Zq3aLO99KmP9aL+XjK9TxPvFvRPixNuo6aJQ2GkwAGyOkZUSQ7RxsdgMM5ePr3vEvXJmD9Gi6XSwJfWWLWZIgjFBzHQaFQkPmBvPPnTZQuW/X7fRwcHIhXhYGF7Eaiz4dBh8C0E5aJxNxnnS1Erwmvs3zcYDDA8vIySqWSZI3pBhUAcuy4jSx50Tejxw9xSjtLZJ1OB4FAAOfOnRP/JXPL6PVkCYzbMxgMEA6HEYlExP+UTCbFn8PutEwmI0oVX4elQd7AM5eIvhrm+PBcUvUBDomibjTRZS3uH1//aDcwofN/+DdeozW51hlLR7vKjhNuW/LDE3uU6R5d2EejkSyiWrVwu91IJpOyULPrgAt1OBxGLpeTQaf8AvHvZPFcrPnlIQlhuyMwXcivXLkirJqzV6g63bhxA81mc2bUht/vlxEU/X4foVBIfk4kElhZWcH58+cBTBUg7jMvFPyZ/gEGLGo5HJgdjcHjou8q9bGl5L6/vw8A0tbKLwUvjiR1HFLIi4zX65VgQ55D3oXwGOpWT/3FNBhOCkh2dFmJpIALlL7G8XszmUykZKavVSxX6eaDSCQiYam1Wg25XA6nT5+WG6x4PI5EIoHJZIK9vT0ZuaO9lsBhJhkzgkKhEFZWVuByuVAqlWZavBldMRgMpBmFoxnoJyKhmJ+fx9zcHNrtNjY2NlAul2dMxd1uF6lUCpFIBLu7u3Kt7/V6mIwncLldM5Pqg8EgQqGQKBo0IvNmTavOo9EI5XJZqgaamHHu2FFfDq9xVIc46BSAjEXi+3K8EBOeGXJYr9fFhMxzTSWI6hOvj4lEQspdJLS89uvrN/eP13tteqZqpX2WwGEFhf/mOdfBiiz98fWOI25b8kPZUUvDOngLOBw2quu7uly2srIiHVTb29t47rnn5O80/JE08IOlVZBkMikXAW4H33tra0sUp0KhgK2tLVFyWNflF6bRaCAcDs+kTVMa5mvTEMd9j0ajM9vCQaW8MOgZY3roncfjmcne6ff7mJ+fn2H7OqVZG4/ZgUBzG1tg+do62wI4nPXFY8O7Kr4270p0wOLR47i0tAQAQkINhuMO7Uth+UPfFBztztFlaRptea3TPhfe9EQiEWmgYJ7NqVOncNddd6HdbuPq1avY3d3FysqKdD5RqZDBx68tiiRi0WhUZm3F43EcHBxIlxhHEbHc5nK5EI/HpWwDHCpVnU4H8XgcKysrCIfD8Hg8yGazcj1kOjMAUUCYBE2C4TgOXHDJseNNGhUZGrJJPNjIQiWf11UqNvRd+f1+5PN5dDodUZCAw2skjceBQECuo+ze0r4Z2i94c8jyF6+PPBZ8ni5fanNyrzedfJ9OpxGPx5FKpcSQTkWGnxMeF3Z38TV4LnletN1Ae8P4eKpyvM5bt9ctAEtM2hfCD45WXGh0A6akg4QjEomgVCrJF5AppVRf5ubmJNRKvyehjWD675oI0LPzwgsvzLR0sy6sX48fdOAww4gEwO/3IxKJCBF4y1veMpM5NBqNRI0hk9ejLzqdjhwn3vFQ8aKBkGrN0YBFdsUB046zarUqdflwODxDjviFoGGPw1u1xKrvGun/0RPpScoA/LVt7gbDcYY232pjMnCozOprD0sVVAq4kHERpImV301madXrdQSDQSwvL8u1anNzE6+88opcv5aXl+VmR/s/SLT6/T6azSZisRiWlpakY4x+GLbEk8jxuq1LNlrFohrSbrexs7MjkSP0TVarVSEavBkkmXCc6SzHlq8Ft8eNubm5GWVCp1QzU4fX4KM3aJzJRQV/PB7LJHkqXZqU8vrO4do6I4jKEo8Zg2JTqRRarRY2Nzelm1iXqo6GCPKY0iDNwav0GrGcx3OvB52y5Mcbax2iqKMQjnYUclgtFSWWYgHM3OgeR9y25IcLr86hmUwmMqcGeH3AH3MXgOmJZhkJmMqI4XAYly5dAjBVfvRoBprhuLDzrkQrFbpVe2VlRUjCzZs3Z6TBcDg8o67oCe8A5M6BX8xer4dKpYL5+XnZz8uXL8u2dTqdGWLFFlE+V78XP9w6iZp3H290zHRrO01/uhuBWRMAZr78wOFke91uq8kRJyjz/fx+v5DN43xHYTB8PXAB42wnPeWbC5s2uQKHBIglEqoY9PIw74yLXafTQSwWw/nz50X9rtfrkmbc6/Vw8+ZN7O/vS6wGy+Ns5+b7cyBpMpmUEQ8sHXG8BTC9Ruop6hx/w5tWqhYkM61WC/1+X+Yw5nI59Pt9GU2hR+twW/x+P1xuF+Ac3lzRzqDJBXCo2PBmk2RT2wIY78HgQ64xRw3pBG+Yo9Eo8vm8XOv5nLm5OeTzeSwuLiIUCuHmzZvY2tqS4649S5zMTsJG8sj2fgZG8vV5XvTYjqNkmKSZRIfPoSKk11N9vuhhIiHUitBxxW1LfrRhjF9KthNSSWCJRYfwadc/00OBaWBftVoVJYjp0HxtfgC4sGt1ia8Xj8flDi0cDktmELMedP4NZUu+Fu+E+Hj9c7VaRaPREMLSbDaxs7MzM6eGH0LmhPBntl1qn81oNJKyWCaTQbvdlv3WBkFgSgqpKnFYq1aFaEgEpuqZHnTKL4i+kz3agaENc7yw8LHZbHZmBIjBcBJAzwazbrS6rdvWj2a16AwdLmZHza0sQQHTxom77757ZjHXpeeDgwNRefRzucDSP+jz+dDpdFCpVBCLxZBIJNBqtWZyY7gAk8hxe/XraIOynniu91UnRGu1gzdpJDCO40jHFf1PuvREMuHxeOSazn1l9xNvaLV6oreR4DHQHh0qRlSIFhYWkMvlJO/H5/Oh1Wphd3cXzWZT2uZJWPlvbfzm9TGVSmF+fh7ZbFaIjh5WzTwlHVjI7dJeThLso51eJEA6G0kPxyb51grQccRtS3447uGoJHzUuKalQ92dRTJC6fTmzZuIRqNSWgKAcrk8k/CpzdW8COiFWy/ezWZTyjahUGjGn8QPFN+bSg9fizHyBGuzJEvMnqBKorMfHMdBOp2Wu5tGo/G6tvzJZCL+IiZPcz4XgNcNCeR26zZVAGIkZMkslUqJ9wg4LE1qD5Ymo1SBSPSY7sp9tnZ2w0kEO5N4s6U9GfoxhP4+HW0i0NERoVAIyWQSjuNgfn4e9913H7LZrCzyvV5P/nO73chkMshkMnC5XNjd3ZWbLCos+lrLlncqJ7wp0uRM+1V4HdAdRbrFn6V3qkL1el1GaDA0kDdwLCtpKwENuvQh8XiSdOlyF9cHvd0kIR6PZ6ZcphU2Ho9IJCLEUOcoVSoVVKtV6XLTXa1Ukmq1mtgzmOfEbjIq6Tz/emYir8EkrlyfNNHja5Ewc42ip4ifC62E8dxwX/X+cP0kadKdhMcRty35IasGMJMwrEmCx+OREC3g9aMV2L4JQKRKzZ51SQeYLvYkHDzxR/0u7Pa6ePHizJdKXyg0AQAw454HIMoNf2bWBn9ut9uIx+PyGj6fb8bwzM4QPpYfeB6TRqMh21av15HJZGZmgbFVFZiqTlre1dkU/OJpc7Qu1/HCoaMHmALNY6hn5Oi7ieMspxoMXw9c7KjEAJDrmG5h199VPkfnaunyhu4YcrlcokQA0xu1TqcjAz1HoxFCoRDW19clVsPr9WJjY0NywLSplv81Gg3UajXpRKKawW2kv4ekht2xHo9nZrQDyQ8zfFjKZ4dsNpuVazVvmEjGeL3TSrj+v8vlEg8PCQOJC6/hfH9NPN6osUUTh6P5PGyXZ7mx1+vh4OAAgUBASB5wSDhotCZR47WQPkkSx1arhW63i3a7DcdxsLKygng8LgSNihOvrdrbw2PO7ea1lqSJBImklMddP95xHBkbwkiV4+r7uW3JDxdRbbriB1STCG1C1l1N5XIZk8lEVIezZ89iY2NjpiNLM9p2uy0mOeDQvMsvGZNZSQzYKglgphQGQGrWOlCq1WrJ4+jaZ8mHbakkOI7jIB6Py3sxrh2AyLN64OpodDgRWbe/AtMLXzKZnOmI6/V6wGu77nIdjsKgHMv3Go/Hcmx5zDSB4fN5PnT+Bd/L7XaLCqWj8/keBsNJBBfBfr8/QxioXujvGJUMfld16ZrGXfpeGCzKawQbIhqNBobDoagYWo0gweD7U1GhqsJrClOKSWjY0UrCpokZ06N5o8bRQ1Qt2A6vPYjhcFjM09xHXkPoa6rX60IceSNLNUnfxHFxZ4I1r79ut/t1+Wo83ryJ5DWM1QPeAOpyHnDYIczH85jzmkmCpJV1nk/d/KK7lnkcC4UCfD6f5DE1Go0Z5ZyKIU3O+vNC8sp90jfy2jTNrkFNJrltvFazDHkccduSH8q02qHOEpg2PHNgHADJtQCA/f19LC0tzUTDj0Yj+WDqGjFwGBzFDxFlRZ54dk1xfhf9McBhW7wOImQLKHB418DH806O28oBoCypsV2d762JAi8CWv3SJja3241UKiVfNJJDTrSncW44mv5dkyxeGEgAKVfzeLMtVCtc+u5ImzC5bUdNg7oNfn19HRsbG3/dR8BgOJagx4KLl/ZWaDM0lVUqQlql4OP4b96ht9tt6Vait9Dn84kKwpsRkqnd3V3cuHEDOzs7oh6RCPHGSqsmvV5POpd0uRuYnQ3F59ZqNfG1MGWZgYhaPQIgXiMqG7r8zmuJ3+8HHIjKwWNFAklVhesGSQ8nyfOYagWHx5A+HBKaSCQiOWf0P/JxzNfhdZbHSAdYsqwVCoVmFDKthHOANQkZX59lt2q1Kp8VrhHcB65Z2uiss4eAw6w1Qpf4+BxdqmQi91Hl6jjitiU/wCwrBmZNXPwZOFQ7dnd3ZwzNWrnRGToAXsd4+Zq6LVMv5LxQsYNM+4EoX+sP2Xg8lpo1uwh0GKD23TQaDXS7XXnvaDQqrYvAbAs+2bhWZyKRiLye3+9HMpmU8h27KnZ3dwFMc3Uoa3K/+FrxeFwkWmBWFeIxe6NWSG0K1x4hys16vIVWpvTxMhhOCnRnF2+wuBjrFmv9/eDPugwDQO7egUO1OpPJYH5+Xq5JXPy50MdiMbnR293dxc7ODrrdrhiMCa28kGh0u10Ui0WxIHAb6EvhTR0XbyoMVJF0OnEoFEIsFpPxF7wm0+/JCI9kMinHhdEm3BeqXtrUOxwOpUOWRJPP4esyy4bvqUkYjykVN6pUvB7yBlD7QenxZG6STkzmuSVJ4fEhQdTnXN8c6s+A9jHxGJK0abM4/63VGhJZri/6Jp8qH5/Pm2vdeWhlr+8wNIslWFPWk9Z5VwFMSzxUOPL5vOQ2AJBsCtbBWc/UH9CjbZW6pMO6q27V5iKvJUjgcK6YNgtqszSZPNk+vUl8bTJvTX64z7wz45eCP/ODrVseeYzcbrcoXgxA08dU+290XhEvxvrOjooYf2a7JH/WHh96C/izJnFXr16FwXASQaU2GAyi0+mIj47XIN29QzVBL3b6usWf+R0lqdClHV1q4ve70+mgUCigWq2KR0V3M2m/EW+Wut2uPJ/XSi7yuuGDZRgurFxA9aJPZUYv4CQp7XZb0oZZdmN5MBQKAS6IqkOyxRsz3hjy+SRvDEJk8wiPrS730QxNokBfKHB47WM1guSJxwyA+GVSqZTc6HEftXrP4+vxTOcecl2gssUoAP6dN5Q0UfO6zHWC13t9Mw7MzjnTxFSLCTzPvNknidR+Ta4Hxw23LfnRBi7NeDlME4DIsDrbR5uGmaHBn3V+Bj8sPLFkvrpl/KifhV1Y/LvOztEmZy0fA4etoDoLSNd92d1QKBQAHPp09GN1TbZUKsl+8Auv3f06JZUhVvy5VqvNfFF0eW4ymUjbO4+nvgDwjkJnIWnDNGVcvrbL5ZoJoeRjAGB5eRnb29tvdOoNhmMNjh3g4kMFg98rLvSE7lzSd/nAdMENhUIzxlWWWbgA83uoF9xKpYJCoSCDirnocuFjuB6vg7w28nV7vd5MNxdJliYyehupQAGH6jpJjb5p0qZilrp4nWbpzgWXKES8lvEaxiRlelZYatM3ZVSISIL09ZnHQEd4UEFj6UyTDX3OeHxIatk5BcxGGPCazf94Ix2LxZBOp6UjL5vNys0jb0JZBmUZjqRYG7l57nT7O4mdJmuaBOnP1lGibeMtvsPQkeL6ZOnWd35ReXFIJBKyyDNbgSALpgJyVFnSznn+XcvMfE1CK0Uej0cmH+vX0iMm9Jep2+3OzMWZm5vD2bNnZdt5R6Nlby1zazLEUCyqUFSgeDEIBoPyZQKmJbajCc/8EnS7XVSrVZn/o+9ACV0K09OCgdlMJGBKvDTJ08f3jQLEDIaTAL04kSwQvE5xoSMp4iKty2OBQECCAvk6R9UIbZ4mIWBJvlarSQAr8720QVYrJNpTow3HVKbY3q19MUd9Nfwbb6K0QsxrOX05R9Wvfr8v10WX2wWXc3hdZkmNJI1qCveX15putyuNLrwW8VpKVUl7J/ne/D+3kURTExkSJpbJ9HWRr6WvgyS9WnWiSpXNZmU8B48z1zlex/UMM76WNs7rLma+H29EqdTpsitvgml74DHhenMccduuQEfd68DhYkzww8WFXg/OjEajKJfLM5Iih+/x9Wkk5Gvr8g8/bDpJ+WgXBreL3Vzs/nK5XNJtxvdmuzswvTvR6gw/lGfOnAEwbT/nxYKvp6VMPXBP3ykBkP3VPhv+m++tTZb6w82yG43ZzN8g82dgmZbUtdrGizSPA+/OdNmM0CGPBsNJglaNeWfOBezoDQQfo+/gucjye8/vOLu9qtUq6vX6zOgZ3U1GksJQVb/fLzEf7Hol0WLTA0mAVqH4OBIxvehqD4720rCMxWsvF36+lz4e9J5o47fP54ML00U7mUzKjSaJIn2GugWciz2PKVUYKi8kjMDhCAiqKdqXw1KTTo7WpSOS0Ha7Ld1mLBVq07oePMprO6/bvV4PiURCiA87AjkglsSKqszR46u7erkP+oZee2Z1Jy+fr6Fv9o8jblvyw4uBLlVppg5MLyLhcFi+xPyiavCDUS6XZ0pRPLH67oPvydfSf+cHWy/kRx3xLKHRwc/t0gwfOJynpWXVRqOBbDYLYNoKv7e3N5M+rdOeB4OB/I2p0zwmDFzU5Ib1fGBK8nRpSvuBRqMR0un0jFytjXMkP9wWDv/TJI8dA3q/dfAk/31c68gGwzcCvR36ZksTB36PWILR6isXa01sSEq4yDNT56h3gyRKk5a5uTm5Tt28eVO+03pOH19fL6wkR1zE2c3FBVp3gOrsIqoN2nKgm0o0CdSGW/238WQMOIfNHlRb/H4/QqGQDAQlaQFeP5SZRID/kayRPPFnqkcsG9LErdUmx3HEl0Nv1FEiy33hvnJb9PboLmatFtHvw+48w98Nblvyo5UNfZEAZoOotIKg504NBgMkk8mZ1r9UKiWkR3dY8Gfg0JNytKOMXwhtLNbloGg0imKxKNs1Pz8v21YqlTAaHQ5R5XuyNZ6DTXW319EW2KNlKhIMRgLokROO40h3GxUnPp8XqO7k0KxNjEYjCb7ia/HCoo+3ziOiIZLP16GH7Bzgz8FgcIb8LC0t2TR3w4kD1Vtm4DAsjzcp/I6Hw+GZzjCXyyUeGb2Y6xI+fZD9fh+JRGLGNkDlhNfFbreLYDCIdDotI3L4mkfNy1zMSUq034XlKJZM9PZyIdfGYhqo9c0siYb21jCHiJ4Uko/xaHqN5rWcz+G/jyo9fP+jpShuB83AbAYZDAYol8tyvGg65vN4nkjG9LbTd8N9INnTXkrD7YHblvwAh5KwhlYaaGTjF5UGPeBwArwOB+QdAYCZLyZw2PKtMydisZh8qUhAdIAft41hi/w5Go0imUzOTFbXCgixsLAg+6SzeviefLzjODOTl7XU2Ww2ZzKCeOHU87iYWcH9kuGAgHQPAJC5NNrgrDsGOF2YJI5fbL4XL8g6n0OyOXB4BwTAzM6GE4vBYCBJwLzp4kJMBZrfc63WcJHVxEI3VWjTb61Wk7IQF34qIeyAZdcXzbQ60E/fdPLax5tFnfrO8QgkZdwuXXKh8kQiRxVJl+95HddGW743cNhUohWnyWQi11hNmo56RLVvhmSO20lPFbtvFxYWxF90/fr1b/tnwXDrcNuSHz2QjWSHdVqdaaP/r03Co9E0YEuTJ5oDgcOFmxcPuup1SYevCUwvKJQ+gVnD82g0QqlUEvk4GAyiVqu9zjzN5zL4itvSbrdn5rkwRExvi67d6+GFvDuh4ZnHQ5MhXToEZkllrVabUZxIGgHIa+qSmW5p5fZwPyjJcz9IEPlzrVaTY2QwnGSwcYM3VPxZe034M8mDbhjQfhUqQiRH/X4fBwcHyGQySCQSM+VrraKPx2MJPEwmk3J9400KSZBWlUhaGEaYTCbh9XpRLBYl2oLqE/NxQqGQeIK4TyRrVIPpA+K1S3fe8hojpUEX4OCQAOlOuaMdq18PRz0vhpOF25b8lMvlb/gYjk34m+Cee+6ZUSGOqjHaNQ8cGom5cOsaOH8mqtXqTAcVOwp0rg9wWFILhUKIRCIy3oJdCAxQ1DVz4LBVFDj0OZFwUF3Rao3X65VW+UqlArfbLUSG9evauCb7qS9uVHWAQwJI8K6MFyXdXsv91J4rBjfyGJNAAtMcJpYJDYaTBn5vdDeULm/rmyUSIE1c6OXT3zdtDi4Wi5ifn0csFpPrlr52eTzT8RP1eh2NRmNGRdcKLckVt4/lIqpO3I5QKIROpzNTqud+MGmaHWV8fa3wMObjb4TX7uN4vTQYvhXctuTn7xoXL178un8nEdH4RgRsfX0dAMSATIJC07EmMDrVeXFxEe12Wy5cR8tLwKyKpQmG3+8X0zMwvZAxhp1/16TtqAmcnWdyIYRPSmLA7MT5o+3+vFuj2ZpDEvWMM62I0S+ky4H8mxEfw0nG0QgJGnupRusyu27y4HWFjyEpOtqN1Wg0UKlUkEqlpBOM5SatvCSTSRSLRVSrVSE2R43WLB1R7SGZ2djY+BsZcE1hMdyOODHk59uBb3Uu1d7e3hv+fm1tDcChd+bogFBg6g1gNxn/xpRYABKSRSWI0HeRmpSxJRM4LHOROLGjRJcOtb+IF0ZuC8kdy2K86PL1GThmMJx0UEXR5WgSHpaztOKj/6Y7qej/0Z4WEpWtrS3EYjHMz8/PlKp/53d+52+17UevLQbDnQgjP7cRODT160GXpd4IR+/ElpaWZoK+WIt3XhvrHovF5DWZdKrb+3mXCUDyRXRQZDgcnhlvoZUi1v+12fq4BmYZDN8sWDLS5IVlZZqImS92tAT9N0Gn08HW1ta3YcsNhjsfRn6OOf7aVvLXOMi3cnGkaVnnBwGHPimCipVutWV3xvz8/DdX5zcYjhH0DYfBYPjOw8iP4ZuGruEz8+eNwBwOy7gwGAwGw+0E9zd+iMFgMBgMBsPxgZEfg8FgMBgMJwpGfgwGg8FgMJwoGPkxGAwGg8FwomDkx2AwGAwGw4mCkR+DwWAwGAwnCkZ+DAaDwWAwnCgY+TEYDAaDwXCiYOTHYDAYDAbDiYKRH4PBYDAYDCcKRn4MBoPBYDCcKBj5MRgMBoPBcKJg5MdgMBgMBsOJgpEfg8FgMBgMJwpGfgwGg8FgMJwoGPkxGAwGg8FwomDkx2AwGAwGw4mCkR+DwWAwGAwnCkZ+DAaDwWAwnCgY+TEYDAaDwXCiYOTHYDAYDAbDiYKRH4PBYDAYDCcKRn4MBoPBYDCcKBj5MRgMBoPBcKLgvdUbYDAYDN8KHMdx3eptMNxCOLd6Awx3Mkz5MRgMBsMdBZfL1brV22C4s2Hkx2AwGAwGw4mCkR+DwWAwGAwnCkZ+DAaDwWAwnCjctoZnMzN+h2CmQYPBYDCcMJjyY/iWYaZDg8FgMNyJMPJjMBgMBoPhRMHIj8FgMBgMhhMFIz8Gg8FguKPgOE70Vm+D4c6GkR+DwWAwGAwnCkZ+DAaDwWAwnCgY+TEYDAaDwXCiYOTHYDAYDAbDiYLLcSzlzmAwGAwGw8mBKT8Gg8FgMBhOFIz8GAwGg8FgOFEw8mMwGAwGg+FEwciPwWAwGAyGEwUjPwaDwWAwGE4UjPwYDAaDwWA4UTDyYzAYDAaD4UTByI/BYDAYDIYTBSM/BoPBYDAYThSM/BgMBoPBYDhRMPJjMBgMBoPhRMHIj8FgMBgMhhMFIz8Gg8FgMBhOFIz8GAwGg8FgOFEw8mMwGAwGg+FEwciPwWAwGAyGEwUjPwaDwWAwGE4UjPwYDAaDwWA4UTDyYzAYDAaD4UTByI/BYDAYDIYTBSM/BoPBYDAYThSM/BgMBoPBYDhRMPJjMBgMBoPhRMHIj8FgMBgMhhMFIz8Gg8FgMBhOFIz8GAwGg8FgOFEw8mMwGAwGg+FEwciPwWAwGAyGEwUjPwaDwWAwGE4UjPwYDAaDwWA4UTDyYzAYDAaD4UTByI/BYDAYDIYTBSM/BoPBYDAYThSM/BgMBoPBYDhRMPJj+Jbgcrm+3+VyOS6X655bvS0Gg8FgMHwzMPJj+FbxIwA+99r/DQaDwWC4Y+ByHOdWb4PhDoPL5YoCuATg7QD+h+M4d9/iTTIYDAaD4W8MU34M3wr+FwCfcBznMoCyy+V6+FZvkMFgMBgMf1MY+TF8K/gRAP/na//+P2GlL4PBYDDcQbCyl+GbgsvlSgPYBnAAwAHgee3/a459mAwGg8FwB8CUH8M3ix8E8F8dx1lzHGfdcZwVADcAfPct3i6DwWAwGP5GMPJj+GbxIwD+4Mjv/n+w0pfBYDAY7hBY2ctgMBgMBsOJgik/hm8aLrg+6ILrg7d6OwwGg8Fg+Fbwt1J+XC7Xt1U2SiQSCAQC6Ha7cLlc8Hq9GA6HGAwG8Hq9cLun3G0ymcBxHHg8HjiOg8lkArfbDZfLBb/fj8FgAJfLhfF4DLfbDcdx4HK5AAButxtutxvD4RBerxcejwfdblce4/V64TiOvN5kMsFkMgEAjEYjuFwueazH45Hfeb1eefx4PIbH45H35usNh8OZbdC/C4VCsm/9fh88T9zG8XgMn88383qDwWBmP3i8uK98rN5evo7X60W/34fb7YbX68VgMJB9AQCfzwcAGAwG6H2iBwBI//20PM7tdsPj8chxDgQCGAwGGI/HcBxHjgH/G4/H8jyv14vRaCTHejKZzJxLn88nxxYAhsMhhsMhAoGA7Dvfw+VyyTmZTCbwer1yHPRnw+fzyevwWOnzy/3t9Xrwer3w+/3y/PF4LI8JhULodrvy+ahWq9/Gb8TtBcdxXLd6GwwGg+FbgfdWb8DXw2AwmCEbXKS54Gli4ff75Wc+lwvwaDRCMBgEAIzHY1ncQ6EQRqORLJKj0Uj+zoWfixwXTy6cmmwBgMvlwmg0gsfjgc/nmyFLbrcbo9EIjuPIc/l7AAgGgxiPx7IvLpcL/X4fAOD3++HxeGa2kYs9SREJB9/b5/NhNBrJY0kIuJCTBE4mE7hcrhmio8kUSUgoFJp5PwBwu9xCOv1+P4ApUQgGg3C5XOh2uwCAcDg8Q3TG4zE6nQ48Hg8CgQA8Hg+Gw6GQNxI3x3EQCARkn/lePp9vhqQcJTo8zsCU7I1GoxkC4/F40O/30ev1XkfYSFBHoxH6/T6CwSBisZh8LgDIOeZ2jkYjIUrcZ4PBYDDc3rCyl8FgMBgMhhOF27rsFYlEZpQE3qX7/X70ej30+334/X5RArRqARyWebxerygtLFmwZELFhooIFR0+nv8Bh+U1XfagaqAVJF2y8Xq98lyqC9wuvm8wGJS/8zHEZDLBcDicKYPxPfW+BQIB2QZup9vtht/vF9WKrx8KhURpYQmK5S2telH9aTQasj2ZTAa1P6jBcRyE3xOWx1CNAiDKE1UnqiVaTeP7cvu1kuP3+zEajTAcDmeUL24jX1OX01gSY5mL+8rt0ueUSuLRUikwVdpYfuR50tvDfeR2saTK7Wq1Wn/XX4PbFlb2MhgMdypu67IXMC0lsIzEBarf78+UY/QCxwWLiysJAv8NQBY3Lqh8LHBYKuF7DgYDIUl8LEtfgUAA/X5/hiywlMLX58JPsjWZTNDr9RAIBGRRHQwGUoYjWaFPhcSG+87F/ijZ4TEhkQkGg/LaJCEkAI7jiGeGz+djeBz8fj/6/b6UdIhyuQxMHwKXyyVlNpbOJpOJlH/eyAekzx/PlSZ8POYA5HealB4lQNzucDgsj2W5czKZiOeL557/5rZpgsS/6+PAcwlAiDg/I3wt+oX4HIPBYDDc3rityQ8XFHpCiKMKifYEUYXhXToXK96l8/GTyUQWs8FgIL4VLqpUlfx+P8bj8YxKQhJz1CzLhVIbmfl6XBi16sHnA4feGJKXfr8v287XolIyHo/R7XZn9oHPIxHRJJAqRbPZFM9Us9mU4xmJRGaOoTYdVyqVv/b86Nf4RiDR4vEOBAJC4PQ5otcHgBzH4XAoJIzKkT6+Wr3heeZjaJbXJE4bwPm5okLH1yeZJWnt9/vodDozRnq9/Tx3BoPBYLj9cVuXvZLJpCxiwGHpiKUSLmLsIGK3Ehd+4LD0xUWMr0MlSf/utX0SBefo47lQHu1U0mUbbgtVG62ocLFlaUyrBfzbeDxGMBhEv9+fUXGGw+HM4qpVHG6zJlh6gdedcfV6/W9/Yj712v/f/s09LZ1Oz3RsARDVhp1qJEkkQ7pURvVNG515/HW5DsBMGZLqDo+RLglqpYsKnNfrhc/nk88EyapWyQDMnF99zL8ZUngnw8peBoPhToUZng0Gg8FgMJwo3NZlr1qthlAoJMoFS0Rseeadvi6bABBVRZdZtDGVJQ6Wo2hc1v/p1mfgsFTGfwOYUY+oABAsx3Dbjnpn6LsBMJNFRM8PVQmdT0MlSrfYUx15I6NtJpOR40AlKpFI/N2oP98iHMdBMBhEMBgUtUf7urR6czSLCJiqbjQld7td8Q/p8hMjEVj+CwQCotwAECWNGUi6FAccmswJqkH6nPL9tM/Kyl4Gg8FwZ+C2Jj/AoemVCyIzcVh2InE4am4GDktJjuMgFArNZNqwK4j5P6PRSEy+2rQcCoXQ7/elFEKvjTYLA5hZ+OhRIYHRploSMr3I05ir/UJc1NvtNtrt9rd07Mrl8rf0vG8XXC4XIpEIhsOhGMHpR9LkhaSC+Tyj0WimbEniSIJ6tDTKjCN6wEgs6Qfqdrvyvr1eT8hvp9N5XY4Qze2aPPG19GeN+6C3xWAwGAy3J2578gNMzbJcGPv9/kxrNVue6bfRPoxgMAiPx4NGo4FerychgKPRCKFQCOPxWBQTpgjrNm0qOwxDZJKvfh8dWMhFUYfeHfUX0aOjvSM6IZnEyu12o1gsfkeO79LSEtrttnhkvp7J+W8DdmFR2QKmwYjcf3bAdTodAEAsFhPiwqgDqjV+v1+6/dgxpo8hPTs6NoDPI0HSni4SUX5OdPu7TgXXnWvakE7Sqt/PYDAYDLcnzPNjMBgMBoPhROG2V36Y+6K7q6iYcA4Xf8cSBRUX3t2zPDGZTKS0Ua1WEQgERKVxHAedTkcUBM5t6vf7aLfbouxolQGYqglsHwcg5RiWyfh4qlR6rIQue2m1gZ1e3268/e1vRyQSQSQSQTabRSqVmum6ikQiAKbeKz1P7f9a/r8AAD/8b34YnU4HxWIR9XodvV5PztVgMMDLL788837pdBrAtBxXr9fFP0M/DSMGAIj/h6VNABJVQPWGChLLTTzeuuNPxwpQ1WM+kw7JJI6WSXWrPF8jGo1K3IAeb6JLcwaDwWC4fXHbkx96Q7RBlj4ft9uNYDAoc5pIdnRWDMmELmvpeVQkJnrhZLs6yRAXZ/14/R8AIUZHyzC6vTocDs+YtOkr0a+pW9b/LrC4uIhkMom1tTU8+OCDiEQi4gXKZDIYjUb4xV/8RQDAv/7X/xqBQACnT58GALTbbXg8HqyvryOTycDr9aLRaCAajQKuaT5Qp9PB2bNnxVRM8jAYDPC+970PXq8XzWYTlUoFW1tb2NzcBACsr68DOCRCRw3teiYXySjPMXN8dISATmM+mroMHAYushwWj8eFZOqQQj2IleeBnzmSnl6vJ+VJfkb5uKPGdoPBYDDcfritc36Aqe9D38nTEEsVCICoOlQn4vE4Op2O+DSAwwVKG2RJevQIBq1E6Gnd7AyjQsOuI21e1lPWuYDr13S73ej1erIdXGC5H3wvPvbg4OCbPl7ZbBanT5/GhQsXkEql5PgwiZqK1crKCh544AEEAgF0u13E43E0Gg2MRiM8+OCDmJ+fF2L51a9+FblcDqurq9jY2MBP3PUTcLlc+I1rv4G/+Iu/QLFYRDgcRigUQqlUEpNxp9NBOj2d/F4sFpFKpYSc1Go1XLx4EcViUUznvV4P5XJZ1BaSR4ZWBgKBmWnuWrFhCOXRHCCqRCTAmpjy/ACQ7jCdmaRJNE32uiNQZ0EBh12A9Jkdd1jOj8FguFNhnh+DwWAwGAwnCre98gNMk551pxRLF7yb17O4+v0+gsEgfD4fer3eTJcXO674HJbJ2KbM9nSqSBo6z4cKBDAdS8EWdfpCCLZpH80MogKj84G4f7oE89fl8aysrIgfZ3d3F+fOncOpU6ewtraGlZUV9Ho91Go1JJNJrKyswOfzodlsIhAI4OzZszh37hwSicQ3dQ729/eRy+XgdrvxNrwNAPBpfPoNH3vz5k3s7u6i0+lgZ2cHXq8X2WwWu7u7KJfLiMViKJVKKJVKSCaTaDab+NSnPoXt7W0sLS2h3++jWCy+rrxFtY8KDZWbo8Nn9ZgLjg2hF0yrRSxdUYWjOsiuPX4+dNozf8dtodLE7xFLZd9qPMGdBFN+DAbDnYrb3vMDHOb1sBxxdEo5/SYAZNxBp9OZmQru9/tnggD14E+W0jhTi7k79K5ojxAXv3A4jE6ng0ajIT4P+pC4LUcXb5IhLtD0IelyHud9fb2W6UAggIceeggXLlyQIEOv14t6vY4bN27A7/fj/PnzePDBBxEKhdBut3H27FnEYrG/8fG+ceMGyuUyNjY28PLLLyOVSuHxxx9HPp/HaHkEr++v/+isra1hbW3tDf+2sbGBUqmEer2OV155BV/72teQTCbxYz/2Y9jd3cUXv/hFfO1rXxPzda/XmzEXj0YjhMNhITgkn/1+H6FQSIbCdjodKXHyc6In3JNo8pzpIbder1c+B3qwqfYRaRN9IBCA1+uVbTTfj8FgMNzeuCOUH+a56Dtw+jG4CNEcqz0ZOpmXw1G5yOlBnlwg9RRxbcDVHhK+Ft9Xm3TZ8cMuNO0JIaFh+nCz2RRVSm93u92W16hWqzPH4d3vfjcuXLiAtbU1HBwcoFwui7rjdruxv7+PdDqN97znPVheXv6Gx7Xb7eI//sf/iC996Uu4cuXKTEcUSRkHrDJfJ5lM4sZv3sBkMsHSP1qSfTh//jwef/xxnD59GslkEvfff7+Ymr8e/uiP/ggvvvgiRqMR2u02gsEg0uk0rly5go997GNCdDqdjmwLwfPE86dJydG5aboLjCSHnwm+jjaiE3r6PD8rJE160Kz+rLCb8LjDlB+DwXCn4o4gP7FYbGYB01O3WeqimRU4XLBIWHTpCTjs/HmjFmedyMzSBs2vr+2zvCfbpvWiyv9zAWbLPQAZaKqnuevSFxdOn8+HVquFWCyG1dVVPPHEE/iVX/kVfPCDH0Sn00EgEMDS0hIefvhh5PN57O3t4e677/6Gx/HXfu3X8Ou//uvY3t7GcDjEYDCAz+fDuXPn4PV6EQ6HEY1GpQU8FovB6/VK1xdLSZ/6f30KE2eCJ/8fT6LVauHg4ADNZhPNZlPSs6PRKPx+P4rFIu6//37803/6T/GjP/qjf+22Xbt2DZ/97GfxzDPPAIB0nD333HP49Kc/jVQqhX6/j1arJeMpeP6o0pDUAJABsTz2OrSQJVKeb0KHGmpCRDM1E7t12TIQCMyoiFSJGNR4nGHkx2Aw3Kkww7PBYDAYDIYThTtC+YlGowiHwwCAVqs1c9dOxYclj16vJ5lAbE1muzKAmecxxJDKEUsZVIZooqVqwOBCYjKZyJwo3WJNtUFvG9UUKhM6NFAP6nS73Wg0GnjkkUfwQz/0Q8jlcvhH/+gf4ad/+qfhdrvxyCOP4F3vehcymczXPWYf//jH8dGPfhRf+cpXUC6XUS6Xkcvl4Pf7EQwGkclk4Pf7MT8/D8dxEI/HcffddyMajco+cKRErVZDs9mUcSG/9WO/BY/bgx/9jR/FYDDA/Py85PI0Gg1sb2/L0NFqtYpisYhSqQSPx4NMJoPz58/jx37sx/B93/d9b7jtL774Ij75yU/i2WefxfLyMkajEf7sz/5MWurH4zHa7bb4svSIkX6/Lx4tKnbah6VHkujATJa89Pw1reDpTKdoNCo+I3qJgMM8oEAggEKh8M19yO9AmPJjMBjuVNwR5AeYBvJxMQsGg+h0OvJ/lmS4+OnkZBIZPQBV5wZx/48OsGS5jARGz3/SGTE0ztJkC0DIlg7ho/mWJIgzw0jGEokEisUi3vSmN+HJJ5/EwsICwuGwZAAlk0m8613v+mu7tD72sY/hYx/7GL72ta/h4sWLGI1GyGazOHPmDO666y4sLS1JuS0QCCAQCGBhYQHBYBDdbhf5fB7Ly8twHAe1Wg2FQgHVahWNRgMejweRSAStVguTyQQf/l8/DK/Hi//04n+SROtkMin7EwwGsbe3h6997WvS9VWpVNBoNFCtVoX4tVotRKNR/LN/9s/esCT2e7/3e/izP/sz1Go1zM/Po91u41Of+pQQmVqtJoSM5TlNPHWiNs3LLJnpc0Ivlh6USp+T9ntx6C3PCckwPy+6ZOr1elEqlf5OPvu3K4z8GAyGOxV3DPmJx+NiMOV4An03zwUqFArNjMNgNw+7wPhYejO44JHIAIfqEEmM7sgigfB6vfLvwWAgapPb7Z7pMuO0eCo+vV5PFt7hcIhcLgev14tyuYzv+77vw/d8z/dgPB6jUCggHA7j1KlTeOSRR97QwPypT30Kv/mbv4lPfOITCAaDqFareMtb3iJpzIlEAktLSzh16hTy+bwcxzNnziAUCqFQKKDRaKBer6NQKCCXyyEQCOD555/HK6+8gnK5jF6vh3a7jUQigWw2i3g8jo/+3z4Kr8+Lf//5f49kMolWq4Ver4dcLof19XUJaOz3+6I2lUol7Ozs4Pr167h06ZKoTZ/73OdQLBYRj8fx5JNP4kMf+pCEHgLAX/7lX+K//bf/hk6ng2w2CwDY2dnBs88+i0AggFarJeeGRJSEhaocyZYmQ1RrdPo3Pw88751OZ0bxY3gjzzF9UoxN0J8nAGg2m39XH//bEkZ+DAbDnQrz/BgMBoPBYDhRuGOUn0QiIR6eUCiEbrcLAOLtYJnj6CiK8XgsQyp1ng4A8bTwTl93YVElolKgf8fHsjsrGAxKWYS/Y2mNioNuo+Y4iYWFBVy9ehXvfve78fTTT0tpr1ar4ezZs3jyySdfl81TqVTwoQ99CJ/5zGdw5coVTCYTXLhwAevr61hZWZHuM5ax8vm8bB+PUbFYRKFQQKfTQafTkUC+8XiMzc1NPP/887h8+TIikQguXLiAWCyGWq0m5Z3P//vPw+vx4q3/z7diPB5jaWkJ8XhctqFcLiOTyeC+++5DtVrFxYsXMR6PMTc3h8FggBdeeAHNZhM+nw/Xrl1DpVJBPB7H/v4+Wq0WstksPv7xj8s+1+t1/PZv/zYuXryIWCyG+fl5bG1t4Y//+I/Rbrfh8/lQr9dFZfP7/eh2u3I++RkfDofSOs82d3b9ae8Qz5cOouR55THQapPuLuRny+/3o1KpfFu+C7cLTPkxGAx3Ku4Y8kMSwIWKpQyWljhZXQ8M1ZPV9ZBMbUzm4/XwVC6IrVYLPp8PkUhETLb0h3Dx1K8DQAgPU6bpA2KpLRQKYWlpmo/TarXwnve8BxcuXEC/38fOzg6Wlpbwlre8BY8//vjM/v+n//Sf8F//639FvV5Hp9PBo48+iieeeAKpVAq5XA7nz59HOBxGuVyG2+1Gq9XC3t4eJpMJgsGg+KFoFPb7/VLyunbtGp577jns7e2h1+uhXq9jbW0N+XwevV4Pe3t7qNfrUvpp/Y8W4ALWf2wdsVgM+Xwebrcb2WwWFy5ckETuRCKBTCYDt9uNWq2GTqcjBIxkkSGKo9FIfEWXL1/GZDLB29/+dvzn//yf5RiUy2X85m/+Jv7yL/8SuVwOmUwGn/3sZ7G1tYVkMolyuSxZQZyjRr8Xz5cuhwKHE+L1rDCd7cNE78lkIqZvfmf0vDiSYhqumap9nGHkx2Aw3Km4Y8hPIpEQPwYJB+/S9Z03wd+RCPEunx1h7A7TYzMAiHpA8yq9OVSX2O3FxZXP1YGGTBqmL4QhfLFYDKlUCvV6HQsLC/iH//Af4r777kOz2cRwOMT6+jre+ta3zuz3r/3ar+G///f/jhs3bqBarWJ1dRUrKyt46KGH8MM//MM4ffo0bt68KYbtvb09RKNRBINBbG9vy37u7OxIDs1zzz2HV199FcPhEDs7O5KinEwmMRwOJd8HgHh++v0+YrEYkskkrv76VTgTB+v/eOrvCQaDcl5isZgQv3vuuQd33323jNuIxWKIx+My9JTktVaroVQqod/vo9vtolKpoFAooFgsot/v4z/8h/+AH/zBH5RjcvHiRXz4wx/GxYsXkclkUK1W8ZnPfAZLS0syDHYymYjKQxM7E5h5rPQkd3aEkchogzqPhc6G4udPP1Z/lyaTybEfcWHkx2Aw3Kkwz4/BYDAYDIYThTtG+YnH41JeopoCHM7aem17pLSlxxfw3yyB6bEH/D/9GrqNHTgcbaFTnpn6TBXI7/eL30dDKwKhUAjZbBb1eh1PPfUU3vve98Lj8WB/fx+nT5/Ge9/73pnn7u7u4sd//Mdx+fJlDIdDGWZ677334tFHH8XS0hLC4bAoEZztVa1WEY1GMR6Pcf36dQSDQcRiMXz5y1/G888/j0KhgHq9Lt1yHE3BNvdgMIhGo4HNzU1sb29LcnE6ncZgMEC320X1D6qYTCZY+JEFeQ3GCMTjcSkf5vN5LCwsIBqNIpvNiqoTDAYRiUSQTCaRz+cRCASQTCYRjUZRKpWwsbEh2UK9Xg/lchkA8MlPflKOz97eHn7pl34JX/7yl+VYfOYzn5HPRrlcxnA4nEng1koh1SBdBuVzdUp3MBiEy+VCr9fDaDRCMBgUNZHlLZZh9WfDcZxjn/Jsyo/BYLhTcceQH+BwujsJih5zwQWNfg7HcRCJRMSXEQgEpEwFYGaR0y3weqq6nhHFkhkXTRqZSXAASFCinhPF10ylUiiXy3jqqafw5je/GfF4HCsrK/iBH/iB1+3nv/yX/xJ//Md/jFarhUAggEcffRSPPvoo3vrWt6Lf72MwGGBhYQHXr1/HcDjEY489hm63i2effRZ+vx+pVArFYhH1el2ydQ4ODnDt2jUxOvv9fpw6dQrnzp1DKBRCs9nECy+8AJfLhVwuh5deeklKe8z6WV5eRi6XwwsffAEAcNdP3IVqtSp+Ke3ricfjKJfLSCQScpwjkYhkF+XzeZw5cwbxeBzRaBRLS0sIhUJotVoYDAa4efMmLl26JHPbXn31VXQ6Hfzbf/tv8dM//dMApgbtX/mVX8H169fh8Xiwvb2Nl156Sbw5rVZrJtdJDyYlWeG55OeA/+bnhuZoluiY/8Shqv1+/3VkSqNer/8dfwtuHxj5MRgMdyruKPITi8Xkrlv7bHiHTt+GVmC0twM49GpwMdOdX8yX4cyrQCAgXV1MYe50OkLAaLamqsDFkYsiZ01FIhH0ej38/b//9/HEE0+gUqnA6/XiJ37iJyRvBpimV7///e/Hn//5n2N1dRVzc3PIZrN49NFH8dBDD2E0GuHKlStwHAerq6ticKaxll1mu7u7uH79OjqdDg4ODvDKK69Idxx9O91uF/V6XY5VPp9HMpkEABwcHKBQKCCZTKJarWJ5eRnxeFy8QF/491+A4zh46P/+kBi3A4GABCT2ej34fD4JdKRaovfV6/UinU7jrrvuQjqdFiP4eDzGzs4O2u02BoMBSqWS+KVeffVVvPDCC3j/+9+PD3/4w/Jaf/AHf4CPfvSj4s16+eWXUS6XEQ6HxU+lzxnJMbdHJz7r4Eua30mI6BcDgGAwOJPZxEnwwOH8t+FweKzVHyM/BoPhTsUdRX7C4bDcZfv9fpmuzQGTLL1wsT2a5qxHGjDk8LX9AAApdbFcxq4udnCxjMKOJQ655Pvr9yThWl1dRavVwtvf/nZ893d/t7RL/8AP/MBMG/vnPvc5/Lt/9+/w7LPP4uzZs1heXsb58+dx4cIFBAIBpNNpnD9/HuPxGJcvX8bW1ha2t7exvb2NUqkkC3ulUhH1pNPpoNfrodfriWq2tbUlQZDBYFCSqTOZDE6fPg232y3kw+12Ix6PY25uTtrUy+Uynvvfn4PjOLj/X96PQCCAdruNRqMhYZKNRgMHBwdwu90Ih8Pw+/2SyD2ZTESFGwwGCIVCePLJJ8VUzKBFEpGLFy+iXq/j3nvvhd/vx6c//WlsbW1hYWEBH/nIR3DmzBkAwG/8xm/gj/7ojxAKheDz+XD16lVcvXoVqVQKzWZTlB2mNPf7fenc4nuRwJDEMLmbJEgPT+VnSBvdgcNSKn9vyo/BYDDcfjDDs8FgMBgMhhOFO0r5yeVyUubyeDzSuhyJRDAYDNDr9UQR4uOY4/La9s5k8gCHZmcdWKdzXvi30WiEeDwu+S1UjqgE0BTL0tx4PBaD8/ve9z685z3vwfLyMgaDAR555JGZEQ6/8zu/g1/5lV9BtVpFJpPBY489hnvvvRdvetOb0O12sb+/j1gshmg0KuWg7e1t/Omf/ilefvllhEIhAFNPVDAYRKlUQqFQQCwWQzgcRqvVEoOzVrFcLpeoW+FwGMvLy4jFYlIyWl1dlfDAhYUF+Hw+7O/v45n/9zNwHAf3/W/3iWG41+uJ0gNMy4ilUgn1el1UEO5zt9tFMplEPB5Ho9FArVbD6dOnsba2Ji3z0WgUi4uLCAQCODg4QKPRgN/vR6vVQqFQQLlcxsbGBn7v934P73rXuwBM1bMPfvCDqFQqiEajuHHjBsrlspzHVqsl6h0/IzrriX4xRh2wlEno7aeKyAwhqkDa88N8o+MKU34MBsOdijuK/LC7iZO9uYDTj8GZXixLsDx1dEo3/R9cuPVsMJ0ITGM1jc9HSxzA1EPEMhK7v7hohsNhvPe978Vjjz2GcrmMfr+Pf/yP/zHm5+fl+R/4wAfwu7/7u9IFlkgkkMvlsLq6KunRAHDp0iVJM242m9ja2sLe3h5qtRp8Pp8s5iQftVpN9m97e3umq4lkMRqNYjQaScLyYDDA2tqaeIcSiYQEItI/UywW8dL/9yVMJhPM/fAcwuGwZPYsLS0hlUoBgMxf29jYwO7uLvr9PkKhEBKJhJCbdDot4Y6DwUA6xWignpubw6lTp5DL5VCtVjEcDrG0tIROp4O//Mu/lNLf7//+7+N7v/d7AUx9Ux/4wAewu7uLUCiEq1ev4qWXXpoJu+Q5Yh4TAzAJfq40YaOfCMAMIeK/+VkhseZjj3PWj5Efg8Fwp8L7jR9y++BowJwOGDw6wJTKD1Ufeja0GVqPsAAg7fAkT7rTh51eJEKTyUQIBBc6kp5MJoNer4cf/MEfxEMPPYSdnR14PB78zM/8zIzq9JM/+ZP4xCc+gV6vhze96U0Ih8MIhUKyD/1+H41GA81mE3t7e2g0GojH42i329K55Xa7EQqF0G63ZTo898VxHBmyyt/xOD7wwAMy9qPb7eLy5cti4KXyEQwGsbCwgH6/j1qthnK5LAbkyXh6HOv1Omq1GpaXl8Xc3O/3RQmam5uDx+NBvV5Ht9uFy+XC/Py8kKNeryeT1iuVCpLJJFKpFGKxGFwuF27evIlqtYpEIoFQKIRqtYparSZenkQigZ/7uZ/DX/3VX+EXfuEXEI1G8fM///P4uZ/7Oezt7eH06dPY29tDu92WcSYksY1GQ4geoZVEKlocTOtyuRAIBNDtduF2u2WMBoknP5t6UKrBYDAYbj+Y58dgMBgMBsOJwh1V9gKmvhaWvPQ8r8FgAJfLJSqHHlbJMDrO19KzmPx+v6gf4/FYfBzAYYAi3w84HHFAlcnj8UjZx3EczM/Po9/v421vexuefPJJ9Ho9hMNh/JN/8k9m9uNnf/Zn8Xu/93vo9Xp4/PHHcfbsWSwsLCCbzSIajcLr9aJarWJ7exsHBwfodrsoFAp4+eWX0e12pSspk8kgk8lImF+v10OxWJShmm63W1rtw+EwAoEAwuEwzp49K63s7XYbzWYTtVoN8Xgc4XBY/Eurq6uIRqNoNBool8vodrv40i9/CZPJBMnvT0quj8/nQyqVwpkzZ5BMJkVRqVQquHz5Mvb39+Hz+RAOh5HNZpHL5RCJRGRfCoUCqtWqqFXxeBz33HMPVldXEYlEJL8oFAphd3cXe3t7GAwGuH79Og4ODtDr9fDkk0/iQx/6EOLxOCqVCn7zN38T165dQ6/Xw7PPPotSqSSlL25fp9ORwbb0drF1XedFcTQIPV46LJPeskgkIjlM7L7zeDwycuO4wcpeBoPhTsUdVfYCIGWcQCCA8XgsCxTnOOlyFz08bC/nwqVLV9oDxL/z93pYqW5v1qW2YDAoZadcLodGo4G3vvWteOyxxxAMBhEMBvGjP/qjM/vwC7/wC/jYxz6Ger2Ot7zlLTh79ixWVlYAQAaPbm9v4+WXX8b+/j76/T6q1Sp2d3fF5O04Du677z6cOXMG4/EYnU5HTNd+vx+Li4uS5cPFmPuVy+UQDAZlZhcAyTHiDK5sNitlqXw+j2w2i3w+j+3t7WkZ0OPFE088gWvXrsk2siwIAJFIBMFgEJ1OB/F4HK1WSzKBmCc0HA7FpxUOh1EqlQBM/UIcuFooFOD3+7G+vo5UKoW5uTkxQV++fBlutxvJZBKBQADPPvss/vk//+f4hV/4BZw7dw7/5t/8G/ziL/4inn/+eTzwwAP49Kc/jbm5Oezt7cmAUxI9TXpJbugDYqs8y1mj0UiG6erH1+t18ZWxdMiwRIPBYDDcPrjjyA8XeE5b9/v9sojTv0OVhwSJhEGnNdP7wwWs3W4LOeDvSJAAiHmaZIq+HP5ufn4evV4PFy5cwOOPP45AIIBOp4Of/MmfnNn+X/7lX8ZHPvIR7O7u4t5778VkMsHu7i5SqRTS6bQswJ1OB9euXUOr1UIul0OpVEKtVpMRC4uLi1haWpLFNRaLyRT6fD4vi3M8Hpf8H058Z1bR4uIiarWakJdyuSzpz/Pz8wiHwxgMBrh69SoikQhWV1fxwAMP4DOhzwgJnJubQ6vVEiXM5/OJSToUCslxYMdZLBYTX9JoNEI0GkWr1UKn04Hb7ZbOtuFwiI2NDTFGO46DdruNUqmElZUVpFIpOVaxWAwHBwcYDAb4H//jfyCfz+ODH/wgAODnf/7n8Uu/9Et44YUX8F3f9V34q7/6K+TzeWxtbSEYDKLZbM4kdo9GIwlnBKY+LuYBUQHU4zIIKqj8bDAc07w/BoPBcPvBPD8Gg8FgMBhOFO445edo4i5biXmnzjIW/6Z9P0fLFEwa5pBSrfTQe8LnslWbpSC/3z9TLmMa8oULF7C4uIidnR38i3/xL2a2/Vd/9Vfx27/929JSfv78edx3330yPDOTyaBcLuPVV1/F5uYmhsMhQqEQrl+/jr29PXg8HqRSKSlBsZQXj8cBQFKT2dZdr9el+yyRSIhiEY1GEYvFUKlUZNYXy1XhcBg7OzuoVCrI5/NYWVlBMplEu93G1tYW1tbW4Pcdjqng+AYep2aziWQyCbfbjWq1ilKpNDMLK5fLSS4Ru6kmkwlSqRR6vZ6UlbLZLBKJBCqVirT0Hxwc4ObNm1hbW8P8/Dyi0Sh2dnYAQLrBAOAjH/kIut0u/st/+S8AgJ/5mZ/Bz/7sz+L69et48MEH8YUvfAHZbBa1Wk2iAXiOqfTQ70WlkQqOzoNiWjUAKR/ydZgkrlvoDQaDwXB74I67MnMRHQ6HMnBTD6LUA0VJZGhuZjmD5IdtyvQEAZAWbU2QRqORhO+xpMMSSDKZxOLiIiaTCb7ru74Li4uLqNfrePrpp2eCDP/kT/4Ev/3bvy2zwb7ne74Hy8vL6Ha7aLVaWF1dxXA4xLVr17CxsYFQKIR8Po9Lly5hd3dXSnqhUAiLi4vIZrNwu91Ip9PyPrlcDoPBQEqAkUgEXq8X3W4Xw+EQmUxGCBPLZzQ9029Tr9cRjUZRrVbR7XZx9epVrK2tIZ/Pw+v1olwuywiPVCqFdruNYDAowX+RSATxeFzmYHEyO/1P8XhcyoudTkdCBWkOTqfTaDabcBxHzNyNRgP9fh97e3sYj8fY3d3F5uYmkskkHn30URnMGolEEIlEsLOzg7/4i7/A+973Pvzqr/4q1tbW8FM/9VP48Ic/jEajIfsViURkHly/34fjOAiFQlJWZRQCPWT0hAGYeSznyrndbmmnZ+nLPD8Gg8Fw++GOIz+866ZJ1nEcmdANQBYr3nHrx/MunUqNzguiV4iLHhdD7RGigsGwQfo6Ll++jO/+7u/GW9/6VmQyGUSjUTz88MOyTR/72Mfwr/7VvxJv0MrKCkajEfb39yXTptvtYnNzE8ViEcPhEP1+H1euXMGNGzeEhHk8HgkC5P4zWLHVaqFWq4l6wQWc3WPBYFAeS4WmXq+j3W4jEAggn88jGo3C7Xaj0WgIKXS5XCgUCmi1WuKPGY1G8Pq8QnJ4TP1+vxwXKkK9Xk8CFzlclanRnU4H/X4fkUgEHo8H5XIZk8kEiUQCyWQSHo8HsVhMuvWy2ax0eWWzWXi9XkSjUdx3330oFAr42te+hslkIsfzU5/6FD7wgQ/g13/917GysoJ3vvOd+MM//EM8+uijKJVKM+nZwWAQfr9fkqTb7TZCoZCQMhrG6f0CDgfg8lgDU88PjfjsOGQQpMFgMBhuD9xx5IejC6gsMN3Z4/EIGTlabmDJi8ZWgo/lUFQahhlgR4WHChHb5Nkyz66lu+66C0899RRcLhe2t7dnursuXbqEX/7lX0YymcS1a9dw3333YW5uDuPxGO12G4lEAru7u7h48SICgQCCwSD29vZw8+ZNtNttRCIR2QfdXs/94bb1ej0x61Ld6Ha72Nvbw8rKikxO1+QpGAwKceQQVMdxEIvF0Ov1kEqlsLCwIBPMG40Gbt68idF4JIbmdDotk9xp8M1ms4jFYrh69SoGgwEikQhCoRCi0ShCoRAqlYrEA/BccnuoZvl8PjQaDWk15/NpzA4Gg5ifn8dzzz2H559/HktLS0KQdEn0Ix/5CJrNJn7rt34LTz31FPb29vDKK6/gzW9+M/7kT/4E8/PzqFQqEl7Ikmk0GpV90oNw9fgTlsN0aYzdhLprUIcoGgwGg+HWw67KBoPBYDAYThTuOOUHODQsh8NhjEYjdLtdUWyoijBsjq3XvAtnq3IgEJARC81mUwIA9cww3vnrUEOarV0uF+r1OiKRiJica7UafuAHfmBmW3//938fV65cQTgcxiOPPCKDV/v9vuwHyyjVahV7e3szYyuSySRcLhcajYaU41qtFoBpizf3GZiqNwwibLfb6PV66Ha7iEQiUiJrNBoYDodotVpot9tot9szZZzRaIRIJIJoNIq5uTlkMhnxuiSTSWQyGWz7tuFgOgaDShJLiizZUQVZXV1FJpORcRnMTgoGg1KeDIVC8Pl8MrqDXhmamD0ej5SWzp8/j5s3b6JcLmNzc1P8RtVqFQsLC8hkMlhfX0c8HhfF5fOf/zw+8IEP4Od+7ufwvve9D8ViES6XC8vLywAgx4uKHz8rjDXQvh2OQNE+Mf25pBJJFYyKocFgMBhuH9yR5IdlCGbD0OfD1GcaftmRRa8JyY1+LMtI9LKwtKW7fVjKoAeGAyz9fj/Onj2L1dVVbG1tSRAf8ed//uf4tV/7NSnRnDlzBktLSyiVShJEWKlUsLm5KV6iSqUiJuVcLicdT9zXVqs18/7RaFS8Nvwd/TSFQgH9fh/dbldKSB6PB4PBQEgPvU8HBwfY29sTUnf69GkEAgEMh0NEIhHpMguHw9OusYkjA0qXl5cl0JBzwKLRKPL5vKRLc66X1+tFp9NBqVQSckrfDGep9Xo99Pt9yRxaWFiQFG6WxSaTCTqdjkxN57T2QCAgpbL5+XlcunQJwWAQn/rUp3DhwgX80A/9EB566CH86Z/+KR5++GF88pOfRCqVws2bNyVfiIGG2rtDIsVtZKcXB9/ybww4BPA6P5rBYDAYbg/ckeTH6/VKEjIXJj21nK3e7PoCIKSFYyiYDB0IBKRdnqSJpIgLG1vEuRDSGByLxfCWt7xFEon/wT/4B7KNL7/8Mn7qp34K3W4Xi4uLePrpp3H+/HkcHBzA4/FgZWVFUps5lqJQKAhJ6PV6qFQqMow1HA6j0WigVqtha2tLhphyrASVlUwmg8FggN3dXbRarZkwPqoVHPKp/U/c5263i2QyKWQwGAyK74gjLzxuDxw40lG2urqKubk5MVz7fD4Z+sqwyWw2C2CqToXDYTEVMzW5Xq+j0+mg0+lIRMF4PMaVK1fQbDaxtLSEYDAofhx6oOr1Ovr9viRZu1wu5PN5pFIprKys4LHHHsNzzz2HGzdu4JlnnsE73vEOPPnkk7h27Rry+Tw2NzdRqVQwNzeHer0uJJKfCR2OCUCiDej50Z2GNIn3ej2EQqGZz6AZng0Gg+H2gXl+DAaDwWAwnCjckcpPu92W0hTzY0ajkQyfZO5Nt9tFIBCQcELtyaCngx1OAMSjQS8Ku8RYZqKKNBwO0e12kc/nkU6n4fV68eCDD8r2DQYDfP/3fz9arRYuXLiAp59+Gvfdd5+0gc/Pz2M0GmFra0tyfhhkyMGowDRQj63YurTXbDZRLpeRyWRkrtjc3Bzm5+fF68PyVrfblZIS/UZsZ282m6KGcd7Xzs6OeIzYDVatVkVNA4CJM4Hb5Ua/34fL5UI4HEYikUA2m8VwOMRwOJRuNnqA6OOhSsIBq2zL57bqUiQHnxYKBQDAwsICAEh5jSofh9JOJhNUq1UpKc7NzWEwGODSpUuoVCr4whe+gI9//ON4//vfj/e85z34+Mc/jieeeAK/9Vu/hWAwKKqV9up4vV5RfrhdLGWxjMjfMxiTPiFuk8FgMBhuL9yR5Mfv92MwGIjJlv4cDuck0aGRlqUjEh6WslgmYVgdyRH9Hmxz5mynbreLeDyO4XCIWCyGBx54AD6fD3t7e/iRH/kRAMDe3h7e9a53SfDe4uIi+v2+zOPy+/2SoNxoNFAoFCRHh6F+TEdmWz+3ZW5uDp1OB81mUwavut1urK6uSggjt5kT2VkS8/v9SCQSiEQi6HQ60jpeq9VkMnswGMRwOMTu7i4CgQAqlYr4fRzHQblcnnqFhtOcHwBCFmlIZjwAy0T0F5VKJZRKJSnhra2tyVww3WK/sLCA8Xgss8wYIshzVK/XMZlM5OeDgwMpK6VSKaRSKbRaLWxsbEgA5dNPP40/+qM/QqVSwf/8n/8Tb3vb27C0tCTE7dFHH8Wzzz4rz9UGenrAeLz5OSP54uex1+sBgBAmZgIB05JrKpVCtVr9jn5PDAaDwfDGuCPJD8cSsKNGm1C5iFO14dgFj8cjhmeCd+UcYxEKhSQ7R/tk+Hd6c7xeryysmUwGTz/9NHK5HADgQx/6kJCxaDSKSCSCubk5FItFMR9fuXJFUooPDg7g9XqxurqK+fl5hEIhDAYDUS04sJOdWPF4XBbfarWKcrmMcDgsKhD9MFRgvF6vBCTStBsIBOD3+5HL5VCtVrGzsyMpyh6PB36/X7xJPLaZTEbydqiENBoN2R56rrTBl6bgbrcr4ylIToEpaY3FYqJsxWIxANMsJypSi4uL8hrtdlt8SgcHB3JsQqEQQqGQjLoIh8PY29tDIBDAuXPnsLa2hgceeAAXL17El7/8ZfzhH/4hfuiHfggXLlxAr9fDuXPnsLe3h62tLSHLVA1J5riPVAK10sOfgcM0cRIjPofkyGAwGAy3Hub5MRgMBoPBcKJwRyo/AKQNm63J2p9D1QCAqCZsAR+PxwiFQpL343K5pOW61WqJQsLSF7uV2JHEfJpYLCYt3o8//jiAqcfoT/7kT1Aul+H1evGOd7wDb3/727G6uor9/X3J1QEg2+jz+UTJop9kMBhIKSkYDEoLdqvVQqPRmOloyuVySKVS8Hg8MtoCmPqFwuGw5PuwK4l/5/wslgfZZabVIratc2YWjwmPHed2RaNRBAIB2R/GELAExzllzWYTtVpNfFflchnRaBSZTAbBYFD+zq42njNmFSWTSelk4wDVs2fPIhKJoNlsolgsSodbLpeD2+0WP9IjjzyCS5cuAYC0vT/88MPY2tpCp9PBI488gl6vh1arhWKxKOfZ5/OJ/4c5SyzvcQ6czgY6mu5MDxH9SwaDwWC49bhjyc9wOJQRFO12e2batm5F1qUxmm05XJQBgZwvBRzOa6LPAzgsfbHF3e/347HHHsP58+el3AUAH/jAB3DlyhUxH991110IBAIzw0CZRVOr1XDlyhUUi0WZLj8ajbC4uAiv14v9/X3J9JlMJigWiygWiwiFQlhYWMDCwgJWVlaEmLBkR6LHfSNhIenyeDySAcQRE/1+X/aVmUFs0yYR5ALf6/UwHE1D+4rFInw+H8rlMhKJBPL5PEKh0MxjmQvE8SFsYadBHThsEfd6vWg0GqjX6+JhGg6HSKVSiEQickzC4bBkCPE8ptNpAECj0RCvTqFQwHg8Ri6XQzabxeOPP47Pfe5zeP755/HJT34SjzzyCL73e78XwJS4nj17Fi+99BJisRja7baQHT1CxeVyiYGb700CzjEpJIcsu1rIocFgMNxeuGPJDzN9SHCo+nDR14Zg/kfvhe7u0iZoGmB1rgunyHOh9vv9iEQiuOeee+DxePDAAw8AAK5fv46LFy8ikUig1+vhySefRDabxcHBAebm5sT389WvfhUvv/wyrl69inq9Lh1Yk8kErVYLnU4H0WgUAGQY6P7+Pvr9PhYXF7G2toZ4PC7byRyearU60wHF/SWpCAaDqFQquHHjBg4ODkRZAqYqms/nE7WHE90ByPEleWMnF83VDEtkzhBfRxNLr9eLcDgsXW5UxiKRiPizarUaNjY2hPiQILlcLjEhB4NB5HI5UaHYGTYcDlGv18WcDhx2+pH0xeNxnDt3Dp///OcxGo3w5S9/GV/60pfw1FNP4fz58yiVSmI+p6+LBJqdYCTRfJ/BYCCDcR3HEU8SjeM8HwxeNBgMBsPtgTuW/ACQThwuyiy9UF0ADqe8M+yPRIgGYt7Vc3inz+dDp9MRI+1wOJTEYpbSFhcXxaBMY+vzzz+PL33pSzh9+jTcbjfW19cxPz8voXcvvvgiNjc3USgURM04ffq0lIw4loIKk+M4KJVKqNfr6PV6CAQCiMfjSKVSQpii0agECbIkFggEZFup4NRqNdy8eVMmmXNB9vl86Pf7EhLYaDSEzDCkbzweS1kOmKo93W4XzsTBV7/6VTFak0Rym/hcqiTcfr/fL/EE7Ibqdrsol8uiRJH4MAyRpb94PC7HOxQKSXr01taWdFKxTObxeNBqtWT0htvtxtraGp566ik5Vx/96EflPGWzWZw7dw6O42Bvb09GmdCEzTEkLB92Oh1R1nhMaXZmiYxxDLoz0WAwGAy3HmZ4NhgMBoPBcKJwxyo/DOvT6o3Ol2HpiqZTPQIDwEweDedN0f9ChYcZQMBUnWC2zIULFxCLxaRlu16v48///M/h8XhQLBbx7ne/W0y4LI0Eg0Gk02kUCgUZ0TAejyVAkLOwaLweDodIJBJIJpOoVqsz+8kxHPF4HF6vV/JwWq0WqtWqKBkspVFRqtfrqFQqMwNbGZoIQNQxDo1l2aZUKuHg4EBmiTmOg4kzEUM0S2LtdhvNZhPZbFYGjtIjRaVnfX0dyWQSPp8PkUgEtVoNu7u7qNfrGI1GElK4vLyMeDwuA08ByEgPXb4cDAYyFoO+Inq4XC6XGKfT6TTq9TqWl5fx2c9+FoFAAH/2Z3+GBx54AD/+4z+Od7zjHfj93/99pNNp3H///Xj55ZclP4glTyo53CeOSKEvij60wWAwM0iWbfHxeByNRuM79yUxGAwGwxvijiU/TCDmcEl2e3FhYumEHV30hujwQoKT2lme0Um/unNnMpkgEolIDs4TTzwBYFoK2tzcFEJy//33iy8mFAphNBpJx9Xu7i5effVVuFwupNNp3HXXXVK6o8enVqtJXlEikUAul0Ov10O1WsX+/j6SyaRsF424TBaOxWJivA0Gg5JiXSwWUalUxHRNT4/f70c+n4fL5UKlUhHCQ1M4vT1c5LWn6NSpU2IA7vV6QsBYfgMgBm8+h/PHdIlyMpnIbK5wOIxTp04hkUgAgHh7aMRut9vSdRePx2dKZDR8N5tNIaYAUCgUsLa2hqWlJclo2t/fx97eHp599lk89dRTOHv2LO6++25cv34d4XBYCBeJoSY7NHNzSj3Jkc6BGo/Hkhelww4NBoPBcOtxx5IfYLogsdOGd9cMqOOEc7aNkwCRLIxGI/T7femMYsKzHgNBUtJqtZBOpzEajbC8vIylpSUkk0lZ4D/72c9ie3sbALC2toZIJIJ4PI5ut4tOp4NWq4WLFy9iZ2cHhUIBtVoN+XxeQvmoSOl9CQaDiEaj0hLPrqn9/X1ZVOPxuATxhcNhMU6T+JDIMVWZZKHf78NxHPh8PiFnJEO6K63X6yGdTmM4HCKXy4kfCoCYgXnsRqPRTMecbntvtVqyXwwk1OnINDLH43Ekk0kxdPf7fbTbbZne3u12RZljqGAqlcLc3JyEReZyOfFLAZAOsueffx7ZbBbZbBbz8/Mol8twuVx4+eWXUSqVcPbsWZw7dw43b96Ex+NBo9HA3t6efFZImPm+3DeSGxIeHkOqQDoE0bq+DAaD4faAeX4MBoPBYDCcKNzRyg99MABmhlxyCClLSex64uBM+l0YKsjHsUNpMBggHo+j1+uJF4iPW11dRT6fx9rammzH5uYmSqUSfD4fVlZWJMjvnnvuQbvdxhe/+EXUajV5D51BVKvVsLS0BI/HIwNb2+22lNFisRgmk4l0Yum5Zn6/XzqsWKZjpxczabTHyev1ot1uiyrG16Ai5jiOtHozM6nZbCKdTkvpxu/3w4XpMR8MBvLYUCg0E6rIc6KHyQaDQSnt6RZ4ls9YZmOAIOebHRwcoNlsIhAIIBaLIZ/Pi9IVCoUwNzeHyWQiHisAWF5eFi+Qz+fDwcEBnnnmGTz66KNYWFjA888/j1gshsuXL+OP//iP8eijjyKZTGJ1dRWvvvoq4vE4FhYWsLOzI+ee6iI7vqj2sAxGBY/dZVTg6D+jamQwGAyGW4s7mvyQTJDoMIuFixRBkuTz+YQIsWTBdnaWhmiipe+HeTlc2DweD7rdriyylUoFly5dQiKRQKvVEt+Pz+cTIlEqldDpdNDtdlGv14UcPPnkk8jn87KINhoNCSmkWZkL73A4lPLScDhEu92Wdn2Px4P5+Xk4joNOpyPlFZavotEodnd30el0ZvKLSD76/b74kjjFnMRQ+1RoBHccB6PhCDs7O/B6vTIwNRQKzSzwLPv5/X6kUikhBCytceAs/83z2Gw2sb+/j93dXdRqNdlPAMjn80gkElKOIiHi4NdarYZisSivRTLs8/mwv7+PnZ0d5HI5LC0tCbF67rnnsL29jbW1NTz44IO4dOkSCoUC0um0GMS134efC24ziQ2PFY+bzv45+pk0GAwGw63DHU1+dAeU/h3NuVyc9BRu+mr4PC5YJDjMiOEix4ygTqeDfD6PhYUFSRgGgN3dXXS7XUkSfutb34poNIp6vY5CoYDr16+jWq3C5XLh2rVr2N7eRjKZxLlz55BMJiXDZjKZIJFIiFLACeZc/KnUAJBJ6MPhUML/AoGAGKDpywkGg6JQRKNR6cCiYuLz+cQUzi4yLtzsbotEIuL/AaZDQ+HCDBGIRCISukj1iQGIVHw4mDQUCsloEq2sTSYT7O7uYm9vD5VKRcgiz3EymUQ6nZYxI3xfnjsGKnICfbvdRiAQQKlUkhEczWYTX/7yl/H444/j1KlTqFQqCIVCKJfLeOaZZ/D+979fVKxMJoNarYZ0Oi2qElUe7d2husbPFAMNNVlmlpI22RsMBoPh1sGuxgaDwWAwGE4U7mjlp9frIZFISCs2gJmyF1UNlnT0aAYAMjiTScf0w1DV0GUo3dIcDodlphdzfPL5PJaWlrC3t4d+v4/l5WUA0zb4UqmE/f19HBwcIJVK4YEHHsCZM2dEMaHvhv9myY2t7uxKo6LCtvB4PI5MJiPPYUcR/w1A2sT5WjwenHXm9XoRi8Xg9/vRbDZFpQmFQuj3+wiFQpIirctWbP/Xvp/BYIBGoyGdcGxx5/nhGBGWKrnP9GNVq1UZNMrH8LwyA4jDSjudDkajEZLJpPiMCoWCdLfxNVgqHI1GWFhYkM/EY489hoODAwSDQbz00ku4dOmS7O873/lO9Pt9XLt2TVrneZ51/MHRgbp6tAq9VlQaqU5y2w0Gg8Fw63BHkx8AsrAyrHA4HMpwUh18yJIQgJnhmn6/X8oU9KJwEvl4PJ6Z3r2+vo5UKoV6vS6Lcq/Xw7Vr11CtVnHjxg0Ui0WcP38ebrdbggHdbjf29vaQTqfx0EMPIZvNzkwEJwnb3t6WvByWs0hMODDUcRwkk0nxKNEAzSBFlrhYquOivLS0hEAggIODA8nbYds7CUkul0MgEECxWBSfDXOUdAs+p9xzJAjncfG9aEJmqz4JALNvSLrotzo4OMD+/j5KpRJcLheWlpaQy+Xg9XrRarVwcHAg0+b5+Fwuh0wmA6/XK9Pl+/0+KpUK/H4/6vU6rl27Jts+GAwQiURkXEckEsHDDz+MT37yk4jFYrh48SK++MUv4umnnxYDeDKZlHlkm5ubkp/E4Ew9NoWfLX6G+H+Sbn4WA4GAkR+DwWC4xbjjyQ+zeOjp0SqI9lnQg0FPi/4ZmOa2hMNhDIdD8aNQQWI31dLSkviGiP39fVnk3G43kskkIpEIKpUKtra20Gw2sbW1hX6/j3PnzmFpaQnAYfcVgwMDgQDC4TCq1SqKxaJMJKcyQ38QzcPZbBbA4RR6LrgkKVrlIgFMpVLiiWFmT7FYxMHBAdrttgwnjcfjKJVKsmDTW8PHUNVIJpMyhR047LiLxWKIxWLSFUU1hObtSCQiydI6cNLr9SKbzSKVSgmB4/bXajU0m000Gg2kUikkk0npeiMBYXjj9evXxSyuyYnjOBJQWa1W8aY3vQmFQgGf/vSn8fnPfx75fB4bGxt4+OGH8cQTT6BUKiEYDGIymWBvb08+MwysBKZEmkSSZnKdFO44jkyo57w4g8FgMNxa3PHkh+UXkhI9pZ1Be4PBQAgLzbYkCbrjiGZpvWCOx2N0Oh1cuHAB8/PzklDMshLD9ahAuFwutNttlEolbG1tSYnq3LlzOH/+vIyvACDvT/VFl+aoqADTlOFms4loNIpEIiFjLWi25RR0BjSS3JEActEnaWH5KhAIIJPJoN1uS0J0s9mUkQ5UiFjeIQlyuVxwu9ySdk2VrN1uSwozy0K6vMbyUKfTkW3hVPdQKIREIiHbFIvFMBwOUS6XZ8gdx2SwQ4+KC0ktSZfH45FU6G63i0KhgN3dXaTTaaysrEgpcWVlBaFQCBsbG7h+/ToeeughrK+vIxqN4v7778eLL74IAHK8+X783HFwrZ74TkWRqiQJaigUkjBJg8FgMNw6mOHZYDAYDAbDicIdr/zocEKWXQBIyYVKDksrLHNRlaBCohUYloWAqWE1GAwiHA7D4/Gg0+kgFovJ+1+7dg21Wk1GVjiOg0qlgt3dXZRKJZk7lclkEAwGAQBzc3OikFBJCgaDUqZZX1/H8vKyeH9otI1GozIXq9lsiueHqg9VBm63nsfFNnk996rb7aJcLktmENWycDiMZrMpWUAs8dAP5XF7MHFNpJzD48nSYSgUkhIRfUqNRkPa2lm+Y/loOBzK6AkqJp1ORwaxcr5WIpGQFncOR6Wfxu/3IxKJYGVlRfaH2+B2uzE3NyfGZWYInT59GtlsVsqW1WpVFCkA0l6/v7+PCxcu4MUXX5ShqgBENaQaRB9QOBwWEzg/dzwf/AwYDAaD4dbhjic/XPjpDeEizwWepIAlLC6uXBRJdtiNFAwGxffDskYmk8HS0hL6/T4SiQROnToFAOh0Otjf35dOKobk1Wo1ef2bN2/C7XYjkUhIKjEXwFgsJtk+XETT6bTM8WLeD8MKuTCzrEfoieo0EvNxPEb68ZznVa1WZRAn/SnslmL5DMBMuWo8HmM4GsKZTEkevUp8v3A4LBPVmXDMbQiHw4hGoxLEGI1GAWDm3ywzVioV2T6WvQAgEokgFouJuZvnkIZwmonpCSqXy3Ju9Ry3l156CdFoFCsrK8hms+h0Orh8+TJeeOEFvPOd70Q0GsX6+jpWV1dx6dIl1Ot1JBIJFItFIZw8Lvw3fT4sa7LTjeSVvieDwWAw3Frc8eSHCghboDlqAJguxrwbp3m51+vJIghgZrI4/SEkHiRL0WgU2WwWyWQSp06dwtzcHACIcZadRN///d+PdDqNz3/+86jX65hMJkilUvB6vajX6/D7/TOt2YFAQHwgJDlUUNglxREWDCKkEqMn1JPckYTQZ8PfAxDPTjAYlC4whgvqpGsqKTwemmixDR4AJs5EOtNICqPRqPh/SHZIqDTBomJDJUiDpIkBj3xfDjsNBAJIJBLw+/0oFoty7jg+hKZnHheSoVqthp2dHYRCIRmP0Wg04Pf7MTc3h+3tbXi9Xuzv7+OZZ57B6dOnkUwm8e53vxutVguvvvoqOp0OPve5zwGAjCfh6BN2uukWfRJrklCSyWg0KgTJYDAYDN95mOfHYDAYDAbDicIdr/wAh3fUDLbj/C4AUiqi/4IjH/g7Pj8UCqHT6ciMJrYvM9eGHiGqPgDwsY99DM8884yUu1ZXV7G3t4dWqyV3//TAUIWIRCKihlC1YImn2+3K+AkqPolEAnNzc4hEIjJagT4l7gM7neLxuKgQLPPx34PBAJ1OZ8b/w44phgACh6Mi6HliWYylJ6pmbpcb8/PzMqS02WzKzDGfz4dsNisKkJ4RRmWN+USj0UhKWN1uF61WC/V6XcIJqbDQ3+R2u1Gv19FqtaTcxUBKqmXNZhNutxvRaBS9Xg/lclmGnNIHNBqNpB1+cXER165dw+bmJg4ODrC9vY0bN27g1KlTCIfD6Ha7uHnzJjweD+6++2585StfkZIXt43q2HA4lLljwGHEAsdfHC1ZGgwGg+E7j2NBfsSI+xoh4KLEsgRzb3Qpwu/3AzgsEbHVm4s+w/gI+leSyaT8jjOjQqEQ7r33XiSTSWxsbEi68xe+8AU0Gg2cPXtWTMA6YXp/fx/lchmFQgHtdhuj0QiDwUB8O+FwGAsLCzINnX4llrRYmmKrO8tgg8FAzMr0p4xGI0QiESnt6bwaPoclGw0u1Nz+0WgEj9uD8WR6HGOxGFKpFFqtlpjBWfo62uJN4sNBrSzzsQzH9242m6hUKpKxxDIhB84y1LHT6eDq1auo1WpyfgFIcOTu7q6cb+3RIaG7efMmkskkFhcXZf8uXryIlZUVfPnLX8bKygr8fj/W1tawuLiISqUihmv6jRqNhhA1fqZYWuUAXZYtud824NRgMBhuLY4F+WHQHT0rOueGygiJEcdV8P9MMQYO1SEu2vTQLCwsiJKSyWTkfa9fvy4pxktLS/B4PEin06jVatjd3ZVsHi66HDPRaDRQKBRQq9XQaDRQLpclDFBPQucgz0gkIiSLuTg02lLZ8fv9aDQa6Ha7YqymGkPyxKnzNE/T60OTMwlLv98XMqgHrUp3l2vqp8pkMjIWBADS6TTS6bQcT91JRwWM6hUN6VS4SFiZv0PzMg3CJEitVkuyiICpqTgejyOZTIp/aDgciseKx56KS6vVQqPRQDweR6PRwKVLl5DL5ZBOpxGNRtHtdjEcDlGpVDAYDJDJZHDffffJUNpwOIy1tTVcvXpVfFQ8t8yS8vl8Mi5Ed8SR+Bj5MRgMhlsL8/wYDAaDwWA4UTgWyg+9OvT68I6cacvA4TwvqhlUGpjxQiWF5RgqI4uLi1hdXZXXXFhYADAtB9XrdYxGIyQSCSwuLmJtbU1atRcXF7G1tYVqtTrja+l0Ouh2u1KqmUwmiMfjcLlcqNfr6Pf70v3FgassaVEtYamIKgmHiwKQ1nK+p8fjQaPRQKvVQigUwuLiopRk2CXFbWGXF/efmUXpdFq65pjzA9ehUhYIBKSTq9frSWmPnh6dLM2uNZ3PRE8MzxP/zuNcqVRwcHAgLfjRaBQLCwtIpVKS+Exlh+34HDyrvTY+n098Pb1eT9rhd3d3kc/ncfHiRXS7Xezt7ckg2rW1NZw/fx5f/epX8eqrr6JSqUgSNVUyliG5f0wUj0Qics50SjVToQ0Gg8Fwa3AsyA8XXu1babfb0hauR1rQlMtMGpZ4tF+GZQsabFkS0nOZrl+/joODAwnm83q9uHjxIl588UVsbm5ia2sLtVoNgUBAfCssufT7fVkUAYjhmAs/J6kvLy8jkUjIvjHfRufw0NNC0kMCw7KQx+OZKQvR4wNA/Cp60jsXdG1Ijsfj4iGKRqPyeHqlSAAYXsgAQz0Kgi3fLIPxtf1+P2KxGLxer4zFoLdqNBqhVqvJfDHmKdGM3m63hXBpsKxHUsxjQv8S85Dy+TyazSb29/dx7tw5rKysAAAuX76Mc+fO4dq1a1hbW0M0GsUjjzyCS5cuodfrIR6PIx6Py5R5fVy5rzyvJGUkRjxnBoPBYLh1OBbkp9FoiBeHCgnvtOkjoSeIAX8A5PdcoKgccTiqy+XC3Nwc8vk8/H6/hBsCEE9PvV5HPp/HYDBAKpVCPB6X4Z3MuXnxxRdRKBQQj8fltUke2HFEI3O328V4PJZhqvSM0N/DzCIambn4Ui2iH4g+Jy7+PDbaB8WFut/vo9friTeIBJGBjFTH6CWil4eEq9VqyXyrXq+HZrMpRmd6pfj4yWQixudgMIh+v4/9/X2Ew2EJQex2u3Iu9HsMh0Px5ACHxmaqajxeVI34meDwVb5nNBpFpVLBxsaGdIT1ej2k02khtdvb27h69Sre8Y53AABOnz6NN7/5zahWq6hUKshkMtjb2wMA6ezj50jn+pAQ0dDt8XgwGAws68dgMBhuIY4F+QEgCz+7tVguohmaE95JGPQYC5IEmnqHw6EsViQWiUQCqVRK3i8ej88QFCpHVDZyuRyWlpbwyiuvYDgcSqCeVj2AqUrBbiCWwWKxGLLZLObm5mT8gk5eZpmICynLYrq8w/IfF2C287NcQxLE40KDMcMFWRbr9/sIhUIIhUJSKtJdVZpM0XxM47M+3pPJRAhVIBAQpYslM2C25BUOh5HP57Gzs4NGo4FwOCxEDDgsofV6PXQ6HQlAbLVaM117JIrJZFL2lZEAHIFBQsztcrvd2NzcxOXLl/Hyyy/j/vvvh9/vl0BHYGqCv3btmpA5nRLO48P3ozJJYkTF0WAwGAy3BmZ4NhgMBoPBcKJwbJQfelR0SUiPuuDv6JVheJ72+/AuHoCMzGDIIcP3iOFwiF6vB5fLJTk0ejhqv9+XUg1b75n5QsO1Hv3AUsnCwgJyuRxyuZwMO/X5fIhGo/B4PFIm42uxLEYfTrPZlKGuOpSQYzP0PgOQklan00Gj0ZBRHTozieqSVp2opnB7mE8UiUTE88NBsMCsEZvHjJ4hbjvjB/L5PEKhEHw+nwxEZVAiZ3jx+AFTw3u73UYkEpG8Hv6fJTq2yvP59CpxP6gqzc/PYzweY3t7G9evX8fVq1dx//33IxAI4L3vfS/q9To+8YlPIBgMYmlpCaVSSUqNVHeo8GgD+Wg0EtM4FTweG4PBYDB8Z3FsyA9NvPTxML+GpR96eOi1oQGWRISlJwBSevH5fDJoNBqN4syZM/J+5XJ5pnTBBOdisYi9vT1sbW2hXC6L0ZnvybIITc3ZbFaMwsyaIeEJBAJIp9NIpVJSkqE3iWUdend0KjMXci647A7rdDrS2cauKG2Q5mKsS0z0y2jzMskWc5A4mZ7HjnO76HVh6ZGkjcNjOdsrkUiI34nnIhKJSI4QZ4HR90Rix8BCkig9RZ0GdSZqk5iSjMTjcSm51et1dLtdOR/7+/syC2xjY0Nygfx+v5TXut0ucrkcdnZ25PyzbMk8I50DpMua9AgZDAaD4dbg2JAfemi4uHCUAQAZV0HCQ8WEhISqEYAZAkHvT6PRwOrqqnQDAcDe3h5KpRLm5+elpZyLqdvtRiqVkhBDn8+HVCqFfr+PYDCIfD6PBx54APF4HL1eT8iRNvmm02nkcjkZhEoCpyfOk/DQQ0QPUKfTQSgUEqJFwzSVMd3Nxins4XAY8XhcOqpIdsLhMKrVKobDobTLTyYTTJxpVxYHpdbr9RlfEZUj+qlImNgZRaVHRw6QwAJTEpVKpUQpq1QqaLfbM+3lVLaobpEMARCFBYAMkOU+kYhQVVpfX8ddd92FYDCIUCiE7e1ttNtt7Ozs4Ktf/So+85nP4O/9vb+Hfr+PaDSK+fl5IW5MDyfRjkQiMySO50x7gaiiGQwGg+HWwDw/BoPBYDAYThSOjfLD4D4GFVJtYLcRgBmVgS3YfAzD8ABIxxa7rgKBwMxAUwDY2NgQr0cikUA2m0W320W1WpV26F6vh1AohHa7LUM7I5EIlpaWEAwG0Ww2EQqFpJzGNnvOj6LKQm8R1Rt6kRjqx99R4WBZjaUmzsNi2CHzgubm5pBMJhGPx8VXpFUyAFJq4twuHke4ICMwotGohA3ytVmeos9Fz05jeS0Wi72uhZ+KkdfrFRUtEAggkUjILDRuA8toLO3RMwTMdv9xlhmVOXacUf3b2NiAz+fD8vKy5DJFo1HUajWUSiVsb29jc3MTkUgEuVwOCwsL+MpXvoJUKoWFhQXs7e1JaY3dZlQCWabk51D7vRKJxIyPzGAwGAzfGRwb8lOtVpFIJMTgyknbzIRhYjLbjkmEuNCzJZyLWCAQQDKZFDKih5wCkEGhLFO1Wq2Z16NBuVAoAJiSiHA4jGaziW63C7/fL5lAbImORqMyF4smYhqVA4GAeF90CjXLZiQB9KS0220xJpPMcf/prwGmJcFEIoFwOIxGoyGhgsPhEOVyGY1GQ1roe72etHXTZxSPx2fSszlVneUqGqtZtuPvh8Oh7B/Tnvl/ng8SN5/Ph5WVFayurqJer6NYLKLRaMgsM2Ba7oxEIhJwyGPa7Xbh9XqldAhAgiRpYvf7/ahUKnIOU6kUisUier0eXnnlFdx999149NFHcffddwOYBlxubm6KJ2x/f19meZF88fPE8hyPO4MXj5b5DAaDwfCdw7EhPwBmxlLQ88Pf0VRLrwY7oDjWgSSJAYPBYBCxWAyBQAC9Xg9LS0vyPjdu3MDGxgZKpRIWFhbQ6/Vw9epVdLtd3Lx5E51OB7VaDfV6XVQldm6Nx2O89NJL2NjYQDKZRCqVkgWTfpuFhQUsLS3B6/WKgkEy1Ov1hGD0ej2ZCt/tdhGJRJDP57G4uCh5PFqB4lgFHiOagqm2kGCxYwkAotEoGo0GqtWqjAoZjoZCLLhvWkkjAeHCT0WEA06pYukuN51+THWOhIhhkADkXMbjcclMol+L3hsOeqWnSJuL9UiR8XiMZrOJVquF/f19hEIhpNNpIb3xeBytVgu7u7v4+Mc/jsFggFOnTknY4Ve+8hVEIpEZLxUTs3UHGMkhlSgjPgaDwXBrYZ4fg8FgMBgMJwrHSvlhyzOzfFhW8Xq9aDabM2nDVANYEtLdSSyb5HI58fucP39e3mdxcREApEQVDoeRy+WkSygQCCAcDmN3d1dKOEw3ZupxuVwW/wk7g9ie7/f7kUgkkEgkEAqFRNFgV1mlUkGhUJChpHt7e5hMJsjn8zKugf4Zvo6ev9Xv99FoNKSbjWMZ2JlEVULnJIVCIWQyGSSTSRRQgMvlkjygbrcrOTqxWAyRSERmgLXbbVGMqAixTZ6vT4VOJz4zRoCZPlR46Ofi79lKH4vFRAnSQ2SpnPG4N5tNKWHyuS6XC61WSyIHHMfB3NwcqtXqzGDZVquFeDyOBx54ANevX8f+/j4AYHV1VXw/LKtS+eLx5vw33XJvMBgMhluDY0V+SHq4qNEwzIWVZEMbhz0ejwzYZHkJmJZ7Tp8+jWaziVgshnq9jkQiAQCSC8MWdpa0ut0u1tfXkcvlcPHiRcRiMbTbbfj9fgnhu+eee7CysoJoNCrZQMyqGY1GUgqiYZt5OPTh9Ho91Go1VCoV7O/vYzgcir8omUxKSCLbuXVbOFu92UrP12WLuuM4ODg4EJ8SjcSTyQTpdFpa4EniKpWKHC/uA6e60/BL8za9SyQRnU5nJo+IpSrgsDTFbeacMJfLhW63K+cOOMxk4kw0Zg+VSiWMRiMpYRWLRdRqNWlDbzQaKBQKGA6HyGaz0gofDofh9/tRrVYRiUTQ6/Vw8eJF3HfffVhbW5PcJrfbjXq9Dp/Ph0QigWKxKOZqlvB4DjjpXRMj+n5survBYDB85/ENyY/L5Wo5jhP9TmzM3xZc6Gmupd+C+TnAbCgg1SEunsBh4rHP5xMFIZfLCfEBgC996UtoNpsSAphMJtHpdLCzs4NyuSxEpdvtisk3k8lgbW0NCwsLkl8DAOl0WkgGFQ6qBVRlqB5RuWg2m3AcB9FoVFSVdDqN+fl5RKPTU9XtdmWfSGxIgqhkkCiQtNy8eRObm5tyDAuFAprNJobDIeLxuKQjk3j0ej3x1dCLRPLDjift42m1WvI37f1hpx1JqlZbCHqy+JpUVpigzceQkLCrqlwuo1qtotFoAJh21JXLZZRKJXQ6HXm/5eVlMWwnk0ksLy/j0qVLqNVq8hr0B3W7XaytrWF+fh4HBweIxWLodDpirtZT3Knw8OfBYHBsAw5dLtcYwEuYXlduAHi/4zi1W7pRBoPB8AY4VsoPO5q4iOpho1xIuThxgecwyqMkY3FxEYlEAo7j4J577pl5HyoCbrcbiURCFBqSKZIYABLEl0wmkU6nEYlEEAwGkUwmhUzQLMvFkeUnvaiTJLBjq9frod1uS8v6/Pw8wuGwKBjsKorFYhiNRuj3+zNGZKosLHfduHED169flxINF32SskqlgnQ6LQrMZDJBuVzGcDhEOByWEESaxBmASFJDskU1iuUrjtxguzsJA6ez61IXCRFb9/WoEioq/Hu32xWlh+e3XC5LqSoWi2Fubk462Th+g9vi9XqRTqeFOH3pS1/CQw89hIWFBYlAWFhYwNbWFnw+H5LJpJTPeN6oagGQMESeZ342Y7EYms3mt/mb8R1D13GcCwDgcrl+G8D/BuD/c0u3yGAwGN4AZng2GAzfDnwBwNI3fJTBYDDcAhwr5afT6SCRSMhdNdupOSvrqJGW7cdsd+fPXq8X8Xhc5oEdxeXLl7G3tyd+D6/Xi1arhVqtJm3u9OMsLS0hm83CcRw0m01RfTjmgWZcGmF7vR46nY6UfgDIwFAarPm+VJHm5+eRy+Xk+QBkrAVNzQBEAZpMJjN5P7VabcYIzGOXTCYxHA5Rq9XEn+Pz+QAHmGAix5EGcs4EY3QAR15o/xFNzbFYTM4Hfx8MBqU8R5Wr1+vJNlMV437yXAGQY6Wzg+hlGgwGuHHjBjqdDvL5PJaWlhAOh+H1esU/VSwWEQqFEIvFEA6HkUwm8cADD0iUQLvdxo0bN1Cv18VcnUqlpA0/kUiI8ZxlPH7+WNbkflBtYqjlcYPL5fIA+B4Av3Grt8VgMBjeCMeK/ACYyZbRZRWWimhy1iUxPo+lFpqlw+EwFhcXcf/998+8R7vdRrPZRCQSQSQSEdNyNpuVRc3n8yGTyWBubk4WdRKYbrcr2TucMdXr9dBsNsXrwwnkwHRh59ytbrcr28YFnwGEXGh12Y/lM5aHAEj44Hg8Rq/Xw+7uLg4ODuT1uCgzm4flOU6EJ7lgQjM76rxer5SP+J4kVDQTk8w0Go2ZBGe+NktQk8lEMorG47EEMJJUcD9IXnU+0Hg8Rq1WQ7FYRLVahd/vFyM4zeHaVO04DuLxONrtNhzHwfr6OkKhkARZktzW63UhSBzISv9WqVSSSe7cRm4nS3qcJk/zM7v3jhFCLpfrBUwVn1cB/Nmt3RyDwWB4Yxw78sM7fpqcOe0bgCw0XJS4UHIRpsG43W4LAZmfn3/de7z44ouSIp1OpxGLxeDz+VAul1Eul1GpVOD3+xGNRpFKpQBAvEVMaKYniGoOlZ9SqSTbRkJGcqOJBD01HGZKEgEcGpF11xg7sEg+jpqLAUhrNr069L9woGir1ZoqRuMRfK6pz4WDVPk8tumzDZ4qFomZJlh6+Cj/rhUwABIT0G63MR6PJViRviQeT/qIOMKDQYanTp3C0tIS8vm8kDEmdtO0PplMUKvVRJFrt9tyLkejEcLhMAaDAYrFIm7evCnk6Lu+67vwhS98Ad1uF/l8HtevX58xZrOTUAdput1uiTQgYT1G6DqOc8HlcoUBfBJTz0Z0TaoAAF8QSURBVM+HbvE2GQwGw+tgnh+DwfB3CsdxOgD+FYB/43K5jhW7MxgMxwN/kwtT2OVybauf/w/Hcf6Pb9cG/W1BdUePFTjafaOVEHp92BrN8kQmk5EcHI39/X1Uq1UZWhkKhWTelc/nE19KPB7H2toa0un0zOiMRCKB5eVlKb+wO2k0GiGdTiOdTotixb+zs4llOd1yTsWEbfUsibEdnQqPbj2nEtZqtdBsNlGr1WRAK9UVKmJ8LL1BHG7qYHp8k8kk8vm8lNDC4TDy+bzk4LDkyHweeod01o2e/0VFjBlN7NgqlUrY2tpCuVyWzjzdSu73+8WjlcvlcPfdd2NtbU3UHn4GHMdBq9VCpVJBvV4Xtazf76NYLOKLX/wi2u02zp8/L0Nf2+02qtUqisUiSqUSut2ulCBjsRguXrwoniUeO/p6AMj54HgTfl50N9hxg+M4X3G5XC8C+BEA//VWb4/BYDBofEPy4zjOHaUO6RIOfSckOGyl1l4ZmpxphE4kEjLxezweS9mKYDmEScYej0daq3d3d7G/v49AIID5+XkhCpFIBH6/H5FIBJlMRhZ2LpJzc3OSlPxGGTf009AMTL8KgxndbjcikQgAyPBRmqRJEPiaetI7J8KzRMNSTLPZhM/nQzabFZM037NcLmM8mm4bU6tZZkylUpKVQ9LW6XTEQEyDtd/vFyMyW9m5PzRik7wyBdpxHGQyGZnRxrwmlsn6/b5MWc9kMsjn8+KZarVaaLVaKJfLaLfbM/PEDg4OxKgei8VQq9Xw6quvwu/3SymPxG1/fx8HBwczc8jy+byUMbn9LEXq88hUa247z8NxKnsdzQNzHOd9t2pbDAaD4evh+Fx5X4NWd6h2kNj4fD4AkAWWd90kETQLr6ysIJPJYHl5GW9605tmXv/5559HrVabUXzYicQOIKoxXq9XcmGoNHk8HplGTkJTqVQQiURmjMUkOQzHazabaLfbQnTC4TB8Ph9arZYsvACkA0qTPN1RxQBAvTCTQPj9/pkFnObxaDQq3WCtVgvjyRiuiUsISqPRgM/nQzweF2M5VTT6gBi4mEgk4PF4JNuG4zhobOa5I+ELh8MyKsPtdks3WbfbRbvdFnLEJO1oNCpT6pvNJhqNhuT70NPE/ahUKnAcB4lEQnJ6aG6+dOmSDIdlflS73cbzzz+PM2fO4J3vfCdWVlaQSCSQTqfRbDaRTCYl/ZufRU1Q9YgTdhtyGj3DKg0Gg8Hw7ccdpeoYDAaDwWAw/G1x7JQfzuCiz4a5Kvy3HijJLByqRSyvuFwuRKNRrK6uvu71Dw4OpGMqGo1KyavX60m6MbN1qtUqxuOxzLaiwkJFg+UXqhkHBwcolUrSJk8/CgBRYFi60l1TAGY8TFQX+v0+2u22lJ3a7TYqlQqAw3wejspIJpPo9Xoyr4ut9SwNZrNZuFwuZDIZXPJdgtfrRSqVkiGuWi3ifrJtXbff66wbx3HQ7XblnHCG12AwkOGsTE/O5XIyI20wGMi4EapMfF2WLUulEm7evInd3V00Go2ZtnsAco5PnTqF9fV1aa1nonOhUMDCwgLe9KY3oV6vo1gsolKpoFariYIIAPl8HplMRs7r0URx/W+eP/qjmP9znEpfBoPBcCfgWF51OT+K4wQAyGKrs11oSm21WlLu8fl8mJubk+yZo9jf35+ZixWNRhEMBhGPx5HP51EsFvGZz3wG1WpVWuGZUxMMBhGNRtHpdHBwcCClq1qtJv4X5uqwVOLz+RCNRhGNRmUbuXjSe0JyR/M0ACn5BYNBaQV3uVxIpVLo9XooFAoywDQWi6HRaEhpiySKZmUGObJ05nK7pHTDPByOhOBx46LPNm+2vfNnbQgmGdTBgPF4XEgDvTLD4VDIFvfV5/MhEAggGo3OtLCzjTwajcogUz1w9dSpUxJSSELI89rr9aRlnqbnvb09IWM89xzSWqlUpKRGUzqPAcuJjAzQZVl6uI7rrC+DwWC4XXEsyQ8XXpID7YdhhxEAUVE4fZy/X1paksC7oyBBGI1GiMfjAIB4PI7FxUWMx2OUSiWUSiXcuHEDk8kEn/3sZ8VXwoXa7/cjlUohmUwiHA4jEolgPB6jUChga2sLtVoNHo8HCwsLOH36NObm5qT7i8QgGo3OzP7if1Q3qCqQZFBtqdfruHLligxgZccXVSGaddPpNILBoCz0zOARpWkyxpUrVzAcDiW8kb4fbb4mgWLuErdJkz2ai/kcAEIkqCQFAoHXpT+TaPD3JDcAZIgqp7Dz9bvdrpwDYGpGT6fTQpqo4IxGI+zu7kpQ5Y0bN9BsNnHz5k1cunQJ9957LyaTCcLhMLLZLHq9nqhpnU5nZkaZ/hxx+5i5xFBMg8FgMHzncCzJD4kPlR6WPHTXFLuO+FgGCi4sLODMmTNYW1vDW9/61pnXffHFF7G3t4d6vS6BeOzaOTg4QKfTQbvdRiQSkc4olmwSiQTi8Ti63S729vZw5coVMexS5RgOh7JINxoNDIdDbGxsSHv8+vq6DOMkASC5YflOBwkCkNbx8XiM3d1dbG1tiRpB03CtVkO73RbyFIvFEI1GEY/HpZOJHWLAlGy54JIwQZq3qd5QTavX6zJ8FJgGOg4GA7TbbRnRoUd4UKljazi73/g3looACFFtt9tS1qLC0mq1ZKgru82o2lCpo1oUjUZFmWq32zMBkO12G91uF5FIREqpu7u7ePnll/Hd3/3dWF9fx/nz53Hjxg3psuPwVpZYWe4EMKP0kIi/kbpoMBgMhm8vzPBsMBgMBoPhROFYKj9sL6Z/hUbcTqcjnhn9WJqjHcfBwsKC+IX00MlWq4VnnnkGN27cEGWHd/9ra2tYWlrC1tYW9vf3xZR7//334+GHH0YikYDL5UI8HhdFo1Qq4aWXXsIrr7yCer2OVCqF++67D2fOnEEsFpNMnO3tbVy/fh03b95EvV7H+vo6zp49KwbgVqslChe32eVyySiKaDQKx3Gws7OD7e1tybopl8vo9/sIh8PI5XI4ffo0fD6f5AyxvZxKDtvqaQqfOBMUi0VRN5ip43a7pZwzHA7RbDbRbDaxuLiI1dVVUds4QoOjOXRJrdfriTLDEqbf75dSkW5J397eFi8QQxSj0agMZG00Guh0OvB4PKL6sHSYTqfFR8X97nQ6cuwYyNjtdtHtdtFsNqXtv1wuS4s7PVicAwZAZqBp/xRVILbO8xgds/leBoPBcNvjWJIflke4sLBMpLunaK5lLg5zeXRiMD0qL7zwAj784Q/j1VdflZKX3+9HuVzGmTNnxPfBXJlarSZhe8FgEIlEQrJnSDjo91lcXAQApNNpyQgaDocIhUJIp9PIZDI4e/Ys2u22lNZ2d3fFYB2JRIT86DldNCJ3u10pBbVaLezt7eHg4EAGeK6trYkxVw/y1HPDhsMhWq0WCoXCtNOtNzVPM+maZSU9j2s8Hgs5WlpakgwgAGLC1oNlSaBYkhwMBgiFQohGo1KW4zw1Di7d2tpCsVjEYDCQsmUqlRJCxWnywDRfKJfLSSccu8K0D4lz4Og1arVaODg4kFIb/U/xeBwrKyuIx+MIh8OYn5+Hx+NBJBJBLpdDvV4XkqhnywEQs7ce5Mpuu1qt9u36ShgMBoNB4ViSn06nI4stk3bZPUXVgb4ULkyO46DT6QhRCYVC4lW5du0azp8/j4sXL0qXFk3KXHRrtRquXr2KarUq3plwOCzBe3Nzc+LzSKVSsiiyDZ5KAB+j4ff7EQ6HJVgxk8mI5wiA+G608sOFNRqNiiISDAYRCoWQyWSEgOnuN/qfSAJ57Px+P5LJpByz697romT0+30MBgOZ0s4xE8PhENlsVkZdAJA2dh4bAHIMud8c5EqfEaHTlre3t7G3tyev1el0pHuqUCiIb+rg4ECMz0zTTiaToobx+LhcrplgQp6bWCyGZDIJn88n5LjdboufKZ/Pw+v1Ym5u7nWT6nUXHM+rJjw03pN8H9cxFwaDwXA7wvW36TRxfdp127apeDweOHAA57U2dzhwYfp/t9sNF6bqBFzTv7tdbkycCdLpNHLZHHz+ab4MMF1EB/0BSqUSGs0GPG4P3B43UqkUYrEYYrEYvB4vms0mKtWKZOcwtTkQCCDgD8DBa632rumiNxgOZFSEy+2CC6/lETmHJliWg6imTMbTkok/4IfHfTjHbDKZYOJMXxsuYDKeAK7XjsPEQa/fQ6fTkSRlj9sDr88Lr+dQieDz3e7paziOA2fyGjmEg/FoSiK693QBB/C8fEjSXJiqJy63C85kWhbz+Xzw+/zweD1yHPg3PWuLn0G32z09D3pfMO0s49+Z6DzoDzBxJpiMJzPnkY8DIGM2HMeBz+uDz+8TczPN7ySaw+EQ/V4fw9EQ49EYE2eqSoVCIbhdbjSaDTlXuXwOy0vLCIWnpb5mo4md3R2MR2MMR0N0O11MnInsm8vlwvSjOP086m2Vz6fKbroj8ALg/GvHdas3w2AwGL4VHEvlB8B0gXmNTMAFYALADbjhloXI5T7MyJk4E1l8gdfCA19bqHK53JRA9HqoN+rT/JzQdGyDzzstr/W6PQyGA7jggs87bZuXQaKTqao0HE1LMF7Pa2U2j/cwc2i6wXAmU5IxGo6mi76jiJELsoCT0OE1+un2uOFyXDNkgsTJ4/FIJ9RgMMBoOMJoMh1dMXFPCYSEQboOyQa3fzwZS8u5HCPXVI3RIYv///beNEjSs7wSPbnv+15Ze1f1pu5WS5aEmBGWwDAzGFsork3gYTzjIOy54yB8wx6Hfa8dzGXmx8R1hGP+MBD+NcSEHWNsILAZvCgwIEsWAolGUqvVe3VX15aZlfv2ZX65Vt4fxXnqzZKwjVAbSfUeoqO7qrK+/L73+9B78nnOc85wMATG+2vG0XcLLHKNw+EQVov1YOOHQpq+fz3j8RjjvX3CMrF+vyWF75OqyQT9Xh/93kF7z2bf9zKC5fvX+31yNR6NYbfZ4XQ45f4KGeH/vn9v6CE03hvLOlgm32+L7k2wZ92DzWrDxDaR62g0G+j3+3B79jVEbpcbA8tg6j0mmMBusws5s2J/3Ugs+WzuTfYOnlUNDQ0NjbuOH63yY3nrVn6CwaB86lYdhNnaoSCVRII6mw9+8IN473vfC4vFgg9/+MNS/QGAz372s/i93/s9LC8vYzQa4WMf+xgSiQSKxaKMVX/ve99Dv9+XllI0GoXNZpPwTL5PMpkU3QkrFcA+iajVarh9+7YcN5vNIhaLIRgMirCYuV+qozS9iigk5ig9f6dUKmFtbQ3r6+solUriR8OWEEXiwL4GKZFITAWuMjH+8mcvAwDO/l9n4fF44PF40O12UalUYBiGiLtVj6BYLIZEIiFtJJIM3hNaEbDlxdF9OjfbbDZZl0qlIqaCgUBA1oXj6t1uV66J7UJaCQBAOBwW76Td3V2USiU5b7bb+OwsLCzA7/fj9u3b2N7eRqfTwcLCAt7//vfD6/Xi8ccfRyqVwpNPPomLFy9iZ2cHm5ubkv7OdWXri3leFFqr5PHtpvmZTHTlR0ND4+2Jd2zl53Dsg+oPw41W9WJh+6NcLqNeryOVSr0mdoBCYNM0xdWZE0Qejwfr6+vinlwsFsX0bjweI5fLodVqAdh3hd7c3ES/30cikcCxY8eQTCYRjUZFtzIYDCSdnESJYmAA0v6hWHs8HqPZbIquxeFwSLVnMplIuysWiwnpu337NiwWCwKBgPjUqNNxw+FQrq/ZbIqrM6tjrPwYhgHTNMWdmTEVAMS7iCaCXq9XfHzUZPtAICDXxLYUY0SoPyqVSuLezOuiHxHvF8XGXBMSDZIfr9eLUCiEfr+PXC6HjY0NcfjmJBgASbsngSIh4v3zer34yEc+gjNnzmBtbU30PPRD4uQcny96GHEqja9VIzE0NDQ0NP5poH1+NDQ0NDQ0NI4U3rGVH7ZUGHtAzQr/cBSaI/H0p1lfX5ex6m63C7/fL8cslUpT3i2pVAonTpzYF0J/f9KHf3q9HsrlMi5duiQVA0452e12aSu5XC74fD5kMhmpKoXDYXg8HtRqNZRKJQyHQ1QqFfj9fkQiEQD7Aa6GYcDj8WB2dhZ+vx/VanUq14qj9Yxe4OtYpQiHwxKrUa1W4Xa7peKUSqUQi8XkWnq9HgzDQKPRkMk5andCoRDC4TCq1Sq63S5GoxGKxSK63S4AiG/Q2bNnMTc3BwBT7Syn04lutwvTNCVQtVwui1u2y+VCs9mUcX5O6Yn26fuVLYfDIRNanLSiVxMrObyvXCtWezg11u12xYfH7/fD4/FMVaDS6TRWVlbwgQ98AGfOnJH7vL6+LsGvly9flmNSz0T9FbPNWH3ks/q2EjtraGhovM3xjiU/rVZL2kj0cyEJ4RQPx8NVzQ0jDlwuF2q1GpLJpPzsxIkTcLvdYpx37do1mKaJSCSCVCqFWq0meU/AfgjqaLQvLOa4+T333CNmf2zJWCwWtNtt2ZwdDgei0SisVisMw0CxWJTxbJ/PB7/fL1ofh8MhbRSGe7I9B2CqHcOE+bm5OTidTrRaLYxGI0QiEaTTaRSLRVSrVUkut1qt8Pl8MmrPpHZOrXHDbjabor+x2Wxot9viDQQA8Xgc6XRafJZUQkICw1F/tsc4hs62EGMsVEITCoUQjUYRCATg8Xhk5J+6JabDAwc+Qmwfsm2mGg8yzJR2B5FIBD6fT3yGWq0WfD4fut2uhLzyOdrY2IDP50OxWITVahXizMk/1TeK5JTnxpZsMBiU1qiGhoaGxt3DO5b8AAejzqruhxsfRbX8OfUspmmi0WiICZ8KimpZrThx4gTm5+dRKpXg8/lw5swZfO1rX8Pu7q74DLH6wapENBqVQNTBYIBAIACv1wvTNKXaEIlEpIpgGIakgft8PgyHQ6mIUBzM66NTMa+NAt9AIIDBYCBuxD6fD9lsFhaLRQgaCYXFYhFnY543J9ecTqd471gsFhFUM8+Kr2X1J5PJYH5+HpFIBDabDbFYDKFQaMr3h9fAIFmn0ynv1+v15NqpWWIe12g0gt/vh8/nQzgcFlJKPVOlUsH29rboebxeLyKRiPgYud1udDodWCwW+Hw+MTUkYaIZJSs/9B8iyUulUnA6najVarh165YQt1wuJ+vPDDOev2qiOR6P5frpWq2ScA0NDQ2Nu4d3NPkBIGJXABI86XA40Ov1xGl4PB6LQDUYDKJUKomD8OnTp+VYjz76KJLJJHZ2dhAIBFCr1TA7O4uZmRmZHrv//vuxs7ODO3fuoNlsSuWJDs8M++Qm3263MRgM4HK5xJOG50hRNSM51HNlFYXGghQKcyNnm4aRDWqkRKVSQalUQqVSQafTmUpM59RTr9fDYDAQAkDCCGDfC8i2TxYZIUGiQndlGkEOh0MxBGQ4KAXBrHhZLBaEQiGYpintLZfLte+fZLejWq1iOBwKeQH23ZaZID+ZTISwApD4CZ6vYRhCQoPBINLptLTT6vW6iLUzmQz8fj96vZ6QUk6mMYGeovFsNgvgIEQ3HA5LJdHhcAhp4++oQbuqvxDBqTsNDQ0NjbsP/VFTQ0NDQ0ND40jhHV35Ud2RB4OB6GP6/b5UfQDIp3u2vtrtNkajEQqFAgzDENEzdSA3b97EaDTC2toaTp48CcMwsLm5KRWIwWCAZrMJh8OBZDIJr9eLbreLUqkkVZl4PC5VGmY7hUIh0e1QVEy9C4XLHCv3+XxIpVISsspqCjU+rDbwWC6XCx6PB6VSCevr69jd3ZU2U7lcxp07d9Dv96Vt5Pf7kU6nRaBNcTM1Sox/iEajME0T+XwerVYLHo8HhmHISDm1R61WCzdv3kQwGEQ8HhcNDNtFpVJJAkcnk4m0AAHIWqj5Xh6PR6opwL6YfHd3F7VaTapdvV4PXq8XmUxG7ns+n8dgMEA8HpfcNZvNhnq9Lu3DyWSCSCSC2dlZeZZYqQKA+++/X7Rg/X5/agSfYaWs4qgVPba8+Nzx3jOG5bC1goaGhobG3cE7+r+2JAPUuahZVcxYYltMTd+mCaDf78edO3dw9uxZOWY2m5WWFZPOXS4XUqmUZIZ973vfQzabFU8XEjASGAp6ma/ldn/fLdrhgGmaEghaq9Wws7Mj/jdbW1tyLSRpNOFzuVxTmhJqkxh2SoE187BM00S5XEatVhPNj9pqYxwFsC8C53rRqdpu2390eOz5+XlkMhkhVfV6HbVaTUgQxdCc4pqfnxfyRzJA4kYCxkk9CoNDoZBomer1uoSxUjfTarUwHo/RbrfR6XRkoo4tTTUrzGazyXXy+ySRrVYL5XIZpmkim82iVquJOSV1SwCQz+fx9NNPI5fLSXI89TvUCJGEkohzupDBrWxn8lnV0NDQ0Lj7eEeTH+pPWPnhaDU3TGpbnE6nCGNZ/eEGdth1d2lpCcC+dmRjYwP/6l/9KywuLsLtdqPb7aJWqyEcDouDssPhwMrKCvx+P9bX16XyQJdiYJ9QDYdD1Go1uFwudDod3LlzB5VKBb1eD4VCAVtbW/D5fDh9+jTi8biMa/d6PaRSKczOzopzNafLqGuiyaDVakUsFsPMzIxcn6oJohA6GAyKOR8JFafluDYAxLCw3W6j2dyP/Zibm8Pi4qKYEvLcWenJZDIIh8PodDoiROaIPe+Rx+ORsf/xeCwkslKpyCQZSRErLDw/6o7C4TCi0aj8PZlMUK1WhcgMh0O5bo/HMzVtxok5wzCwvb2Nra0t0VABEFKYTqdx7NgxPP/885I+T7JJwrO3tyfHPHx8ipxpxqgrPxoaGhr/NNCaHw0NDQ0NDY0jhXf0R81ut4tAIAAA0npi3AI/kfP7jKhgNWhvbw/hcFimhIiHH34Yn/nMZ9BqtVAsFrGzs4MHHngAAFCpVCSL69q1a9I6KhaL2Nvbkwmh7e1t0XiUy2Xs7e3J+DsrF2yZXLlyBcViEUtLS1hZWUEoFBLNzMbGBjY2NtBsNgEAy8vLcDgc0oJhvhgrFmzBORwOOBwOhEIhZLNZ5HI51Go19Pt93Lx5E9lsVrQ6k8lE2nLNZlMsAFhlGo1GiMViiMfjGI1G4odjsViwurqKY8eOYXt7G+vr63J9avWDlRtWQTgarvr11Ot1FAoFlMtltFotaWvxHgIQrx8Aom8Kh8NIJpMyop9MJmVMnvEXg8EA1WoVpmnKuL/T6ZR2Gd/H7Xaj3+9jcXERZ86cAbA/oZXJZOB0OtHpdCReZDgcIhQKyci/OsnFa1a/x8kvml/WarU34/HX0NDQ0PgBeEeTHwBTotjhcCjmgNxwJpPJVEuM4mDmd7Xb7anj0SMmGo3CMAzcd9998jMSqXg8jmPHjolpYCAQgN/vRzgchsvlQq/Xw9ramrSVGFBKkz6LxYKNjQ1cvHgRFosFp06dwqlTp+D3+xEKhRCLxTAejxGNRtFoNGAYBgzDkJYX23wApJVHD6DxeIxgMIh6vY5GoyEePABQq9VgsViws7OD8XiMVCoFv98Pq9UqJLLb7SJvzQMTiMiX7TWbzSaGj81mE5VKBePxGKFQCMlkEuVyWXK5aGzI1huJGXVRLpcLFosFhmGgWq2iXC6jVCqJx9Hi4qJcJ6/L5XKJ/olBqiSR1FPRvZltMxpEqoLyeDyOaDS6f635PAqFguiurFYrtre3MTMzA2A/C041S6Rzc7vdnhKcc4157RRA22w2yZtT23gaGhoaGncPR4b8qNUG6kNIPiiupYfMZDLBzZs3cfbs2SmHZ2B/0uef//N/josXL0ooJ3CQxh6JRPDTP/3TcLlcePLJJ7G7uytp4yQIq6ursFqtWFtbw40bN3Dr1i0Eg0FEo1HR+BiGAbfbjbm5OTmHUCiEdDot+hJu3sA+KSkWixIaqpI7GgiSAFSrVQkTbTQacDqdOHbsGMLhMHK5HKxWK/x+P3K5nEQ8ABDPntFoBLvNLkRye3sbAER0HY/Hkc1m4fV6kcvlsLu7i0ajMZVyT9M/u90+JagmWSExME1TfIna7TYCgYD49Hg8HtFrUTRMshcMBoXMtlotNJtN8f6JRqMIhULo9XqiIyJY5eH6LC8vS1TH0tISHA6HVNqAfRdvVod2d3cBHBAcThgCkEBaVhl5vaygqeG6GhoaGhp3F1rzo6GhoaGhoXGk8I6v/LDqw2oI2w1qsClHvU3TRCwWQ6fTwc2bN9Hr9VAsFnHp0iWcO3dOjnny5Ek899xzyGQyeOGFFzA/Py+ZVoVCQfQ4Xq8XDz74oIyRc2Q7GAzi3LlzSKfT0lZptVoy3eXz+fDP/tk/w+zsrEQ/8Fw5mTQcDmGaJvr9vmSA3blzR/RDMzMzCIfD4tPDdlg+n8fm5qbkbnE9CoUCdnZ2EAwG0e12YbVacfbsWYRCIXGA5oj5oD/AAANcuHABw+EQyWQSsVhM4kJoL6CO2XP6iVNTqg0B2360HmCOWbvdFh8djuTH43GEw2H4fD75EwwG0el0xJWZ97bX6yGXy8m6mqYJwzDQbreRTCZlrJ85Z/QnikQi8Pv9cDqdUvWJRqOw2+0Ih8NiG2AYBra2tjAYDOB0OmUyzuPxyH3hs8frZ0WIFR8AMunFtdPQ0NDQuLt4x5MfNUuJGxE1QACmNiLGTdhsNlSrVfGKqVQqU8d85JFH8LnPfQ5OpxNPPvkkzp07h2w2i8XFRfj9fozHY1SrVTz//PMol8s4fvw4YrGYhGF2Oh3Rn6TTadhsNly6dAmGYWBpaQn33XcfEokERqMRwuGw6G6YeA7sa3mCwaDEQhSLRfT7fUQiESQSCUSjURlxp8D7xo0buHjxogSzFgoF0dCoJpBer1eCSNvtNra2trC7uytiXpvdJhqbhx9+GMFgEL1eT9Y1FAqJyJitHLYHaTPg8/nkvEga6PnjdDolhHRvbw/Hjh1Dr9eT1PXNzU3EYjFEIhEhe8FgcKrd12g0kMvlJBqj2WyiVquJeSLvgTpezrViZAa9lSiwPn36NMbjsWh6aFvAdWHwK4X0JNn0GeK4P0kOSSGT31UfIg0NDQ2Nu4d3PPkBMBUYyU/bdA2mu67L5RJTPW5Mm5ubWFpaes2ncbfbjXQ6jc3NTWxubuKb3/wmMpmMVA7i8TgWFxfxUz/1U/jbv/1bbG1tYTQaCSngVA9diXd2dlCv15HNZnH27FmpLmSzWalyAJDN0W63IxQKYW5uDoFAAKPRaN98cDiEz+cT4kIht8VigWmamEwmCIVCKBQKWFtbg8vlksBVbtjc3GmiSOKn5l5dxVW4XW48+uijGA6HaLVayGQyElrq9/sxHA5RrVZF/8JgWYaRct0tFgucTie63a7ooqglslqtiEQimJ+fx2QyQT6fF9ExfYJ4HCa87+3toV6vo1QqCflaW1vDaDSCz+cTA0nqd0ajEVwuF/r9PobDIWKxmKx1r9fDnTt3MJlMEI1GEYvF8IEPfACPPPIIAIiAm7oiElpV4MxpOxIzCrz5rFE3plaEdLq7hoaGxt3FO578tFot+P3+qQgBCp7p7MxNHYC0XkajEer1OtLp9NSneADw+/1YWlrC7u4uRqMRrl+/LhtnJBKRcfVUKoWVlRWsra3JqDZN/UzTxNbWFvL5vJgPLi4uwuv1ot/vIxAIIBgMSvgmA0dZmQiFQvB4PKjVauLabLfbEYlEABwEbpqmCdM04fF4EIlEUCgU0O/34fP5sLCwgOXlZXi9XgCQqSrDMLC7uyvtqVQqhfn5eWmbMeohn88jGo1iaWkJsVhMAlZLpRLK5bKEljocDmm/ZTIZiZYADiIeOH7f7XbRarVEEMxWWiQSgcPhEPdlRllQEE6BtMViQbfbRb/fR7fbxe3bt9FsNsWwktUXhtN6PB655tOnT4sdASM7BoMBDMNAMBjE7OystOPcbjdKpRLq9bqYMpLIcXqNhI/xFiRYFHXzOVQrRHwGNTQ0NDTuHrTgWUNDQ0NDQ+NI4R1f+QEgFQGKbCm0ZbaSaZqiBWK7ZTKZ4OrVq1hfX8eJEyfQarUQDocBAIuLi/jFX/xFfOc734HT6cSzzz47ZYJ37733AgA2NzfR7/dht9uxtbWFa9euwTAMEQJTXGu1WpHNZpFKpRAKhRAMBpFOp+FwOLC1tYVcLieBqcFgEJlMRqIlIpGIxDfwuhhdQQ0LoxVo2OhyuRAMBmG328Wo0Gq1IhQKiZ5FDUalTxHPmTqVwy2sZrOJZrOJbrcrLRwGunY6HcRiMYTDYakcsdrDYFXqj3jMTqcjY/q8b6qmimLkarWKbDYrOiFmt+VyOdTrddEwsXXl9XrRaDSwubmJ3d1dpFIpnDp1CtFoVDyBLBaLVNW8Xi/8fr9YIwQCARFT12o1dDodMavkCL/H45F4DoaXsiXGahc1UvQIstvtIsrX0NDQ0Lh7OBLkB4A4AbN15fV6pdVFg0HgQKMxGAzQarVQKBQwMzODp59+Gk888YQcb3FxEQ8++CAuXLiAVquFfr+P8+fPY2ZmBjs7O2g2m8hkMnj3u9+Nvb09uFwuOJ1OfPe730Wj0ZCATGaHsWXEtHSGdu7t7aFarWJzcxNWqxW1Wg25XA7RaBSpVArtdltIi6o18Xg8kibf7XYRi8Xgdrvh9/sxNzeHTqcDl8sFt9uNmZkZhEIhca2mKNtmsyEUCiGRSMDlcklyOwkW9UAUL/f7fZimKVqjwWCAWq2GbrcLn8+HWCyGZDIp7snUvNRqNdRqNdFaMeyzXq+jXq/LVJvb7UYgEEC9XhezRNUUMRwOIxgMitOy3+/HqVOnRChOMscWWTabRSKRQLPZhGmaaDabkmhPAkdfn0QigXe/+934l//yXwLYd/O+du0aBoMBfD7flFcQ3ZtJepxOp0y5kVjbbLYpp2wSV56bhoaGhsbdw5EgP4xOIJngKDIJT7vdhs/ng81mE12Q0+lEvV7Hd77zHczOziKbzaJarYogdn5+Ho8//jiee+45AMD169fx/ve/HxaLRbQ6q6urAIDd3V2sra2h3W4jHA4jk8kgkUgI+dnd3RXDvclkIpoeipSPHz8On8+Hl156CRsbGwgEAlJlyWazojHiBhwOh4Vceb1eeDweTCYTeL1eZLNZtNttuU66TtOA0O/3S3gop7JcLhfK5bIEgh4eT2+1WnL8SCQi01GsDDUaDYxGIyFsXq9XDA0Nw8BgMEC324VhGGi1WkKOSHAMwxBTRwqCFxYWpHrFUf5SqQRgX5N15swZLCwsiIiagawcnWdVptfrIRaLYWlpScJpe72e6IooTn/Pe96Dxx57THQ5jUZDNE2dTkc0WRR3q7EWjBvheQJAv98HACFCHJ8HoF2eNTQ0NO4ytOZHQ0NDQ0ND40jhSFR+qLMAINEKAGSaifEW1F1wXNtisWBtbU0iDyqVilR+gH2zw+PHj2NtbQ03b97EZz/7WTzxxBP48Ic/jKWlJQDArVu3cPv2bWxtbWE8HiMej8NqtcLtdiMcDmN1dRW7u7v49re/jVqtJpEVKysrSCQSGI/H8Pl8MpF16dIl7O7uwjRNmQJLJBLS4mq1Wtjc3EQqlcLS0hJSqRScTqeYAbKlks1mpTIDQCpjtVoNjUZDqhjj8RiJREJaRw6HAxZYMBqNsLW1hW63K9+PxWKIRqMyzq22tpxOJ2ZmZsTAENi3HWg0GtLm4ho1Gg2Ew2FYLBY0m03U63XY7Xa5HrUyBEDMH6n1sdlsMk3GKAnTNNFqtaRlRh0Rq4Iulwter1eyworFIgKBAIrFIn72Z38WH/zgBzE7Oyv33jAMaTVubm7KJBn1T5wkZCWILS1qoVht6/f7oskCMBWZoqGhoaFxd3AkyI+6mag5StSXEMz7ot7H7XajWq2Ki7Oa87W2toa//uu/RjweF7Eu21s3btyA2+2WfK6PfvSjmJubw9e//nXUajXxeen3+zAMA+FwGA8++CCuX7+OVquF3d1d9Pt9rKysYHZ2Vlo/gUAAJ0+ehMvlEoPCmzdv4saNGzL6ro6HO51OaXUxobzf7yMUCom3T7/fx2AwQKPRQD6fR7vdlpaQ2+0WokFLgPn5ebzqehU9sydaJjpOV6tVGf32er0YDodwu91YWVkRvY7H4xEyYhgGyuWy6IuoxykWiygWi3ItLpcLiUQC6XR6ysyQHkjBYHBqxN3pdMrYPp2d2XpSQ155rufPnxdDxXg8jnw+j1u3bsHlciEQCCCRSGBxcVGIzfXr1/HMM8+Ir1Oz2RQhNvVOFIVTBM2gVuAgb45kGzjwcOLzGggEXhOqq6GhoaHx5uBIkB9WEnq9nrjqstJDkS3NDwk14f2ZZ57BAw88gEAgIJ/+V1dXxexwfn4em5ubKJfLqNVqeOaZZ1Cr1bC8vAy73S76n3K5jGeffVaEtZ1OBwBEK3Pvvffixo0b2NnZkXMbDoc4duwY/H6/TDvNzs6iWq0il8vhxo0bojfJZrPiGD0ejyWJncGbk8kE3W4Xk8lEAkG3trZkYomuxxT60hMHgBCgTqcjVY2ZmRkJKiWJ4jlTCBwKheB2u0V3M5lMxECQE1/NZlP8g1ZXVxGJRCTF3Wq1wul0CjlhpQmAJMqbpik6Kxo6UtNEnZJqFGmxWGQSbmFhAX6/H/1+H/1+H1tbW3jllVcA7IfFnjx5Ev/6X/9rxONxABDnavolVSoVVCoVdDodmRKjfodrrsZbMEqFpJtElc8chdC6+qOhoaFx93AkyA8AcS5W86S40QCQT+AUpFKoarFYUCqV8Pzzz8tmdvbsWQDAiRMn0Gw2ce7cObz00ksIBAK4cOGCiHCB/fbSZDJBPB7HY489hnK5jC9/+cvo9XrIZDJSYWHOVDKZxOXLl3Ht2jW0223cunULo9EIq6uriMVi2Nvbg2ma8Hq9iMVi8Hg8kjjOtHjGYfh8PszNzSEUCmE0GqFYLKJer8PpdKJWq+H69evI5/PSEmT1wev1iskiKzXcmD0eDxx2B0aWkVRRotEonE4nOp0O2u22ECePxyPX5Ha7MRgMMBwO4fF4ZBR8OByiUChgPB5jcXERc3Nzcm2BQADXr19Hp9NBs9nEwsICvF4vCoWCnFen05HqVjabRSgUEmEz20put1uE2czQ6na7SCaTWFxclEm7ZrOJSqWCRqMhUR+PPPIIHn74YQD7wvVnn30WTz31lFR9SqWSVJVYVeTEFis8k8kEpmlKi5UkSH3eAAghJznX0NDQ0Lg70IJnDQ0NDQ0NjSOFI1P5ocCUbQdWIdgqYXtCTdqmMWAoFMILL7yA+++/H8ViUSo/P/VTP4V8Po9Tp07BYrHgT//0T9Hr9ZDP59Hv9+HxeLC6uopwOCxRCY899hjC4TC++tWvYnt7W2IY6Dljs9lw//33Y25uDt/61rdkfLvVauH48eNYWFgAAMnDajQasNvtkpvl9/vh8Xjgcrkk1qPZbEr1JhaLSZUDAAKBgIy7szKktri4Fo1GQ9pZE0wkroFp6ul0GouLizJG7/P54PF45HwY7cAoDq/XKxEjyWQSkUgEkUhEKig2mw3z8/Nwu924ffs2DMPAq6++ilgshlQqJf4/o9FINEe0EbBYLDAMQ4TqLpcLpmkC2G8z9Xo9LC8v4+TJk4hEIiK4rlar2NnZkYyuTCaDBx54QJ6hdDqNpaUlzM3NYWtrSypSfr9f2mmsDlLYzLYqK19sEdJ3SDU6ZOWNlUkNDQ0NjbuDI0N+ut2ueL3QYZcbjsfjAQDRoRzOZ5pMJtje3sZ3v/tdrKys4Nq1azh16hQA4N/+23+L559/HmfPnsXa2hquXLmCF154AQ888AAqlQrK5TLm5+cBALlcDm63G8vLyzhz5gyGwyEMw0C73cbVq1dx6tQpLC8vYzweI5PJ4F/8i3+BV155RTx2OBFEX5lWq4Ver4eZmRnMzc3B4XAgGo0iHA6LzodaHAZpOp1OOUapVEKv10MwGITf75e2Czdfn88Hq9UKwzBkk3a73XDYHeKGXavV4Ha7xVOIjtX0r6FIeG9vD6FQCOPxWCac/H4/ksmkkDgmvqfTafHtYe4XTRkNwxDSwzR1tgObzabopTi1Va/X4Xa7pQ1Zq9VgtVrFC8lms6Hdbkv2Gt2+R6MRjh07Ji0volqtot/vw+v1Ip/Po1gswuFwiIBZJW8UPQOQe+ByueRrEm7V4ZmvPxymq6GhoaHx5uHIkB+SHoImdEz2BiB6i36/L5sXtSNutxvXrl3DrVu3UK/X0el0pCrw8MMP48SJE0in0/jkJz8Jv9+Py5cv49ixY/j2t78NwzBEz8KN/5FHHkG328WVK1fQ7Xbh8Xiwvb2NbrcrAafpdBperxe5XA4ulwupVErSz/P5PK5fvy7VlWazCYfDgUwmI9UFxlRw2igWi4kxYSwWQyaTAbBv2EiNzWg0Qj6fF+dpVXzLNSQZojC6UqlMVXmYyk7tDUfSGXcxmUxkdJ6C43w+L9NxLpdLtEsclR8Oh+j1ejJCT1IGQITNzWZTQlPL5TLq9brobbrdLtrtNmZnZ3Hy5Emk02kRZ4/HYxSLRdy4cUPu97ve9S588YtfhN/vl2fmwoULePHFF9Hv9yXglJotipp5vm63WwgaCRwriXzOaKZJsqZO6unKj4aGhsbdg/4vrIaGhoaGhsaRwpGp/Ljdbmll8RM3WxQApApEHYpa/eG00NWrV3H58mWcP38e+Xx+Sg8SiUTwvve9D//m3/wb/M//+T/h9/vxd3/3d1haWkKhUMDP//zPI5VKodPpoFgsIhKJ4EMf+hCsViteeOEF1Ot1MdyzWq2S/xWPx5FOp9HpdFCtVmEYBiqViuhgYrEYFhcXMTs7i1gsBtM0cfXqVakQ0UTQ5/PBNE0sLy8jHo9LzEOj0UCn05EML7Zv8vk88vk8CoUC/H6/jJPTo4j6KJvNhmAwiNFoJOaBnU5HoijYSqMRos/nE+8bZoex2hEOh+HxeGQsHdiv0AUCASwtLYkXEOM6Ll++jN3dXdRqNTmHnZ0dMRT0er3odDpSbVleXsbx48eRSqWk2tJoNHDjxg1sbW1JLEUmk8Fv//ZvT1V9er0eLl++jHw+L3lpu7u78jNqfex2O9xuN1qtluTHUftjtVqlxcr2Ip8zVfMDQHyMWIHT0NDQ0HjzcGTIT7Vahd/vl/F26i248QAH4/Bq4jYdoW02GyKRCL72ta9hcXERi4uL+OpXv4rHH3986n3uvfdePProo/jyl7+MdrstPj9PPfUUAoGAHN8wDNGLRCIRbGxsYGtrC8lkUvKiut0urFYrms0mtre3RUR77do1bGxsyIg8PWXoXkwtUbValZT1SCQCh8MhIaiRSASnT59Gq9WSJPW9vT3MzMxI62o0GsHv9yMYDCIej4s785PBJ9Ef9CXninlmdKTO5/Po9XqIx+Pw+XySvE4iSRNEADIan0qlxGuHSe78PoNDqdMC9snq+fPncePGDUlXTyQSco+73S5yuRxGoxFSqRRmZ2cxOzuLZDIpAm4m0ZfLZWnlra6u4vd///fx3ve+d+q+Pv3009jc3JSQVmaxMbtM9e0BIBoqjrZTw0Ndk5ryTt8kvobPpNb9aGhoaNwdHBnyAxxsTKpYltoKblSsvjASg5taNBqVhPUXX3xRNuuvfe1rkvQN7G96jz76KAzDwDPPPINCoQBg3yOm2+3i3Llz2NvbQ6vVgmEYmJ+fRyKRwJUrV2T6q1gsIpFIwG63Y21tDV6vF36/H8PhEC+//DLW19fh8XgwHo/hcrnEMJGVqkKhgBdffBGmaUpQaalUwnA4RL/fx3g8RjQaxXg8ht/vFxEuIx7cbrfocrrdrqwXp+W4RowCcblciEajGA6HuH79OtbX1yUehCJtamu40VN4TQ0QX8cqCl2YSQ4tFgvq9TqAfR8ikodYLIZGoyGi9GaziVAoJFUUn88n5+L1ehGPx2EYBjqdDgqFAr773e9ifX0dg8EAp0+fxh/90R+JmJ0ol8sol8uicRoMBrhz544Ez7JqyGgKCs2pvaKomUnvrK7xmaQuiF/rgFMNDQ2Nuwut+dHQ0NDQ0NA4UjhSlR8AU60FNdySYZysOnBihxWgdrst3jnPPvssZmdncfbsWdTrdalmAMB9992HRqOBX/mVX0E4HMbnP/95bG5uYnZ2Fs8++yxeeuklvP/978fi4iLS6TQsFgvsdjsSiQRefvll3LhxA61WC9VqFYPBAC6XC4PBAFeuXMHNmzcxHo8lxLPX62F7exvtdhvLy8sAgI2NDdRqNQAHWWXRaFTaMM1mU6orXq8XVqtVnJaB/TYUp6hSqdSUh5BhGNjc3JRKxmg0kjZOr9dDt9uV6gy1Lz6fD/V6XVpysVgMPp8PtVpNgly9Xi+SyaREeFitVgSDQQyHQ6yvr2N7e1uiLCwWCzKZDJLJJFwuF5aWluDxePC9730PhUIBdrtdctgikQhM05SWWLfbxUsvvSROzpcvX0a9XofVasXJkyfxh3/4h6+p+gDAd77zHVy9ehWbm5sIBALY2NjAtWvXpP0FAKZpTmWqsQrESA+1ssOv1Uokq18ul0uqc3riS0NDQ+Pu4EiRH2p8uDGxhUHSo25QwH6rhnlNAKSd4XK58PTTTyOZTGJ2dhZf+tKXcP78eZw8eRIARC/y0EMP4fd///fxW7/1W/gf/+N/AABCoRCeeuopnD59GsvLy8hms4hEIqK1oVcNz7dWq6FQKIhAlq0oamUmk4m0ZTgGbrfbEQgEMDMzg5WVFYRCIZimKaJljlfzGjkm3mq1ZKScbarJZAKv1wufzydEyGqzwmrZH0V3u93S3hoOh9LOi0Qi8Hq9GI/HqNfr2N7ehs1mQyqVgsPhQKlUwubmJkzTlBBYJtC7XC6JyqD2icaRHCEfjUYIhUJyvSRbi4uLWF5eFgKRTqcl2b1erwthuXXrlgS9xmIx/NZv/RZOnz79mmfmmWeewZUrV1Cr1bCwsIBcLocLFy7A5/NJq5HklwSGX1PoTMKjEhr+fDgcSguQgnyK3jX50dDQ0Lg7OFLkxzRN2O12qdLwEzeDPlnN4BSTOh1GAtTr9eDxeFAqlfBnf/Zn+MVf/EWEQiHcuHED8/PzIuRV8d/+23/DE088gY9//OPY3d0Vc8ByuYyzZ89KVcliseD06dOoVCq4efMmKpUKEokEHnzwQdTrdVy5ckV0JnSnZgWB1+DxeBCLxRCLxZBMJjGZTMTNmZ5GnFiam5uDzWaDaZqoVCrI5XJot9toNpvY3d2Vqa75+XmsrKwgkUgAACw4qFiMRiMMBgNJq3c6nYjH45idnUU0GkWr1UKxWMRgMEAsFoPVakW73Ua320Wz2USz2UQymZR1U123SWicTid8Pt+UVxM9dfr9PnZ2duB0OvETP/ETSCQSqNVqiEajWFpaQrfbFbPEfr+PRqMha2sYBo4fP44vfvGL4pyt4tKlS7hx4wbK5bJovC5cuCDkTE1vVx3C1edFPWdV66SGmVIQrf6c5E1DQ0ND483HkSI/FPZSnMrNi60tABJHQFJEokRTP7vdjlqthlgshjt37uB//a//hX/37/4dIpEIvvrVr+LRRx8V80AVjzzyCNbW1vCrv/qr+NKXvoStrS2ZbLrnnnuQzWbRaDTQarXwwAMPYHV1FZcuXZKJpWg0irNnz6JQKKBYLCIQCEglxOl0SoXKbrfD5/NJS8tqtYqIGYCEga6vr2NnZ0ccmDm5xbaZ3++H0+mE0+nE5uYmDMPAPffcg2AwKBUyVoiY3D4cDtHtdjEejyWU1TAM1Go1mKYJn8+HYDAoVaharYZSqQSPx4NyuYxarYZUKoVkMonxeIxGo4F6vS7kwDAMDIdDeX+O4Nvtdtxzzz0YjUbY2tpCOByG0+lELpcT48fxeIxCoYBbt25hbW0N3W4XP/ETP4H/+l//6+sSn36/jzt37mBjYwM+nw/D4RBPPfUU8vn8VMWGzw3/TWJKAqS2vFTna1X87fP5MBqN0Ol0JFqDVR9WmDQ0NDQ03jzourqGhoaGhobGkYKFn0rf0C9bLG/8l39MYPuE7Qga29lsNhkfZ7uLr2MFhSZ2NpsNg8FAvHPC4TB+6Zd+CYlEAv1+H6dPn54yQDyMSqWCJ554Aq+88gqi0SjS6TSOHz+OxcVF7OzsYHd3VyIntre3sb29jUajAY/Hg7m5ORHr0ryP0Qlerxcej0fOkcaCHEnnqLrL5UIkEoHf74fP50MkEkG73caLL76IW7duwel0IpFIwOPxoFarTXkkZTIZvPqZV9HtdOH9kFcqT+12W7yQKOZ2OBwwTROlUglOpxNLS0vi+5PL5fDiiy+i0+ngzJkzyGazCAQCSCQSIjQ3DAONRgOGYUgVpt1uA4B4Fs3Pz4tWiGJn3p9gMAiLxSItvatXr6JYLKLT6eBd73oXvvGNb/zAe3T58mX85V/+JUqlEtxuNy5cuIDnnnsOfr9/KieOo+zUR7FSqD5fPF91xJ3PlFrlYeVI1Z/Z7Xa0Wq0f/cG/C5hMJpYf9zloaGhovBEcOfITCASmvHzo9UOHXbU95PV6RSfDzY2iYtUEMRKJoNVq4Vd+5Vfw0EMPIZfLYWZm5jUGiIfxuc99Dr/2a78Gj8eDhYUFZLNZzM7OIpVKifswWyPNZhO3b99Go9FANBoVPU8+n8f6+rpspExO93g8ck3Uk9hsNsnfAvY9dmKxGNxuNzY2NtDr9eB2u3HixAncc889SCQSMAwDOzs7uHPnDhqNxn5r7qv7m/GxXz6GVCqFVquF9fV1OBwO8QpaXFyE3+/Hzs4ODMNAJpMRo0S73Y719XVcv34dvV4PiUQC4XAYqVQKJ0+eRDQaRT6fF60OTR7ZFpxMJkilUjh16pRMdNntdqTTaQQCgakcrXq9LhNjbL898sgj+MxnPvO67S4AuHbtGr71rW8hl8vBbrejWCzii1/8oqTFq748h00x6WbNZ0j19aE+i/eK4L3hxJ163NFohG63+0M/5/8U0ORHQ0Pj7YojR36CwSAACOmhWR43pMNTX/xUfniChw7Eo9EIHo8Hy8vLqNVq+NjHPob3ve99qNfrCAaDeOyxx/7e89nY2MDHP/5xFAoFVCoVZLNZJJNJrKys4Ny5c2LIZ7Vakc/nUavV0Gw2Zez6vvvug9PpxCuvvIIrV66IgJnhn4xXmEwmcLlcCAaDSKfT6PV6Ep1AgXcikUAymUQ4HIbP54PD4YDT6RSRci6XQ7FYxOXPXAYswMO/8zASiQQajQZyuRzK5TI8Ho9MydFwMJvNIhgMIhQKIRAIoNvt4uLFi8jlcohGozh16hTS6TQikQiGwyHy+TxM00QkEoHdbsfLL7+MarWKbreLeDyO5eVlqQ6FQiH4/X4hPQxBZbTE1atXcePGDdRqNdjtdszOzuLTn/40fvInf/J178crr7yCl19+GTs7O3A4HDAMA3/2Z3+GSqUiJIRTWxxtpyMzK0AOhwP9fl9IEjU+fNZUwT3vDX+uCqV5bzT50dDQ0HhzoTU/GhoaGhoaGkcKR67y4/V6ZZyYbQl+clencgBIy0iNvRgMBhKwSX8bfoLPZDLY2dnBL/zCL+DRRx9Fv9+H3W7HysoKVldXEYvFfuB5/cZv/Ab++I//WKazstksjh8/LjoZaoF8Pp9kaN25cwd37tzBaDSSllKj0cDOzg7q9bqcv9PpRCqVQiaTQSqVkvMol8toNpuw2+3w+/0IhULw+XxSVen1ejJO3u12sbOzgytXrqD5v5uwWCx41//zLqyurqLb7eLChQtotVpIJBIyyaUGxc7MzGB+fh52ux2vvvoqms0mRqMRZmZmMDs7i0wmg0AggMFggE6nIxlhr7zyCsbjMTKZDFZXV6UKFQgEMDs7i3g8LlWeXq8nYaTr6+u4evUqdnd3xd/n0Ucfxa//+q/jkUceec36X7x4EXfu3EGhUMDu7q4En37961/HnTt3xGeJJoTAweg6M8kYuMpnAtifMOQEl2pNwJYkdUH8HqtDnL4bDoeic3qrQVd+NDQ03q44cuQnEAjIBqSmaavthsOmdBQ6U79x2CzR5XLBMAwJ4mw0GvjJn/xJfOhDH4LFYoFpmlhcXMTMzAzOnj37A8/t8uXL+A//4T/g4sWLcDgcCAaDOH/+PB544AHE43EZ0+b70NOGehaKtoH9lgoT4AHI6DoNFdnWItGjeNfpdMLv96PT6aDT6cDlcqHZbGJtbQ0bGxtoNpswnzTh9Xrx7t99N/x+P2ZmZkSc/PLLL8NqtaJarYrWhdYBpmkKYWDL6NSpU1haWpLA1UajIflgNpsNCwsLmJmZgdvtRrfbxWAwQCaTwczMjBxzMpnISHihUMBgMEC5XMbt27ext7eHbDaL3/md38FHP/rR1133559/HmtrayiVSjAMA6FQCKVSCV/4whfEaJEZZiQ+ai7c4Z8xn4utMY6+q0SZRpNsoZIs8dnj+litVt320tDQ0HiTceTID7A/8QXsi0qBgxgIghuX1WqVCRySHGDfA4af5Bm+SR8gv9+PSCSCRqOB973vffjgBz+IwWCAbreLhYUFPPjgg5iZmfl7z+9Tn/oUPv/5z6NarUql59ixY1hdXZVzJxHg5jmZTFAsFnHr1i30ej3ZUJeXlzE7O4uNjQ1Uq1XR9fj9fni9Xjk3p9OJQCAgmiEAcLvduHXrFv72b/8Wg8EA9957L+LxOL79/30bFlhw/j+eR7VaRSAQwPLyMkajEWq1GgzDwO3bt9HtdmUTZyDqYVM/3gOPxyOVEKfTiWg0Cq/XC8MwMBgMEI1Gsby8jIWFBTQaDRQKBdEmmaaJZrOJarWKSqUilap+v493vetd+MpXviLVPhU7OzvI5/PI5XK4ceMGOp0OIpEIXn31VXzlK1+B3+9HpVKR+073ZYqVWcmh0Fw1PFSFzSpRVr1/1MR3fs1Kkpr0brPZ3pITX5r8aGhovF2hNT8aGhoaGhoaRwpHsvITCAQkfJKfsjmmDRxM4ajtL7vdjl6vN+XcOxgM4PV6pXqhZjNFo1EYhoH77rsP73vf+2SSiVWS8+fPIxAI/MBzvHDhAn73d38X3/3ud0VLks1mkUgkcObMGSwvL0srZXd3F3fu3JFzoPcMHZIZvkmHZbfbLRohjlkPBgO0Wi0JdHU4HNjc3MT3vvc92Gw2PPDAA4jFYuh0Onjq/30K4/EYix9fxGQyQTQaRbvdlgpNJpNBOBxGpVLB1tYWyuWy5Gj1+/2pXCu291jhYuuL2WJ2ux2Li4tYXFwEsD8dV6lUxNeo2+1Kxlan08HW1hYsFgve/e5349Of/jSOHz/+mrV96aWXcPPmTYRCIVSrVVy5cgWBQAAejwd/8zd/g+985zviR8SKDc+FlZ7DuV1qYC5ff/h1bKESrC6qVUe1Sqb+eSvqfnTlR0ND4+2KI0l+XC4XHA4H3G63iHqBg9F2bjjctNSfAweCaHXMGXgtafL7/ZKm/nM/93OYm5sDACSTSaTTaaysrMj3fhC+/OUv47/8l/+C7e1tWCwWGRcPh8PIZrOIRqMS8Mn2VTQaxWg0QrFYRLPZxGAwQLVaRaFQkAwwACJ05pg4R9udTicGgwFM05T0+EuXLqHRaMDn8yH3xznsjfcQeDwgOhav14tYLAaXy4VYLCaCcPoNkQxQx8KxdEZWcCx/NBohk8lgeXlZjBJ7vR4KhQIuXryIfr+PUCgkWV0A0Ol0sLGxIQTlE5/4BD71qU+97nrevHkTX/nKV1AulyVHLJlMYnd3F3/xF3+BYrEIAGJlMBgMJP6DhHkwGEzpv1wulzwrfD6GwyFGo9EUWeYzRN8ol8slLTGSJx6TRInC52q1+kM94/8U0ORHQ0Pj7YojSX7C4bC4HQOQRG6LxSK+NqPRSDY/ldRQo0Fw8otVDAAwDANut1uqKqwwfOhDH8K5c+fg8Xjg8/kQDodx8uRJnDp16h8857/7u7/DJz/5SVy6dElypObn55HNZuHz+TCZTBAMBrG0tAS3242dnR2Uy2WEw2HJwGKul2EYUskyTRNWqxWFQgGlUgnBYBBWqxXFYhEOhwNer1dyuRYXF+Hz+fD87z2P4WiI6P8RhdPpxMzMDIbDIarVqrhJ93o9OJ1OESr3ej0kk0nxAbLZbGIYuLS0hGg0ikAggIWFBQSDQbRaLXGXpqC53+9jd3cXu7u7aLfb6PV62NzcRCqVQjQaxcc+9jH82q/9mvjoqGA22O3bt/FXf/VXuHLlChYWFhCNRvGtb30L3/zmNxEMBsX7iORNJSOqyJk/43PCqS3+m5UriuOBA3LM82HFUH2WSBRVsf14PJbq3VsJmvxoaGi8XXEkyY/f739NcCkAITHqxqS2NAia2fFv4ED8yg1ODa90OByYnZ1Fq9XCyZMn8d73vleqT16vF5FIBGfPnsXKyso/eO6tVguf/exn8Sd/8icoFAoyfh+JRBCLxaaIFSe8QqEQ4vG4bOB7e3toNpvodruw2WyoVqtoNptSReLGXq1WYRgGYrEYIpEILBYLut0uvvSJL6HX7yH+83FpYWUyGSSTSaRSKZl+83q94hLNqTO32y0EyDRNieMYjUbw+XxSpXI4HPD7/RgMBtjd3UU+n5cqXavVQrlcRiAQQDwexy//8i/jE5/4xOuu1+bmJvL5PFqtFgqFAm7evIler4d0Oo3d3V38+Z//Odrttlwbycve3h78fr9UntSJQAbckjSrxoUkLxaL5TXPE4mMKoZW2152ux39fl9CU1V38bdiuKkmPxoaGm9XaMGzhoaGhoaGxpHCkaz8BAKBqWBJCoTV9pea3WW326WtRYE02xccV1bXkWPLrAxRQ+T3+9Hv93H8+HE88cQTCAQC4s+TyWQwOzuLs2fPIh6P/6Ouo1Kp4FOf+hS+8IUvIB6Pw2KxoNVqwTRNhEIhpNNpeL1eqTAkk0lkMhkJd3W5XAiFQnC73djb20On05EgURoj2mw25HI51Go1uFwu2O12fP7//Dx6vR5mPjaDVqsFq9WKWCyGdDqN1dVVLCwsYDQaiQja6/UinU7Dbrfj9u3b6Pf7SKVS4oFkmiaKxaKMybtcLkSjUdH6cGS+3W6jVqvB7XbDbrfjwx/+MP79v//3OHbsGBqNBhKJhFTgTNOU/LB6vY47d+5gMBhgYWEB3W4Xzz33HL797W8D2K/kABDrAlZy2PqiPmcwGEjLjrEh1PzQz4dVQnXkHYA8I/wdtQLECqGqHeNasyKl214aGhoabx6OJPkBIE7Aaqq7Km5WJ3NU35/D5oc0B+QGqOY7Wa3Wqc10b28PwWBQNC0f+chHcOrUKXQ6HQyHQ0QiEWlRnTlzBqlU6oe6pqeffhr//b//d3zrW98SsSw33GQyKRNnHo8H2WwWoVAIuVwO1WoV0WgUJ0+eRDqdRrlcRr1eRzwex2g0QqVSQSgUwng8RqVSwV//33+N4WCI5EeTEqQaCoWwvLyMbDaLmZkZhMNhuN1uWCwWMUw0TROGYcBms8Hn84kRY6vVQrfbxWg0QrlcRrFYhNvthsvlwu7uLprNphARq9WKxx9/HA899BDcbjcWFhbwnve8B8C+1urKlSvodruw2+2o1+si1M5ms2i323j66afFRJI5YMCBMaE63cVnguSX5Fj1gFJJDH+mkmf1eVGfIVUvxHYknztVaK96A73VWl+a/GhoaLxdcWTJDyeGAIiuYzwei5iYWhaaHKpEYjQaSQI8NymSKcMwZOOkYR8djjkp5HA4EIlEsLu7i/e+97147LHHEAqFsLOzg+FwiFOnTiEWiyEYDOLee+9FKBR6Q9f4wgsv4Atf+AKefPJJ1Gq1qSrG3NwcfD4fms2miKXdbjc6nY6YNVLwbRgG+v2+EKhvfPIbGI6GiP98HL1eDxaLBSsrKxIz4XQ64fV6AUCIiNvtRrvdFl0N154Bq9z8TdMUSwHqgB566CE89NBDOHHiBI4dO4YzZ85MXed4PMbly5dx9epVFItFCQPtdrvweDwwDAPPPPOMjLeTbLLi9Xp/gAPzS5oz8r4SFEDTLPJw1Ud1BefzcJgs2e12qfwQfD+VFAHQ5EdDQ0PjTYLW/GhoaGhoaGgcKRzZyg9jIlTQf4baDX7yZ14Wq0Nsg9ntdvnkTnNDtX12eOR6MplMGSmyyhSNRvGzP/uzOHfuHMbjMdrtNvb29pBMJhEIBOB2u8XTp9lsIhKJIJ1O/1DX22638Qd/8AfI5XJ49tln0ev14PP5UK1W0ev1sLe3J9ep5p653W6MRiOxBRgOhyh8voC9vT3EPxKXthewX03jqLzX65Ux+Xq9LpUNvhcrIczN4vpzBD8cDuM3fuM38Ju/+Zt/73WVy2Xs7Ozg0qVLuHHjBhqNBoLBoERTPPfcc7h27ZpUfBhHwrWnFw8rfKzIUHfDSpDL5Zq6z7Q14HSfWiXitBjbZKpBojrqrk6LMQON1R5qzahHGw6Hbzndj678aGhovF1xZMmPx+ORVga1GKofi+q6C+B12xjUiNAhmaGdbG+om5+qJ1JT5K1WK4LBIMbjMbxeLx566CHcf//9WF1dRb/fx82bN0Wom06n4ff74XK5xFSQIt83iuvXr+Pq1avY2trCc889h4sXL6Ldbouo93ByvdPpRP3P63DYHTj/H8+LVmdvbw+tVguNRgMul0taNNS0kCBw7Jvap/n5eZw7dw7Hjh3DZDJBPp+H2+3GBz7wgan21mQywebmJprNppxXs9kU359CoSCj/fl8HpcuXcIrr7yCVCqFWq0mWieLxSLZZ3TO7na7U2RENbkkueX32BpVnZgBCMEhaVFFzKoWjCSSzxHXiO/Hn1GQzmvd29t7ywWcavKjoaHxdsWRJz/8JM4NaDAYyEamOu+qjruq2FU9hlo9oE8QNRv8+rBRIolTIBCA3W6Hw+FApVLBPffcg/vuuw+Li4uIx+MSbkkdjM/nQygUQiwWQyaTgcPhQKFQEMHxm4Ver4dr167B7XZjbm4Ofr8fj+ExAMDTeHrqtSR6pVIJGxsbMsnGNHuu09/3XsC+FxCxubmJUqkEl8uFra0t5PN5MagcjUZoNBowTRMOhwOlUgkvv/wyNjY2EAqFZPKN5GMwGMDj8UzpblhZIdFhtYUVHIfDIc8C/6b/Dl+nEiVOfR3W9FA4zX+rRHA4HIoGie7aqmAa2H8+6/X6m3RX3xxo8qOhofF2hdb8aGhoaGhoaBwpHNnKTygUmtJmUOPzetEIbGWw2sBx5uFwOOXcy6oP9RtqG00dbSbULCi1Nebz+aSVZRgG5ufn8Z73vAcnTpyQ32+328jn8wgEAshkMgAAp9MpuWFsm7B19OlPfxrHjh3Dz/zMz/zIa/eDKj//GLzwwgtwOp247777pr7fbDbxjW98A5PJBPF4HIZhyHQY3ag7nY60B/f29tDr9dDv95HP53HhwgV0Oh14PB7RTLE9xbVnlUVt5ak2BqzuAAdOzWo4KY/ldDrleWAFicdl1W9vb2/q+WA1iK1Wp9OJvb090UvxnID9+8jXs103Ho8leuOtAl350dDQeLviyJIfABKtwDaKaliojmNTn8END5gON2W7hGGW1I6o49HcQFUBLUmRaoqoCq2ZNebxeGCaJgaDAc6ePYv7778f8/PziMViGI1GqNfrYspHz6FkMomZmRkEg0F4PB65xvF4DI/Hg2AwCGC/HfePNVUk3ij52djYwNe//nW85z3vwcmTJwFAstNM08Qrr7yCW7duAdgfkc/lciIM93g8cDqd6Ha7Em9x+fJl5PN58S9qtVpCNA577qiaKxohktyQeKjCZ9XQkm0uAJLXxuOStDIAVtUGqd4/qkiepEklzGyZqePy/X5fiDmfOcMwfqg1v5vQ5EdDQ+PtiiNNfkgAuBlxQ6JwlVM33CRJalwulwRXkiSpJIabGjdRet9Qd6JWeriZqqGXNOCjToRuwswDI8lKJBI4fvw4VlZWkM1mMTs7Ky7P4/EY9Xodk8kEKysrsFgsWF9fh9/vh8fjQafTkQoGq03Hjx9HMpnEeDxGKBRCNBp9zZq9/PLL+KX5X4LL5cKf7P4JLBYLjh07BmA/ebxWqwHYd9EOBoMoFovodDrwer1oNpvodDpwuVzodrvY2trC9vY2nE4nwuEwarUayuUyJpMJAoEAfD4fTNNEtVpFpVLB7du3sbW1JT5EwL6myjAMIa/q2rJ6Qo0WSQjvIwAhp6rmi2nrakCpejyv1yvf5z3nfeExgQOSpXr1AAehpiQ2/KOeqyqkdjgcU55LbxVo8qOhofF2xZEmP16vFxaLBV6vV1LGuTmyWsONjH+rU2D8RM7qgeoKzU/yaqWIGyo3MrXKw0oQ34dTY6rAWn0fh8MBl8slI9iGYSAYDEpMxurqKpaWlqRlMhwOpY3k8XgQjUZht9tF4M0KjNfrhdPphGEYYjbo8/kQj8fh8/mQz+fxnx/9z7BZbfhP3/xP8Hq94v5MAXCj0RBxdrfbhdVqhc/nQ7fblZR2v98/5YBNsS8AcXpeW1vD2toaqtUqvF6vnC8T6lUS4XA4JCWea2Sz2dDv9wFgqgqnVlxUJ2dWilj9Uc0K1WdBvf8kqGxhMZZCFUPzNQCkOkSCS9PJw27TJDydTmeKXLXb7Tf//whvEJr8aGhovF2hBc8aGhoaGhoaRwr2H/cJvBWgVnwATOlG3G63fCpXYwfUDDBWDtT4ArYx3G63+MqwUmSa5pSBIsNV1VbHcDicEsWycsDj9/t9mKYp50I9TC6XQ6PRwIULF1Cv1xEIBHDPPfdgZmYGTqdT2nS5XA7JZBLRaBROp1MqL263W86PQaydTge9Xk/iPBz2gzyq27dvi6C41WqJhojtNWaecTTf4/FgNBqJeWKj0UCn08HOzg62trZQLBZRLpfR6/VkbRgq2u12p1pIqhZnMplIO4oRIvxd0zQBQLQ5rLiwwgdAxOysPrEKpFZ7DrepWJWjfw9/znNke009R1YE2f5kK9ThcMDn88lzQH2PGtTKZ0BDQ0ND40fDkf6vqervQnLCHC7+XCVB3PQOp3VTxErRLAD5HkkT9T9suZBsDQYDaXNQU6K6RvM9uUmrrRgAcny2gnq9HqrVKhwOB9xuN9xuNzY3N5HP54VUTSYTSSgfjUbweDxIpVLwer0IBoOIRqPw+/3w+XyIxWKIx+MSFEqX69F4hFKphMlkAp/PB6fTKRqdUqmEtbU1dLtdITJ2ux3tdhumaaLf76NWq4mvD80TVePJ0WgkgaYkD1wn3hfVr0c1rGTLkKSJZoY8Lu8v107VbtHpWnVeHg6HcDgccn+p51J9oPh6HlsVNB++7yQ9vHa2HlVNGVtoKnni3xoaGhoaPxqOtOZHTXbnJsYNjQJYdQIHONB5cJqLpISfzA87QwOQT/bUoKjTQtxMuUmr+h5uutyEOVUGYKoCcnhKjNUpamRonMdzByAu0dQbcfMnQaCbMDdfvs5qtWL3T3cxmUwQ+7mYXDvfV60uHTY1pOjY4XAICXK5XOj3+0IAeD8Oa3I4Ys4KDUmRKlJWjSlVM0nVnJDkg6STP1en8XifeZ9UN2+ukUqS1QqRGnVBbRdF9MPhUHRarFB5vV6p9jAOhJVHHkPVjY3H47eM07PW/GhoaLxdoTU/GhoaGhoaGkcKR7rtZRgG4vE4HA4H2u32VDgpgKlKidreoNEg9TiqzofVIuCgRcWWGQ3tVDNFVjJoakh9EKesOp0O3G731O8C0xUG1byPfj6sRvAcGNLJc2Pbi9UManBYYQEgbSS2bThNNR6NMcFENEesNPX7fdjtdnQ6nSnjR1VfQz8cvobvyeoYj6dWWnjepmmK7sjtdqPVakmFRo0U4bnzWllJ47odNjxU25mqN4967awUqdNf6lQY9UV8b95vdQqQx1K1Yd1uV6pq6vXya7X684NMODU0NDQ0fjgcafIDAP1+H71eDzabDcFgcEpjwzFsAPJ9boBsQaibNT191NRytkfYXuHrVIM8tlwAyKar+sqYpimEhG0e6k7UDCmbzSav5Wu4qfNcCdWcUTXl45qoI/ccK+f7TzCB1bL/vtQAkYDwPSnYBjBF3OhYzBF8EhO21EiISCTYpuK6Uz/U6XSm2msctQcwpZFhgjvJlNrGVP1/+Ed1+mbmlsPhkMR7EhquDcXIbJH1ej04HA4EAgF0Oh1xeaa5JImsKoDf29uDYRiSI0a92OHxerfbLeRXQ0NDQ+ON48iTH+pIhsOhVC5UfxiVZLBaQNNBbsgAhKTQs4ZQAy+pXVHFutzAudmx8qJWkXw+n1RzVCNFNRIDgBAvYNrXhqSGOiGaG3Jzp+5IdbXme1CjxGuZTCaY7E0wtoynBMY8ZxIlee3rkDQSJhJHXj8F5wCEIJAsMC6E58xzpI6Hv0PNEUkmr1uduFK9gEjCVM0U7636Oq41v1YJq9PplAk8t9s9RVh5/pxWU0XLe3t78Hq9AA6MNknSVENGViTb7fbraso0NDQ0NH44aM2PhoaGhoaGxpHCkZ72AiAaksM5TOon/MN6Hmo27Ha7jCXTQ4ZVCVUTAhxUHNgOY7WGlSbgQEOkngv1Hnw/VcfDio06raa2bVh1AaZH5FWfmsM6IVaEAEilhdfD45h/bWKCCfw/45f14LUBB9NtfE+1NaXGf6jrTh0Ov384/0rVA7EdqOqKAEj1Sa2icf1USwOuk6qHUn+HrwUOqmmqjuvwqLraolTzwbi2rOaxAqVWq/hMqREnPA9Ow7H6QzsFPe2loaGh8aPhyLe9uGFzU6aXC0ejVeEsgKlkb2563MDY9gKm2yvcBA/nhXH8naSHkRXqaDMJi0p8SFy40asb92HCQSLB16paH2poVMJEMTHJm+qfw+vf29sDLAdp9VwLlUizvcc1Vts6PA4JkSoiV0f0KSwmESEJUHVZHBvn6DiA10SC8BwHg8GUXxLPh+T1sJ+O3W6Hx+ORNufhFh3fQ3021HF99b1VDZg6Rs/jkGCphom8Xr6WhFJDQ0ND40fDkSc/6qdziom5yXHT4yav5kCxQqBOKakbNwmKOunEaSf6uvCY3OTVySa1CsDNXhUsq545h7VHqj5IJUM8B7WKxYqV3++X6S1OgpEIqkRif9H2/1IDPylQVoW6PGeVCPJcfpBnDwC5B+p5HiY9PBYrInSOVqfnDvvwqFUdVlZeb3pKFULz/gAHVTX1HOx2uwif+d4kjMCBYJzX1O/3RXPFtQMg16ISUxpfqhVJm80Gj8cjrtUaGhoaGj88jjz5MQwDPp9v6tO3Sja4CarTTNxIGagJHHzqVwXGAKamvdTwShrgqcSEmys3RjoZkwCo76tu3oeN+FSxMzdUbrSq0JgkazQawTAM+R0AEusxRXq+f95Wy/71+f1+OebrkQuuKf/mxs+pKIqlgQMDPxJI9V4AB0GxrICpxEQlZ2ocidpiI8kCDib3aIxIwTsAWUveR7UVyfuqTsixosN7B+A17TjVQZzHVFtjJJokcACmyB+rfF6vV65FQ0NDQ+ONQwueNTQ0NDQ0NI4UjnzlBzhoi6j+L6pOBTjQBqkCWFYPbDabjG/TF0at/qjaFlXErOo+qAFSxc5qqwgAfD7fVPWGxoVqZYEtGv4eW1dsn6ij5Op7ULjNSpdalWIYKKsyE0ymPIR4rXt7+7lYFP8SrLSwSsM1YXCpai7JihgAWUveG7ayuEbqdajj4Ye1Sod1PmxJcTTfYrFM5XnxvVWROJ8DVpC4RgRbVOqYv9pSe724isP+StQE8eeqRxMrZ6wWaWhoaGi8cWjyA0wRETW5G8BrND7qhJTVap3akNQ2C4CpdgyJBnDgt6OSEgBitsg2BzdpVf+hkhLgYMLJat1PkKc/0eH2EF8LHLhVq+0qNWme7RySGVXka5omMAEwgRgOkgjY7XY5jtr2Uifd1MkpVbBNIkcCwfU6LCA+PH3G4/l8Pmmn8drUKTGuB8kL211cC1VjpSar87wPi7dJVrnmXHfeL4LHd7vdcDqd4pJ9+LnjOrEFppJVdS1UDZKGhoaGxhuD/q8oIAJSjj2zQqBWMA5XhtSpLnVUHTgYh+a/qSFRJ7P453Al4PDUFl+jTk+pP+v1evD7/VMEhOJg/m6/3xfSplagWI2x2+2ix6FRIH9GXZEa4tpDD7C8Vh9D0sNKiWmacn2qKFg1aaRRIYkUiQBF1Kx8cA0OrzdJEckHz4f3jgRHnXJTBebqWlDAzQkyrgHXUrUy4HqyonOYoKgibz4XPEdqrdSKkqoHUgmfKgJXBeYaGhoaGm8cP5LPj4aGhoaGhobG2w268qPxQ8NisYwBvIr95+cagF+aTCZvDec9DQ0NDQ2NfwB62kvjjcCcTCbnJ5PJGQADAL/64z4hDQ0NDQ2Nfyw0+dH4UfEsgJUf90loaGhoaGj8Y6HJj8YbhsVisQP4IPZbYBoaGhoaGm8LaM2PxhuBx2KxXPz+v58F8Lkf47loaGhoaGj8UNDTXho/NCwWizGZTPw/7vPQ0NDQ0NB4I9BtLw0NDQ0NDY0jBU1+NDQ0NDQ0NI4UdNtLQ0NDQ0ND40hBV340NDQ0NDQ0jhQ0+dHQ0NDQ0NA4UtDkR0NDQ0NDQ+NIQZMfDQ0NDQ0NjSMFTX40NDQ0NDQ0jhQ0+dHQ0NDQ0NA4UtDkR0NDQ0NDQ+NI4f8H0J9vGeO87zUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = t1w.orthoview() # Requires matplotlib, occasionally glitchy in OSX setups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The crosshair is focused at the origin $(0, 0, 0)$.\n", + "\n", + "All of this information is encoded in the affine:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 9.99131918e-01 -5.16291820e-02 1.25127016e-02 -1.25263863e+02]\n", + " [ 4.07721959e-02 1.29202044e+00 -9.81179178e-02 -7.31330109e+01]\n", + " [-8.54506902e-03 1.28044292e-01 9.95096147e-01 -1.77554291e+02]\n", + " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n" + ] + } + ], + "source": [ + "print(affine)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Affine transforms\n", + "\n", + "The affine is a 4 x 4 numpy array. This describes the transformation from the voxel space (indices $(i, j, k)$) to a *reference* space (coordinates $(x, y, z)$). These coordinates are, by convention, distance in mm *right*, *anterior* and *superior* of an origin\\*.\n", + "\n", + "$$\n", + " \\begin{bmatrix}\n", + " x\\\\\n", + " y\\\\\n", + " z\\\\\n", + " 1\\\\\n", + " \\end{bmatrix} =\n", + " \\mathbf A\n", + " \\begin{bmatrix}\n", + " i\\\\\n", + " j\\\\\n", + " k\\\\\n", + " 1\\\\\n", + " \\end{bmatrix} =\\begin{bmatrix}\n", + " m_{1,1} & m_{1,2} & m_{1,3} & a \\\\\n", + " m_{2,1} & m_{2,2} & m_{2,3} & b \\\\\n", + " m_{3,1} & m_{3,2} & m_{3,3} & c \\\\\n", + " 0 & 0 & 0 & 1 \\\\\n", + " \\end{bmatrix}\n", + " \\begin{bmatrix}\n", + " i\\\\\n", + " j\\\\\n", + " k\\\\\n", + " 1\\\\\n", + " \\end{bmatrix}\n", + "$$\n", + "\n", + "For an unmodified image, this reference space typically refers to an origin in the isocenter of the imaging magnet, and the directions right, anterior and superior are defined assuming a subject is lying in the scanner, face up.\n", + "\n", + "![](https://nipy.org/nibabel/_images/localizer.png)\n", + "\n", + "\\* *If a file format uses an alternative convention, NiBabel converts on read/write, so affines are always RAS+.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### The affine as a series of transformations\n", + "\n", + "\n", + "\n", + "![](https://nipy.org/nibabel/_images/illustrating_affine.png)\n", + "\n", + "An affine transformation can be decomposed into translation, rotation, scaling (zooms) and shear transformations, applied right-to-left." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Translation: [-125.26386261 -73.13301086 -177.55429077]\n", + "Rotation:\n", + "[[ 0.99913193 -0.03973383 0.01251451]\n", + " [ 0.0407722 0.99433924 -0.09811785]\n", + " [-0.00854507 0.09854292 0.99509611]]\n", + "Zooms: [0.99999999 1.2993759 1.00000002]\n", + "Max shear: 1.8087155968217092e-06\n" + ] + } + ], + "source": [ + "T, R, Z, S = transforms3d.affines.decompose44(affine) # External library\n", + "print(f\"Translation: {T}\\nRotation:\\n{R}\\nZooms: {Z}\\nMax shear: {np.abs(S).max()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Tm, Rm, Zm, Sm = [np.eye(4) for _ in range(4)]\n", + "Tm[:3, 3] = T\n", + "Rm[:3, :3] = R\n", + "Zm[:3, :3] = np.diag(Z)\n", + "Sm[[0, 0, 1], [1, 2, 2]] = S\n", + "np.allclose(Tm @ Rm @ Zm @ Sm, affine)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "NiBabel provides functions for extracting information from affines:\n", + "\n", + "* Orientation (or axis codes) indicates the direction an axis encodes. If increasing index along an axis moves to the right or left, the axis is coded \"R\" or \"L\", respectively.\n", + "* Voxel sizes (or zooms)\n", + "* Obliquity measures the amount of rotation from \"canonical\" axes." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Orientation: ('R', 'A', 'S')\n", + "Zooms: [0.99999999 1.2993759 1.00000002]\n", + "Obliquity: [0.04167007 0.10645293 0.09907456]\n" + ] + } + ], + "source": [ + "print(\"Orientation:\", nb.aff2axcodes(affine))\n", + "print(\"Zooms:\", nb.affines.voxel_sizes(affine))\n", + "print(\"Obliquity:\", nb.affines.obliquity(affine))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "You can also use it to answer specific questions. For instance, the inverse affine allows you to calculate indices from RAS coordinates and look up the image intensity value at that location." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Center: (126, 65, 171)\n", + "Value: 312.0\n" + ] + } + ], + "source": [ + "i, j, k, _ = np.linalg.pinv(affine) @ [0, 0, 0, 1]\n", + "print(f\"Center: ({int(i)}, {int(j)}, {int(k)})\")\n", + "print(f\"Value: \", data[int(i), int(j), int(k)])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Don't Panic\n", + "\n", + "
\n", + "\n", + "If you didn't follow all of the above, that's okay. Here are the important points:\n", + "\n", + "1. Affines provide the spatial interpretation of the data.\n", + "2. NiBabel has some useful methods for working with them.\n", + "\n", + "You'll go over this again with Noah Benson in [Introduction to the Geometry and Structure of the Human Brain](https://neurohackademy.org/course/introduction-to-the-geometry-and-structure-of-the-human-brain/).\n", + "\n", + "Matthew Brett's [Coordinate systems and affines](https://nipy.org/nibabel/coordinate_systems.html) tutorial is an excellent resource." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Image headers\n", + "\n", + "The image header is specific to each file format. Minimally, it contains information to construct the data and affine arrays, but some methods are useful beyond that. \n", + "\n", + "* `get_zooms()` method returns voxel sizes. If the data is 4D, then repetition time is included as a temporal zoom.\n", + "* `get_data_dtype()` returns the numpy data type in which the image data is stored (or will be stored when the image is saved)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4.0, 4.0, 3.999975, 2.5)\n", + "int16\n" + ] + } + ], + "source": [ + "print(bold.header.get_zooms())\n", + "print(bold.header.get_data_dtype())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "For images with fixed header structures, header fields are exposed through a `dict`-like interface." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " object, endian='<'\n", + "sizeof_hdr : 348\n", + "data_type : b''\n", + "db_name : b''\n", + "extents : 0\n", + "session_error : 0\n", + "regular : b'r'\n", + "dim_info : 0\n", + "dim : [ 3 256 156 256 1 1 1 1]\n", + "intent_p1 : 0.0\n", + "intent_p2 : 0.0\n", + "intent_p3 : 0.0\n", + "intent_code : none\n", + "datatype : float32\n", + "bitpix : 32\n", + "slice_start : 0\n", + "pixdim : [1. 1. 1.2993759 1. 0.009668 0. 0.\n", + " 0. ]\n", + "vox_offset : 0.0\n", + "scl_slope : nan\n", + "scl_inter : nan\n", + "slice_end : 0\n", + "slice_code : unknown\n", + "xyzt_units : 10\n", + "cal_max : 0.0\n", + "cal_min : 0.0\n", + "slice_duration : 0.0\n", + "toffset : 0.0\n", + "glmax : 0\n", + "glmin : 0\n", + "descrip : b'FSL5.0'\n", + "aux_file : b''\n", + "qform_code : scanner\n", + "sform_code : scanner\n", + "quatern_b : 0.049235623\n", + "quatern_c : 0.005271982\n", + "quatern_d : 0.020155331\n", + "qoffset_x : -125.26386\n", + "qoffset_y : -73.13301\n", + "qoffset_z : -177.55429\n", + "srow_x : [ 9.9913192e-01 -5.1629182e-02 1.2512702e-02 -1.2526386e+02]\n", + "srow_y : [ 4.0772196e-02 1.2920204e+00 -9.8117918e-02 -7.3133011e+01]\n", + "srow_z : [-8.5450690e-03 1.2804429e-01 9.9509615e-01 -1.7755429e+02]\n", + "intent_name : b''\n", + "magic : b'n+1'\n" + ] + } + ], + "source": [ + "print(header)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'FSL5.0'\n" + ] + } + ], + "source": [ + "print(header['descrip'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The MGH header is similarly accessible, but its structure is quite different:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " object, endian='>'\n", + "version : 1\n", + "dims : [256 156 256 1]\n", + "type : 3\n", + "dof : 0\n", + "goodRASFlag : 1\n", + "delta : [1. 1.2993759 1. ]\n", + "Mdc : [[ 0.9991319 0.0407722 -0.00854507]\n", + " [-0.03973383 0.9943392 0.09854292]\n", + " [ 0.0125127 -0.09811792 0.99509615]]\n", + "Pxyz_c : [ 0.1995725 20.30433 -41.2883 ]\n", + "tr : 0.0\n", + "flip_angle : 0.0\n", + "te : 0.0\n", + "ti : 0.0\n", + "fov : 0.0\n" + ] + } + ], + "source": [ + "mghheader = nb.load('/tmp/img.mgz').header\n", + "print(mghheader)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.1995725 20.30433 -41.2883 ]\n", + "(1.0, 1.2993759, 1.0)\n", + ">f4\n" + ] + } + ], + "source": [ + "print(mghheader['Pxyz_c'])\n", + "print(mghheader.get_zooms())\n", + "print(mghheader.get_data_dtype())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other formats use text key-value pairs (PAR/REC, BRIK/HEAD) or keys in NetCDF (MINC 1.0) or HDF5 (MINC 2.0) containers, and their values are accessible by other means.\n", + "\n", + "Often, we're not particularly interested in the header, or even the affine. But it's important to know they're there and, especially, to remember to copy them when making new images, so that derivatives stay aligned with the original image." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Creating new images\n", + "\n", + "Reading data will only take you so far. In many cases, you will want to use the image to compute a new image in the same space. Such a function might take the form:\n", + "\n", + "```Python\n", + "def my_function(image):\n", + " orig_data = image.get_fdata()\n", + " new_data = some_operation_on(orig_data)\n", + " return image.__class__(new_data, affine=image.affine, header=image.header)\n", + "```\n", + "\n", + "Note the `image.__class__` ensures that the output image is the same type as the input. If your operation requires specific format features, you might use a specific class like `nb.Nifti1Image`.\n", + "\n", + "For example, perhaps we want to save space and rescale our T1w image to an unsigned byte:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def rescale(img):\n", + " data = img.get_fdata()\n", + " rescaled = ((data - data.min()) * 255. / (data.max() - data.min())).astype(np.uint8)\n", + " \n", + " rescaled_img = img.__class__(rescaled, affine=img.affine, header=img.header)\n", + " \n", + " rescaled_img.header.set_data_dtype('uint8') # Ensure data is saved as this type\n", + " return rescaled_img\n", + "\n", + "rescaled_t1w = rescale(t1w)\n", + "rescaled_t1w.to_filename('/tmp/rescaled_t1w.nii.gz')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data objects - Arrays and Array Proxies\n", + "\n", + "Recall that the spatial image contains data, affine and header objects. When creating a new image, the `data` array is typically an array." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[244 245 246 247]\n", + " [248 249 250 251]\n", + " [252 253 254 255]]\n", + "\n", + " [[256 257 258 259]\n", + " [260 261 262 263]\n", + " [264 265 266 267]]]\n" + ] + } + ], + "source": [ + "array_img = nb.Nifti1Image(np.arange(244, 268).reshape(2, 3, 4), affine=np.diag([2, 2, 2, 1]))\n", + "print(array_img.dataobj)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "When loading a file, an `ArrayProxy` is used for most image types." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "array_img.to_filename('/tmp/array_img.nii')\n", + "proxy_img = nb.load('/tmp/array_img.nii')\n", + "print(proxy_img.dataobj)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "An array proxy is an object that knows how to access data, but does not read it until requested. The usual way to request data is `get_fdata()`, which returns all data as floating point:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "memmap([[[244., 245., 246., 247.],\n", + " [248., 249., 250., 251.],\n", + " [252., 253., 254., 255.]],\n", + "\n", + " [[256., 257., 258., 259.],\n", + " [260., 261., 262., 263.],\n", + " [264., 265., 266., 267.]]], dtype=float32)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "proxy_img.get_fdata(dtype=np.float32) # The default is float64, but you can choose any floating point type." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`get_fdata()` provides a very predictable interface. When you need more control, you'll want to work with the `ArrayProxy` directly." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Converting array proxies to arrays\n", + "\n", + "Array proxies are designed to be one step away from a numpy array:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "int64\n" + ] + }, + { + "data": { + "text/plain": [ + "memmap([[[244, 245, 246, 247],\n", + " [248, 249, 250, 251],\n", + " [252, 253, 254, 255]],\n", + "\n", + " [[256, 257, 258, 259],\n", + " [260, 261, 262, 263],\n", + " [264, 265, 266, 267]]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr = np.asanyarray(proxy_img.dataobj) # array will create a copy; asarray passes through arrays; asanyarray passes subclasses like memmap through\n", + "print(arr.dtype)\n", + "arr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Memory maps are arrays that remain on disk, rather than in RAM. This is only possible with uncompressed images.\n", + "\n", + "We can also cast to any type we please, however unwisely." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[244 245 246 247]\n", + " [248 249 250 251]\n", + " [252 253 254 255]]\n", + "\n", + " [[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]]\n", + "[[[244.+0.j 245.+0.j 246.+0.j 247.+0.j]\n", + " [248.+0.j 249.+0.j 250.+0.j 251.+0.j]\n", + " [252.+0.j 253.+0.j 254.+0.j 255.+0.j]]\n", + "\n", + " [[256.+0.j 257.+0.j 258.+0.j 259.+0.j]\n", + " [260.+0.j 261.+0.j 262.+0.j 263.+0.j]\n", + " [264.+0.j 265.+0.j 266.+0.j 267.+0.j]]]\n" + ] + } + ], + "source": [ + "print(np.uint8(proxy_img.dataobj)) # Values over 255 will be truncated\n", + "print(np.complex256(proxy_img.dataobj)) # A life less ordinal" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Indexing and slicing array proxies\n", + "\n", + "One of the primary motivations for an array proxy is to avoid loading data unnecessarily. Accessing the array proxy with array indices or slices will return the requested values without loading other data:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[244 245 246 247]\n", + " [248 249 250 251]\n", + " [252 253 254 255]]\n", + "[[[245 246]\n", + " [249 250]\n", + " [253 254]]\n", + "\n", + " [[257 258]\n", + " [261 262]\n", + " [265 266]]]\n" + ] + } + ], + "source": [ + "print(proxy_img.dataobj[0])\n", + "print(proxy_img.dataobj[..., 1:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, this is useful for fetching a single volume from a BOLD series:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(64, 64, 30)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vol0 = bold.dataobj[..., 0]\n", + "vol0.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Slicing works with compressed data, as well. Install the [indexed-gzip](https://pypi.org/project/indexed-gzip/) package for significant speedups with gzipped files." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Scaling array proxies\n", + "\n", + "Several image types, including NIfTI, have a slope and intercept (scaling factors) in the header that allows them to extend the data range and/or precision beyond those of the on-disk type.\n", + "\n", + "For example `uint16` can take the values 0-65535. If a BOLD series includes values from 500-2000, we can calculate a slope that will allow us to utilize the 16 bits of precision to encode our desired values." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.02288818359375 500\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pr = (500, 2000) # Plausible range\n", + "nbits = 16 # 16-bits of precision\n", + "scl_slope = (pr[1] - pr[0]) / (2 ** nbits) # Resolvable difference\n", + "scl_inter = pr[0] # Minimum value\n", + "print(scl_slope, scl_inter)\n", + "\"Saving space by collapsing plotting into one line.\"; x = np.arange(2 ** nbits); plt.step(x, x * scl_slope + scl_inter); vlim = np.array([120, 150]); plt.xlim(vlim); plt.ylim(vlim * scl_slope + scl_inter); plt.xlabel(\"On-disk value\"); plt.ylabel('\"True\" value'); _ = plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Let's create an image from some random values in our plausible range:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 840.39886182 1081.43237313 835.40125694 982.33174656]\n", + " [1573.21571784 1261.74029839 600.53166939 1131.41563477]\n", + " [1638.7430394 1913.94671681 1364.22730446 711.51449619]]\n", + "\n", + " [[ 642.17083878 1226.3384657 1562.95952987 1962.86543456]\n", + " [1148.47532746 1302.16166916 877.44302527 1921.37256987]\n", + " [1301.64831329 1167.99390276 1585.47466315 1442.73636725]]]\n" + ] + } + ], + "source": [ + "float_img = nb.Nifti1Image(np.random.default_rng().uniform(500, 2000, (2, 3, 4)), # 64-bit float\n", + " affine=np.diag([2, 2, 2, 1]))\n", + "print(float_img.get_fdata())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Save as `uint16` and check its values:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 840.40302254 1081.43848911 835.3931435 982.32187811]\n", + " [1573.21736212 1261.73177083 600.53167725 1131.41255221]\n", + " [1638.74075933 1913.95150033 1364.23680624 711.51816763]]\n", + "\n", + " [[ 642.16980053 1226.33001149 1562.96893737 1962.86538155]\n", + " [1148.47940154 1302.16419709 877.44702439 1921.37277342]\n", + " [1301.64450009 1167.9992207 1585.48221122 1442.73184046]]]\n" + ] + } + ], + "source": [ + "float_img.header.set_data_dtype(np.uint16)\n", + "float_img.to_filename(\"/tmp/uint16_img.nii\")\n", + "\n", + "uint16_img = nb.load(\"/tmp/uint16_img.nii\")\n", + "print(uint16_img.get_fdata())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We clearly lost some precision..." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.009868452619912205" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.abs(float_img.get_fdata() - uint16_img.get_fdata()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what's going on?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The `ArrayProxy` keeps track of scaling factors:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slope: 0.020787879824638367; Intercept: 600.5316772460938\n", + "[[[11539 23134 11298 18366]\n", + " [46791 31807 0 25538]\n", + " [49943 63182 36738 5339]]\n", + "\n", + " [[ 2003 30104 46298 65535]\n", + " [26359 33752 13321 63539]\n", + " [33727 27298 47381 40514]]]\n" + ] + } + ], + "source": [ + "print(f\"Slope: {uint16_img.dataobj.slope}; Intercept: {uint16_img.dataobj.inter}\")\n", + "print(uint16_img.dataobj.get_unscaled())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scaling is done automatically when the data is accessed, by slice or whole." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 840.40302254 1081.43848911 835.3931435 982.32187811]\n", + " [1573.21736212 1261.73177083 600.53167725 1131.41255221]\n", + " [1638.74075933 1913.95150033 1364.23680624 711.51816763]]\n", + "\n", + " [[ 642.16980053 1226.33001149 1562.96893737 1962.86538155]\n", + " [1148.47940154 1302.16419709 877.44702439 1921.37277342]\n", + " [1301.64450009 1167.9992207 1585.48221122 1442.73184046]]]\n", + "840.4030225425959\n" + ] + } + ], + "source": [ + "print(np.asanyarray(uint16_img.dataobj))\n", + "print(uint16_img.dataobj[0, 0, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `ArrayProxy` guarantees that the data has the intended *value*, but the *type* can vary based on the on-disk type and the values of scaling factors." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "int64\n", + "float64\n" + ] + } + ], + "source": [ + "print(proxy_img.dataobj[0, 0, 0].dtype) # Our earlier integer image\n", + "print(uint16_img.dataobj[0, 0, 0].dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`get_fdata()` sweeps these details under the rug and always gives you the same type." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Don't Panic\n", + "\n", + "
\n", + "\n", + "If you didn't follow all of the above, that's okay. Here are the important points:\n", + "\n", + "1. When in doubt, use `img.get_fdata()` will fetch all of the data, and it will always be a float\n", + "2. `img.dataobj` exists if you want to load only some data or control the data type\n", + "3. Both methods transparently scale data when needed\n", + "\n", + "In the NiBabel docs, [The image data array](https://nipy.org/nibabel/nibabel_images.html#the-image-data-array) gives you an overview of both methods, and [Images and memory](https://nipy.org/nibabel/images_and_memory.html) has even more details." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Slicing images\n", + "\n", + "Slicing array proxies is nice. Wouldn't it be nicer to keep track of the affine and header?\n", + "\n", + "The `slicer` attribute provides an interface that allows you to apply slices to an image, and updates the affine to ensure that the spatial information matches.\n", + "\n", + "Consider the T1-weighted image from earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = t1w.orthoview()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "We can use the slicer to crop unneeded voxels in the left-right and inferior-superior directions:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cropped = t1w.slicer[40:216, :, 50:226]\n", + "cropped.orthoview()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the origin crosshair points to the same structure. The affines now differ in translation:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 0. 0. 40.5909118 ]\n", + " [ 0. 0. 0. -3.27500805]\n", + " [ 0. 0. 0. 49.41300459]\n", + " [ 0. 0. 0. 0. ]]\n" + ] + } + ], + "source": [ + "print(cropped.affine - t1w.affine)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "You can even downsample an image, and the zooms will reflect the increased distance between voxels." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4.0, 5.1975036, 4.0)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cheap_downsample = cropped.slicer[2::4, 2::4, 2::4]\n", + "print(cheap_downsample.header.get_zooms())\n", + "cheap_downsample.orthoview()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this is a bad idea in *most* circumstances because it induces aliasing." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The better approach would be to anti-alias and then slice:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def blur(img, sigma): # Isotropic in voxel space, not world space\n", + " return img.__class__(ndi.gaussian_filter(img.dataobj, sigma), img.affine, img.header)\n", + "\n", + "better_downsample = blur(cropped, sigma=1.5).slicer[2::4, 2::4, 2::4]\n", + "better_downsample.orthoview()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "For non-spatial dimensions, slices or indices may be used to select one or more volumes." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BOLD shape: (64, 64, 30, 184); Zooms: (4.0, 4.0, 3.999975, 2.5)\n", + "Time pts 1-5 shape: (64, 64, 30, 5); Zooms: (4.0, 4.0, 3.999975, 2.5)\n", + "Time pt 1 shape: (64, 64, 30); Zooms: (4.0, 4.0, 3.999975)\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tp15 = bold.slicer[..., :5]\n", + "tp1 = bold.slicer[..., 0]\n", + "print(f\"BOLD shape: {bold.shape}; Zooms: {bold.header.get_zooms()}\")\n", + "print(f\"Time pts 1-5 shape: {tp15.shape}; Zooms: {tp15.header.get_zooms()}\")\n", + "print(f\"Time pt 1 shape: {tp1.shape}; Zooms: {tp1.header.get_zooms()}\")\n", + "np.array_equal(tp15.get_fdata(), bold.dataobj[..., :5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aliasing considerations apply to time series as well, so be careful with down-sampling here, too." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
Break!
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Surface Images\n", + "\n", + "Although the scanner samples data in three dimensions, some brain structures are better represented as a convoluted sheet than a volume. Data may be usefully resampled onto a cortical sheet, but in the process, it loses the intrinsic geometry of a 3D array.\n", + "\n", + "To represent data on a surface, you need the following structures:\n", + "\n", + "1. The surface *mesh*\n", + " 1. Vertices: a list of coordinates in world space\n", + " 1. Faces: a list of 3-tuples of indices into the coordinate list\n", + "2. The *data* array: a 1D or 2D array of values or vectors at each vertex\n", + "\n", + "Unlike spatial images, these components are frequently kept in separate files.\n", + "\n", + "*Terminological note*: You are likely to encounter multiple names for each of these. *Vertices* may be called *coordinates* or a *point set*. *Faces* are often called *triangles*. When a data array is plotted on the surface, it might be called a *texture*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Image-specific interfaces\n", + "\n", + "The two supported surface formats at present are GIFTI and FreeSurfer geometry files. Unlike spatial images, there is not yet a common interface for working with surface-based data.\n", + "\n", + "FreeSurfer encodes a great deal of information in its directory structure and file names, allowing the necessary data arrays to have relatively simple formats.\n", + "\n", + "GIFTI is more of an interchange format, and so has a rich metadata structure and can store different kinds of data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Surface meshes\n", + "\n", + "To load a surface mesh in a FreeSurfer directory, use `nb.freesurfer.read_geometry`:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ -9.01226521 -96.7989502 -31.38132668]\n", + " [ -9.62946892 -96.77184296 -31.43518829]]\n", + "[[0 1 3]\n", + " [4 3 1]]\n", + "OrderedDict([('head', array([ 2, 0, 20], dtype=int32)),\n", + " ('valid', '1 # volume info valid'),\n", + " ('filename', '../mri/filled-pretess255.mgz'),\n", + " ('volume', array([256, 256, 256])),\n", + " ('voxelsize', array([1., 1., 1.])),\n", + " ('xras', array([-0.99999994, 0. , 0. ])),\n", + " ('yras', array([ 0. , 0. , -0.99999994])),\n", + " ('zras', array([0. , 0.99999994, 0. ])),\n", + " ('cras', array([-0.99998474, -5.00001526, -1.00003815]))])\n" + ] + } + ], + "source": [ + "fs_verts, fs_faces, fs_meta = nb.freesurfer.read_geometry(data_dir / 'ds005-preproc/freesurfer/sub-01/surf/lh.pial', read_metadata=True)\n", + "print(fs_verts[:2])\n", + "print(fs_faces[:2])\n", + "pprint(fs_meta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NiBabel does not have any viewer for surfaces, but Nilearn has plotting utilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = nlp.plot_surf((fs_verts, fs_faces))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Let's do the same thing with GIFTI. Here, the file-level metadata is minimal:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Date': 'Mon Jan 13 17:14:05 2020',\n", + " 'UserName': 'root',\n", + " 'gifticlib-version': 'gifti library version 1.09, 28 June, 2010'}\n" + ] + } + ], + "source": [ + "gii_pial = nb.load(data_dir / 'ds005-preproc/fmriprep/sub-01/anat/sub-01_hemi-L_pial.surf.gii')\n", + "pprint(gii_pial.meta.metadata) # .meta maps onto the XML object, its .metadata property exposes a Python dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The vertices and faces are stored as separate data arrays, each with their own metadata. These can be queried by NIfTI *intent code*:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'AnatomicalStructurePrimary': 'CortexLeft',\n", + " 'AnatomicalStructureSecondary': 'Pial',\n", + " 'GeometricType': 'Anatomical',\n", + " 'Name': '/out/freesurfer/sub-01/surf/lh.pial',\n", + " 'SurfaceCenterX': '-32.284454',\n", + " 'SurfaceCenterY': '-22.393669',\n", + " 'SurfaceCenterZ': '-2.683327',\n", + " 'VolGeomC_A': '0.000000',\n", + " 'VolGeomC_R': '0.000000',\n", + " 'VolGeomC_S': '0.000000',\n", + " 'VolGeomDepth': '256',\n", + " 'VolGeomHeight': '256',\n", + " 'VolGeomWidth': '256',\n", + " 'VolGeomX_A': '0.000000',\n", + " 'VolGeomX_R': '-1.000000',\n", + " 'VolGeomX_S': '0.000000',\n", + " 'VolGeomXsize': '1.000000',\n", + " 'VolGeomY_A': '0.000000',\n", + " 'VolGeomY_R': '0.000000',\n", + " 'VolGeomY_S': '-1.000000',\n", + " 'VolGeomYsize': '1.000000',\n", + " 'VolGeomZ_A': '1.000000',\n", + " 'VolGeomZ_R': '0.000000',\n", + " 'VolGeomZ_S': '0.000000',\n", + " 'VolGeomZsize': '1.000000'}\n", + "{'Name': '/out/freesurfer/sub-01/surf/lh.pial', 'TopologicalType': 'Closed'}\n" + ] + } + ], + "source": [ + "pointset_darray = gii_pial.get_arrays_from_intent('pointset')[0]\n", + "triangle_darray = gii_pial.get_arrays_from_intent('triangle')[0]\n", + "pprint(pointset_darray.meta.metadata)\n", + "pprint(triangle_darray.meta.metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The actual values are stored as the `.data` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ -10.000125 -101.81076 -32.395775]\n", + " [ -10.617342 -101.78375 -32.44954 ]]\n", + "[[0 1 3]\n", + " [4 3 1]]\n" + ] + } + ], + "source": [ + "gii_verts, gii_faces = pointset_darray.data, triangle_darray.data\n", + "print(gii_verts[:2])\n", + "print(gii_faces[:2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This API can be cumbersome, if all you want is the NumPy arrays. Thanks to a project started at Neurohackademy 2019, the `agg_data()` (aggregate data) method will find the requested data array(s) and return just the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ -10.000125 -101.81076 -32.395775]\n", + " [ -10.617342 -101.78375 -32.44954 ]]\n", + "[[0 1 3]\n", + " [4 3 1]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gii_verts, gii_faces = gii_pial.agg_data(('pointset', 'triangle'))\n", + "print(gii_verts[:2])\n", + "print(gii_faces[:2])\n", + "_ = nlp.plot_surf((gii_verts, gii_faces))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Surface-sampled data\n", + "\n", + "For data sampled to the surface, FreeSurfer has a few data types:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(126142,)\n", + "(126142,)\n", + "(126142, 1, 1)\n" + ] + } + ], + "source": [ + "# Morphometry\n", + "curv = nb.freesurfer.read_morph_data(data_dir / 'ds005-preproc/freesurfer/sub-01/surf/lh.curv')\n", + "# Annotations\n", + "labels, color_table, names = nb.freesurfer.read_annot(data_dir / 'ds005-preproc/freesurfer/sub-01/label/lh.aparc.annot')\n", + "# MGH files...\n", + "mgh = nb.load(data_dir / 'ds005-preproc/freesurfer/sub-01/surf/lh.w-g.pct.mgh')\n", + "print(curv.shape)\n", + "print(labels.shape)\n", + "print(mgh.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, subplot_kw={'projection': '3d'})\n", + "_ = nlp.plot_surf((fs_verts, fs_faces), curv, axes=axes[0])\n", + "_ = nlp.plot_surf((fs_verts, fs_faces), labels, axes=axes[1])\n", + "_ = nlp.plot_surf((fs_verts, fs_faces), mgh.get_fdata(), axes=axes[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "GIFTIs will be GIFTIs. This one is a BOLD series sampled to the `fsaverage5` surface:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "bold_gii = nb.load(data_dir / 'ds005-preproc/fmriprep/sub-01/func/sub-01_task-mixedgamblestask_run-1_space-fsaverage5_hemi-L_bold.func.gii')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Each time point is an individual data array with intent `NIFTI_INTENT_TIME_SERIES`. `agg_data()` will aggregate these into a single array." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10242, 240)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = bold_gii.agg_data('time series')\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "We can plot the mean BOLD signal. This time we will use a FreeSurfer surface, which Nilearn knows what to do with:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = nlp.plot_surf(str(data_dir / 'ds005-preproc/freesurfer/fsaverage5/surf/lh.inflated'), data.mean(axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Don't Panic\n", + "\n", + "
\n", + "\n", + "If you didn't follow all of the above, that's okay. Here are the important points:\n", + "\n", + "1. Practical considerations make dealing with surfaces a multi-file affair\n", + "2. The data structures are common (vertices, faces, per-vertex data arrays), but the arrangement can vary\n", + "3. For working with FreeSurfer files, learn to traverse the directory and identify file types\n", + "4. For working with GIFTI, learn to query the file for its contents\n", + "\n", + "Hopefully next year, we'll be able to talk about a common interface that will simplify things." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## CIFTI-2\n", + "\n", + "
\n", + "
\n", + "
\n", + " From Glasser, et al., 2016. doi:10.1038/nn.4361\n", + "
\n", + "
\n", + "\n", + "CIFTI-2 is a file format intended to cover many use cases for connectivity analysis.\n", + "\n", + "Files have 2-3 dimensions and each dimension is described by one of 5 types of axis.\n", + "\n", + "* Brain models: each row/column is a voxel or vertex\n", + "* Parcels: each row/column is a group of voxels and/or vertices\n", + "* Scalars: each row/column has a unique name\n", + "* Labels: each row/column has a unique name and label table\n", + "* Series: each row/column is a point in a series (typically time series), which increases monotonically\n", + "\n", + "For example, a \"parcellated dense connectivity\" CIFTI-2 file has two dimensions, indexed by a brain models axis and a parcels axis, respectively. The interpretation is \"connectivity from parcels to vertices and/or voxels\"." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "
\n", + " \n", + "
\n", + "\n", + "On disk, the file is a NIfTI-2 file with an alternative XML header as an extension, schematized here.\n", + "\n", + "NiBabel loads a header that closely mirrors this structure, and makes the NIfTI-2 header accessible as a `nifti_header` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "cifti = nb.load(data_dir / 'ds005-preproc/fmriprep/sub-01/func/sub-01_task-mixedgamblestask_run-1_space-fsLR_den-91k_bold.dtseries.nii')\n", + "cifti_data = cifti.get_fdata(dtype=np.float32)\n", + "cifti_hdr = cifti.header\n", + "nifti_hdr = cifti.nifti_header" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "The `Cifti2Header` is useful if you're familiar with the XML structure and need to fetch an exact value or have fine control over the header that is written." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + "[0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n" + ] + } + ], + "source": [ + "bm0 = next(cifti_hdr.matrix[1].brain_models)\n", + "print(bm0.voxel_indices_ijk)\n", + "print(list(bm0.vertex_indices)[:20])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Most of the time, the `Axis` format will be more useful:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "axes = [cifti_hdr.get_axis(i) for i in range(cifti.ndim)]\n", + "axes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The simplest way to get a handle on CIFTI-2 data is to use it. Let's take an axis and a data block and repackage the voxels as a regular NIfTI-1 image:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "def volume_from_cifti(data, axis):\n", + " assert isinstance(axis, nb.cifti2.BrainModelAxis)\n", + " data = data.T[axis.volume_mask] # Assume brainmodels axis is last, move it to front\n", + " volmask = axis.volume_mask # Which indices on this axis are for voxels?\n", + " vox_indices = tuple(axis.voxel[axis.volume_mask].T) # ([x0, x1, ...], [y0, ...], [z0, ...])\n", + " vol_data = np.zeros(axis.volume_shape + data.shape[1:], # Volume + any extra dimensions\n", + " dtype=data.dtype)\n", + " vol_data[vox_indices] = data # \"Fancy indexing\"\n", + " return nb.Nifti1Image(vol_data, axis.affine) # Add affine for spatial interpretation" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "volume_from_cifti(cifti_data, axes[1]).orthoview()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Now we can extract the values on a surface vertex. This time, as a simple numpy array:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def surf_data_from_cifti(data, axis, surf_name):\n", + " assert isinstance(axis, nb.cifti2.BrainModelAxis)\n", + " for name, data_indices, model in axis.iter_structures(): # Iterates over volumetric and surface structures\n", + " if name == surf_name: # Just looking for a surface\n", + " data = data.T[data_indices] # Assume brainmodels axis is last, move it to front\n", + " vtx_indices = model.vertex # Generally 1-N, except medial wall vertices\n", + " surf_data = np.zeros((vtx_indices.max() + 1,) + data.shape[1:], dtype=data.dtype)\n", + " surf_data[vtx_indices] = data\n", + " return surf_data\n", + " raise ValueError(f\"No structure named {surf_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_ = nlp.plot_surf(str(data_dir / \"conte69/Conte69.L.inflated.32k_fs_LR.surf.gii\"),\n", + " surf_data_from_cifti(cifti_data, axes[1], 'CIFTI_STRUCTURE_CORTEX_LEFT').mean(axis=1),\n", + " cmap='plasma')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Finally, combine into a function that will break a CIFTI-2 matrix into a volume and two surface components:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "def decompose_cifti(img):\n", + " data = img.get_fdata(dtype=np.float32)\n", + " brain_models = img.header.get_axis(1) # Assume we know this\n", + " return (volume_from_cifti(data, brain_models),\n", + " surf_data_from_cifti(data, brain_models, \"CIFTI_STRUCTURE_CORTEX_LEFT\"),\n", + " surf_data_from_cifti(data, brain_models, \"CIFTI_STRUCTURE_CORTEX_RIGHT\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(91, 109, 91, 240) (32492, 240) (32492, 240)\n" + ] + } + ], + "source": [ + "vol, left, right = decompose_cifti(cifti)\n", + "print(vol.shape, left.shape, right.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Tractography\n", + "\n", + "
\n", + "
\n", + "
\n", + " From DIPY documentation\n", + "
\n", + "
\n", + "\n", + "Diffusion MRI uses estimates of the directional flow of water to detect white matter tracts. These tracts are represented as a series of points in world space.\n", + "\n", + "NiBabel represents tractogram files as a collection of these things:\n", + "\n", + "1. A *tractogram*, which is:\n", + " 1. A set of *streamlines*: each streamline is a series of coordinates describing a path\n", + " 1. Streamline-level data: a set of data arrays associated with each streamline\n", + " 1. Point-level data: a set of data arrays associated with each point of each streamline\n", + "1. An *affine* matrix: 4x4 array relating voxel coordinates of a reference image and world coordinates\n", + "1. File-level metadata: a format-specific header\n", + "\n", + "Files of this type are considered `TractogramFile`s. Like `SpatialImage`, the goal is to provide a uniform interface where possible.\n", + "\n", + "The two currently-supported file types are TCK (MRtrix) and TRK (TrackVis). We will load some very small ones that were created for testing:" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "tck = nb.streamlines.load(Path(nb.testing.data_path) / 'standard.tck')\n", + "trk = nb.streamlines.load(Path(nb.testing.data_path) / 'complex.trk')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Like a spatial image, printing the tractogram file gives an overview:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAGIC NUMBER: TRACK\n", + "v.2\n", + "dim: [1 1 1]\n", + "voxel_sizes: [1. 1. 1.]\n", + "origin: [0. 0. 0.]\n", + "nb_scalars: 4\n", + "scalar_names:\n", + " colors\u00003\n", + " fa\n", + "nb_properties: 5\n", + "property_names:\n", + " mean_colors\u00003\n", + " mean_curvature\n", + " mean_torsion\n", + "vox_to_world:\n", + "[[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]]\n", + "voxel_order: RAS\n", + "image_orientation_patient: [0. 0. 0. 0. 0. 0.]\n", + "pad1: \n", + "pad2: \n", + "invert_x: \n", + "invert_y: \n", + "invert_z: \n", + "swap_xy: \n", + "swap_yz: \n", + "swap_zx: \n", + "n_count: 3\n", + "hdr_size: 1000\n" + ] + } + ], + "source": [ + "print(trk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The streamlines API\n", + "\n", + "The overall API is as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# File-level\n", + "tractogram = trk.tractogram\n", + "trk_affine = trk.affine # Describes relation between voxels and coordinates in an associated image\n", + "trk_header = trk.header\n", + "\n", + "# All streamlines\n", + "streamlines = tractogram.streamlines\n", + "data_per_point = tractogram.data_per_point\n", + "data_per_streamline = tractogram.data_per_streamline\n", + "\n", + "# One streamline at a time\n", + "tractogram_item = tractogram[2] # Most to look at\n", + "streamline_coords = tractogram_item.streamline\n", + "streamline_data = tractogram_item.data_for_streamline\n", + "point_data = tractogram_item.data_for_points" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### A single streamline\n", + "\n", + "When looking at an individual streamline, the objects are familiar: NumPy arrays and Python dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 1. 2.]\n", + " [ 3. 4. 5.]\n", + " [ 6. 7. 8.]\n", + " [ 9. 10. 11.]\n", + " [12. 13. 14.]]\n", + "{'mean_colors': array([0., 0., 1.], dtype=float32),\n", + " 'mean_curvature': array([3.11], dtype=float32),\n", + " 'mean_torsion': array([3.22], dtype=float32)}\n", + "{'colors': array([[0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.]], dtype=float32),\n", + " 'fa': array([[0.5],\n", + " [0.6],\n", + " [0.6],\n", + " [0.7],\n", + " [0.8]], dtype=float32)}\n" + ] + } + ], + "source": [ + "print(streamline_coords)\n", + "pprint(streamline_data)\n", + "pprint(point_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### All streamlines\n", + "\n", + "When looking at all streamlines, the objects are custom. These deal with issues arising from the fact that each streamline may have a different number of coordinates, so N-dimensional arrays are not appropriate." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ArraySequence([array([[0., 1., 2.]], dtype=float32), array([[0., 1., 2.],\n", + " [3., 4., 5.]], dtype=float32), array([[ 0., 1., 2.],\n", + " [ 3., 4., 5.],\n", + " [ 6., 7., 8.],\n", + " [ 9., 10., 11.],\n", + " [12., 13., 14.]], dtype=float32)])\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(streamlines)\n", + "print(data_per_streamline)\n", + "print(data_per_point)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dictionaries operate like normal dictionaries, providing NumPy array and `ArraySequence` values." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.11]\n", + " [2.11]\n", + " [3.11]]\n", + "ArraySequence([array([[0.2]], dtype=float32), array([[0.3],\n", + " [0.4]], dtype=float32), array([[0.5],\n", + " [0.6],\n", + " [0.6],\n", + " [0.7],\n", + " [0.8]], dtype=float32)])\n" + ] + } + ], + "source": [ + "print(data_per_streamline['mean_curvature'])\n", + "print(data_per_point['fa'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Each component is also indexable by integer:" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 1. 2.]\n", + " [ 3. 4. 5.]\n", + " [ 6. 7. 8.]\n", + " [ 9. 10. 11.]\n", + " [12. 13. 14.]]\n", + "{'mean_colors': array([[0.],\n", + " [0.],\n", + " [1.]], dtype=float32),\n", + " 'mean_curvature': array([[3.11]], dtype=float32),\n", + " 'mean_torsion': array([[3.22]], dtype=float32)}\n", + "\n", + "{'colors': ArraySequence([array([0., 0., 1.], dtype=float32), array([0., 0., 1.], dtype=float32), array([0., 0., 1.], dtype=float32), array([0., 0., 1.], dtype=float32), array([0., 0., 1.], dtype=float32)]),\n", + " 'fa': ArraySequence([array([0.5], dtype=float32), array([0.6], dtype=float32), array([0.6], dtype=float32), array([0.7], dtype=float32), array([0.8], dtype=float32)])}\n" + ] + } + ], + "source": [ + "print(streamlines[2])\n", + "pprint(dict(data_per_streamline[2])); print() # For space\n", + "pprint(dict(data_per_point[2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, there are a few ways to get at the same data, based on whether you prefer to operate by streamline or by data element (such as `fa`)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Summary\n", + "\n", + "
\n", + "\n", + "You made it!\n", + "\n", + "## Learning objectives (reprise)\n", + "\n", + "1. Be able to load and save different types of files in NiBabel\n", + " * `nb.load`, `nb.save`, `img.to_filename()`, `img.to_bytes()`, `img.from_bytes()`\n", + "1. Become familiar with the `SpatialImage` API and identify its components\n", + " * `img.get_fdata()`/`img.dataobj`\n", + " * `img.affine`\n", + " * `img.header`\n", + "1. Understand the differences between array and proxy images\n", + " * Arrays are what it says on the tin.\n", + " * Proxies conserve memory and automatically scale values\n", + " * `get_fdata()` and `dataobj` provide tradeoffs between consistency and control\n", + "1. Acquire a passing familiarity with:\n", + " * Surfaces: Meshes and per-vertex data, idiosyncratic\n", + " * CIFTI-2: 2-3D NIfTI with some interesting axes; brainmodels can be decomposed into volume and surface components\n", + " * Tractograms: Streamlines are paths in space, and data can be attached to the point or the path" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/evaluate_main.py b/evaluate_main.py new file mode 100644 index 00000000..35c9dbaf --- /dev/null +++ b/evaluate_main.py @@ -0,0 +1,52 @@ +import sys +import yaml +from osl_dynamics.config_api.batch import IndexParser, BatchTrain, batch_check + +def main(index,config_path,analysis_config_path=None): + ''' + This is the main function of all the analysis. + Parameters + ---------- + index: int + index == -1 represents post training checkk or analysis. + config_path: str + where read the config path. + analysis_config_path: str + if analysis_config_path is not None, and index == -1 + then implement the analysis code. + ''' + with open(config_path, 'r') as file: + config_batch = yaml.safe_load(file) + if index > -1: + if isinstance(config_batch,list): + with open(f'{config_batch[index]}batch_config.yaml','r') as file: + config = yaml.safe_load(file) + else: + index_parser = IndexParser(config_batch) + config = index_parser.parse(index) + batch_train = BatchTrain(config) + batch_train.model_train() + else: + # Step 1: batch check whether training is successful + # return the list where training is not successful + batch_check(config_batch) + + # Step 2: if analysis_config_path is not None, implement the analayis code. + if analysis_config_path is not None: + pass + + + + +if __name__ == '__main__': + index = int(sys.argv[1]) - 1 + config_path = sys.argv[2] + + if len(sys.argv) > 3: + analysis_config_path = sys.argv[3] + else: + analysis_config_path = None + + main(index,config_path,analysis_config_path) + + diff --git a/examples/fmri/visualisation/.ipynb_checkpoints/Demo_fmri_visualisation-checkpoint.ipynb b/examples/fmri/visualisation/.ipynb_checkpoints/Demo_fmri_visualisation-checkpoint.ipynb new file mode 100644 index 00000000..f6691c51 --- /dev/null +++ b/examples/fmri/visualisation/.ipynb_checkpoints/Demo_fmri_visualisation-checkpoint.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "61de8eb3", + "metadata": {}, + "source": [ + "# Tutorial of fMRI state visualisation using workbench" + ] + }, + { + "cell_type": "markdown", + "id": "e8df7865", + "metadata": {}, + "source": [ + "Author: Yiming Wei\n", + "\n", + "Date: 22nd January 2024" + ] + }, + { + "cell_type": "markdown", + "id": "c2704308", + "metadata": {}, + "source": [ + "The aim of this tutorial is to provide a first example of fMRI data visualisation using osl-dynamics toolbox within OHBA analysis group. We will utilise the HCP workbench and tools in osl_dynamics.analysis. This tutorial should enable us (Chet, Ben, Brian and myself) to compare dynamic FC models (HMM, Dynemo and mDynemo) trained on different large-scale datasets (HCP, UKBiobank and Cam-CAN). Several prerequisites are listed below:\n", + "\n", + "1. BMRC cluster. \n", + "\n", + "We need to set up workbench properly on BMRC cluster so that `wb_command` can be run properly in the command line. The easiest way is to run `module load ConnectomeWorkbench` before starting this tutorial. (TODO: It seems to me that this command does not work properly if run in jupyter notebook or python subprocess. Why?).\n", + "P.S. There's a `setup` function in `osl_dynamics.analysis.workbench.py` to setup workbench, which requires to specify the workbench path. However, the BMRC CPUs work on two architectures (refer to this [page](https://www.medsci.ox.ac.uk/for-staff/resources/bmrc/python-on-the-bmrc-cluster) for more info), which means that the path is dependent on the actual node. (Maybe different ideas?).\n", + "\n", + "2. Brain surface map.\n", + "\n", + "By default, osl-dynamics uses ParcellationPilot. HCP S1200 map, which is available on the HCP database, might be another option.\n", + "\n", + "3. Spatial map.\n", + "Obtained from group-level spatial ICA. Should be specific to the dataset.\n", + "HCP: surface map.\n", + "UKB: was volumetric, but now surface map - I'll email Fidel.\n", + "CAM-CAN: not sure.\n", + "\n", + "4. State mean activation and FC map.\n", + "These are the parameters in the observation model. For example, if we train HMM model with K states on ICA results with N channels, the state mean activation should be a numpy array with shape K * N, the FC map should be a 3D numpy array with shape K * N * N." + ] + }, + { + "cell_type": "markdown", + "id": "8646ad8d", + "metadata": {}, + "source": [ + "We give a simple example below. Note some of the functions are in `rotation/utils.py`, which is my own function toolbox." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bd6720c5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import nibabel as nib\n", + "from rotation.utils import IC2surface,first_eigenvector\n", + "from osl_dynamics.analysis.workbench import render" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ca0845c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the directories\n", + "spatial_map_dir = '../../../data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d25.ica/melodic_IC.dscalar.nii'\n", + "mean_activation_dir = '../../../results_202310/HMM_ICA_25_state_8/state_means.npy'\n", + "fc_dir = '../../../results_202310/HMM_ICA_25_state_8/state_correlations.npy'\n", + "save_dir ='./'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "09731120", + "metadata": {}, + "outputs": [], + "source": [ + "# Load files\n", + "spatial_surface_map = nib.load(spatial_map_dir)\n", + "state_means = np.load(mean_activation_dir)\n", + "correlations = np.load(fc_dir)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b773c4ef", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate surface map of mean activation\n", + "mean_activation_surface_map = IC2surface(spatial_surface_map,state_means.T)\n", + "mean_activation_surface_map.to_filename(f'{save_dir}mean_activation_surface_map.dscalar.nii')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1ed26bc5", + "metadata": {}, + "outputs": [], + "source": [ + "# Rank-one approximation\n", + "r1_approxs = []\n", + "for i in range(len(correlations)):\n", + " correlation = correlations[i,:,:]\n", + " r1_approxs.append(first_eigenvector(correlation))\n", + "r1_approxs = np.array(r1_approxs)\n", + "np.save(f'{save_dir}r1_approx_FC.npy', r1_approxs)\n", + "\n", + "# Calculate surface map of FC\n", + "FC_degree_surface_map = IC2surface(spatial_surface_map,r1_approxs.T)\n", + "FC_degree_surface_map.to_filename(f'{save_dir}FC_surface_map.dscalar.nii')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4614726b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving images: 100%|██████████████████████████████| 8/8 [00:09<00:00, 1.15s/it]\n", + "Saving images: 100%|██████████████████████████████| 8/8 [00:07<00:00, 1.02it/s]\n" + ] + } + ], + "source": [ + "# Render\n", + "render(nii=f'{save_dir}mean_activation_surface_map.dscalar.nii',\n", + " save_dir=f'{save_dir}/visualisation',\n", + " gui=False,\n", + " inflation=0,\n", + " image_name='./visualisation/mean_mid_thickness',\n", + " input_cifti=True\n", + " )\n", + "render(nii=f'{save_dir}FC_surface_map.dscalar.nii',\n", + " save_dir=f'{save_dir}/visualisation',\n", + " gui=False,\n", + " inflation=0,\n", + " image_name='./visualisation/FC_mid_thickness',\n", + " input_cifti=True\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef3c0bdd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/fmri/visualisation/Demo_fmri_visualisation.ipynb b/examples/fmri/visualisation/Demo_fmri_visualisation.ipynb new file mode 100644 index 00000000..f6691c51 --- /dev/null +++ b/examples/fmri/visualisation/Demo_fmri_visualisation.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "61de8eb3", + "metadata": {}, + "source": [ + "# Tutorial of fMRI state visualisation using workbench" + ] + }, + { + "cell_type": "markdown", + "id": "e8df7865", + "metadata": {}, + "source": [ + "Author: Yiming Wei\n", + "\n", + "Date: 22nd January 2024" + ] + }, + { + "cell_type": "markdown", + "id": "c2704308", + "metadata": {}, + "source": [ + "The aim of this tutorial is to provide a first example of fMRI data visualisation using osl-dynamics toolbox within OHBA analysis group. We will utilise the HCP workbench and tools in osl_dynamics.analysis. This tutorial should enable us (Chet, Ben, Brian and myself) to compare dynamic FC models (HMM, Dynemo and mDynemo) trained on different large-scale datasets (HCP, UKBiobank and Cam-CAN). Several prerequisites are listed below:\n", + "\n", + "1. BMRC cluster. \n", + "\n", + "We need to set up workbench properly on BMRC cluster so that `wb_command` can be run properly in the command line. The easiest way is to run `module load ConnectomeWorkbench` before starting this tutorial. (TODO: It seems to me that this command does not work properly if run in jupyter notebook or python subprocess. Why?).\n", + "P.S. There's a `setup` function in `osl_dynamics.analysis.workbench.py` to setup workbench, which requires to specify the workbench path. However, the BMRC CPUs work on two architectures (refer to this [page](https://www.medsci.ox.ac.uk/for-staff/resources/bmrc/python-on-the-bmrc-cluster) for more info), which means that the path is dependent on the actual node. (Maybe different ideas?).\n", + "\n", + "2. Brain surface map.\n", + "\n", + "By default, osl-dynamics uses ParcellationPilot. HCP S1200 map, which is available on the HCP database, might be another option.\n", + "\n", + "3. Spatial map.\n", + "Obtained from group-level spatial ICA. Should be specific to the dataset.\n", + "HCP: surface map.\n", + "UKB: was volumetric, but now surface map - I'll email Fidel.\n", + "CAM-CAN: not sure.\n", + "\n", + "4. State mean activation and FC map.\n", + "These are the parameters in the observation model. For example, if we train HMM model with K states on ICA results with N channels, the state mean activation should be a numpy array with shape K * N, the FC map should be a 3D numpy array with shape K * N * N." + ] + }, + { + "cell_type": "markdown", + "id": "8646ad8d", + "metadata": {}, + "source": [ + "We give a simple example below. Note some of the functions are in `rotation/utils.py`, which is my own function toolbox." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bd6720c5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import nibabel as nib\n", + "from rotation.utils import IC2surface,first_eigenvector\n", + "from osl_dynamics.analysis.workbench import render" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ca0845c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the directories\n", + "spatial_map_dir = '../../../data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d25.ica/melodic_IC.dscalar.nii'\n", + "mean_activation_dir = '../../../results_202310/HMM_ICA_25_state_8/state_means.npy'\n", + "fc_dir = '../../../results_202310/HMM_ICA_25_state_8/state_correlations.npy'\n", + "save_dir ='./'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "09731120", + "metadata": {}, + "outputs": [], + "source": [ + "# Load files\n", + "spatial_surface_map = nib.load(spatial_map_dir)\n", + "state_means = np.load(mean_activation_dir)\n", + "correlations = np.load(fc_dir)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b773c4ef", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate surface map of mean activation\n", + "mean_activation_surface_map = IC2surface(spatial_surface_map,state_means.T)\n", + "mean_activation_surface_map.to_filename(f'{save_dir}mean_activation_surface_map.dscalar.nii')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1ed26bc5", + "metadata": {}, + "outputs": [], + "source": [ + "# Rank-one approximation\n", + "r1_approxs = []\n", + "for i in range(len(correlations)):\n", + " correlation = correlations[i,:,:]\n", + " r1_approxs.append(first_eigenvector(correlation))\n", + "r1_approxs = np.array(r1_approxs)\n", + "np.save(f'{save_dir}r1_approx_FC.npy', r1_approxs)\n", + "\n", + "# Calculate surface map of FC\n", + "FC_degree_surface_map = IC2surface(spatial_surface_map,r1_approxs.T)\n", + "FC_degree_surface_map.to_filename(f'{save_dir}FC_surface_map.dscalar.nii')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4614726b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving images: 100%|██████████████████████████████| 8/8 [00:09<00:00, 1.15s/it]\n", + "Saving images: 100%|██████████████████████████████| 8/8 [00:07<00:00, 1.02it/s]\n" + ] + } + ], + "source": [ + "# Render\n", + "render(nii=f'{save_dir}mean_activation_surface_map.dscalar.nii',\n", + " save_dir=f'{save_dir}/visualisation',\n", + " gui=False,\n", + " inflation=0,\n", + " image_name='./visualisation/mean_mid_thickness',\n", + " input_cifti=True\n", + " )\n", + "render(nii=f'{save_dir}FC_surface_map.dscalar.nii',\n", + " save_dir=f'{save_dir}/visualisation',\n", + " gui=False,\n", + " inflation=0,\n", + " image_name='./visualisation/FC_mid_thickness',\n", + " input_cifti=True\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef3c0bdd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/fmrib_qsub.sh b/fmrib_qsub.sh new file mode 100644 index 00000000..e76b18a6 --- /dev/null +++ b/fmrib_qsub.sh @@ -0,0 +1,55 @@ +#!/bin/bash + +# Specify a job name +#$ -N swimming + +# --- Parameters for the Queue Master --- +# target queue (Please specify in the command line!) +##$ -q short.q + +# Run the job in the current working directory +#$ -cwd -j y + +# Log locations which are relative to the current +# working directory of the submission +#$ -o ./log/ +#$ -e ./log/ + +# Parallel environemnt settings +# For more information on these please see the wiki +# Allowed settings: +# shmem +# mpi +# node_mpi +# ramdisk +##$ -pe shmem 1 + +# Print some useful data about the job to help with debugging +echo "------------------------------------------------" +echo "SGE Job ID: $JOB_ID" +echo "SGE Job ID: $SGE_JOB_ID" +echo "Run on host: "`hostname` +echo "Operating system: "`uname -s` +echo "Username: "`whoami` +echo "Started at: "`date` +echo "------------------------------------------------" + +# >>> conda initialize >>> +# !! Contents within this block are managed by 'conda init' !! +__conda_setup="$('/opt/fmrib/conda/python3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" +if [ $? -eq 0 ]; then + eval "$__conda_setup" +else + if [ -f "/opt/fmrib/conda/python3/etc/profile.d/conda.sh" ]; then + . "/opt/fmrib/conda/python3/etc/profile.d/conda.sh" + else + export PATH="/opt/fmrib/conda/python3/bin:$PATH" + fi +fi +unset __conda_setup +# <<< conda initialize <<< +# Finally, we can run our real computing job + +conda activate osld # Environment name +python main.py +# End of job script diff --git a/high_pass_filter.ipynb b/high_pass_filter.ipynb new file mode 100644 index 00000000..0c2ae3c2 --- /dev/null +++ b/high_pass_filter.ipynb @@ -0,0 +1,666 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b4deb1e4-aa9d-42e3-b85c-8c6885c8fb27", + "metadata": {}, + "source": [ + "# Understanding High-pass Filter" + ] + }, + { + "cell_type": "markdown", + "id": "047622c1-436f-4856-8c7f-78dda3da9f13", + "metadata": {}, + "source": [ + "This is a jupyter notebook to understand how high-pass filter works" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d9dce5f-4a90-4384-b08d-e8173144f0bd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.signal import butter, filtfilt\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "5fbf4306-0a84-494c-b531-be2ec88d2b5c", + "metadata": {}, + "source": [ + "### Step 1: Generate the mocked sigmals" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ee5c4b6c-f9a2-44d6-ac33-63d9a3958f54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Amplitude')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parameters\n", + "fs = 1000 # Sampling frequency (Hz)\n", + "t = np.arange(0, 1, 1/fs) # Time vector\n", + "\n", + "# Generate a signal (sum of sinusoids)\n", + "f1 = 5 # Low-frequency component (Hz)\n", + "f2 = 50 # High-frequency component (Hz)\n", + "signal = np.sin(2*np.pi*f1*t) + 0.5*np.sin(2*np.pi*f2*t)\n", + "\n", + "# Plot the original signal in time domain\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(t, signal)\n", + "plt.title('Original Signal')\n", + "plt.xlabel('Timbe (s)')\n", + "plt.ylabel('Amplitude')" + ] + }, + { + "cell_type": "markdown", + "id": "5cfab458-da0a-4375-b2a9-2cf409c9a7df", + "metadata": {}, + "source": [ + "### Step 2: Plot the original spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "696a7edc-7b24-4e18-b2bf-8be4e720f16a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Magnitude')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute the frequency spectrum of the original signal\n", + "N = len(signal)\n", + "frequencies = np.fft.fftfreq(N, 1/fs)\n", + "spectrum = np.fft.fft(signal,norm='forward')\n", + "\n", + "# Plot the original signal's frequency spectrum\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(frequencies, np.abs(spectrum))\n", + "plt.title('Frequency Spectrum of Original Signal')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Magnitude')\n", + "#print(np.around(np.abs(spectrum), decimals=2))\n", + "#print(frequencies)" + ] + }, + { + "cell_type": "markdown", + "id": "24ddac2b-a476-4848-84c6-84aea280c815", + "metadata": {}, + "source": [ + "### Step 3: Apply the high-pass filter" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e881ec2a-554f-4382-a886-cec595cd3de2", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply a high-pass filter (example using a Butterworth filter)\n", + "fc = 10 # Cutoff frequency of the high-pass filter (Hz)\n", + "order = 4 # Filter order\n", + "b, a = butter(order, fc/(fs/2), btype='high')\n", + "filtered_signal = filtfilt(b, a, signal)" + ] + }, + { + "cell_type": "markdown", + "id": "caabbbc2-cb14-47c7-9025-d87d1011431f", + "metadata": {}, + "source": [ + "### Step 4: Plot the post-processed signal" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b5c24f16-e66e-4fc0-9a09-f8918eb0fdee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the filtered signal in time domain\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(t, filtered_signal)\n", + "plt.title('Filtered Signal')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Amplitude')\n", + "\n", + "# Compute the frequency spectrum of the filtered signal\n", + "filtered_spectrum = np.fft.fft(filtered_signal)\n", + "\n", + "# Plot the filtered signal's frequency spectrum\n", + "plt.figure()\n", + "plt.plot(frequencies, np.abs(filtered_spectrum))\n", + "plt.title('Frequency Spectrum of Filtered Signal')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Magnitude')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "47f1f351-4695-4499-9b01-604279409a81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.+0.j 0.-1.j 3.+0.j 0.+1.j]\n", + "[ 0. 1. -2. -1.]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#data = np.array([8.,4.,8.,0.])\n", + "#print(np.fft.fft(data,norm='forward'))\n", + "#print(np.fft.fftfreq(n=4,d=0.25))\n", + "#plt.plot(np.fft.fftfreq(n=4,d=0.25), np.abs(np.fft.fft(data,norm='forward')))" + ] + }, + { + "cell_type": "markdown", + "id": "9b11cb9c-0f6d-4992-9153-3001120a6d1c", + "metadata": {}, + "source": [ + "### Step 5: Compare the effect of different high-pass filters." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9aff62aa-0054-49b1-8f2b-a402ffd33e34", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\n# Design filters\\nb_butter, a_butter = butter(order, cutoff_freq / nyquist, btype=\\'high\\')\\nb_cheby1, a_cheby1 = cheby1(order, 0.5, cutoff_freq / nyquist, btype=\\'high\\')\\nb_cheby2, a_cheby2 = cheby2(order, 20, cutoff_freq / nyquist, btype=\\'high\\')\\nb_ellip, a_ellip = ellip(order, 0.5, 20, cutoff_freq / nyquist, btype=\\'high\\')\\n\\n# Frequency response\\nw, h_butter = freqz(b_butter, a_butter, worN=N, fs=fs)\\n_, h_cheby1 = freqz(b_cheby1, a_cheby1, worN=N, fs=fs)\\n_, h_cheby2 = freqz(b_cheby2, a_cheby2, worN=N, fs=fs)\\n_, h_ellip = freqz(b_ellip, a_ellip, worN=N, fs=fs)\\n\\n# Plotting\\nplt.figure(figsize=(10, 6))\\n\\nplt.plot(0.5 * fs * w / np.pi, np.abs(h_butter), \\'b\\', label=\\'Butterworth\\')\\nplt.plot(0.5 * fs * w / np.pi, np.abs(h_cheby1), \\'g\\', label=\\'Chebyshev Type I\\')\\nplt.plot(0.5 * fs * w / np.pi, np.abs(h_cheby2), \\'r\\', label=\\'Chebyshev Type II\\')\\nplt.plot(0.5 * fs * w / np.pi, np.abs(h_ellip), \\'m\\', label=\\'Elliptic\\')\\n\\nplt.title(\"High-Pass Filter Frequency Response\")\\nplt.xlabel(\\'Frequency (Hz)\\')\\nplt.ylabel(\\'Gain\\')\\nplt.grid(True)\\nplt.legend()\\n\\nplt.tight_layout()\\nplt.show()\\n'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.signal import butter, cheby1, cheby2, ellip, freqz\n", + "\n", + "# Parameters\n", + "N = 1200 # Number of time points\n", + "T = 0.7 # Time resolution (seconds)\n", + "fs = 1 / T # Sampling frequency\n", + "nyquist = 0.5 * fs # Nyquist frequency\n", + "\n", + "# Filter design\n", + "cutoff_freq = 0.25 # Cutoff frequency (Hz)\n", + "order = 16 # Filter order\n", + "\n", + "# Design filters\n", + "b_butter, a_butter = butter(order, cutoff_freq, btype='highpass',fs=fs)\n", + "b_cheby1, a_cheby1 = cheby1(order, 0.5, cutoff_freq, btype='highpass',fs=fs)\n", + "b_cheby2, a_cheby2 = cheby2(order, 20, cutoff_freq, btype='highpass',fs=fs)\n", + "#b_ellip, a_ellip = ellip(order, 0.5, 20, cutoff_freq, btype='highpass',fs=fs)\n", + "\n", + "# Frequency response\n", + "w, h_butter = freqz(b_butter, a_butter, worN=N, fs=fs)\n", + "_, h_cheby1 = freqz(b_cheby1, a_cheby1, worN=N, fs=fs)\n", + "_, h_cheby2 = freqz(b_cheby2, a_cheby2, worN=N, fs=fs)\n", + "#_, h_ellip = freqz(b_ellip, a_ellip, worN=N, fs=fs)\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.plot(w, np.abs(h_butter), 'b', label='Butterworth')\n", + "plt.plot(w, np.abs(h_cheby1), 'g', label='Chebyshev Type I')\n", + "plt.plot(w, np.abs(h_cheby2), 'r', label='Chebyshev Type II')\n", + "#plt.plot(w, np.abs(h_ellip), 'm', label='Elliptic')\n", + "\n", + "plt.title(\"High-Pass Filter Frequency Response\")\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Gain')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "'''\n", + "# Design filters\n", + "b_butter, a_butter = butter(order, cutoff_freq / nyquist, btype='high')\n", + "b_cheby1, a_cheby1 = cheby1(order, 0.5, cutoff_freq / nyquist, btype='high')\n", + "b_cheby2, a_cheby2 = cheby2(order, 20, cutoff_freq / nyquist, btype='high')\n", + "b_ellip, a_ellip = ellip(order, 0.5, 20, cutoff_freq / nyquist, btype='high')\n", + "\n", + "# Frequency response\n", + "w, h_butter = freqz(b_butter, a_butter, worN=N, fs=fs)\n", + "_, h_cheby1 = freqz(b_cheby1, a_cheby1, worN=N, fs=fs)\n", + "_, h_cheby2 = freqz(b_cheby2, a_cheby2, worN=N, fs=fs)\n", + "_, h_ellip = freqz(b_ellip, a_ellip, worN=N, fs=fs)\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.plot(0.5 * fs * w / np.pi, np.abs(h_butter), 'b', label='Butterworth')\n", + "plt.plot(0.5 * fs * w / np.pi, np.abs(h_cheby1), 'g', label='Chebyshev Type I')\n", + "plt.plot(0.5 * fs * w / np.pi, np.abs(h_cheby2), 'r', label='Chebyshev Type II')\n", + "plt.plot(0.5 * fs * w / np.pi, np.abs(h_ellip), 'm', label='Elliptic')\n", + "\n", + "plt.title(\"High-Pass Filter Frequency Response\")\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Gain')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "id": "6129b210-d522-4c47-89c5-4772fecee1f6", + "metadata": {}, + "source": [ + "### Step 6: Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7f2f08fa-f670-4f9a-8c06-85b95e1379e7", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from scipy.signal import butter, cheby1, cheby2, ellip, freqz" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b13205d9-c805-4a45-ba90-5217c0096342", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the parameters of fMRI signal\n", + "N = 1200 # Number of time points\n", + "T = 0.7 # Time resolution (seconds)\n", + "fs = 1 / T # Sampling frequency\n", + "nyquist = 0.5 * fs # Nyquist frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "1809d116-90b8-4e5a-832e-299f7d630782", + "metadata": {}, + "outputs": [], + "source": [ + "# Filter design\n", + "cutoff_freq = 0.25 # Cutoff frequency (Hz)\n", + "order = 32 # Filter order\n", + "\n", + "# Design filters\n", + "b_butter, a_butter = butter(order, cutoff_freq, btype='highpass',fs=fs)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c04409af-ef23-4188-9cbb-bf13ecb73760", + "metadata": {}, + "outputs": [], + "source": [ + "# Original signal\n", + "ff_1 = np.linspace(0.1,0.2,6)\n", + "ff_2 = np.linspace(0.05,0.2,4)\n", + "signal_1 = np.zeros(N)\n", + "signal_2 = np.zeros(N)\n", + "t = np.array([i * T for i in range(N)])\n", + "\n", + "#for i,f in enumerate(ff_1):\n", + "# signal_1 += np.sin(2*np.pi * f * t + np.pi / 3 * i)\n", + "signal_1 = np.sin(2 * np.pi * 0.2 * t)\n", + "signal_2 = np.sin(2 * np.pi * 0.3 * t)\n", + "#for i,f in enumerate(ff_2):\n", + "# signal_2 += np.sin(2*np.pi * f * t + np.pi / 3 * i)\n", + "\n", + "signals = np.array([signal_1,signal_2])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0ec71ed7-2d2f-444b-8aac-d4b28d7fe05c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the signal in time space and frequency space\n", + "plt.plot(t,signals[0,:])\n", + "plt.xlabel('time (s)',fontsize=15)\n", + "plt.ylabel('Amplitude',fontsize=15)\n", + "plt.title('Original signal 1 in time space',fontsize=20)\n", + "plt.show()\n", + "\n", + "frequencies = np.fft.fftfreq(N, 1/fs)\n", + "spectrum = np.fft.fft(signals[0,:],norm='forward')\n", + "\n", + "# Plot the original signal's frequency spectrum\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(frequencies, np.abs(spectrum))\n", + "plt.title('Frequency Spectrum of Original Signal 1')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Magnitude')\n", + "plt.show()\n", + "\n", + "# Plot the signal in time space and frequency space\n", + "plt.plot(t,signals[1,:])\n", + "plt.xlabel('time (s)',fontsize=15)\n", + "plt.ylabel('Amplitude',fontsize=15)\n", + "plt.title('Original signal 2 in time space',fontsize=20)\n", + "plt.show()\n", + "\n", + "frequencies = np.fft.fftfreq(N, 1/fs)\n", + "spectrum = np.fft.fft(signals[1,:],norm='forward')\n", + "\n", + "# Plot the original signal's frequency spectrum\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(frequencies, np.abs(spectrum))\n", + "plt.title('Frequency Spectrum of Original Signal')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Magnitude')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "81a4e8e6-0a91-4952-b8cd-33e11db8f0f3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Filter and plot again\n", + "filtered_signals = filtfilt(b_butter, a_butter, signals)\n", + "\n", + "plt.plot(t,filtered_signals[0,:])\n", + "plt.xlabel('time (s)',fontsize=15)\n", + "plt.ylabel('Amplitude',fontsize=15)\n", + "plt.title('Filtered signal 1 in time space',fontsize=20)\n", + "plt.show()\n", + "\n", + "frequencies = np.fft.fftfreq(N, 1/fs)\n", + "spectrum = np.fft.fft(filtered_signals[0,:],norm='forward')\n", + "\n", + "# Plot the filtered signal's frequency spectrum\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(frequencies, np.abs(spectrum))\n", + "plt.title('Frequency Spectrum of Filtered Signal 1')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Magnitude')\n", + "plt.show()\n", + "\n", + "plt.plot(t,filtered_signals[1,:])\n", + "plt.xlabel('time (s)',fontsize=15)\n", + "plt.ylabel('Amplitude',fontsize=15)\n", + "plt.title('Filtered signal 2 in time space',fontsize=20)\n", + "plt.show()\n", + "\n", + "frequencies = np.fft.fftfreq(N, 1/fs)\n", + "spectrum = np.fft.fft(filtered_signals[1,:],norm='forward')\n", + "\n", + "# Plot the filtered signal's frequency spectrum\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(frequencies, np.abs(spectrum))\n", + "plt.title('Frequency Spectrum of Filtered Signal 2')\n", + "plt.xlabel('Frequency (Hz)')\n", + "plt.ylabel('Magnitude')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df360d3d-f796-45e5-8d04-123eb51d68c8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c6a1e35-cade-4cf1-a641-6cf81ac78597", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4d06d6d-a9a8-48a1-b5ec-fb08d091c9d8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/interactive.ipynb b/interactive.ipynb new file mode 100644 index 00000000..4e98d838 --- /dev/null +++ b/interactive.ipynb @@ -0,0 +1,27013 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ac1bdd74", + "metadata": {}, + "source": [ + "### Check whether the pre-processed data has been de-meaned" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a15f1ed9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fbc862c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4800, 15)\n" + ] + } + ], + "source": [ + "data_dir = '/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/'\n", + "data = np.loadtxt(f\"{data_dir}100206.txt\")\n", + "print(data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3a5004fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "73.97078169525533\n", + "71.50405805815484\n", + "59.740868684702896\n", + "60.80229374610422\n" + ] + } + ], + "source": [ + "print(np.std(data[:1200,5]))\n", + "print(np.std(data[1200:2400,5]))\n", + "print(np.std(data[2400:3600,5]))\n", + "print(np.std(data[3600:4800,5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fb2216ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.squeeze(data[:,0]))" + ] + }, + { + "cell_type": "markdown", + "id": "656e7fff", + "metadata": {}, + "source": [ + "So we need to z-score each session (1200 time points). Let's write a function for this!" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "2be16c13", + "metadata": {}, + "outputs": [], + "source": [ + "def z_score(data:np.ndarray,n_session:int = 1):\n", + " \"\"\"\n", + " z_score the input data.\n", + " If n_session = 1, then z_score directly\n", + " If n_session > 1, then divide the data into different sessions,\n", + " z-score separately, and then concatenate back.\n", + " \n", + " Parameters:\n", + " data (np.ndarray): (n_timepoints,n_channel)\n", + " n_session (int): the number of sessions\n", + " \n", + " Returns\n", + " np.ndarray: z-scored data\n", + " \"\"\"\n", + " import scipy.stats as stats\n", + " \n", + " if n_session == 1:\n", + " return stats.zscore(data,axis=0)\n", + " else:\n", + " if len(data) % n_session > 0:\n", + " raise ValueError('Number of time points is not divisible by n_session!')\n", + " n_timepoint, n_channel = data.shape\n", + " \n", + " # Split to n sessions\n", + " data = np.reshape(data,(n_session,-1,n_channel))\n", + " \n", + " # z-score\n", + " data = stats.zscore(data,axis=1)\n", + " \n", + " # Concatenate and reshape\n", + " return np.reshape(data,(n_timepoint,n_channel))\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "c6cc1dc6", + "metadata": {}, + "outputs": [], + "source": [ + "def test_z_score():\n", + " import numpy as np\n", + " import numpy.testing as npt\n", + " \n", + " # Example 1: one session\n", + " x = np.array([[0.5,1.5,2.5],[-2.0,-1.0,0.0]]).T\n", + " y = z_score(x)\n", + " npt.assert_equal(y[:,0], y[:,1])\n", + " \n", + " # Example 2: two sessions\n", + " np.random.seed(42)\n", + " x1 = np.random.normal(loc=100.0,scale=100.0,size=(100,2))\n", + " x2 = np.random.normal(loc=0.0,scale=2.0,size=(100,2))\n", + " x = np.concatenate([x1,x2])\n", + " y = z_score(x,n_session=2)\n", + " \n", + " # Check the data are z-scored\n", + " npt.assert_almost_equal(np.mean(y[:100,:]),0.0,decimal=3)\n", + " npt.assert_almost_equal(np.std(y[:100,:]),1.0,decimal=3)\n", + " npt.assert_almost_equal(np.mean(y[100:,:]),0.0,decimal=3)\n", + " npt.assert_almost_equal(np.std(y[100:,:]),1.0,decimal=3)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "5727936b", + "metadata": {}, + "outputs": [], + "source": [ + "test_z_score()" + ] + }, + { + "cell_type": "markdown", + "id": "e74147ff", + "metadata": {}, + "source": [ + "### Write a function to load data" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "89d6ed13", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fs0/lmh182/.conda/envs/osld/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import pathlib\n", + "from osl_dynamics.data import Data\n", + "class PrepareData():\n", + " def __init__(self,data_dir:pathlib.Path,n_session:int=4):\n", + " self.data_dir = data_dir\n", + " self.n_session = n_session\n", + " \n", + " def load(self,):\n", + " '''\n", + " Load data from specified directories\n", + " Returns:\n", + " tuple: A tuple containing the following\n", + " - subjs (list): A list of read-in subjects\n", + " - dataset (osl_dynamics.data.Data): the wrappeed dataset\n", + " '''\n", + " subjs = []\n", + " data_list = []\n", + " for file in self.data_dir.glob('*.txt'):\n", + " subjs.append(file.stem)\n", + " data_list.append(z_score(np.loadtxt(file),self.n_session))\n", + " \n", + " print('Read from directory: ',self.data_dir)\n", + " print('Number of subjects: ',len(subjs))\n", + " \n", + " return subjs, Data(data_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "b26e8766", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read from directory: /vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2\n", + "Number of subjects: 1003\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading files: 100%|██████| 1003/1003 [00:13<00:00, 76.75it/s]\n" + ] + } + ], + "source": [ + "import pathlib\n", + "data_dir = pathlib.Path('/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/')\n", + "prepare_data = PrepareData(data_dir)\n", + "subjs, dataset = prepare_data.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "cacef84f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1003\n" + ] + } + ], + "source": [ + "print(len(subjs))" + ] + }, + { + "cell_type": "markdown", + "id": "bc2ddf66", + "metadata": {}, + "source": [ + "Attention: We need to sort the input files otherwise something would go wrong. Let's give it a try here!" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "28ed4a86", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = pathlib.Path('/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "eb312b40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(data_dir.glob('*.txt'))" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "d48d92db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/100206.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/100307.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/100408.txt\n", + 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"/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/987074.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/987983.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/989987.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/990366.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/991267.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/992673.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/992774.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/993675.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/994273.txt\n", + "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/996782.txt\n" + ] + } + ], + "source": [ + "for file in sorted(data_dir.glob('*.txt')):\n", + " print(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "3b7957e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "279.5" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1118 / 4" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "f0dff153", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]\n", + " [12 13 14 15]\n", + " [16 17 18 19]\n", + " [20 21 22 23]]\n", + "####################\n", + "[array([[0, 1, 2, 3],\n", + " [4, 5, 6, 7]]), array([[ 8, 9, 10, 11],\n", + " [12, 13, 14, 15]]), array([[16, 17, 18, 19],\n", + " [20, 21, 22, 23]])]\n" + ] + } + ], + "source": [ + "x = np.reshape(np.arange(24),(6,4))\n", + "print(x)\n", + "print('####################')\n", + "y = np.split(x,3)\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "11655d99", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'stats' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstats\u001b[49m\u001b[38;5;241m.\u001b[39mzscore(np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m]))\n", + "\u001b[0;31mNameError\u001b[0m: name 'stats' is not defined" + ] + } + ], + "source": [ + "stats.zscore(np.array([-1,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8c593eaf", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.testing as npt\n", + "\n", + "def test_prepare_data():\n", + " import shutil, os\n", + " import pathlib\n", + " from rotation.preprocessing import PrepareData\n", + " temp_dir = './test_temp/'\n", + " # Create the directory if not exists\n", + " if not os.path.exists(temp_dir):\n", + " print(f'Create the temporary directory {temp_dir}')\n", + " os.makedirs(temp_dir)\n", + " \n", + " # Create a subject\n", + " subj_name = '10001'\n", + " data = np.array([[-1,1,-1,1],[1,-1,1,-1]]).T\n", + " np.savetxt(f'{temp_dir}{subj_name}.txt', data)\n", + " \n", + " # Use the PrepareData class\n", + " prepare_data = PrepareData(pathlib.Path(temp_dir),2)\n", + " subj,result = prepare_data.load()\n", + " npt.assert_equal(subj[0],'10001')\n", + " npt.assert_equal(result[0],np.array([[-1,1],[1,-1]]))\n", + " npt.assert_equal(result[1],np.array([[-1,1],[1,-1]]))\n", + " \n", + " # Delete the directory\n", + " if os.path.exists(temp_dir):\n", + " shutil.rmtree(temp_dir)\n", + " print(f'Delete temporary directory {temp_dir}')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "73b4e942", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Create the temporary directory ./test_temp/\n", + "Read from directory: test_temp\n", + "Number of subjects: 1\n", + "Delete temporary directory ./test_temp/\n" + ] + } + ], + "source": [ + "test_prepare_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5c20a043", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1444320000" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4800 * 300 * 1003" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a74d1499", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "432000000000" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4000 * 300 * 300 * 1200" + ] + }, + { + "cell_type": "markdown", + "id": "f64b3647", + "metadata": {}, + "source": [ + "### training on HMM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e10bdf", + "metadata": {}, + "outputs": [], + "source": [ + "def HMM_training(dataset,n_states,n_channels,save_dir):\n", + " from osl_dynamics.models.hmm import Config, Model\n", + " # Create a config object\n", + " config = Config(\n", + " n_states=n_states,\n", + " n_channels=n_channels,\n", + " sequence_length=600,\n", + " learn_means=True,\n", + " learn_covariances=True,\n", + " batch_size=32,\n", + " learning_rate=1e-3,\n", + " n_epochs=40,\n", + " )\n", + " \n", + " # Initiate a Model class and print a summary\n", + " model = Model(config)\n", + " model.summary()\n", + " \n", + " # Initialization\n", + " init_history = model.random_state_time_course_initialization(training_data, n_epochs=1, n_init=3)\n", + " \n", + " # Full training\n", + " history = model.fit(training_data)\n", + " model.save(save_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "f98f385f", + "metadata": {}, + "source": [ + "## Post-training analysis" + ] + }, + { + "cell_type": "markdown", + "id": "8bb95b6a", + "metadata": {}, + "source": [ + "### Question: What does the model look like?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d4b61e55", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "43d78fed", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result_dir = './results/'\n", + "result_1_dir = f'{result_dir}HMM_ICA_15_state_12/'\n", + "trans_prob = np.load(f'{result_1_dir}trans_prob.npy')\n", + "np.fill_diagonal(trans_prob,0)\n", + "seaborn.heatmap(trans_prob)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9c5a430d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'batch_size': 32, 'covariances_epsilon': 1e-06, 'covariances_regularizer': None, 'diagonal_covariances': False, 'gradient_clip': None, 'initial_covariances': None, 'initial_means': None, 'initial_trans_prob': None, 'learn_covariances': True, 'learn_means': True, 'learn_trans_prob': True, 'learning_rate': 0.001, 'means_regularizer': None, 'model_name': 'HMM', 'multi_gpu': False, 'multiple_dynamics': False, 'n_channels': 15, 'n_epochs': 40, 'n_modes': None, 'n_states': 12, 'observation_update_decay': 0.1, 'optimizer': 'adam', 'sequence_length': 600, 'state_probs_t0': None, 'strategy': None, 'trans_prob_update_delay': 5, 'trans_prob_update_forget': 0.7}\n" + ] + } + ], + "source": [ + "import yaml\n", + "with open(f'{result_1_dir}config.yml','r') as file:\n", + " config = yaml.safe_load(file)\n", + " \n", + "print(config)" + ] + }, + { + "cell_type": "markdown", + "id": "12e8dd57", + "metadata": {}, + "source": [ + "### Question: how to load the model and infer the hidden states?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0cfe4c84", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:49:54 INFO osl-dynamics [mod_base.py:485:load]: Loading model: ./results/HMM_ICA_15_state_12/\n" + ] + } + ], + "source": [ + "from osl_dynamics.models import load\n", + "\n", + "model = load(result_1_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "47afbdad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read from directory: /vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2\n", + "Number of subjects: 1003\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading files: 100%|█████| 4012/4012 [00:29<00:00, 136.52it/s]\n", + "2023-07-10 14:50:57 INFO osl-dynamics [hmm.py:1093:get_alpha]: Getting alpha\n", + "2023-07-10 14:50:57.301349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:03 INFO numba.core.transforms [transforms.py:58:_extract_loop_lifting_candidates]: finding looplift candidates\n", + "2023-07-10 14:51:05.718853: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:05.971241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:06.256129: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:06.891903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:07.529349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:08.192589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:08.446964: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:08.724794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:09.393028: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:10.053924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:10.698082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:10.938372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:11.186895: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:11.746559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:12.385129: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:12.976705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:13.441367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:13.882135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:14.535466: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:15.186307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:15.425076: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:15.669013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:51:16.227648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:16.857340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:17.421342: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:17.875047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:18.260140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:18.584902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:18.903672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:19.577791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:20.219490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:20.469246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:20.722770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:21.267987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:21.927980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:22.582917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:23.121648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:23.607360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:24.012014: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:24.319802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:24.567259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:25.208229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:25.456760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:26.145445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:26.788256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:27.401365: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:51:28.035225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:28.372031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:28.610290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:29.161839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:29.807305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:30.421951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:30.912650: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:31.313048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:31.660495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:32.248626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:32.770069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:33.389283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:33.977793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:34.567480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:35.135426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:35.650945: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:36.095667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:36.526391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:37.126035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:37.734156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:38.002464: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:38.687447: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:38.944277: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:39.191641: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:51:39.814941: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:40.366794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:41.028605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:41.699647: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:42.301194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:42.921179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:43.543000: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:44.201586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:44.447819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:44.698305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:44.944103: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:45.196896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:45.831289: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:46.508263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:47.176391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:47.701432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:48.391682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:49.065577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:49.702304: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:49.944494: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:50.610355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:51.246635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:51.937958: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:52.523652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:51:52.982565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:53.365637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:53.864192: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:54.554560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:55.117923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:55.553995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:56.221678: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:56.802319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:57.283243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:57.696391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:58.024034: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:58.584082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:59.142245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:51:59.630038: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:00.051808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:00.411345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:00.816091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:01.481886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:02.047175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:02.494451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:03.167178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:03.808315: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:04.423131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:05.048584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:52:05.623308: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:06.129105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:06.540725: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:07.207536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:07.553277: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:07.998758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:08.689594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:09.252911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:09.508194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:10.165187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:10.693451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:10.935551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:11.583627: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:12.213271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:12.461445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:12.708978: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:13.414819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:14.105838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:14.727674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:14.976877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:15.230512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:15.484942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:16.159693: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:16.707141: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:52:16.955174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:17.629527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:18.191423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:18.440287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:18.684141: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:18.938697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:19.191446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:19.862836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:20.514502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:21.175408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:21.830829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:22.437123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:23.100872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:23.723622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:23.970521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:24.216855: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:24.461241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:25.122204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:25.398417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:25.639359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:26.209999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:26.453553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:26.746840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:27.372006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:52:27.870218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:28.284571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:28.622601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:28.908923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:29.186351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:29.428041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:29.669671: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:30.236700: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:30.493921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:30.741216: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:30.989253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:31.240115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:31.485716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:31.833201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:32.506197: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:33.070680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:33.514164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:33.882897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:34.206004: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:34.445111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:34.691813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:34.942249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:35.191215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:35.874459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:52:36.543952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:37.114423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:37.627824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:38.081762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:38.463243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:38.803509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:39.103675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:39.366512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:39.613485: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:39.864189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:40.108766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:40.358887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:40.594976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:40.836914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:41.074617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:41.322755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:41.565706: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:42.039565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:42.691854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:42.941049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:43.182392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:43.850691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:44.518388: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:45.171534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:52:45.414560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:45.655916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:45.895600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:46.132760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:46.371188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:46.609533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:47.158901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:47.706494: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:47.954752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:48.199635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:48.440161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:48.680942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:48.926374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:49.165858: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:49.726626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:49.970112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:50.253608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:50.872585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:51.358977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:52.038825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:52.696521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:52.935987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:53.173888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:53.415916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:52:53.659843: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:54.238366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:54.911754: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:55.539274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:56.191912: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:56.649807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:57.232709: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:57.477926: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:57.785677: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:58.390925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:58.966499: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:59.537152: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:52:59.979626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:00.358349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:00.698675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:00.982311: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:01.370330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:02.051910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:02.597163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:02.834507: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:03.071489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:03.332286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:03.615916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:04.185027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:53:04.422015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:04.659115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:04.894723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:05.143148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:05.711710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:05.950548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:06.189509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:06.741544: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:07.205333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:07.622763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:07.976108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:08.277297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:08.515364: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:08.888651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:09.481718: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:09.945487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:10.328420: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:10.665210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:10.943536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:11.288812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:11.946799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:12.605042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:13.199316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:13.445390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:53:14.128828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:14.728812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:15.335146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:15.998917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:16.562034: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:17.005503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:17.317403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:17.571278: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:17.811958: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:18.052966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:18.312775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:18.598213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:18.847952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:19.092006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:19.347373: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:19.605518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:19.850188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:20.091644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:20.594848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:21.211120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:21.447002: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:21.685733: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:21.924566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:22.168468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:53:22.737688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:22.987227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:23.309887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:23.915052: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:24.504509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:24.857816: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:25.104087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:25.719598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:25.961017: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:26.207167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:26.449351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:27.112189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:27.715395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:27.970543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:28.211757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:28.451731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:28.689730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:28.926743: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:29.165047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:29.405041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:29.644493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:29.887146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:30.127373: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:30.376174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:53:30.619117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:30.882907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:31.148365: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:31.388995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:31.628695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:32.194476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:32.434927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:32.677344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:33.196828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:33.437900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:33.677170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:33.914006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:34.154628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:34.722259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:34.963562: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:35.233505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:35.851821: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:36.337615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:36.740776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:37.075001: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:37.417726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:37.756632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:38.032621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:38.496347: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:53:39.063680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:39.509478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:39.880748: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:40.215568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:40.489549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:40.867504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:41.527237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:42.184699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:42.430495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:42.682299: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:43.243179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:43.488744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:43.805440: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:44.414986: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:45.012838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:45.673362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:46.199151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:46.630543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:46.987904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:47.591411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:48.176598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:48.652475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:49.055778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:49.407582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:53:50.073229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:50.697802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:51.360161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:51.976291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:52.597230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:53.136176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:53.558531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:53.915040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:54.318408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:54.970366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:55.628664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:56.206465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:56.447478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:56.693135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:57.243989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:57.491045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:57.740410: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:58.384140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:59.087047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:53:59.745885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:00.391881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:00.944449: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:01.401191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:01.790050: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:54:02.121476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:02.429683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:02.728643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:02.974856: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:03.289928: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:03.948015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:04.518646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:05.205931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:05.839988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:06.438290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:07.021228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:07.641443: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:08.175359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:08.625389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:09.061666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:09.428423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:09.753541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:10.031445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:10.294313: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:10.543170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:10.955084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:11.627855: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:12.228605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:12.478717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:54:13.173047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:13.702200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:13.952903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:14.223016: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:14.906484: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:15.506866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:16.119528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:16.656680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:17.105851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:17.599901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:18.210502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:18.454344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:18.710016: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:19.353584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:19.875870: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:20.320969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:20.702170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:21.015495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:21.298474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:21.555886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:21.801768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:22.045802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:22.306886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:22.587881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:54:22.843552: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:23.502980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:24.130020: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:24.807931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:25.415065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:26.002788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:26.568993: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:27.014317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:27.585153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:28.142044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:28.571273: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:28.937907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:29.360584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:30.039172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:30.608338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:31.099866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:31.659353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:32.239874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:32.483464: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:32.735889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:32.977953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:33.284597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:33.965524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:34.653922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:54:35.192506: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:35.445920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:35.707039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:35.961544: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:36.212350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:36.463080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:36.719015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:36.970743: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:37.262284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:37.881841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:38.367990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:38.789376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:39.169498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:39.847084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:40.512215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:41.085550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:41.754560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:42.422375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:42.998488: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:43.469815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:44.143647: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:44.700340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:45.005066: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:45.679096: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:54:46.330705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:47.018921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:47.610421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:48.163759: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:48.708064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:49.325690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:49.814436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:50.228185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:50.587371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:51.188109: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:51.435918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:51.691916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:51.942721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:52.193385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:52.854071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:53.364382: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:53.872389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:54.467987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:54.938423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:55.456712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:56.126253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:56.705791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:56.943379: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:57.194449: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:54:57.435497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:57.685069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:57.926999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:58.173881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:58.813588: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:54:59.477333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:00.149638: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:00.707203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:00.963101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:01.208838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:01.448727: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:01.695415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:01.935405: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:02.178951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:02.827901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:03.366547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:03.957105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:04.635527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:05.317067: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:05.932755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:06.407006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:06.801082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:07.136682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:07.714149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:55:07.953815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:08.210921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:08.841260: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:09.336286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:10.015167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:10.580102: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:11.013933: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:11.393229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:11.729295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:12.016586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:12.463392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:13.036705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:13.482732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:13.861527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:14.204244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:14.835802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:15.330494: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:15.746874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:16.083147: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:16.748555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:17.361906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:17.847917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:18.249824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:18.596557: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:55:18.909897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:19.220937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:19.478259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:19.826231: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:20.576861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:21.217169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:21.870524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:22.522703: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:23.099030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:23.550230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:23.917815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:24.250296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:25.058054: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:25.707880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:26.337079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:26.985395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:27.402121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:27.681119: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:28.127659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:28.516807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:28.851599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:29.175632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:29.867012: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:30.546451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:55:31.232387: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:31.477754: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:32.058577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:32.621724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:33.064595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:33.446371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:33.779997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:34.075446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:34.632572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:35.198271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:35.455203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:35.712590: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:35.962571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:36.218013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:36.470201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:36.723705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:36.979177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:37.655377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:38.187753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:38.577207: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:39.272859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:39.538758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:39.798422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:40.071331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:55:40.329653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:40.585091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:41.094621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:41.652075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:42.086721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:42.447972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:42.894291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:43.491010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:43.967979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:44.307689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:44.558859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:44.899844: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:45.203489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:45.466169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:45.720758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:45.972184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:46.227840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:46.477339: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:46.729555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:46.991162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:47.362039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:47.972841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:48.455415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:48.852127: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:55:49.197203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:49.455436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:49.721055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:49.966208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:50.264433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:50.925044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:51.480040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:51.841213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:52.084235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:52.643255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:53.245754: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:53.891133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:54.547229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:55.119820: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:55.639751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:56.057995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:56.670646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:57.355960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:58.034359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:58.711527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:59.310207: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:55:59.999810: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:00.606582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:01.223377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:56:01.472225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:01.870994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:02.632931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:03.233486: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:03.483989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:03.796635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:04.461859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:05.139578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:05.695551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:05.951536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:06.242497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:06.485018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:06.727848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:06.977338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:07.229204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:07.510447: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:08.160218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:08.760567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:09.021840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:09.281176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:09.534987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:09.800276: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:10.487085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:11.059855: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:56:11.618003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:12.100868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:12.535209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:13.222352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:13.870709: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:14.641601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:15.239165: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:15.489078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:16.159989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:16.797463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:17.426854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:17.955034: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:18.672432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:19.355675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:19.964090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:20.591196: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:21.224234: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:21.476642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:21.733111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:22.024521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:22.269616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:22.516433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:22.901718: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:23.494850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:56:23.958863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:24.357158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:24.708329: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:24.990644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:25.265755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:25.525016: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:25.950105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:26.533460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:27.194512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:27.443653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:28.199508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:28.455270: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:28.703639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:28.950662: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:29.211601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:29.855659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:30.341049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:30.744032: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:31.074774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:31.369495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:31.643080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:31.892162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:32.139420: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:32.834928: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:56:33.528099: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:34.216005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:34.882885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:35.468353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:35.945674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:36.481578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:37.070600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:37.663026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:38.129097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:38.551244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:38.916077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:39.239181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:39.514424: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:40.039266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:40.701491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:41.321389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:41.795830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:42.204399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:42.832404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:43.420510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:44.117528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:44.425459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:44.677046: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:45.248448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:56:45.905320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:46.476400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:47.133962: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:47.714982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:47.960667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:48.219203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:48.459564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:48.777840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:49.433957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:50.000274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:50.606409: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:51.168691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:51.627843: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:52.010287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:52.311104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:52.560305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:53.013297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:53.702990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:54.378412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:55.050821: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:55.619794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:56.062168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:56.444853: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:56.784106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:56:57.231367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:57.870271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:58.352148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:58.736659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:56:59.406889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:00.099478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:00.660226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:01.111002: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:01.491341: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:02.016654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:02.698771: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:03.343697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:03.986375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:04.555822: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:05.110504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:05.594226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:06.080284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:06.482029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:07.009077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:07.364749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:07.608965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:07.857647: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:08.098002: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:08.617009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:57:09.162260: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:09.592389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:09.959155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:10.283834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:10.562492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:10.820184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:11.065751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:11.535000: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:12.083375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:12.523636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:12.890449: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:13.217220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:13.499652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:13.773043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:14.017648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:14.702489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:15.360035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:15.926603: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:16.378191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:16.760322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:17.076974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:17.402809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:17.724024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:17.992032: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:57:18.362865: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:19.034060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:19.603894: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:20.041048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:20.409492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:20.802577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:21.411337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:21.891925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:22.290500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:22.641919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:22.937454: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:23.269280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:23.876241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:24.540601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:24.877929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:25.125897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:25.372729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:25.623118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:25.869583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:26.117580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:26.694869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:27.322618: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:27.872560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:28.544446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:57:29.208222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:29.796459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:30.272675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:30.681794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:31.062672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:31.468910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:31.855237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:32.200665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:32.475396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:32.847939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:33.439073: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:33.908434: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:34.403544: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:34.992205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:35.592143: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:36.134476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:36.558090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:36.913454: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:37.231325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:37.507929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:37.907596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:38.488617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:39.108467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:39.653081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:57:40.123238: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:40.576599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:40.958105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:41.423822: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:42.006283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:42.340019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:42.581085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:43.078116: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:43.627377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:44.142303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:44.410911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:44.656971: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:45.225912: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:45.473103: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:46.143178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:46.415124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:46.664006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:46.960927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:47.627511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:48.177407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:48.625698: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:49.026347: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:49.380069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:49.713064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:57:50.365974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:51.005923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:51.569306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:52.021244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:52.599267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:53.178014: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:53.470924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:53.732423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:53.970631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:54.211613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:54.450913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:54.696189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:55.329203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:55.768553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:56.016256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:56.398520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:56.979239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:57.612015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:58.164863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:58.612669: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:58.989255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:57:59.513919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:00.083819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:00.544317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:00.996527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:01.424300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:02.115866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:02.728624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:02.976493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:03.218059: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:03.458429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:03.696678: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:03.935874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:04.176748: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:04.737489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:04.978135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:05.300724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:05.920838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:06.436758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:06.875070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:07.253416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:07.565684: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:07.866791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:08.153041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:08.394268: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:08.634516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:08.874508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:09.115280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:09.366426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:09.620271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:10.193879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:10.444094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:10.686208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:10.933452: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:11.175689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:11.420300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:11.662306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:11.904503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:12.149880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:12.396160: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:12.643677: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:12.889776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:13.131019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:13.698617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:13.955203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:14.202448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:14.445468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:14.690024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:14.936451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:15.178080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:15.742603: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:15.989489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:16.228036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:16.469089: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:16.709533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:16.955417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:17.203489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:17.827982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:18.324728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:18.741648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:19.109284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:19.464130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:19.789436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:20.079121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:20.598353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:21.138867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:21.571260: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:21.923973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:22.255013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:22.524454: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:22.779335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:23.022745: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:23.267405: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:23.510935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:23.858616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:24.526750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:25.088989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:25.594226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:26.007576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:26.366445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:26.683659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:26.950626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:27.628759: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:28.279823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:28.897730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:29.376913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:29.776476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:30.126717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:30.420789: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:31.475451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:31.772626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:32.381812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:32.861297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:33.273248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:33.610119: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:33.937685: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:34.254371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:34.522628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:34.930473: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:35.511558: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:35.963652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:36.343538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:36.676928: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:36.949559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:37.214579: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:37.458103: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:37.699374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:37.942613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:38.183560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:38.742164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:38.988205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:39.232255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:39.480366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:39.796726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:40.454333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:41.024074: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:41.477328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:41.860675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:42.204368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:42.490224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:42.886811: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:43.483312: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:43.958008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:44.364295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:44.721583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:45.022620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:45.299158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:45.548156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:45.793726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:46.043835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:46.453835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:47.022777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:47.637203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:48.183650: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:48.425293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:48.666836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:48.905731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:49.146340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:49.387400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:49.630236: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:49.886709: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:50.134673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:50.380723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:50.628826: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:51.198044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:51.440337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:51.684413: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:51.926436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:58:52.165535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:52.418520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:52.661227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:52.903440: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:53.145975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:53.715063: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:53.964952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:54.212237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:54.453008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:54.710829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:55.347413: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:55.784087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:56.036003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:56.465430: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:57.147826: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:57.700408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:58.341176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:58.951106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:58:59.622861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:00.214987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:00.461750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:00.773999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:01.393197: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:01.982432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:02.640380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:03.199823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:03.439790: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:03.686296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:03.950626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:04.195087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:04.438902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:04.685325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:05.251164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:05.496360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:05.741612: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:05.984498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:06.291118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:06.909271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:07.499228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:07.856985: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:08.098457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:08.341423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:08.579570: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:08.821799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:09.064407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:09.304164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:09.554945: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:09.994635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:10.558340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:11.003149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:11.381182: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:11.722929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:12.002881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:12.270316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:12.515431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:12.761205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:13.009333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:13.390198: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:13.980335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:14.444935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:14.967942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:15.544271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:16.166778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:16.707052: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:16.945501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:17.186175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:17.424950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:17.668497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:18.235707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:18.479770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:18.723097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:18.963474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:19.219041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:19.839524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:20.329550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:20.750577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:21.212481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:21.590865: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:21.922355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:22.232643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:22.491864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:22.747709: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:22.986463: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:23.222864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:23.461241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:23.716527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:24.337726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:24.901959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:25.478375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:26.089568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:26.717138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:26.964044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:27.223894: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:27.848347: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:28.368233: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:28.896228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:29.477262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:29.939076: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:30.321974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:30.657087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:30.939151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:31.221200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:31.465880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:31.706661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:31.944896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:32.186534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:32.427689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:32.669630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:32.909421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:33.151935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:33.391730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:33.633824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:33.875519: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:34.118690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:34.359511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:34.602155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:34.843181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:35.090411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:35.712708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:35.973694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:36.223833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:36.859683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:37.425096: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:37.877084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:38.538813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:38.874907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:39.119854: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:39.693293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:40.222673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:40.468528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:40.712375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:40.950965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:41.199298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:41.832124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:42.511786: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:43.085566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:43.626541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:44.230598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:44.479604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:45.153482: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:45.754156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:46.283057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:46.592555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:47.109779: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:47.650796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:47.908042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:48.149407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:48.719262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:48.959459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:49.204948: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:49.447571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:49.696861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:49.940563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:50.182044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:50.700158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:50.949346: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:51.620784: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:52.211178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:52.460051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:52.709492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:52.956596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:53.210593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:53.461953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:53.714615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:53.965476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:54.287762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:54.936816: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:55.490829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 14:59:56.011716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:56.672016: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:57.196891: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:57.435370: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:57.674188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:57.914783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:58.152169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:58.394100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:58.634645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:58.880597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:59.120327: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 14:59:59.669048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:00.191266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:00.429532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:00.669547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:00.910108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:01.151563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:01.393263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:01.635526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:02.202163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:02.441575: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:02.681199: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:02.919888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:03.161594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:00:03.729750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:03.972191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:04.239918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:04.761501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:05.006366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:05.680786: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:05.935581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:06.188215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:06.822462: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:07.373879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:07.916159: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:08.501817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:09.165171: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:09.801424: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:10.356131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:10.910841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:11.515378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:12.157356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:12.689195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:12.931249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:13.170379: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:13.410485: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:13.644563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:13.891141: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:00:14.135438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:14.713188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:14.971259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:15.224223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:15.484480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:15.740674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:15.992452: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:16.249966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:16.496724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:16.817268: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:17.412605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:17.886065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:18.284509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:18.643245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:18.984040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:19.453422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:20.122368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:20.405304: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:20.654464: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:20.897901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:21.134758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:21.699753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:21.937469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:22.173435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:00:22.408532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:22.648391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:23.219484: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:23.472849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:23.713751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:23.958899: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:24.216210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:24.857212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:25.413362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:26.085226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:26.580083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:27.132704: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:27.692045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:28.346457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:28.959320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:29.615122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:29.898497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:30.141240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:30.381424: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:30.622081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:30.872340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:31.120049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:31.688907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:31.936123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:00:32.177278: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:32.418977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:32.662840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:33.342795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:34.006580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:34.730449: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:34.997600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:35.350435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:35.969899: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:36.454335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:37.135324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:37.408356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:37.650952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:37.970799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:38.652316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:39.216545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:39.868193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:40.418409: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:40.994414: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:41.576481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:42.166407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:42.746203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:43.396827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:44.036490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:00:44.605389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:45.262178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:45.801596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:46.278859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:46.740135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:47.125915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:47.512467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:48.112772: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:48.667058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:48.933313: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:49.247346: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:49.919332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:50.515697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:50.859598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:51.102479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:51.642502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:52.237151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:52.490988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:52.826380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:53.514686: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:54.173458: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:54.718214: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:54.967759: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:55.642130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:00:56.183445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:56.435956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:56.684767: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:56.932847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:57.179526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:57.670357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:58.362675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:59.042457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:00:59.765753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:00.398437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:01.003001: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:01.681299: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:02.257286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:02.912630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:03.580297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:04.274479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:04.939940: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:05.494274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:05.938363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:06.315618: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:06.651128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:07.237914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:07.489602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:07.746490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:01:08.005268: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:08.357219: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:09.043570: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:09.620340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:10.058637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:10.691077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:11.351667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:11.977349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:12.623538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:13.300267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:13.933244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:14.438812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:14.842459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:15.184758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:15.752055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:16.005011: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:16.665139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:17.299085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:17.922636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:18.414468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:19.087255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:19.667497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:20.238006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:20.487600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:01:20.735159: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:20.982843: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:21.232959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:21.826307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:22.499942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:23.083699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:23.540227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:23.921920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:24.272723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:24.572799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:24.840871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:25.093000: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:25.349483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:25.601996: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:26.076507: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:26.604727: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:27.177038: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:27.433875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:27.684553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:27.949671: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:28.200346: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:28.448727: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:29.133267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:29.766212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:01:30.020542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:30.421027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:31.095214: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:31.773237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:32.433726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:32.996689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:33.449751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:33.853172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:34.346217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:34.749350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:35.421453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:36.105025: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:36.726970: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:36.983470: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:37.232208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:37.479157: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:37.732777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:37.984441: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:38.242181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:38.491884: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:38.820776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:39.426158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:39.902561: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:40.300295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:01:40.642190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:41.226036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:41.471694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:41.781548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:42.387619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:42.877287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:43.278334: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:43.638623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:43.935848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:44.221043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:44.469963: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:44.763224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:45.381499: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:45.868490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:46.361032: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:46.954184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:47.353903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:47.653958: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:47.943125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:48.204679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:48.861664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:49.363817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:49.779204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:50.141172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:01:50.445971: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:50.740616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:50.994344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:51.368329: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:52.031712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:52.606988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:53.041395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:53.417133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:53.753520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:54.052426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:54.540700: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:55.103784: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:55.725990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:55.975675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:56.222545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:56.470615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:56.723245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:56.971832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:57.217178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:57.462421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:57.708510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:58.112110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:58.722744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:58.969488: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:01:59.224348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:59.472542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:59.719825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:01:59.966249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:00.629308: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:01.172630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:01.619058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:02.202448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:02.863138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:03.508071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:04.068979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:04.702156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:04.949529: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:05.197897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:05.445340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:05.693218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:05.940069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:06.189964: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:06.440123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:07.121354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:07.730379: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:08.366693: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:09.053056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:09.712587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:02:10.369353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:11.033152: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:11.696241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:11.942850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:12.188647: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:12.436457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:12.690080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:12.940675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:13.187635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:13.433595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:13.677470: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:14.215474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:14.457903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:14.705881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:14.948975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:15.191456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:15.432637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:15.679202: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:16.243969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:16.487962: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:16.800649: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:17.410636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:18.002101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:18.680948: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:02:18.930768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:19.178071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:19.423079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:19.668152: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:19.911421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:20.154280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:20.731601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:20.975646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:21.217500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:21.458901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:21.698503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:21.944337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:22.187147: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:22.824680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:23.354664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:23.878025: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:24.467999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:24.941981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:25.326835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:25.663401: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:25.946239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:26.223345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:26.465225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:26.710560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:02:27.165234: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:27.733184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:27.981381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:28.229719: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:28.474984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:28.763239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:29.381687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:29.961676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:30.554662: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:31.161722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:31.675318: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:32.098445: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:32.438622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:32.751752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:33.016770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:33.276380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:33.526453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:33.784250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:34.030169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:34.288183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:34.955010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:35.611896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:36.215573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:36.469564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:02:36.719682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:36.978493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:37.230622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:37.485277: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:37.892826: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:38.577178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:39.138136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:39.575791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:39.945703: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:40.267834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:40.538516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:40.972373: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:41.633864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:42.174602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:42.429010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:42.678167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:43.257243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:43.597616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:44.218264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:44.470089: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:45.137048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:45.844791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:46.518634: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:47.095826: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:02:47.651505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:48.097776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:48.512431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:48.884216: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:49.210756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:49.479428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:49.824485: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:50.533624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:51.207399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:51.451996: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:51.693505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:51.940468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:52.179750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:52.747080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:53.218799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:53.640477: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:54.054973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:54.494175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:55.073628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:55.638926: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:56.081730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:56.722916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:56.976326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:57.275872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:02:57.890490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:58.378402: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:59.032197: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:59.699213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:02:59.939772: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:00.180039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:00.742309: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:00.980998: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:01.288220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:01.902985: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:02.393574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:02.798660: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:03.156063: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:03.456062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:03.747846: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:03.994764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:04.669302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:05.338602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:05.962411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:06.345283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:06.584933: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:06.850801: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:07.122223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:07.385950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:03:07.642078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:07.890586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:08.135112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:08.379029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:08.626426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:08.873708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:09.414141: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:10.091538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:10.740576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:11.360227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:11.973018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:12.634648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:13.181412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:13.636278: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:14.240447: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:14.496085: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:14.747250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:14.998489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:15.253310: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:15.995069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:16.669929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:17.203222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:17.841234: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:18.528274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:03:19.198941: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:19.454770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:20.149610: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:20.745374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:20.986333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:21.300224: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:21.904459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:22.382380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:22.876974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:23.554023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:24.118934: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:24.628324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:25.084309: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:25.425873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:25.735674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:25.996710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:26.253271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:26.497400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:26.831660: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:27.496075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:28.186805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:28.714011: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:28.956695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:29.214153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:03:29.457198: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:29.738497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:30.431297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:31.094821: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:31.762965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:32.328179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:32.904838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:33.573287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:33.890831: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:34.138904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:34.385315: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:34.634809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:34.878694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:35.131418: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:35.710067: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:35.960093: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:36.210548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:36.840589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:37.508120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:37.877161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:38.127452: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:38.380532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:38.634537: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:39.210992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:03:39.451684: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:39.718065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:40.399374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:40.954955: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:41.530167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:42.088929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:42.624980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:43.073058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:43.510447: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:43.903590: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:44.254565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:44.552940: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:44.821266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:45.083862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:45.579756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:46.126488: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:46.561588: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:47.210106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:47.475531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:47.811833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:48.505349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:49.146811: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:49.671533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:50.104322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:03:50.519799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:50.889213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:51.214905: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:51.478630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:51.827691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:52.464464: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:52.925763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:53.316290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:53.657225: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:53.937581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:54.205401: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:54.456107: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:54.702510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:55.326644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:55.874419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:56.401731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:56.835417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:57.218917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:57.526980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:58.025165: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:58.688125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:58.930657: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:59.181397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:03:59.718067: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:03:59.964132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:00.210022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:00.461947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:00.706064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:00.950080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:01.610886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:02.153212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:02.582098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:02.958596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:03.497128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:03.973476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:04.556148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:05.208492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:05.463752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:05.708386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:05.952533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:06.205661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:06.844512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:07.345417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:07.822871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:08.439386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:08.960292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:09.528715: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:10.196322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:04:10.449597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:10.696370: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:11.252190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:11.502797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:11.755213: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:12.000075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:12.243708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:12.487618: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:12.727931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:12.969297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:13.256972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:13.872133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:14.360872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:14.830566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:15.440818: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:16.054713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:16.611378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:17.056513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:17.420647: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:17.808683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:18.479927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:19.056106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:19.681786: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:20.206195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:04:20.449169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:20.693073: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:20.935353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:21.179918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:21.414659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:21.656562: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:22.230442: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:22.530272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:22.783352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:23.066969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:23.739556: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:24.402012: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:24.982044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:25.442356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:26.123716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:26.708861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:27.351852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:27.855469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:28.243101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:28.507503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:28.748629: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:28.998709: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:29.349137: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:29.949267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:04:30.415988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:30.783677: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:31.029002: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:31.274696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:31.526606: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:31.786098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:32.174766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:32.746188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:32.995321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:33.250078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:33.491248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:33.826871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:34.504859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:34.865840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:35.114119: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:35.356047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:35.598320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:36.110288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:36.652255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:37.082593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:37.525555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:37.922266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:38.264600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:38.567298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:04:38.834696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:39.090181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:39.333001: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:39.581308: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:40.072784: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:40.714182: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:40.951674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:41.193475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:41.437541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:41.682273: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:42.246673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:42.489531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:42.807545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:43.415535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:44.017913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:44.693320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:44.942571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:45.185487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:45.747268: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:45.999586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:46.337778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:46.948819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:47.427253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:47.957163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:04:48.532462: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:48.991371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:49.382095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:49.725984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:50.004279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:50.422780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:51.008661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:51.462621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:51.856252: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:52.200688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:52.478107: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:52.752101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:52.992261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:53.310091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:53.986168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:54.569624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:55.017483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:55.395110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:55.762708: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:56.414257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:56.960344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:57.538805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:58.199181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:58.442548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:04:58.682554: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:59.243775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:59.490190: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:59.739472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:04:59.982587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:00.661999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:01.242235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:01.484492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:01.729615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:01.980612: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:02.231876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:02.483960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:02.799904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:03.416481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:04.030786: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:04.586424: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:05.110283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:05.543939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:06.127086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:06.687246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:06.943893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:07.193471: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:07.755271: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:07.998607: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:05:08.349296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:08.964917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:09.584546: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:10.215574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:10.466939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:10.717833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:10.964981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:11.233130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:11.879961: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:12.419304: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:12.864515: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:13.241391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:13.558465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:14.086472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:14.643372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:14.906920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:15.155052: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:15.724921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:15.965118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:16.209921: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:16.449897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:16.698640: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:16.939543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:17.183413: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:05:17.870459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:18.561432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:19.234729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:19.896933: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:20.539000: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:21.199589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:21.442566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:21.697128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:22.224457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:22.473961: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:22.723695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:22.971833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:23.262233: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:23.943240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:24.614073: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:24.909989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:25.157307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:25.733773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:25.983057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:26.230617: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:26.484587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:26.730259: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:26.975528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:27.222954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:05:27.464789: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:27.710283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:27.955686: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:28.217701: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:28.848828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:29.350713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:29.740142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:29.985560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:30.237430: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:30.484055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:30.726927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:30.975326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:31.227305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:31.473772: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:31.718376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:31.969359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:32.663239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:33.245574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:33.499581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:33.746800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:33.994705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:34.332250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:35.027758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:35.620835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:05:36.059934: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:36.433298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:36.853932: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:37.442639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:38.052500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:38.624248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:39.246282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:39.897323: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:40.433665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:40.872381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:41.303828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:41.926777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:42.522151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:43.151753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:43.798117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:44.346707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:44.926356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:45.512994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:46.121058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:46.672520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:47.093493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:47.451189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:47.773773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:48.043465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:05:48.315233: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:48.576861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:48.832852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:49.074688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:49.317620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:49.571889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:50.065419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:50.711547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:50.957402: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:51.207427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:51.450930: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:51.706567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:51.956431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:52.206035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:52.763798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:53.416201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:53.964008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:54.414499: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:54.797811: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:55.156406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:55.459496: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:55.745092: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:55.992865: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:56.238053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:05:56.480904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:56.724077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:56.967805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:57.211290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:57.460929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:57.703605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:57.943911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:58.187155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:58.435597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:59.124924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:59.726433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:05:59.979729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:00.261336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:00.870332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:01.358582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:01.758782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:02.100668: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:02.421187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:02.773029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:03.385407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:03.909709: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:04.392159: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:04.782505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:05.124140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:06:05.703808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:05.943687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:06.187642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:06.749989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:06.993599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:07.244890: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:07.498104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:07.838099: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:08.437207: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:08.904690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:09.268670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:09.510573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:09.860482: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:10.464501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:10.934334: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:11.339575: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:11.691337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:11.976616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:12.249788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:12.899726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:13.562892: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:14.221034: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:14.472081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:14.893132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:06:15.582939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:16.205122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:16.446138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:16.686971: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:16.928611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:17.168799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:17.405233: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:17.650764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:18.215321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:18.464882: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:18.714799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:18.967587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:19.220453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:19.470944: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:19.745950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:20.431091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:21.120051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:21.714597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:21.957566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:22.213279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:22.846522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:23.433379: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:24.027834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:24.492947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:06:24.884520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:25.274817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:25.887774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:26.368997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:26.772631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:27.112863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:27.446789: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:27.867048: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:28.539597: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:29.200630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:29.442139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:29.693803: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:29.939762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:30.193058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:30.439376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:30.687595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:31.324722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:31.864518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:32.311627: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:32.694532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:33.011799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:33.316484: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:33.589220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:33.844176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:06:34.086398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:34.332026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:34.575091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:35.033472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:35.593886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:35.884326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:36.128102: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:36.380119: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:36.619987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:37.175601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:37.700215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:37.941605: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:38.190357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:38.438670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:38.686095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:38.941147: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:39.183460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:39.722712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:39.969235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:40.215477: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:40.465416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:40.744309: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:41.376723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:41.870513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:06:42.544314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:43.203974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:43.456450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:43.926951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:44.588016: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:44.900697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:45.151209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:45.827070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:46.512220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:47.177945: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:47.707257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:47.953105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:48.200830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:48.445523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:48.697787: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:48.945178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:49.189953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:49.436673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:49.682611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:50.252459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:50.498732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:50.746593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:50.988469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:51.234957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:06:51.485773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:51.751699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:51.992600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:52.307403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:52.957018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:53.660091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:54.243071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:54.488581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:54.802349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:55.455596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:56.109662: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:56.684780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:57.169138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:57.747352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:58.359513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:58.966707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:59.435650: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:06:59.825813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:00.163367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:00.849086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:01.525621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:02.213590: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:02.832593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:03.325573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:07:03.738690: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:04.083595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:04.655524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:05.195574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:05.446594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:05.697278: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:06.265691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:06.924661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:07.515592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:07.982049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:08.546461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:09.116552: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:09.626396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:10.043527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:10.394354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:10.716257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:10.986582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:11.685674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:12.252267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:12.493591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:12.734160: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:12.976839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:13.221532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:13.463722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:07:13.704355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:13.952188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:14.200104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:14.764927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:15.008131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:15.264802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:15.525907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:16.194139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:16.716192: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:16.977236: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:17.243120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:17.615162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:18.227432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:18.474032: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:18.718185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:18.960461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:19.206517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:19.836351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:20.276014: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:20.524083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:21.206018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:21.903611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:22.582820: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:23.263369: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:07:23.935991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:24.627332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:25.232828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:25.681274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:26.345796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:27.027870: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:27.696483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:27.943331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:28.187756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:28.439140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:28.682057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:29.246949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:29.720077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:30.103291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:30.433296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:31.123704: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:31.679833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:32.270476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:32.858386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:33.399335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:33.849079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:34.229425: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:34.542676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:34.837504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:07:35.495509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:36.181649: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:36.433976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:36.674106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:37.239652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:37.483156: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:37.766079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:38.375053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:38.863056: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:39.263209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:39.593334: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:39.890966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:40.167982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:40.735504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:40.983989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:41.657242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:42.246879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:42.500809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:42.836579: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:43.510142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:44.099615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:44.640793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:45.153380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:45.432960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:07:45.681489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:46.133655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:46.712674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:46.959503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:47.203628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:47.448313: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:47.689919: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:47.943177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:48.182641: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:48.837257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:49.446026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:49.822324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:50.059492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:50.500118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:51.165901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:51.698623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:51.942832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:52.189068: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:52.431431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:52.682350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:52.928191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:53.172553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:53.416545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:53.660187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:07:53.902335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:54.153501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:54.397037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:54.648868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:54.901843: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:55.153545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:55.852665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:56.509072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:57.094826: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:57.388591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:57.633448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:58.204592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:58.448167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:58.701486: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:58.949382: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:59.197391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:59.437922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:07:59.689216: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:00.258964: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:00.920045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:01.587924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:01.894161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:02.132986: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:02.381615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:02.628992: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:02.874417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:03.114121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:03.367591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:03.608365: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:04.142244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:04.682855: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:05.123390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:05.556341: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:05.857632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:06.103103: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:06.354511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:06.605506: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:06.863174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:07.119201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:07.367813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:07.610491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:08.165763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:08.712237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:08.952575: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:09.192403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:09.435564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:09.675630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:10.243540: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:10.481392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:10.717654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:10.961311: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:11.204746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:11.446555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:11.690886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:11.933061: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:12.615505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:13.224221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:13.464807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:13.704798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:13.946478: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:14.187337: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:14.427684: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:14.673081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:14.921514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:15.613361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:16.223546: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:16.465896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:16.718567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:17.349180: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:17.875175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:18.394661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:19.006675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:19.682395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:20.248880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:20.492248: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:20.735732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:20.977600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:21.223227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:21.474599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:21.746836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:22.063428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:22.506653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:23.066069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:23.706386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:23.953798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:24.281017: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:24.565487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:24.841328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:25.112821: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:25.798749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:26.456800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:27.117757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:27.718654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:27.989950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:28.254656: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:28.530645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:28.794657: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:29.062780: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:29.317046: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:29.570562: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:29.836238: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:30.270864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:30.577131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:30.854698: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:31.157277: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:31.733814: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:32.017098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:32.314551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:32.859325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:33.113343: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:33.383468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:33.641046: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:34.176915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:34.444342: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:34.710416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:34.966722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:35.396406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:35.961565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:36.462150: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:36.775623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:37.444309: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:38.053805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:38.703626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:39.055226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:39.405077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:39.666841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:39.933376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:40.505407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:41.189793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:41.725898: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:42.141805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:42.525312: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:43.051128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:43.715915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:44.206535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:44.772254: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:45.392332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:45.936199: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:46.522108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:46.856147: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:47.092169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:47.597140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:48.149995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:48.626355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:49.023136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:49.571783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:50.145338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:50.619452: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:51.066297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:51.456766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:51.797243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:52.101504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:52.406366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:52.709047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:52.945974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:53.198918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:53.754227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:53.992959: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:54.296320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:54.952663: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:55.555867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:55.875508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:56.544860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:57.208115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:57.457508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:58.139127: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:58.735896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:08:59.126837: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:08:59.725285: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:00.366138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:01.047873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:01.701885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:01.941086: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:02.184519: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:02.427969: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:02.671680: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:02.911511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:03.158175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:03.721840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:03.984596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:04.229474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:04.502689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:04.751609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:04.999475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:05.308955: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:05.962765: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:06.621495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:07.211706: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:07.462285: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:07.757632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:08.407823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:08.997417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:09:09.611015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:10.155384: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:10.581180: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:11.024911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:11.600428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:12.193984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:12.666667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:13.251139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:13.504935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:13.845082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:14.449303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:15.063683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:15.392657: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:15.631429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:16.183495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:16.706568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:16.944975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:17.186658: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:17.432990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:17.677330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:17.923435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:18.168362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:18.416682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:20.094990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:09:20.347532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:20.610653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:20.856165: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:21.101923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:21.640131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:22.188285: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:22.634031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:23.012010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:23.349598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:24.049064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:24.704162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:24.971281: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:25.273409: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:25.713177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:26.101246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:26.487994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:26.857019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:27.543800: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:28.116178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:28.403673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:28.660611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:28.914272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:29.158091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:29.723816: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:09:29.969161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:30.213889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:30.456012: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:30.727267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:31.398387: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:32.096561: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:32.790571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:33.435987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:33.984578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:34.644131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:35.226223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:35.490559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:35.750361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:36.162543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:36.749084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:37.000856: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:37.247127: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:37.654409: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:38.228226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:38.471157: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:38.741400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:39.363656: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:39.856882: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:40.353020: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:09:40.973989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:41.338347: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:41.583174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:42.076912: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:42.709892: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:42.953221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:43.193825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:43.824370: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:44.273936: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:44.523871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:44.982289: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:45.353112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:45.598624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:46.110008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:46.727983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:46.976009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:47.223168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:47.471906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:48.134971: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:48.719748: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:48.967360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:49.234355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:49.776047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:50.019937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:09:50.263994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:50.500493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:50.744599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:50.995158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:51.244081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:51.486428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:51.739822: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:51.989999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:52.636356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:52.917244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:53.169723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:53.745576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:54.379758: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:54.934975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:55.388775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:55.776872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:56.106243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:56.385055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:56.655288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:56.898427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:57.142844: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:57.799340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:58.416695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:09:59.005170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:09:59.667911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:00.205209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:00.455867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:01.133900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:01.416606: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:01.671553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:01.926527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:02.180179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:02.746883: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:03.018598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:03.273697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:03.535580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:03.790962: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:04.039550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:04.461081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:05.144929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:05.680371: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:05.929470: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:06.185097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:06.863155: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:07.474918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:08.102145: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:08.657068: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:09.133084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:09.595611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:09.980348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:10.458263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:11.027914: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:11.475505: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:11.807873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:12.054980: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:12.304908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:12.545205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:12.955902: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:13.619368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:13.903796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:14.148280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:14.403485: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:14.643487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:15.221841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:15.467440: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:15.733572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:16.268264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:16.512088: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:16.760843: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:17.013182: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:17.399390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:18.065642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:18.624367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:19.063036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:19.528282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:20.136635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:20.669105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:21.110819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:21.484520: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:21.822177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:22.120503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:22.440101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:22.750604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:22.994120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:23.234998: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:23.480593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:23.720210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:23.967702: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:24.206964: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:24.445120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:24.682762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:24.936158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:25.176164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:25.418713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:25.657262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:25.899260: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:26.148091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:26.805954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:27.407330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:27.995666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:28.561075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:28.870398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:29.113472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:29.370344: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:29.617931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:30.167492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:30.705112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:30.951766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:31.196788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:31.445602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:31.692807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:31.944925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:32.203707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:32.463297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:32.704944: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:32.947997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:33.188744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:33.431027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:33.672018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:33.918966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:34.164482: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:34.417653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:34.662006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:34.912855: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:35.153016: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:35.717051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:35.965984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:36.434889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:36.824104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:37.071348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:37.321527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:37.584820: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:37.868722: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:38.560423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:39.218764: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:39.480183: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:39.732173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:39.981455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:40.233864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:40.485005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:40.737814: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:40.992388: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:41.314374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:41.986881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:42.570757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:43.212960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:43.458788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:43.712487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:43.965717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:44.220341: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:44.464357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:44.718787: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:44.963426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:45.211276: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:45.456468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:45.703812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:46.262412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:46.510362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:46.753158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:47.014054: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:47.264924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:47.518456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:47.767817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:48.016908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:48.264888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:48.513367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:48.756857: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:49.001806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:49.246555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:49.506362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:49.757207: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:50.017422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:50.383857: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:51.064517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:51.633108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:52.143721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:52.742965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:52.998257: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:53.655981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:53.932674: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:54.182521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:54.437675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:54.686973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:55.216044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:55.468264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:55.888953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:56.558782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:56.888352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:57.130313: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:57.709977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:57.959963: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:58.218446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:10:58.886221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:59.523612: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:10:59.862971: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:00.104957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:00.354632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:00.598092: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:00.838502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:01.086511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:01.326707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:01.569090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:01.830107: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:02.112330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:02.666264: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:03.203394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:03.657792: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:04.034437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:04.371228: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:04.677869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:04.930911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:05.188161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:05.760144: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:06.006760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:06.341825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:06.944073: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:11:07.426467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:08.102523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:08.732284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:08.991360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:09.521069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:10.096956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:10.737483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:11.323414: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:11.793927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:12.200429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:12.520851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:12.818404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:13.080987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:13.336314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:13.576614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:14.037788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:14.596545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:14.888124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:15.135860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:15.710881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:15.957645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:16.206284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:16.456118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:17.136422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:11:17.737702: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:17.988266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:18.239306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:18.488752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:18.784687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:19.443340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:19.989666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:20.432132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:20.805929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:21.130794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:21.406524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:21.671910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:22.238208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:22.480752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:22.721759: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:22.963216: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:23.210164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:23.453610: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:23.695493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:23.939140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:24.181280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:24.423377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:24.663955: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:24.914352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:11:25.152203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:25.394283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:25.634498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:25.877242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:26.118038: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:26.360629: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:26.601422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:27.119290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:27.662272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:28.139446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:28.533098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:28.885309: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:29.206210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:29.472622: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:30.143272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:30.741591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:30.988314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:31.234654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:31.482599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:31.732283: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:31.974937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:32.262095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:32.886089: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:33.471064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:11:34.139439: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:34.422518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:34.671649: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:35.246621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:35.493770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:35.743105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:35.993982: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:36.267230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:36.535099: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:36.930064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:37.542361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:37.878385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:38.133414: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:38.707911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:38.962393: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:39.213920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:39.455104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:39.712098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:40.333671: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:40.825757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:41.227872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:41.621237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:42.189516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:42.439273: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:11:42.692829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:42.951731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:43.198993: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:43.448036: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:43.692166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:43.948682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:44.192877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:44.754446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:44.993267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:45.369829: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:46.039899: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:46.711731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:46.983732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:47.245280: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:47.495448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:47.739972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:47.988852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:48.546319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:49.104461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:49.550367: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:50.219221: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:50.791082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:51.299710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:51.720862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:11:52.062718: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:52.374167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:52.657442: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:52.908973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:53.156601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:53.396804: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:53.638522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:54.206868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:54.455542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:55.129061: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:55.738106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:55.996512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:56.352474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:56.953844: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:57.546077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:57.878083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:58.121428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:58.680865: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:58.931666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:59.173347: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:59.418510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:59.665824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:11:59.908900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:00.149390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:00.716899: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:00.960611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:01.202883: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:01.757723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:02.014345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:02.254868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:02.497377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:02.797009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:03.398235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:03.983953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:04.657640: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:04.926076: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:05.242340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:05.864387: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:06.353274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:06.759338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:07.100200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:07.433256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:07.751983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:08.022778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:08.282995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:08.531681: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:08.774932: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:09.016210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:09.259908: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:09.500363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:09.828031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:10.440580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:11.039761: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:11.600175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:12.239330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:12.877298: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:13.401485: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:13.839220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:14.215894: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:14.527172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:14.819691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:15.090090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:15.369087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:15.650324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:16.228836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:16.475295: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:16.722988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:16.967973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:17.217372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:17.890609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:18.546746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:19.198037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:19.438809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:19.704343: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:20.276242: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:20.517426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:20.785951: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:21.044148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:21.295961: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:21.547326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:21.793851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:22.045779: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:22.300274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:22.564670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:22.827137: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:23.074839: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:23.320008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:23.562799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:23.819803: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:24.492527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:25.188079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:25.445269: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:25.699922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:25.951068: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:26.196942: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:26.444856: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:26.699241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:26.951905: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:27.648071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:28.206535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:28.473557: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:28.732507: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:28.992263: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:29.243395: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:29.499275: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:29.857024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:30.473822: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:31.106175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:31.795619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:32.468397: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:33.137917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:33.801693: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:34.484654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:35.073110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:35.656635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:36.141166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:36.542315: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:36.892849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:37.210888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:37.470668: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:37.731887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:37.984518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:38.277092: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:38.925017: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:39.478769: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:39.923436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:40.304290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:40.631359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:41.216074: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:41.463849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:41.725628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:42.351062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:42.863578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:43.270436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:43.514935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:43.876479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:44.478275: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:44.954585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:45.347486: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:45.688202: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:45.966374: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:46.300434: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:46.924825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:47.545128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:48.212677: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:48.906218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:49.592504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:50.229517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:50.477126: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:50.736611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:51.385783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:52.061984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:52.716117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:52.970129: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:53.214316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:53.465880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:53.713421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:53.967762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:54.246145: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:54.885596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:55.386442: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:55.794901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:56.143035: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:56.729243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:56.977121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:57.279563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:57.930482: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:58.478483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:12:58.927515: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:12:59.436398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:00.028178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:00.658804: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:01.193164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:01.645335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:02.042753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:02.390577: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:02.700457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:02.963074: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:03.253204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:03.878166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:04.473411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:05.139303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:05.729781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:05.980206: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:06.650405: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:07.270188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:07.942361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:08.621318: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:09.318302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:09.940093: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:10.416601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:10.818241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:13:11.161343: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:11.441978: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:11.737252: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:12.376013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:12.904301: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:13.449968: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:14.120401: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:14.659679: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:15.286664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:15.942534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:16.338074: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:17.018017: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:17.688973: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:17.942621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:18.182195: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:18.425658: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:18.670087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:18.910637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:19.155848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:19.398549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:19.638305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:19.886459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:20.124535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:20.367172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:13:20.616672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:21.160534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:21.721540: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:21.966163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:22.207128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:22.451389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:22.691214: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:23.252269: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:23.493862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:23.740956: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:23.981364: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:24.269328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:24.895066: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:25.421258: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:25.921273: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:26.592361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:27.152320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:27.417806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:27.658961: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:28.227913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:28.469851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:28.734389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:29.362202: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:29.951795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:13:30.535428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:30.995744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:31.372437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:31.885113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:32.479989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:32.957350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:33.490563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:34.153576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:34.702807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:34.949369: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:35.195778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:35.444079: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:35.680465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:35.963717: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:36.214534: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:36.765666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:37.018058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:37.261170: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:37.507122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:37.843219: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:38.519828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:39.181859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:39.711687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:39.961229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:13:40.207637: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:40.457671: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:40.704122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:40.953667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:41.207915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:41.457198: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:41.712999: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:42.341907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:43.023031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:43.605654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:44.151765: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:44.623754: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:45.013891: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:45.356064: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:45.663497: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:45.922122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:46.173333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:46.420519: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:46.665798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:47.236660: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:47.483866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:47.725072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:47.967232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:48.212052: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:13:48.457189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:48.705261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:48.948216: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:49.203244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:49.761671: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:50.226084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:50.611697: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:51.002508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:51.355599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:51.670498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:52.246149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:52.495448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:52.812289: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:53.479184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:54.158193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:54.738113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:55.356330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:55.846424: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:56.237222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:56.483655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:56.729446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:56.975997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:57.225021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:57.469373: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:13:57.750721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:58.377776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:58.870801: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:59.275185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:59.614045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:13:59.910931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:00.191381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:00.439635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:00.684192: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:01.262455: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:01.518649: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:01.786058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:02.428966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:02.972715: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:03.558469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:04.115456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:04.615169: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:04.899977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:05.141728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:05.385707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:05.632230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:05.878189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:06.122141: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:06.677484: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:14:07.213369: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:07.463868: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:07.717513: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:08.344073: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:08.835091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:09.242411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:09.578573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:09.875406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:10.157552: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:10.402422: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:10.641407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:10.881089: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:11.128599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:11.692265: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:11.939123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:12.181250: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:12.750803: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:12.993918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:13.300660: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:13.967172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:14.628412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:15.167325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:15.707787: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:16.380847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:14:16.980954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:17.586784: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:18.219947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:18.461849: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:19.136710: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:19.722038: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:19.970937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:20.261986: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:20.933595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:21.613871: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:22.161833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:22.427756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:22.672389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:22.916860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:23.160654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:23.405866: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:23.654916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:23.898202: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:24.143443: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:24.395523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:24.647537: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:24.891414: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:25.130381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:25.688255: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:14:25.942300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:26.187549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:26.430539: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:26.677396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:27.195644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:27.839584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:28.422652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:28.976549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:29.419208: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:29.797464: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:30.121121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:30.478276: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:30.809947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:31.103061: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:31.362220: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:31.605139: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:31.847189: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:32.090664: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:32.350776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:32.615057: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:32.862416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:33.106431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:33.349307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:33.594955: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:14:34.103461: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:34.727212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:34.967345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:35.230458: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:35.865114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:36.495893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:37.170550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:37.730977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:37.984581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:38.241841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:38.487673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:38.808047: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:39.492457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:40.184331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:40.729837: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:40.980400: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:41.279943: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:41.904376: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:42.397662: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:42.900972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:43.497193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:44.136796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:44.727578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:45.372526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:14:46.075381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:46.718572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:46.966629: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:47.250414: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:47.875929: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:48.366360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:48.775922: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:49.133378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:49.464482: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:49.890399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:50.555778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:51.120454: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:51.559681: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:52.253203: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:52.931500: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:53.520125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:54.160749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:54.748620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:55.083946: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:55.721678: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:55.971483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:56.216100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:56.462952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:56.716206: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:14:57.356947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:57.854644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:58.264150: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:58.622807: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:58.957985: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:59.278805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:59.554134: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:14:59.833842: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:00.121010: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:00.374872: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:00.614678: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:01.153456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:01.691012: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:01.943128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:02.186794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:02.754726: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:03.000636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:03.241974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:03.482332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:03.765287: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:04.373121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:04.863338: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:05.263802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:05.616273: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:15:05.965512: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:06.294818: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:06.577819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:06.910380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:07.244460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:07.866833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:08.361297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:09.034568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:09.611581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:10.251353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:10.896249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:11.455135: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:12.141098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:12.734925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:13.397399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:13.937825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:14.625853: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:15.191415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:15.441789: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:15.689879: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:16.226642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:16.471997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:16.715567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:16.962864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:15:17.214728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:17.861222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:18.491336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:19.074563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:19.532784: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:19.906251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:20.289411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:20.962303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:21.557583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:22.018713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:22.594755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:23.155989: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:23.594483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:23.960284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:24.282873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:24.568437: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:24.836399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:25.102694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:25.363372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:25.630731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:25.880390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:26.138529: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:26.713545: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:26.961162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:15:27.229111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:27.864793: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:28.298241: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:28.541050: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:28.930695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:29.521003: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:30.140901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:30.680151: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:31.111514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:31.462124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:31.889167: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:32.481011: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:32.941641: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:33.329510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:33.662161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:33.975932: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:34.359005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:34.958349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:35.569452: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:36.125077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:36.562175: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:36.988426: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:37.535252: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:38.219240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:15:38.847947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:39.427412: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:40.056939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:40.387322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:40.630511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:40.874128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:41.114351: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:41.638256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:42.221836: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:42.462218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:42.701033: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:42.941101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:43.176827: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:43.422290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:43.669325: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:44.237768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:44.477778: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:44.725176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:44.966670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:45.210436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:45.448782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:45.688923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:46.223392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:46.463585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:15:46.702683: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:46.941621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:47.186901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:47.427292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:47.662282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:47.901728: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:48.141524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:48.709966: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:48.950132: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:49.192240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:49.855657: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:50.458632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:50.945054: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:51.607653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:52.291900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:52.894084: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:53.557154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:54.211904: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:54.464646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:54.707514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:55.325261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:55.820023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:56.228288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:56.578988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:15:57.128903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:57.672191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:58.088442: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:58.532937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:59.202114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:15:59.525081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:00.202015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:00.886196: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:01.567013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:02.235273: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:02.857247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:03.445090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:04.024429: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:04.479556: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:04.863494: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:05.217209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:05.503715: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:05.889536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:06.474027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:07.082553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:07.630335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:08.063342: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:08.423774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:08.751024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:16:09.021593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:09.431166: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:10.087511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:10.644432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:11.071350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:11.502104: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:11.895164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:12.244205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:12.546339: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:13.082161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:13.408184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:13.650444: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:14.218193: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:14.476467: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:14.726567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:14.977965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:15.227593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:15.475979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:15.723161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:15.963819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:16.208601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:16.454901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:16.698142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:16.945227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:16:17.192837: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:17.720609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:17.962321: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:18.213482: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:18.454305: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:18.699122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:18.947215: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:19.203949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:19.458668: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:19.702610: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:19.947504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:20.199103: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:20.763968: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:21.017642: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:21.365715: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:21.972294: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:22.604308: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:23.271668: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:23.849724: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:24.541433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:25.110256: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:25.557568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:26.142638: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:26.790906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:16:27.407406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:28.008666: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:28.585126: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:29.037995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:29.410326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:29.737508: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:30.012702: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:30.282673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:30.524398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:30.774990: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:31.018246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:31.368647: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:32.055294: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:32.388044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:32.636682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:33.305573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:33.999453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:34.680665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:35.220533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:35.656962: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:36.237050: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:36.497307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:36.751360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:37.001475: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:16:37.253200: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:37.516146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:37.905446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:38.517586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:39.155237: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:39.724772: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:39.974613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:40.233142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:40.493906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:40.893533: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:41.517653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:42.027751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:42.453163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:42.811372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:43.129828: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:43.402931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:43.663886: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:43.904648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:44.164441: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:44.727750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:44.969613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:45.231516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:45.875384: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:46.411120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:16:46.854933: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:47.243147: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:47.554808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:48.058805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:48.616320: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:49.047655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:49.708479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:50.261229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:50.507108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:50.824297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:51.479948: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:52.048653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:52.687899: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:52.934788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:53.176528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:53.419566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:53.661953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:53.916430: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:54.158796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:54.724119: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:54.963173: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:55.205417: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:55.767965: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:56.021123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:16:56.277931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:56.670153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:57.255382: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:57.502348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:57.747372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:57.999769: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:58.304067: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:58.914428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:59.391319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:16:59.794665: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:00.151469: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:00.483407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:00.791613: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:01.054070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:01.496883: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:02.062566: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:02.512694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:02.881953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:03.237348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:03.862976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:04.367483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:04.840217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:05.442378: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:06.050388: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:17:06.610389: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:07.059521: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:07.644783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:08.191646: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:08.438268: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:08.682576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:08.937042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:09.185328: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:09.755336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:09.998924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:10.240547: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:10.487958: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:10.734832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:10.984714: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:11.229159: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:11.480823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:12.159154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:12.751509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:13.001317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:13.576897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:14.231503: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:14.866022: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:15.494924: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:16.123350: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:17:16.665390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:17.088333: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:17.442406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:17.762058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:18.043026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:18.486688: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:19.149281: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:19.700974: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:19.947479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:20.193518: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:20.437888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:20.687972: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:21.261209: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:21.520383: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:22.195920: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:22.450005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:22.696272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:22.942816: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:23.187240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:23.759115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:23.999244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:24.312453: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:24.986935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:25.650844: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:17:26.226873: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:26.468889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:26.737041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:27.360528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:27.867357: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:28.395651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:29.063913: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:29.718752: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:29.964319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:30.211667: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:30.833524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:31.418194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:32.084194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:32.654644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:33.086427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:33.447892: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:33.768706: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:34.043970: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:34.308822: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:34.566689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:35.019830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:35.688433: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:36.263495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:36.516168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:17:36.760850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:37.014957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:37.684082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:37.937881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:38.190586: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:38.444563: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:38.696621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:39.265994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:39.514841: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:39.764080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:40.009106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:40.407015: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:41.064188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:41.613916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:42.043774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:42.407654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:42.768696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:43.409149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:43.955798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:44.614877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:45.168191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:45.713777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:46.355548: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:46.942032: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:17:47.637713: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:48.207087: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:48.753875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:49.384981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:49.993118: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:50.570493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:51.133773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:51.615953: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:52.015373: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:52.322210: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:52.565981: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:53.255005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:53.934687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:54.533041: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:55.228343: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:55.916451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:56.603895: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:57.240443: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:57.932682: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:58.602466: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:59.278262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:17:59.923402: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:00.580583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:00.915081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:18:01.607604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:02.239867: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:02.495684: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:02.829335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:03.451706: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:04.076386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:04.647223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:05.143785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:05.549326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:05.899927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:06.214238: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:06.594438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:07.065495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:07.542396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:08.218952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:08.853110: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:09.346359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:09.757029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:10.099363: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:10.427486: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:10.806798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:11.486859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:12.172824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:12.782510: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:18:13.310917: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:13.798786: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:14.202952: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:14.567689: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:15.128654: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:15.690254: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:15.947875: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:16.194466: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:16.763546: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:17.021744: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:17.357804: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:18.002598: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:18.678288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:19.369568: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:20.043184: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:20.612083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:21.056205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:21.439592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:21.775178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:22.074023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:22.341131: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:22.587976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:22.842185: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:23.091896: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:18:23.728489: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:24.058890: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:24.717492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:24.969394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:25.223249: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:25.477069: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:25.724639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:25.975524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:26.226245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:26.473760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:26.771149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:27.450127: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:28.099805: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:28.728610: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:28.972632: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:29.220775: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:29.462833: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:29.713024: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:29.965915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:30.219229: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:30.469033: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:30.743511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:31.441782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:32.129031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:18:32.674840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:33.298403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:33.935601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:34.536201: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:35.002148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:35.398143: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:35.748881: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:36.039777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:36.310732: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:36.564661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:37.008465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:37.685535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:38.220425: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:38.868062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:39.404122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:39.948885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:40.634375: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:41.231975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:41.879361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:42.415541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:42.857488: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:43.231681: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:43.480812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:43.781782: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-07-10 15:18:44.460900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:45.079574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:45.758030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:46.531915: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n", + "2023-07-10 15:18:47.217005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [1200,15]\n", + "\t [[{{node Placeholder/_0}}]]\n" + ] + } + ], + "source": [ + "import pathlib\n", + "from rotation.preprocessing import PrepareData\n", + "n_channels = 15\n", + "data_dir = pathlib.Path(f'/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d{n_channels}_ts2/')\n", + "prepare_data = PrepareData(data_dir)\n", + "subj,dataset = prepare_data.load()\n", + " \n", + "alpha = model.get_alpha(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "53ed822b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fa7cc1cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(alpha[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "438b2300", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1200, 12)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alpha[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9259dfed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4012" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "20fd7da4", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "pickle.dump(alpha, open(f'{result_1_dir}alpha.pkl', \"wb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e6875ef5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.9256483e-04, 2.1780960e-03, 6.2531843e-03, ..., 2.4948446e-03,\n", + " 7.9063408e-04, 3.1569682e-04],\n", + " [1.4581109e-05, 8.3765259e-04, 5.9707104e-03, ..., 1.4968008e-03,\n", + " 1.6649136e-03, 1.2321266e-05],\n", + " [1.1396520e-05, 6.6842531e-06, 7.3152315e-04, ..., 8.4383806e-05,\n", + " 7.1660667e-03, 5.6155511e-07],\n", + " ...,\n", + " [7.1074951e-01, 4.1825572e-08, 3.1663360e-05, ..., 4.8747900e-04,\n", + " 1.8685320e-09, 5.9881685e-08],\n", + " [8.2679802e-01, 7.4637230e-09, 2.7055499e-05, ..., 8.5863030e-07,\n", + " 2.5648489e-10, 2.9916586e-10],\n", + " [9.1423517e-01, 7.6308275e-08, 4.1581033e-04, ..., 2.3310297e-06,\n", + " 1.9075143e-08, 2.3776222e-07]], dtype=float32)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alpha[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b31abc41", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.2677984e-10, 1.9777464e-07, 3.3698305e-07, 1.0830148e-06,\n", + " 9.6019214e-08, 5.5629685e-06, 9.5552868e-01, 1.8513897e-06,\n", + " 2.0398751e-08, 4.4461805e-02, 1.4254192e-08, 3.5274979e-07],\n", + " dtype=float32)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alpha[0][300,:]" + ] + }, + { + "cell_type": "markdown", + "id": "514fd14e", + "metadata": {}, + "source": [ + "### Understanding summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c708b3f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(alpha))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "f9b09846", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, [])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from osl_dynamics.utils import plotting\n", + "\n", + "# Plot the state probability time course for the first subject\n", + "plotting.plot_alpha(alpha[0],n_samples=1200)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "f4055eaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, [])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from osl_dynamics.inference import modes\n", + "\n", + "# Hard classify the state probabilities\n", + "stc = modes.argmax_time_courses(alpha)\n", + "plotting.plot_alpha(stc[0],n_samples=1200)" + ] + }, + { + "cell_type": "markdown", + "id": "b9557fe3", + "metadata": {}, + "source": [ + "### Fractional occupancy" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "5465c6f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4012, 12)\n" + ] + } + ], + "source": [ + "# Calculate fractional occupancies\n", + "fo = modes.fractional_occupancies(stc)\n", + "print(fo.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "e572d14b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.07360523 0.06035041 0.08186378 0.06777231 0.11493582 0.09527522\n", + " 0.07094965 0.07823488 0.09277812 0.0869288 0.09690512 0.08040067]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "print(np.mean(fo,axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "d1c31450", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the distribution of fractional occupancy (FO) across subjects\n", + "plotting.plot_violin(fo.T, x_label=\"State\", y_label=\"FO\")" + ] + }, + { + "cell_type": "markdown", + "id": "f5cf4c2e", + "metadata": {}, + "source": [ + "### Mean Lifetime" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "30eba68f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.67132269 4.79102603 5.32466506 6.06323505 4.45195085 4.54564241\n", + " 5.22813657 5.37935488 6.70049335 4.94312067 6.28557787 6.32329685]\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate mean lifetimes (in seconds)\n", + "mlt = modes.mean_lifetimes(stc, sampling_frequency=1/0.72)\n", + "\n", + "# Convert to ms\n", + "#mlt *= 1000\n", + "\n", + "# Print the group average\n", + "print(np.mean(mlt, axis=0))\n", + "\n", + "# Plot distribution across subjects\n", + "plotting.plot_violin(mlt.T, x_label=\"State\", y_label=\"Mean Lifetime (s)\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e6c889e", + "metadata": {}, + "source": [ + "### Mean Interval\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "5d8bc7e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[65.76852283 95.07600701 92.34632847 98.83552669 35.05508174 52.21079136\n", + " 73.15320108 80.92260451 77.35193795 89.73243718 69.88843844 81.15131273]\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate mean intervals (in seconds)\n", + "mintv = modes.mean_intervals(stc, sampling_frequency=1/0.72)\n", + "\n", + "# Print the group average\n", + "print(np.mean(mintv, axis=0))\n", + "\n", + "# Plot distribution across subjects\n", + "plotting.plot_violin(mintv.T, x_label=\"State\", y_label=\"Mean Interval (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "9a8a36c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "127575000" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "50 * 50 *126 * 405" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "5252adf2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "402965280000" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4012 * 300 * 300 * 1116" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "411f7b2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1225.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "50 * 49 / 2" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4f7d15f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The partition is: {0: 0, 1: 0, 2: 0, 3: 0, 4: 2, 5: 2, 6: 2, 7: 0, 8: 1, 9: 1, 10: 2, 11: 0, 12: 0, 13: 0, 14: 1, 15: 1, 16: 2, 17: 0, 18: 1, 19: 0, 20: 1, 21: 0, 22: 1, 23: 3, 24: 3, 25: 3, 26: 1, 27: 3, 28: 3, 29: 1, 30: 1, 31: 3, 32: 1, 33: 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/slurm-22854570/ipykernel_336595/3638768981.py:17: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n", + " cmap = cm.get_cmap('viridis', max(partition.values()) + 1)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from community import community_louvain\n", + "import matplotlib.cm as cm\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "%matplotlib inline\n", + "\n", + "# load the karate club graph\n", + "G = nx.karate_club_graph()\n", + "\n", + "# compute the best partition\n", + "partition = community_louvain.best_partition(G)\n", + "\n", + "print('The partition is: ',partition)\n", + "# draw the graph\n", + "pos = nx.spring_layout(G)\n", + "# color the nodes according to their partition\n", + "cmap = cm.get_cmap('viridis', max(partition.values()) + 1)\n", + "nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=40,\n", + " cmap=cmap, node_color=list(partition.values()))\n", + "nx.draw_networkx_edges(G, pos, alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c5f65e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[8.49689927e-01 2.15284653e-02 1.19195069e-02 1.54485338e-05\n", + " 2.77014542e-12 1.37034552e-02 2.02857930e-02 2.61173983e-02\n", + " 1.73635491e-02 6.59600873e-03 1.27933011e-02 1.99871467e-02]\n", + " [4.79083359e-02 8.55376697e-01 1.15627801e-02 5.28669526e-04\n", + " 6.66584300e-03 4.93814862e-03 2.43874492e-02 9.30345589e-03\n", + " 1.21323045e-02 6.77407477e-03 8.57175995e-03 1.18504817e-02]\n", + " [1.41233283e-02 1.05032523e-02 8.93749375e-01 2.29123417e-04\n", + " 1.10931641e-03 1.79559309e-02 1.45212626e-02 3.50934095e-03\n", + " 2.47075221e-02 5.19086912e-05 4.80684142e-03 1.47327976e-02]\n", + " [1.57715744e-04 1.53065570e-03 2.94738585e-04 9.41656736e-01\n", + " 3.34574605e-02 1.69650598e-03 4.35216258e-04 9.58674644e-04\n", + " 4.30217763e-04 1.86982730e-02 3.41186259e-05 6.49687266e-04]\n", + " [1.06665419e-35 1.02306734e-02 2.93840760e-03 2.77247536e-02\n", + " 8.50642440e-01 1.25564639e-02 2.22954519e-03 3.92910590e-03\n", + " 2.33229645e-03 6.23344522e-02 2.32952751e-02 1.78658685e-03]\n", + " [1.65657802e-02 4.95731722e-03 1.57759241e-02 1.20766136e-03\n", + " 7.00006024e-03 8.69620568e-01 7.01292800e-03 8.99554983e-04\n", + " 1.72069475e-02 3.10718428e-02 1.81529490e-02 1.05284668e-02]\n", + " [3.44109998e-02 1.18469051e-02 1.67971622e-02 3.26265311e-04\n", + " 2.63364114e-03 1.13741144e-02 8.73334015e-01 1.01339727e-02\n", + " 1.26141076e-02 5.92796273e-03 1.13133445e-02 9.28750915e-03]\n", + " [4.07768980e-02 1.30805760e-02 5.49637888e-03 4.81526608e-04\n", + " 4.02197923e-03 1.82069761e-03 1.68047454e-02 8.76128660e-01\n", + " 1.20392319e-02 5.88570868e-03 1.45145916e-02 8.94900580e-03]\n", + " [2.52947348e-02 1.43696663e-02 2.14050353e-02 2.54171306e-04\n", + " 1.21649670e-03 1.41421653e-02 8.24622460e-03 1.05229881e-02\n", + " 8.81378136e-01 3.72172223e-04 8.13556888e-03 1.46626406e-02]\n", + " [8.46873830e-03 4.66446384e-03 2.25382467e-06 1.94642459e-02\n", + " 4.75655062e-02 1.94406880e-02 2.74753478e-03 4.91006363e-03\n", + " 2.08963990e-04 8.78523738e-01 1.38192128e-02 1.84590746e-04]\n", + " [2.16284342e-02 1.14849295e-02 4.26891896e-03 3.24563194e-05\n", + " 1.96581813e-02 1.30447785e-02 5.57744158e-03 1.65814559e-02\n", + " 8.98771765e-03 2.41250022e-02 8.66760951e-01 7.84973266e-03]\n", + " [3.21970056e-02 1.24208325e-02 1.43365042e-02 2.88745916e-04\n", + " 7.39701493e-04 1.24579454e-02 8.19022845e-03 6.58568004e-03\n", + " 1.68202950e-02 1.47158264e-03 1.17019301e-02 8.82789549e-01]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "data_dir = './results/HMM_ICA_100_state_12/'\n", + "tpm = np.load(f'{data_dir}trans_prob.npy')\n", + "print(tpm)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ca19c014", + "metadata": {}, + "outputs": [], + "source": [ + "# For directed weighted graph\n", + "G = nx.DiGraph()\n", + "\n", + "for i in range(len(tpm)):\n", + " G.add_node(i) # Add nodes to the graph\n", + "\n", + " for j in range(i + 1, len(tpm)):\n", + " weight = tpm[i][j]\n", + "\n", + " if weight > 0:\n", + " G.add_edge(i, j, weight=weight) # Add edge with weight to the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9a273fe2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Draw the graph\n", + "pos = nx.spring_layout(G, seed=42)\n", + "nx.draw(G, pos, with_labels=True, node_size=500, node_color='skyblue', font_size=10, font_weight='bold')\n", + "labels = nx.get_edge_attributes(G, 'weight')\n", + "#nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6dfd95dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{0, 1, 6, 7}, {3, 4, 5, 9, 10}, {2, 8, 11}]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nx.community.louvain_communities(G, seed=123)" + ] + }, + { + "cell_type": "markdown", + "id": "32336ef9", + "metadata": {}, + "source": [ + "## Learn how to use Keras and tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "566a4405", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-04 12:58:01.765041: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2023-11-04 12:58:02.256567: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2023-11-04 12:58:02.256603: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "fashion_mnist = tf.keras.datasets.fashion_mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a5a069b2", + "metadata": {}, + "outputs": [], + "source": [ + "(X_train_full,y_train_full),(X_test,y_test)=fashion_mnist" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0e98e12a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(60000, 28, 28)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_full.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e5e665bc", + "metadata": {}, + "outputs": [], + "source": [ + "X_train,y_train = X_train_full[:-5000],y_train_full[:-5000]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4f447ea1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(55000, 28, 28)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bcc06abe", + "metadata": {}, + "outputs": [], + "source": [ + "X_valid, y_valid = X_train_full[-5000:],y_train_full[-5000:]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "acb430b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5000, 28, 28)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_valid.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "66d558d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('uint8')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "477202c9", + "metadata": {}, + "outputs": [], + "source": [ + "X_train,X_valid,X_test = X_train/255.,X_valid/255., X_test/255." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1bdf3563", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.dtype" + ] + }, + { + "cell_type": "markdown", + "id": "63971fbf", + "metadata": {}, + "source": [ + "### Build a model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3bc95125", + "metadata": {}, + "outputs": [], + "source": [ + "tf.random.set_seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8c148ee3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-04 13:07:37.706983: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", + "2023-11-04 13:07:37.707082: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2023-11-04 13:07:37.707117: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (win002.hpc.in.bmrc.ox.ac.uk): /proc/driver/nvidia/version does not exist\n", + "2023-11-04 13:07:37.708268: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "model = tf.keras.Sequential()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6000e1f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "51104b38", + "metadata": {}, + "outputs": [], + "source": [ + "model.add(tf.keras.layers.Input(shape=[28,28]))\n", + "model.add(tf.keras.layers.Flatten())\n", + "model.add(tf.keras.layers.Dense(300,activation=\"relu\"))\n", + "model.add(tf.keras.layers.Dense(100,activation=\"relu\"))\n", + "model.add(tf.keras.layers.Dense(10,activation=\"softmax\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "adedf0ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " flatten (Flatten) (None, 784) 0 \n", + " \n", + " dense (Dense) (None, 300) 235500 \n", + " \n", + " dense_1 (Dense) (None, 100) 30100 \n", + " \n", + " dense_2 (Dense) (None, 10) 1010 \n", + " \n", + "=================================================================\n", + "Total params: 266,610\n", + "Trainable params: 266,610\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "369630cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.layers" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a8e37506", + "metadata": {}, + "outputs": [], + "source": [ + "hidden1 = model.layers[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "dfe5c68a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'dense'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hidden1.name" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "83df89be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_layer('dense')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "12baf02f", + "metadata": {}, + "outputs": [], + "source": [ + "weights,biases = hidden1.get_weights()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b88b0ebb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "05a0dbc9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(784, 300)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8d640182", + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5183a42d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "1719/1719 [==============================] - 8s 4ms/step - loss: 0.7220 - accuracy: 0.7649 - val_loss: 0.4959 - val_accuracy: 0.8332\n", + "Epoch 2/30\n", + "1719/1719 [==============================] - 8s 5ms/step - loss: 0.4825 - accuracy: 0.8331 - val_loss: 0.4561 - val_accuracy: 0.8384\n", + "Epoch 3/30\n", + "1719/1719 [==============================] - 6s 4ms/step - loss: 0.4369 - accuracy: 0.8478 - val_loss: 0.4233 - val_accuracy: 0.8556\n", + "Epoch 4/30\n", + "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4121 - accuracy: 0.8562 - val_loss: 0.3964 - val_accuracy: 0.8620\n", + "Epoch 5/30\n", + "1719/1719 [==============================] - 6s 4ms/step - loss: 0.3911 - accuracy: 0.8629 - val_loss: 0.3884 - val_accuracy: 0.8618\n", + "Epoch 6/30\n", + "1719/1719 [==============================] - 10s 6ms/step - loss: 0.3751 - accuracy: 0.8691 - val_loss: 0.3906 - val_accuracy: 0.8612\n", + "Epoch 7/30\n", + "1719/1719 [==============================] - 9s 5ms/step - loss: 0.3629 - accuracy: 0.8713 - val_loss: 0.3705 - val_accuracy: 0.8694\n", + "Epoch 8/30\n", + "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3516 - accuracy: 0.8756 - val_loss: 0.3782 - val_accuracy: 0.8614\n", + "Epoch 9/30\n", + "1719/1719 [==============================] - 10s 6ms/step - loss: 0.3408 - accuracy: 0.8792 - val_loss: 0.3508 - val_accuracy: 0.8726\n", + "Epoch 10/30\n", + "1719/1719 [==============================] - 15s 9ms/step - loss: 0.3308 - accuracy: 0.8815 - val_loss: 0.3545 - val_accuracy: 0.8740\n", + "Epoch 11/30\n", + "1719/1719 [==============================] - 11s 6ms/step - loss: 0.3225 - accuracy: 0.8853 - val_loss: 0.3610 - val_accuracy: 0.8694\n", + "Epoch 12/30\n", + "1719/1719 [==============================] - 18s 10ms/step - loss: 0.3145 - accuracy: 0.8872 - val_loss: 0.3487 - val_accuracy: 0.8736\n", + "Epoch 13/30\n", + "1719/1719 [==============================] - 17s 10ms/step - loss: 0.3071 - accuracy: 0.8906 - val_loss: 0.3285 - val_accuracy: 0.8782\n", + "Epoch 14/30\n", + "1719/1719 [==============================] - 16s 9ms/step - loss: 0.3002 - accuracy: 0.8923 - val_loss: 0.3419 - val_accuracy: 0.8776\n", + "Epoch 15/30\n", + "1719/1719 [==============================] - 12s 7ms/step - loss: 0.2938 - accuracy: 0.8948 - val_loss: 0.3395 - val_accuracy: 0.8810\n", + "Epoch 16/30\n", + "1719/1719 [==============================] - 12s 7ms/step - loss: 0.2868 - accuracy: 0.8971 - val_loss: 0.3276 - val_accuracy: 0.8792\n", + "Epoch 17/30\n", + "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2825 - accuracy: 0.8969 - val_loss: 0.3347 - val_accuracy: 0.8788\n", + "Epoch 18/30\n", + "1719/1719 [==============================] - 11s 6ms/step - loss: 0.2759 - accuracy: 0.9006 - val_loss: 0.3247 - val_accuracy: 0.8818\n", + "Epoch 19/30\n", + "1719/1719 [==============================] - 14s 8ms/step - loss: 0.2711 - accuracy: 0.9029 - val_loss: 0.3473 - val_accuracy: 0.8720\n", + "Epoch 20/30\n", + "1719/1719 [==============================] - 16s 9ms/step - loss: 0.2664 - accuracy: 0.9044 - val_loss: 0.3205 - val_accuracy: 0.8814\n", + "Epoch 21/30\n", + "1719/1719 [==============================] - 13s 8ms/step - loss: 0.2614 - accuracy: 0.9041 - val_loss: 0.3180 - val_accuracy: 0.8864\n", + "Epoch 22/30\n", + "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2564 - accuracy: 0.9068 - val_loss: 0.3123 - val_accuracy: 0.8852\n", + "Epoch 23/30\n", + "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2519 - accuracy: 0.9100 - val_loss: 0.3492 - val_accuracy: 0.8722\n", + "Epoch 24/30\n", + "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2468 - accuracy: 0.9113 - val_loss: 0.3221 - val_accuracy: 0.8860\n", + "Epoch 25/30\n", + "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2427 - accuracy: 0.9126 - val_loss: 0.3165 - val_accuracy: 0.8836\n", + "Epoch 26/30\n", + "1719/1719 [==============================] - 16s 10ms/step - loss: 0.2391 - accuracy: 0.9143 - val_loss: 0.3122 - val_accuracy: 0.8868\n", + "Epoch 27/30\n", + "1719/1719 [==============================] - 17s 10ms/step - loss: 0.2339 - accuracy: 0.9149 - val_loss: 0.3214 - val_accuracy: 0.8852\n", + "Epoch 28/30\n", + "1719/1719 [==============================] - 17s 10ms/step - loss: 0.2313 - accuracy: 0.9169 - val_loss: 0.3091 - val_accuracy: 0.8908\n", + "Epoch 29/30\n", + "1719/1719 [==============================] - 12s 7ms/step - loss: 0.2266 - accuracy: 0.9188 - val_loss: 0.3234 - val_accuracy: 0.8852\n", + "Epoch 30/30\n", + "1719/1719 [==============================] - 10s 6ms/step - loss: 0.2234 - accuracy: 0.9200 - val_loss: 0.3053 - val_accuracy: 0.8894\n" + ] + } + ], + "source": [ + "history = model.fit(X_train,y_train,epochs=30,validation_data=(X_valid,y_valid))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "d116c497", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "keras.callbacks.History" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "cb69611a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'verbose': 1, 'epochs': 30, 'steps': 1719}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "history.params" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "c1dd70ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0,\n", + " 1,\n", + " 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0.8907999992370605,\n", + " 0.885200023651123,\n", + " 0.8894000053405762]}" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "history.history" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "50ba98c2", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "23c1f557", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(history.history).plot(figsize=(8,5),xlim=[0,29],ylim=[0,1],grid=True,xlabel='epoch',style=['r--','r--.','b-','b-*'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "9fe8df55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 2s 6ms/step - loss: 0.3251 - accuracy: 0.8856\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.3251431882381439, 0.8855999708175659]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(X_test,y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "3a89f1df", + "metadata": {}, + "outputs": [], + "source": [ + "X_new = X_test[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "12810b75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 219ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0. , 0. , 0. , 0. , 0. , 0.01, 0. , 0.02, 0. , 0.97],\n", + " [0. , 0. , 0.99, 0. , 0.01, 0. , 0. , 0. , 0. , 0. ],\n", + " [0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]],\n", + " dtype=float32)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_proba = model.predict(X_new)\n", + "\n", + "y_proba.round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "d828fb0e", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = y_proba.argmax(axis=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "62418178", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9, 2, 1])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "markdown", + "id": "28d7f1f2", + "metadata": {}, + "source": [ + "### Build Complex Models Using the Functional API" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "68f4e02c", + "metadata": {}, + "outputs": [], + "source": [ + "normalization_layer = tf.keras.layers.Normalization()\n", + "hidden_layer1 = tf.keras.layers.Dense(30,activation=\"relu\")\n", + "hidden_layer2 = tf.keras.layers.Dense(30,activation=\"relu\")\n", + "concat_layer = tf.keras.layers.Concatenate()\n", + "output_layer = tf.keras.layers.Dense(1)\n", + "\n", + "input_ = tf.keras.layers.Input(shape=X_train.shape[1:])\n", + "normalized = normalization_layer(input_)\n", + "hidden1 = hidden_layer1(normalized)\n", + "hidden2 = hidden_layer2(hidden1)\n", + "concat = concat_layer([normalized,hidden2])\n", + "output = output_layer(concat)\n", + "\n", + "model = tf.keras.Model(inputs=[input_],outputs=[output])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "472f6b4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_2 (InputLayer) [(None, 28, 28)] 0 [] \n", + " \n", + " normalization (Normalization) (None, 28, 28) 57 ['input_2[0][0]'] \n", + " \n", + " dense_3 (Dense) (None, 28, 30) 870 ['normalization[0][0]'] \n", + " \n", + " dense_4 (Dense) (None, 28, 30) 930 ['dense_3[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 28, 58) 0 ['normalization[0][0]', \n", + " 'dense_4[0][0]'] \n", + " \n", + " dense_5 (Dense) (None, 28, 1) 59 ['concatenate[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 1,916\n", + "Trainable params: 1,859\n", + "Non-trainable params: 57\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "f0e70251", + "metadata": {}, + "source": [ + "### Subclassing API" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "17e638eb", + "metadata": {}, + "outputs": [], + "source": [ + "class WideAndDeepModel(tf.keras.Model):\n", + " def __init__(self,units=30,activation=\"relu\",**kwargs):\n", + " super().__init__(**kwargs)\n", + " self.norm_layer_wide = tf.keras.layers.Normalization()\n", + " self.norm_layer_deep = tf.keras.layers.Normalization()\n", + " self.hidden1 = tf.keras.layers.Dense(units,activation=activation)\n", + " self.hidden2 = tf.keras.layers.Dense(units,activation=activation)\n", + " self.main_output = tf.keras.layers.Dense(1)\n", + " self.aux_output = tf.keras.layers.Dense(1)\n", + " \n", + " def call(self,inputs):\n", + " input_wide, input_deep = inputs\n", + " norm_wide = self.norm_layer_wide(input_wide)\n", + " norm_deep = self.norm_layer_deep(input_deep)\n", + " hidden1 = self.hidden1(norm_deep)\n", + " hidden2 = self.hidden2(hidden1)\n", + " concat = tf.keras.layers.concatenate([norm_wide,hidden2])\n", + " output = self.main_otput(concat)\n", + " aux_output = self.aux_output(hidden2)\n", + " return output, aux_output\n", + " \n", + "model = WideAndDeepModel(30,activation=\"relu\",name=\"my_cool_model\")" + ] + }, + { + "cell_type": "markdown", + "id": "76739afd", + "metadata": {}, + "source": [ + "### On tensorflow dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b893f36f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([0 1 2 3 4 5 6 7 8 9], shape=(10,), dtype=int32)\n", + "\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "X = tf.range(10)\n", + "print(X)\n", + "dataset = tf.data.Dataset.from_tensor_slices(X)\n", + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "73fbd793", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(0, shape=(), dtype=int32)\n", + "tf.Tensor(1, shape=(), dtype=int32)\n", + "tf.Tensor(2, shape=(), dtype=int32)\n", + "tf.Tensor(3, shape=(), dtype=int32)\n", + "tf.Tensor(4, shape=(), dtype=int32)\n", + "tf.Tensor(5, shape=(), dtype=int32)\n", + "tf.Tensor(6, shape=(), dtype=int32)\n", + "tf.Tensor(7, shape=(), dtype=int32)\n", + "tf.Tensor(8, shape=(), dtype=int32)\n", + "tf.Tensor(9, shape=(), dtype=int32)\n" + ] + } + ], + "source": [ + "for item in dataset:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d84b8f06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': (, ), 'b': }\n", + "{'a': (, ), 'b': }\n", + "{'a': (, ), 'b': }\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-06 11:17:59.769306: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n" + ] + } + ], + "source": [ + "X_nested = {\"a\":([1,2,3],[4,5,6]),\"b\":[7,8,9]}\n", + "dataset = tf.data.Dataset.from_tensor_slices(X_nested)\n", + "for item in dataset:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fafef85d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensorflow.python.data.ops.dataset_ops.TensorSliceDataset" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4f1c8e76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int32)\n", + "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int32)\n", + "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int32)\n", + "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)\n", + "tf.Tensor([8 9], shape=(2,), dtype=int32)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-06 17:21:52.917276: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n" + ] + } + ], + "source": [ + "dataset = tf.data.Dataset.from_tensor_slices(tf.range(10))\n", + "dataset = dataset.repeat(3).batch(7)\n", + "for item in dataset:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5c8b8cba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([ 0 2 4 6 8 10 12], shape=(7,), dtype=int32)\n", + "tf.Tensor([14 16 18 0 2 4 6], shape=(7,), dtype=int32)\n", + "tf.Tensor([ 8 10 12 14 16 18 0], shape=(7,), dtype=int32)\n", + "tf.Tensor([ 2 4 6 8 10 12 14], shape=(7,), dtype=int32)\n", + "tf.Tensor([16 18], shape=(2,), dtype=int32)\n" + ] + } + ], + "source": [ + "dataset = dataset.map(lambda x:x * 2)\n", + "for item in dataset:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "id": "7203676d", + "metadata": {}, + "source": [ + "#### About shuffling the data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a2f13aeb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(0, shape=(), dtype=int64)\n", + "tf.Tensor(1, shape=(), dtype=int64)\n", + "tf.Tensor(2, shape=(), dtype=int64)\n", + "tf.Tensor(3, shape=(), dtype=int64)\n", + "tf.Tensor(4, shape=(), dtype=int64)\n", + "tf.Tensor(5, shape=(), dtype=int64)\n", + "tf.Tensor(6, shape=(), dtype=int64)\n", + "tf.Tensor(7, shape=(), dtype=int64)\n", + "tf.Tensor(8, shape=(), dtype=int64)\n", + "tf.Tensor(9, shape=(), dtype=int64)\n", + "tf.Tensor(0, shape=(), dtype=int64)\n", + "tf.Tensor(1, shape=(), dtype=int64)\n", + "tf.Tensor(2, shape=(), dtype=int64)\n", + "tf.Tensor(3, shape=(), dtype=int64)\n", + "tf.Tensor(4, shape=(), dtype=int64)\n", + "tf.Tensor(5, shape=(), dtype=int64)\n", + "tf.Tensor(6, shape=(), dtype=int64)\n", + "tf.Tensor(7, shape=(), dtype=int64)\n", + "tf.Tensor(8, shape=(), dtype=int64)\n", + "tf.Tensor(9, shape=(), dtype=int64)\n", + "################\n", + "tf.Tensor([1 4 2 3 5 0 6], shape=(7,), dtype=int64)\n", + "tf.Tensor([9 8 2 0 3 1 4], shape=(7,), dtype=int64)\n", + "tf.Tensor([5 7 9 6 7 8], shape=(6,), dtype=int64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-06 17:27:54.715713: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n", + "2023-11-06 17:27:54.722487: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n" + ] + } + ], + "source": [ + "dataset = tf.data.Dataset.range(10).repeat(2)\n", + "for item in dataset:\n", + " print(item)\n", + "print('################')\n", + "dataset = dataset.shuffle(buffer_size=4,seed=42).batch(7)\n", + "for item in dataset:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "id": "1cbf3f9a", + "metadata": {}, + "source": [ + "### Shuffling the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ab08f0a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([3 2 1 6 4 7 8], shape=(7,), dtype=int64)\n", + "tf.Tensor([0 5 9 2 0 3 6], shape=(7,), dtype=int64)\n", + "tf.Tensor([1 7 8 5 4 9], shape=(6,), dtype=int64)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-10 11:07:06.402865: W tensorflow/core/data/root_dataset.cc:247] Optimization loop failed: CANCELLED: Operation was cancelled\n" + ] + } + ], + "source": [ + "dataset = tf.data.Dataset.range(10).repeat(2)\n", + "dataset = dataset.shuffle(buffer_size=4,seed=43).batch(7)\n", + "for item in dataset:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "id": "9b0baff2", + "metadata": {}, + "source": [ + "### Understand how Rukuang generated the simulation data\n", + "\n", + "Let's use Rukuang's code." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "8d17b983", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n", + "(25600, 40)\n" + ] + } + ], + "source": [ + "print(len(time_series))\n", + "print(time_series[0].shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7196fb34", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading files: 100%|████████████████████████████| 10/10 [00:00<00:00, 57.04it/s]\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "from osl_dynamics import data, simulation\n", + "from osl_dynamics.inference import tf_ops\n", + "from osl_dynamics.models.hmm import Config, Model\n", + "from osl_dynamics.utils import plotting\n", + "from sklearn.model_selection import KFold\n", + "from matplotlib import pyplot as plt\n", + "\n", + "train = False\n", + "n_subjects = 10\n", + "n_states_list = np.array([2, 3, 4, 5, 6, 7])\n", + "n_folds = 5\n", + "#results_dir = \"/well/woolrich/users/tjo747/python/sehmm/results/n_modes/hmm_vs_hmm\"\n", + "#os.makedirs(results_dir, exist_ok=True)\n", + "\n", + "tf_ops.gpu_growth()\n", + "\n", + "\n", + "time_series = []\n", + "for i in range(n_subjects):\n", + " sim = simulation.HMM_MVN(\n", + " n_samples=25600,\n", + " n_states=4,\n", + " n_channels=40,\n", + " trans_prob=\"sequence\",\n", + " stay_prob=0.9,\n", + " means=\"zero\",\n", + " covariances=\"random\",\n", + " random_seed=123,\n", + " )\n", + " time_series.append(sim.time_series)\n", + "\n", + "training_data = data.Data(time_series,load_memmaps=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "634979b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type((training_data.arrays)[0]))" + ] + }, + { + "cell_type": "markdown", + "id": "e10ff221", + "metadata": {}, + "source": [ + "### Now it's time to understand sklearn cross validation." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "882be880", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold\n", + "kf = KFold(shuffle=True,random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "15620231", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 0:\n", + " Train: index=[ 2 3 4 5 6 7 8 9 10 11 12 13 14 16 18 19]\n", + " Test: index=[ 0 1 15 17]\n", + "Fold 1:\n", + " Train: index=[ 0 1 2 4 6 7 9 10 12 13 14 15 16 17 18 19]\n", + " Test: index=[ 3 5 8 11]\n", + "Fold 2:\n", + " Train: index=[ 0 1 3 4 5 6 7 8 9 10 11 12 14 15 17 19]\n", + " Test: index=[ 2 13 16 18]\n", + "Fold 3:\n", + " Train: index=[ 0 1 2 3 5 6 7 8 10 11 13 14 15 16 17 18]\n", + " Test: index=[ 4 9 12 19]\n", + "Fold 4:\n", + " Train: index=[ 0 1 2 3 4 5 8 9 11 12 13 15 16 17 18 19]\n", + " Test: index=[ 6 7 10 14]\n" + ] + } + ], + "source": [ + "for i, (train_index, test_index) in enumerate(kf.split(range(20))):\n", + " print(f\"Fold {i}:\")\n", + " print(f\" Train: index={train_index}\")\n", + " print(f\" Test: index={test_index}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "10f2f656", + "metadata": {}, + "source": [ + "### Understand the relationship between \"true\" states and inferred states\n", + "\n", + "This is really important. We have generated the data using ground truth with 4 states. Our model has states as many as 11. So how will these states look like?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3b512c9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f85e9b8f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Object `like` not found.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "true_dir = './results_HCP_202311_no_mean/HMM_ICA_50_state_4/'\n", + "true_means = np.load(f'{true_dir}state_means.npy')\n", + "true_correlations = np.load(f'{true_dir}state_correlations.npy')\n", + "print(true_means.shape)\n", + "print(true_correlations.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "46d21327", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(11, 50)\n", + "(11, 50, 50)\n" + ] + } + ], + "source": [ + "n_states = 11\n", + "fit_dir = f'./results_simulation_202311_no_mean/HMM_ICA_50_state_{n_states}/'\n", + "fit_means = np.load(f'{fit_dir}state_means.npy')\n", + "fit_correlations = np.load(f'{fit_dir}state_correlations.npy')\n", + "print(fit_means.shape)\n", + "print(fit_correlations.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4a03fd4e", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.utils import twopair_riemannian_distance, twopair_vector_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d87039fe", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9aafeec1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n", + " c /= stddev[:, None]\n", + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/seaborn/matrix.py:202: RuntimeWarning: All-NaN slice encountered\n", + " vmin = np.nanmin(calc_data)\n", + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/seaborn/matrix.py:207: RuntimeWarning: All-NaN slice encountered\n", + " vmax = np.nanmax(calc_data)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "true_mean_correlation = twopair_vector_correlation(true_means,true_means)\n", + "sns.heatmap(true_mean_correlation, cmap=\"coolwarm\", square=True,\n", + " linewidths=.5, cbar_kws={\"shrink\": 0.75}, annot=True, fmt=\".2f\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ad498484", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mean_correlation = twopair_vector_correlation(true_means,fit_means)\n", + "sns.heatmap(mean_correlation, cmap=\"coolwarm\", square=True,\n", + " linewidths=.5, cbar_kws={\"shrink\": 0.75}, annot=True, fmt=\".2f\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ed0e78cc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 4/4 [00:00<00:00, 361.48it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SUhJeeeUVZGRkwN7eHv3790d4eDjs7e1r3AcTKxERSUJDJNatW7f+4z6YWImISBL4EH4iIiIR8ftYiYiIRMRvtyEiImqGOGIlIiJJ4BorERGRiKQyFczESkREksDESkREJCJOBRMREYmII1YiIiIRccRKREQkJhlHrERERKLhVDAREZGIOBVMREQkIo5YiYiIRCSVEatMEARB30EQERE9TsoHk+rUzvGrn0WO5NEa1Yj1Ttw1fYfQbLh26Ij+o0P0HUazErZ3EIpObtZ3GM2G8WBf7Df00HcYzcZzpdf1HUKj0agSKxERUXW4xkpERCQmiayxMrESEZEkyPiACCIiIvFIZVcwEysREUkC11iJiIjExBErERGReDhiJSIiEpFMxhErERGReDhiJSIiEg93BRMREYmIa6xERERiksgaqzSiJCIikgiOWImISBI4FUxERCQmbl4iIiISDx/CT0REJCaOWImIiMTDNVYiIiIxSeR2GyZWIiKSBo5YiYiIxMOH8BMREYmJI1YiIiLxSOUh/NKIkoiISCar21FHixcvhkwmw9y5c2vVjiNWIiKShgYcsUZERGDt2rXo2rVrrdtyxEpERPSQ/Px8+Pr64ocffoC1tXWt2zOxEhGRNNRxKlitViM3N1fnUKvV1b7MzJkz8dxzz2HYsGF1CpOJlYiIJEEml9fpCAwMhKWlpc4RGBhY5Wts3boVFy5cqPZ8TXCNlYiIpKGO97H6+/vDz89Pp0ypVFaql5iYiDlz5uDo0aNQqVR1ei2AiZWIiKSijvexKpXKKhPp30VFRSE1NRVPPvlkRZlGo8Hp06exatUqqNVqKBSKx/bDxEpERJJQ309eGjp0KKKjo3XKXn/9dXh6euKDDz6oUVIFmFiJiEgq6vnJS+bm5vDy8tIpMzU1ha2tbaXyR2FiJSIiaeCzgomIiET0D56iVFenTp2qdRsmViIikgaJPCuYiZWIiKSBU8FEREQi4tfGERERiUgiI1ZpRElERCQRHLE+8GvwDoSdDUdiUhKURkp06uiB6a9NgYtzq0e2+333Huw9cAipaemwtDDHgH7/wrQpk2BkZFSp7tbtv2Hdxp8xbswovPPm9Pq6FEl5/tmWeOV5F9hYG+FGQj6WrY3Htbi8ausP7meH6RPbwMlBhaTkQgRtSEB4VKZOnWm+bhj9tBPMTQ0QfS0XS9bEIeleUX1fSqMWHBKJ7acjkZyRDQBo18Iebz43EP29OlRZf/eZiwjYtEenzMhAgfOrFlRZ//PN+7EjNArvv/Q0Jg7tI2rsUtXho1lw/3i2Tll+zE2EdBn52LYtxj+LJzcvw/3dxxD14syKcoWpCTy/fA+OY4bByNYKhQlJuLX6Z9z5fqvo8TdKetgVXBdMrA/8efkKxjw3Eh4dOkCj0eCnTb/gw48+wY9BK2FczTMjT5wKwY8bfsb7c2ahU0dPJN1NxjfLv4UMMsx4Y6pO3euxcdh/6DDaurk1wNVIw5D+9pg1vR2WrI7F1dg8jB/TCksXdcErMyKQnVNaqb6XpwUC5nfC2o03cSYiE8MHOSBwQWdMnRuFhDuFAADfF1zw4qhW+GJ5DO6lFGO6rxuWLuqCie9EoKRUaOhLbDQcrc3xrs9QuDrYAAD2nL2EuUHbsHXBm2jf0qHKNmYqJXZ9+tebenXvaSf+iMGfCUmwtzQXPW6py7sci3MjXq/4WVumeWwb49at0PGrD5ARGlHpXKclH8L2qT64OGU+im7fhd3wfvBaGYDi5FSk7jshauyNkkR2BUsjygYQuCgAzwwbCrfWrmjXtg3mz3sXqWlpiIu/UW2bK9euo3NHTwx5ahCcHB3R48knMHjgAMTExenUKyoqQuCSZZg3eybMzEzr+1Ik42UfZ+w9fA8HjqfgVmIhvlkTh2K1FqOGO1VZ/6UxrXDuQiZ+3ZmE20mF+HHzLcTeyMcLo1rp1NkUfBth5zJw41YBPl8WA1sbJQb0sWuoy2qUBnX1wIAuHdDa0RatHW0x22cITJRGiE64W30jGWBnaVZx2FqYVaqSkpWLxdsO4sup42Cg4NvJ32k1GqhT0iuO0oysRzeQy9Ft0xLELVqJwoTESqet+zyBpJ93IfP0eRTdvovEH4OR92cMrHrW/su4JamOXxvX0PgvoRoFBeUjIHOzym8m/9O5owfibtxAzPVYAMC9+/dxPvICevV4UqfeyqDv0btndzzZzbv+ApYYAwMZ3NubI/LSX280ggBEXsxCZw+LKtt4eVog8qLuG9O5PzLh5Vlev6WjCnY2SkQ8VKegUIOrsbkVdQjQaLU4FHEZRSWl6NrGudp6ReoSjPzPCjzjvxxz12xFfHKqznmtVsDCDbswZfi/qh31Nnem7Vtj6O1QDL5+DN02LYHKpcUj63dYOBMlqRlIXL+jyvNZ4X/AcfQQKB/8vm0H9YZphzZIPxomeuyNkkxet6OBiT4VnJiYiICAAPz000/V1lGr1ZW+ZLYm3zzQULRaLYJ+WIfOnTqijVvrausNeWoQcnLzMO+D/0AQBGg0GowaOQKvjn+pos7JkFDE3biB1cuWNETokmFpYQgDhQyZWbpTvpnZpWjtbFJlGxsrI2Rll+iUZWWXwsaqfD3bxtqooky3TknFueYs7m4KJn/9E0pKy2CsNMLSt8ajXUv7Kuu6Odnik8lj0KGVI/KLirHp6Fm89vV6/BbwNhytyz+krD/yf1DI5Xh1SK+GvAzJyD7/Jy5N80dBbAKUTvZw/2gm+p7cjNPdRkOTX1CpvnW/7nB5/UWE9vCpts8rcz5Dl+8+w7DbodCWlkLQCoiesRCZYZH1eCWNSHOdCs7MzMTGjRsfWac2XzqrDyuDvset27ex4N/vPbLepT+j8WvwDsx++y0ErfgvAv7zIc5FRuKXX7cBAFLT0rDmhx/h/75flZuZiBqSm6Mdti14Cz9/MA3jB/bAxxt340ZyWpV1vdu6YHQfb3i6OKGHuxv+O2M8rM1NsCM0CgBw9XYytpw4h0VTxkImkQ0lDS3t8Gnc/+0Q8qKvI/1oGM6PfhOGVhZo+VLlzUsKM1N0W/81omd89MjpYreZk2DVqxsifGYgrPcLuPbvxfD6NgC2Q/rW56U0HhKZCq71iHXPnj2PPH/z5s3H9lHdl86m3Hl82/q2Muh7nIuIwH8Xfwl7u0evy234ZQuGDXkKzz4zHADQxs0NxepiLF+1Bq9OeAlx8TeQnZ2Dt+f8da1arRbRV65i974DOLBze42/hqipycktRZlGgI21oU65jZUhMrJKqmyTmV0CayvdDyjWVobIfDCKzXzQzvpvfVhbGSH+Zr6Y4UuSoYGiYvNSp9YtceV2MracPIePfEc9vq1CAQ8XJySmlu/AvhB/B5l5BRj5n+UVdTRaAUt3HMXm4+dw8Ms59XINUlaWk4eCuFswaeda6ZxpOxeYtHFGj11BFWWyB6OzkUVXENJ5BIqTU+Hx+TxEvTgLqQdDAAB50ddh4d0Rbf2mIePE2Ya5EH2SyH2stU6sPj4+kMlkEITqd1g+7hNsTb90tiEJgoBV3/2A/zsbjiWBn6OFk+Nj26jV6krXKn/wj0EQBDzh7Y3vV63QOb9kxUq4OLfChBeeb7ZJFQDKygTExuehe1drhIZnACj/YNnd2xq/7696Q83lmFz08LbG9j1/ne/ZzRqXY3IBAMkpxUjPVKOHtzXiE8qn2kyMFejkboFdB5Lr+YqkRysIKCl9/C5VoHxdNv5uKvp7tQcAjOrdFX082+rUefvbzRjVpwvG9u0mdqhNgsLUBCZtXaDeXHmWID/mJkK66X7A8fh0LgzMTXHF7wsUJd6HQmUEuZERBK3ue6+g0UAmkScS/WMSmR2pdWJt0aIF1qxZg7Fjx1Z5/uLFi+jevfs/DqyhrQxaixMhp/Hpwv/AxMQYmVnl0zGmJiYVHwK++u9y2NnaYtprkwAAfXr1xG+79qB927bw9HBH8r172PjLFvTp1RMKhQImJsaV1mhVSiUszM0fuXbbXGzdlYQF8zwRE5+Ha7F5GD+2FYxVcuw/dh8AsHCeB9IySrB2UwIAYPueu1gV6I2XfZxxJjIDwwY4wLO9Ob5eFVvR5/Y9dzFlgisSk4vKb7eZ6IaMTDVCw9P1co2Nxbc7j6OfV3s4WVuiUK3GwfOXERl7C2tm+wIAFq7fBQcrc7w7bigAYO3+EHRp4wxXexvkFRVj45EzuJeZg3H9yjfmWZmZwMpMdy3cQCGHrYUZ3Jya9w7s/+n41b+Rsu8kiu4kQ9XSAR0+ng1Bo0Xy1n0AAO/1X6H4bgquL1wKrboE+Vd07yYozSn/wPi/8rLSUmSEnEPHxfOhKSpG0Z1k2A7sCeeJPrg6f3HDXpy+SGSNtdaJtXv37oiKiqo2sT5uNNtY7T1wCADwvv9CnfL3587GM8PK32xS09J0Phn6vjweMpkMG37ZjPSMTFhaWqBPr56YOsm34QKXsBNhabCyNMR0XzfYWJdP174XEF2x+cjRXoWHP5xfjsnFp0uu4Y2JbfDm5DZISi6C/xdXKu5hBYDNvyVCpVLg37PcYWZqgOirOXgvILpZ38MKAJl5BVi4fhfSc/NhZqyEeytHrJnti76d2gEA7mXm6My+5BYU47Nf9iE9Nx8WJip0dG2BjfNfr3azE1WmauWEJ35ZCkNbK5SkZSLr/6Jwpv94lKSXf2g3dmkBQautVZ9/+PrB4ws/PLFpCQxtLFF0OxnXP16GO2t/rY9LaHQEiYxYZUIts2BoaCgKCgowYsSIKs8XFBQgMjISgwYNqnUwd+Ku1boN1Y1rh47oPzpE32E0K2F7B6Ho5GZ9h9FsGA/2xX5DD32H0Ww8V3q93l+jrv9+jAc37GCn1iPWAQMGPPK8qalpnZIqERHRIzXVzUtERET6IJWpYGmkfyIiIongiJWIiKSBU8FEREQikshUMBMrERFJQ1O9j5WIiEgfpLJ5iYmViIikgWusRERE4hGYWImIiETEqWAiIiLxcMRKREQkJo5YiYiIRMQRKxERkXh4uw0REZGYJDJilUaUREREEsERKxERSYIATgUTERGJhrfbEBERiYmJlYiISDzcFUxERCQiTgUTERGJiSNWIiIi8UhlxCqNKImIqNkTIKvTURtBQUHo2rUrLCwsYGFhgb59++LgwYO16oMjViIikoSGGLE6Oztj8eLF6NChAwRBwMaNGzF27Fj88ccf6Ny5c436YGIlIiJpaIA11tGjR+v8/MUXXyAoKAjh4eFMrERE1LQIdVy9VKvVUKvVOmVKpRJKpfKR7TQaDbZv346CggL07du3xq/HNVYiIpIEQSar0xEYGAhLS0udIzAwsNrXiY6OhpmZGZRKJWbMmIGdO3eiU6dONY6TI1YiImrS/P394efnp1P2qNGqh4cHLl68iJycHOzYsQNTpkxBSEhIjZMrEysREUlCXTcv1WTa92FGRkZo3749AKB79+6IiIjAihUrsHbt2hq1Z2IlIiJJ0Ne322i12kprtI/CxEpERJLQELfb+Pv7Y+TIkXB1dUVeXh62bNmCU6dO4fDhwzXug4mViIgkoSEewp+amorJkyfj3r17sLS0RNeuXXH48GEMHz68xn0wsRIRkSQ0xFTwunXr/nEfTKxERCQJUnlWMBMrERFJgr42L9UWEysREUkCR6xEREQi4oiViIhIRByxEhERiUgqI1aZIAiCvoMgIiJ6nBs3b9apXbu2bUWO5NEa1Yj1eHSxvkNoNoZ2UaHo5GZ9h9GsGA/2Rf/RIfoOo9kI2zsIxbu+1XcYzYbK5119h9BoNKrESkREVB1BkMZUMBMrERFJQl2/6LyhMbESEZEkSGXzEhMrERFJAhMrERGRiJhYiYiIRMTESkREJCLuCiYiIhIRR6xEREQiYmIlIiISERMrERGRiLjGSkREJCKtREas0ng+FBERkURwxEpERJLANVYiIiIRcY2ViIhIRByxEhERiYgjViIiIhFxxEpERCQijliJiIhEpNV3ADXExEpERJLAESsREZGIuMZKREQkIo5YiYiIRMQRKxERkYi0gr4jqBk+hJ+IiEhEHLESEZEkcCqYiIhIRNy8REREJCJBImusTKxERCQJWk4FExERiYdTwURERCKSylQwb7chIiJJECCr01EbgYGB6NmzJ8zNzeHg4AAfHx9cv369Vn0wsRIRkSRohbodtRESEoKZM2ciPDwcR48eRWlpKZ5++mkUFBTUuA9OBRMRkSQ0xBrroUOHdH7esGEDHBwcEBUVhYEDB9aoDyZWIiKShLqusarVaqjVap0ypVIJpVL52LY5OTkAABsbmxq/HqeCiYhIErSQ1ekIDAyEpaWlzhEYGPj419NqMXfuXPTr1w9eXl41jpMjViIikoS6jlj9/f3h5+enU1aT0erMmTNx+fJlhIWF1er1mFiJiKhJq+m078NmzZqFffv24fTp03B2dq5VWybWBw79vg4Xzx1Hyt0EGBop0dajG8ZNnAvHVm7VtklOjMe+rWtw5+Y1ZKYl48XX5mPIqIn/uN/mIDgkEttPRyI5IxsA0K6FPd58biD6e3Wosv7uMxcRsGmPTpmRgQLnVy2osv7nm/djR2gU3n/paUwc2kfU2KXs+Wdb4pXnXWBjbYQbCflYtjYe1+Lyqq0/uJ8dpk9sAycHFZKSCxG0IQHhUZk6dab5umH0004wNzVA9LVcLFkTh6R7RfV9KY1e8NnLCA6/jOSsXABAO0cbvDW0J/p7tq62zS+hlxAcfhn3s/NgZWqM4V3a4d0RfaA0/OuteuuZaGw8/QfS8wrh3sIWH44diC4ujvV+PY1BQ2xeEgQBs2fPxs6dO3Hq1Cm0adOm1n0wsT4QfzUSg0ZMQOv2naHVaLB7y0qs/GwGPlr+O5QqkyrblKiLYefojCf7DseODUtE67c5cLQ2x7s+Q+HqUL4hYM/ZS5gbtA1bF7yJ9i0dqmxjplJi16czK36WVfNv7MQfMfgzIQn2luaixy1lQ/rbY9b0dliyOhZXY/MwfkwrLF3UBa/MiEB2Tmml+l6eFgiY3wlrN97EmYhMDB/kgMAFnTF1bhQS7hQCAHxfcMGLo1rhi+UxuJdSjOm+bli6qAsmvhOBklKJ3M1fTxwsTTFnZB+42llBEIC9UTGYs+kAtr07Hu2dbCvVP/BHLFYcOotPXxwC79ZOuJ2ejY+DjwMA5o/uDwA4dCkOS/aFYeG4p9DF1RGbwy7h7XV7sfv9V2Fr1vTfTxri+1hnzpyJLVu2YPfu3TA3N8f9+/cBAJaWljA2Nq5RH9y89MCshUHoO3gsWrq0h7ObBybPXITM9Hu4c/NatW3c2nvh+cl+6NF/JAwMjUTrtzkY1NUDA7p0QGtHW7R2tMVsnyEwURohOuFu9Y1kgJ2lWcVha2FWqUpKVi4WbzuIL6eOg4GCf94Pe9nHGXsP38OB4ym4lViIb9bEoVitxajhTlXWf2lMK5y7kIlfdybhdlIhftx8C7E38vHCqFY6dTYF30bYuQzcuFWAz5fFwNZGiQF97Brqshqtpzq1wQBPN7S2s4KbvRVmj+gDEyND/Hknpcr6F2/fR7fWTnj2CXe0srHAv9xdMaJbB1xOSq2o83PoRTzfqzN8enZEO0cbLBz3FFSGBtgV0TzeTwShbkdtBAUFIScnB0899RRatGhRcWzbtq3GffCdpxpFhfkAAFMzC0n0K2UarRaHIi6jqKQUXdtUv5ZRpC7ByP+swDP+yzF3zVbEJ6fqnNdqBSzcsAtThv+r2lFvc2VgIIN7e3NEXsqqKBMEIPJiFjp7VP236OVpgciLWTpl5/7IhJdnef2WjirY2SgR8VCdgkINrsbmVtShchqtFgcvxqGopBTerav+INOttROu3U1DdGJ54k3KyEFYzB0M8HAFAJSWaXDtbhr6dPjr34hcLkOf9s748879+r+IRqAhnrwkCEKVx2uvvVbjPmo9FVxUVISoqCjY2NigU6dOOueKi4sRHByMyZMn17bbRkWr1WLH+q/RzrMbWrpWvebXmPqVqri7KZj89U8oKS2DsdIIS98aj3Yt7aus6+Zki08mj0GHVo7ILyrGpqNn8drX6/FbwNtwtC5/E19/5P+gkMvx6pBeDXkZkmBpYQgDhQyZWbpTvpnZpWjtXPUUoo2VEbKyS3TKsrJLYWNVPjtjY21UUaZbp6TiXHMXdy8Dk9bsQEmZBiZGhlg2eSTaOVZ9P+SzT7gjq7AIrwX9DghAmVaLl/p0xvQhPQAAWYXF0GiFSlO+tuYmSEjLqqrLJqchpoLFUKsRa2xsLDp27IiBAweiS5cuGDRoEO7du1dxPicnB6+//vpj+1Gr1cjNzdU5/n7zrj5t+/FLJCfewNR5X0uiX6lyc7TDtgVv4ecPpmH8wB74eONu3EhOq7Kud1sXjO7jDU8XJ/Rwd8N/Z4yHtbkJdoRGAQCu3k7GlhPnsGjKWMiqW3wlamBu9lYInjMBv8x8ES/18cJHwcdxIyWzyroRN+5i3YkoLPAZhK3vjsfSSSMReu021h6LaOCoG6+GmAoWQ60S6wcffAAvLy+kpqbi+vXrMDc3R79+/XDnzp1avWhdb9ZtCNt+/BLRUacx95MfYG0r3k67+upXygwNFHB1sEGn1i3x7rihcHd2xJaT52rWVqGAh4sTElPL36QuxN9BZl4BRv5nObq/8xm6v/MZ7mXmYOmOoxj5nxX1eRmSkJNbijKNABtrQ51yGytDZGSVVNkmM7sE1la6I09rK0NkPhjFZj5oZ21l+Lc6RhXnmjtDAwVc7azQydkBc0b2hXsLO2wOu1Rl3dVHzmHUkx54vlcndGhhi6FebTF7RB/8dOoCtFoB1iYqKOQyZOQX6rTLyCuEnXnT37gESCex1moq+MyZMzh27Bjs7OxgZ2eHvXv34p133sGAAQNw8uRJmJqa1qif6m7WDYvV3zhfEAQErwvExfMnMO/TdbBzrN19Sw3db1OkFQSUlGpqVFej1SL+bir6e7UHAIzq3RV9PNvq1Hn7280Y1acLxvbtJnaoklNWJiA2Pg/du1ojNDwDQPmu6u7e1vh9f9Ubxi7H5KKHtzW27/nrfM9u1rgcU377SHJKMdIz1ejhbY34hPIHlJsYK9DJ3QK7DiTX8xVJk1YQUKrRVnmuuLSs0myLQl7+swABhgYKdGxlj3PxSRjSufxvXasVcC4+CS//q0v9Bt5IaJvi97EWFRXBwOCvJjKZDEFBQZg1axYGDRqELVu21Kif6m/WLa5NOKLa+uOXiAw9iLc+WA6lyhQ5WekAAGMTMxgpVQCADd8ugJWtA3x85wAAykpLcS/pBgBAU1aK7MxUJCbEQKkygUML1xr32xx9u/M4+nm1h5O1JQrVahw8fxmRsbewZrYvAGDh+l1wsDLHu+OGAgDW7g9BlzbOcLW3QV5RMTYeOYN7mTkY1+9JAICVmQms/rb2ZKCQw9bCDG5O3KEKAFt3JWHBPE/ExOfhWmwexo9tBWOVHPuPlW98WTjPA2kZJVi7KQEAsH3PXawK9MbLPs44E5mBYQMc4NneHF+viq3oc/ueu5gywRWJyUXlt9tMdENGphqh4el6ucbGZMXBs+jv0RpOVmYoVJfiwMVYRN68i6CpYwAAC7Ydg4OFKeaM7AsAGNTRDT+HXoRnS3t0cXVEYnoOVh85h4Ed3aCQl08uThrQDR8FH0dnZwd4OTvgl7BLKCotg0+Pjnq7zoYkle9jrVVi9fT0RGRkJDp21P2fuGrVKgDAmDFjxIusgYUeDgYALA+YplM+aeYi9B08FgCQlX4fcvlfs+c5WakInD+h4udjezbi2J6N6NCpB+YtWlfjfpujzLwCLFy/C+m5+TAzVsK9lSPWzPZF307tAAD3MnN0Pr3nFhTjs1/2IT03HxYmKnR0bYGN81+vdrMTVXYiLA1WloaY7usGG2sjxN/Mx3sB0RWbjxztVTqbQy7H5OLTJdfwxsQ2eHNyGyQlF8H/iysV97ACwObfEqFSKfDvWe4wMzVA9NUcvBcQ3ezvYQWAzPwiLAw+hrTcApiplHBvYYugqWPQ190FAHA/Ow/yh/7G3xjSAzIAq4+EIzWnANamxhjUyQ2znvnrAScjvDsgq6AIa46cQ3peITxa2mHN1FGwbUZTwVIgE4SahxoYGIjQ0FAcOHCgyvPvvPMOvvvuO2i1VU91PM7xaP2NWJuboV1UKDq5Wd9hNCvGg33Rf3SIvsNoNsL2DkLxrm/1HUazofJ5t95f45fQumXWiQMadgq5VpuX/P39q02qALBmzZo6J1UiIqJHEQRZnY6GxgdEEBERiYjPCiYiIkmQyhorEysREUmCVJ68xMRKRESSwBErERGRiJhYiYiIRMSpYCIiIhFxxEpERCQiqTwmgYmViIgkgSNWIiIiETGxEhERiYibl4iIiERUi++M+ZuGfV4wEysREUmCVKaC+RB+IiIiEXHESkREksDbbYiIiEQklalgJlYiIpIE7gomIiISEUesREREIhLqPGTl7TZERESVcCqYiIhIRJwKJiIiEpFWIkNWJlYiIpIEjliJiIhExMRKREQkIq1EMisTKxERSYIgkUca8iH8REREIuKIlYiIJKHu38fasJhYiYhIEvjtNkRERCLiiJWIiEhEEnk+BDcvERGRNAhaoU5HbZw+fRqjR49Gy5YtIZPJsGvXrlrHycRKRESSIAh1O2qjoKAA3t7eWL16dZ3j5FQwERFJQkM8K3jkyJEYOXLkP+qDiZWIiCSBm5eIiIhEVNcnL6nVaqjVap0ypVIJpVIpQlSVcY2ViIgkQSsIdToCAwNhaWmpcwQGBtZbnByxEhGRJNR1Ktjf3x9+fn46ZfU1WgWYWImISCLqunmpPqd9q8LESkRE9EB+fj7i4+Mrfk5ISMDFixdhY2MDV1fXGvUhE6SyzYqIiJq1uSvz69Ru+WyzGtc9deoUBg8eXKl8ypQp2LBhQ436aFQj1pxvZus7hGbDcv5K7Df00HcYzcpzpddRvOtbfYfRbKh83kX/0SH6DqPZCNs7qN5fo7ZPUaqLp5566h/f1tOoEisREVF1tBKZYGViJSIiSWiIEasYmFiJiEgSmFiJiIhEJJG8ysRKRETSwBErERGRiKRydygTKxERSUJDfG2cGJhYiYhIEjhiJSIiEhHXWImIiEQklcTK72MlIiISEUesREQkCXykIRERkYikMhXMxEpERJLAXcFEREQi4n2sREREIuJUMBERkYg4FUxERCQiQavVdwg1wsRKRESSwDVWIiIiEXEqmIiISETcvERERCQiJlYiIiIRaQVpbF7iQ/iJiIhExBErERFJAqeCiYiIRMTESkREJCLebkNERCQiLZ+8REREJB5OBRMREYlIkMjtNkysREQkCRyxEhERiYiJlYiISERSefISEysREUkCR6xEREQi4hedExERiYgjViIiIhFJ5XYbfrsNERGRiDhirYKy13CoBo2BOvIkik/+XmUdgw7eUPZ5GgorO0CugDY7DeqIEyi9GlFeQS6Hqv8oGLTtDLmlLYSSYpTdvo7ikN0QCnIb8Goapw4fzYL7x7N1yvJjbiKky8jHtm0x/lk8uXkZ7u8+hqgXZ1aUK0xN4Pnle3AcMwxGtlYoTEjCrdU/4873W0WPX2qCz15GcPhlJGeV/+21c7TBW0N7or9n62rb/BJ6CcHhl3E/Ow9WpsYY3qUd3h3RB0rDv942tp6JxsbTfyA9rxDuLWzx4diB6OLiWO/XIyXPP9sSrzzvAhtrI9xIyMeytfG4FpdXbf3B/ewwfWIbODmokJRciKANCQiPytSpM83XDaOfdoK5qQGir+ViyZo4JN0rqu9L0Tstp4KlSeHkCiPvftCk3n1kPaG4AOrww9BmpABaDQzadobxSF8IhXkouxUDGBhB7ugC9dlD0KTehUxlAtWQF2Dy/Fso+PmbBrqaxi3vcizOjXi94mdtmeaxbYxbt0LHrz5ARmhEpXOdlnwI26f64OKU+Si6fRd2w/vBa2UAipNTkbrvhKixS42DpSnmjOwDVzsrCAKwNyoGczYdwLZ3x6O9k22l+gf+iMWKQ2fx6YtD4N3aCbfTs/Fx8HEAwPzR/QEAhy7FYcm+MCwc9xS6uDpic9glvL1uL3a//ypszUwa8vIarSH97TFrejssWR2Lq7F5GD+mFZYu6oJXZkQgO6e0Un0vTwsEzO+EtRtv4kxEJoYPckDggs6YOjcKCXcKAQC+L7jgxVGt8MXyGNxLKcZ0XzcsXdQFE9+JQEmpNBJPXUll8xKngh9maATj56ag8MivEIoLH1lVkxiPsrg/oc1MgTY7HSUXQqBNS4aiVbvyCiXFKNy+GqXX/4A2KxWae7dQfHw7DJxcITO3boCLafy0Gg3UKekVR2lG1qMbyOXotmkJ4hatRGFCYqXT1n2eQNLPu5B5+jyKbt9F4o/ByPszBlY9u9bTFUjHU53aYICnG1rbWcHN3gqzR/SBiZEh/ryTUmX9i7fvo1trJzz7hDta2VjgX+6uGNGtAy4npVbU+Tn0Ip7v1Rk+PTuinaMNFo57CipDA+yKuNZAV9X4vezjjL2H7+HA8RTcSizEN2viUKzWYtRwpyrrvzSmFc5dyMSvO5NwO6kQP26+hdgb+XhhVCudOpuCbyPsXAZu3CrA58tiYGujxIA+dg11WXojaIU6HXWxevVquLm5QaVSoXfv3jh//nyN2zKxPsR42HiU3bwCze3rtW6rcHWH3NoBmqT46ispjSEIWgjqpj9lUxOm7Vtj6O1QDL5+DN02LYHKpcUj63dYOBMlqRlIXL+jyvNZ4X/AcfQQKFs6AABsB/WGaYc2SD8aJnrsUqbRanHwYhyKSkrh3brqN/hurZ1w7W4aohPLE29SRg7CYu5ggIcrAKC0TINrd9PQp4NzRRu5XIY+7Z3x55379X8REmBgIIN7e3NEXvrrA6MgAJEXs9DZw6LKNl6eFoi8qPsB89wfmfDyLK/f0lEFOxslIh6qU1CowdXY3Io6TZkgaOt01Na2bdvg5+eHgIAAXLhwAd7e3njmmWeQmpr6+MbgVHAFQ88noXB0QX5tpmmNVLB4+3NAYQAIWhQdDUZZdUlZYQDjgWNQei0KKCkWJ2gJyz7/Jy5N80dBbAKUTvZw/2gm+p7cjNPdRkOTX1CpvnW/7nB5/UWE9vCpts8rcz5Dl+8+w7DbodCWlkLQCoiesRCZYZH1eCXSEXcvA5PW7EBJmQYmRoZYNnkk2jnaVFn32SfckVVYhNeCfgcEoEyrxUt9OmP6kB4AgKzCYmi0QqUpX1tzEySkPWbmoZmwtDCEgUKGzCzdKd/M7FK0dq56qtzGyghZ2SU6ZVnZpbCxMio/b21UUaZbp6TiXFPWULfbLF26FG+88QZef718qeq7777D/v378dNPP+HDDz98bPtaJ9Zr164hPDwcffv2haenJ2JiYrBixQqo1WpMnDgRQ4YMeWwfarUaarVap0ypVNY2FNHIzK2gGvICCravBjRlNW9Yokb+xsWAkRIGrh4wHjwO2px0aBL/NmqVy2EyZiogk6HoaLC4wUtU2uHTFf+dF30d2ecvYciNk2j50shKI1KFmSm6rf8a0TM+euR0sdvMSbDq1Q0RPjNQdCcZNgN6wOvb8jXWjBNn6+1apMLN3grBcyYgv7gER6Nv4KPg41j31rgqk2vEjbtYdyIKC3wGoYuLI+5k5ODrPaFYax6Bt4b11EP0RHVfY60u51SVd0pKShAVFQV/f/+KMrlcjmHDhuHs2Zq9j9QqsR46dAhjx46FmZkZCgsLsXPnTkyePBne3t7QarV4+umnceTIkccm18DAQHz66ac6ZQEBAZhnWptoxKNwdIXc1AJmk/9dUSaTK6BwaQejJwcid+m88jmcSgRos9MBACWpd6GwdYSy99MofDixPkiqcgsbFGz7lqPVapTl5KEg7hZM2rlWOmfazgUmbZzRY1dQRZlMXr6KMbLoCkI6j0Bxcio8Pp+HqBdnIfVgCIDyhG3h3RFt/aYxsQIwNFDA1c4KANDJ2QFXklKxOewSPn5hcKW6q4+cw6gnPfB8r04AgA4tbFFUUorPfj+FN4b0gLWJCgq5DBn5unsRMvIKYWfOjUsAkJNbijKNABtrQ51yGytDZGSVVNkmM7sE1la6I09rK0NkPhjFZj5oZ/23PqytjBB/M1/M8BulsL2D6tTuk08+qTLnfPLJJ5XqpqenQ6PRwNFRd3e7o6MjYmJiavR6tVpjXbRoEebPn4+MjAysX78er776Kt544w0cPXoUx48fx/z587F48eLH9uPv74+cnByd4+FPBw2t7PZ15K3/Evkbv6o4yu7dRunVSORv/KqapFoFmQwyxUOfVf6XVK3sURC86rEbopozhakJTNq6QH0/rdK5/JibCOk2CqE9fCqOlL0nkHHqHEJ7+KAo8T7khgaQGxlVmioSNBrI5LKGugxJ0QoCSjVVjwCKS8sgk+n+3hQPfo8CBBgaKNCxlT3OxSf91Z9WwLn4JHR1rXrdtrkpKxMQG5+H7l3/2qwokwHdva1x5XrVt9xdjslFD2/dzY09u1njckx5/eSUYqRnqnXqmBgr0MndoqIOVdbQOadWI9YrV65g06ZNAIDx48dj0qRJePHFFyvO+/r6Yv369Y/tp7ohuN7GcqVqaNPv/a2sBEJRQUW58bOToM3Lhjp0LwBA2Xs4NPfvQJOdDpnCAAZtO8OwUy8UHd1W3l4uh8mYaVA4uqDg97WAXAaZqTkAQCgqBLSPv7WkKev41b+Rsu8kiu4kQ9XSAR0+ng1Bo0Xy1n0AAO/1X6H4bgquL1wKrboE+VfidNqX5pS/ifyvvKy0FBkh59Bx8XxoiopRdCcZtgN7wnmiD67Of/yHvaZuxcGz6O/RGk5WZihUl+LAxVhE3ryLoKljAAALth2Dg4Up5ozsCwAY1NENP4dehGdLe3RxdURieg5WHzmHgR3doHgwWzBpQDd8FHwcnZ0d4OXsgF/CLqGotAw+PTrq7Tobm627krBgnidi4vNwLTYP48e2grFKjv3Hyjd4LZzngbSMEqzdlAAA2L7nLlYFeuNlH2eciczAsAEO8Gxvjq9XxVb0uX3PXUyZ4IrE5KLy220muiEjU43Q8HS9XKMUVJdzqmJnZweFQoGUFN0d8ykpKXByqtmHxlqvsf7vU6xcLodKpYKlpWXFOXNzc+Tk5NS2S0mQm1vrjlwNjaAaPh5yMysIZaXQZqagaP8mlF6/AACQmVnBsEP5bR7mr+kududvXVF5HbaZUbVywhO/LIWhrRVK0jKR9X9RONN/PErSy9dQjV1a1Ho95Q9fP3h84YcnNi2BoY0lim4n4/rHy3Bn7a/1cQmSkplfhIXBx5CWWwAzlRLuLWwRNHUM+rq7AADuZ+dB/tAI9Y0hPSADsPpIOFJzCmBtaoxBndww65k+FXVGeHdAVkER1hw5h/S8Qni0tMOaqaNgy6ngCifC0mBlaYjpvm6wsS6frn0vILpi85GjvQoPT7JcjsnFp0uu4Y2JbfDm5DZISi6C/xdXKu5hBYDNvyVCpVLg37PcYWZqgOirOXgvILrJ38PaUIyMjNC9e3ccP34cPj4+AACtVovjx49j1qxZNepDJgg1necEvL298dVXX2HEiBEAgMuXL8PT0xMGBuX5OTQ0FFOmTMHNmzdreSnlcr6Z/fhKJArL+Sux39BD32E0K8+VXkfxrm/1HUazofJ5F/1Hh+g7jGajruufjdG2bdswZcoUrF27Fr169cLy5csRHByMmJiYSmuvVanViPXtt9+GRvPXFKaXl5fO+YMHD9ZoVzAREVFjNWHCBKSlpeHjjz/G/fv30a1bNxw6dKhGSRWoZWKdMWPGI89/+eWXtemOiIioUZo1a1aNp37/jk9eIiIiEhETKxERkYiYWImIiETExEpERCQiJlYiIiIRMbESERGJiImViIhIREysREREImJiJSIiEhETKxERkYiYWImIiETExEpERCQiJlYiIiIRMbESERGJiImViIhIREysREREImJiJSIiEhETKxERkYiYWImIiETExEpERCQiJlYiIiIRMbESERGJiImViIhIREysREREImJiJSIiEhETKxERkYiYWImIiETExEpERCQiJlYiIiIRMbESERGJiImViIhIREysREREImJiJSIiEhETKxERkYiYWImIiETExEpERCQiJlYiIiIRMbESERGJiImViIhIRDJBEAR9ByFVarUagYGB8Pf3h1Kp1Hc4TR5/3w2Pv/OGxd9308DE+g/k5ubC0tISOTk5sLCw0Hc4TR5/3w2Pv/OGxd9308CpYCIiIhExsRIREYmIiZWIiEhETKz/gFKpREBAADcZNBD+vhsef+cNi7/vpoGbl4iIiETEESsREZGImFiJiIhExMRKREQkIiZWIiIiETGx1tHq1avh5uYGlUqF3r174/z58/oOqck6ffo0Ro8ejZYtW0Imk2HXrl36DqlJCwwMRM+ePWFubg4HBwf4+Pjg+vXr+g6rSQsKCkLXrl1hYWEBCwsL9O3bFwcPHtR3WFRHTKx1sG3bNvj5+SEgIAAXLlyAt7c3nnnmGaSmpuo7tCapoKAA3t7eWL16tb5DaRZCQkIwc+ZMhIeH4+jRoygtLcXTTz+NgoICfYfWZDk7O2Px4sWIiopCZGQkhgwZgrFjx+LKlSv6Do3qgLfb1EHv3r3Rs2dPrFq1CgCg1Wrh4uKC2bNn48MPP9RzdE2bTCbDzp074ePjo+9Qmo20tDQ4ODggJCQEAwcO1Hc4zYaNjQ2++eYbTJs2Td+hUC1xxFpLJSUliIqKwrBhwyrK5HI5hg0bhrNnz+oxMqL6kZOTA6D8jZ7qn0ajwdatW1FQUIC+ffvqOxyqAwN9ByA16enp0Gg0cHR01Cl3dHRETEyMnqIiqh9arRZz585Fv3794OXlpe9wmrTo6Gj07dsXxcXFMDMzw86dO9GpUyd9h0V1wMRKRNWaOXMmLl++jLCwMH2H0uR5eHjg4sWLyMnJwY4dOzBlyhSEhIQwuUoQE2st2dnZQaFQICUlRac8JSUFTk5OeoqKSHyzZs3Cvn37cPr0aTg7O+s7nCbPyMgI7du3BwB0794dERERWLFiBdauXavnyKi2uMZaS0ZGRujevTuOHz9eUabVanH8+HGuh1CTIAgCZs2ahZ07d+LEiRNo06aNvkNqlrRaLdRqtb7DoDrgiLUO/Pz8MGXKFPTo0QO9evXC8uXLUVBQgNdff13foTVJ+fn5iI+Pr/g5ISEBFy9ehI2NDVxdXfUYWdM0c+ZMbNmyBbt374a5uTnu378PALC0tISxsbGeo2ua/P39MXLkSLi6uiIvLw9btmzBqVOncPjwYX2HRnUhUJ2sXLlScHV1FYyMjIRevXoJ4eHh+g6pyTp58qQAoNIxZcoUfYfWJFX1uwYgrF+/Xt+hNVlTp04VWrduLRgZGQn29vbC0KFDhSNHjug7LKoj3sdKREQkIq6xEhERiYiJlYiISERMrERERCJiYiUiIhIREysREZGImFiJiIhExMRKREQkIiZWIiIiETGxEhERiYiJlYiISERMrERERCJiYiUiIhLR/wN7qXEeAPa2SgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "true_correlation_distance = twopair_riemannian_distance(true_correlations,true_correlations)\n", + "sns.heatmap(true_correlation_distance, cmap=\"coolwarm\", square=True,\n", + " linewidths=.5, cbar_kws={\"shrink\": 0.75}, annot=True, fmt=\".2f\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ea5f75f0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 4/4 [00:00<00:00, 203.96it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "correlation_distance = twopair_riemannian_distance(true_correlations,fit_correlations)\n", + "sns.heatmap(correlation_distance, cmap=\"coolwarm\", square=True,\n", + " linewidths=.5, cbar_kws={\"shrink\": 0.75}, annot=True, fmt=\".2f\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae10c328", + "metadata": {}, + "source": [ + "### Calculate the free energy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "98da228c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from rotation.analysis import comparison_analysis\n", + "models = ['HMM']\n", + "list_channels = [50,200,300]\n", + "list_states = [2,3,4,5,6,7,8,10,11]\n", + "result_dir = './results_simulation_202311_no_mean/'\n", + "save_dir = f'{result_dir}comparison/'\n", + "comparison_analysis(models,list_channels,list_states,result_dir,save_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af8aa576", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97d32f19", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "878ab0c4", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "incomplete input (390902673.py, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[18], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n" + ] + } + ], + "source": [ + "for n_state in n_states:\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "79c5cb42", + "metadata": {}, + "source": [ + "### Calculate the fractional occupancy across different number of states" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0e1fbe0d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def fo_correlation(fo:np.ndarray):\n", + " # Calculate the fractional occupancy of different fo vectors\n", + " # Assuming 'data' is your numpy array with shape (N, M) N vectors with length M\n", + "\n", + " # Calculate the correlation matrix\n", + " correlation_matrix = np.corrcoef(fo.T, rowvar=False)\n", + "\n", + " # Exclude the diagonal elements (self-correlations) and get the upper triangular part\n", + " upper_triangle = np.triu(correlation_matrix, k=1)\n", + "\n", + " # Count the number of non-zero elements in the upper triangle\n", + " num_pairs = np.count_nonzero(upper_triangle)\n", + " \n", + " # Calculate the sum of all pairwise correlations\n", + " sum_of_correlations = np.sum(upper_triangle)\n", + "\n", + " # Calculate the average pairwise correlation\n", + " return sum_of_correlations / num_pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bf9e6da3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i=0\n", + "0.042105263157894736\n", + "0.21052631578947367\n", + "-0.042105263157894736\n", + "0.8\n", + "nan\n", + "i=1\n", + "0.9183370386832441\n", + "0.9463488740095206\n", + "0.9411147483720742\n", + "0.9479281312823751\n", + "0.9556415158536743\n", + "i=2\n", + "0.9511191383429114\n", + "0.9556662976776074\n", + "0.948607280170677\n", + "0.9669538225835946\n", + "0.9240976070927012\n", + "i=3\n", + "0.9375842311066614\n", + "0.9525775952206605\n", + "0.9699362796469595\n", + "0.9542083909222998\n", + "0.9135600820495006\n", + "i=4\n", + "0.9111264550715703\n", + "0.9491493528236157\n", + "0.9634356466876499\n", + "0.9713298524011053\n", + "0.9367415043867453\n", + "i=5\n", + "0.9340361617828553\n", + "0.9626198044627088\n", + "0.9641395686266944\n", + "0.9551746258113176\n", + "0.9526132969356207\n", + "i=6\n", + "0.949777726880377\n", + "0.9595423443698502\n", + "0.9631031288761055\n", + "0.9696107537292914\n", + "0.9202475966719372\n", + "i=7\n", + "0.9410888269148104\n", + "0.9568535377496098\n", + "0.9708032262313706\n", + "0.9375470465690332\n", + "0.9574928518485503\n", + "i=8\n", + "0.949532571760413\n", + "0.9371033825853317\n", + "0.9615600972648601\n", + "0.9694784170409859\n", + "0.9110265025318101\n", + "i=9\n", + "0.9541858315465755\n", + "0.957618093117654\n", + "0.9626041581868663\n", + "0.9713708376961047\n", + "0.9536545075497675\n", + "[ nan 0.94187406 0.94928883 0.94557332 0.94635656 0.95371669\n", + " 0.95245631 0.9527571 0.94574019 0.95988669]\n" + ] + } + ], + "source": [ + "states = [2,3,4,5,6,7,8,9,10,11]\n", + "fo_correlations = np.zeros((10,5))\n", + "for i in range(len(states)):\n", + " print(f'i={i}')\n", + " for j in range(5):\n", + " fo = np.load(f'./results_simulation_202311_toy_2/HMM_ICA_2_state_{states[i]}/cross_validation_{j}/validation/fo.npy')\n", + " print(fo_correlation(fo))\n", + " fo_correlations[i,j] = fo_correlation(fo)\n", + "print(np.mean(fo_correlations,axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9ef8a87c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9986660732168026\n" + ] + } + ], + "source": [ + "test_fo = np.array([[0.3,0.5,0.2],[0.31,0.49,0.2]])\n", + "print(fo_correlation(test_fo))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e2ba0523", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i=0\n", + "18820.51122430424\n", + "18782.153813231864\n", + "18780.841405648996\n", + "18775.62434233668\n", + "18767.98849251772\n", + "i=1\n", + "18724.1065514451\n", + "18733.49469636096\n", + "18738.084964307363\n", + "18715.404912188344\n", + "18718.402158949175\n", + "i=2\n", + "18722.885068781037\n", + "18696.135788137897\n", + "18705.13166627112\n", + "18693.672438870202\n", + "18678.349688922415\n", + "i=3\n", + "18668.82389817432\n", + "18679.142361001654\n", + "18683.204304575465\n", + "18671.608178990136\n", + "18674.836264112902\n", + "i=4\n", + "18671.705090399613\n", + "18689.503478831884\n", + "18680.233481246483\n", + "18670.97798611928\n", + "18677.027130241506\n", + "i=5\n", + "18689.107597432627\n", + "18671.14019175027\n", + "18668.723368922827\n", + "18667.38634036229\n", + "18659.258738979228\n", + "i=6\n", + "18662.602186571345\n", + "18669.79750533006\n", + "18676.8635364364\n", + "18663.506920928776\n", + "18655.42425720338\n", + "i=7\n", + "18664.66346765958\n", + "18702.333462930077\n", + "18669.004252332907\n", + "18673.91737320727\n", + "18651.488141422025\n", + "i=8\n", + "18658.82845534392\n", + "18666.882401369206\n", + "18664.032669724565\n", + "18662.826929013587\n", + "18647.9126888127\n", + "i=9\n", + "18655.47791142228\n", + "18659.068787866043\n", + "18665.929507294004\n", + "18676.623428440722\n", + "18647.32099139024\n", + "i=10\n", + "18660.02333126184\n", + "18655.65849572116\n", + "18668.97741581328\n", + "18656.14604548119\n", + "18649.270351927426\n", + "i=11\n", + "18659.52341200683\n", + "18658.106136835533\n", + "18658.84082031358\n", + "18646.814848625407\n", + "18649.631255450975\n", + "i=12\n", + "18656.812596279335\n", + "18655.80335376825\n", + "18660.798543501838\n", + "18651.309678539998\n", + "18644.7830098455\n", + "i=13\n", + "18647.37133315644\n", + "18653.894544755705\n", + "18654.59602277117\n", + "18645.783659470115\n", + "18641.475587629582\n", + "i=14\n", + "18651.947774771394\n", + "18649.804479794788\n", + "18657.69801034643\n", + "18650.661006064624\n", + "18636.12379213078\n", + "[18785.42385561 18725.89865665 18699.2349302 18675.52300137\n", + " 18677.88943337 18671.12324749 18665.63888129 18672.28133951\n", + " 18660.09662885 18660.88412528 18658.01512804 18654.58329465\n", + " 18653.90143639 18648.62422956 18649.24701262]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import json\n", + "states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "free_energy = np.zeros((15,5))\n", + "for i in range(len(states)):\n", + " print(f'i={i}')\n", + " for j in range(5):\n", + " with open(f'./results_simulation_202311_toy_5/HMM_ICA_25_state_{states[i]}/cross_validation_{j}/metrics.json', 'r') as file:\n", + " # Load the JSON data from the file\n", + " data = json.load(file)\n", + " print(data['free_energy'])\n", + " free_energy[i,j] = data ['free_energy']\n", + "print(np.mean(free_energy,axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1b5bb4c9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Calculate mean and standard deviation along axis 1 (across the 5 repetitions)\n", + "mean_free_energy = np.mean(free_energy, axis=1)\n", + "std_dev_free_energy = np.std(free_energy, axis=1)\n", + "\n", + "# Number of states (assuming it ranges from 2 to 16)\n", + "num_states = np.arange(2, 17)\n", + "\n", + "# Plotting the mean free energy with error bars\n", + "plt.errorbar(num_states, mean_free_energy, yerr=std_dev_free_energy, fmt='o-', label='Free Energy')\n", + "\n", + "# Customize the plot\n", + "plt.title('Free Energy vs Number of States')\n", + "plt.xlabel('Number of States')\n", + "plt.ylabel('Free Energy')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.savefig('./temp_plot_20231121/free_energy.pdf')\n", + "plt.savefig('./temp_plot_20231121/free_energy.jpg')\n", + "plt.close()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "de7b49e9", + "metadata": {}, + "source": [ + "### Plot the reproducibility analysis for new data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3f97b58c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Directory path\n", + "base_directory = './results_simulation_202311_toy_5/'\n", + "\n", + "# Number of states range\n", + "num_states_range = np.arange(2, 17)\n", + "\n", + "# List to store average diagonal values\n", + "average_diagonal_values = []\n", + "\n", + "# Loop through different numbers of states\n", + "for n_state in num_states_range:\n", + " # File path\n", + " file_path = os.path.join(base_directory, f'HMM_ICA_25_state_{n_state}/reproduce_analysis/FCs_fisher_correlation_reorder_split_1.npy')\n", + " \n", + " # Check if the file exists\n", + " if os.path.exists(file_path):\n", + " # Load the 2D numpy array from the file\n", + " data = np.load(file_path)\n", + " \n", + " # Calculate the average value of diagonals and append to the list\n", + " average_diagonal = np.mean(np.diagonal(data))\n", + " average_diagonal_values.append(average_diagonal)\n", + " else:\n", + " print(f\"File not found for {n_state} states.\")\n", + "\n", + "# Plotting the average diagonal values against the number of states\n", + "plt.plot(num_states_range, average_diagonal_values, 'o-')\n", + "\n", + "# Customize the plot\n", + "plt.title('Average Diagonal vs Number of States')\n", + "plt.xlabel('Number of States')\n", + "plt.ylabel('Average Diagonal Value')\n", + "plt.grid(True)\n", + "plt.savefig('./temp_plot_20231121/reproduce_analysis.pdf')\n", + "plt.savefig('./temp_plot_20231121/reproduce_analysis.jpg')\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3f72cc30", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from osl_dynamics.analysis.modes import calc_trans_prob_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "566d2e88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200, 5)\n" + ] + } + ], + "source": [ + "import pickle\n", + "with open('./results_simulation_202311_toy_2/HMM_ICA_2_state_5/cross_validation_0/validation/alpha.pkl','rb') as file:\n", + " alpha = pickle.load(file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3057b7ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.20776352, 0.19857886, 0.23280662, 0.17427129, 0.1865797 ],\n", + " [0.21297948, 0.1951258 , 0.2378353 , 0.16826051, 0.1857989 ],\n", + " [0.2206108 , 0.19000335, 0.24536414, 0.161661 , 0.18236071],\n", + " ...,\n", + " [0.17803954, 0.21357319, 0.1828571 , 0.17252667, 0.2530035 ],\n", + " [0.1780189 , 0.21357313, 0.18247892, 0.17248656, 0.2534425 ],\n", + " [0.1794829 , 0.21233372, 0.18231668, 0.17321044, 0.25265625]],\n", + " dtype=float32)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alpha[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d83ba536", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.94789579, 0. , 0.02204409, 0. , 0.03006012],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.0625 , 0. , 0.9 , 0. , 0.0375 ],\n", + " [0.5 , 0. , 0. , 0.5 , 0. ],\n", + " [0.02788104, 0. , 0.00743494, 0.00185874, 0.96282528]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calc_trans_prob_matrix(alpha[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4c9f8394", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.97667638, 0. , 0.00874636, 0. , 0.01457726],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.05839416, 0. , 0.9270073 , 0. , 0.01459854],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.0212766 , 0. , 0.0106383 , 0. , 0.96808511]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calc_trans_prob_matrix(alpha[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "10185244", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.96174863, 0. , 0.0136612 , 0. , 0.02459016],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.03333333, 0. , 0.88333333, 0. , 0.08333333],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.01423027, 0. , 0.00258732, 0. , 0.98318241]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calc_trans_prob_matrix(alpha[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5a38eff1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.9673913 , 0. , 0.01086957, 0. , 0.02173913],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.04724409, 0. , 0.93700787, 0. , 0.01574803],\n", + " [0. , 0. , 0. , 0. , 0. ],\n", + " [0.01470588, 0. , 0.00326797, 0. , 0.98202614]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calc_trans_prob_matrix(alpha[3])" + ] + }, + { + "cell_type": "markdown", + "id": "5276f815", + "metadata": {}, + "source": [ + "### Calculate the Fractional Occupancy across scans in the same subject in real data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "dd2194c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.97950602 0.95918963 0.93670589 0.86264682 0.53929757]\n", + " [0.99130442 0.9369285 0.88698215 0.8225825 0.75641117]\n", + " [0.97339019 0.98070914 0.93987138 0.89433092 0.88025788]\n", + " [0.952394 0.95726558 0.92490503 0.8963217 0.9219986 ]]\n" + ] + } + ], + "source": [ + "save_dir = './results_202309/'\n", + "N_channels = [15,25,50,100]\n", + "N_states = [4,8,12,16,20]\n", + "fo_correlation = np.zeros((4,5))\n", + "for i in range(len(N_channels)):\n", + " for j in range(len(N_states)):\n", + " fo = np.load(f'{save_dir}HMM_ICA_{N_channels[i]}_state_{N_states[j]}/fo.npy')\n", + " temp = np.zeros(1003)\n", + " for k in range(1003):\n", + " temp[k] = np.corrcoef(fo[4*k+1,:],fo[4*k+2,:])[0,1]\n", + " fo_correlation[i,j] = np.mean(temp[k])\n", + "\n", + "print(fo_correlation)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "57cf757c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of states: 2\n", + "[[[1.08675 0.79293466]\n", + " [0.79293466 0.9776847 ]]\n", + "\n", + " [[0.9148042 0.8080214 ]\n", + " [0.8080214 1.0383565 ]]]\n", + "[[[1.0810381 0.8155787 ]\n", + " [0.8155787 1.0047741 ]]\n", + "\n", + " [[0.92099375 0.7886471 ]\n", + " [0.7886471 1.0076351 ]]]\n", + "[[[0.93030304 0.77806693]\n", + " [0.77806693 0.9832886 ]]\n", + "\n", + " [[1.0721947 0.8260784 ]\n", + " [0.8260784 1.0260663 ]]]\n", + "[[[1.0838544 0.784845 ]\n", + " [0.784845 0.95867985]]\n", + "\n", + " [[0.9233475 0.81727713]\n", + " [0.81727713 1.0550208 ]]]\n", + "[[[1.095637 0.81208014]\n", + " [0.81208014 0.99377495]]\n", + "\n", + " [[0.90951 0.7931748 ]\n", + " [0.7931748 1.0220174 ]]]\n", + "Number of states: 3\n", + "[[[1.1032157 0.7987267 ]\n", + " [0.7987267 0.9879487 ]]\n", + "\n", + " [[0.8952596 0.79460204]\n", + " [0.79460204 1.0230927 ]]\n", + "\n", + " [[1.0007254 0.8182104 ]\n", + " [0.8182104 1.0128049 ]]]\n", + "[[[1.0669298 0.82418317]\n", + " [0.82418317 1.0236919 ]]\n", + "\n", + " [[1.0958679 0.81990016]\n", + " [0.81990016 1.0085359 ]]\n", + "\n", + " [[0.8995982 0.7878066 ]\n", + " [0.7878066 1.0147293 ]]]\n", + "[[[1.0885408 0.80925983]\n", + " [0.80925983 1.0021284 ]]\n", + "\n", + " [[0.9954629 0.8204736 ]\n", + " [0.8204736 1.0414991 ]]\n", + "\n", + " [[0.922631 0.78843963]\n", + " [0.78843963 0.9955502 ]]]\n", + "[[[1.0918213 0.7932222 ]\n", + " [0.7932222 0.97037256]]\n", + "\n", + " [[0.90349346 0.7934673 ]\n", + " [0.7934673 1.018952 ]]\n", + "\n", + " [[1.0614822 0.8461554 ]\n", + " [0.8461554 1.0662968 ]]]\n", + "[[[1.1220093 0.80450374]\n", + " [0.80450374 0.98128814]]\n", + "\n", + " [[0.9352439 0.8061465 ]\n", + " [0.8061465 1.0182924 ]]\n", + "\n", + " [[0.8869685 0.78550744]\n", + " [0.78550744 1.0210403 ]]]\n", + "Number of states: 4\n", + "[[[1.0726527 0.80181324]\n", + " [0.80181324 0.9995523 ]]\n", + "\n", + " [[0.93225044 0.81576705]\n", + " [0.81576705 1.0421999 ]]\n", + "\n", + " [[0.9267251 0.725251 ]\n", + " [0.725251 0.8880574 ]]\n", + "\n", + " [[1.043531 0.8433696 ]\n", + " [0.8433696 1.0695951 ]]]\n", + "[[[0.9491423 0.73392546]\n", + " [0.73392546 0.8933551 ]]\n", + "\n", + " [[1.0944494 0.77909493]\n", + " [0.77909493 0.94969434]]\n", + "\n", + " [[0.90808654 0.79951066]\n", + " [0.79951066 1.0226042 ]]\n", + "\n", + " [[1.0505387 0.8875914 ]\n", + " [0.8875914 1.1531518 ]]]\n", + "[[[1.0821586 0.7951551 ]\n", + " [0.7951551 0.98791355]]\n", + "\n", + " [[0.9309569 0.80292296]\n", + " [0.80292296 1.017394 ]]\n", + "\n", + " [[1.0732206 0.77658033]\n", + " [0.77658033 0.9355518 ]]\n", + "\n", + " [[0.91213745 0.82247835]\n", + " [0.82247835 1.0685633 ]]]\n", + "[[[1.0882446 0.7952631 ]\n", + " [0.7952631 0.9679963 ]]\n", + "\n", + " [[0.9282799 0.8124639 ]\n", + " [0.8124639 1.0431492 ]]\n", + "\n", + " [[1.0624554 0.77895933]\n", + " [0.77895933 0.9692205 ]]\n", + "\n", + " [[0.9086455 0.80183136]\n", + " [0.80183136 1.0308828 ]]]\n", + "[[[0.90558934 0.80551666]\n", + " [0.80551666 1.039056 ]]\n", + "\n", + " [[1.0282356 0.78476596]\n", + " [0.78476596 0.9496183 ]]\n", + "\n", + " [[1.1011893 0.7931686 ]\n", + " [0.7931686 0.97522336]]\n", + "\n", + " [[0.94960654 0.82229984]\n", + " [0.82229984 1.0529885 ]]]\n", + "Number of states: 5\n", + "[[[0.9319123 0.7125365 ]\n", + " [0.7125365 0.86663955]]\n", + "\n", + " [[1.0782242 0.8664532 ]\n", + " [0.8664532 1.0892551 ]]\n", + "\n", + " [[0.9164421 0.81377083]\n", + " [0.81377083 1.0507293 ]]\n", + "\n", + " [[0.9785252 0.85083526]\n", + " [0.85083526 1.1203527 ]]\n", + "\n", + " [[1.0729201 0.76326936]\n", + " [0.76326936 0.93385714]]]\n", + "[[[0.9469946 0.73214024]\n", + " [0.73214024 0.8962373 ]]\n", + "\n", + " [[0.9165874 0.80494744]\n", + " [0.80494744 1.0636287 ]]\n", + "\n", + " [[1.0957134 0.87279916]\n", + " [0.87279916 1.0928024 ]]\n", + "\n", + " [[1.1023351 0.7989377 ]\n", + " [0.7989377 0.9732984 ]]\n", + "\n", + " [[0.91298836 0.7900779 ]\n", + " [0.7900779 1.0002004 ]]]\n", + "[[[1.1001004 0.8083552 ]\n", + " [0.8083552 0.9966412 ]]\n", + "\n", + " [[1.0877159 0.7969398 ]\n", + " [0.7969398 0.97867316]]\n", + "\n", + " [[0.9190506 0.79609823]\n", + " 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]]\n", + "\n", + " [[0.9013916 0.81050354]\n", + " [0.81050354 1.0566108 ]]]\n", + "[[[1.0919648 0.9109333 ]\n", + " [0.9109333 1.145589 ]]\n", + "\n", + " [[0.8939783 0.7104139 ]\n", + " [0.7104139 0.87911 ]]\n", + "\n", + " [[0.9351206 0.8100244 ]\n", + " [0.8100244 1.0351114 ]]\n", + "\n", + " [[1.0166657 0.76961863]\n", + " [0.76961863 0.92237467]]\n", + "\n", + " [[0.9342803 0.83475703]\n", + " [0.83475703 1.1069928 ]]\n", + "\n", + " [[1.1179576 0.790441 ]\n", + " [0.790441 0.9789712 ]]\n", + "\n", + " [[0.89790636 0.7744306 ]\n", + " [0.7744306 0.98943526]]]\n", + "Number of states: 8\n", + "[[[0.9411132 0.71977043]\n", + " [0.71977043 0.8799462 ]]\n", + "\n", + " [[1.1088034 0.77691674]\n", + " [0.77691674 0.9388825 ]]\n", + "\n", + " [[0.90482783 0.801472 ]\n", + " [0.801472 1.0326971 ]]\n", + "\n", + " [[1.1097981 0.78768414]\n", + " [0.78768414 0.9564774 ]]\n", + "\n", + " [[0.9779234 0.85432655]\n", + " [0.85432655 1.0981984 ]]\n", + "\n", + " [[1.0783397 0.89900565]\n", + 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"\n", + " [[1.0796893 0.78214794]\n", + " [0.78214794 0.9504097 ]]]\n", + "[[[1.1159911 0.8045311 ]\n", + " [0.8045311 0.9874347 ]]\n", + "\n", + " [[0.9329353 0.8306007 ]\n", + " [0.8306007 1.0732872 ]]\n", + "\n", + " [[1.1030232 0.7951052 ]\n", + " [0.7951052 0.95002556]]\n", + "\n", + " [[0.9115653 0.81348884]\n", + " [0.81348884 1.0480678 ]]\n", + "\n", + " [[0.90932244 0.75502586]\n", + " [0.75502586 0.9450001 ]]\n", + "\n", + " [[1.0786418 0.78822607]\n", + " [0.78822607 0.9747451 ]]\n", + "\n", + " [[1.0997459 0.9156894 ]\n", + " [0.9156894 1.1636195 ]]\n", + "\n", + " [[1.014504 0.74202794]\n", + " [0.74202794 0.90847236]]\n", + "\n", + " [[0.8850151 0.76066697]\n", + " [0.76066697 0.97350353]]]\n", + "Number of states: 10\n", + "[[[1.0995213 0.8049352 ]\n", + " [0.8049352 1.0055742 ]]\n", + "\n", + " [[0.9155789 0.7991195 ]\n", + " [0.7991195 1.0196173 ]]\n", + "\n", + " [[1.1224087 0.8130994 ]\n", + " [0.8130994 0.98669213]]\n", + "\n", + " [[0.92655087 0.70666516]\n", + " 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[0.7401763 0.9521546 ]]\n", + "\n", + " [[1.0705005 0.7841949 ]\n", + " [0.7841949 0.96062094]]\n", + "\n", + " [[0.89148885 0.74523896]\n", + " [0.74523896 0.9418921 ]]]\n", + "[[[1.0941514 0.82723844]\n", + " [0.82723844 1.0320293 ]]\n", + "\n", + " [[0.9016379 0.69968784]\n", + " [0.69968784 0.8568471 ]]\n", + "\n", + " [[1.0813881 0.7594175 ]\n", + " [0.7594175 0.9287067 ]]\n", + "\n", + " [[0.91807646 0.8041493 ]\n", + " [0.8041493 1.0169271 ]]\n", + "\n", + " [[1.076788 0.8726259 ]\n", + " [0.8726259 1.1118598 ]]\n", + "\n", + " [[1.0775548 0.7697792 ]\n", + " [0.7697792 0.9286327 ]]\n", + "\n", + " [[0.90590435 0.82226545]\n", + " [0.82226545 1.132757 ]]\n", + "\n", + " [[1.1147788 0.92580116]\n", + " [0.92580116 1.1602373 ]]\n", + "\n", + " [[0.93718207 0.8311381 ]\n", + " [0.8311381 1.0607282 ]]\n", + "\n", + " [[0.8815293 0.6801496 ]\n", + " [0.6801496 0.849087 ]]]\n", + "[[[0.90979713 0.81381065]\n", + " [0.81381065 1.0739056 ]]\n", + "\n", + " [[0.89193577 0.7935337 ]\n", + " [0.7935337 1.0256634 ]]\n", + "\n", + " [[1.1245558 0.7973786 ]\n", + " [0.7973786 0.96197623]]\n", + "\n", + " [[0.9111174 0.79068214]\n", + " [0.79068214 1.0093589 ]]\n", + "\n", + " [[1.0925175 0.7967137 ]\n", + " [0.7967137 0.99147093]]\n", + "\n", + " [[0.90541 0.7147688 ]\n", + " [0.7147688 0.87973446]]\n", + "\n", + " [[1.1482975 0.8526439 ]\n", + " [0.8526439 1.0107005 ]]\n", + "\n", + " [[1.0649587 0.79239887]\n", + " [0.79239887 0.9894664 ]]\n", + "\n", + " [[0.92274594 0.7144027 ]\n", + " [0.7144027 0.9126962 ]]\n", + "\n", + " [[1.0998946 0.919964 ]\n", + " [0.919964 1.1826246 ]]]\n", + "[[[0.9064722 0.7542487 ]\n", + " [0.7542487 0.95098907]]\n", + "\n", + " [[0.8934469 0.7182921 ]\n", + " [0.7182921 0.9031713 ]]\n", + "\n", + " [[1.1081653 0.79542196]\n", + " [0.79542196 0.9601845 ]]\n", + "\n", + " [[0.88297284 0.78889495]\n", + " [0.78889495 1.0312548 ]]\n", + "\n", + " [[1.0994778 0.78786844]\n", + " [0.78786844 0.95702755]]\n", + "\n", + " [[0.9782854 0.85507184]\n", + " [0.85507184 1.1265575 ]]\n", + "\n", + " [[0.89301753 0.7539302 ]\n", + " [0.7539302 0.9624738 ]]\n", + "\n", + " [[1.1376988 0.9103765 ]\n", + " [0.9103765 1.1270065 ]]\n", + "\n", + " [[1.1136822 0.8032045 ]\n", + " [0.8032045 0.9740425 ]]\n", + "\n", + " [[0.9057937 0.80348754]\n", + " [0.80348754 1.0434362 ]]]\n", + "Number of states: 11\n", + "[[[0.8852692 0.723381 ]\n", + " [0.723381 0.91271234]]\n", + "\n", + " [[0.9012995 0.8131417 ]\n", + " [0.8131417 1.0475258 ]]\n", + "\n", + " [[1.0967735 0.79045016]\n", + " [0.79045016 0.98148626]]\n", + "\n", + " [[1.1103526 0.797415 ]\n", + " [0.797415 0.97060245]]\n", + "\n", + " [[0.9087478 0.8097946 ]\n", + " [0.8097946 1.0439487 ]]\n", + "\n", + " [[1.1515263 0.97499806]\n", + " [0.97499806 1.2322832 ]]\n", + "\n", + " [[1.0459596 0.7449017 ]\n", + " [0.7449017 0.93945307]]\n", + "\n", + " [[0.8921368 0.6771741 ]\n", + " [0.6771741 0.8380078 ]]\n", + "\n", + " [[0.96657157 0.7657426 ]\n", + " [0.7657426 0.9302686 ]]\n", + "\n", + " [[1.1156647 0.7895195 ]\n", + " [0.7895195 0.948476 ]]\n", + "\n", + " [[0.89366114 0.7949121 ]\n", + " [0.7949121 1.0347984 ]]]\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './results_simulation_202311_no_mean/HMM_ICA_2_state_11/cross_validation_1/state_covariances.npy'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of states: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_state\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m5\u001b[39m):\n\u001b[0;32m----> 6\u001b[0m state_covariances \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msave_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43mHMM_ICA_2_state_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_state\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/cross_validation_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/state_covariances.npy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(state_covariances)\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/numpy/lib/npyio.py:405\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[1;32m 403\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 405\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 406\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './results_simulation_202311_no_mean/HMM_ICA_2_state_11/cross_validation_1/state_covariances.npy'" + ] + } + ], + "source": [ + "save_dir = './results_simulation_202311_no_mean/'\n", + "N_states = [2,3,4,5,6,7,8,9,10,11]\n", + "for N_state in N_states:\n", + " print(f'Number of states: {N_state}')\n", + " for i in range(5):\n", + " state_covariances = np.load(f'{save_dir}HMM_ICA_2_state_{N_state}/cross_validation_{i}/state_covariances.npy')\n", + " print(state_covariances)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ac7106fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of states: 2\n", + "[ 0.05378785 -0.05817798]\n", + "[ 0.03055509 -0.03699611]\n", + "[ 0.03142449 -0.053673 ]\n", + "[ 0.00698425 -0.01987016]\n", + "[-0.07265587 0.05860319]\n", + "Number of states: 3\n", + "[ 0.0032953 0.02264835 -0.03192652]\n", + "[ 0.00457314 0.04954706 -0.05906602]\n", + "[ 0.06761466 -0.0995445 0.03209297]\n", + "[-0.03605026 0.0521932 -0.05851068]\n", + "[-0.06545039 -0.05777635 0.07059249]\n", + "Number of states: 4\n", + "[-0.04064161 0.06690485 0.07405905 -0.08863751]\n", + "[ 0.02542547 0.02809798 0.0671682 -0.08260437]\n", + "[ 0.05866564 0.05036159 -0.0081642 -0.09026714]\n", + "[ 0.04701238 -0.07437423 -0.06356851 0.05871381]\n", + "[-0.05338547 -0.07001888 0.03161782 0.05346838]\n", + "Number of states: 5\n", + "[-0.08420586 0.06099431 -0.08054513 0.04679568 0.08057624]\n", + "[ 0.07587091 -0.0709579 -0.05981565 -0.06444271 0.06637251]\n", + "[-0.0656861 0.06845136 -0.05104404 0.04163178 -0.07282606]\n", + "[ 0.07013366 -0.09021921 -0.03429725 -0.06053928 0.05789986]\n", + "[ 0.02650632 -0.07583283 0.06019732 -0.0878945 0.06101407]\n", + "Number of states: 6\n", + "[ 0.06913672 -0.07443216 0.04648639 -0.04011662 -0.07492031 0.06794517]\n", + "[-0.08522308 -0.08360135 0.06677228 0.09654101 -0.08057284 0.06279697]\n", + "[-0.01960698 0.0215756 -0.07255942 0.0811163 0.02272382 -0.0898149 ]\n", + "[ 0.04533216 -0.08820518 -0.01338984 -0.04166112 0.02610259 0.08490394]\n", + "[-0.0552978 0.0626842 -0.07912572 0.07296418 -0.04753168 -0.06884173]\n", + "Number of states: 7\n", + "[-0.0165484 -0.03881793 0.07443994 0.07753289 -0.07943892 -0.07253899\n", + " 0.00535229]\n", + "[-0.07031019 0.09496401 0.04225563 -0.08064965 0.07338312 -0.06674851\n", + " -0.07066887]\n", + "[-0.03195983 0.04996205 0.04498854 0.07943308 -0.01651228 -0.08219504\n", + " -0.08763611]\n", + "[ 0.08950624 -0.05736653 -0.08779222 -0.06441915 0.08194077 -0.08854005\n", + " 0.06041614]\n", + "[ 0.0693223 -0.08220051 0.03052643 -0.09132991 0.07997594 -0.07840721\n", + " 0.04257052]\n", + "Number of states: 8\n", + "[-0.07426192 -0.06631325 0.06664959 0.07173371 0.04799342 0.10961056\n", + " -0.08637299 -0.10419428]\n", + "[-0.08590391 0.01996911 -0.097292 0.08047415 0.09446725 -0.0377831\n", + " 0.05465854 -0.07640402]\n", + "[-0.09300229 -0.03322732 0.06886524 -0.04354902 0.00764134 -0.05482689\n", + " 0.05022756 0.03715887]\n", + "[ 0.00480494 0.09056635 -0.02550225 0.01716321 0.04994705 -0.11816809\n", + " 0.04426184 -0.05583297]\n", + "[ 0.05878424 0.06643647 0.0482417 -0.04387363 -0.08761579 0.07158856\n", + " -0.10145143 -0.07337826]\n", + "Number of states: 9\n", + "[ 0.04325328 -0.08891437 0.07061021 0.09753501 0.07472928 -0.09056534\n", + " -0.07803094 0.03758293 -0.05648017]\n", + "[ 0.04491829 0.08385213 -0.08150613 0.07402823 0.06421802 -0.01929308\n", + " -0.06592368 -0.06214871 -0.09157357]\n", + "[-0.06542633 0.05349273 -0.07068787 -0.03975571 -0.00716919 -0.03812329\n", + " 0.054536 -0.07993523 0.07854546]\n", + "[ 0.07045947 -0.0388016 0.04863743 0.06474826 0.07314412 -0.05674222\n", + " -0.04149754 -0.08754565 -0.09310263]\n", + "[ 0.05831068 0.03338916 0.06071126 0.02018529 0.03852322 0.01773181\n", + " -0.06702943 -0.10803106 -0.05483945]\n", + "Number of states: 10\n", + "[-0.06008232 -0.05011758 -0.10983821 0.08948838 0.06704243 0.04343844\n", + " -0.00689303 -0.05834287 0.1046019 -0.08377858]\n", + "[ 0.08451819 0.05700795 -0.10219782 0.08057369 -0.04059291 0.06785374\n", + " -0.05857968 -0.08072656 0.03313854 -0.08559453]\n", + "[ 0.04538562 -0.07259376 0.08970674 -0.07299656 -0.05347917 -0.10256691\n", + " 0.04632372 0.08137444 0.08520433 -0.1225581 ]\n", + "[ 0.08154624 -0.10580292 0.09192292 0.06342277 -0.08089446 -0.010922\n", + " -0.04150415 0.05079705 -0.04719571 -0.07636671]\n", + "[ 0.0620327 -0.09668449 0.07826731 -0.09707957 0.06518662 0.08413901\n", + " 0.07052303 -0.03159323 -0.08502768 -0.07597245]\n", + "Number of states: 11\n", + "[ 0.0709854 -0.02583526 0.05892346 0.08871533 0.00793011 0.08973899\n", + " -0.09189811 -0.08322483 0.07399978 -0.03489035 -0.08675573]\n", + "[ 0.10529378 -0.09239631 -0.07645413 0.08090511 -0.08114367 -0.04591209\n", + " 0.09167133 -0.08044995 0.06440311 -0.08502547 0.04203067]\n", + "[-0.0380872 0.05580013 0.06451462 -0.10980576 -0.12321924 0.08457466\n", + " -0.0728386 0.05501027 0.03763775 0.0654284 0.04368911]\n", + "[-0.04316735 0.06450094 0.08433216 0.03005417 -0.06907897 -0.09505352\n", + " -0.10082199 -0.11531677 0.09201629 0.06914324 0.07435288]\n", + "[-0.08153591 0.01762052 0.00829857 0.02757959 -0.06632363 0.09837821\n", + " -0.08513532 -0.10385049 0.07643584 0.0612738 -0.07630572]\n" + ] + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_2/'\n", + "N_states = [2,3,4,5,6,7,8,9,10,11]\n", + "for N_state in N_states:\n", + " print(f'Number of states: {N_state}')\n", + " for i in range(5):\n", + " state_correlations = np.load(f'{save_dir}HMM_ICA_2_state_{N_state}/cross_validation_{i}/state_correlations.npy')\n", + " print(state_correlations[:,0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "acb80655", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of states: 7\n", + "[ 0.00977522 -0.00216067 -0.04186209 -0.00309481 -0.01425217 0.00180625\n", + " -0.00749647]\n", + "[ 0.00697951 -0.02204215 -0.01073844 -0.00860576 -0.00532291 -0.0174249\n", + " -0.00679664]\n", + "[ 0.00836213 -0.01460998 0.02311867 0.01421786 -0.02223931 0.00256739\n", + " -0.03726145]\n", + "[ 0.00063448 -0.04301171 -0.01582496 -0.00191165 -0.02111614 0.01215602\n", + " 0.01461784]\n", + "[-0.0091382 -0.01258997 -0.02721128 -0.01043992 0.00412478 -0.003901\n", + " 0.00160206]\n", + "Number of states: 8\n", + "[ 0.02120858 0.00138292 -0.00860462 -0.04425818 -0.02230405 -0.01213971\n", + " 0.01356055 -0.01442865]\n", + "[-0.00314609 -0.0276848 -0.02269739 -0.01341339 0.00860837 -0.0143698\n", + " 0.00391661 -0.00491389]\n", + "[ 0.00709645 -0.00814363 0.00938956 -0.00922521 -0.01709046 0.00981777\n", + " -0.01093454 -0.01117289]\n", + "[-3.04418802e-03 -1.06103225e-02 1.50493262e-02 -5.17292414e-03\n", + " -5.50436052e-05 -2.81314943e-02 -1.07052019e-02 -1.92632992e-02]\n", + "[ 0.01360544 -0.03258251 0.00748951 -0.02245267 -0.02273977 0.01826198\n", + " -0.01254019 -0.01618685]\n", + "Number of states: 9\n", + "[-0.01179869 -0.00571245 0.00409122 -0.02100811 0.02756275 -0.01439051\n", + " -0.02377279 -0.03478543 0.00529194]\n", + "[ 0.00375088 0.02035892 -0.00212054 -0.02739521 -0.02717134 -0.02171133\n", + " -0.00314388 -0.01235729 -0.01351377]\n", + "[-0.01802572 0.0045763 0.00195038 -0.00408031 -0.02530781 0.02166127\n", + " -0.03707371 0.01013616 0.01192861]\n", + "[ 0.0019297 -0.02587766 0.03127304 -0.00653402 -0.00464 -0.00559579\n", + " -0.01631909 -0.0108276 -0.03290645]\n", + "[-0.00120062 -0.03855875 0.00757642 0.00359447 -0.0041353 -0.02611521\n", + " -0.0086405 -0.00435529 -0.00271284]\n", + "Number of states: 10\n", + "[-0.02167779 -0.03852338 -0.01466324 0.00247627 0.01906777 -0.02516842\n", + " 0.01831823 0.00076302 -0.02897827 0.00622892]\n", + "[-0.03247461 0.00275455 0.02500498 -0.01865078 -0.01959419 -0.00968632\n", + " -0.00989918 0.00307149 -0.02589762 -0.00564326]\n", + "[-0.01206478 -0.02945036 0.01897653 -0.00738699 0.01698951 0.02052747\n", + " -0.00903526 -0.02643565 -0.02988176 0.01985603]\n", + "[-0.03552136 -0.01151674 0.01400451 -0.01894022 -0.02218681 0.0168243\n", + " -0.01350647 -0.01319719 -0.00301904 0.00937803]\n", + "[-0.02274193 -0.02574296 0.02625424 -0.03361758 0.00921834 -0.0311893\n", + " 0.00620306 -0.03197261 0.00333963 0.01819849]\n", + "Number of states: 11\n", + "[ 0.00438812 0.01558816 0.00134302 0.00556871 -0.03971866 0.02477977\n", + " -0.01753416 -0.03299552 -0.01847226 -0.0191732 -0.01373924]\n", + "[-0.02916676 -0.00388115 0.00280239 0.01439322 -0.01186108 -0.01526006\n", + " -0.03406967 -0.00401795 -0.00351917 -0.00766596 -0.00771629]\n", + "[-0.01390815 -0.00455057 -0.03265508 -0.00792545 0.01257343 0.02284257\n", + " -0.01011136 0.01207617 -0.01206919 -0.02383347 0.01728538]\n", + "[-0.00678425 -0.02053609 -0.01356743 -0.02419263 0.03486868 -0.02166568\n", + " -0.00945422 -0.01947442 0.02032727 0.006252 -0.03238673]\n", + "[-0.01113094 -0.03754055 0.0020637 -0.00870018 0.0031254 -0.01282913\n", + " -0.00244791 -0.02705385 0.0080309 0.00801032 -0.01204374]\n" + ] + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_4/'\n", + "N_states = [7,8,9,10,11]\n", + "for N_state in N_states:\n", + " print(f'Number of states: {N_state}')\n", + " for i in range(5):\n", + " state_correlations = np.load(f'{save_dir}HMM_ICA_2_state_{N_state}/cross_validation_{i}/state_correlations.npy')\n", + " print(state_correlations[:,0,1])" + ] + }, + { + "cell_type": "markdown", + "id": "a188d136", + "metadata": {}, + "source": [ + "### Generate some real time series" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f0b70b94", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f94db9d1", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir ='./data/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/100307.txt'\n", + "data = np.loadtxt(data_dir)\n", + "save_dir = './temp_plot_202311_grey/'\n", + "if not os.path.exists(save_dir):\n", + " os.makedirs(save_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "79cc9ca6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4800, 15)\n" + ] + } + ], + "source": [ + "print(data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c41eedcd", + "metadata": {}, + "outputs": [], + "source": [ + "T, N_channels = data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8a8163c6", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(N_channels):\n", + " plt.plot(data[:1000,i],color='grey')\n", + " plt.savefig(f'{save_dir}time_series_{i}.pdf')\n", + " plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "368d07bf", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "swc_dir = './results_202309/SWC_ICA_50/cor_swc.npy'\n", + "data = np.load(swc_dir)\n", + "cmap = sns.color_palette(\"RdBu_r\",as_cmap=True)\n", + "for subject in range(2):\n", + " for i in range(9):\n", + " seaborn.heatmap(data[subject,i,:,:],vmin=-1.0,vmax=1.0,square=True,cmap=cmap,xticklabels=False,yticklabels=False,cbar=False)\n", + " plt.savefig(f'{save_dir}swc_cor_subject_{subject}_index_{i}.pdf')\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "10a95f99", + "metadata": {}, + "source": [ + "### Look at your data! Check the ground truth" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "70af67f1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8b080be0", + "metadata": {}, + "outputs": [], + "source": [ + "covariances = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy')\n", + "correlations = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3b3b697d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8, 25, 25)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "covariances.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "68d5d6eb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 942.62it/s\n" + ] + } + ], + "source": [ + "from rotation.utils import pairwise_fisher_z_correlations\n", + "fisher_z = pairwise_fisher_z_correlations(correlations)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c51c4f52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8, 8)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fisher_z.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "69e96f95", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(fisher_z)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b39f31d1", + "metadata": {}, + "outputs": [], + "source": [ + "correlations_fit = np.load('./results_simulation_202311_toy_5/HMM_ICA_25_state_8/state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9df665b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8, 25, 25)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correlations_fit.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a847c057", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 979.92it/s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fisher_z_fit = pairwise_fisher_z_correlations(correlations_fit)\n", + "seaborn.heatmap(fisher_z_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c5ff1c02", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 46\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(50.722222222222214, 0.5, 'Truth')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rotation.utils import twopair_fisher_z_transformed_correlation\n", + "correlation_truth_fit = twopair_fisher_z_transformed_correlation(correlations,correlations_fit)\n", + "seaborn.heatmap(correlation_truth_fit)\n", + "plt.xlabel('Fit')\n", + "plt.ylabel('Truth')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a66deca1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.83624251, 0.87012094, 0.90166191, 0.89066245, 0.78281659,\n", + " 0.89151127, 0.89456208, 0.87908712],\n", + " [0.79781565, 0.5858404 , 0.75091116, 0.71923662, 0.59430407,\n", + " 0.87088876, 0.87367178, 0.74740529],\n", + " [0.91702652, 0.83562061, 0.90244091, 0.89670103, 0.78085487,\n", + " 0.89063369, 0.89573097, 0.91516119],\n", + " [0.8103752 , 0.58814984, 0.72152761, 0.70510112, 0.61213004,\n", + " 0.7483853 , 0.74353096, 0.74833301],\n", + " [0.6085179 , 0.73552822, 0.69491321, 0.72115188, 0.89684871,\n", + " 0.61288534, 0.59125805, 0.69913748],\n", + " [0.95398829, 0.80609457, 0.91538969, 0.89929312, 0.77331355,\n", + " 0.93748001, 0.94056036, 0.92279008],\n", + " [0.89885839, 0.81364203, 0.87124894, 0.87434477, 0.80985473,\n", + " 0.8150764 , 0.8061176 , 0.88355588],\n", + " [0.6283374 , 0.92686738, 0.80095794, 0.82506889, 0.85472505,\n", + " 0.65338245, 0.65070348, 0.76353957]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correlation_truth_fit" + ] + }, + { + "cell_type": "markdown", + "id": "6a2a52dd", + "metadata": {}, + "source": [ + "### Test with Riemannian distance" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "727e66b0", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.utils import twopair_riemannian_distance\n", + "covariances_fit = np.load('./results_simulation_202311_toy_5/HMM_ICA_25_state_8/state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c949011e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 137.89it/s]\n", + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 668.04it/s]\n", + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 464.47it/s]\n" + ] + } + ], + "source": [ + "riemann_truth = twopair_riemannian_distance(covariances,covariances)\n", + "riemann_fit = twopair_riemannian_distance(covariances_fit,covariances_fit)\n", + "riemann_truth_fit = twopair_riemannian_distance(covariances,covariances_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3387f74f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FYQIAAFyOygAAAGYMEwAA4G5uGyYgGQAAwMxllQHmDAAA4HJUBgAAMDFcVhloPsnAny84HUGjfVv0J6dDsEXZ42OcDsEWvmf3OB1Co1VMn+N0CLYIHyt0OoRGMy5963QItmjVO8npEFoGlyUDDBMAAOByzacyAABAM8EwAQAAbueyZIBhAgAAXI7KAAAAJgwTAADgciQDAAC4nNuSAeYMAADgclQGAAAwMzxOR3BVkQwAAGDCMAEAAHAVKgMAAJgYYYYJAABwNYYJAACAq1AZAADAxGA1AQAA7sYwAQAAcBUqAwAAmLCaAAAAlzMMpyO4ukgGAAAwcVtlgDkDAAC4nOVk4JNPPtGGDRt07NgxSdKxY8c0b948PfDAA/rwww/r1UcoFFJ5eXlEC31baTUUAACahBH22NasCAaDGjlypGJiYtSlSxelpKSosLCwzms2btwoj8cT0aKjoy3d11IysH37dg0ZMkSPPvqohg4dqu3bt2vMmDE6fvy4Tp48qbvvvrteCUEwGJTP54toz+ccsxQ4AABNxTDsa1bs3r1baWlp2rdvn7KysnT58mXdfffdqqioqPO62NhYFRcXV7WTJ09auq+lZODpp5/WokWL9L//+7/asGGD/vEf/1Fz585VVlaWdu7cqUWLFmnp0qVX7CcQCKisrCyiPZrc31LgAABca7Zv3677779ft9xyi5KSkrRx40YVFRUpPz+/zus8Ho/i4+OrWlxcnKX7WkoGjh49qvvvv1+SdO+99+rcuXP6h3/4h6rv77vvPv3P//zPFfvxer2KjY2NaN42rS0FDgBAU7FzmKDGofFQqF5xlJWVSZI6depU53nnz59Xr1691KNHD02bNk1Hjx619LyW5wx4PN+Nf7Rq1UrR0dHy+XxV38XExFQFDgBAS2UYHttaTUPjwWDwijGEw2EtXLhQo0eP1qBBg2o9r1+/flq/fr3eeecdbdq0SeFwWMnJyfrjH/9Y7+e1tLTwpptu0meffabvf//7kqTc3Fz17Nmz6vuioiIlJCRY6RIAgGtaIBBQenp6xDGv13vF69LS0nTkyBHt3bu3zvP8fr/8fn/V5+TkZA0YMEBr167VkiVL6hWjpWRg3rx5qqz866x/c6aybds23XnnnVa6BACg2bHz3QRer7deP/5/a/78+Xrvvfe0Z88ede/e3dK1bdu21dChQ3X8+PF6X2MpGXjooYfq/P7ZZ5+10h0AAM1S2KG3FhqGoYcfflibN29Wdna2evfubbmPyspKHT58WJMnT673NexACABAM5GWlqaMjAy98847iomJUUlJiSTJ5/PpuuuukySlpqaqW7duVfMOnn76ad12223q06ePvvnmGz333HM6efKk5syZU+/7kgwAAGBiOFQZWL16tSRp3LhxEcc3bNhQtZqvqKhIrVr9df7/119/rblz56qkpEQdO3bU8OHDlZOTo4EDB9b7viQDAACYOPVuAqMeuxRlZ2dHfF6+fLmWL1/eqPuSDAAAYOK2txbyoiIAAFyOygAAACZue4UxyQAAACZOLS10CsMEAAC4HJUBAABMnFpa6BSSAQAATFhNAAAAXIXKAAAAJm6bQEgyAACAidvmDDBMAACAy1EZAADAxG0TCEkGAAAwYc6AQ9rMWuB0CI3Wtp3P6RDwNyqm1/9d3s1V+6GpTodgiwtf/d7pEBrt2yPZTodgi8rszU6HYI9bxjdp98wZAAAArtJsKgMAADQXDBMAAOByLps/yDABAABuR2UAAAAThgkAAHA5VhMAAABXoTIAAIBJ2OkArjKSAQAATAwxTAAAAFyEygAAACZhl200QDIAAIBJ2GXDBCQDAACYMGcAAAC4CpUBAABMWFoIAIDLMUwAAAAcEQwGNXLkSMXExKhLly5KSUlRYWHhFa9766231L9/f0VHR2vw4MHaunWrpfuSDAAAYBK2sVmxe/dupaWlad++fcrKytLly5d19913q6KiotZrcnJyNHPmTD344IM6dOiQUlJSlJKSoiNHjtT7vh7DMJrFasrQp3udDqHRPO18ToeAv2GUnXY6hEZrPzTV6RBsceGr3zsdQqN9eyTb6RBsYRw96HQItmiXtrJJ+98a90Pb+ppcmtnga8+cOaMuXbpo9+7dGjNmTI3nzJgxQxUVFXrvvfeqjt12220aMmSI1qxZU6/72FIZaCb5BAAAzU4oFFJ5eXlEC4VC9bq2rKxMktSpU6daz8nNzdWECRMijk2cOFG5ubn1jtGWZMDr9eqTTz6xoysAABxnyGNbCwaD8vl8ES0YDF4xhnA4rIULF2r06NEaNGhQreeVlJQoLi4u4lhcXJxKSkrq/byWVhOkp6fXeLyyslJLly7VDTfcIEl64YUXrHQLAECzErZxMUEgEKj2++n1eq94XVpamo4cOaK9e5t+GN1SMvDiiy8qKSlJ119/fcRxwzD0ySefqH379vJ4rvxfMBQKVS+RXLokb1SUlXAAAGj2vF5vvX78/9b8+fP13nvvac+ePerevXud58bHx6u0tDTiWGlpqeLj4+t9P0vDBM8++6zKysr0b//2b9q1a1dVa926tTZu3Khdu3bpww8/vGI/NZVM/n3tJiuhAADQZMLy2NasMAxD8+fP1+bNm/Xhhx+qd+/eV7zG7/dr586dEceysrLk9/vrfV9LycBPf/pTvfHGG5o3b54effRRXb582crlVQKBgMrKyiLaYz/+UYP6AgDAboaNzYq0tDRt2rRJGRkZiomJUUlJiUpKSnThwoWqc1JTUxUIBKo+L1iwQNu3b9eyZct07NgxPfXUU8rLy9P8+fPrfV/LEwhHjhyp/Px8nTlzRiNGjNCRI0fqNTTwt7xer2JjYyMaQwQAgObCqX0GVq9erbKyMo0bN04JCQlV7Y033qg6p6ioSMXFxVWfk5OTlZGRoXXr1ikpKUlvv/22tmzZUuekQ7MGbUfcoUMHvf7668rMzNSECRNUWVnZkG4AAMDfqM9S/ezs7GrHpk+frunTpzf4vo16N8EPf/hD3X777crPz1evXr0a0xUAAM1G2GLFu6Vr9IuKunfvfsWZjgAAtCRu20qPdxMAAOByvMIYAAATqxP/WjqSAQAATOzcgbAlYJgAAACXozIAAICJ1Z0DWzqSAQAATFhNAAAAXIXKAAAAJm6bQEgyAACACUsLAQBwOeYMAAAAV6EyAACACXMGAABwObfNGWCYAAAAl6MyAACAidsqAyQDAACYGC6bM8AwAQAALtdsKgMXn1nsdAiN1qp9lNMh2KLN6BFOh2CL8LFCp0NotAtf/d7pEGxxXdc7nA6h0c7vXOp0CLao/LzI6RBaBIYJAABwObclAwwTAADgclQGAAAwcdt2xCQDAACYsAMhAAAux5wBAADgKlQGAAAwcVtlgGQAAAATt00gZJgAAACXozIAAICJ21YTUBkAAMAkbGOzYs+ePZo6daq6du0qj8ejLVu21Hl+dna2PB5PtVZSUmLpviQDAAA0ExUVFUpKStKqVassXVdYWKji4uKq1qVLF0vXM0wAAICJUxMIJ02apEmTJlm+rkuXLrr++usbfF8qAwAAmIRl2NZCoZDKy8sjWigUsjXeIUOGKCEhQXfddZf+8Ic/WL6eZAAAgCYUDAbl8/kiWjAYtKXvhIQErVmzRr/5zW/0m9/8Rj169NC4ceN08OBBS/0wTAAAgImdmw4FAgGlp6dHHPN6vbb03a9fP/Xr16/qc3Jysj7//HMtX75cv/rVr+rdD8kAAAAmds4Z8Hq9tv3418eoUaO0d+9eS9eQDAAAYNKStyMuKChQQkKCpWtIBgAAaCbOnz+v48ePV30+ceKECgoK1KlTJ/Xs2VOBQECnTp3SL3/5S0nSiy++qN69e+uWW27RxYsX9eqrr+rDDz/U+++/b+m+JAMAAJg4tQNhXl6efvCDH1R9/stcg1mzZmnjxo0qLi5WUVFR1feXLl3Sv/7rv+rUqVNq166dEhMT9cEHH0T0UR+NSgYqKir05ptv6vjx40pISNDMmTN1ww03NKZLAAAcF3Zop4Fx48bJMGq/98aNGyM+P/bYY3rssccafV9LycDAgQO1d+9ederUSV9++aXGjBmjr7/+WjfffLM+//xzLVmyRPv27VPv3r3r7CcUClVbYxmqDMvbmpWOAABcbZZ+fY8dO6Zvv/1W0ndLJbp27aqTJ09q//79OnnypBITE/XEE09csZ+a1ly+cPj/NugBAACwm2Fjawka/Kd4bm6unnrqKfl8PklShw4d9POf/7xeyxkCgYDKysoiWvrgmxoaCgAAtnLqRUVOsTxnwOP5blbFxYsXqy1d6Natm86cOXPFPmpac2kwRAAAgCMsJwPjx49XmzZtVF5ersLCQg0aNKjqu5MnTzKBEADQ4jk1gdAplpKBxYsXR3zu0KFDxOd3331Xd9xxR+OjAgDAQe5KBRqZDJg999xzjQoGAABcfWw6BACASUuZ+GcXkgEAAEyYMwAAgMu5KxVoxD4DAADg2kBlAAAAE+YMAADgcobLBgoYJgAAwOWoDAAAYMIwAQAALue2pYUMEwAA4HJUBgAAMHFXXYBkAACAahgmAAAArkJlAAAAE1YTAADgcm7bdIhkAAAAE7dVBpgzAACAyzWbykDUHUlOh9BoxvkKp0OwRbjwM6dDsIVx6VunQ2i0b49kOx2CLc7vXOp0CI3WYfxPnQ7BFufeDTgdQovAMAEAAC7HMAEAAHAVKgMAAJiEDYYJAABwNXelAgwTAADgeiQDAACYhGXY1qzYs2ePpk6dqq5du8rj8WjLli1XvCY7O1vDhg2T1+tVnz59tHHjRsvPSzIAAICJYeM/VlRUVCgpKUmrVq2q1/knTpzQlClT9IMf/EAFBQVauHCh5syZox07dli6L3MGAABoJiZNmqRJkybV+/w1a9aod+/eWrZsmSRpwIAB2rt3r5YvX66JEyfWux8qAwAAmIRtbKFQSOXl5REtFArZEmdubq4mTJgQcWzixInKzc211A/JAAAAJnbOGQgGg/L5fBEtGAzaEmdJSYni4uIijsXFxam8vFwXLlyodz8MEwAAYGLndsSBQEDp6ekRx7xer23924FkAACAJuT1epvsxz8+Pl6lpaURx0pLSxUbG6vrrruu3v2QDAAAYNJS3k3g9/u1devWiGNZWVny+/2W+mHOAAAAJoZh2NasOH/+vAoKClRQUCDpu6WDBQUFKioqkvTdkENqamrV+Q899JC++OILPfbYYzp27Jhefvllvfnmm3rkkUcs3ZdkAACAZiIvL09Dhw7V0KFDJUnp6ekaOnSonnzySUlScXFxVWIgSb1799Z//dd/KSsrS0lJSVq2bJleffVVS8sKJYYJAACoxurOgXYZN25cndWEmnYXHDdunA4dOtSo+5IMAABg0lLmDNiFYQIAAFyOygAAACZ27jPQEpAMAABg4tScAacwTAAAgMtZSgYOHjyoEydOVH3+1a9+pdGjR6tHjx66/fbblZmZWa9+anxpw+VvrUUOAEATcWqfAadYSgZmz56tzz//XJL06quv6sc//rFGjBihJ554QiNHjtTcuXO1fv36K/ZT00sbntt2oGFPAACAzex8a2FLYGnOwGeffaa+fftKkl5++WWtWLFCc+fOrfp+5MiReuaZZ/TAAw/U2U9NL20I//pnVkIBAKDJMIGwDu3atdPZs2fVq1cvnTp1SqNGjYr4/tZbb40YRqhNTS9tuNCWuYwAADjB0jDBpEmTtHr1aknS2LFj9fbbb0d8/+abb6pPnz72RQcAgAPCMmxrLYGlP8d/8YtfaPTo0Ro7dqxGjBihZcuWKTs7WwMGDFBhYaH27dunzZs3N1WsAABcFS1l4p9dLFUGunbtqkOHDsnv92v79u0yDEP79+/X+++/r+7du+sPf/iDJk+e3FSxAgCAJmB5oP7666/X0qVLtXTp0qaIBwAAx7WU8r5dmLUHAICJ21YTsAMhAAAuR2UAAACTsMsmEJIMAABg4q5UgGECAABcj8oAAAAmrCYAAMDlSAYAAHA5diAEAACuQmUAAAAThgkAAHA5diAEAACuQmUAAAATt00gJBkAAMDEbXMGGCYAAMDlqAwAAGDCMIFDjPJzTofQaG1S5jodgi0qP97rdAi2aNU7yekQGq0ye7PTIdii8vMip0NotHPvBpwOwRYxU4NOh2CLby/Nb9L+GSYAAACOWbVqlW666SZFR0fr1ltv1f79+2s9d+PGjfJ4PBEtOjra8j1JBgAAMDFs/MeKN954Q+np6Vq8eLEOHjyopKQkTZw4UadPn671mtjYWBUXF1e1kydPWn5ekgEAAEzChmFbs+KFF17Q3LlzNXv2bA0cOFBr1qxRu3bttH79+lqv8Xg8io+Pr2pxcXGWn5dkAAAAEzsrA6FQSOXl5REtFApVu+elS5eUn5+vCRMmVB1r1aqVJkyYoNzc3FpjPX/+vHr16qUePXpo2rRpOnr0qOXnJRkAAKAJBYNB+Xy+iBYMVp/IefbsWVVWVlb7yz4uLk4lJSU19t2vXz+tX79e77zzjjZt2qRwOKzk5GT98Y9/tBRjs1lNAABAc2G1vF+XQCCg9PT0iGNer9eWvv1+v/x+f9Xn5ORkDRgwQGvXrtWSJUvq3Q/JAAAAJna+qMjr9dbrx79z585q3bq1SktLI46XlpYqPj6+Xvdq27athg4dquPHj1uKkWECAACagaioKA0fPlw7d+6sOhYOh7Vz586Iv/7rUllZqcOHDyshIcHSvakMAABgYucwgRXp6emaNWuWRowYoVGjRunFF19URUWFZs+eLUlKTU1Vt27dquYcPP3007rtttvUp08fffPNN3ruued08uRJzZkzx9J9SQYAADCxc5jAihkzZujMmTN68sknVVJSoiFDhmj79u1VkwqLiorUqtVfi/pff/215s6dq5KSEnXs2FHDhw9XTk6OBg4caOm+HqOZbMD85xda/la+bEfcvLAdcfMRvga2I2498W6nQ7DFtbMd8akm7b/vjcNt6+uzM/m29dVUqAwAAGDi1DCBU0gGAAAwcWqYwCmsJgAAwOWoDAAAYGIYYadDuKpIBgAAMAm7bJiAZAAAAJNmstDuqmHOAAAALkdlAAAAE4YJAABwOYYJAACAq1hKBh5++GH9/ve/b/RNQ6GQysvLI1ro28pG9wsAgB3ChmFbawksJQOrVq3SuHHjdPPNN+sXv/iFSkpKGnTTYDAon88X0Z7fWdCgvgAAsJth4z8tgeVhgvfff1+TJ0/W888/r549e2ratGl67733FA7Xf4OGQCCgsrKyiPbo+CFWQwEAADawnAwMHjxYL774or766itt2rRJoVBIKSkp6tGjh5544gkdP378in14vV7FxsZGNG+b1g16AAAA7GYYhm2tJWjwBMK2bdvq3nvv1fbt2/XFF19o7ty5+vWvf61+/frZGR8AAFddWIZtrSWwZTVBz5499dRTT+nEiRPavn27HV0CAICrxNI+A7169VLr1rWX8z0ej+66665GBwUAgJNaSnnfLpaSgRMnTjRVHAAANBstZUmgXdiBEAAAE7dVBtiBEAAAl6MyAACASUtZBWAXkgEAAEwYJgAAAK5CZQAAABNWEwAA4HIt5QVDdmGYAAAAl6MyAACACcMEAAC4HKsJAACAq1AZAADAhAmEAAC4nGEYtjWrVq1apZtuuknR0dG69dZbtX///jrPf+utt9S/f39FR0dr8ODB2rp1q+V7kgwAAGDiVDLwxhtvKD09XYsXL9bBgweVlJSkiRMn6vTp0zWen5OTo5kzZ+rBBx/UoUOHlJKSopSUFB05csTSfUkGAABoJl544QXNnTtXs2fP1sCBA7VmzRq1a9dO69evr/H8FStW6J577tGiRYs0YMAALVmyRMOGDdPKlSst3ZdkAAAAE8PGFgqFVF5eHtFCoVC1e166dEn5+fmaMGFC1bFWrVppwoQJys3NrTHO3NzciPMlaeLEibWeX/sDu8TFixeNxYsXGxcvXnQ6lAa7Fp7BMK6N57gWnsEweI7m5Fp4BsO4dp7DTosXL66WIyxevLjaeadOnTIkGTk5ORHHFy1aZIwaNarGvtu2bWtkZGREHFu1apXRpUsXSzF6DMMdiynLy8vl8/lUVlam2NhYp8NpkGvhGaRr4zmuhWeQeI7m5Fp4BunaeQ47hUKhapUAr9crr9cbceyrr75St27dlJOTI7/fX3X8scce0+7du/XRRx9V6zsqKkqvv/66Zs6cWXXs5Zdf1s9//nOVlpbWO0aWFgIA0IRq+uGvSefOndW6detqP+KlpaWKj4+v8Zr4+HhL59eGOQMAADQDUVFRGj58uHbu3Fl1LBwOa+fOnRGVgr/l9/sjzpekrKysWs+vDZUBAACaifT0dM2aNUsjRozQqFGj9OKLL6qiokKzZ8+WJKWmpqpbt24KBoOSpAULFmjs2LFatmyZpkyZoszMTOXl5WndunWW7uuaZMDr9Wrx4sX1KtU0V9fCM0jXxnNcC88g8RzNybXwDNK18xxOmTFjhs6cOaMnn3xSJSUlGjJkiLZv3664uDhJUlFRkVq1+mtRPzk5WRkZGfrZz36mxx9/XH379tWWLVs0aNAgS/d1zQRCAABQM+YMAADgciQDAAC4HMkAAAAuRzIAAIDLuSIZsPo6yOZmz549mjp1qrp27SqPx6MtW7Y4HZJlwWBQI0eOVExMjLp06aKUlBQVFhY6HZZlq1evVmJiomJjYxUbGyu/369t27Y5HVajLF26VB6PRwsXLnQ6FEueeuopeTyeiNa/f3+nw2qQU6dO6Uc/+pFuuOEGXXfddRo8eLDy8vKcDqvebrrppmr/Lzwej9LS0pwODfV0zScDVl8H2RxVVFQoKSlJq1atcjqUBtu9e7fS0tK0b98+ZWVl6fLly7r77rtVUVHhdGiWdO/eXUuXLlV+fr7y8vJ05513atq0aTp69KjToTXIgQMHtHbtWiUmJjodSoPccsstKi4urmp79+51OiTLvv76a40ePVpt27bVtm3b9PHHH2vZsmXq2LGj06HV24EDByL+P2RlZUmSpk+f7nBkqDdLbzJogUaNGmWkpaVVfa6srDS6du1qBINBB6NqOEnG5s2bnQ6j0U6fPm1IMnbv3u10KI3WsWNH49VXX3U6DMvOnTtn9O3b18jKyjLGjh1rLFiwwOmQLFm8eLGRlJTkdBiN9pOf/MS4/fbbnQ7DVgsWLDC+//3vG+Fw2OlQUE/XdGWgIa+DxNVRVlYmSerUqZPDkTRcZWWlMjMzVVFRYXnrz+YgLS1NU6ZMqfb605bks88+U9euXfW9731P9913n4qKipwOybLf/e53GjFihKZPn64uXbpo6NCheuWVV5wOq8EuXbqkTZs26YEHHpDH43E6HNTTNZ0MnD17VpWVlVU7N/1FXFycSkpKHIoK4XBYCxcu1OjRoy3vktUcHD58WB06dJDX69VDDz2kzZs3a+DAgU6HZUlmZqYOHjxYtaVpS3Trrbdq48aN2r59u1avXq0TJ07ojjvu0Llz55wOzZIvvvhCq1evVt++fbVjxw7NmzdP//Iv/6LXX3/d6dAaZMuWLfrmm290//33Ox0KLHDNdsRoPtLS0nTkyJEWOb4rSf369VNBQYHKysr09ttva9asWdq9e3eLSQi+/PJLLViwQFlZWYqOjnY6nAabNGlS1b8nJibq1ltvVa9evfTmm2/qwQcfdDAya8LhsEaMGKFnn31WkjR06FAdOXJEa9as0axZsxyOzrrXXntNkyZNUteuXZ0OBRZc05WBhrwOEk1r/vz5eu+997Rr1y51797d6XAaJCoqSn369NHw4cMVDAaVlJSkFStWOB1WveXn5+v06dMaNmyY2rRpozZt2mj37t166aWX1KZNG1VWVjodYoNcf/31uvnmm3X8+HGnQ7EkISGhWiI5YMCAFjnkcfLkSX3wwQeaM2eO06HAoms6GWjI6yDRNAzD0Pz587V582Z9+OGH6t27t9Mh2SYcDisUCjkdRr2NHz9ehw8fVkFBQVUbMWKE7rvvPhUUFKh169ZOh9gg58+f1+eff66EhASnQ7Fk9OjR1ZbZfvrpp+rVq5dDETXchg0b1KVLF02ZMsXpUGDRNT9McKXXQbYE58+fj/hr58SJEyooKFCnTp3Us2dPByOrv7S0NGVkZOidd95RTExM1ZwNn8+n6667zuHo6i8QCGjSpEnq2bOnzp07p4yMDGVnZ2vHjh1Oh1ZvMTEx1eZqtG/fXjfccEOLmsPx6KOPaurUqerVq5e++uorLV68WK1bt9bMmTOdDs2SRx55RMnJyXr22Wd17733av/+/Vq3bp3lV9A6LRwOa8OGDZo1a5batLnmf1quPU4vZ7ga/uM//sPo2bOnERUVZYwaNcrYt2+f0yFZsmvXLkNStTZr1iynQ6u3muKXZGzYsMHp0Cx54IEHjF69ehlRUVHGjTfeaIwfP954//33nQ6r0Vri0sIZM2YYCQkJRlRUlNGtWzdjxowZxvHjx50Oq0HeffddY9CgQYbX6zX69+9vrFu3zumQLNuxY4chySgsLHQ6FDQArzAGAMDlruk5AwAA4MpIBgAAcDmSAQAAXI5kAAAAlyMZAADA5UgGAABwOZIBAABcjmQAAACXIxkAAMDlSAYAAHA5kgEAAFyOZAAAAJf7fyHO3uVLGjINAAAAAElFTkSuQmCC", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(riemann_truth_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c7273207", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.utils import hungarian_pair" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "4dce8240", + "metadata": {}, + "outputs": [], + "source": [ + "fisher_indices, fisher_reordered = hungarian_pair(correlation_truth_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "5803ee25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PPvigYlp5am/fvn01ZMgQ1dbWRnQetCQAADALGPaNNoqNjVVGRoYqKyu/DiMQUGVlpXJyclr9zN/+9jdLUuB2uyVJhtH6d3/xxRfau3evkpOT2xybRIUBAAArhy6r9Hq9mj59ujIzM5WVlaWysjI1NTUFr5qYNm2aUlJSgnsgJk+erNLSUo0ZMybYkpg/f74mT54cTBzmzJmjyZMnKzU1VR9//LFKSkrkdrs1derUiGIjYQAAwMyhyyrz8/N15MgRLViwQHV1dRo9erQqKiqCGyEPHjwYUlGYN2+eXC6X5s2bp0OHDqlfv36aPHmyHn300eAxH330kaZOnaqjR4+qX79+GjdunLZt26Z+/fpFFJvLOFXN4hv25atPOh1CxzV+5nQEtnCde4HTIdijld7dmWbfrS84HYItzvvReKdD6Lgu8L8nSTq5qTL8QWeAPs9s7tT1v/D+i21r9S59xba1nESFAQAAE4NnSViQMAAAYEbCYNE1amwAAKBTRZwwfPnll3rrrbf0wQcfWN47ceKEfvazn4Vdo9XbZJ78KtJQAADoHIGAfaOLiChh2L17t4YPH67LLrtMI0eO1IQJE/TJJ58E3z927Fjw0o/Tae02mY+v3RJ59AAAdAYH7sMQ7SJKGO6//35dfPHFOnz4sHbt2qWzzjpLY8eOjfghFsXFxTp27FjIuDf/qojWAAAA35yINj2+/fbbev3115WQkKCEhAT9+te/1n/8x39o/PjxeuONN9SrV682rdPabTK/7M7+SwBAlOhClQG7RFRh+PLLL9Wt29c/2F0ul5YtW6bJkydrwoQJ2r17t+0BAgDwTTMMw7bRVUT0a/2wYcP03nvvafjw4SHzP/nJTyRJ//Iv9t3oAgAARI+IKgzXX3+9/vu//7vV937yk59o6tSpXSqbAgB8S7Hp0SKihKG4uFibNm065ftPP/20Al3oEhIAwLcUCYMFOw0BADDh1tBW3OkRAACERYUBAAAzKgwWJAwAAJixHc+ClgQAAAiLCgMAACZserQiYQAAwIyEwYKWBAAACIsKAwAAZmx6tCBhAADAhD0MVrQkAABAWFQYAAAwoyVhQcIAAIAJLQkrEgYAAMyoMFiwhwEAAIRFhQEAABODCoNF1CQMK+6qdjqEDjuvucXpEGwRcO10OgRb/J+RHzsdQodd/dlRp0OwxZMz4p0OocP+6HE5HYItHv1kh9Mh2OLkM538BSQMFrQkAABAWFFTYQAAIFrQkrAiYQAAwIyEwYKWBAAACIsKAwAAJrQkrEgYAAAwIWGwImEAAMCEhMGKPQwAACAsKgwAAJgZXeNGXXYiYQAAwISWhBUtCQAAEBYVBgAATIwALQkzKgwAAJgYAftGpJYuXaq0tDTFxcUpOztb1dWnfzhjWVmZhg4dqh49emjgwIGaPXu2Tpw40aE1W0PCAABAlFi7dq28Xq9KSkq0fft2paenKy8vT4cPH271+BdffFFz585VSUmJPvzwQ61YsUJr167VAw880O41T4WEAQAAE8Nw2Tb8fr8aGxtDht/vb/V7S0tLVVhYqIKCAo0YMULl5eXq2bOnVq5c2erxb7/9tsaOHaubb75ZaWlpuvrqqzV16tSQCkKka54KCQMAACZ2tiR8Pp/i4+NDhs/ns3xnc3OzampqlJubG5yLiYlRbm6uqqqqWo3z0ksvVU1NTTBB2LdvnzZt2qRJkya1e81TYdMjAACdqLi4WF6vN2TO4/FYjmtoaFBLS4sSExND5hMTE7Vz585W17755pvV0NCgcePGyTAMffXVV7r77ruDLYn2rHkqVBgAADAxAi7bhsfjUZ8+fUJGawlDe2zdulWLFi3S008/re3bt+ull17Sxo0b9cMf/tCW9f8ZFQYAAEwM45v/zoSEBLndbtXX14fM19fXKykpqdXPzJ8/X9/73vd05513SpJGjhyppqYm3XXXXXrwwQfbteapUGEAAMDEzgpDW8XGxiojI0OVlZXBuUAgoMrKSuXk5LT6mb/97W+KiQn9Ue52u/9+DobRrjVPhQoDAABRwuv1avr06crMzFRWVpbKysrU1NSkgoICSdK0adOUkpIS3DQ5efJklZaWasyYMcrOzlZtba3mz5+vyZMnBxOHcGu2lSMJg9/vt1xSctJoUXeX24lwAAAI4dSdHvPz83XkyBEtWLBAdXV1Gj16tCoqKoKbFg8ePBhSUZg3b55cLpfmzZunQ4cOqV+/fpo8ebIeffTRNq/ZVi7DiKxT8+GHH2rbtm3KycnRsGHDtHPnTj3xxBPy+/269dZbdcUVV4RdY+HChXrooYdC5iaeNVKT4kdFFHy0Oa+5xekQbNFV7oj6f0Z+7HQIHTam5qjTIdjiydgz+++2JP3R0zX+Yjz6yVanQ7DFyeZDnbr+/vSrbFvrvD9ssW0tJ0W0h6GiokKjR4/WnDlzNGbMGFVUVOiyyy5TbW2tDhw4oKuvvlq//e1vw65TXFysY8eOhYyr+lzU7pMAAACdK6KE4eGHH9a9996ro0eP6rnnntPNN9+swsJCbdmyRZWVlbr33nu1ePHisOu0dokJ7QgAQLRwYtNjtIsoYfjzn/+s2267TZJ044036vjx4/r3f//34Pu33HKL/vjHP9oaIAAA3zQ7bw3dVUR8WaXL9feTj4mJUVxcnOLj44PvnXXWWTp27Jh90QEAgKgQUcKQlpamPXv2BF9XVVVp0KBBwdcHDx5UcnKyfdEBAOAAJx9vHa0iuqxyxowZamn5+kqAiy++OOT9V199tU1XSQAAEM0CXaiVYJeIEoa77777tO8vWrSoQ8EAAIDoxJ0eAQAw6UqbFe1CwgAAgElXuhzSLiQMAACYOPG0ymjH0yoBAEBYVBgAADChJWFFwgAAgAmXVVrRkgAAAGFRYQAAwITLKq1IGAAAMOEqCStaEgAAICwqDAAAmLDp0YqEAQAAE/YwWNGSAAAAYVFhAADAhE2PViQMAACYsIfBKmoShr+4W5wOocMWn/yT0yHY4nt9Rjkdgi0K3z3sdAgdtn91gdMh2KJv/lNOh9BhS/pNcDoEW6xK+L9Oh3BGYA+DFXsYAABAWFFTYQAAIFrQkrAiYQAAwIQ9j1a0JAAAQFhUGAAAMKElYUXCAACACVdJWNGSAAAAYVFhAADAJOB0AFGIhAEAABNDtCTMaEkAAICwqDAAAGAS4EYMFiQMAACYBGhJWJAwAABgwh4GK/YwAACAsKgwAABgwmWVViQMAACY0JKwoiUBAEAUWbp0qdLS0hQXF6fs7GxVV1ef8tjLL79cLpfLMq699trgMbfddpvl/WuuuSbiuKgwAABg4lRLYu3atfJ6vSovL1d2drbKysqUl5enXbt2qX///pbjX3rpJTU3NwdfHz16VOnp6brhhhtCjrvmmmv03HPPBV97PJ6IYyNhAADAxM6Ewe/3y+/3h8x5PJ5Wf2iXlpaqsLBQBQUFkqTy8nJt3LhRK1eu1Ny5cy3Hn3322SGv16xZo549e1oSBo/Ho6SkpA6dBy0JAAA6kc/nU3x8fMjw+XyW45qbm1VTU6Pc3NzgXExMjHJzc1VVVdWm71qxYoVuuukm9erVK2R+69at6t+/v4YOHaoZM2bo6NGjEZ+HLRUGwzDkcrFBBADQNdi56bG4uFherzdkrrXqQkNDg1paWpSYmBgyn5iYqJ07d4b9nurqau3YsUMrVqwImb/mmmv0r//6rzrvvPO0d+9ePfDAA5o4caKqqqrkdrvbfB62JAwej0d/+MMfNHz4cDuWAwDAUQEbfwc+VfvBbitWrNDIkSOVlZUVMn/TTTcF/33kyJEaNWqUzj//fG3dulVXXnllm9ePKGEwZ0j/0NLSosWLF+ucc86R9PcezOm01s/5ymhRN1fbMx0AALqShIQEud1u1dfXh8zX19eH3X/Q1NSkNWvW6OGHHw77PYMHD1ZCQoJqa2s7L2EoKytTenq6+vbtGzJvGIY+/PBD9erVq02tCZ/Pp4ceeihkLif+Il3ad2Qk4QAA0CmceJZEbGysMjIyVFlZqSlTpvw9jkBAlZWVmjlz5mk/+4tf/EJ+v1+33npr2O/56KOPdPToUSUnJ0cUX0QJw6JFi7R8+XItWbJEV1xxRXC+e/fuWrVqlUaMGNGmdVrr5ywYeWckoQAA0Gmcelil1+vV9OnTlZmZqaysLJWVlampqSl41cS0adOUkpJi2TS5YsUKTZkyJVjp/4cvvvhCDz30kP7t3/5NSUlJ2rt3r+677z5dcMEFysvLiyi2iBKGuXPn6sorr9Stt96qyZMny+fzqXv37hF9odR6P4d2BAAgWjh1H4b8/HwdOXJECxYsUF1dnUaPHq2KiorgRsiDBw8qJib0Asddu3bprbfe0muvvWZZz+12649//KNWr16tzz//XAMGDNDVV1+tH/7whxHvq4h40+N3v/td1dTUqKioSJmZmfr5z3/OFRIAANhk5syZp2xBbN261TI3dOhQGUbrNZEePXpo8+bNtsTVrqskevfurdWrV2vNmjXKzc1VS0uLLcEAABANAvwibNGhyypvuukmjRs3TjU1NUpNTbUrJgAAHOXUHoZo1uH7MJx77rk699xz7YgFAABEKZ4lAQCAiVObHqMZCQMAACZ23umxq+DhUwAAICwqDAAAmDhxp8doR8IAAIAJV0lY0ZIAAABhUWEAAMCETY9WJAwAAJhwWaUVCQMAACbsYbBiDwMAAAiLCgMAACbsYbAiYQAAwIQ9DFa0JAAAQFhUGAAAMKHCYEXCAACAicEeBgtaEgAAIKyoqTA88vP/z+kQOuzR/vc4HYItjMbDTodgi4dTH3E6hA7bcclsp0OwxfEPful0CB3WsuN3Todgi8bS3zgdwhmBloRV1CQMAABECxIGK1oSAAAgLCoMAACYcGtoKxIGAABMuNOjFQkDAAAm7GGwYg8DAAAIiwoDAAAmVBisSBgAADBh06MVLQkAABAWFQYAAEy4SsKKhAEAABP2MFjRkgAAAGFRYQAAwIRNj1YkDAAAmARIGSxoSQAAgLCoMAAAYMKmRysSBgAATGhIWJEwAABgQoXBij0MAAAgLBIGAABMAi77RqSWLl2qtLQ0xcXFKTs7W9XV1ac89vLLL5fL5bKMa6+9NniMYRhasGCBkpOT1aNHD+Xm5mrPnj0Rx0XCAACASUCGbSMSa9euldfrVUlJibZv36709HTl5eXp8OHDrR7/0ksv6ZNPPgmOHTt2yO1264Ybbgge81//9V968sknVV5ernfeeUe9evVSXl6eTpw4EVFsHdrD0NTUpHXr1qm2tlbJycmaOnWqzjnnnLCf8/v98vv9IXNG80l5Yrt3JBwAAM5opaWlKiwsVEFBgSSpvLxcGzdu1MqVKzV37lzL8WeffXbI6zVr1qhnz57BhMEwDJWVlWnevHm67rrrJEk/+9nPlJiYqA0bNuimm25qc2wRVRhGjBihTz/9VJL017/+VRdffLFmz56tLVu2qKSkRCNGjND+/fvDruPz+RQfHx8yHn/+lUhCAQCg0xg2Dr/fr8bGxpBh/qVZkpqbm1VTU6Pc3NzgXExMjHJzc1VVVdWmuFesWKGbbrpJvXr1kiTt379fdXV1IWvGx8crOzu7zWsGY4nk4J07d+qrr76SJBUXF2vAgAE6cOCAqqurdeDAAY0aNUoPPvhg2HWKi4t17NixkHHv9/4losABAOgsARtHa78k+3w+y3c2NDSopaVFiYmJIfOJiYmqq6sLG3N1dbV27NihO++8Mzj3j8+1d81/1u6WRFVVlcrLyxUfHy9J6t27tx566KE2lTc8Ho88Hk/I3AnaEQCALqi4uFherzdkzvwz0A4rVqzQyJEjlZWVZfvaUjs2Pbpcf9/yeeLECSUnJ4e8l5KSoiNHjtgTGQAADrFz06PH41GfPn1CRmsJQ0JCgtxut+rr60Pm6+vrlZSUdNp4m5qatGbNGt1xxx0h8//4XHvWNIs4Ybjyyit1ySWXqLGxUbt27Qp578CBA23a9AgAQDSzcw9DW8XGxiojI0OVlZXBuUAgoMrKSuXk5Jz2s7/4xS/k9/t16623hsyfd955SkpKClmzsbFR77zzTtg1zSJqSZSUlIS87t27d8jrX//61xo/fnxEAQAAgL/zer2aPn26MjMzlZWVpbKyMjU1NQWvmpg2bZpSUlIseyBWrFihKVOmWH5pd7lc+sEPfqBHHnlEF154oc477zzNnz9fAwYM0JQpUyKKrUMJg9njjz8e0ZcDABCNnLo1dH5+vo4cOaIFCxaorq5Oo0ePVkVFRXDT4sGDBxUTE9oc2LVrl9566y299tprra553333qampSXfddZc+//xzjRs3ThUVFYqLi4soNpdhGFHxjI0T//tzp0PoMFf/85wOwRZGY+s3CDnTuFNHOR1Ch+24ZLbTIdjioq3hr56Kdi07fud0CLZoLP2N0yHYInHr1k5d35vW9vsThFP6lzW2reUkHj4FAIBJVPwmHWW4NTQAAAiLCgMAACY83tqKhAEAABODpoQFLQkAABAWFQYAAExoSViRMAAAYBKgJWFBSwIAAIRFhQEAABPqC1YkDAAAmNCSsKIlAQAAwqLCAACACVdJWJEwAABgwo2brEgYAAAwocJgxR4GAAAQVtRUGALb/9fpEDrsy4rlTodgi575lzodgi2+OlTrdAgdlnLh506HYIvPbr/P6RA67Kz8kU6HYAt3D0rtbUFLwipqEgYAAKIFLQkrWhIAACAsKgwAAJgEDFoSZiQMAACYkC5Y0ZIAAABhUWEAAMCEZ0lYkTAAAGDCZZVWtCQAAEBYVBgAADDhPgxWJAwAAJiwh8GKhAEAABP2MFixhwEAAIRFhQEAABP2MFiRMAAAYGJwa2gLWhIAACAsKgwAAJhwlYQVCQMAACbsYbCiJQEAAMKiwgAAgAn3YbAiYQAAwIQ9DFa0JAAAQFgRJQzbt2/X/v37g6+ff/55jR07VgMHDtS4ceO0Zs2aNq3j9/vV2NgYMvwnWyKLHACATmIYhm2jq4goYSgoKNDevXslST/96U/1/e9/X5mZmXrwwQf13e9+V4WFhVq5cmXYdXw+n+Lj40PGj7Zsb98ZAABgs4CNo6uIKGHYs2ePLrzwQknS008/rSeeeEJPPPGE7r77bv34xz/WM888oyVLloRdp7i4WMeOHQsZc666pH1nAACAzQwb/4nU0qVLlZaWpri4OGVnZ6u6uvq0x3/++ecqKipScnKyPB6PhgwZok2bNgXfX7hwoVwuV8gYNmxYxHFFtOmxZ8+eamhoUGpqqg4dOqSsrKyQ97Ozs0NaFqfi8Xjk8XhC5v7W3R1JKAAAdDlr166V1+tVeXm5srOzVVZWpry8PO3atUv9+/e3HN/c3KyrrrpK/fv31/r165WSkqIDBw6ob9++IcdddNFFev3114Ovu3WL/JqHiCoMEydO1LJlyyRJEyZM0Pr160PeX7dunS644IKIgwAAIJoEZNg2IlFaWqrCwkIVFBRoxIgRKi8vV8+ePU/Z7l+5cqU+/fRTbdiwQWPHjlVaWpomTJig9PT0kOO6deumpKSk4EhISIj4zySiFOOxxx7T2LFjNWHCBGVmZmrJkiXaunWrhg8frl27dmnbtm16+eWXIw4CAIBoYudmRb/fL7/fHzLXWqW9ublZNTU1Ki4uDs7FxMQoNzdXVVVVra79yiuvKCcnR0VFRfrVr36lfv366eabb9b9998vt/vryv2ePXs0YMAAxcXFKScnRz6fT4MGDYroPCKqMAwYMEC///3vlZOTo4qKChmGoerqar322ms699xz9b//+7+aNGlSRAEAANCVtbbR3+fzWY5raGhQS0uLEhMTQ+YTExNVV1fX6tr79u3T+vXr1dLSok2bNmn+/PlasmSJHnnkkeAx2dnZWrVqlSoqKrRs2TLt379f48eP1/HjxyM6j4ibGH379tXixYu1ePHiSD8KAMAZwc4bNxUXF8vr9YbMmasL7RUIBNS/f38tX75cbrdbGRkZOnTokB5//HGVlJRI+vt2gn8YNWqUsrOzlZqaqnXr1umOO+5o83dxp0cAAEzsvDV0a+2H1iQkJMjtdqu+vj5kvr6+XklJSa1+Jjk5Wd27dw9pPwwfPlx1dXVqbm5WbGys5TN9+/bVkCFDVFtbG9F5cKdHAACiQGxsrDIyMlRZWRmcCwQCqqysVE5OTqufGTt2rGpraxUIfH3Hh927dys5ObnVZEGSvvjiC+3du1fJyckRxUfCAACAScAwbBuR8Hq9evbZZ7V69Wp9+OGHmjFjhpqamlRQUCBJmjZtWsimyBkzZujTTz/VrFmztHv3bm3cuFGLFi1SUVFR8Jg5c+bozTff1F/+8he9/fbbuv766+V2uzV16tSIYqMlAQCAiVM3dM7Pz9eRI0e0YMEC1dXVafTo0aqoqAhuhDx48KBiYr7+XX/gwIHavHmzZs+erVGjRiklJUWzZs3S/fffHzzmo48+0tSpU3X06FH169dP48aN07Zt29SvX7+IYiNhAAAgisycOVMzZ85s9b2tW7da5nJycrRt27ZTrtfW5zyFQ8IAAIAJj7e2ImEAAMCEhMGKhAEAAJOu9Fhqu3CVBAAACIsKAwAAJrQkrEgYAAAwsfNOj10FLQkAABAWFQYAAEzY9GhFwgAAgAl7GKxoSQAAgLCoMAAAYEJLwipqEobPXtzpdAgdVvBRD6dDsEVJzR6nQ7DF9403nA6hw975fprTIdjiuuc+czqEDlv/ux1Oh2CL3ovvD38QaEm0gpYEAAAIK2oqDAAARAvuw2BFwgAAgEmAPQwWJAwAAJhQYbBiDwMAAAiLCgMAACa0JKxIGAAAMKElYUVLAgAAhEWFAQAAE1oSViQMAACY0JKwoiUBAADCosIAAIAJLQkrEgYAAExoSVjRkgAAAGFRYQAAwMQwAk6HEHVIGAAAMAnQkrAgYQAAwMRg06MFexgAAEBYVBgAADChJWFFwgAAgAktCStaEgAAIKyIEoZ77rlH//M//9PhL/X7/WpsbAwZ/gCXsAAAokPAMGwbXUVECcPSpUt1+eWXa8iQIXrsscdUV1fXri/1+XyKj48PGT85dKBdawEAYDfDxn+6iohbEq+99pomTZqkH/3oRxo0aJCuu+46/eY3v1EgggpBcXGxjh07FjJmpqRGGgoAAPiGRJwwjBw5UmVlZfr444/1wgsvyO/3a8qUKRo4cKAefPBB1dbWhl3D4/GoT58+IcMTw3YKAEB0MAzDttFVtPundPfu3XXjjTeqoqJC+/btU2FhoX7+859r6NChdsYHAMA3LiDDttFV2PJr/aBBg7Rw4ULt379fFRUVdiwJAACiSEQJQ2pqqtxu9ynfd7lcuuqqqzocFAAATnKyJbF06VKlpaUpLi5O2dnZqq6uPu3xn3/+uYqKipScnCyPx6MhQ4Zo06ZNHVqzNRElDPv379c555wT8ZcAAHAmceqyyrVr18rr9aqkpETbt29Xenq68vLydPjw4VaPb25u1lVXXaW//OUvWr9+vXbt2qVnn31WKSkp7V7zVNhpCACAiVMVhtLSUhUWFqqgoEAjRoxQeXm5evbsqZUrV7Z6/MqVK/Xpp59qw4YNGjt2rNLS0jRhwgSlp6e3e81TIWEAAKATtXqzQr/fclxzc7NqamqUm5sbnIuJiVFubq6qqqpaXfuVV15RTk6OioqKlJiYqIsvvliLFi1SS0tLu9c8FRIGAABM7LxKorWbFfp8Pst3NjQ0qKWlRYmJiSHziYmJp7xR4r59+7R+/Xq1tLRo06ZNmj9/vpYsWaJHHnmk3WueCg+fAgDAxM77JxQXF8vr9YbMeTweW9YOBALq37+/li9fLrfbrYyMDB06dEiPP/64SkpKbPmOfyBhAACgE3k8njYlCAkJCXK73aqvrw+Zr6+vV1JSUqufSU5OVvfu3UOuYBw+fLjq6urU3NzcrjVPhZYEAAAmTlwlERsbq4yMDFVWVn4dRyCgyspK5eTktPqZsWPHqra2NuTxDLt371ZycrJiY2PbteapkDAAAGDi1MOnvF6vnn32Wa1evVoffvihZsyYoaamJhUUFEiSpk2bpuLi4uDxM2bM0KeffqpZs2Zp9+7d2rhxoxYtWqSioqI2r9lWtCQAAIgS+fn5OnLkiBYsWKC6ujqNHj1aFRUVwU2LBw8eVMw/PXtp4MCB2rx5s2bPnq1Ro0YpJSVFs2bN0v3339/mNdvKZUTJkzEO5VzhdAgdVvBRrNMh2KLkZG+nQ7DF942PnA6hw975fprTIdjiuuc+czqEDls/rtnpEGwRN/dep0OwRVz6pE5dv0cP+56g/OWXB2xby0lUGAAAMImS36WjCnsYAABAWFQYAAAwiXSz4rcBCQMAACa0JKxIGAAAMCFhsGIPAwAACIsKAwAAJtQXWmF8S5w4ccIoKSkxTpw44XQo7dYVzsEwusZ5dIVzMAzOI5p0hXMwjK5zHrCKmhs3dbbGxkbFx8fr2LFj6tOnj9PhtEtXOAepa5xHVzgHifOIJl3hHKSucx6wYg8DAAAIi4QBAACERcIAAADC+tYkDB6PRyUlJfJ4PE6H0m5d4RykrnEeXeEcJM4jmnSFc5C6znnA6luz6REAALTft6bCAAAA2o+EAQAAhEXCAAAAwiJhAAAAYZEwAACAsL4VCcPSpUuVlpamuLg4ZWdnq7q62umQIvK73/1OkydP1oABA+RyubRhwwanQ4qYz+fTd7/7XZ111lnq37+/pkyZol27djkdVsSWLVumUaNGqU+fPurTp49ycnL06quvOh1WhyxevFgul0s/+MEPnA4lIgsXLpTL5QoZw4YNczqsdjl06JBuvfVWnXPOOerRo4dGjhyp9957z+mw2iwtLc3y38LlcqmoqMjp0GCjLp8wrF27Vl6vVyUlJdq+fbvS09OVl5enw4cPOx1amzU1NSk9PV1Lly51OpR2e/PNN1VUVKRt27Zpy5YtOnnypK6++mo1NTU5HVpEzj33XC1evFg1NTV67733dMUVV+i6667Tn//8Z6dDa5d3331XzzzzjEaNGuV0KO1y0UUX6ZNPPgmOt956y+mQIvbZZ59p7Nix6t69u1599VV98MEHWrJkib7zne84HVqbvfvuuyH/HbZs2SJJuuGGGxyODLZy9tlXnS8rK8soKioKvm5paTEGDBhg+Hw+B6NqP0nGyy+/7HQYHXb48GFDkvHmm286HUqHfec73zF++tOfOh1GxI4fP25ceOGFxpYtW4wJEyYYs2bNcjqkiJSUlBjp6elOh9Fh999/vzFu3Dinw7DVrFmzjPPPP98IBAJOhwIbdekKQ3Nzs2pqapSbmxuci4mJUW5urqqqqhyMDMeOHZMknX322Q5H0n4tLS1as2aNmpqalJOT43Q4ESsqKtK1114b8vfjTLNnzx4NGDBAgwcP1i233KKDBw86HVLEXnnlFWVmZuqGG25Q//79NWbMGD377LNOh9Vuzc3NeuGFF3T77bfL5XI5HQ5s1KUThoaGBrW0tCgxMTFkPjExUXV1dQ5FhUAgoB/84AcaO3asLr74YqfDidif/vQn9e7dWx6PR3fffbdefvlljRgxwumwIrJmzRpt375dPp/P6VDaLTs7W6tWrVJFRYWWLVum/fv3a/z48Tp+/LjToUVk3759WrZsmS688EJt3rxZM2bM0H/+539q9erVTofWLhs2bNDnn3+u2267zelQYLNuTgeAb5+ioiLt2LHjjOw3S9LQoUP1/vvv69ixY1q/fr2mT5+uN99884xJGv76179q1qxZ2rJli+Li4pwOp90mTpwY/PdRo0YpOztbqampWrdune644w4HI4tMIBBQZmamFi1aJEkaM2aMduzYofLyck2fPt3h6CK3YsUKTZw4UQMGDHA6FNisS1cYEhIS5Ha7VV9fHzJfX1+vpKQkh6L6dps5c6Z+85vf6I033tC5557rdDjtEhsbqwsuuEAZGRny+XxKT0/XE0884XRYbVZTU6PDhw/rkksuUbdu3dStWze9+eabevLJJ9WtWze1tLQ4HWK79O3bV0OGDFFtba3ToUQkOTnZkmwOHz78jGyvHDhwQK+//rruvPNOp0NBJ+jSCUNsbKwyMjJUWVkZnAsEAqqsrDwje85nMsMwNHPmTL388sv67W9/q/POO8/pkGwTCATk9/udDqPNrrzySv3pT3/S+++/HxyZmZm65ZZb9P7778vtdjsdYrt88cUX2rt3r5KTk50OJSJjx461XGK8e/dupaamOhRR+z333HPq37+/rr32WqdDQSfo8i0Jr9er6dOnKzMzU1lZWSorK1NTU5MKCgqcDq3Nvvjii5Dfmvbv36/3339fZ599tgYNGuRgZG1XVFSkF198Ub/61a901llnBfeQxMfHq0ePHg5H13bFxcWaOHGiBg0apOPHj+vFF1/U1q1btXnzZqdDa7OzzjrLsnekV69eOuecc86oPSVz5szR5MmTlZqaqo8//lglJSVyu92aOnWq06FFZPbs2br00ku1aNEi3Xjjjaqurtby5cu1fPlyp0OLSCAQ0HPPPafp06erW7cu/6Pl28npyzS+CU899ZQxaNAgIzY21sjKyjK2bdvmdEgReeONNwxJljF9+nSnQ2uz1uKXZDz33HNOhxaR22+/3UhNTTViY2ONfv36GVdeeaXx2muvOR1Wh52Jl1Xm5+cbycnJRmxsrJGSkmLk5+cbtbW1TofVLr/+9a+Niy++2PB4PMawYcOM5cuXOx1SxDZv3mxIMnbt2uV0KOgkLsMwDGdSFQAAcKbo0nsYAACAPUgYAABAWCQMAAAgLBIGAAAQFgkDAAAIi4QBAACERcIAAADCImEAAABhkTAAAICwSBgAAEBYJAwAACCs/wfAihxY2wf7ggAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(fisher_reordered)" + ] + }, + { + "cell_type": "markdown", + "id": "e977e1ab", + "metadata": {}, + "source": [ + "### Check the effect of z_score" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "de2adc71", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "import numpy as np\n", + "from rotation.preprocessing import PrepareData\n", + "original_data = np.loadtxt('data/node_timeseries/simulation_toy_5/10001.txt')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6bb7d8d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(120000, 25)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "original_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ee634e89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read from directory: data/node_timeseries/simulation_toy_5\n", + "Number of subjects: 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading files: 100%|███████████████████████| 100/100 [00:00<00:00, 10272.86it/s]\n" + ] + } + ], + "source": [ + "data_dir = pathlib.Path('data/node_timeseries/simulation_toy_5/')\n", + "prepare_data = PrepareData(data_dir)\n", + "subj,dataset = prepare_data.load(z_score_data=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1483b572", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['10001']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subj" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7487c714", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b1039e67", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30000" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(dataset[0]!=original_data[:1200,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "eb3c60fb", + "metadata": {}, + "outputs": [], + "source": [ + "covariances_no_z_score = np.load('./results_simulation_playground/no_z_score/HMM_ICA_25_state_8/state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "52c8acfb", + "metadata": {}, + "outputs": [], + "source": [ + "correlations_no_z_score = np.load('./results_simulation_playground/no_z_score/HMM_ICA_25_state_8/state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "33233053", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|█| 8/8 [00:00<00:00, 58.45it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn\n", + "from rotation.utils import pairwise_fisher_z_correlations\n", + "seaborn.heatmap(pairwise_fisher_z_correlations(correlations_no_z_score))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "51e4cb00", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|█████████████| 8/8 [00:00<00:00, 50.33it/s]\n" + ] + } + ], + "source": [ + "from rotation.utils import twopair_riemannian_distance\n", + "covariances = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy')\n", + "covariances_no_z_score_truth = twopair_riemannian_distance(covariances_no_z_score,covariances)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9d349be5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fisher_indices, fisher_reordered = hungarian_pair(covariances_no_z_score_truth,distance=True)\n", + "seaborn.heatmap(fisher_reordered)" + ] + }, + { + "cell_type": "markdown", + "id": "9eb5635c", + "metadata": {}, + "source": [ + "### Update 29th November 2023: Using Sliding window correlation to check model simulation" + ] + }, + { + "cell_type": "markdown", + "id": "dda7135c", + "metadata": {}, + "source": [ + "#### Step 1: read in the ground-truth correlations and covariances." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6a6cead8", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "correlations_truth = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_correlations.npy')\n", + "covariances_truth = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy')" + ] + }, + { + "cell_type": "markdown", + "id": "b3a7299d", + "metadata": {}, + "source": [ + "#### Step 2: read in the sliding window correlations and covariances." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "94b828ac", + "metadata": {}, + "outputs": [], + "source": [ + "correlations_SWC = np.load('./results_simulation_202311_toy_6/SWC_ICA_25_state_8/state_correlations.npy')\n", + "covariances_SWC = np.load('./results_simulation_202311_toy_6/SWC_ICA_25_state_8/state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8fd262ba", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from rotation.utils import twopair_riemannian_distance, twopair_fisher_z_transformed_correlation, hungarian_pair" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1f7414e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|█████████████| 8/8 [00:00<00:00, 63.66it/s]\n", + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 582.54it/s]\n", + "Computing Riemannian distances: 0%| | 0/8 [00:00 4\u001b[0m riemannian_cor_SWC \u001b[38;5;241m=\u001b[39m \u001b[43mtwopair_riemannian_distance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcorrelations_SWC\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcorrelations_SWC\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m riemannian_cov_SWC \u001b[38;5;241m=\u001b[39m twopair_riemannian_distance(covariances_SWC,covariances_SWC)\n\u001b[1;32m 6\u001b[0m riemannian_cor_truth_SWC \u001b[38;5;241m=\u001b[39m twopair_riemannian_distance(correlations_truth,correlations_SWC)\n", + "File \u001b[0;32m/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:429\u001b[0m, in \u001b[0;36mtwopair_riemannian_distance\u001b[0;34m(matrices_1, matrices_2, eps_value, threshold)\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m np\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mLinAlgError:\n\u001b[1;32m 427\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCalculation of Riemannian distance failed for eps=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00meps\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 429\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRiemannian distance failed for all eps values!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: Riemannian distance failed for all eps values!" + ] + } + ], + "source": [ + "### Riemannian distance of ground truth correlations/covariances\n", + "riemannian_cor_truth = twopair_riemannian_distance(correlations_truth,correlations_truth)\n", + "riemannian_cov_truth = twopair_riemannian_distance(covariances_truth,covariances_truth)\n", + "riemannian_cor_SWC = twopair_riemannian_distance(correlations_SWC,correlations_SWC)\n", + "riemannian_cov_SWC = twopair_riemannian_distance(covariances_SWC,covariances_SWC)\n", + "riemannian_cor_truth_SWC = twopair_riemannian_distance(correlations_truth,correlations_SWC)\n", + "riemannian_cov_truth_SWC = twopair_riemannian_distance(covariances_truth,covariances_SWC)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cb421a57", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|█| 8/8 [00:00<00:00, 43\n", + "Compute twopair Fisher-z transformated correlation: 100%|█| 8/8 [00:00<00:00, 27\n", + "Compute twopair Fisher-z transformated correlation: 100%|█| 8/8 [00:00<00:00, 44\n" + ] + } + ], + "source": [ + "### Fisher-z transformed of ground truth correlations/covariances\n", + "fisher_cor_truth = twopair_fisher_z_transformed_correlation(correlations_truth,correlations_truth)\n", + "fisher_cor_SWC = twopair_fisher_z_transformed_correlation(correlations_SWC,correlations_SWC)\n", + "fisher_cor_truth_SWC = twopair_fisher_z_transformed_correlation(correlations_truth,correlations_SWC)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db33d861", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn\n", + "seaborn.heatmap(fisher_cor_truth)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d01ea2ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(fisher_cor_SWC)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "833a453b", + "metadata": {}, + "outputs": [], + "source": [ + "indices, fisher_cor_truth_SWC_reordered = hungarian_pair(fisher_cor_truth_SWC)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d93d5a9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(fisher_cor_truth_SWC_reordered)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b5a73911", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99665347, 0.79065424, 0.86175603, 0.67923163, 0.64137114,\n", + " 0.83951469, 0.79072613, 0.73808677],\n", + " [0.69531901, 0.97180593, 0.56825185, 0.69969413, 0.49478473,\n", + " 0.77561 , 0.70770739, 0.35748703],\n", + " [0.83670896, 0.81624049, 0.79788602, 0.74443762, 0.63394945,\n", + " 0.98448696, 0.81566564, 0.63466101],\n", + " [0.606599 , 0.70847455, 0.57585796, 0.98390002, 0.53809844,\n", + " 0.71286777, 0.71860203, 0.37846639],\n", + " [0.56450886, 0.531393 , 0.75437802, 0.53637612, 0.98684594,\n", + " 0.57883474, 0.67088507, 0.71001059],\n", + " [0.82633156, 0.89953065, 0.76747896, 0.84420265, 0.63545595,\n", + " 0.96842436, 0.89347347, 0.57129213],\n", + " [0.73816928, 0.77253745, 0.79246946, 0.76510963, 0.71058002,\n", + " 0.81414476, 0.98449903, 0.65564675],\n", + " [0.73440623, 0.50396578, 0.94752085, 0.462748 , 0.75510815,\n", + " 0.63700018, 0.70084349, 0.99896867]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fisher_cor_truth_SWC_reordered" + ] + }, + { + "cell_type": "markdown", + "id": "3594eb6f", + "metadata": {}, + "source": [ + "#### Step 3: read in the HMM states " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "29606f73", + "metadata": {}, + "outputs": [], + "source": [ + "correlations_HMM = np.load('./results_simulation_202311_toy_6/HMM_ICA_25_state_8/state_correlations.npy')\n", + "covariances_HMM = np.load('./results_simulation_202311_toy_6/HMM_ICA_25_state_8/state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "64f2fae8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 42\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 46\n" + ] + } + ], + "source": [ + "fisher_cor_HMM = twopair_fisher_z_transformed_correlation(correlations_HMM,correlations_HMM)\n", + "fisher_cor_truth_HMM = twopair_fisher_z_transformed_correlation(correlations_truth,correlations_HMM)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bc87fb87", + "metadata": {}, + "outputs": [], + "source": [ + "indices, fisher_cor_truth_HMM_reordered = hungarian_pair(fisher_cor_truth_HMM)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "648fb950", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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Ab7zxhl599VVt3rxZBw4c0G233ebfz0wkFQAAdBqlpaXKy8tTbm6uBg4cqKVLl6pbt25atmxZm/NXrlypRx55RFlZWRowYIDuvfdeZWVl6amnnvKZ16VLF8XExHhHZGSk97Vjx47pxRdfVGlpqW644QalpKRo+fLlev/997V161a/4iepAADAyOO2bLhcLh0/ftxnuFyuVm/Z3NysqqoqZWRkeK8FBAQoIyNDlZWVbYbpcrkUEhLicy00NLRVJWL37t2Ki4vTgAEDdOedd6q2ttb7WlVVlU6dOuXzvklJSerXr98Z3/dMSCoAADCysP1RUlKiiIgIn1FSUtLqLY8cOaKWlhZFR0f7XI+OjlZ9fX2bYWZmZqq0tFS7d++W2+3Whg0btG7dOh08eNA7Jy0tTStWrFB5ebmWLFmiffv26frrr9eJEyckSfX19QoKClKPHj3a/b5nwpFSAADOocLCQjmdTp9rwcHBlqy9cOFC5eXlKSkpSQ6HQ4mJicrNzfVpl4wfP9775yFDhigtLU39+/fXK6+8orvvvtuSOP6JSgUAAAYet9uyERwcrPDwcJ/RVlIRGRmpwMDAVqcuGhoaFBMT02acUVFRWr9+vRobG7V//37t2rVLYWFhGjBgwBk/W48ePXTFFVeopqZGkhQTE6Pm5mYdPXq03e97JiQVAAAY2XD6IygoSCkpKaqoqPg2DLdbFRUVSk9P/87vDQkJUXx8vE6fPq3XXntNN9988xnnnjx5Unv27FFsbKwkKSUlRV27dvV53+rqatXW1p71fY1ofwAA0Ek4nU7l5OQoNTVVw4cPV1lZmRobG5WbmytJmjJliuLj4717Mj744APV1dVp6NChqqur0/z58+V2u/XQQw9513zggQeUnZ2t/v3768CBAyouLlZgYKAmTZokSYqIiNDdd98tp9Opnj17Kjw8XLNmzVJ6erquu+46v+InqQAAwMim23RPnDhRhw8f1rx581RfX6+hQ4eqvLzcu3mztrZWAQHfNhmamppUVFSkvXv3KiwsTFlZWVq5cqXPpsu//e1vmjRpkr788ktFRUVp1KhR2rp1q6Kiorxznn76aQUEBGjChAlyuVzKzMzU4sWL/Y7f4fF4OsUNzht+OMbuEDosLKO/3SFYIuhn8+0OwRL3pD5odwgddrUn1O4QLJHhPmF3CB0WFXPS7hAsETlruN0hWCJ0SuvTE1Y6+cCZ2wf+Cvvl7yxbq7OjUgEAgBEPFDOFjZoAAMASVCoAADDwUKkwhaQCAAAjkgpTaH8AAABL+J1UfP3119qyZYs+++yzVq81NTXpN7/5zVnXaPPhKiaeOQ8AwDnhdls3LiJ+JRVffPGFrrrqKo0ePVqDBw/WmDFjfB5acuzYMe8NOr5LWw9X+dX+2rN+HwAA3wsb7qh5IfArqXj44Yc1aNAgHTp0SNXV1erevbtGjhzp8wjV9igsLNSxY8d8xn/27+fXGgAAoHPxa6Pm+++/r40bNyoyMlKRkZF644039B//8R+6/vrrtWnTJl1yySXtWic4OLjVw1S+DmB7BwCgk7jIKgxW8es3+ddff60uXb7NQxwOh5YsWaLs7GyNGTNGX3zxheUBAgDwffN4PJaNi4lflYqkpCR99NFHuuqqq3yuP/vss5Kkf//3f7cuMgAAcF7xq1Jx66236r//+7/bfO3ZZ5/VpEmTLrqsDABwAWKjpil+JRWFhYV6++23z/j64sWL5b7Ijs8AAC5AJBWmcEdNAAAMuE23ORy5AAAAlqBSAQCAEZUKU0gqAAAwYnugKbQ/AACAJahUAABgwEZNc0gqAAAwIqkwhfYHAACwBJUKAACM2KhpCkkFAAAG7Kkwh/YHAACwBJUKAACMaH+YQlIBAIAB7Q9zSCoAADCiUmEKeyoAAIAlqFQAAGDgoVJhisPj8XSKxlGXoHi7Q+iwEVFJdodgiUu79rA7BEu88NF/2R1Chx38tzy7Q7DEuwdj7Q4B/1/mpXV2h2CJ2C2bzun6X940xrK1er212bK1OjvaHwAAwBK0PwAAMKD9YQ5JBQAARiQVptD+AAAAlqBSAQCAAe0Pc0gqAAAwIKkwh6QCAAADkgpz2FMBAAAsQaUCAAAjj8PuCM5LJBUAABjQ/jCH9gcAAJ3IokWLlJCQoJCQEKWlpWnbtm1nnHvq1CktWLBAiYmJCgkJUXJyssrLy33mlJSUaNiwYerevbt69+6tW265RdXV1T5zxo4dK4fD4TNmzJjhd+wkFQAAGHjcDsuGP9auXSun06ni4mJt375dycnJyszM1KFDh9qcX1RUpOeee07PPPOMPvvsM82YMUO33nqrPv74Y++czZs3Kz8/X1u3btWGDRt06tQp3XjjjWpsbPRZKy8vTwcPHvSOX/ziF37/3Gh/AABgYFf7o7S0VHl5ecrNzZUkLV26VG+99ZaWLVum2bNnt5q/cuVKzZkzR1lZWZKke++9Vxs3btRTTz2lVatWSVKrysWKFSvUu3dvVVVVafTo0d7r3bp1U0xMTIfip1IBAMA55HK5dPz4cZ/hcrlazWtublZVVZUyMjK81wICApSRkaHKysozrh0SEuJzLTQ0VFu2bDljPMeOHZMk9ezZ0+f66tWrFRkZqUGDBqmwsFBfffVVuz+jN16/vwMAgAucx+OwbJSUlCgiIsJnlJSUtHrPI0eOqKWlRdHR0T7Xo6OjVV9f32acmZmZKi0t1e7du+V2u7VhwwatW7dOBw8ebHO+2+3W/fffr5EjR2rQoEHe65MnT9aqVau0adMmFRYWauXKlbrrrrv8/rnR/gAAwMDK9kdhYaGcTqfPteDgYEvWXrhwofLy8pSUlCSHw6HExETl5uZq2bJlbc7Pz8/XJ5980qqSMX36dO+fBw8erNjYWI0bN0579uxRYmJiu+OhUgEAwDkUHBys8PBwn9FWUhEZGanAwEA1NDT4XG9oaDjjXoeoqCitX79ejY2N2r9/v3bt2qWwsDANGDCg1dyZM2fqzTff1KZNm9SnT5/vjDktLU2SVFNT096PKYmkAgCAVuw4/REUFKSUlBRVVFR4r7ndblVUVCg9Pf07vzckJETx8fE6ffq0XnvtNd18883ffhaPRzNnztTrr7+ud999V5deeulZY9mxY4ckKTY2tt3xS7Q/AABoxeOx532dTqdycnKUmpqq4cOHq6ysTI2Njd7TIFOmTFF8fLx3T8YHH3yguro6DR06VHV1dZo/f77cbrceeugh75r5+flas2aNfve736l79+7e/RkREREKDQ3Vnj17tGbNGmVlZalXr17auXOnCgoKNHr0aA0ZMsSv+EkqAAAw8Pf+ElaZOHGiDh8+rHnz5qm+vl5Dhw5VeXm5d/NmbW2tAgK+bTI0NTWpqKhIe/fuVVhYmLKysrRy5Ur16NHDO2fJkiWSvrnB1b9avny5pk6dqqCgIG3cuNGbwPTt21cTJkxQUVGR3/E7PB678jFfXYLi7Q6hw0ZEJdkdgiUu7drD7hAs8cJH/2V3CB128N/y7A7BEu8e9K+EinMn89I6u0OwROyWTed0/f3XZpx9Ujv1377RsrU6O1sqFS6Xq9UZXY/HI4eDB7gAAOxnV6XifOf3Rs3PP/9cy5cv165duyRJu3bt0r333qtp06bp3XffbdcabZ3Z9bhP+BsKAADnhMdj3biY+JVUlJeXa+jQoXrggQd0zTXXqLy8XKNHj1ZNTY3279+vG2+8sV2JRWFhoY4dO+YzHAHdTX8IAABgP7+SigULFujBBx/Ul19+qeXLl2vy5MnKy8vThg0bVFFRoQcffFBPPvnkWddp68wurQ8AQGdh1wPFznd+JRWffvqppk6dKkm64447dOLECf34xz/2vn7nnXdq586dlgYIAMD3zcrbdF9M/N5T8c+KQkBAgEJCQhQREeF9rXv37t4HlQAAgIuLX0lFQkKCdu/e7f26srJS/fr1835dW1vr9923AADobDxu68bFxK8jpffee69aWlq8X//rE84k6Q9/+INuuOEGayIDAMAm7ousbWEVv5KKGTNmfOfrTzzxRIeCAQAA5y9u0w0AgMHFtsHSKiQVAAAYXGxHQa1CUgEAgMHFdidMq/h9pBQAAKAtVCoAADCg/WEOSQUAAAYcKTWH9gcAALAElQoAAAw4UmoOSQUAAAac/jCH9gcAALAElQoAAAzYqGkOSQUAAAbsqTCH9gcAALAElQoAAAzYqGkOSQUAAAbsqTCn0yQVRbFj7Q6hw65vOm13CJao6tLV7hAscfDf8uwOocNiy39tdwiWCB081+4QOuxCuWtz10vcdodwXmBPhTnsqQAAAJboNJUKAAA6C9of5pBUAABgwD5Nc2h/AAAAS1CpAADAgPaHOSQVAAAYcPrDHNofAADAElQqAAAw4G4e5pBUAABg4BHtDzNofwAAAEtQqQAAwMDNjSpMIakAAMDATfvDFJIKAAAM2FNhDnsqAACAJahUAABgwJFSc0gqAAAwoP1hDu0PAAA6kUWLFikhIUEhISFKS0vTtm3bzjj31KlTWrBggRITExUSEqLk5GSVl5f7vWZTU5Py8/PVq1cvhYWFacKECWpoaPA7dpIKAAAM3BYOf6xdu1ZOp1PFxcXavn27kpOTlZmZqUOHDrU5v6ioSM8995yeeeYZffbZZ5oxY4ZuvfVWffzxx36tWVBQoDfeeEOvvvqqNm/erAMHDui2227zM3rJ4fF4OsVp3Pn977Q7hA67vum03SFYoiqkq90hWOInUQftDqHDYst/bXcIllg3eK7dIXSY+wKphv9o8N/sDsESke9sPqfrvx39E8vWGlf7klwul8+14OBgBQcHt5qblpamYcOG6dlnn5Ukud1u9e3bV7NmzdLs2bNbzY+Li9OcOXOUn5/vvTZhwgSFhoZq1apV7Vrz2LFjioqK0po1a/TjH/9YkrRr1y5dddVVqqys1HXXXdfuz0qlAgCAc6ikpEQRERE+o6SkpNW85uZmVVVVKSMjw3stICBAGRkZqqysbHNtl8ulkJAQn2uhoaHasmVLu9esqqrSqVOnfOYkJSWpX79+Z3zfM7Fko6bH45HDcYGk8QCAi56VGzULCwvldDp9rrVVpThy5IhaWloUHR3tcz06Olq7du1qc+3MzEyVlpZq9OjRSkxMVEVFhdatW6eWlpZ2r1lfX6+goCD16NGj1Zz6+nq/PqsllYrg4GB9/vnnViwFAIDt3A7rRnBwsMLDw31GW0mFGQsXLtTll1+upKQkBQUFaebMmcrNzVVAgD2NCL8qFcZM659aWlr05JNPqlevXpKk0tLS71zH5XK16i+d9rSoiyPQn3AAALhgREZGKjAwsNWpi4aGBsXExLT5PVFRUVq/fr2ampr05ZdfKi4uTrNnz9aAAQPavWZMTIyam5t19OhRn2rFd73vmfiVypSVlWnTpk36+OOPfYbH49Hnn3+ujz/+WDt27DjrOm31l7Yc+9SvwAEAOFfcclg22isoKEgpKSmqqKj4Ng63WxUVFUpPT//O7w0JCVF8fLxOnz6t1157TTfffHO710xJSVHXrl195lRXV6u2tvas72vkV6XiiSee0PPPP6+nnnpKN9xwg/d6165dtWLFCg0cOLBd67TVX/rFoOn+hAIAwDlj17FIp9OpnJwcpaamavjw4SorK1NjY6Nyc3MlSVOmTFF8fLx3o+cHH3yguro6DR06VHV1dZo/f77cbrceeuihdq8ZERGhu+++W06nUz179lR4eLhmzZql9PR0v05+SH4mFbNnz9a4ceN01113KTs7WyUlJera1f/jh20dpaH1AQDoLOy6TffEiRN1+PBhzZs3T/X19Ro6dKjKy8u9Gy1ra2t99ks0NTWpqKhIe/fuVVhYmLKysrRy5UqfNsbZ1pSkp59+WgEBAZowYYJcLpcyMzO1ePFiv+M3dZ+KkydPKj8/Xzt27NDq1at17bXXaseOHe2uVLSF+1R0HtynovPgPhWdB/ep6FzO9X0q1sVMtmyt2+rXWLZWZ2fqSGlYWJheeuklvfzyy8rIyPAeXQEA4ELg5jYJpnToPhU/+clPNGrUKFVVVal///5WxQQAgK06xa2mz0MdvvlVnz591KdPHytiAQAA5zEefQ4AgIFdGzXPdyQVAAAYXCgbc79vPFAMAABYgkoFAAAG/twJE98iqQAAwIDTH+bQ/gAAAJagUgEAgAEbNc0hqQAAwIAjpeaQVAAAYMCeCnPYUwEAACxBpQIAAAP2VJhDUgEAgAF7Ksyh/QEAACxBpQIAAAMqFeaQVAAAYOBhT4UptD8AAIAlOk2lYvypr+0OocNi4o/bHYIlfnD4ErtDsMS7B2PtDqHDQgfPtTsES9z2l8fsDqHDXhkyz+4QLBEytJfdIZwXaH+Y02mSCgAAOguSCnNofwAAAEtQqQAAwIDbdJtDUgEAgAF31DSHpAIAAAP2VJjDngoAAGAJKhUAABhQqTCHpAIAAAM2appD+wMAAFiCSgUAAAac/jCHpAIAAAP2VJhD+wMAAFiCSgUAAAZs1DSHpAIAAAM3aYUptD8AAIAlqFQAAGDARk1zSCoAADCg+WEOSQUAAAZUKsxhTwUAALAElQoAAAy4o6Y5VCoAADBwy2PZ8NeiRYuUkJCgkJAQpaWladu2bd85v6ysTFdeeaVCQ0PVt29fFRQUqKmpyft6QkKCHA5Hq5Gfn++dM3bs2Favz5gxw+/YO1SpaGxs1CuvvKKamhrFxsZq0qRJ6tWr11m/z+VyyeVy+Vxr9rQoyBHYkXAAADivrV27Vk6nU0uXLlVaWprKysqUmZmp6upq9e7du9X8NWvWaPbs2Vq2bJlGjBihL774QlOnTpXD4VBpaakk6cMPP1RLS4v3ez755BP96Ec/0u233+6zVl5enhYsWOD9ulu3bn7H71elYuDAgfr73/8uSfrrX/+qQYMGqaCgQBs2bFBxcbEGDhyoffv2nXWdkpISRURE+IyXTn7hd/AAAJwLHguHy+XS8ePHfYbxL9b/VFpaqry8POXm5mrgwIFaunSpunXrpmXLlrU5//3339fIkSM1efJkJSQk6MYbb9SkSZN8qhtRUVGKiYnxjjfffFOJiYkaM2aMz1rdunXzmRceHu73z82vpGLXrl06ffq0JKmwsFBxcXHav3+/tm3bpv3792vIkCGaM2fOWdcpLCzUsWPHfEZO2BV+Bw8AwLngtnC09RfpkpKSVu/Z3NysqqoqZWRkeK8FBAQoIyNDlZWVbcY5YsQIVVVVeZOIvXv36u2331ZWVlab85ubm7Vq1SpNmzZNDofvxpHVq1crMjJSgwYNUmFhob766qt2/az+len2R2VlpZYuXaqIiAhJUlhYmB599FH95Cc/Oev3BgcHKzg42OcarQ8AwIWosLBQTqfT55rxd6AkHTlyRC0tLYqOjva5Hh0drV27drW59uTJk3XkyBGNGjVKHo9Hp0+f1owZM/TII4+0OX/9+vU6evSopk6d2mqd/v37Ky4uTjt37tTDDz+s6upqrVu3zo9PaiKp+Gdm09TUpNjYWJ/X4uPjdfjwYX+XBACgU7Hy2R9t/UXaKu+9956eeOIJLV68WGlpaaqpqdF9992nxx57THPnzm01/8UXX9T48eMVFxfnc3369OnePw8ePFixsbEaN26c9uzZo8TExHbH43dSMW7cOHXp0kXHjx9XdXW1Bg0a5H1t//797dqoCQBAZ2bHHTUjIyMVGBiohoYGn+sNDQ2KiYlp83vmzp2rn/70p7rnnnskfZMQNDY2avr06ZozZ44CAr7d5bB//35t3LixXdWHtLQ0SVJNTc25SyqKi4t9vg4LC/P5+o033tD111/vz5IAAEBSUFCQUlJSVFFRoVtuuUWS5Ha7VVFRoZkzZ7b5PV999ZVP4iBJgYHfbCfweHxTo+XLl6t379666aabzhrLjh07JKlVR+JsOpRUGP3Xf/2XX28OAEBnZNdtup1Op3JycpSamqrhw4errKxMjY2Nys3NlSRNmTJF8fHx3o2e2dnZKi0t1TXXXONtf8ydO1fZ2dne5EL6JjlZvny5cnJy1KWL76/+PXv2aM2aNcrKylKvXr20c+dOFRQUaPTo0RoyZIhf8XNHTQAADKzcU+GPiRMn6vDhw5o3b57q6+s1dOhQlZeXezdv1tbW+lQmioqK5HA4VFRUpLq6OkVFRSk7O1uPP/64z7obN25UbW2tpk2b1uo9g4KCtHHjRm8C07dvX02YMEFFRUV+x+/wGOsjNvkg7ja7Q+iwmJjjdodgiS8PX2J3CJbY6e5udwgdFuruFP95dthtf3nM7hA67JUh8+wOwRI3/9T/Y4KdUdjP/TuV4K+ChLOfZGyvp//vZcvW6uy4TTcAALAE7Q8AAAx49Lk5JBUAABh4bNpTcb6j/QEAACxBpQIAAAPaH+aQVAAAYGDXkdLzHe0PAABgCSoVAAAYUKcwh6QCAAAD2h/m0P4AAACWoFIBAIABpz/MIakAAMCAm1+ZQ1IBAIABlQpz2FMBAAAs0WkqFdWObnaH0GEDR5+yOwRLBPz5pN0hWOPA+f/oc7fD7giscSE8NvyOnQvsDsESzc/OsTuE8wLtD3M6TVIBAEBnQfvDHNofAADAElQqAAAwcHtof5hBUgEAgAEphTm0PwAAgCWoVAAAYMCzP8whqQAAwIAjpebQ/gAAAJagUgEAgAH3qTCHpAIAAAP2VJhDUgEAgAF7KsxhTwUAALAElQoAAAzYU2EOSQUAAAYebtNtCu0PAABgCSoVAAAYcPrDHJIKAAAM2FNhDu0PAABgCSoVAAAYcJ8Kc0gqAAAwYE+FObQ/AACAJfxKKrZv3659+/Z5v165cqVGjhypvn37atSoUXr55ZfbtY7L5dLx48d9xilPi3+RAwBwjng8HsvGxcSvpCI3N1d79uyRJL3wwgv62c9+ptTUVM2ZM0fDhg1TXl6eli1bdtZ1SkpKFBER4TPePPGpuU8AAIDF3BaOi4lfeyp2796tyy+/XJK0ePFiLVy4UHl5ed7Xhw0bpscff1zTpk37znUKCwvldDp9rr2a9DN/QgEA4Jxho6Y5fiUV3bp105EjR9S/f3/V1dVp+PDhPq+npaX5tEfOJDg4WMHBwT7XujoC/QkFAAB0Mn61P8aPH68lS5ZIksaMGaPf/va3Pq+/8soruuyyy6yLDgAAG7jlsWz4a9GiRUpISFBISIjS0tK0bdu275xfVlamK6+8UqGhoerbt68KCgrU1NTkfX3+/PlyOBw+IykpyWeNpqYm5efnq1evXgoLC9OECRPU0NDgd+x+VSp+/vOfa+TIkRozZoxSU1P11FNP6b333tNVV12l6upqbd26Va+//rrfQQAA0JnYtcFy7dq1cjqdWrp0qdLS0lRWVqbMzExVV1erd+/ereavWbNGs2fP1rJlyzRixAh98cUXmjp1qhwOh0pLS73zrr76am3cuNH7dZcuvr/+CwoK9NZbb+nVV19VRESEZs6cqdtuu01//vOf/Yrfr0pFXFycPv74Y6Wnp6u8vFwej0fbtm3TH//4R/Xp00d//vOflZWV5VcAAADgG6WlpcrLy1Nubq4GDhyopUuXqlu3bmc8BPH+++9r5MiRmjx5shISEnTjjTdq0qRJraobXbp0UUxMjHdERkZ6Xzt27JhefPFFlZaW6oYbblBKSoqWL1+u999/X1u3bvUrfr/vU9GjRw89+eST+vTTT/X111/L5XLp//7v/7R69Wqlpqb6uxwAAJ2Ole2Ptm6j4HK5Wr1nc3OzqqqqlJGR4b0WEBCgjIwMVVZWthnniBEjVFVV5U0i9u7dq7fffrvVX/B3796tuLg4DRgwQHfeeadqa2u9r1VVVenUqVM+75uUlKR+/fqd8X3PhJtfAQBg4LHwn7Zuo1BSUtLqPY8cOaKWlhZFR0f7XI+OjlZ9fX2bcU6ePFkLFizQqFGj1LVrVyUmJmrs2LF65JFHvHPS0tK0YsUKlZeXa8mSJdq3b5+uv/56nThxQpJUX1+voKAg9ejRo93veybcphsAgHOordsoGE9AmvXee+/piSee0OLFi5WWlqaamhrdd999euyxxzR37lxJ3xyy+KchQ4YoLS1N/fv31yuvvKK7777bkjj+iaQCAAADt4UbNdu6jUJbIiMjFRgY2OrURUNDg2JiYtr8nrlz5+qnP/2p7rnnHknS4MGD1djYqOnTp2vOnDkKCGjdkOjRo4euuOIK1dTUSJJiYmLU3Nyso0eP+lQrvut9z4T2BwAABh4LR3sFBQUpJSVFFRUV3mtut1sVFRVKT09v83u++uqrVolDYOA393060wmWkydPas+ePYqNjZUkpaSkqGvXrj7vW11drdra2jO+75lQqQAAoJNwOp3KyclRamqqhg8frrKyMjU2Nio3N1eSNGXKFMXHx3v3ZGRnZ6u0tFTXXHONt/0xd+5cZWdne5OLBx54QNnZ2erfv78OHDig4uJiBQYGatKkSZKkiIgI3X333XI6nerZs6fCw8M1a9Yspaen67rrrvMrfpIKAAAM7Hr0+cSJE3X48GHNmzdP9fX1Gjp0qMrLy72bN2tra30qE0VFRXI4HCoqKlJdXZ2ioqKUnZ2txx9/3Dvnb3/7myZNmqQvv/xSUVFRGjVqlLZu3aqoqCjvnKeffloBAQGaMGGCXC6XMjMztXjxYr/jd3g6ySPUfhN/l90hdNittx+zOwRLHP3zV3aHYImKA7F2h9BhwZ3jP88Oc8thdwgddsfOBXaHYInmZ+fYHYIlLpn/3+d0/fT4H1q2VmXdJsvW6uyoVAAAYNBJ/r593mGjJgAAsASVCgAADOzaU3G+I6kAAMDAQ1JhCu0PAABgCSoVAAAYsFHTHJIKAAAM2FNhDu0PAABgCSoVAAAY0P4wp9MkFf8+9oDdIXRYl2Ej7Q7BEpHXnrY7BEtkPr/V7hA6rOslbrtDsETI0F52h9BhF8qdKINmPn72SaD9YRLtDwAAYIlOU6kAAKCz4D4V5pBUAABg4GZPhSkkFQAAGFCpMIc9FQAAwBJUKgAAMKD9YQ5JBQAABrQ/zKH9AQAALEGlAgAAA9of5pBUAABgQPvDHNofAADAElQqAAAwoP1hDkkFAAAGtD/Mof0BAAAsQaUCAAADj8dtdwjnJZIKAAAM3LQ/TCGpAADAwMNGTVPYUwEAACxBpQIAAAPaH+aQVAAAYED7wxzaHwAAwBJ+JRWzZs3S//zP/3T4TV0ul44fP+4zXC0c3wEAdA5uj8eycTHxK6lYtGiRxo4dqyuuuEI///nPVV9fb+pNS0pKFBER4TOe/nS/qbUAALCax8J/LiZ+tz/++Mc/KisrS7/85S/Vr18/3XzzzXrzzTfldre/0lBYWKhjx475jIKr+/sbCgAA6ET8TioGDx6ssrIyHThwQKtWrZLL5dItt9yivn37as6cOaqpqTnrGsHBwQoPD/cZwYFs7wAAdA4ej8eycTEx/Zu8a9euuuOOO1ReXq69e/cqLy9Pq1ev1pVXXmllfAAAfO/c8lg2LiaWlAf69eun+fPna9++fSovL7diSQAAcJ7x6z4V/fv3V2Bg4Blfdzgc+tGPftThoAAAsNPF1rawil9Jxb59+85VHAAAdBoX21FQq3BHTQAADKhUmMORCwAAOpFFixYpISFBISEhSktL07Zt275zfllZma688kqFhoaqb9++KigoUFNTk/f1kpISDRs2TN27d1fv3r11yy23qLq62meNsWPHyuFw+IwZM2b4HTtJBQAABnad/li7dq2cTqeKi4u1fft2JScnKzMzU4cOHWpz/po1azR79mwVFxfr888/14svvqi1a9fqkUce8c7ZvHmz8vPztXXrVm3YsEGnTp3SjTfeqMbGRp+18vLydPDgQe/4xS9+4ffPjfYHAAAGdrU/SktLlZeXp9zcXEnS0qVL9dZbb2nZsmWaPXt2q/nvv/++Ro4cqcmTJ0uSEhISNGnSJH3wwQfeOcZTmStWrFDv3r1VVVWl0aNHe69369ZNMTExHYqfSgUAAOdQm8+7crlazWtublZVVZUyMjK81wICApSRkaHKyso21x4xYoSqqqq8LZK9e/fq7bffVlZW1hnjOXbsmCSpZ8+ePtdXr16tyMhIDRo0SIWFhfrqq6/8/qxUKgAAMLDy9EdJSYkeffRRn2vFxcWaP3++z7UjR46opaVF0dHRPtejo6O1a9euNteePHmyjhw5olGjRsnj8ej06dOaMWOGT/vjX7ndbt1///0aOXKkBg0a5LNO//79FRcXp507d+rhhx9WdXW11q1b59dnJakAAMDAygeBFRYWyul0+lwLDg62ZO333ntPTzzxhBYvXqy0tDTV1NTovvvu02OPPaa5c+e2mp+fn69PPvlEW7Zs8bk+ffp0758HDx6s2NhYjRs3Tnv27FFiYmK74yGpAADgHAoODm5XEhEZGanAwEA1NDT4XG9oaDjjXoe5c+fqpz/9qe655x5J3yQEjY2Nmj59uubMmaOAgG93OcycOVNvvvmm/vSnP6lPnz7fGUtaWpokqaamxq+kgj0VAAAYuD0ey0Z7BQUFKSUlRRUVFd/G4XaroqJC6enpbX7PV1995ZM4SPLe+fqfm009Ho9mzpyp119/Xe+++64uvfTSs8ayY8cOSVJsbGy745eoVAAA0Ipdpz+cTqdycnKUmpqq4cOHq6ysTI2Njd7TIFOmTFF8fLxKSkokSdnZ2SotLdU111zjbX/MnTtX2dnZ3uQiPz9fa9as0e9+9zt1795d9fX1kqSIiAiFhoZqz549WrNmjbKystSrVy/t3LlTBQUFGj16tIYMGeJX/CQVAAB0EhMnTtThw4c1b9481dfXa+jQoSovL/du3qytrfWpTBQVFcnhcKioqEh1dXWKiopSdna2Hn/8ce+cJUuWSPrmBlf/avny5Zo6daqCgoK0ceNGbwLTt29fTZgwQUVFRX7H7/B0knuRHr3zBrtD6LDgrJF2h2CNltN2R2CJo89vtTuEDut6idvuECwRMrSX3SF0mCPUmo11dgua+fjZJ50HukYOOKfrB4f0tWwtV9NfLVurs6NSAQCAQSf5+/Z5h6QCAAADkgpzOP0BAAAsQaUCAAAD6hQmeS4STU1NnuLiYk9TU5PdoZh2IXwGj+fC+BwXwmfwePgcncmF8Bk8ngvnc8CcTnP641w7fvy4IiIidOzYMYWHh9sdjikXwmeQLozPcSF8BonP0ZlcCJ9BunA+B8xhTwUAALAESQUAALAESQUAALDERZNUBAcHq7i42LLHzdrhQvgM0oXxOS6EzyDxOTqTC+EzSBfO54A5F81GTQAAcG5dNJUKAABwbpFUAAAAS5BUAAAAS5BUAAAAS5BUAAAAS1wUScWiRYuUkJCgkJAQpaWladu2bXaH5Jc//elPys7OVlxcnBwOh9avX293SH4rKSnRsGHD1L17d/Xu3Vu33HKLqqur7Q7Lb0uWLNGQIUMUHh6u8PBwpaen6w9/+IPdYXXIk08+KYfDofvvv9/uUPwyf/58ORwOn5GUlGR3WKbU1dXprrvuUq9evRQaGqrBgwfro48+sjusdktISGj178LhcCg/P9/u0PA9u+CTirVr18rpdKq4uFjbt29XcnKyMjMzdejQIbtDa7fGxkYlJydr0aJFdodi2ubNm5Wfn6+tW7dqw4YNOnXqlG688UY1NjbaHZpf+vTpoyeffFJVVVX66KOPdMMNN+jmm2/Wp59+andopnz44Yd67rnnNGTIELtDMeXqq6/WwYMHvWPLli12h+S3f/zjHxo5cqS6du2qP/zhD/rss8/01FNP6Qc/+IHdobXbhx9+6PPvYcOGDZKk22+/3ebI8L2z93lm597w4cM9+fn53q9bWlo8cXFxnpKSEhujMk+S5/XXX7c7jA47dOiQR5Jn8+bNdofSYT/4wQ88L7zwgt1h+O3EiROeyy+/3LNhwwbPmDFjPPfdd5/dIfmluLjYk5ycbHcYHfbwww97Ro0aZXcYlrrvvvs8iYmJHrfbbXco+J5d0JWK5uZmVVVVKSMjw3stICBAGRkZqqystDEyHDt2TJLUs2dPmyMxr6WlRS+//LIaGxuVnp5udzh+y8/P10033eTz38f5Zvfu3YqLi9OAAQN05513qra21u6Q/Pb73/9eqampuv3229W7d29dc801+vWvf213WKY1Nzdr1apVmjZtmhwOh93h4Ht2QScVR44cUUtLi6Kjo32uR0dHq76+3qao4Ha7df/992vkyJEaNGiQ3eH47S9/+YvCwsIUHBysGTNm6PXXX9fAgQPtDssvL7/8srZv366SkhK7QzEtLS1NK1asUHl5uZYsWaJ9+/bp+uuv14kTJ+wOzS979+7VkiVLdPnll+udd97Rvffeq//8z//USy+9ZHdopqxfv15Hjx7V1KlT7Q4FNuhidwC4+OTn5+uTTz45L/vfknTllVdqx44dOnbsmH77298qJydHmzdvPm8Si7/+9a+67777tGHDBoWEhNgdjmnjx4/3/nnIkCFKS0tT//799corr+juu++2MTL/uN1upaam6oknnpAkXXPNNfrkk0+0dOlS5eTk2Byd/1588UWNHz9ecXFxdocCG1zQlYrIyEgFBgaqoaHB53pDQ4NiYmJsiuriNnPmTL355pvatGmT+vTpY3c4pgQFBemyyy5TSkqKSkpKlJycrIULF9odVrtVVVXp0KFDuvbaa9WlSxd16dJFmzdv1q9+9St16dJFLS0tdodoSo8ePXTFFVeopqbG7lD8Ehsb2yohveqqq87LVs7+/fu1ceNG3XPPPXaHAptc0ElFUFCQUlJSVFFR4b3mdrtVUVFxXvbAz2cej0czZ87U66+/rnfffVeXXnqp3SFZxu12y+Vy2R1Gu40bN05/+ctftGPHDu9ITU3VnXfeqR07digwMNDuEE05efKk9uzZo9jYWLtD8cvIkSNbHa/+4osv1L9/f5siMm/58uXq3bu3brrpJrtDgU0u+PaH0+lUTk6OUlNTNXz4cJWVlamxsVG5ubl2h9ZuJ0+e9Pnb1759+7Rjxw717NlT/fr1szGy9svPz9eaNWv0u9/9Tt27d/fuaYmIiFBoaKjN0bVfYWGhxo8fr379+unEiRNas2aN3nvvPb3zzjt2h9Zu3bt3b7WX5ZJLLlGvXr3Oqz0uDzzwgLKzs9W/f38dOHBAxcXFCgwM1KRJk+wOzS8FBQUaMWKEnnjiCd1xxx3atm2bnn/+eT3//PN2h+YXt9ut5cuXKycnR126XPC/WnAmdh8/+T4888wznn79+nmCgoI8w4cP92zdutXukPyyadMmj6RWIycnx+7Q2q2t+CV5li9fbndofpk2bZqnf//+nqCgIE9UVJRn3Lhxnj/+8Y92h9Vh5+OR0okTJ3piY2M9QUFBnvj4eM/EiRM9NTU1dodlyhtvvOEZNGiQJzg42JOUlOR5/vnn7Q7Jb++8845Hkqe6utruUGAjh8fj8diTzgAAgAvJBb2nAgAAfH9IKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCVIKgAAgCX+H2v8Bcrz0Wq0AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(fisher_cor_HMM)" + ] + }, + { + "cell_type": "markdown", + "id": "12ea5ac1", + "metadata": {}, + "source": [ + "### Update 1st Dec 2023" + ] + }, + { + "cell_type": "markdown", + "id": "e550447d", + "metadata": {}, + "source": [ + "As suggested by Chet, now let's check whether the osl-dynamics HMM inference is correct. First, check the non-trainable case." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b0fcbb93", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import seaborn\n", + "from rotation.utils import twopair_riemannian_distance, hungarian_pair" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d6e6dbe0", + "metadata": {}, + "outputs": [], + "source": [ + "state_covariance_fit = np.load('./results_simulation_202311_toy_6/HMM_ICA_25_state_8_final/state_covariances.npy')\n", + "state_correlation_fit = np.load('./results_simulation_202311_toy_6/HMM_ICA_25_state_8_final/state_correlations.npy')\n", + "state_covariance_truth = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy')\n", + "state_correlation_truth = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d7cad6fa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 703.06it/s]\n" + ] + } + ], + "source": [ + "riemannian_fit_truth = twopair_riemannian_distance(state_covariance_fit,state_covariance_truth)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8687b86f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[3.40165214 4.30717011 3.84038278 4.20373658 3.51038735 3.12401759\n", + " 3.87697419 0.2220812 ]\n", + " [3.52225279 3.43193624 3.2468047 3.18809499 3.97682885 3.94893485\n", + " 0.27046988 3.8413184 ]\n", + " [3.34266174 3.25839884 0.22693022 3.28144366 4.3971542 3.68080528\n", + " 3.27287083 3.84530398]\n", + " [2.99361127 3.09763207 3.6748324 3.28287213 4.49290185 0.2408669\n", + " 4.00648284 3.13329171]\n", + " [4.28933351 4.40521676 4.40702048 4.23416973 0.23888105 4.52361696\n", + " 3.96486466 3.53856864]\n", + " [2.91095785 0.24518114 3.26954234 2.98285989 4.41633575 3.08779298\n", + " 3.48844738 4.34197844]\n", + " [3.27353471 2.99365311 3.3194696 0.23079059 4.24406806 3.27762597\n", + " 3.23906169 4.25418187]\n", + " [0.18944531 2.91233698 3.35909665 3.24848635 4.30739919 2.99555427\n", + " 3.6033023 3.45915863]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, riemannian_fit_truth_reordered = hungarian_pair(riemannian_fit_truth,distance=True)\n", + "seaborn.heatmap(riemannian_fit_truth_reordered)\n", + "print(riemannian_fit_truth)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2f1b94b7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 1076.43it/\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rotation.utils import pairwise_fisher_z_correlations\n", + "seaborn.heatmap(pairwise_fisher_z_correlations(state_correlation_fit))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "84fd5720", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 1026.95it/\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(pairwise_fisher_z_correlations(state_correlation_truth))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e2449fbf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 675.18it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AwFFObZ6rqamJ+XnZsmXq3bu36uvrNWrUqLjmsJTYKyoqznk9EomoqqpKPXv2lCQ9+uijVqYFACClRG3M6+FwWOFwOOaa3++X3+9v9XcbGxslST169Ij7+ywl9scee0xDhgxRt27dYq4bhqEPPvhAXbp0kc/X+n8b57pJffWV/JmZVsIBACDlhUIhPfDAAzHXKisrtWDBggv+XjQa1Zw5c1RSUqKCgoK4v89SYl+4cKGWLFmiRx55RDfffHPL9YyMDC1btkyDBw+Oa55z3eT95WX671nTrIQDAECbsPOs+GAweFbHO55qvby8XHv27NHmzZstfZ+lxP6zn/1MY8eO1V133aXx48crFAopIyPD0hdK575J/e/blucBAKAt2LklPN62+3+aNWuWXnvtNW3atEl9+vSx9LuWd8UPGzZM9fX1+uKLL1RUVKQ9e/bE1X7/T36/X9nZ2TGDNjwAIFU49bibYRiaNWuWVq1apTfffFNXXXWV5dgT2hXftWtXLV++XCtXrtS4ceMUiUQSmQYAAPyH8vJyrVixQq+88oqysrJ05MgRSVIgENBFF10U1xxJPe723e9+VzfddJPq6+uVn5+fzFQAAKSMqMVOtF2qq6slSWPGjIm5vnTpUk2dOjWuOZJ+jr1Pnz6W+/8AAKQyp45ds+PAN46UBQDAQ3gJDAAAJm5+CQyJHQAAEztPnmtvtOIBAPAQKnYAAEzsPHmuvZHYAQAwcfPLyGnFAwDgIVTsAACYuHnzHIkdAAATHncDAMBDWGMHAAApgYodAAAT1tgBAPAQN6+x04oHAMBDqNgBADBxc8VOYgcAwMRw8Ro7rXgAADwkZSr2U6FfOB1C0jp1v9jpEGyRXlTodAi2iH6w1+kQknby8F+dDsEWF+WOdDqEpJ1YV+V0CLY48/5+p0NwBVrxAAB4iJsTO614AAA8hIodAAATNx8pS2IHAMCEk+cAAPAQ1tgBAEBKoGIHAMDEzRU7iR0AABM3b56jFQ8AgIdQsQMAYMKueAAAPMTNa+y04gEA8BAqdgAATNy8eY7EDgCASdTFqZ1WPAAAHkLFDgCAiZs3z5HYAQAwcW8jnsQOAMBZ3Fyxs8YOAICHULEDAGDSYU+ea25u1osvvqj9+/crJydHd955p3r27GlXbAAAOMLNj7tZSuyDBw/W5s2b1aNHDx06dEijRo3Sl19+qf79++ujjz7SL3/5S9XV1emqq6664DzhcFjhcDj2WiQqfxorAwAAJMNSJv3www915swZSVIwGFRubq4OHjyobdu26eDBgyosLNT999/f6jyhUEiBQCBmPLr7fxO6AQAA7GbYONpbwiXy1q1btWDBAgUCAUlS165d9cADD2jz5s2t/m4wGFRjY2PMqLj2ykRDAQDAVlEbR3uzvMbu8/1rR8GpU6eUk5MT89nll1+uL774otU5/H6//H5/zDWDNjwAAEmznNjHjh2r9PR0NTU1qaGhQQUFBS2fHTx4kM1zAADX6zCb5yorK2N+7tq1a8zPq1ev1siRI5OPCgAAB7k3rSeZ2M1+9atfJRUMAABIDgfUAABg4uYjZUnsAACYdJg1dgAAOgL3pnVeAgMAgKdQsQMAYMIaOwAAHmK4uBlPKx4AAA+hYgcAwIRWPAAAHuLmx91oxQMA4CFU7AAAmLi3XiexAwBwFlrxAAAgJVCxAwBgwq54AAA8xM0H1JDYAQAwcXPFzho7AAAekjIVe+aYoU6HkDSjsdHpEGwRbdjndAi2MKJu/jv3v5zZs8HpEGxxYl2V0yEkrevYnzkdgi2Orw46HYIr0IoHAMBD3FwW0IoHAMBDqNgBADCJGrTiAQDwDPemdVrxAAB4ChU7AAAmnBUPAICHGDb+Y8WmTZs0fvx45ebmyufz6eWXX7YcO4kdAIAU0dzcrCFDhmjx4sUJz0ErHgAAE6eeYy8tLVVpaWlSc5DYAQAwsXONPRwOKxwOx1zz+/3y+/22fcd/ohUPAICJnWvsoVBIgUAgZoRCoTaLnYodAIA2FAwGVVFREXOtrap1icQOAMBZ7Fxjb8u2+7mQ2AEAMDE4UhYAACTrxIkT2r9/f8vPBw4c0K5du9SjRw/l5eXFNQeJHQAAE6dOntuxY4e+/vWvt/z877X5srIyLVu2LK45SOwAAJg49Rz7mDFjkl4G4HE3AAA8hIodAAATq2e8pxISOwAAJrzdDQAApARLiX3nzp06cOBAy8+/+93vVFJSoiuuuEI33XSTVq5cGdc84XBYTU1NMSN8+oy1yAEAaCOGYdg22pulxD5t2jR99NFHkqRnn31WP/7xj1VUVKT7779fw4YN04wZM/T888+3Os+5zs391f+8ndgdAABgs6iNo71ZWmPft2+f+vXrJ0l68skntWjRIs2YMaPl82HDhunBBx/U9OnTLzjPuc7Nja5cYCUUAADaTIfZPHfxxRfr2LFjys/P16effqrhw4fHfD5ixIiYVv35nOvc3JMZ7OMDACBZllrxpaWlqq6uliSNHj1aL730UsznL774ovr27WtfdAAAOCAqw7bR3iyVyQ899JBKSko0evRoFRUV6ZFHHtGGDRs0aNAgNTQ0qK6uTqtWrWqrWAEAaBdufgmMpYo9NzdX77zzjoqLi1VTUyPDMLRt2za9/vrr6tOnj9566y3ddtttbRUrAABoheWF7W7duqmqqkpVVVVtEQ8AAI5z8wE17FgDAMDEzbviOXkOAAAPoWIHAMAk6uLNcyR2AABM3JvWacUDAOApVOwAAJiwKx4AAA8hsQMA4CEd5uQ5AACQ2qjYAQAwoRUPAICHcPIcAABICVTsAACYuHnzHIkdAAATN6+x04oHAMBDqNgBADChFW8D49gxp0NIWvq3Zzodgi0iezY5HYItOl09xOkQkhbZ8henQ7DFmff3Ox1C0o6vDjodgi2yxoecDsEWZ76a1abz04oHAAApIWUqdgAAUoWbn2MnsQMAYBJljR0AAO9wc8XOGjsAAB5CxQ4AgAmteAAAPIRWPAAASAlU7AAAmNCKBwDAQ2jFAwCAlEDFDgCACa14AAA8hFY8AABICVTsAACYGEbU6RASRmIHAMDEze9jJ7EDAGBiuHjzHGvsAAB4CBU7AAAmtOIBAPAQWvEAACAlWErs99xzj/76178m/aXhcFhNTU0xI3wmkvS8AADYIWoYto32ZimxL168WGPGjFH//v310EMP6ciRIwl9aSgUUiAQiBkPv/m3hOYCAMBuho3/tDfLrfjXX39dt912mx5++GHl5eXpjjvu0GuvvaZoNP6H+YPBoBobG2PG3JsLrYYCAABMLCf2a6+9Vo899pgOHz6sF154QeFwWBMmTNAVV1yh+++/X/v37291Dr/fr+zs7JjhT09L6AYAALCbYRi2jfaW8Oa5jIwMTZo0STU1Nfr44481Y8YM/f73v9eAAQPsjA8AgHYXlWHbaG+27IrPy8vTggULdODAAdXU1NgxJQAASICl59jz8/OVlnb+lrnP59Mtt9ySdFAAADjJzc+xW0rsBw4caKs4AABIGU48pmYXTp4DAMDEzRU7J88BAOAhVOwAAJjwEhgAADyEVjwAAEgJVOwAAJiwKx4AAA9x4uUtdqEVDwCAh1CxAwBgQiseAAAPYVc8AABICVTsAACYsHkOAAAPMQzDtmHV4sWLdeWVV6pz584aMWKEtm3bZun3SewAAJg4ldj/+Mc/qqKiQpWVldq5c6eGDBmib3zjGzp69Gjcc5DYAQBIEY8++qhmzJihadOmafDgwXrqqad08cUX6/nnn497DhI7AAAmho0jHA6rqakpZoTD4bO+86uvvlJ9fb3GjRvXcq1Tp04aN26ctm7daiH4DuLUqVNGZWWlcerUKadDSZgX7sEwvHEfXrgHw+A+UokX7sEwvHMfdqqsrDwr31dWVp715z799FNDkrFly5aY6/PmzTOGDx8e9/f5DMPFD+tZ0NTUpEAgoMbGRmVnZzsdTkK8cA+SN+7DC/cgcR+pxAv3IHnnPuwUDofPqtD9fr/8fn/MtcOHD+vyyy/Xli1bVFxc3HL9pz/9qTZu3Ki33347ru/jcTcAANrQuZL4ufTq1UtpaWn6/PPPY65//vnnuuyyy+L+PtbYAQBIAZmZmRo6dKjWrVvXci0ajWrdunUxFXxrqNgBAEgRFRUVKisrU1FRkYYPH67HHntMzc3NmjZtWtxzdJjE7vf7VVlZGVc7JFV54R4kb9yHF+5B4j5SiRfuQfLOfThl8uTJ+uKLL/Tzn/9cR44c0XXXXaeamhpdeumlcc/RYTbPAQDQEbDGDgCAh5DYAQDwEBI7AAAeQmIHAMBDOkRiT/YVeE7btGmTxo8fr9zcXPl8Pr388stOh2RZKBTSsGHDlJWVpd69e2vChAlqaGhwOizLqqurVVhYqOzsbGVnZ6u4uFhr1qxxOqykVFVVyefzac6cOU6HYsmCBQvk8/lixsCBA50OKyGffvqp7rrrLvXs2VMXXXSRrr32Wu3YscPpsOJ25ZVXnvXvwufzqby83OnQOiTPJ3Y7XoHntObmZg0ZMkSLFy92OpSEbdy4UeXl5aqrq1Ntba1Onz6tW2+9Vc3NzU6HZkmfPn1UVVWl+vp67dixQzfffLPuuOMOvffee06HlpDt27fr6aefVmFhodOhJOSaa67RZ5991jI2b97sdEiWffnllyopKVFGRobWrFmj999/X4888oi6d+/udGhx2759e8y/h9raWknSxIkTHY6sg0rmYHs3GD58uFFeXt7ycyQSMXJzc41QKORgVImTZKxatcrpMJJ29OhRQ5KxceNGp0NJWvfu3Y1nn33W6TAsO378uNGvXz+jtrbWGD16tDF79mynQ7KksrLSGDJkiNNhJG3+/PnGTTfd5HQYtpo9e7bxta99zYhGo06H0iF5umK37RV4sF1jY6MkqUePHg5HkrhIJKKVK1equbnZ0nGPqaK8vFy33357zP8+3Gbfvn3Kzc3V1VdfrSlTpuiTTz5xOiTLXn31VRUVFWnixInq3bu3rr/+ej3zzDNOh5Wwr776Si+88IKmT58un8/ndDgdkqcT+7FjxxSJRM46sefSSy/VkSNHHIoK0WhUc+bMUUlJiQoKCpwOx7Ldu3era9eu8vv9mjlzplatWqXBgwc7HZYlK1eu1M6dOxUKhZwOJWEjRozQsmXLVFNTo+rqah04cEAjR47U8ePHnQ7Nko8//ljV1dXq16+f1q5dq7vvvlv33nuvli9f7nRoCXn55Zf1z3/+U1OnTnU6lA6rwxwpi9RRXl6uPXv2uHI9VJIGDBigXbt2qbGxUS+99JLKysq0ceNG1yT3Q4cOafbs2aqtrVXnzp2dDidhpaWlLf+5sLBQI0aMUH5+vl588UX94Ac/cDAya6LRqIqKirRw4UJJ0vXXX689e/boqaeeUllZmcPRWffcc8+ptLRUubm5TofSYXm6YrfrFXiwz6xZs/Taa69p/fr16tOnj9PhJCQzM1N9+/bV0KFDFQqFNGTIEC1atMjpsOJWX1+vo0eP6oYbblB6errS09O1ceNGPf7440pPT1ckEnE6xIR069ZN/fv31/79+50OxZKcnJyz/lI4aNAgVy4rHDx4UG+88YZ++MMfOh1Kh+bpxG7XK/CQPMMwNGvWLK1atUpvvvmmrrrqKqdDsk00GlU4HHY6jLiNHTtWu3fv1q5du1pGUVGRpkyZol27diktLc3pEBNy4sQJffTRR8rJyXE6FEtKSkrOevRz7969ys/PdyiixC1dulS9e/fW7bff7nQoHZrnW/F2vALPaSdOnIipQg4cOKBdu3apR48eysvLczCy+JWXl2vFihV65ZVXlJWV1bLHIRAI6KKLLnI4uvgFg0GVlpYqLy9Px48f14oVK7RhwwatXbvW6dDilpWVddbehi5duqhnz56u2vMwd+5cjR8/Xvn5+Tp8+LAqKyuVlpamO++80+nQLLnvvvt04403auHChZo0aZK2bdumJUuWaMmSJU6HZkk0GtXSpUtVVlam9HTPp5bU5vS2/Pbwm9/8xsjLyzMyMzON4cOHG3V1dU6HZMn69esNSWeNsrIyp0OL27nil2QsXbrU6dAsmT59upGfn29kZmYal1xyiTF27Fjj9ddfdzqspLnxcbfJkycbOTk5RmZmpnH55ZcbkydPNvbv3+90WAlZvXq1UVBQYPj9fmPgwIHGkiVLnA7JsrVr1xqSjIaGBqdD6fB4bSsAAB7i6TV2AAA6GhI7AAAeQmIHAMBDSOwAAHgIiR0AAA8hsQMA4CEkdgAAPITEDgCAh5DYAQDwEBI7AAAeQmIHAMBDSOwAAHjI/wPy5rLvuqkIYwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_riemannian_distance(state_covariance_fit,state_covariance_fit))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8b8af493", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ed5b613d", + "metadata": {}, + "outputs": [], + "source": [ + "with open('./results_simulation_202311_toy_6/HMM_ICA_25_state_8/alpha.pkl','rb') as file:\n", + " posterior = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1b2ce618", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200,)\n" + ] + } + ], + "source": [ + "posterior_hard = np.argmax(posterior[7],axis=1)\n", + "print(posterior_hard.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "08225e61", + "metadata": {}, + "outputs": [], + "source": [ + "truth = np.load('data/node_timeseries/simulation_toy_6/10001_state_time_course.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "7c595d18", + "metadata": {}, + "outputs": [], + "source": [ + "truth_0 = np.argmax(truth[8400:9600,:],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "7241cb80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1000" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(truth_0!=posterior_hard)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "5a7b0088", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13\n", + "4\n", + "5\n", + "13\n", + "13\n", + "5\n", + "68\n", + "14\n", + "17\n", + "18\n", + "26\n", + "12\n", + "22\n", + "19\n", + "23\n", + "10\n", + "3\n", + "10\n", + "15\n", + "5\n", + "10\n", + "14\n", + 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"cell_type": "markdown", + "id": "29c2048f", + "metadata": {}, + "source": [ + "### Understand the different training configurations and loss function" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "45e7331b", + "metadata": {}, + "outputs": [], + "source": [ + "sequence_lengths = [600,400,200,100,50,25]\n", + "batch_sizes = [1024,512,256,128,64,32]\n", + "learning_rates = [0.0005,0.001,0.005,0.01]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "873f9ff2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "70cab68e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sequence_length_25_batch_size_32_learning_rate_0.0005\n", + "sequence_length_25_batch_size_32_learning_rate_0.001\n", + "sequence_length_25_batch_size_32_learning_rate_0.005\n", + "sequence_length_25_batch_size_32_learning_rate_0.01\n" + ] + } + ], + "source": [ + "for sequence_length in sequence_lengths:\n", + " for batch_size in batch_sizes:\n", + " for learning_rate in learning_rates:\n", + " if not os.path.exists(f'./results_training_config/sl_{sequence_length}_bs_{batch_size}_lr_{learning_rate}/trans_prob.npy'):\n", + " print(f'sequence_length_{sequence_length}_batch_size_{batch_size}_learning_rate_{learning_rate}')" + ] + }, + { + "cell_type": "markdown", + "id": "ca3cedd1", + "metadata": {}, + "source": [ + "### Update the state covariances after initialised training" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d84f8abf", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "%matplotlib inline\n", + "from rotation.utils import cov2stdcor, pairwise_fisher_z_correlations, twopair_riemannian_distance" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4d0d0fca", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 862.83it/s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_6/HMM_ICA_25_state_8_miscellaneous_real/'\n", + "n_init = 10\n", + "for i in range(1,2):\n", + " covariances = np.load(f'{save_dir}covariances_{i}.npy')\n", + " _, correlations = cov2stdcor(covariances)\n", + " seaborn.heatmap(pairwise_fisher_z_correlations(correlations))\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "426e9033", + "metadata": {}, + "source": [ + "### Check Chet's simulation - What these covariance matrices look like" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a0653d4d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "%matplotlib inline\n", + "from rotation.utils import twopair_riemannian_distance, pairwise_fisher_z_correlations, cov2stdcor" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3b8be68b", + "metadata": {}, + "outputs": [], + "source": [ + "covariances = np.load('./data/node_timeseries/simulation_toy_8/10001_state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bea5c5e3", + "metadata": {}, + "outputs": [], + "source": [ + "_,correlations = cov2stdcor(covariances)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "23df0e8c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|█████████████| 5/5 [00:00<00:00, 28.93it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_riemannian_distance(covariances, covariances))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "21baaedf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1758.47it/\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(pairwise_fisher_z_correlations(correlations))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "36e7b3f4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1686.36it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 2539.85it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1579.30it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1889.84it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1555.17it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1830.77it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1892.22it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1795.35it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1858.52it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute Fisher-z transformated correlation: 100%|â–ˆ| 5/5 [00:00<00:00, 1958.67it/\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_8/'\n", + "n_init = 10\n", + "for i in range(n_init):\n", + " covariances = np.load(f'{save_dir}covariances_{i}.npy')\n", + " _, correlations = cov2stdcor(covariances)\n", + " seaborn.heatmap(pairwise_fisher_z_correlations(correlations))\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bf27cb3a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|███████████| 5/5 [00:00<00:00, 1592.37it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results_covariance = np.load(f'{save_dir}HMM_ICA_11_state_5/state_covariances.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(results_covariance,results_covariance))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bafe6f13", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|███████████| 5/5 [00:00<00:00, 1654.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[7.48701844 6.75399839 7.17971632 7.91314317 7.60459501]\n", + " [5.89449839 6.6039659 6.08214241 6.20640782 3.83590764]\n", + " [5.71553436 6.57674421 6.36538851 4.12601537 6.65066772]\n", + " [5.14404274 6.70249356 4.57809857 6.55693776 6.41183871]\n", + " [6.37435824 5.69786049 6.67285536 6.69623953 6.98998333]]\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "QuadMesh.set() got an unexpected keyword argument 'distance'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m _, riem_reordered \u001b[38;5;241m=\u001b[39m hungarian_pair(riem)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(riem_reordered)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mseaborn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheatmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mriem_reordered\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/seaborn/matrix.py:459\u001b[0m, in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m square:\n\u001b[1;32m 458\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_aspect(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mequal\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 459\u001b[0m \u001b[43mplotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbar_ax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 460\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/seaborn/matrix.py:306\u001b[0m, in \u001b[0;36m_HeatMapper.plot\u001b[0;34m(self, ax, cax, kws)\u001b[0m\n\u001b[1;32m 303\u001b[0m kws\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvmax)\n\u001b[1;32m 305\u001b[0m \u001b[38;5;66;03m# Draw the heatmap\u001b[39;00m\n\u001b[0;32m--> 306\u001b[0m mesh \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkws\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# Set the axis limits\u001b[39;00m\n\u001b[1;32m 309\u001b[0m ax\u001b[38;5;241m.\u001b[39mset(xlim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]), ylim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]))\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1444\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1445\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1446\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/axes/_axes.py:6229\u001b[0m, in \u001b[0;36mAxes.pcolormesh\u001b[0;34m(self, alpha, norm, cmap, vmin, vmax, shading, antialiased, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6225\u001b[0m C \u001b[38;5;241m=\u001b[39m C\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m 6227\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msnap\u001b[39m\u001b[38;5;124m'\u001b[39m, mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpcolormesh.snap\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m-> 6229\u001b[0m collection \u001b[38;5;241m=\u001b[39m \u001b[43mmcoll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuadMesh\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 6230\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialiased\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mantialiased\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshading\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshading\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6231\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6232\u001b[0m collection\u001b[38;5;241m.\u001b[39m_scale_norm(norm, vmin, vmax)\n\u001b[1;32m 6234\u001b[0m coords \u001b[38;5;241m=\u001b[39m coords\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m) \u001b[38;5;66;03m# flatten the grid structure; keep x, y\u001b[39;00m\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/collections.py:1939\u001b[0m, in \u001b[0;36mQuadMesh.__init__\u001b[0;34m(self, coordinates, antialiased, shading, **kwargs)\u001b[0m\n\u001b[1;32m 1936\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bbox\u001b[38;5;241m.\u001b[39mupdate_from_data_xy(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_coordinates\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m 1937\u001b[0m \u001b[38;5;66;03m# super init delayed after own init because array kwarg requires\u001b[39;00m\n\u001b[1;32m 1938\u001b[0m \u001b[38;5;66;03m# self._coordinates and self._shading\u001b[39;00m\n\u001b[0;32m-> 1939\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1940\u001b[0m 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\u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 453\u001b[0m name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/collections.py:201\u001b[0m, in \u001b[0;36mCollection.__init__\u001b[0;34m(self, edgecolors, facecolors, linewidths, linestyles, capstyle, joinstyle, antialiaseds, offsets, offset_transform, norm, cmap, pickradius, hatch, urls, zorder, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset_transform \u001b[38;5;241m=\u001b[39m offset_transform\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_path_effects \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_internal_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_paths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/artist.py:1223\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 1216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[1;32m 1217\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1218\u001b[0m \u001b[38;5;124;03m Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[1;32m 1219\u001b[0m \u001b[38;5;124;03m errors as if calling `set`.\u001b[39;00m\n\u001b[1;32m 1220\u001b[0m \n\u001b[1;32m 1221\u001b[0m \u001b[38;5;124;03m The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[1;32m 1222\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1223\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_props\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1224\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{cls.__name__}\u001b[39;49;00m\u001b[38;5;124;43m.set() got an unexpected keyword argument \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 1225\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{prop_name!r}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/artist.py:1197\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m 1195\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m callable(func):\n\u001b[0;32m-> 1197\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 1198\u001b[0m errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[1;32m 1199\u001b[0m ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[1;32m 1200\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n", + "\u001b[0;31mAttributeError\u001b[0m: QuadMesh.set() got an unexpected keyword argument 'distance'" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rotation.utils import hungarian_pair\n", + "riem = twopair_riemannian_distance(results_covariance,covariances)\n", + "_, riem_reordered = hungarian_pair(riem)\n", + "print(riem_reordered)\n", + "seaborn.heatmap(riem_reordered,distance=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5d85c4d2", + "metadata": {}, + "source": [ + "Now we have finished the training. Just look at your data!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1c0d1d3b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import twopair_riemannian_distance, twopair_fisher_z_transformed_correlation, cov2stdcor,hungarian_pair" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4ffe4803", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 517.44it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 56\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ground_truth_dir = './results_HCP_202311_no_mean/'\n", + "covariances_ground_truth = np.load(f'{ground_truth_dir}HMM_ICA_25_state_8/state_covariances.npy')\n", + "correlations_ground_truth = np.load(f'{ground_truth_dir}HMM_ICA_25_state_8/state_correlations.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(covariances_ground_truth,covariances_ground_truth))\n", + "plt.show()\n", + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(correlations_ground_truth,correlations_ground_truth))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "daee1b5b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|██████████| 16/16 [00:00<00:00, 292.92it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 16/16 [00:00<00:00, \n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|██████████| 16/16 [00:00<00:00, 787.76it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_6/'\n", + "n_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "covariances = np.load(f'{save_dir}HMM_ICA_25_state_16/state_covariances.npy')\n", + "correlations = np.load(f'{save_dir}HMM_ICA_25_state_16/state_correlations.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(covariances,covariances))\n", + "plt.show()\n", + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(correlations,correlations))\n", + "plt.show()\n", + "covariance_fit_truth = twopair_riemannian_distance(covariances,covariances_ground_truth)\n", + "seaborn.heatmap(covariance_fit_truth)" + ] + }, + { + "cell_type": "markdown", + "id": "5569af73", + "metadata": {}, + "source": [ + "check the fractional occupancy when the states are very large" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a8032dc1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.02820833 0.17531667 0.00204167 0.12139167 0.111325 0.02111667\n", + " 0.00235833 0.10115 0.10949167 0.00215833 0.07358333 0.00185\n", + " 0.10640833 0.00259167 0.03178333 0.109225 ]\n" + ] + } + ], + "source": [ + "fo = np.load(f'{save_dir}HMM_ICA_25_state_16/fo.npy')\n", + "print(np.mean(fo,axis=0))" + ] + }, + { + "cell_type": "markdown", + "id": "f9d875ee", + "metadata": {}, + "source": [ + "### Check the results of all training configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a918b84c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import json\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "439ff202", + "metadata": {}, + "outputs": [], + "source": [ + "sequence_lengths = [25,50,100,200,400,600]\n", + "batch_sizes = [32,64,128,256,512,1024]\n", + "#learning_rates = [0.0005,0.001,0.005,0.01]\n", + "learning_rates = [0.001]" + ] + }, + { + "cell_type": "markdown", + "id": "b6210e28", + "metadata": {}, + "source": [ + "Check whether the training has finished" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1e603db0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sl_25_bs_32_lr_0.0005\n", + "sl_25_bs_32_lr_0.001\n", + "sl_25_bs_32_lr_0.005\n", + "sl_25_bs_32_lr_0.01\n" + ] + } + ], + "source": [ + "save_dir = './results_training_config/'\n", + "for sequence_length in sequence_lengths:\n", + " for batch_size in batch_sizes:\n", + " for learning_rate in learning_rates:\n", + " if not os.path.isfile(f'{save_dir}sl_{sequence_length}_bs_{batch_size}_lr_{learning_rate}/time.json'):\n", + " print(f'sl_{sequence_length}_bs_{batch_size}_lr_{learning_rate}')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9a18c6ac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/slurm-39097824/ipykernel_37046/3657015509.py:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_xticklabels(configurations, rotation=45, ha='right')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initialize lists to store data\n", + "configurations = []\n", + "durations = []\n", + "\n", + "# Loop through configurations and read JSON files\n", + "for sequence_length in sequence_lengths:\n", + " for batch_size in batch_sizes:\n", + " for learning_rate in learning_rates:\n", + " # Skip the missing file\n", + " if sequence_length == 25 and batch_size == 32:\n", + " continue\n", + " \n", + " # Construct the file path\n", + " file_path = f'./results_training_config/sl_{sequence_length}_bs_{batch_size}_lr_{learning_rate}/time.json'\n", + " \n", + " # Check if the file exists\n", + " if os.path.exists(file_path):\n", + " # Read the JSON file\n", + " with open(file_path, 'r') as json_file:\n", + " data = json.load(json_file)\n", + " duration = data['duration']\n", + " \n", + " # Append data to lists\n", + " configurations.append(f'SL {sequence_length}, BS {batch_size}, LR {learning_rate}')\n", + " durations.append(duration)\n", + "\n", + "# Create a bar plot\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "bars = ax.bar(configurations, durations, color='skyblue')\n", + "ax.set_ylabel('Training Time (seconds)')\n", + "ax.set_title('Training Time Across Different Configurations')\n", + "ax.set_xticklabels(configurations, rotation=45, ha='right')\n", + "\n", + "# Add data labels\n", + "for bar, duration in zip(bars, durations):\n", + " height = bar.get_height()\n", + " ax.annotate(f'{duration:.2f} s', xy=(bar.get_x() + bar.get_width() / 2, height),\n", + " xytext=(0, 3), # 3 points vertical offset\n", + " textcoords=\"offset points\",\n", + " ha='center', va='bottom')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "85a1c47f", + "metadata": {}, + "source": [ + "## Update 8th Dec 2023" + ] + }, + { + "cell_type": "markdown", + "id": "bca40288", + "metadata": {}, + "source": [ + "### Step 1: Show our current training model is successful" + ] + }, + { + "cell_type": "markdown", + "id": "cce56930", + "metadata": {}, + "source": [ + "Method: use the ground-truth HMM model obtained from HCP (results_HCP_202311_no_mean), generate some simulation data (results_simulation_202311_toy_6), and then compare the fitted states with respect to the ground truth." + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "4545c9df", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import *" + ] + }, + { + "cell_type": "markdown", + "id": "79a47896", + "metadata": {}, + "source": [ + "first read through the ground truth" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "28b606af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "covariances ground truth shape: (8, 25, 25)\n", + "correlations_truth shape: (8, 25, 25)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 695.04it/s]\n", + "Compute twopair Fisher-z transformated correlation: 100%|█| 8/8 [00:00<00:00, 46\n" + ] + }, + { + "data": { + "image/png": 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", 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BBYROspQM5Ofna+fOnd+4fc2aNQo5cDsGAAC2clkywBMIAQAwMbrIj7hdeAIhAAAuR2UAAAAzl1UGSAYAADBz2fI32gQAALgclQEAAEzctoCQZAAAADOXJQO0CQAAcDkqAwAAmLlsASHJAAAAJm5bM0CbAAAAl6MyAACAGW0CAADczW1tApIBAADMXFYZYM0AAAAuR2UAAAATw2WVAY9hGJ2iMbJy+J1Oh9BuIxu7x397ehtBp0OwxTUf+J0Ood3+z6Rcp0OwxYsT/+50CO32yZ9inA7BFlNP/8npEGzR8Pc/d+jx/3bjDNuONXDHm7Ydq6PQJgAAwOVoEwAAYOK2NgHJAAAAZi5LBmgTAADgclQGAAAwoU0AAIDLkQwAAOBybksGWDMAAIDLURkAAMDM8DgdwUVFMgAAgAltAgAA4CpUBgAAMDFCtAkAAHA12gQAAMBVqAwAAGBicDcBAADuRpsAAAC4CpUBAABMuJsAAACXMwynI7i4SAYAADBxW2WANQMAALicI5WBQCCgQCAQNve1EVQPT6QT4QAAEIbKwAUcOHBAGzdu1MGDByVJBw8e1KJFizR//nz97ne/a9Ux/H6/YmJiwkZp/QdWQwEAoEMYhn2jK7CUDJSUlGjixIm69957NWnSJJWUlGj69Ok6cuSIjh8/ruuvv75VCUF+fr7OnDkTNmb2/3abLwIAALSdpWRg2bJluu+++/S3v/1NGzdu1O23366cnBzt3r1bpaWluu+++7RixYoLHsfr9ap///5hgxYBAKCzMEIe24ZVhYWFSkxMVHR0tNLS0lRRUfGN+15zzTXyeDzNxo033mjpnJaSgQ8++EB33XWXJOmWW27R2bNn9YMf/KBp+x133KE//elPlgIAAKCzMQyPbcOKrVu3yufzqaCgQPv27VNycrIyMzN16tSpFvd/+eWXdfLkyaaxf/9+RUZG6uabb7Z0XstrBjyef1xYRESEoqOjFRMT07StX79+OnPmjNVDAgAASatWrVJOTo6ys7OVlJSkoqIi9e7dW8XFxS3uf+mllyo+Pr5p7N69W7179+7YZCAxMVEfffRR0+fy8nINHz686XN1dbUSEhIsBQAAQGdjhOwbgUBA9fX1YcN8R50kNTY2qrKyUhkZGU1zERERysjIUHl5eavi3rBhg2699Vb16dPH0vVaSgYWLVqkYDDY9HncuHHq0eOfdye+/vrruu666ywFAABAZxMyPLaNlu6g8/v9zc5ZV1enYDCouLi4sPm4uDjV1NRcMOaKigrt379fCxYssHy9lp4zsHDhwvNuX758ueUAAADozvLz8+Xz+cLmvF6v7efZsGGDxo8fr8mTJ1v+Lo8jBgDAxOrCv/Pxer2t+vGPjY1VZGSkamtrw+Zra2sVHx9/3u82NDRoy5YtWrZsWZti5HHEAACYOHFrYVRUlFJSUlRaWto0FwqFVFpaqvT09PN+97/+678UCAR05513tul6qQwAAGDi1JMDfT6fsrKylJqaqsmTJ2v16tVqaGhQdna2JGnevHkaMmRIszUHGzZs0Jw5czRw4MA2nZdkAACATmLu3Lk6ffq0li5dqpqaGk2cOFElJSVNiwqrq6sVERFe1D906JDeeustvfHGG20+L8kAAAAmTr6oKC8vT3l5eS1uKysrazY3evRoGe0sZZAMAABgErJxAWFXwAJCAABcjsoAAAAmdt5a2BWQDAAAYOLU3QROoU0AAIDLURkAAMDEbQsISQYAADBx25oB2gQAALgclQEAAEzctoCQZAAAABPWDDikNiLodAjtVhQ87HQItpjd50qnQ7DFE5NynQ6h3X7zXqHTIdji/tQHnQ6h3a4OdI+u6nsjxjgdQpfAmgEAAOAqnaYyAABAZ0GbAAAAl3PZ+kHaBAAAuB2VAQAATGgTAADgctxNAAAAXIXKAAAAJiGnA7jISAYAADAxRJsAAAC4CJUBAABMQi570ADJAAAAJiGXtQlIBgAAMGHNAAAAcBUqAwAAmHBrIQAALkebAAAAuAqVAQAATGgTAADgcm5LBmgTAADgcrZUBgzDkMfjrsUWAIDuiwWEbeD1enXgwAE7DgUAgONCHvtGV2CpMuDz+VqcDwaDWrFihQYOHChJWrVq1XmPEwgEFAgEwua+NoLq4Ym0Eg4AALCBpWRg9erVSk5O1oABA8LmDcPQgQMH1KdPn1a1C/x+vx577LGwuSkx39bUAeOthAMAQIfg3QTnsXz5cq1bt04rV67Udddd1zTfs2dPbdq0SUlJSa06Tn5+frMqw6PjF1gJBQCADuOylxZaSwYeeOABzZw5U3feeadmz54tv9+vnj17Wj6p1+uV1+sND4QWAQCgk+DWwgu46qqrVFlZqdOnTys1NVX79+/nTgIAALqwNt1a2LdvX23evFlbtmxRRkaGgsGg3XEBAOCYkMv+yG3XcwZuvfVWXX311aqsrNSIESPsigkAAEexZsCioUOHaujQoXbEAgAAHMC7CQAAMHHbAkKSAQAATLrKkwPtwouKAABwOSoDAACYuO0JhFQGAAAwMWwcVhUWFioxMVHR0dFKS0tTRUXFeff//PPPlZubq4SEBHm9Xo0aNUo7d+60dE4qAwAAdBJbt26Vz+dTUVGR0tLStHr1amVmZurQoUMaNGhQs/0bGxv13e9+V4MGDdJLL72kIUOG6Pjx483eIXQhJAMAAJg4tYBw1apVysnJUXZ2tiSpqKhIO3bsUHFxsR544IFm+xcXF+vTTz/V22+/3fR6gMTERMvnpU0AAIBJyMYRCARUX18fNgKBQLNzNjY2qrKyUhkZGU1zERERysjIUHl5eYtxvvrqq0pPT1dubq7i4uI0btw4LV++3PKTgUkGAAAwsXPNgN/vV0xMTNjw+/3NzllXV6dgMKi4uLiw+bi4ONXU1LQY59GjR/XSSy8pGAxq586deuSRR7Ry5Ur9+7//u6XrpU0AAEAHys/Pl8/nC5szv7m3rUKhkAYNGqR169YpMjJSKSkpOnHihB5//HEVFBS0+jgkAwAAmNi5ZsDr9bbqxz82NlaRkZGqra0Nm6+trVV8fHyL30lISFDPnj0VGRnZNDd27FjV1NSosbFRUVFRrYqRNgEAACZ2rhloraioKKWkpKi0tPSfcYRCKi0tVXp6eovfmTp1qo4cOaJQ6J9nOnz4sBISElqdCEgkAwAAdBo+n0/r16/X5s2bdeDAAS1atEgNDQ1NdxfMmzdP+fn5TfsvWrRIn376qRYvXqzDhw9rx44dWr58uXJzcy2dlzYBAAAmTr2oaO7cuTp9+rSWLl2qmpoaTZw4USUlJU2LCqurqxUR8c+/44cNG6Zdu3ZpyZIlmjBhgoYMGaLFixfr/vvvt3RekgEAAEwMB59GnJeXp7y8vBa3lZWVNZtLT0/XO++8065z0iYAAMDlOk1l4JFZnzsdQrstjUl0OgRbRGZe73QItvj7ys1Oh9Bu96c+6HQItvjl3uVOh9BuX23q+tcgSU880z1ewLO0g4/vVJvAKZ0mGQAAoLNwWzJAmwAAAJejMgAAgElbXj3clZEMAABg4tRbC51CMgAAgAlrBgAAgKtQGQAAwMRtlQGSAQAATNy2gJA2AQAALkdlAAAAE+4mAADA5dy2ZoA2AQAALkdlAAAAE7ctICQZAADAJOSydIA2AQAALkdlAAAAE7ctICQZAADAxF1NApIBAACacVtlgDUDAAC4HJUBAABMeAIhAAAu57ZbC9uVDDQ0NGjbtm06cuSIEhISdNttt2ngwIEX/F4gEFAgEAibawyG5I2kawEAwMVm6dc3KSlJn376qSTpk08+0bhx47RkyRLt3r1bBQUFSkpK0rFjxy54HL/fr5iYmLCxct/HbbsCAABsZtg4ugJLycDBgwf19ddfS5Ly8/M1ePBgHT9+XBUVFTp+/LgmTJighx566ILHyc/P15kzZ8LGv33nirZdAQAANgvZOLqCNrcJysvLVVRUpJiYGElS37599dhjj+nWW2+94He9Xq+8Xm/Y3FlaBAAAOMJyMuDx/GOJ5blz55SQkBC2bciQITp9+rQ9kQEA4BAWEF7AzJkz1aNHD9XX1+vQoUMaN25c07bjx4+3agEhAACdmbtSAYvJQEFBQdjnvn37hn1+7bXXNG3atPZHBQAALpp2JQNmjz/+eLuCAQCgM+gqC//swkOHAAAwYc0AAAAu565UgBcVAQDgelQGAAAwYc0AAAAuZ7isUUCbAAAAl6MyAACACW0CAABczm23FtImAADA5agMAABg4q66AMkAAADN0CYAAACuQmUAAAAT7iYAAMDleOgQAAAuF7JxWFVYWKjExERFR0crLS1NFRUV37jvpk2b5PF4wkZ0dLTlc5IMAADQSWzdulU+n08FBQXat2+fkpOTlZmZqVOnTn3jd/r376+TJ082jePHj1s+b6dpE5zc0/VLMrWfeZwOwRaj3tnsdAi2OHk0xukQ2u3qQPfI17/atNzpENqt510POh2CLdIfz3c6hC7BqTbBqlWrlJOTo+zsbElSUVGRduzYoeLiYj3wwAMtfsfj8Sg+Pr5d5+0e/08DAICN7GwTBAIB1dfXh41AINDsnI2NjaqsrFRGRkbTXEREhDIyMlReXv6NsX7xxRcaMWKEhg0bpptuukkffPCB5eslGQAAoAP5/X7FxMSEDb/f32y/uro6BYNBxcXFhc3HxcWppqamxWOPHj1axcXF+vWvf60XXnhBoVBIU6ZM0V/+8hdLMXaaNgEAAJ1FyLCvTZCfny+fzxc25/V6bTl2enq60tPTmz5PmTJFY8eO1bPPPquf//znrT4OyQAAACZ2rhjwer2t+vGPjY1VZGSkamtrw+Zra2tbvSagZ8+emjRpko4cOWIpRtoEAAB0AlFRUUpJSVFpaWnTXCgUUmlpadhf/+cTDAb1/vvvKyEhwdK5qQwAAGDi1LsJfD6fsrKylJqaqsmTJ2v16tVqaGhourtg3rx5GjJkSNOag2XLlulf/uVfNHLkSH3++ed6/PHHdfz4cS1YsMDSeUkGAAAwcerWwrlz5+r06dNaunSpampqNHHiRJWUlDQtKqyurlZExD+L+p999plycnJUU1OjSy65RCkpKXr77beVlJRk6bwkAwAAdCJ5eXnKy8trcVtZWVnY5yeffFJPPvlku89JMgAAgAkvKgIAwOWcWjPgFJIBAABMeGshAABwFSoDAACYsGYAAACXM2x8HHFXQJsAAACXozIAAIAJdxMAAOByblszQJsAAACXozIAAICJ254zQDIAAICJ29YM0CYAAMDlLCUD+/bt07Fjx5o+P//885o6daqGDRumq6++Wlu2bGnVcQKBgOrr68NGY8htyzUAAJ2VYRi2ja7AUjKQnZ2tjz/+WJL03HPP6Sc/+YlSU1P10EMP6aqrrlJOTo6Ki4sveBy/36+YmJiw8ezfjrbtCgAAsFnIxtEVWFoz8NFHH+nKK6+UJK1Zs0ZPPfWUcnJymrZfddVV+sUvfqH58+ef9zj5+fny+Xxhc9VX/cBKKAAAdBgWEJ5H7969VVdXpxEjRujEiROaPHly2Pa0tLSwNsI38Xq98nq9YXNRESxfAADACZZ+gW+44QatXbtWkjRjxgy99NJLYdu3bdumkSNH2hcdAAAOCMmwbXQFlioDv/zlLzV16lTNmDFDqampWrlypcrKyjR27FgdOnRI77zzjrZv395RsQIAcFF0lYV/drFUGRg8eLDee+89paenq6SkRIZhqKKiQm+88YaGDh2q//3f/9WsWbM6KlYAANABLD90aMCAAVqxYoVWrFjREfEAAOC4rlLetwtPIAQAwMRtdxOwhB8AAJejMgAAgEnIZQsISQYAADBxVypAmwAAANejMgAAgAl3EwAA4HIkAwAAuBxPIAQAAK5CZQAAABPaBAAAuBxPIAQAAK5CZQAAABO3LSAkGQAAwMRtawZoEwAA4HJUBgAAMKFN4JB+AwNOh9BuSR+/73QItth2YIbTIdjih/VvOx1Cu703YozTIdjiiWc8TofQbumP5zsdgi2u+cDvdAhdAm0CAADgKp2mMgAAQGfhtucMkAwAAGASYs0AAADu5rbKAGsGAABwOSoDAACY0CYAAMDlaBMAAADHFBYWKjExUdHR0UpLS1NFRUWrvrdlyxZ5PB7NmTPH8jlJBgAAMAkZhm3Diq1bt8rn86mgoED79u1TcnKyMjMzderUqfN+789//rPuvfdeTZs2rU3XSzIAAICJYeO/AoGA6uvrw0Yg0PJTd1etWqWcnBxlZ2crKSlJRUVF6t27t4qLi78x1mAwqDvuuEOPPfaYLr/88jZdL8kAAAAdyO/3KyYmJmz4/c0fC93Y2KjKykplZGQ0zUVERCgjI0Pl5eXfePxly5Zp0KBB+tGPftTmGFlACACAiZ13E+Tn58vn84XNeb3eZvvV1dUpGAwqLi4ubD4uLk4HDx5s8dhvvfWWNmzYoKqqqnbFSDIAAICJnXcTeL3eFn/82+vs2bP64Q9/qPXr1ys2NrZdxyIZAACgE4iNjVVkZKRqa2vD5mtraxUfH99s/48//lh//vOfNXv27Ka5UCgkSerRo4cOHTqkK664olXnZs0AAAAmhhGybbRWVFSUUlJSVFpa2jQXCoVUWlqq9PT0ZvuPGTNG77//vqqqqprG9773PV177bWqqqrSsGHDWn1uKgMAAJiEHHrokM/nU1ZWllJTUzV58mStXr1aDQ0Nys7OliTNmzdPQ4YMkd/vV3R0tMaNGxf2/QEDBkhSs/kLIRkAAMDEcOhxxHPnztXp06e1dOlS1dTUaOLEiSopKWlaVFhdXa2ICPuL+iQDAAB0Inl5ecrLy2txW1lZ2Xm/u2nTpjadk2QAAAATp9oETiEZAADAxKk2gVO4mwAAAJezlAzcfffd+p//+Z92n7TF5zSHWn/7BQAAHcmpFxU5xVIyUFhYqGuuuUajRo3SL3/5S9XU1LTppC09p/mZvxxv07EAALCbnS8q6gostwneeOMNzZo1S0888YSGDx+um266Sb/5zW+annrUGvn5+Tpz5kzYuHvoCKuhAAAAG1hOBsaPH6/Vq1frr3/9q1544QUFAgHNmTNHw4YN00MPPaQjR45c8Bher1f9+/cPG94OuG8SAIC2MAzDttEVtPkXuGfPnrrllltUUlKio0ePKicnR//xH/+h0aNH2xkfAAAXXUiGbaMrsOXP8eHDh+vRRx/VsWPHVFJSYschAQDARWLpOQMjRoxQZGTkN273eDz67ne/2+6gAABwUlcp79vFUjJw7NixjooDAIBOo6vcEmgXnkAIAICJ2yoDLOEHAMDlqAwAAGDSVe4CsAvJAAAAJrQJAACAq1AZAADAhLsJAABwua7ygiG70CYAAMDlqAwAAGBCmwAAAJfjbgIAAOAqVAYAADBx2wJCkgEAAEzc1iYgGQAAwMRtyQBrBgAAcDkqAwAAmLirLiDJcIlz584ZBQUFxrlz55wOpc26wzUYRve4ju5wDYbBdXQm3eEaDKP7XIfbeAzDHY2R+vp6xcTE6MyZM+rfv7/T4bRJd7gGqXtcR3e4Bonr6Ey6wzVI3ec63IY1AwAAuBzJAAAALkcyAACAy7kmGfB6vSooKJDX63U6lDbrDtcgdY/r6A7XIHEdnUl3uAap+1yH27hmASEAAGiZayoDAACgZSQDAAC4HMkAAAAuRzIAAIDLkQwAAOByrkgGCgsLlZiYqOjoaKWlpamiosLpkCz5/e9/r9mzZ2vw4MHyeDx65ZVXnA7JMr/fr6uuukr9+vXToEGDNGfOHB06dMjpsCxbu3atJkyYoP79+6t///5KT0/X66+/7nRY7bJixQp5PB7dc889TodiyaOPPiqPxxM2xowZ43RYbXLixAndeeedGjhwoHr16qXx48dr7969TofVaomJic3+s/B4PMrNzXU6NLRSt08Gtm7dKp/Pp4KCAu3bt0/JycnKzMzUqVOnnA6t1RoaGpScnKzCwkKnQ2mzN998U7m5uXrnnXe0e/duffXVV7r++uvV0NDgdGiWDB06VCtWrFBlZaX27t2r6667TjfddJM++OADp0Nrk3fffVfPPvusJkyY4HQobfLtb39bJ0+ebBpvvfWW0yFZ9tlnn2nq1Knq2bOnXn/9dX344YdauXKlLrnkEqdDa7V333037D+H3bt3S5JuvvlmhyNDqzn7nqSON3nyZCM3N7fpczAYNAYPHmz4/X4Ho2o7Scb27dudDqPdTp06ZUgy3nzzTadDabdLLrnEeO6555wOw7KzZ88aV155pbF7925jxowZxuLFi50OyZKCggIjOTnZ6TDa7f777zeuvvpqp8Ow1eLFi40rrrjCCIVCToeCVurWlYHGxkZVVlYqIyOjaS4iIkIZGRkqLy93MDKcOXNGknTppZc6HEnbBYNBbdmyRQ0NDUpPT3c6HMtyc3N14403hv3vo6v56KOPNHjwYF1++eW64447VF1d7XRIlr366qtKTU3VzTffrEGDBmnSpElav36902G1WWNjo1544QXNnz9fHo/H6XDQSt06Gairq1MwGFRcXFzYfFxcnGpqahyKCqFQSPfcc4+mTp2qcePGOR2OZe+//7769u0rr9erhQsXavv27UpKSnI6LEu2bNmiffv2ye/3Ox1Km6WlpWnTpk0qKSnR2rVrdezYMU2bNk1nz551OjRLjh49qrVr1+rKK6/Url27tGjRIv30pz/V5s2bnQ6tTV555RV9/vnnuuuuu5wOBRb0cDoAuE9ubq7279/fJfu7kjR69GhVVVXpzJkzeumll5SVlaU333yzyyQEn3zyiRYvXqzdu3crOjra6XDa7IYbbmj69xMmTFBaWppGjBihbdu26Uc/+pGDkVkTCoWUmpqq5cuXS5ImTZqk/fv3q6ioSFlZWQ5HZ92GDRt0ww03aPDgwU6HAgu6dWUgNjZWkZGRqq2tDZuvra1VfHy8Q1G5W15enn7zm99oz549Gjp0qNPhtElUVJRGjhyplJQU+f1+JScn66mnnnI6rFarrKzUqVOn9J3vfEc9evRQjx499Oabb+rpp59Wjx49FAwGnQ6xTQYMGKBRo0bpyJEjTodiSUJCQrNEcuzYsV2y5XH8+HH99re/1YIFC5wOBRZ162QgKipKKSkpKi0tbZoLhUIqLS3tkj3erswwDOXl5Wn79u363e9+p29961tOh2SbUCikQCDgdBitNnPmTL3//vuqqqpqGqmpqbrjjjtUVVWlyMhIp0Nsky+++EIff/yxEhISnA7FkqlTpza7zfbw4cMaMWKEQxG13caNGzVo0CDdeOONTocCi7p9m8Dn8ykrK0upqamaPHmyVq9erYaGBmVnZzsdWqt98cUXYX/tHDt2TFVVVbr00ks1fPhwByNrvdzcXL344ov69a9/rX79+jWt2YiJiVGvXr0cjq718vPzdcMNN2j48OE6e/asXnzxRZWVlWnXrl1Oh9Zq/fr1a7ZWo0+fPho4cGCXWsNx7733avbs2RoxYoT++te/qqCgQJGRkbrtttucDs2SJUuWaMqUKVq+fLluueUWVVRUaN26dVq3bp3ToVkSCoW0ceNGZWVlqUePbv/T0v04fTvDxfDMM88Yw4cPN6KioozJkycb77zzjtMhWbJnzx5DUrORlZXldGit1lL8koyNGzc6HZol8+fPN0aMGGFERUUZl112mTFz5kzjjTfecDqsduuKtxbOnTvXSEhIMKKioowhQ4YYc+fONY4cOeJ0WG3y2muvGePGjTO8Xq8xZswYY926dU6HZNmuXbsMScahQ4ecDgVt4DEMw3AmDQEAAJ1Bt14zAAAALoxkAAAAlyMZAADA5UgGAABwOZIBAABcjmQAAACXIxkAAMDlSAYAAHA5kgEAAFyOZAAAAJcjGQAAwOX+HyfIXlmMAhzFAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir_truth = './results_HCP_202311_no_mean/HMM_ICA_25_state_8/'\n", + "covariances_truth = np.load(f'{save_dir_truth}state_covariances.npy')\n", + "correlations_truth = np.load(f'{save_dir_truth}state_correlations.npy')\n", + "print(f'covariances ground truth shape: {covariances_truth.shape}')\n", + "print(f'correlations_truth shape: {correlations_truth.shape}')\n", + "riemannian_truth = twopair_riemannian_distance(covariances_truth,covariances_truth)\n", + "fisher_z_truth = twopair_fisher_z_transformed_correlation(correlations_truth,correlations_truth)\n", + "seaborn.heatmap(riemannian_truth)\n", + "plt.show()\n", + "seaborn.heatmap(fisher_z_truth)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "71c75117", + "metadata": {}, + "source": [ + "then read the model fitting (HMM_ICA_25_state_8)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "a4866a89", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir_fit ='./results_simulation_202311_toy_9/HMM_ICA_25_state_8/'" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "c200922e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "covariances fit shape: (8, 25, 25)\n", + "correlations fit shape: (8, 25, 25)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 681.72it/s]\n", + "Compute twopair Fisher-z transformated correlation: 100%|█| 8/8 [00:00<00:00, 50\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covariances_fit = np.load(f'{save_dir_fit}state_covariances.npy')\n", + "correlations_fit = np.load(f'{save_dir_fit}state_correlations.npy')\n", + "print(f'covariances fit shape: {covariances_fit.shape}')\n", + "print(f'correlations fit shape: {correlations_fit.shape}')\n", + "riemannian_fit = twopair_riemannian_distance(covariances_fit,covariances_fit)\n", + "fisher_z_fit = twopair_fisher_z_transformed_correlation(correlations_fit,correlations_fit)\n", + "seaborn.heatmap(riemannian_fit)\n", + "plt.show()\n", + "seaborn.heatmap(fisher_z_fit)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d86622a8", + "metadata": {}, + "source": [ + "At least, the within-model distance/similarity distribution are similar.\n", + "Now try to pair them!" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "73a115f7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 720.35it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.163 3.347 3.119 4.247 3.421 3.345 3.327 4.34 ]\n", + " [3.356 1.226 3.121 4.355 3.152 3.706 3.545 3.588]\n", + " [3.081 3.12 1.264 4.543 3.225 3.639 3.523 4.51 ]\n", + " [4.222 4.341 4.39 1.298 4.625 3.869 4.339 3.702]\n", + " [3.485 3.201 3.378 4.678 1.366 4.053 3.962 3.515]\n", + " [3.327 3.76 3.614 4.184 4.12 1.155 3.413 4.052]\n", + " [3.42 3.607 3.412 4.615 3.867 3.519 1.252 4.134]\n", + " [4.27 3.691 4.459 3.622 3.385 3.847 3.892 1.439]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "riemannian_fit_truth = twopair_riemannian_distance(covariances_fit,covariances_truth)\n", + "_,riemannian_fit_truth_reorder = hungarian_pair(riemannian_fit_truth,distance=True)\n", + "print(riemannian_fit_truth_reorder)\n", + "seaborn.heatmap(riemannian_fit_truth_reorder)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "01cf633b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loss_history = np.load(f'{save_dir_fit}loss_history.npy')\n", + "plt.plot(loss_history)" + ] + }, + { + "cell_type": "markdown", + "id": "7d1a2b15", + "metadata": {}, + "source": [ + "I should say the model recover the majority of states quite well! So we can confirm that the model training on simulation is successful." + ] + }, + { + "cell_type": "markdown", + "id": "8091a029", + "metadata": {}, + "source": [ + "### Step 2: Compare across different number of states" + ] + }, + { + "cell_type": "markdown", + "id": "1417b960", + "metadata": {}, + "source": [ + "#### Question 1: Has training converged?" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "id": "e19a4356", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "238d7827", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XXniB8PBw2rdvT1ZWVo52N9s+uzfLw8OD9u3b5/nKHoo9d+4c5cqVy9dry5YtOY6TlZVFly5dOHr0KKtXryYsLOyur5UoOiRpEv9qGo2Gjh07snLlyhxDLZcvX2bhwoW0bNnSNXT0z2EbLy8vqlatitlsBpzF9P75D/cDDzyAt7e3q01hsdvtuYaAAgMDKV++fI5jm0ymXO0AHn/8ceLi4nLcAWWz2Zg6dSpeXl60adMm37G88MIL/Pbbb0yePJnSpUvz2GOP3Xab5s2b06lTJ2bOnMmKFStyrbdYLAwfPhxwftjWr1+fuXPnkpSU5GoTGxvLb7/9xuOPP57vWPNr3rx5pKamur5funQply5dcp1bdu/Ljb0tiqLkKg1wp/75M6dWq13Dwdnv6+OPP86OHTvYunWrq116ejpff/01lStXLnIf1vf6s5rXtd6+fXuO8wdcdyTe+DOSfay2bdsyY8YMLl26lGv/N5ZPuNs5TXa7nZ49e7J161aWLFlC8+bNb3dZRDEhc5rEv8K3337LmjVrci0fPHgwH3/8MdHR0bRs2ZI33ngDrVbLjBkzMJvNTJgwwdU2LCyMtm3b0rBhQ/z9/fnzzz9dt00DHD16lEcffZQePXoQFhaGVqtl+fLlXL58mf/85z+Fen6pqalUrFiRZ555hnr16uHl5cXatWvZuXNnjl6uhg0bsmjRIoYNG0bjxo3x8vKiS5cu9O/fnxkzZtC3b1927dpF5cqVWbp0KX/88QeTJ0/O1wTtbL169eLtt99m+fLlvP766/ku2Dhv3jw6duzI008/TZcuXXj00UcxmUwcO3aMH374gUuXLrlqNX322Wc89thjNG/enH79+rlKDvj6+hbK8/j8/f1p2bIlL730EpcvX2by5MlUrVqVV199FXAOLz7wwAMMHz6cCxcu4OPjw7Jly+76hoBsr7zyCteuXeORRx6hYsWKnDlzhqlTp1K/fn3XnKV3332X77//nscee4xBgwbh7+/P3LlzOXXqFMuWLcvXMOA/bdy4kXbt2jFy5MgCv573+rP6xBNP8OOPP9KtWzc6d+7MqVOnmD59OmFhYaSlpbm2NxgMhIWFsWjRIh588EH8/f2pXbs2tWvX5quvvqJly5bUqVOHV199lSpVqnD58mW2bt3K+fPn2bt3L8Bdz2l68803WbVqFV26dOHatWu5elBvd2OEKMLcdNeeEPdF9m3SN3udO3dOURRF2b17txIeHq54eXkpRqNRadeunbJly5Yc+/r444+VJk2aKH5+forBYFBq1KihfPLJJ4rFYlEURVESEhKUAQMGKDVq1FBMJpPi6+urNG3aVFm8ePFt48y+NT4+Pj7P9ZUqVbplyQGz2ay89dZbSr169RRvb2/FZDIp9erVU/7v//4vxzZpaWlKr169FD8/PwXIcUv35cuXlZdeekkpU6aM4unpqdSpU0eZPXt2ju2zb8G+XVmFxx9/XAFyXcPbycjIUCZOnKg0btxY8fLyUjw9PZVq1aopAwcOVI4fP56j7dq1a5WHH35YMRgMio+Pj9KlSxfl4MGDOdrc7Lr26dNHMZlMuY7fpk0bpVatWq7vs2/T//7775X33ntPCQwMVAwGg9K5c+ccZQQURVEOHjyotG/fXvHy8lLKlCmjvPrqq8revXsVIMd1vNmxs9fd+J4sXbpU6dixoxIYGKh4enoqISEhymuvvaZcunQpx3YnTpxQnnnmGcXPz0/R6/VKkyZNlNWrV+dok30uS5YsybE8+z29McaffvpJAZTp06e7lt2sfEf2fm93y322e/1ZdTgcyqeffqpUqlRJ0el0ykMPPaSsXr0617VTFEXZsmWL0rBhQ8XT0zNX+YETJ04oL774ohIUFKR4eHgoFSpUUJ544gll6dKl+TqPW2nTps0t/90RxZdKUe5i1qMQQtxCt27d2L9/f74qTRdl2T0uS5Ys4ZlnnnF3OPfN22+/zffff8/x48fR6XTuDkeIIkPmNAkhCtSlS5eIiorKd3VvUfRs2LCBjz76SBImIf5B5jQJIQrEqVOn+OOPP5g5cyYeHh689tpr7g5J3KWdO3fe1XZpaWk55hXlJSAgQGoViWJLkiYhRIGIiYnhpZdeIiQkhLlz5+a7oKcoOSZOnMjo0aNv2ebUqVP5eg6jEEWRzGkSQghRIE6ePHnbx7G0bNkyXxXuhSiKJGkSQgghhMgHmQguhBBCCJEPMqepgDgcDi5evIi3t/dNy/cLIYQQomhRFIXU1FTKly9/22KwkjQVkIsXL+Z4RpkQQgghio9z585RsWLFW7aRpKmA3PiAx+xnlQkhhBCiaEtJSSE4ODhfj4uSpKmAZA/J+fj4SNIkhBBCFDP5mVojE8GFEEIIIfJBkiYhhBBCiHyQpEkIIYQQIh9kTpMQQghxB+x2O1ar1d1hiHzy8PAosOcdStIkhBBC5IOiKMTFxZGUlOTuUMQd8vPzIygo6J7rKErSJIQQQuRDdsIUGBiI0WiUQsbFgKIoZGRkcOXKFQDKlSt3T/uTpEkIIYS4Dbvd7kqYSpcu7e5wxB0wGAwAXLlyhcDAwHsaqpOJ4EIIIcRtZM9hMhqNbo5E3I3s9+1e56JJ0iSEEELkkwzJFU8F9b5J0iSEEEIIkQ+SNAkhhBBC5IMkTUIIIUQJ1bdvX1QqFSqVCg8PD0JDQ3n77bfJyspyd2i5bNy4EZVKdduSDllZWfTt25c6deqg1Wrp2rXrfYkP5O65os+aBRkJgAp8K7g7GiGEEMVMp06dmD17NlarlV27dtGnTx9UKhXjx493d2h3xW63YzAYGDRoEMuWLbuvx5aepqIudhl8Xgt+GuTuSIQQQhRDOp2OoKAggoOD6dq1K+3btyc6Otq13uFwEBkZSWhoKAaDgXr16rF06VLX+uweoKioKOrWrYter6dZs2bExsbmOM7mzZtp1aoVBoOB4OBgBg0aRHp6umv9/PnzadSoEd7e3gQFBdGrVy9X/aTTp0/Trl07AEqVKoVKpaJv3755no/JZGLatGm8+uqrBAUFFdRlyhdJmoo6vY/z/1kp7o1DCCFEDoqikGGx3feXoih3HXNsbCxbtmzB09PTtSwyMpJ58+Yxffp0Dhw4wNChQ+nduzcxMTE5tn3rrbeYNGkSO3fuJCAggC5durhu4T9x4gSdOnWie/fu7Nu3j0WLFrF582YiIiJc21utVsaOHcvevXtZsWIFp0+fdiVGwcHBrl6jI0eOcOnSJb744ou7Ps/CIsNzRZ3uetJklqRJCCGKkkyrnbARv9734x4cE47RM/8f36tXr8bLywubzYbZbEatVvPll18CYDab+fTTT1m7di3NmzcHoEqVKmzevJkZM2bQpk0b135GjhxJhw4dAJg7dy4VK1Zk+fLl9OjRg8jISJ5//nmGDBkCQLVq1ZgyZQpt2rRh2rRp6PV6Xn75Zde+qlSpwpQpU2jcuDFpaWl4eXnh7+8PQGBgIH5+fvdyiQqNJE1FnfQ0CSGEuAft2rVj2rRppKen8/nnn6PVaunevTsAx48fJyMjw5UMZbNYLDz00EM5lmUnVQD+/v5Ur16dQ4cOAbB371727dvHggULXG0URcHhcHDq1Clq1qzJrl27GDVqFHv37iUxMRGHwwHA2bNnCQsLK5RzL2iSNBV10tMkhBBFksFDw8Ex4W457p0wmUxUrVoVgG+//ZZ69eoxa9Ys+vXrR1paGgBRUVFUqJDzZiOdTpfvY6SlpfHaa68xaFDu+bchISGkp6cTHh5OeHg4CxYsICAggLNnzxIeHo7FYrmj83EnSZqKOr2v8/+WNHDYQX33z8wRQghRcFQq1R0NkxUFarWa999/n2HDhtGrVy/CwsLQ6XScPXs2x1BcXrZt20ZISAgAiYmJHD16lJo1awLQoEEDDh486ErO/mn//v1cvXqVcePGERwcDMCff/6Zo032PCu73X5P51iYZCJ4Uafz/vtrc6r74hBCCFEiPPvss2g0Gr766iu8vb0ZPnw4Q4cOZe7cuZw4cYLdu3czdepU5s6dm2O7MWPGsG7dOmJjY+nbty9lypRx1Uh655132LJlCxEREezZs4djx46xcuVK10TwkJAQPD09mTp1KidPnmTVqlWMHTs2x/4rVaqESqVi9erVxMfHu3rB8nLw4EH27NnDtWvXSE5OZs+ePezZs6dAr1OeFFEgkpOTFUBJTk4u0P1uPhavmEeWVpSRPoqSeKZA9y2EECJ/MjMzlYMHDyqZmZnuDuWO9OnTR3nqqadyLY+MjFQCAgKUtLQ0xeFwKJMnT1aqV6+ueHh4KAEBAUp4eLgSExOjKIqibNiwQQGUn376SalVq5bi6empNGnSRNm7d2+Ofe7YsUPp0KGD4uXlpZhMJqVu3brKJ5984lq/cOFCpXLlyopOp1OaN2+urFq1SgGUv/76y9VmzJgxSlBQkKJSqZQ+ffrc9LwqVaqkALleN3Or9+9OPr9VinIP9y4Kl5SUFHx9fUlOTsbHx6fA9vvT3os0+7EpAaoU+O8fEFS7wPYthBAif7Kysjh16hShoaHo9Xp3h3Nfbdy4kXbt2pGYmFhk72q7nVu9f3fy+S3Dc0Wct15LqmJ0fiOTwYUQQgi3kaSpiPPWa0nletIkZQeEEEIItyle0/7/hbz1HlxRDM5vpKdJCCHEfda2bdt7qkJekkhPUxHnpfu7p0nJSnZzNEIIIcS/lyRNRZzXDXOabJnS0ySEEEK4iyRNRZyX5989Tdb0JPcGI4QQQvyLSdJUxKnVKiwaEwDWjCT3BiOEEEL8i8lE8CLuyLUjbC19hbIWE+0yZU6TEEII4S6SNBVxJ5JO8JfvaTwzTbSVkgNCCCGE28jwXBHn5ekFQKpaJXWahBBCCDeSpKmI8/Z0PrA3Va1GbZEH9gohhMi/vn37olKpUKlUeHh4EBoayttvv01WVpa7Q8tl48aNqFQqkpKSbtvuqaeeoly5cphMJurXr8+CBQvuS4wyPFfEeXk4e5rS1Gq0VkmahBBC3JlOnToxe/ZsrFYru3btok+fPqhUKsaPH+/u0O7Kli1bqFu3Lu+88w5ly5Zl9erVvPjii/j6+vLEE08U6rGlp6mIu7GnSWNLc3M0QgghihudTkdQUBDBwcF07dqV9u3bEx0d7VrvcDiIjIwkNDQUg8FAvXr1WLp0qWt9dg9QVFQUdevWRa/X06xZM2JjY3McZ/PmzbRq1QqDwUBwcDCDBg0iPT3dtX7+/Pk0atQIb29vgoKC6NWrF1euXAHg9OnTtGvXDoBSpUqhUqno27dvnufz/vvvM3bsWFq0aMEDDzzA4MGD6dSpEz/++GNBXbKbkqSpiMtOmmwqFYo9HaSUvRBCFA2KApb0+/+6h8+B2NhYtmzZgqenp2tZZGQk8+bNY/r06Rw4cIChQ4fSu3dvYmJicmz71ltvMWnSJHbu3ElAQABdunTBarUCcOLECTp16kT37t3Zt28fixYtYvPmzURERLi2t1qtjB07lr1797JixQpOnz7tSoyCg4NZtmwZAEeOHOHSpUt88cUX+T6v5ORk/P397/ay5JsMzxVxRq0RFSoUFNLVYLSkgc7b3WEJIYSwZsCn5e//cd+/CJ6mfDdfvXo1Xl5e2Gw2zGYzarWaL7/8EgCz2cynn37K2rVrad68OQBVqlRh8+bNzJgxgzZt2rj2M3LkSDp06ADA3LlzqVixIsuXL6dHjx5ERkby/PPPM2TIEACqVavGlClTaNOmDdOmTUOv1/Pyyy+79lWlShWmTJlC48aNSUtLw8vLy5X0BAYG4ufnl+/zW7x4MTt37mTGjBn53uZuSdJUxKlUKjzVRsyOdFLVagKyUiRpEkIIkW/t2rVj2rRppKen8/nnn6PVaunevTsAx48fJyMjw5UMZbNYLDz00EM5lmUnVQD+/v5Ur16dQ4cOAbB371727duXY0K2oig4HA5OnTpFzZo12bVrF6NGjWLv3r0kJibicDgAOHv2LGFhYXd1bhs2bOCll17im2++oVatWne1jzshSVMxoNeYMDvSSVOrwZwCVHB3SEIIITyMzl4fdxz3DphMJqpWrQrAt99+S7169Zg1axb9+vUjLc05VzYqKooKFXJ+tuh0unwfIy0tjddee41BgwblWhcSEkJ6ejrh4eGEh4ezYMECAgICOHv2LOHh4Vgsljs6n2wxMTF06dKFzz//nBdffPGu9nGnJGkqBoxaL5KtV5xJk9RqEkKIokGluqNhsqJArVbz/vvvM2zYMHr16kVYWBg6nY6zZ8/mGIrLy7Zt2wgJCQEgMTGRo0ePUrNmTQAaNGjAwYMHXcnZP+3fv5+rV68ybtw4goODAfjzzz9ztMmeZ2W32297Hhs3buSJJ55g/Pjx9O/f/7btC4pMBC8GsssOpLh6moQQQoi78+yzz6LRaPjqq6/w9vZm+PDhDB06lLlz53LixAl2797N1KlTmTt3bo7txowZw7p164iNjaVv376UKVOGrl27AvDOO++wZcsWIiIi2LNnD8eOHWPlypWuieAhISF4enoydepUTp48yapVqxg7dmyO/VeqVAmVSsXq1auJj4939YL904YNG+jcuTODBg2ie/fuxMXFERcXx7Vr1wr+Yv2DJE3FgNf1O+jS1CpJmoQQQtwTrVZLREQEEyZMID09nbFjx/LRRx8RGRlJzZo16dSpE1FRUYSGhubYbty4cQwePJiGDRsSFxfHTz/95Oodqlu3LjExMRw9epRWrVrx0EMPMWLECMqXd06UDwgIYM6cOSxZsoSwsDDGjRvHxIkTc+y/QoUKjB49mnfffZeyZcvmuPPuRnPnziUjI4PIyEjKlSvnej399NOFcLVyUimK3MNeEFJSUvD19SU5ORkfH58C3XdE9FvEXFzDm1cT6dt6LDR6qUD3L4QQ4taysrI4deoUoaGh6PV6d4dzX23cuJF27dqRmJh4R3e1FSW3ev/u5PNbepqKgVJ655uYqlajyJwmIYQQwi3cmjRFRkbSuHFjvL29CQwMpGvXrhw5csS1/tq1awwcOJDq1atjMBgICQlh0KBBJCcnu9rMmTPH9Vydf76yK41mVzP95ysuLi5HPF999RWVK1dGr9fTtGlTduzYcX8uxC0oFgtBWRpKpSqkqtVYM5LcHZIQQgjxr+TWpCkmJoYBAwawbds2oqOjsVqtdOzY0VV2/eLFi1y8eJGJEycSGxvLnDlzWLNmDf369XPto2fPnly6dCnHKzw8nDZt2hAYGJjjeNlVRrNfN65ftGgRw4YNY+TIkezevZt69eoRHh7uSrzcJXl1FI8MnM9rPztIk6RJCCHEfda2bVsURSm2Q3MFya0lB9asWZPj+zlz5hAYGMiuXbto3bo1tWvXdpVVB3jggQf45JNP6N27NzabDa1Wi8FgwGAwuNrEx8ezfv16Zs2alet4t6oy+r///Y9XX32Vl15yzheaPn06UVFRfPvtt7z77rsFcLZ3R+PjnARuMiukqlXYM5Jvs4UQQgghCkORmtOUPex2q+fHZE/U0mrzzvfmzZuH0WjkmWeeybWufv36lCtXjg4dOvDHH3+4llssFnbt2kX79u1dy9RqNe3bt2fr1q15HsdsNpOSkpLjVRjU3s75TKYsSFOrcWRJ0iSEEEK4Q5FJmhwOB0OGDOHhhx+mdu3aebZJSEhg7NixtyxkNWvWLHr16pWj96lcuXJMnz6dZcuWsWzZMoKDg2nbti27d+927ddut1O2bNkc+ypbtmyueU/ZIiMj8fX1db2yi3UVtOyeJqPZORFcilsKIYQQ7lFkKoIPGDCA2NhYNm/enOf6lJQUOnfuTFhYGKNGjcqzzdatWzl06BDz58/Psbx69epUr17d9X2LFi04ceIEn3/+ea62+fXee+8xbNiwHPEVRuL0z54mlTnvYl9CCCGEKFxFImmKiIhg9erV/P7771SsWDHX+tTUVDp16oS3tzfLly/Hw8Mjz/3MnDmT+vXr07Bhw9ses0mTJq4ErUyZMmg0Gi5fvpyjzeXLlwkKCspze51Od0fP5blbGm9nNXCdDTIdoLFKT5MQQgjhDm4dnlMUhYiICJYvX8769etzVR8FZw9Ox44d8fT0ZNWqVTctKpaWlsbixYtz3Fl3K3v27KFcuXKA83k3DRs2ZN26da71DoeDdevW5XiqszuovbxcXzusajRW6WkSQggh3MGtPU0DBgxg4cKFrFy5Em9vb9f8IV9fXwwGgythysjI4Lvvvssx4TogIACNRuPa16JFi7DZbPTu3TvXcSZPnkxoaCi1atUiKyuLmTNnsn79en777TdXm2HDhtGnTx8aNWpEkyZNmDx5Munp6a676dxFpdGg8vJCSUtDb1FhtadhUBTngyKFEEIIcd+4tadp2rRpJCcn07Zt2xzPj1m0aBEAu3fvZvv27ezfv5+qVavmaHPu3Lkc+5o1axZPP/10niUFLBYLb775JnXq1KFNmzbs3buXtWvX8uijj7ra9OzZk4kTJzJixAjq16/Pnj17WLNmTa7J4e6g8b5ediALMlQKWDPdHJEQQojioG/fvq6Czh4eHoSGhvL222+TlZXl7tByyS5EnZSUdMt2R44coV27dpQtWxa9Xk+VKlX48MMPsVqthR6jW3uabvfYu+yCWvmxZcuWm657++23efvtt2+7j4iIiJs+INCdND4+2C5dwmhWSPNQOx/a62l0d1hCCCGKgU6dOjF79mysViu7du2iT58+qFQqxo8f7+7Q7oqHhwcvvvgiDRo0wM/Pj7179/Lqq6/icDj49NNPC/XYRabkgLi5G3ua0tQqKTsghBAi33Q6HUFBQQQHB9O1a1fat29PdHS0a73D4SAyMpLQ0FAMBgP16tVj6dKlrvXZPUBRUVHUrVsXvV5Ps2bNiI2NzXGczZs306pVKwwGA8HBwQwaNMj1hA+A+fPn06hRI7y9vQkKCqJXr16up26cPn2adu3aAVCqVClUKhV9+/bN83yqVKnCSy+9RL169ahUqRJPPvkkzz//PJs2bSqoS3ZTkjQVA+rrT102mSFFfb2nSQghhFspikKGNeO+v/I7ApOX2NhYtmzZgqenp2tZZGQk8+bNY/r06Rw4cIChQ4fSu3dvYmJicmz71ltvMWnSJHbu3ElAQABdunRxDYmdOHGCTp060b17d/bt28eiRYvYvHlzjtEbq9XK2LFj2bt3LytWrOD06dOuxCg4ONj1BJDsR5598cUX+Tqn48ePs2bNGtq0aXPX1yW/ikTJAXFr2T1Nxuu1miRpEkII98u0ZdJ0YdP7ftztvbZj9Mj/FI3Vq1fj5eWFzWbDbDajVqv58ssvAefTLT799FPWrl3rulu8SpUqbN68mRkzZuRIREaOHEmHDh0AmDt3LhUrVmT58uX06NGDyMhInn/+eYYMGQJAtWrVmDJlCm3atGHatGno9Xpefvll176qVKnClClTaNy4MWlpaXh5ebmeBnKrR57dqEWLFuzevRuz2Uz//v0ZM2ZMvq/J3ZKkqRj4u6dJcSZNMjwnhBAin9q1a8e0adNIT0/n888/R6vV0r17d8DZS5ORkeFKhrJZLBYeeuihHMtuLMHj7+9P9erVOXToEAB79+5l3759LFiwwNVGURQcDgenTp2iZs2a7Nq1i1GjRrF3714SExNxOBwAnD17lrCwsDs+r0WLFpGamsrevXt56623mDhxYr7mL98LSZqKgRvnNKWqVdLTJIQQRYBBa2B7r+1uOe6dMJlMVK1aFYBvv/2WevXqMWvWLPr160damrP2X1RUFBUqVMix3Z0UcE5LS+O1115j0KBBudaFhISQnp5OeHg44eHhLFiwgICAAM6ePUt4eDgWi+WOzidb9lM4wsLCsNvt9O/fnzfffDNHOaKCJklTMaC+YXhOnj8nhBBFg0qluqNhsqJArVbz/vvvM2zYMHr16kVYWBg6nY6zZ8/edk7Qtm3bCAkJASAxMZGjR49Ss2ZNABo0aMDBgwddydk/7d+/n6tXrzJu3DhXsvPnn3/maJM9z8put9/xeTkcDqxWKw6Ho1CTJpkIXgzc+NDeNLUaR2aymyMSQghRXD377LNoNBq++uorvL29GT58OEOHDmXu3LmcOHGC3bt3M3XqVObOnZtjuzFjxrBu3TpiY2Pp27cvZcqUoWvXrgC88847bNmyhYiICPbs2cOxY8dYuXKlayJ4SEgInp6eTJ06lZMnT7Jq1SrGjh2bY/+VKlVCpVKxevVq4uPjXb1g/7RgwQIWL17MoUOHOHnyJIsXL+a9996jZ8+eN33MWkGRpKkYyO5pMpkVUtVqrBmSNAkhhLg7Wq2WiIgIJkyYQHp6OmPHjuWjjz4iMjKSmjVr0qlTJ6KionI92mzcuHEMHjyYhg0bEhcXx08//eTqHapbty4xMTEcPXqUVq1a8dBDDzFixAjKly8POJ/iMWfOHJYsWUJYWBjjxo1j4sSJOfZfoUIFRo8ezbvvvkvZsmVvWjdRq9Uyfvx4mjRpQt26dRk9ejQRERHMnDmzEK5WTirlXu5dFC4pKSn4+vqSnJyMz/WJ2wXh6sU0jkX9Reb8b7A4drLqeSsT/B/B1PPrAjuGEEKIW8vKyuLUqVOEhobe9BmoJdXGjRtp164diYmJ+bqrrSi61ft3J5/f0tNUxCWcS2PXLiuXyjXDZHYWt7TL8JwQQghx30nSVMTpTc7xWavWiCnLWdxSMae6OSohhBDi30funividCbnW2TzMKG3QoaiRmWWniYhhBD3x508B7akk56mIk5v/LunCcBhVaGWniYhhBDivpOepiIuu6fJrjXgUKnBAiqrJE1CCCHE/SY9TUWczvB3XmvTGjGaVVjs6bfYQgghhBCFQZKmIk6tUeN5PXFyTgZXyFTZwGZ2c2RCCCHEv4skTcWA3jUZ3IjRDKkqeZSKEEIIcb9J0lQM6FyTwU2uR6nIQ3uFEEKI+0uSpmJAZ/y7p8mU/dBeSZqEEEKI+0qSpmLg7wKXJkxZCqlqlQzPCSGEuK2+ffuiUqlQqVR4eHgQGhrK22+/TVZWlrtDy2Xjxo2oVCqSkpLyvc3x48fx9va+b493kaSpGNBdT5qcd8/J8JwQQoj869SpE5cuXeLkyZN8/vnnzJgxg5EjR7o7rHtmtVp57rnnaNWq1X07piRNxUD28Jz1+vBcmlomggshhMgfnU5HUFAQwcHBdO3alfbt2xMdHe1a73A4iIyMJDQ0FIPBQL169Vi6dKlrfXYPUFRUFHXr1kWv19OsWTNiY2NzHGfz5s20atUKg8FAcHAwgwYNIj397xI58+fPp1GjRnh7exMUFESvXr24cuUKAKdPn6Zdu3YAlCpVCpVKRd++fW95Xh9++CE1atSgR48e93qJ8k2SpmJAf8NEcJNZ5jQJIURRoCgKjoyM+/66l0eaxMbGsmXLFjw9PV3LIiMjmTdvHtOnT+fAgQMMHTqU3r17ExMTk2Pbt956i0mTJrFz504CAgLo0qULVqsVgBMnTtCpUye6d+/Ovn37WLRoEZs3byYiIsK1vdVqZezYsezdu5cVK1Zw+vRpV2IUHBzMsmXLADhy5AiXLl3iiy++uOl5rF+/niVLlvDVV1/d9bW4G1IRvBj4+/lzBoxpMqdJCCGKAiUzkyMNGt7341bfvQuV0Zjv9qtXr8bLywubzYbZbEatVvPll18CYDab+fTTT1m7di3NmzcHoEqVKmzevJkZM2bQpk0b135GjhxJhw4dAJg7dy4VK1Zk+fLl9OjRg8jISJ5//nmGDBkCQLVq1ZgyZQpt2rRh2rRp6PV6Xn75Zde+qlSpwpQpU2jcuDFpaWl4eXnh7+8PQGBg4C3nKF29epW+ffvy3Xff4ePjk+/rUBAkaSoGbuxpKnV9TpMtM0nePCGEELfVrl07pk2bRnp6Op9//jlarZbu3bsDzonUGRkZrmQom8Vi4aGHHsqxLDupAvD396d69eocOnQIgL1797Jv3z4WLFjgaqMoCg6Hg1OnTlGzZk127drFqFGj2Lt3L4mJiTgcDgDOnj1LWFhYvs/n1VdfpVevXrRu3frOLkQBkM/dYkB3Y3HL6yUHbBnJ8uYJIYQbqQwGqu/e5Zbj3gmTyUTVqlUB+Pbbb6lXrx6zZs2iX79+pKWlARAVFUWFChVybKfT6fJ9jLS0NF577TUGDRqUa11ISAjp6emEh4cTHh7OggULCAgI4OzZs4SHh2OxWO7ofNavX8+qVauYOHEi8HdyptVq+frrr3P0aBU0+dwtBm4sOWC8PqfJninDc0II4U4qleqOhsmKArVazfvvv8+wYcPo1asXYWFh6HQ6zp49m2MoLi/btm0jJCQEgMTERI4ePUrNmjUBaNCgAQcPHnQlZ/+0f/9+rl69yrhx4wgODgbgzz//zNEme56V3W6/ZRxbt27N0WblypWMHz+eLVu25Er8CppMBC8GsiuC27RGDBbIQI0jM9nNUQkhhCiOnn32WTQaDV999RXe3t4MHz6coUOHMnfuXE6cOMHu3buZOnUqc+fOzbHdmDFjWLduHbGxsfTt25cyZcrQtWtXAN555x22bNlCREQEe/bs4dixY6xcudI1ETwkJARPT0+mTp3KyZMnWbVqFWPHjs2x/0qVKqFSqVi9ejXx8fGuXrB/qlmzJrVr13a9KlSogFqtpnbt2pQqVargL9gNJGkqBrKH5xS1BrtGh82qkrvnhBBC3BWtVktERAQTJkwgPT2dsWPH8tFHHxEZGUnNmjXp1KkTUVFRhIaG5thu3LhxDB48mIYNGxIXF8dPP/3k6h2qW7cuMTExHD16lFatWvHQQw8xYsQIypcvD0BAQABz5sxhyZIlhIWFMW7cONfwWrYKFSowevRo3n33XcqWLZvjzruiQqXcy72LwiUlJQVfX1+Sk5MLfDa/oijMGBiD3eag+daP+KhvIoscOrze/KtAjyOEECJvWVlZnDp1itDQUPR6vbvDua82btxIu3btSExMvG+Vtwvard6/O/n8lp6mYkClUuWYDK6xqFEsqW6OSgghhPh3kaSpmPh7MrgRY5ZClj3vsV4hhBBCFA65e66YyH6Uis3DWRU8y2gBuxU0Hm6OTAghREnWtm3be6pCXpJIT1MxoTP+3dPkev6cWYbohBBCiPtFkqZiQm/6+6G9Rnn+nBBCCHHfSdJUTOhMf9dqMmYpzqRJnj8nhBBC3DeSNBUT+utzmqxa55ymNOlpEkIIIe4rSZqKCVdVcI/sOU0q6WkSQggh7iNJmoqJvJ4/Jz1NQgghxP0jSVMxcWNxS5PMaRJCCCHuO0maiokbSw4YXXOa5KG9Qgghbq5v376oVCpUKhUeHh6Ehoby9ttvk5WV5e7Qctm4cSMqlYqkpKRbtjt9+rTrnG58bdu2rdBjlOKWxYTedENxyyzn8JySlYrKzXEJIYQo2jp16sTs2bOxWq3s2rWLPn36oFKpGD9+vLtDuydr166lVq1aru9Lly5d6MeUnqZiIrunya7RYbBoSFOrsGVKT5MQQohb0+l0BAUFERwcTNeuXWnfvj3R0dGu9Q6Hg8jISEJDQzEYDNSrV4+lS5e61mf3AEVFRVG3bl30ej3NmjUjNjY2x3E2b95Mq1atMBgMBAcHM2jQINLT013r58+fT6NGjfD29iYoKIhevXpx5coVwNl71K5dOwBKlSqFSqWib9++tzyv0qVLExQU5Hp5eBT+EzIkaSomdAYt2d1KnnYjaaixZyS5NSYhhPg3UxQFq9l+31/38kiT2NhYtmzZgqenp2tZZGQk8+bNY/r06Rw4cIChQ4fSu3dvYmJicmz71ltvMWnSJHbu3ElAQABdunTBarUCcOLECTp16kT37t3Zt28fixYtYvPmzURERLi2t1qtjB07lr1797JixQpOnz7tSoyCg4NZtmwZAEeOHOHSpUt88cUXtzyXJ598ksDAQFq2bMmqVavu+prcCRmeKyZUahU6gxZzhg2rhxGbNQV7pkwEF0IId7FZHHw9OOb2DQtY/y/a4KHT5Lv96tWr8fLywmazYTabUavVfPnllwCYzWY+/fRT1q5dS/PmzQGoUqUKmzdvZsaMGbRp08a1n5EjR9KhQwcA5s6dS8WKFVm+fDk9evQgMjKS559/niFDhgBQrVo1pkyZQps2bZg2bRp6vZ6XX37Zta8qVaowZcoUGjduTFpaGl5eXvj7+wMQGBiIn5/fTc/Hy8uLSZMm8fDDD6NWq1m2bBldu3ZlxYoVPPnkk/m+LndDkqZiRGfycCZNWhM2qwolS4bnhBBC3Fq7du2YNm0a6enpfP7552i1Wrp37w7A8ePHycjIcCVD2SwWCw899FCOZdlJFYC/vz/Vq1fn0KFDAOzdu5d9+/axYMECVxtFUXA4HJw6dYqaNWuya9cuRo0axd69e0lMTMThcABw9uxZwsLC8n0+ZcqUYdiwYa7vGzduzMWLF/nss88kaRJ/0xu1pOAsO4BFhUolPU1CCOEuWk81/b9oc/uGhXDcO2EymahatSoA3377LfXq1WPWrFn069ePtLQ0AKKioqhQoUKO7XQ6Xb6PkZaWxmuvvcagQYNyrQsJCSE9PZ3w8HDCw8NZsGABAQEBnD17lvDwcCwWyx2dT16aNm2aY55WYZGkqRj5u8ClEQ+LCpsq1c0RCSHEv5dKpbqjYbKiQK1W8/777zNs2DB69epFWFgYOp2Os2fP5hiKy8u2bdsICQkBIDExkaNHj1KzZk0AGjRowMGDB13J2T/t37+fq1evMm7cOIKDgwH4888/c7TJnmdlt9vv+Lz27NlDuXLl7ni7OyUTwYsRnfGGsgNmyLKnuTkiIYQQxc2zzz6LRqPhq6++wtvbm+HDhzN06FDmzp3LiRMn2L17N1OnTmXu3Lk5thszZgzr1q0jNjaWvn37UqZMGbp27QrAO++8w5YtW4iIiGDPnj0cO3aMlStXuiaCh4SE4OnpydSpUzl58iSrVq1i7NixOfZfqVIlVCoVq1evJj4+3tUL9k9z587l+++/5/Dhwxw+fJhPP/2Ub7/9loEDBxb8xfoHSZqKEd0NPU3GLMhyZMH1MWEhhBAiP7RaLREREUyYMIH09HTGjh3LRx99RGRkJDVr1qRTp05ERUURGhqaY7tx48YxePBgGjZsSFxcHD/99JOrd6hu3brExMRw9OhRWrVqxUMPPcSIESMoX748AAEBAcyZM4clS5YQFhbGuHHjmDhxYo79V6hQgdGjR/Puu+9StmzZHHfe/dPYsWNp2LAhTZs2ZeXKlSxatIiXXnqpgK9UbirlXu5dFC4pKSn4+vqSnJyMj49PoRxj+6qT/PnzaSpciGFryFJerHCFWsOOg963UI4nhBDCKSsri1OnThEaGoper3d3OPfVxo0badeuHYmJibe8q60ou9X7dyef39LTVIy4hue0RkxmhRS1Sp4/J4QQQtwnbk2aIiMjady4Md7e3gQGBtK1a1eOHDniWn/t2jUGDhxI9erVMRgMhISEMGjQIJKT/77Vfs6cOXk+g0alUrkqjYIzU27QoAE6nY6qVasyZ86cXPF89dVXVK5cGb1eT9OmTdmxY0ehnv+dyvH8uazs589J0iSEEELcD25NmmJiYhgwYADbtm0jOjoaq9VKx44dXWXXL168yMWLF5k4cSKxsbHMmTOHNWvW0K9fP9c+evbsyaVLl3K8wsPDadOmDYGBgQCcOnWKzp07065dO/bs2cOQIUN45ZVX+PXXX137WbRoEcOGDWPkyJHs3r2bevXqER4eniPxcrccz5/Lfmiv9DQJIYQoRG3btkVRlGI7NFeQitScpvj4eAIDA4mJiaF169Z5tlmyZAm9e/cmPT0drTZ3xYT4+HgqVKjArFmzeOGFFwDnrP6oqKgcz8n5z3/+Q1JSEmvWrAGcNR4aN27sqpLqcDgIDg5m4MCBvPvuu7eN/X7Mabp4PInlE3djyLiCJmkMqrYpvPjELHgwvFCOJ4QQwunfPKepJCiRc5qyh92yS6nfrI2Pj0+eCRPAvHnzMBqNPPPMM65lW7dupX379jnahYeHs3XrVsBZ+XTXrl052qjVatq3b+9qUxTorw/P2Tycw3Op0tMkhBD3VRHqZxB3oKDetyJT3NLhcDBkyBAefvhhateunWebhIQExo4dS//+/W+6n1mzZtGrVy8MBoNrWVxcHGXLls3RrmzZsqSkpJCZmUliYiJ2uz3PNocPH87zOGazGbPZ7Po+JaXwkxfd9eE5q9aI0QxpahWY5VEqQghR2Dw8nH+0ZmRk5Ph8EcVDRkYG8Pf7eLeKTNI0YMAAYmNj2bx5c57rU1JS6Ny5M2FhYYwaNSrPNlu3buXQoUPMnz+/ECN1ioyMZPTo0YV+nBtl3z2HSo3BqidVnQlmqQouhBCFTaPR4Ofn55rnajQaUalUbo5K3I6iKGRkZHDlyhX8/PzQaO6tgnuRSJoiIiJYvXo1v//+OxUrVsy1PjU1lU6dOuHt7c3y5ctvminOnDmT+vXr07BhwxzLg4KCuHz5co5lly9fxsfHB4PBgEajQaPR5NkmKCgoz2O99957OR4YmJKS4ioNX1i0Hhq0HipsVgUPu5E0dbIMzwkhxH2S/XlQlG4QEvnj5+d308/zO+HWpElRFAYOHMjy5cvZuHFjruqj4ExGwsPD0el0rFq16qYT8NLS0li8eDGRkZG51jVv3pyff/45x7Lo6GjXE5s9PT1p2LAh69atc5WEdzgcrFu37qYVSXU63R09zLCg6AxabFYrHnYjqSo1jqyUojUxTQghSiiVSkW5cuUIDAzEarW6OxyRTx4eHvfcw5TNrUnTgAEDWLhwIStXrsTb25u4uDgAfH19MRgMpKSk0LFjRzIyMvjuu+9ISUlxzR0KCAjIcREWLVqEzWajd+/euY7z3//+ly+//JK3336bl19+mfXr17N48WKioqJcbYYNG0afPn1o1KgRTZo0YfLkyaSnp9+Xsux3QmfyID3Fit3DhMWmxpaRhKe7gxJCiH+R7NEJ8e/j1qRp2rRpgLMGxI1mz55N37592b17N9u3bwfI9eTkU6dOUblyZdf3s2bN4umnn86zjkRoaChRUVEMHTqUL774gooVKzJz5kzCw/++Vb9nz57Ex8czYsQI4uLiqF+/PmvWrMk1Odzd9F6eQAZWrRGHRYU9UyaCCyGEEPdDkarTVJzdjzpNAL9M38/JPfE8ePQHZnfcxMxSFTG+Fl1oxxNCCCFKsmJbp0ncXnbZAZvWiGJRo2RJT5MQQghxP0jSVMy4nj/nYURnAYs5zc0RCSGEEP8OkjQVM67nz2lNmLIgw57u5oiEEEKIfwdJmoqZG3uaTFmQpWSCTEsTQgghCp0kTcWM3nT9+XPXH6WSrlLAIr1NQgghRGGTpKmYyX6UilVrxJSlOB/aa5aq4EIIIURhk6SpmMnuabJ6mDCZcSZN8igVIYQQotBJ0lTMZPc02bRGjFmQplZJT5MQQghxH0jSVMzorvc0OTSeGC0epKmkp0kIIYS4HyRpKmY89RpUKufXOquBFI0azFLgUgghhChskjQVMyqVCk+dM2vytBudPU3mVDdHJYQQQpR8kjQVQ3qD8+nannYjaTIRXAghhLgvJGkqhrLnNWkVE6kqmQguhBBC3A+SNBVDem8d4LyDzmyXniYhhBDifpCkqRjSuZImEzarGnumTAQXQgghCpskTcXQ3wUuDSgWSZqEEEKI+0GSpmLo7wKXJhQLOCRpEkIIIQqdJE3FkN6Y3dNkRGVR48iSpEkIIYQobJI0FUN6U/ZDe00YzJBplTpNQgghRGGTpKkYyi45YNMaMZkhw5rm5oiEEEKIkk+SpmJId8PwnNEMmfYMUBQ3RyWEEEKUbJI0FUPZw3M2rQlTlkK6ygG2LDdHJYQQQpRskjQVQ9k9TTYPI0azilR5lIoQQghR6CRpKoZ013uaAIxmg/P5c/IoFSGEEKJQSdJUDGk0arQa5xwmvU0e2iuEEELcD5I0FVM6ncr5f5uRFLUazFKrSQghhChMkjQVUzqDBgBPu4k0tQrMUqtJCCGEKEySNBVT2XfQaRSDDM8JIYQQ94EkTcWUzksHgEYxkaqSieBCCCFEYZOkqZjS++gBsGuNZNmlp0kIIYQobJI0FVPZSZNNa8JqU0lPkxBCCFHIJGkqpvSmvx+lYreoUbLk7jkhhBCiMEnSVEzpjNmPUjGiWNQ4MiVpEkIIIQqTJE3F1N89TSawqrBL0iSEEEIUKkmaiilddtKkNaIxq7DKRHAhhBCiUEnSVExl12myaY2YzJBhkaRJCCGEKEySNBVTOuPfE8GNWZBhTXNzREIIIUTJJklTMZU9EVxRe2Aye5Bhy3BzREIIIUTJJklTMeWh06BWKQCYzEbSVTawWdwclRBCCFFySdJUTKlUKjy1DgD0NiNp8igVIYQQolBJ0lSMeXqqANDZTKSqVSAFLoUQQohCI0lTMabTO98+T7uBNLUazKlujkgIIYQouSRpKsayyw5oHSZS1TI8J4QQQhQmSZqKMZ2XJwAaxejsaZICl0IIIUShkaSpGNN76wBQYyRVpZKeJiGEEKIQSdJUjOl9jQDYtSYyHdLTJIQQQhQmSZqKMUMpZ9Jk1RqxWmVOkxBCCFGYJGkqxvTXH9pr8zBis6ql5IAQQghRiCRpKsZcz5/TGnFYpKdJCCGEKEySNBVjuuslB2weJrCqULKkTpMQQghRWCRpKsb0N/Q0qSwq7JkyPCeEEEIUFkmairHsOU12rQGtWYM5M8m9AQkhhBAl2F0lTefOneP8+fOu73fs2MGQIUP4+uuvCywwcXueRq3ray+zkTSLzGkSQgghCstdJU29evViw4YNAMTFxdGhQwd27NjBBx98wJgxY/K9n8jISBo3boy3tzeBgYF07dqVI0eOuNZfu3aNgQMHUr16dQwGAyEhIQwaNIjk5NzDUHPmzKFu3bro9XoCAwMZMGCAa93p06dRqVS5Xtu2bcuxjyVLllCjRg30ej116tTh559/vtNLc1+p1So8VDYATBYD6VaZ0ySEEEIUlrtKmmJjY2nSpAkAixcvpnbt2mzZsoUFCxYwZ86cfO8nJiaGAQMGsG3bNqKjo7FarXTs2JH09HQALl68yMWLF5k4cSKxsbHMmTOHNWvW0K9fvxz7+d///scHH3zAu+++y4EDB1i7di3h4eG5jrd27VouXbrkejVs2NC1bsuWLTz33HP069ePv/76i65du9K1a1diY2Pv4grdP55aBwAGi5EMW4aboxFCCCFKLpWiKMqdbuTl5UVsbCyVK1fmySef5OGHH+add97h7NmzVK9enczMzLsKJj4+nsDAQGJiYmjdunWebZYsWULv3r1JT09Hq9WSmJhIhQoV+Omnn3j00Ufz3Ob06dOEhoby119/Ub9+/Tzb9OzZk/T0dFavXu1a1qxZM+rXr8/06dNvG3tKSgq+vr4kJyfj4+Nz+5MtIAvfXENiuiceif9H/Yd+p8lbF0Gjvf2GQgghhLijz++76mmqVasW06dPZ9OmTURHR9OpUyfA2TNUunTpu9klgGvYzd/f/5ZtfHx80GqdiUF0dDQOh4MLFy5Qs2ZNKlasSI8ePTh37lyubZ988kkCAwNp2bIlq1atyrFu69attG/fPsey8PBwtm7dmmccZrOZlJSUHC930Omdb6Gn3USqWmo1CSGEEIXlrpKm8ePHM2PGDNq2bctzzz1HvXr1AFi1apVr2O5OORwOhgwZwsMPP0zt2rXzbJOQkMDYsWPp37+/a9nJkydxOBx8+umnTJ48maVLl3Lt2jU6dOiAxWIBnD1jkyZNYsmSJURFRdGyZUu6du2aI3GKi4ujbNmyOY5XtmxZ4uLi8owlMjISX19f1ys4OPiuzvte6a5PBvewG0lTq8Es85qEEEKIwnBX4zht27YlISGBlJQUSpUq5Vrev39/jEbjXQUyYMAAYmNj2bx5c57rU1JS6Ny5M2FhYYwaNcq13OFwYLVamTJlCh07dgTg+++/JygoiA0bNhAeHk6ZMmUYNmyYa5vGjRtz8eJFPvvsM5588sm7ive9997Lsc+UlBS3JE7OsgNWNIpRepqEEEKIQnRXSVNmZiaKorgSpjNnzrB8+XJq1qyZ5wTs24mIiGD16tX8/vvvVKxYMdf61NRUOnXqhLe3N8uXL8fDw8O1rly5cgCEhYW5lgUEBFCmTBnOnj1702M2bdqU6Oho1/dBQUFcvnw5R5vLly8TFBSU5/Y6nQ6dTpe/EyxEem8dYEWtMpKqVkGWJE1CCCFEYbir4bmnnnqKefPmAZCUlETTpk2ZNGkSXbt2Zdq0afnej6IoREREsHz5ctavX09oaGiuNikpKXTs2BFPT09WrVqFXq/Psf7hhx8GyFWqICEhgUqVKt302Hv27HElXADNmzdn3bp1OdpER0fTvHnzfJ+PO+h9Dde/Ml0fnpOkSQghhCgMd5U07d69m1atWgGwdOlSypYty5kzZ5g3bx5TpkzJ934GDBjAd999x8KFC/H29iYuLo64uDjX3XfZCVN6ejqzZs0iJSXF1cZutwPw4IMP8tRTTzF48GC2bNlCbGwsffr0oUaNGrRr1w6AuXPn8v3333P48GEOHz7Mp59+yrfffsvAgQNdsQwePJg1a9YwadIkDh8+zKhRo/jzzz+JiIi4m0t03xj8vQBwaI1kONTS0ySEEEIUkrsansvIyMDb2xuA3377jaeffhq1Wk2zZs04c+ZMvveT3SvVtm3bHMtnz55N37592b17N9u3bwegatWqOdqcOnWKypUrAzBv3jyGDh1K586dUavVtGnThjVr1uQYxhs7dixnzpxBq9VSo0YNFi1axDPPPONa36JFCxYuXMiHH37I+++/T7Vq1VixYsVNJ6UXFXo/5xwyq9aIxSo9TUIIIURhuas6TXXr1uWVV16hW7du1K5dmzVr1tC8eXN27dpF586db3rHWUnmrjpNF44msuJ/f2HMiONQjRFMaPlfaD38vh1fCCGEKM4KvU7TiBEjGD58OJUrV6ZJkyaueT+//fYbDz300N3sUtyl7If2WrUmHBbpaRJCCCEKy10Nzz3zzDO0bNmSS5cuuWo0ATz66KN069atwIITt6czOpMmm4cRxSx1moQQQojCctfP2wgKCiIoKIjz588DULFixbsubCnunt7kfAsVlQa1RS8TwYUQQohCclfDcw6HgzFjxuDr60ulSpWoVKkSfn5+jB07FofDUdAxilvQempQK847CTUWE46sZDdHJIQQQpRMd9XT9MEHHzBr1izGjRvnqpO0efNmRo0aRVZWFp988kmBBiluzUNtxaxo0Fu9yMpM4u5qsgshhBDiVu4qaZo7dy4zZ87M8QiSunXrUqFCBd544w1Jmu4zndaB2QoGi4EU80VJmoQQQohCcFfDc9euXaNGjRq5lteoUYNr167dc1DiznheL0dlsBpJs8hEcCGEEKIw3FXSVK9ePb788stcy7/88kvq1q17z0GJO6PTqZz/t5tIt6W7ORohhBCiZLqr4bkJEybQuXNn1q5d66rRtHXrVs6dO8fPP/9coAGK29MZNJAIHg4jGQ4zOBygvqt8WAghhBA3cVefrG3atOHo0aN069aNpKQkkpKSePrppzlw4ADz588v6BjFbWQXuNQ4jKSpVWBJc3NEQgghRMlz13Waypcvn2vC9969e5k1axZff/31PQcm8k/nrQMsqDGSpr5eFVx//x7lIoQQQvwbyBhOCaD30QOgwkiKWi0FLoUQQohCIElTCWAoZXJ+oTb93dMkhBBCiAIlSVMJYCjtBYBdayRDUUlPkxBCCFEI7mhO09NPP33L9UlJSfcSi7hLhtLeAFi1RrKs0tMkhBBCFIY7Spp8fX1vu/7FF1+8p4DEndN7eQJg8zBhtapBnj8nhBBCFLg7Sppmz55dWHGIe6AzOksO2DU67BZP6WkSQgghCoHMaSoBdAYtKIrzG7MXmOVRKkIIIURBk6SpBFCpVWiULADUFpNMBBdCCCEKgSRNJYQWKwAai0mG54QQQohCIElTCeGpsQGgtZlwZCa5NxghhBCiBJKkqYTQeVz/v9VEplnunhNCCCEKmiRNJYRB73wrdTYDKVJyQAghhChwkjSVEDqDBgAPh4kUS5qboxFCCCFKHkmaSgj99VpNWoeRdFu6m6MRQgghSh5JmkoInbezKrgGIxn2rL/rNgkhhBCiQEjSVEIYfPQAqDCSpgasGe4NSAghhChhJGkqIfS+BucXKhOpapUUuBRCCCEKmCRNJYShtDcADo2RNLVaClwKIYQQBeyOHtgriq7spMmmNWJR1NLTJIQQQhQw6WkqIYwBvoAzaTJbNCAFLoUQQogCJUlTCaH3dk4ER6XGKg/tFUIIIQqcJE0lhMZDjdpuAcBh9gJzqpsjEkIIIUoWSZpKEI0jEwCH1UsmggshhBAFTJKmEkSDGQCVDM8JIYQQBU6SphJEq7IBoJaeJiGEEKLASdJUgnho7ABo7NLTJIQQQhQ0SZpKEE/n4+fQ2IwoWVJyQAghhChIkjSVIAaDs1app91EujnJvcEIIYQQJYwkTSWIyUsHgNZhJEWG54QQQogCJUlTCWL0diZNGsVIikXqNAkhhBAFSZKmEsTg66wKrlYZSbOmuzkaIYQQomSRpKkE0fsanF+ojKTbM90bjBBCCFHCSNJUguhLeQHgUJtIVznAmuXmiIQQQoiSQ5KmEsRYxhsAu8ZIqlotBS6FEEKIAiRJUwliCPAFQNF4kuLQSYFLIYQQogBJ0lSC6Ev7oFKcVcEtFi8wS4FLIYQQoqBI0lSCqD090dicE8CtFh/paRJCCCEKkCRNJYzGngGA1eIFZqnVJIQQQhQUSZpKGI3DDIBi8ZKJ4EIIIUQBkqSphFGrLACobCYZnhNCCCEKkCRNJYxGbXV+YTNJT5MQQghRgNyaNEVGRtK4cWO8vb0JDAyka9euHDlyxLX+2rVrDBw4kOrVq2MwGAgJCWHQoEEkJ+e+K2zOnDnUrVsXvV5PYGAgAwYMyLF+3759tGrVCr1eT3BwMBMmTMi1jyVLllCjRg30ej116tTh559/LviTLmRarQKA2u4lPU1CCCFEAXJr0hQTE8OAAQPYtm0b0dHRWK1WOnbsSHq687lpFy9e5OLFi0ycOJHY2FjmzJnDmjVr6NevX479/O9//+ODDz7g3Xff5cCBA6xdu5bw8HDX+pSUFDp27EilSpXYtWsXn332GaNGjeLrr792tdmyZQvPPfcc/fr146+//qJr16507dqV2NjY+3MxCoiHpwoAtd0oJQeEEEKIAqRSFEVxdxDZ4uPjCQwMJCYmhtatW+fZZsmSJfTu3Zv09HS0Wi2JiYlUqFCBn376iUcffTTPbaZNm8YHH3xAXFwcnp6eALz77rusWLGCw4cPA9CzZ0/S09NZvXq1a7tmzZpRv359pk+fftvYU1JS8PX1JTk5GR8fnzs99QIT9dY3nE59ALt1FwM67EXzn+/cFosQQghR1N3J53eRmtOUPezm7+9/yzY+Pj5otVoAoqOjcTgcXLhwgZo1a1KxYkV69OjBuXPnXNts3bqV1q1buxImgPDwcI4cOUJiYqKrTfv27XMcKzw8nK1bt+YZh9lsJiUlJcerKDB56wHQKEbSzUnuDUYIIYQoQYpM0uRwOBgyZAgPP/wwtWvXzrNNQkICY8eOpX///q5lJ0+exOFw8OmnnzJ58mSWLl3KtWvX6NChAxaL806yuLg4ypYtm2Nf2d/HxcXdsk32+n+KjIzE19fX9QoODr67Ey9gXr5GANSYSJE6TUIIIUSBKTJJ04ABA4iNjeWHH37Ic31KSgqdO3cmLCyMUaNGuZY7HA6sVitTpkwhPDycZs2a8f3333Ps2DE2bNhQaPG+9957JCcnu1439my5k4+/CQCVykCKpWj0fgkhhBAlgdbdAQBERESwevVqfv/9dypWrJhrfWpqKp06dcLb25vly5fj4eHhWleuXDkAwsLCXMsCAgIoU6YMZ8+eBSAoKIjLly/n2Gf290FBQbdsk73+n3Q6HTqd7k5PtdCZSpsAK4raRJo13d3hCCGEECWGW3uaFEUhIiKC5cuXs379ekJDQ3O1yb7zzdPTk1WrVqHX63Osf/jhhwFylSpISEigUqVKADRv3pzff/8dq9XqahMdHU316tUpVaqUq826dety7Ds6OprmzZsXzMneJ4bSzklsDo2RVFuWm6MRQgghSg63Jk0DBgzgu+++Y+HChXh7exMXF0dcXByZmc6HzmYnTOnp6cyaNYuUlBRXG7vdDsCDDz7IU089xeDBg9myZQuxsbH06dOHGjVq0K5dOwB69eqFp6cn/fr148CBAyxatIgvvviCYcOGuWIZPHgwa9asYdKkSRw+fJhRo0bx559/EhERcf8vzD0wlvl75n+qQwd26y1aCyGEECK/3Jo0TZs2jeTkZNq2bUu5cuVcr0WLFgGwe/dutm/fzv79+6latWqONjfOIZo3bx5Nmzalc+fOtGnTBg8PD9asWeMaxvP19eW3337j1KlTNGzYkDfffJMRI0bkmFDeokULFi5cyNdff029evVYunQpK1asuOmk9KJKW8oXjc2ZdKZKgUshhBCiwBSpOk3FWVGp06RYLMzqvwqz3h9dudG8MnA2+FdxWzxCCCFEUVZs6zSJe6fy9ERrywDAYvGWniYhhBCigEjSVAKpHc7hOZvVC6RWkxBCCFEgJGkqgVSK8645h9UEZulpEkIIIQqCJE0lkFrlvGNOsclEcCGEEKKgSNJUAqnUNuf/7V7S0ySEEEIUEEmaSiC1h/OGSJXDJD1NQgghRAGRpKkE0uqcb6vKbgRzspujEUIIIUoGSZpKIJ3RWdRTrUhPkxBCCFFQJGkqgQzeBgBUGLFmSU+TEEIIURAkaSqBvEt5A6BSmUiX4TkhhBCiQEjSVAL5lHYmTYraQIr0NAkhhBAFQpKmEsgn8Pqzc1QeJJmz3BuMEEIIUUJI0lQC+QT4oXI4azUlSc4khBBCFAhJmkogTz8/PK4/tDfFonFzNEIIIUTJIElTCaTx8UZrTQcg3aYDh93NEQkhhBDFnyRNJZDa29vV05Rl9ZZHqQghhBAFQJKmEkit06G2O5Mmi0Ue2iuEEEIUBEmaSiiVwzkD3GrzAnOqm6MRQgghij9JmkosMwAOu5cMzwkhhBAFQJKmkkplBUCxecGxaDcHI4QQQhR/kjSVUCqNs04TdhNsnw6pl90bkBBCCFHMSdJUQqm0CgCK4gXWDNg0yc0RCSGEEMWbJE0llFp//a1VTM7///ktJJ5xX0BCCCFEMSdJUwmlMXoCoFaMUKUtOKwQM969QQkhhBDFmCRNJZTeywCACiM8MsK5cO/3cOWwG6MSQgghii9Jmkook7+P8wu1nszAMKjxBCgO2PCJewMTQgghiilJmkoonzKlXF9fTU6GRz4EVHBoFVzY7b7AhBBCiGJKkqYSylTaH+31588lJadAYE2o29O5cv1YN0YmhBBCFE+SNJVQJn8/tFZn0pScnOZc2PZdUGvhxHo4tcmN0QkhhBDFjyRNJZRXaT/05kQADm2MR3Eo4B8KDfs6G6wfC4rivgCFEEKIYkaSphLKWMqPKidXoXLYMB/zYMOKWOeK1m+B1gDntsPRX90bpBBCCFGMSNJUQml8ffBLOUn1oz8AcOi3eI78eQm8g6Dpa85G68eCw+HGKIUQQojiQ5KmEkql02FVaykft5UTZZzzl6JnHyD+bCo8PBh0PnA5Fg786OZIhRBCiOJBkqYSSqVSkaVzFrisWtOPs34HUdnVLJ+6k3SrCVoMcjbc8AnYrW6MVAghhCgeJGkqwRQvbwAuHPHB5/E0Eg1xWFNh1Ve7sTV8DYxl4NpJ2LPAzZEKIYQQRZ8kTSVYqepVAWixZj6PBfTgaNP1ZGnSuXY2k/WLz6K0Gu5suHE8WDPdGKkQQghR9EnSVIJVHjWCdC8/qqRcImHY24zt8B6bwr7HgZ1jO67wV1JH8KkIqRdh5yx3hyuEEEIUaZI0lWCeFStgnPgFmRpPgk8dwDJ2Gv99/AU2hy4DYOvKM5yqPMrZeNMkyEpxX7BCCCFEESdJUwkX1rYJq7pFYFepsa2JosGqo9RpU5HYss476n6LCeCqqRVkXoNt/+fmaIUQQoiiS5Kmf4GOL3VjSr3uAFydNp3XTlfhaoODnPc9gs3sIOrKYDIdPrDlS0i/6uZohRBCiKJJkqZ/geZVShPXsiMLq7cHIGHsp4zzfIY/whaTrI8nNVXDL+ljsJsz4Y/P3RytEEIIUTRJ0vQvoFKpeK31A8yvEU5MaBOw27F8MI5R5XrxS42vMWsyuZReiZiU11C2fwMpF90dshBCCFHkSNL0L9GpdhCVypiYWOdpksLqo2RkUHHMPLqWb8TaanNw4OBQZnv2pbSHmAnuDlcIIYQociRp+pfQqFW82qoKNrWWD+v3xvPBB7EnJPDUtH2UDrKytfIKAP5I7cuZrfvh6gn3BiyEEEIUMZI0/Ys807AiZbw8OZEBh4eMQRsUhPXUaT5cruVs0A4OBW5FQcNviUNIjPrK3eEKIYQQRYokTf8ieg8NfVtUBuDL/SkEz5iB2tsbx94DTP6jCpsrL+aS9wksiomo7Q3IOrnPvQELIYQQRYgkTf8yvZtVwuip4dClFLarSlFx6lTw8MC0aS+j9obya/VZpOuukWwvz6//tx1H3GF3hyyEEEIUCZI0/cv4GT35T+MQAGbEnMDUrCnlP/0EgOq/HqHnUQNR1b/Grs7ifNoDbPnsG+fEcJvFnWELIYQQbidJ079Qv1ahaNUqtpy4yr7zSfh26ULAsGEAdP7pMrUuJrC26gIA9qZ35vDPf8CM1nBuhzvDLnCOrCxOP9+bsy+/jGKRpFAIIcStSdL0L1TBz8CT9coDMCPmJAClX30Fv+f+A4rCgFVWPDL38meFNQBsTHmDyxdsMKsj/Pw2mFPdFntBujpzFpm7dpG+ZSsJM752dzhCCCGKOJWiKIq7gygJUlJS8PX1JTk5GR8fH3eHc1uH41LoNHkTahWsf7MtlcuYUGw2zkcMJG3jRiwmHVM72KiS2R9fc22MmjQeNw/HoCSDoTQ0eBGlbB1wKKA4UBwOUACHA21AGUwtW6JSF92c3HL+Aic7d0Yxm50LtFpCly1DX/1B9wYmhBDivrqTz29JmgpIcUuaAPrO3sHGI/H0bhbCx13rAODIyOBMn75k7d8PgE2j588Gw8kwlcM3+QQP7ZmCWrHddt9+PXsSNGokKpUqX7FkpFjY8uNx/MuZaBBe6e5PKp/ODxpM6m+/YWzaFLWXF2nr1qGvXZvKP3yPSqst9OMLIYQoGu7k87vodgWIQvffNg8AsOTP8ySkOXtc1EYjwdOn4f1YJ7RhNbgQ5MD7yteoHZkk+z7AieavY3igDMZAM8ZAM6bydkx1QjG1bImpdStMrVqBSkXSokVcnT49X3EkXc5g2YQ/ObItjq3LT3D+8LVCO2eA9K1bSf3tN9BoKPv++wSNGIHa25us2FiuzZ1bqMdWFIXVJ1ez+Mhi5O8VIYQoXtyaNEVGRtK4cWO8vb0JDAyka9euHDlyxLX+2rVrDBw4kOrVq2MwGAgJCWHQoEEkJyfn2I9Kpcr1+uGHH1zrN27cmGebuLi4HPv56quvqFy5Mnq9nqZNm7JjR8ma+PxPTUP9qRfsh9nmYO6W067l2tKlqfj551T7cTmhSxYz4cVMfgqbg4LCOc8apL39PZV+WEalXuUJaX2ZkFp/ENLqMiHj3iXkm68p+8EHAMR/MYWkZctuGcOlE8ksm7CLlIQs1Gpnr9TvPxzFbnMUyjkrVitxnzjvFiz13HPoqz+IR9lAyr77jjPmKVMxnzpVKMc22818+MeHvLfpPcZuG8vUv6YWynGKCsVqxZ5aMua/CSEEuDlpiomJYcCAAWzbto3o6GisVisdO3YkPT0dgIsXL3Lx4kUmTpxIbGwsc+bMYc2aNfTr1y/XvmbPns2lS5dcr65du+Zqc+TIkRxtAgMDXesWLVrEsGHDGDlyJLt376ZevXqEh4dz5cqVQjt/d1OpVPy3dRUA5m09Q7o597Bbdf/qjH14LOdKHWZ7yE8AbFp0jIvpleHVDdB+NGj1cCoG/q8F9s1fYH3iKUq/+ioAl0aMJC0mJs/jH991hZWf/0VWupXASt78Z0QTDN4eJMZlsHfduUI558Tvv8dy/ASaUqUIGBjhWu779NOYWrRAMZu59NFHzjlaBSg+I56Xf32ZVSdWoVY5f+2+2f8NCw4tKNDjuJvicJCxcyeXRo3iWKvWHG3chNO9e5P4ww/YEhPdHZ4QQtyTIjWnKT4+nsDAQGJiYmjdunWebZYsWULv3r1JT09He33uiUqlYvny5XkmSuDsaWrXrh2JiYn4+fnl2aZp06Y0btyYL7/8EgCHw0FwcDADBw7k3XffvW3sxXFOE4DdodD+fzGcSkjnoyfC6NcyNM92U3ZP4Zt939Dx+EtUSaiPwduDZ99rjLe/3vmcutVD4NTvAOx3VCal63eELlpA8sqVqAwGKs2dg6FuXcA5RLV33Tn+WHYcFKhctwwd+9XCQ6fh8LZLrJtzCK2nml6jmjn3X0BsV69yotNjOFJTCRozmlI9euRYbzl/gZNPPomSkUHZER/h36tXgRz3QMIBBm0YxJWMK/h4+jCxzUT2xu/lqz1foULFhDYT6FS5U4Ecyx0URcF86BDJUVGkRP2M7R89uC4eHni1bIlvlyfwatcOtcFQ4LHMPzifpUeXMqH1BKr7Vy/w/Re0S2mXMHoY8dX5ujsUUQDSLGkcTTzKQ4EP5Xs+p7vYrl3j7CuvgEMh6MMPMDZq5O6Q3KbYzmnKHnbz9/e/ZRsfHx9XwpRtwIABlClThiZNmvDtt9/mOV+kfv36lCtXjg4dOvDHH3+4llssFnbt2kX79u1dy9RqNe3bt2fr1q15xmE2m0lJScnxKo6yH+QLMGvTSaz2vHtYIh6KoHVwa9ZX+Y4krzgyU638Mn0/VoudVFMIH/p8wlvW/iQpJuqoT6NbM4xyY8dgevhhlMxMzr32XyynT+NwKGxadIw/ljoTpjptKvDYf+vgodMAUL1pEOWq+mKzOPhjybECPdcrn3+OIzUVfa1a+HXvnmu9Z8UKBA4dCkD8xElYL1y452P+fPJn+qzpw5WMK1TxrcLCzgtpXr45r9V9jZ7Ve6Kg8P6m99l+afs9H+t+s5w5Q/z//R8nOz/Bqae7c23Wt9ji4lB7eeH79NOEfDuLquvWEvjWW+hq1gSrlbQNG7gw7E2OPdySi++8S9qmzSi2299YkB974/cy8c+JnEw+yYd/fIjVYS2Q/RYkh+Jgz5U9TN41ma4rutJxWUeeXvk06dZ0d4cm7pFDcTBw/UD6rOnDN/u/cXc4t6RYLJwfNAjzwUOYDx/mTO8XuDRqlAyn50OR6WlyOBw8+eSTJCUlsXnz5jzbJCQk0LBhQ3r37s0n1+elAIwdO5ZHHnkEo9HIb7/9xsiRI5kwYQKDBg0CnMNyGzdupFGjRpjNZmbOnMn8+fPZvn07DRo04OLFi1SoUIEtW7bQvHlz137ffvttYmJi2L499wfaqFGjGD16dK7lxa2nCSDLaqfl+A0kpJn5vGc9uj1UMc92qZZUekX1IiE+mZ6x7+Jh0eNT3Zf/y0ziUkoWAK+HWRh64hU8VTbOtPmcio17crZPH7IOHEAdEsrxzmM4fciZYLboXpX67YNz/UV29UIaiz7ZieJQ6DKwHiG1SucZT4Y1g12Xd7H10lZOJ5+mQdkGPBLyCFV8q+Rqm7l/P6d79ARFodLChRgbPJTnPhWHgzO9XyBz925MLVsS/M3Xd/UXo0NxMPWvqczcPxOA1hVbM77VeLw8vVxt7A47b/3+FtFnojF5mJgdPpuapWve8bHy468rf3E+9TyPhz6ORq256/1Yr1wh9ZdfSF4d5brDEkDl6YlXu3b4PNEZr9atUet0ubY1Hz9O8k+rSVm9OkdCqilTBp/HHsO3yxPo69S5q+udacvk2Z+e5UzKGdeywQ0G80qdV+54XwUt05bJ1otb2XhuIzHnY7iWlftGh+GNhtOnVp/7H9x9oCgKS44u4VTyKfrV6UcZQxl3h1Qolh9bzogtIwBQq9TM7DiTxkGN3RxVboqicOmjj0heugyrwYPEhg8QuNn5uCxt2bIEjRyB9yOPuDnK+6tYlhx4/fXX+eWXX9i8eTMVK+b+0E5JSaFDhw74+/uzatUqPDw8brqvESNGMHv2bM6du/m8mDZt2hASEsL8+fPvKmkym82Ys2v8XI8vODi4WCZNAF9tOM5nvx6hRpA3vwxuddMPrlPJp+gV1QufhCCeODgANWo26q1cqeBJ5NN1aPFAGaK+epPO8TNJV3tjGroLm1nDkd6vsNv/SVJ8KqPRqmj/Ui2qNgzM8xgAm5ceY+/ac/gGGPjPiCZoPTTYHXYOXj3I1ktb2XpxK3vi92Bz5O6lCPUNpX1Iex4NeZSw0mGgKJx+7jmy9u7D96knKT9+/C2vhfnkKU517YpisVAuMhK/bl3v6FqmWdJ4b9N7bDy/EYB+tfsx8KGBeSYrZruZ/0b/lz8v/0lpfWnmPz6fYO/gOzrerdgcNv5vz/+5/vJtXq45ka0iKW3IOxHNiz05mdToaJJXR5GxfTtk/5Oh0WBq3hyfJzrj3b49Gi+vW+/oOkVRyPzrL1JWrybl51+wJyW51nlUCsH3iS74PNEZXWjeQ8V5+XT7p3x/+HsCjYG8VOslxu8cj6fak2VPLqOyb+V876egJGQmEHMuho3nNrL10lbM9r//rfDy8KJVhVa0DW5LQmYCn/35GYGGQH7p/gueGs/7HmthyrJlMWrrKKJORgHgq/Pl7cZv06VKlyI/fHUnrmVd48kVT5JsTqaCVwUupF2gjKEMS7osKXJJ4tXZc7gyfjwOFYx7Vs2eB9T0yWzAU8suYbv+mendqRNBH7yPNiDAzdHeH8UuaYqIiGDlypX8/vvvhObxD2Vqairh4eEYjUZWr16NXn/reS5RUVE88cQTZGVlocvjL16At956i82bN7N161YsFgtGo5GlS5fmmBfVp08fkpKSWLly5W3PobjOacqWnGGlxbh1pFvszH6pMe2q553QKIrCZ5tWMP/kSGrFtaTV6WdQgPDX61CtnvMXbP/ZBFQzH6W2+jRZVR8n89GZ/PTFLlKTbGitaTRVbaXetDGobpH4WrJsLBy5jfRkC8bm6ewLWc/2uO2kWnJ2H5c3lad5+eZU8a3C1ktb2XZpW45EKsgURN8zlWjwzR+ojEYeWPMLHoE3T9ayJXz9DfH/+x9qX18eWP1Tvv/xOJdyjoHrB3Ii+QQ6jY7RLUbTuUrnW26TaknlpTUvcSTxCCHeIcx7bN4dJTU3E58Rzzub3mFn3E4AtGotNoeNQEMgn7X5jAZlG9xye8Vq5eqsb0mYNu3vIqCA4aGH8HmiMz6dOqEtfW9xKlYraX/8QcpPq0ldvx4lM9O1zvfppyn38djbFkndcnELr0W/BsCMDjNoXq45/137X7Zc3EKjso2YFT7LNfm+sCiKwvGk42w8t5GN5zayL2FfjvUVvCrQNrgtbYPb0jCwIR4a58++xW7hsWWPcSXzCqNbjObpak/n63jpSWb2bzxP1UaBlKnoXcBnUzAup19m8IbBHLh6AI1KQ7B3MKdTTgPwcPmHGdF8BOW9yrs3yALy4eYPWXliJQ+WepA5nebwws8vcCL5BE3LNWVG+xn31LtbkI7+/APWN0ejVmDuo2oOtK/ChbQLWB1WWvg35KND1UmduwDsdtS+vpR9+218n+5WohLcvBSbpElRFAYOHMjy5cvZuHEj1apVy9UmJSWF8PBwdDodP//8M0aj8bb7/eSTT5g0aRLXrt283k+HDh3w9vbmxx9/BJwTwZs0acLUqc7bwB0OByEhIURERJToieA3+nj1QWZuPkWzKv780L95rvUXkzL5cEUs6w9fwbP0BnQBv9LuZC+qX2mKzqjlmXcb4RfofH/emrqATxMGEm99kJ/TP8ZsVuPto6b2pk8xJJ7D96mnKDcuMtcvY1JWEtvjtrP14lbO/pVEw/1PYlNZWFQ/klT9Nbw9vGlSrgnNyzWnefnmBHvnHN5LtaSy6fwm1p1dx6YLmyAtgy++tuOXDks7GLH+pzOPhjxKs/LN0GnyTqgBFJuN0z16knXwIN4dO1Jxyhe3vX7bLm3jzY1vkmJJIdAQyBePfEHtMrXzde3jM+J54ZcXuJB2gbDSYXwb/i0mD1O+ts3L9kvbeef3d7iadRWD1sCo5qN4sNSDvBnzJieTT6JRaRjcYDB9avXJM6HI3B/LpY8+wnzY2W2vq1YNny5d8Hn8cTwrVrjruG7FkZ5O6vr1JP/0E+mb/wCHgzIREQREDLjpNsnmZJ5e9TRXMq7wn+r/4YNmznIXF9Iu0G1lNzJtmYxoPoJnH3z21sfOzORy5DgUhx2vlq0wPdwCjXf+kpHNFzbzybZPOJ92PsfyOmXquBKlan7VbvrBM/fAXCb+OZHKPpVZ8dSK237AKorCiv/9xcVjSag1Kpo99YBzmFtddD7Y9sbvZciGISRkJuCr82VSm0k0KNuAuQfmMm3PNCwOCwatgcENBvOf6v8pMknF3dgZt5OXf30ZFSrmPz6fegH1OJF0gueiniPTlskb9d7g9fqvuzXGDGsG3/0yjoYfLcFohpj6Hnh+MJgXar3I7su7GbR+EBm2DGr61+SLCoPJ/HgS5oOHADA2b0a50aPxDAlx6zkUpmKTNL3xxhssXLiQlStXUr3633e6+Pr6YjAYSElJoWPHjmRkZLB8+XJMpr8/RAICAtBoNPz0009cvnyZZs2aodfriY6OZvjw4QwfPtw152jy5MmEhoZSq1YtsrKymDlzJlOnTuW3337j0UcfBZwlB/r06cOMGTNo0qQJkydPZvHixRw+fJiyZcve9lxKQtJ0KTmTVuM3YHMorBjwMPWD/QBwOBQW7DjL+F8Ok2a24alRE9HuAU5rZrDu9DqePjSUMinBlCpn4pl3GuKp17Im9hJH5k5Hl9ocO56UDTHyeEQDHH9t5dwbA8Bup3T//gQOG8qVjCusObWGX0//yv6E/Shc/5FUoMuhCCokV0NVKY2Wr1QmrHQYWnX+KnZn2bKIHfUWpqVriSutZmg/FXaN84PFqDXSskJL2ldqT4vyLfK8eynr0CFOPdsDbDYqfPEFPuEd8zyOoih8f/h7JuycgF2xU7dMXSa3m0yA8c66tk8nn+bFX14k0ZxI83LN+erRr1w9Evlld9j5ev/XTNszDQWFqn5VmdR2kmueV4Y1gzHbxriGS9pWbMvHLT92nb8jM5P4KVOdRT4dDjS+vpR9/z18nnzyvv61mbRsGZc++BCACpM/x6dT3ncXvrfpPVafXE0ln0osfmIxRo+//6iaf3A+E3ZOwMvDixVPraCsKe/fY8Vm4/ygwaStX//3Qq0WY/36mNq0xqt1a3QPPpjn+R+6eog+a/qQactEp9HRrFwz2ga3pU3FNvl+/9Ot6XRc2pEUSwqT2kyiY+W8f86yHd0ZR/SsgzmWVaheivZ9a+JVquDuNr1bK4+vZPTW0VgdVqr6VWXKI1NyDDmfSj7FqC2j2H1lNwD1AuoxusVoHvB7wF0h3zWL3cIzPz3DqeRTPPvgs4xoPsK17qcTP/H+5vdRoXL2gJbP/Yfo/bD+7HqmbPyEwf93kaAkuFjVjxrzf6BCqUqciE/DQ60mlVO8sfYNrmVdI9g7mOnt/g/Tj+uInzIVxWxGpdcTMHAg/n1eLJFPTCg2SdPN/hGePXs2ffv2dZUKyMupU6eoXLkya9as4b333uP48eMoikLVqlV5/fXXefXVV1Ff79afMGECX3/9NRcuXMBoNFK3bl1GjBiRa99ffvkln332GXFxcdSvX58pU6bQtGnTfJ1LSUiaAN5cvJdlu8/zWO0gpvVuyIn4NN5btp8dp529dg1C/JjwTF2qBnqTYc3gxV9e5NzlS/SMfRed2URovTI89loddkefZdvyEwCE6rbTocVJPHo659Xc+IG4tntlvnnwwt+JElDVryrNyjWjefnmVFVqsXLcPhx2hcdfr0NovfwnIuYTJzj5VFew2Sg/YxpHHjSy9uxa1p1dx5WMnPW3AgwBVPWrygN+D1CtVDUe8HuAqn5VSf/qG65On4GmTBkeWP0Tmn+UrLDarXyy/ROWHXMW8exSpQsjW4y8ZS/WrcQmxPLyry+TacvksdDHGNdqXL6Hlq5mXuW9Te+x9ZLzjs9uVbvxXtP3MGhz3tqvKApLjy1l3PZxWBwWypvKM6ntJEKPpXJpxEis1+c1+Dz+OGU/eP+eh+Du1uVx47k2Zw4qvZ5KC77DUKtWjvW/nf6NN2PeRK1SM++xedQLqJdjvd1h58VfXmRfwj7aBbfji3Zf5Po3R1EU4kaNJmnRIlSenvh260bGjh1Y/lHgVFu2LF6tW2Fq3RpT8+ZovLyIz4jnuajnuJxxmRblW/B5289zJG134qs9XzF973TCSofxQ+cfbvpvoyXLxoKR28hIttCkSyhGH082LzmGzeJAZ9TS9vkat5wreC8cWVmk/vYb+jp18pxvZnPY+HzX58w7OA+AR4If4dNWn+bZY+pQHCw9upT/7fof6dZ0PNQe9K/bn361+93xHwru9PW+r5n611T89f6s6roq1x9fI7eM5MdjP+Kv92dpl6V3/IfUvbiQdoFx28ex6cwGPvjBQe2zCvag0tT4cRVaf39iLyTz9P9tweCp4Y93H+Gq+QKvRb/mmo81vf10QlP1XBo5ioxt2wDQh4VR7pOP0dcsnBtW3KXYJE0lSUlJmo5eTqXj57+jUsGrraowZ8tpLDYHRk8Nb4dX54XmldHcMAxwIe0C/1n9HzwTfOl2cAgqh5qAEG/izzrnHp3WXeVTv//iobKR8cy3bDDq+eXULwQuiuHZ3204gP89rcbaqiGPhT7GoyGPEmjM+Y/+1uUn2P3rGbz99Tw3qikenrfvylcUhXOvvEr6H3/g1a4dwdP+z7XOoTg4kHCAdWfXsfPAPpKvZpBgOk+6ZzL847MqWFeOD6ZfxT8ug5RHG+A95gOq+FZBr9VzNfMqwzYOY/eV3ahVaoY1HMaLYS/ec4/MHxf+IGJdBDbFxgthL/BWo7duu88/4/7knd/f4UrmFfQaPR82+5Cnqj51y20OXj3ImxvfJDH+HC9ugHZ77QBog4Kcd9Dc5A+W+0Wx2zn339dJ37QJbVAQlRcvcs1HS8hMoNvKbiSZk3i1zqsMajAoz30cSzxGj596YFNsTGwzkfDK4TnWJ0yfTvzkL0ClosIXk/Hp6OzlsZw7R9rvv5P++ybSt29Hycr6eyOtFn2Dh1gdeJFfguLQVKnMgicW4uN597/3iVmJhC8LJ9OWyYwOM2hRvkWe7f5Yeow9a8/hE2Dgues3SCRdziD62wNcOeP8navRPIhWPR/EU19wPQLWy1c4P2AAWbGxAJhatqTU873wat0alUZDsjmZt39/my0XtwDwWt3XeKP+G7dN+OPS4/h428fEnHcWwK3qV5UxLcZQJ6BOgcVeWM6lnKPbqm6Y7WbGtRqX59zFLFsWvX7uxbHEYzQq24hvOn6T757yu2W1W5l7cC4z9s4gy5ZJ/1+h/V92VEYjlX/4Hv2DD5JpsdN56iZOxjtLXUzoXpcejYOJz4jnv2v/y9HEo3h5eDHlkSk0KtuI5B9/5PL4CThSUkCjofTLL1NmwBuobzO/uLiQpMkNSkrSBNBvzk7WHf67J6b1gwF82q02FUvl/Vf0jks76B/dn6qXG9HuxPWCkCpo+FQV+v0Ry+PGr8EnlhiTkczsD39FYfgGH5psTwRPDyp9++1Ni6tZzXYWjt5G2jUzjR6vTNMnc5cU+KfUtWs5HzEQlYcHVaJW5xqPVxwKO1af4s+fT7uWqQwOrP6pXDVd4JTHIU56HCRFf5VqFxyMnW9HDXzaQ83eB5yTWjNsGSRkJuDl4cWE1hNoVbHVbePKr9UnV/PepvcAGNZwGC/VfinPdg7Fwbex3/LlX19iV+xU8a3CpDaTqFqqar6OczlqJedHj8CYYgHgYJtKdIicg49/UMGcyD2yp6Zyuud/sJw8ib5eXSrNm4fK05OB6wcScz6GGv41WPj4wlv2Tnz515fM2DcjV29A0o/LufT++wCU/fBD/Hs/n+f2DrOZjB07Sdv0O+kxv2M5cybHelXZAHxatcazYjDagAC0gQHO/5cpg8bf/7YT2bON3zGe7w59R9OgpswMn+larlgs2BISiD98iRVLklAUFS0rniJIE4/v093QV6+O3e5g50+n2PXrGVDAp4yeDi/XIqjKvRfNzIw9wPk33sB25Qoqo9E5Wf/6x4ZHcDCOrh35wG8dh23nMWgNjH14bK7k9FYURWHN6TVEbo8k0ZyIWqWmd83eDKg/4K577gqboiiumw2alWvG1x1uXprkVPIp/rP6P2TYMm6Z4BeEnXE7+WTbJ5xIdvbyv3q0Ih2WnQaViopffYX3I84/hN5fvp+F28+6tmtcuRRL/utM1FMsKQxaP4hdl3fhqfZkfOvxtK/UHlt8PHGffErqmjWA807XilOmoq/+YKGdz/0iSZMblKSkadeZRHrO2IqXXstHncN4ukGF2/Z0LDi0gHE7xtH43OM0SwknONyTbYZofjr+K1bl78J9IWo9j9Xuw+OhjxPqXck5l2TdOtQ+PlRe8B26PG4GADj5Vzy/zNiPWqviuY+a4lf25v+YOrKyONn5CawXLlD6tdcIHDokx3pzhpXo2Qc5s/8qAL6BBlISslAcuX8VNDpQlbGgv3SA4AP7cCjn+eD5eDL0zraVfCox5ZEpedaGulfZE4RRFCaEDqXZJS+yDhxAWy4IQ+3amKsG89HBz5wT3nEODX7Y7MN8fdBYL1/h8sdjSY1eC0BmBX8mPJLGgYoOKvlUYlKbSUWmorblzBlO9eiJIzkZnye7sP2VpozcOgoPtQeLnlhEtVJ5/8y4trdbePanZzmZfJKuVbsy9uGxpG3axLn/vu6cW/fqKwS++Wa+45n32wT2/zSXBieh3jkNKsstimhqNGhLl3YmUTe+bkisUBSsV65w7fwJvt/8Fb6pDtoY6mJIysQWH489MREF+KveIJJKVadMwl7qxn4NgMrDg4A3h+H/4ouo1GouHkti7eyDpF7LQqVW0eixSjR6vDJqzd3dPZiyZg0X330PJSsLz6oPEDxtGqhUJH7/PUlLl+G4XpDYrIVd9Uy0GDSWmk0fu6tjJWYlMmHnBFafXA047zgc1WIUzco1u+V2WbYsrmZdJT4jnquZV0nITCA+M56EzAR8dD68UueVe+oFzMsvp37h7d/fxlPtyY9P/Ugln0q3bP/zyZ95Z9M7qFAxrf00Hq7wcIHGczXzKv/b9T9WnVgFgL/en480T1Jh5Gyw2wkc/ialX3HWLPvtQBz95+8CYNKz9Xhr6V4cCmwY3pbQMs6h1CxbFu/8/g7rz61HrVLzUbOPeObBZwBIXbeOuNFjsF25gkf58lRethRtqVIFej73myRNblCSkiaAs1cz8DN54KPP3/wCRVEYsWUEK46vyLXOYfWhVGow0zLXU9tiRtVrMTzo/EvUkZnJ2ZdeJnPPHucQzA/f4xGUu5dDURRWf7mXsweuERzmT5eB9W6ayCVMm0b8F1PQli3LA7/8jPqGOy6vXUzn5+n7SL6SicZDTdvnq1OjWTlsVjtXL6STcC6V+LPO19UL6Xk+OFiNHUMFD9RlrFTwLYeH2tNZgV5xxqmA62uc/4HDuTx7mclPR7VGZSkT7JXnPBvLyZNk7NzJ7jXzMe4/id9NCkZf8YXT5TRUavIIjdr0xFC7Nhrfm/cuKA4HSUuXcuWziThSU0GrpfQr/Sjz+uvsSz7E8JjhXM64jE6j44OmH9CtWreb7ut+St+2jbP9XgG7ncWP6Fja1M6bDd+kb+2++dp+z5U9vPjLiygozKr0Pj7DJqJkZODzZBfKjxuX796gtWfWMnSjs2r8R80+4plKT5IRvYKM6B+weVbGlpSOLT7emezc4u7dO3WlXBNiq/dBrdh41GM9PmW9sJw9S/omZ8JsatGccpHj8CgbiDnTxu/fH+HojssAlA31ocPLYfgG5L/XRlEUEv7v/0iY6nyslKl1Kyr873+uWlyKojBn1wz2LPySTn/aqXzDFEFDo4b4P/883u3b37KsyM1sOr+JMdvGEJfufBxPt6rdaBTUiITMBOcrI4GErL+/TrXeuop1Df8azOjg7GksCCmWFJ5a8RQJmQm8Uf8NXq+Xvzvjxmwdw5KjSyilK8XiLosJMt17b67dYWfZsWVM3j2ZVEsqKlQ8++CzvOH/FAm9X8GRmprjTuXLKVl0mvw7iRlW+reuwvuP16Tv7B1sPBJPRLuqDA//+w8lm8PGx9s+ds3XjKgfQf+6/VGpVNiTkpw9wGfOYGrRnOBvvkGlKb53QErS5AYlLWm6Gxa7hZd+fYl98fvw1fnSoVIHHg99nOm/Kqw7FM93wStpGb8IvMvDgG2gd3642xITOfN8bywnT6Lx98ev+9P49eiBZ3DOIo9JVzL4YcwO7DYH4a/WznPCq/XiRU483hklK4vykybi2/nveQYn98SzdvZBrGY7XqV0PPbfOgRWuvl7Zbc7SIrLcCZR51K5fOAiCRcysGsLbhzfr6yRB5uUpXLZLLTH9pC+YwcZO3Ziv3o1RzuLBk5U1PBgy84knjuBJfYg5a/l/avrUSkEQ63a6GvXRl+7FvqwWmi8TFhOn+bSiJFk7NgBgL5OHcp9PBb9DXeuJmYl8v7m99l8wVmV/6kHnuKDZh/kmkzuDlcXLODK2I9xAD/2q8b7by6/o1vVP93+Keu2LiRyvoJ3usP5j/306ag881dQ8sY75XrV6MV7TZ3Dp3z7GJzdAoG1oN+voHOWKlCsVmxXr15PohJcydQ/Xyq1Gm1gINqAANJ9dSyI/5lrXjCk42gqVq6Lw9efxZ8fJv365O/GnZ2TsBVFIWnRYi6PG4eSlYXG15egsWNc87KO7owjZuFRLJk2PHQaWvV8kBrNg27ba+zIyuLS+++T8vMvAPj36UPg22+5PhSzbFmM3DKSn0/9DMAz1bozVPsYqT8sIuW3aLj+WBxtYCB+/+lJqWef5fBBM/Hn03i4e9V8zbVKt6YzeddkfjjyQ77eG0+1JwHGAEobShNgCKCMoQyBWTq2/LmcXWVSCPWrwjcdvrnpHZR34uNtH7PoyCIq+1Rm2ZP/3955xzdR/3/8maRNuvcutIUCZZZN2bJkykaGqAgKKkvFAU7AxdeNAjJUwMWWPWULQtlQyiyl0NK9R9o0635/HLSUlg4oFH5+ng9Cxl0un/vkmnvde/5V7oKk+aZ8nt36LBfTLtLMoxk/9/gZS+W9B72HJYfxaeinXEiTSwPUc6nHB60/oIHaXxY0165h3aQJfr/9ilKtxmyWeH7xUQ5eSaG+twPrJrRFY6FiS1g8E5adxNvRioNTuxSJWZUkiTmn5hQUxx1RdwTTWk1DqVCiu3yZa8NHIOXmVthae6/oY2KKnRcqAyGaqgAhmmTyjHlcSrtEA9cGBXEmhyJTeOanIzhbGjnuOh1VRhQ0ex76zSl4nyE2luujx2CILvSz27Zrh9Owodh37lxwxXpk01WOb7mGrZOGZ2aEFPsBvvHGG2Rv245Nixb4/f4bCoWiWPySbx0neoxtiLV9xasvx33wEfFb9pFboymWz04AlUo+Ccn/CmrlKBTyf7fOT4XrKJCQSAiPI/pKLiZz4Q+UQ1YUnonH8Uw6gUapx7pJE2xatUTTshnvpi7hn6TDqBQqTJIcsN3PsxtT7AfAxSvkhYejCz9XkPlWBIUCdY0aGGJjC9OHX3sNl+efK/Hq0CyZ+eXsL8w9PRezZKaWUy3GNBxDdfvq+Nr54mrt+sCLRZbEr+d+Jf3TL+hxSgIba2osX1GheIqsxBucGtQLj1Qjmf4utPxrR7mrmN+ZKTev6zw5oDfuFCzqVLhi7R4wYjncR92h1/a8xp6YPfQP7M+n7T/l0F9XOLUzGgc3K0ZMD8HCsui2869eJe6tt9Gdl8sQOA4ZjNe776K0tSUrNY/dSy8QF5EBQGAzdzqNrIuVbckn6yIB3xYWeM+YjtOQIQXLE7QJvL739YKCldNaTWNY0LACIWZITCJj1SrSV63ElJwCQJZzTY43ngIo8G/oSu9XG5XbXXgy8SSLzi7CZDYViKEiNxv53t7SvogYzNy8hYSZMzFnZ7O7nR0LO+Tha1+Nn7v/TDX7kttElYezyWcZuXWkbLHs/gutvFtV6P3RWdEM3TwUrUHL6IajmdJ8SoXHkK5L5/uT3xdYgOwt7ZnQdALDgoahMknEvPwy2kOHsfDxpsaqVbILGPj5wFU+3XIBK0slmyd1oJaHfOzrDCZCPt9NZp6B38a0omOd4hl+t8IvAHoG9OSz9p+hVqnJ2raN2DfkfSitLEtlkLN/PzcmTsJt4kTcXh5XqdsWoqkKEKLp7kiSRO8fDnIhPovZbXIZcOpmP7Dn1kFgYY8jyWAge98+MlauQvvvvwXBphbu7jgOGYzzkCEo3L1Y/vERslJ0NH3Sj7aDCwOetaFHiH7hBVAqqbH2L6zq1i0WvxTcpRptB9dCdY8xHqbsbK4+1RdjYiIuY8bg+c7bBfto1uZiSkm+aV1IwZiSgjElGVOR5ykYU1PBYMCosiLZLZhEz5akOdeFm0JEoYBqQU7UCfGmZhN31NYW5BpyeXHHi4SnhmOptGRqy6kMDRpazGpgTE9Hd+48uvBwdOfCyQs/hzE+vmC5bds2eM2cWa6rtaPxR3nnn3dI1RW1emlUGrxtvfG198XX1le+t/Olml01fOx8cNI4VXpNpyvpVxi2eRgmQz4/bfPD7mwUlr6+BKxehUUpDb5vYc7N5foLo9GFhZHkCB+NsmTe0GXlKj6ab8pnzPYxhKWEEeAQwJ99/iyMkflrLJxdBdVDIP4MGHXQegL0/Pye9zUsOYyRW0diobBgZfv17P76KmazRJ8JwQQ0Krklh6TXkzxnDqk//wKShKW/H75ffYV1cDBms8Spv69zdGMUZrOEraOabqPrU61u0Xm7PeBb5eSE7w/fY9uqUBQcSzjGO/+8Q0peCk4aJ7554pu7igZJryfr752k/bmMA4puZN8W81O3hStdXgx+IHW/TNnZJHz8CVmbNhV5PbSFHd91zcPd1pOfuv9ETafSYxCNehMWd2TpGs1GRmwZwcW0i/St2ZfPO9zbd3yrVAbA3C5zeaL6E2Sn6di2QO7lGNjMncBmHgWFggv27aYr7vuT35Oll/t39g/sz+vNXy9o1ZLwyaek//mnnCm37E+s6tYF4FxcJgPnHUJvMvPZwIaMDCkag/XRhnB+O3ydfo19+GFEyX05t17dyvv/vo/RbKS9cyeeV03C288Fyy1LyVr6C0obG/xXriDfz4Ok3CSSc5NJyksiJS+lyPPk3GQMZgNOGicc1A44aZxw1DjKzzV3PL+53PJYOEmT3kAyGLDv3h3f2d+V26VeHoRoqgKEaCqd1cdjeHtNmGwCDt6G6thP4Fgdxh8ucGfcjj4mhoxVq8lYu7bQVaVQYNexIzkdhrLnICiVCoZ+0BJXHzsko5GogYPIj4jA+ZkReH30UbH4pc4jgwhq7X3f+5K9dy83Xh0PSiXWwcGySEpJKdICpCwUVlZYN22CbatW2LRqhdk/iMiwdCKOJZIYlVWwnspSSUAjN+q08sSxloq1V/+iY7WOFQrSNqakkBcejlKjwaZ16wqdrFLyUlh4ZiGRmZHEZseSkJuAWSoe53U7NhY2BUIq0DGQp2o+Ve5svpIwmAyM3DqSC2kX6ODbgR+afca14cMxREdj3aI5/osXl+pik4xGbkycRM6+fagcHVkzpRnLdQeo7VyblX1Wlpp5J0kS0w5MY2vUVhzUDizrs6ww6DcrDum7YKLymhIXNIPgoGQc/r7ZdPep76DFmHve5xd3vMjR+KO8dP1jLOIdCQh2o8/44DLfpz1ylLhp02ShrFLhPnECruPGoVCpSLqexc7F58lIzEWpVDBkWgvc/eS/vZICvm8Ja4PZwPzT8/n57M8FBVPndJlTLotN+P4b7F9+GQsMBF5azaU6w0GhJLi2nvZTelSqcMo9fpy4d6ZiiIsDpRK3V1/FwtODhBkzwWwmLNieWT1zcbBxYUHXBVRX1SQrJY/M5DyykvPITCm8z9ca8WvgQs9xjbDUyOLpt3O/8dXxr3BQO7BxwMb7anV0q1eio8aRPzot59/5sWQk5hZZx7WaHbWaeVCruQcxykg+O/IZ51LPARDkHMT7rd+nqUehwElfsULeV6Da3DnYd+sGQJ7exFNzDhCZrOXJ+p4seq55sXk/eyOTvnMPorFQcvT9bjhal/w3cSj2EG/vmkr3M+Nwy5U7A5iVJmxzI6geE4bJdI6PRqSTZ1V532vjq2beXmNGbYIz9ayIemsw77b7oNK2D0I0VQlCNJWOzmCi/Rd7SMnR8+PTdeh9YDBkXIeWL0Gfb+76PkmvJ3vPHtJXrCwosAZwtvlkku2D8A6wYeDUENL/+JPEzz5D5ehI4I7tXL9mKIxfctHQ+5XgghNEZRD71ttkbd5c7HWFjQ0Wbm7yzdUVC3c3VLeeu7lh4eKCReYZLEwJKOxcwNr55s1JvrdyIjNbzeXjyVw+mljkh1RjY0FgU3fqtfPBs4ZDlfSDMpgNJGgTiMuJIzYnlhvZN4jNiS14npyXXOL7mno05ek6T/Ok/5NYlRETJpklIo4norayoHo9F+aH/8jCsIU4ahxZ128d7jbu5EdGcm3YcMw5OTg9PQSvjz8ucT4kSSLho+lkrF6NQqPBb8lidPUD6L++Pxn5GUxqOolxwXc39f8U9hM/nPoBC4UFC55cQIi3XOzWbDIT8esCTp60Ic0ol7OwcVTTr+0pXE99AAoVPPsXBN5bratDcYf4cvl8nowYjdJCwTPTW+PoXr64MlNmJgkzZxbEJFk3a4bPl1+iruaLId/Ejp/DuX42FRcfW56e1oL0nxYUDfj+5puCFjLRWdFMOzCNsymyFWRgrYFMazWtXBmauVl6ls0IJT/XSIdhdajlmMzRL9ZwzlnuwtDYdIRW059DXUKD9oogGQwkz51H6k8/gdmMZfXq+Hz5BdaNm5CemEvCjoPcWLaJPLULKW6eJDg5Y5/vikoqO57It44TfSY2JtWQTL/1/cgz5jG9zfSCTLJ7RW/S8/y257mcdIVhl9/GLsMdO2cNTbv7cy0smRuXMopk86baxBLpepoEz8s83244w4KGFan3dHuihPvrr+P2yssFyz5Yf5Y/QqPxsNew/fWOuNgWv8CQJIle3x/gYkI2nw5oyLOtS84GNBnNrPj2XzKuGsizyMGo1GOvL2qxVBoSuBAQidY3GbWvETc7Oc7Mw8YDdxt33K3dUavUZOZnkpGfQWZ+ZrHHmXr5uc+5JF5dlonaBEfrKPhugJI+dfrzWfvP7mv+70SIpipAiKay+W7nZb7fHUFTPyfW9TTAbzeLL47aDDXKrnGUHxVFxqrVZK5bR45OyZGWH2JWqWmmOIrLiXWYs7LwnD6dCNtWlRK/VBrmvDyyduxAaW2DhXuhSFLa3qVfnCTB5R2w5xNIDC9j6wqwckSyciKFelzOakFEal20+YUnKldfWxp08CUoxAu19QMqlpeThPHIT+SeWIGxbj9c+n4KZQg1nVFHnDZOFlHZsRyOP8y+mH0FcVj2anv6BfZjSO0hd7U+Hd8axZGNckVulUbBRYejXHE5xcS+o+lZq7D+T84//8hlA8xmPN97D5fnnyu2reQffyTlhzmgVFLth+8Lrr5v1cGyVFqypt+aEktG3JkpNzRoKEaDiYuHEzi14xpZqXITY7VawspBLluhsbagd8Pt+MTMA40jvLQT3CteukGfZ2Duu9vQ6OygRTITXhpWofdLkkTWxo0kfPwJZq0WpZ0dXh99iEPfvuhyDCz/+Ah52QZqW1yh+q7vgKIB35IksenqJj4L/YxcYy72anumt5leofpLu5ee52JoAm7V7Xj63ZYolQokvZ69n23gQqIrCslE8KUl1Hv+STm+7i7tOa4k5VDdxRqNRfE4sfyoKOLefqeg6KbjwIF4vv8+eQYLti86W8RqeydmhQlrJws8vJ1xdLPGwc0aR3drHNytydca2DI/DIPORLW6zuxr+Du7YnfS1KMpS3surZSYvutp0fz8xd94ZdZEsjIwclp7nL3k34+cLB1rd/7N5ePxeGTURCUV7ruLjy21mnsQ2MwDZzc12iNHiX3zTbkkR9+++Hz5RcEFxM7ziYz97TgAv7/Yig61716R/FbMU+PqTmyYULwkgiRJ7Fp6nstHElGpFeT0OofK3YCrzgdNrAuGc2ayEpRIt8XzWWpUVKvrTEAjN/wauGLnXP5OCdpDh4h5dTxSfj5WnTpi8fk0ssy52FjaUMOxeEX6+0GIpipAiKayScrW0f5/e9GbzKwd35ZmZ2bAiaXgHACvHgJ1+RrUmvPzyf77b46uvchlTXPU+Zm0Pvox6qAgLneeRvQ5OdW7cZfqtB0ceM81aiqNawdh98cQc0R+rnGAoF6g10JeBugyIC9dfmwoubaAWVISp6/PpbzOROjaYUL+8bFQK6nT0pMGHX1LzQSsEInnMR36kbgT54jObUScoT51rfYR0MYL+wHfQgVjCZJyk1h/ZT1/Xf6LOG1cwetNPZoypM4Quvt3L7A+RZ9LZdPcMyCBlZ0FuhxjwfpqKxU1GrtTq7kH1eu5oLJUkrp4CUlffglKJdUXLcKufeGP/e3terymf4TziBEFyyRJYvzu8RyMPUgzj2Ys6bmkyInwzky5Nxu/zbkDcZzeFU1uplwI1EqRSWO3gzSa+jmSQsnWH8OIj8xEZamkR8AqamT/KR/bL+0B24q5cg6vu8LJHdFkaVLY1nIe24dtu6dCj/obN4h7+x3yTp0C5NY4XjOmc/VkEn+viAbJTPOw2QRNGYXz03JT42x9Np+EfsK2KNlS1dyzObPaz8Lbrvyu7biIdNZ9cwoUMPid5njVKCyDIUkSO+cdIyI8B6Upn2anZ+Ne3Rbvjz/BumHRVjnbwxN45Y8TtAxw5s+XWqO2UBZsI2PVajlzMC8PpaMj3jNn4tCzB/GRmWxfeJbcLD0qSyXOXjY4ultjbczEuHE5Vpmx5DjnMvOpBPLtLfmm0zd0qt6p2D7EX8lg45wzGPNNRDudZ1fdpSzvv4w6zvdfzNFsMrN9UThRZ1LQK3VsbDCHD/u+TRe/LoSnhPNp6KcFrrj6to0YbTsJwxVrblxIx3ybBco2LxGPhON4JJ/AtZYn/r//hlIj/zYkZeno+f0B0rR6xnaowft96pc6ppScfFp/vhujWWLnGx2p7VnUMn94fSQnt19HoVTQZ0Iw/g2KH9NJK9ZyYe5qUl0bklGjDXm6ostdq9nh39CVmk3c8fC3v6u1XHv4MDGvvIqUn49d585U+352ubNc7wUhmqoAIZrKx63edn0b+zBnYCD82AaybkDr8dBzVoW2ZTKYWf7RQTLTjXjrI9F61CErwyTHLz1bl6CQKq5qHXdKFkuRNxvBWlhDyMvQ7jWwuUvwslFfVETlpd/2PB3SotCd282l7Nacy+1BuqkwoNvD354GHX2p3cKzIA6j3JjNaM/s5vquvVy/YUuMvjEGqehJurvj1wS0qYVlv9kVFk4gB7Iejj/MmstrSrQ+9XEbwJF5SeRrjdTv4ENo4Dr2HAulYWYbGmW1Iy+rsIik2tqCGo3dCGzmjsWy2eSsX4vS3p6AlSvR1KxBzv79xRpD30l8Tjz9N/Qnz5jH+yHvM7zucECO4xq+eTiJuYl0cOvEKOl1zu6NJV8rCzhbJzVNNauoL63Ass8n8ncKGPQm/v4pnGtnU1EooYvnn9SV1oBfW3h+PViU7yo7PUHLio+PYjZLnGi6nmNWe3mrxVuMajCqwnMOckxXyqJFpMz7EUwmLLy9wWzmjHNPEr1a4eCgYPinHbFUqziVdIpp/0wjThuHSqFifJPxvNjwxQqVdjCZzKz67BhpcVrqd/Ch88i6xdcxmtk87ww3LqRjacih+YmvsNGn4fL887hPnoTSxga90cyT3+3neqrson6hbQAz+jXAmJZG/AcfFjRYtmnTGp9Zs7D08uL8wTj2L7+E2STh6mtLr1eCi7g188LPEfPSS5gyMkjztuXdwTqyHSyZ1WEWPWsUbwp99XwCm+aewcJsickvkwnv9Edlcfdj35SVRf7ly1j6+mLhVXJ5B0mS2Pv7RS4cikdloSTnyfP8mjEfe7U9Xf26suHKBiQk7CztmNh0opydmJVD9r59pO7cz/XLWpKcGpLmXBfpNhddcHt32j3TEKVSgdksMWrJUQ5EFC0vUBZjfzvOzvOJvNyxJu/2Luwvdys2DaDL83Wp19bnrtuInzGDjBUrUTg44jD3N+JSLLkenkpCVBa3tRjFw9+e4C7VqdXMA5Vl4ZxqQ0NlwaTTYdepE74/fI/yAQomEKKpShCiqXyci8ukzw8HUSkVHJzaGe+kf+HPwYACxmwHv9Kr/95JzPk0Nv5wuuD5g4hfqjDJl2DPp3BBrs6L0gKajYKOb4PD/Qeik5sGp5chHfuF+EQrwnO7E6lrixk5RkNtpSIoxIsGHX1x9b17Sr3ZLJF0JZnre//l+oVsknVFY0v0CgOXLRTU8bBDHatDhZ4BLh/h2aolin4/3Fda/Z3WJ5XZgv7hr+Gh9UPtZSJwlAWv/SO3m1jYbSFtvNsQfzWTKyeSiDyZVGDtAVBbq3DPvIDrpd142eXi/eF73Jg0CSkvr0hhv5K4lUptY2HDhgEbcLZyZsz2MVyJu0b71P7UiW+FMV8OfHd0t6ZZT3+CnMNQrRomu9+mnAdN4RybTGb2/n6RS6FyYca2TitoarUSGo+AAfPLdG9KksTG709z42I6AY1c0T15hRmHZ+Bh7cG2wdvKXROoJPJOnyb2nakFZT0UtepzuO5kcnNMNOriS3id3SwMW4hZMuNr58sXHb8o1gS5PJzaGc2hv65gZWfJyBmtsbIrOXZIrzOy7puTpMTkYKvQ0vTgx6gNOVj6+OA1YzqrVdWZuek89hoLsvNlwfpTkB7/X77FlJIiV0N/4w1cXhiFWYKDqyII3x8LQGBTd7qMqldiTaj8K1eIHvMixqQkst1tmDYknxQnJTPazmBQ7UFF1v3m+DfsPHSI3hfHoTJbUrOJO93HNiiWfWuIjSXtt9/JWL0ac64s8pS2tqgDA9EEBqKpFYg6UL4dP6Lj9K4YFAro+XIjqjdy4oXtLxCWElawvb41+/Ka33NYHjxJ9q5d5B49BiZTwXJLHx80XXqQXrM90Wk2XA+Xret+DVzo/lJDfj8efVt5gfbU8ijf7+GOcwm8/PsJ3O01HJ7WBQuVkqgzyWxbcBZJokidsLth1uuJfu558s6cQRMURMDyZShtbMjL0RN9Lo3rZ1O4ejqloHCwtYOahh18aNDRFy6eIebll5F0Omyf6Ei1OXMeuGACIZqqBCGays/wRYcJvZrGq50CmdqzLqwfD6f/BNda8MpBsKxYIcW/fw4n4ngSvkFO9Hip8uOXyk1GNOz7As4sA8kMKCB4GHSaBi6V64MHwGyGq3vh2C/kXTzIxdxOnMvtTqap8CrQq6YjDTv6ENjMAwu1Cp3WQMz5NK6fusH1cyno8oue0DycMvEIrs6XlzI5p9PRq5EXc0c0ZeXsU6RHZGKlyGSI61Qcm3eB/vPuSzhBofXpnz8jcLzqT55FDn8Ff02OJh2AYUHD+KB10UwZySzdVUBZGHNxSTuPJj8TGx933IcNwMpOg9rGEo2NBRprC/nexhILtRKzZGbU9lGcST5Dx2odcdK7kx6qpG5SSEGgsKuvHc17+RPYzAOlUgG/9oWof6DtZOj+SbF9kswSh9Ze4fQuuWZWU9v1tLH7FUW3j6BD6QUAr5xIYsdP4agslIyYHoK1i4pef/UiKS+JmW1nFpzUT0anM3tXBN4OVtTztqeetwN1vR3umvVUMN85WlLmzMGcq8XjnXe4cV3P5rlnANhQ/wfiHSPpW7Mv74W8h526fDWsbicnXcefM45gzDeVaZEA0Gbm89cXJ8hO0+HmCo1D/4cUK8/bv/7NmFuvL++MaEt8UjrGH+fQL+pfANS1AvH9+mus6tYlN0vPjp/CC2pRhfSrQfNeAaUmSuhv3CB69BgMMTHkOVnz3tN6Yt0UTG05lWfrPwvApbRLcpkLycT/AuYQvVrCbJSo1dyDJ8fUR6lSojt/ntTFS8jatq1A1Kjc3DBlZBQU+byda35PcrXmAACaWp2lTj1rNLUCyfC25+VzHxGQpebljKY4HLmILiysyHs1tWtj/2Q37Lt1Q1OvXpH9u3Iiid1Lz2M0mLFxs+JHYybJmEsN6i4Jg8lM6893k6rV88uoFjSwsmLDt6cwGszUa+dN52frlisBxZCYSNTgIZhSUnB46il8vvqyyPvysvWcOxhH+P5YtBlybKBSCR5JJ6h2fTfeTf1kwaQpfwzU/SBEUxUgRFP5udX7yNHaktB3u2JtyoJ5rSEnAer1hUZPg08zcKxW5pU5yPEBSdHZePjZV038Uk4SHPgGji8G080TeN2noPP74Fl6HEGlkREDJ5YinfiNG+lenMvtQVR+K8zIV9oaGwucPG1IupbF7X/xaoWW6naXCQj2xK9nL9Quzjy/+CiHIlOp6W7LhgntsLeyRK8zsuSTIxhT83FSxTDEdRqaxn1gwAJQ3V8g+vl/49j7+0VQgOqpWNbl/UGcNg5/B39WPbWq1FgeySwRH5nJlZNJRB6NI1dbejmE21EqFahtLFBaSVzXXcWg0OOVXQMlshD0rOFAi14B+DdyLfzBjw+DhR3k7LjXw+Rj9C6c/Ps6h9fKjVPrWu+ms8OPKIcugQYDSlzfkG9i2YxQctLzadEngJC+cnD6rR6EAQ4BrO+/ngvxOYxYFFpgfbkdXydr6nk7UP+mkKrn7YCfi40s9kpgW9Q2dvwaRu2ElmRbpVH/FSv61X2qvFNYjO2LzhJ5Mhmvmo4MeqtZQbHX0kiL17L2qxPk5xrxr+9E85y/Sf/9dxSSGa3GBv+Jr5K1fj36SHku99XvxMjFX+LgZE9ydDZbF4SRk5aPpZWKJ0fXp0bjuwc7344hMYmYl14kP+IKejsNHw4xEuWtYGKTiYwNHstz254jLDmMbn7d+K7zd1wLS2HbwrOYTRI1A5TUu/QbeYcPF2zPpk1rXMe8iG37dmAwoI+OJv9KJPmRV9BHRnIlzopwJzkRodaVtfjd2F10QBYWRYWWQoF1kybYd+uKfbduqP1LFz/J0dls/vEMuRl68hQSN+rZ8t2kkApn2X6y+Ty/HIyif6A7wZfy0eUY8GvgSu/xjSpU3y73+HGuvzAajEY833sXl+efL7aOyWTm6qlkTm+6SFJioSXNw9+Oxl39CGzmUcQdGnYjg+BqThXan/IgRFMVIERT+TGZJTp/vY/otNzCK6GLW2HFiKIr2nqAb/Obt2bg0/TusUAVGoARsuNky1BGNBjywNJGtnAVu7cq+trtNX3yMuDQDxA6Hww3SwPU6Ahdp0O1Fvc/znvBqJfdgsd+QRt1ngt5XTmf251sc2HLGReLaPw1J/CvlotX176oGjxVIHy+2H6R+fsisVGrWD+hHXVuCwbNSc9n8czDqHRmfNVh9HX+GFXDfjDop6LzUgGSrmex9quTmIxmQvrVpEXvAExmE+Gp4fjb++Nk5VTubUlmiaitR4k5eBFVo2YYsCRfa0SfZyA/1yjf8uT7kpoz38LST0+fwSH41CmhSOe6V2VLYoNB8PSSMsd04ZAsCCUJAjRH6eE2D4sx6+Vj+g4Or4vk5I7r2Lta8cz0kILiilqDlu5rupOlz+Kdpp8ze4MVqVo9LfydaV3TlQvxWVyIzyIuU1dsmwA2ahVBXoUiqr63PX5uKmaf+pKNkRuxNGp4NvwjNHl2NOjoS6dn7pLtZzLA9UNy+yPPBsW+8+hzqWyacwaFUsHQ91riVq38lqr4KxlsmH0ak9FMQIgn847+y/iTKwnMLEwcULq68k3TYfxtV5OeDbyYEuTL3t8vYjSYcfSwpverwbh4ly+Z5BbG9HRixo5DFx6O0VrNx4NNXKyuoJlHM04mnSxw23rZeiHp9YQv+ZsDJzVIChVe8Yepd2UFjj174DJmNNYNGtz1c66cSOLvn8ORJGjc2pHG1dJvCqpbouoqUn4+WFpiGxKCfbdu2HXpjKVH8VZRpTF9xRmMB5LxMSlRKBV0HF6Hhh19S145LwNWPSd3JR+yGKzk89aF+CwGfXeAZ3M0OJmVuPvZM2BK03K1v7mTtN9+J/Hzz0Glwm/J4iLFUm+Re+IE0WPHkal0JaHZUOIsAzGb5L9PGwc1DZ/wpUEHXw7GpDHu9xMMbOrLt0Pv3nv0XhCiqQoQoqliLD4YxcebzxPobsvON56Qr4QjdsHFzRB3EhLPgbn4lTQuNW8TUs3Bq1Fxd57ZBNkJch2oW8Io/Xrh86zYkrddHpQWhSIqP6cw2823OXT9CGp2KvXtRpOZ2bsiyNYZ+OCp+lg+SMtY4jk49gvmM6uIyamF1uRCNc05HILbQpsJxYTd7d3P54xoSt/GxV0rCdcyWfXlCVRmqGu9iy4O81DU7wuDF4NFxdyiuhwDqz4/RnaajoBgN3q/0qhclon7RZIkDPkm9DcFVH6ukVytjrXnNuDmY8+Yzs+U/MbsRJjdULYmvrS73MI46kwyO34+h8lgxtvyHH2q/YzmlS1FrFTpCVpWfHIUs0mi96uNillL5p6ay8KwhSj11cmMHE9DX0eWjW1dpKF2Rq6eC/HZXEzIuimksrmUmI3+jqbTSuvr2PisQqFORYGSccFj6Ws1jM3fy7WY+k5ujF/9OzKjsuJhzWiIvmlZsbAG78byHPg2x+jZjBU/xJOZnEfjrtVp/3Ttcs3N7USeSmL7onCQ4ICVAUWQLXPUl0hduAjbkBC8Zs7gbI6CYQsO01qrIuSma9mvgQvdX2yAxubehLspJ4cbr44n99gxTGoL/jfAzJlA+e9yasupjKjen4xVq0n77TeMiYkkuTflXP3RSAoVdZs60mVs6Ra1mPNpbJ53BrNJokEHH554Jqh4k26TCUN8AipHh4IaWRVl94VEXvz1OBYSzPT2JuNiBgCNnvCl3dDaRa1EZhP8+TRE3rR21egII9eAhQaD3sSXU//BKU9CYWfBCx+1xsbh3kIeJEkibupUsjZuQuXqSo2/1hRpyJ578iQxL43FnJuLbdu2VPtxHjq9kvMHYzm7P7bA9a5UKYjQmPlXmc+gbjXlsI5KRIimKkCIpoqRrTPQZtYecvKNLB3dkk5Bd1xRGfIg4SzEnii8pV0tviGlBXjUB/e6oE26KZJiwGwovu7tqNRyRXJnf1kEGXXyZxpy77i/+fhuVbDd60GXD6BunzJdiUaTmTdXn2HDafnq+c4MlQeGLgvC18jZd42GglPxFipRKVr6zTlIdr6R0e0CmN737lfN4ccS2PvLeZRAK7s/aGn3FwT1hqeXljtDzGyW2DL3DNHn03Bwt2bouy3u+aT30NjzGfzzpdwy5cW/K/TWuIh0tswLQ68z4WoRRd86q7F9eQ1o7OSaSHPOEHM+Df+GrvSZULzFyJXUBAZu6g0KA46ZE9gwZjSudmXPtdFkJipFy/n4LPZfP8aR9JVkKeSaRmaDE7rYYXhp6jOomS9BcUauhyZi56xh+IetCr+PqH9gzRjQJoOlrfw3l59Z5HOO5QzlaM4IbNVanukfgbpGM9nFblWx38K/118hYrscqF67rz/d+wQiSVLBfOi0BpZ+exxTrFx93zPEnUGjGt7V/VhezDodsa+9Ts7+/ZgtVHzfV4FFo/p8GNOMrNVrMOfkAKByd8Pl2edIqfske1ZEIUnQ8AlfOg6XyxDsupBEeGwmHWq70dzfmcRrWWyYfRpjvonAZh50f6nBfY+1JJKydfScLZcXeLF9DT7oU4+TO64Tul7+zaxW15keYxsW9hzc8T4cniuLX6UK9DlQfwDmgb+w7afzXAtLIU8hcaSGJavf6XhfYzPn5XFtxDPkX7yIVeNg/H//HaVaTe7JU8S89NJNwdSGaj/+iNKqsPCtyWgm8lQSYXtuFKm35dfIlb4TKp6gUBpCNFUBQjRVnI83nWfxv1F0rOPOb2PK0fgyN01O4489WSiktEklr6u0kK/knfzByU8WR7ceO/mDnWf50+YlSXZN3CmokGTBVo5gaJNZYsqq02w4HYdKqcB00z20ZHRLOt8pGB8yuXojA+cd4lJiNi38nVk+rnWZFrBdG65waZt8cnvS6RvqWB2Um9UO/U12aZbBrcbLFpZKBk9tUSFXTpVgyIPvGkBuKjz9613jkkoj5UY2m74/SW62CQdVAn2b78fphR+JPJPK9oXhKC0UjPgopFjPscw8AyMWhRJp/gO1yyGauLXg9z5luwZBvtI/knCERWGLOJZwDAClQkk7zx7YaQexLSyTbJ1sdbWUYFyeDTZ6icBWnvR8oR4c/Bb2fiZfNHg2lL9f5xqQegVij8ON42RejWT5+Vcxoaa741fUtj5089MVcmFP3xZQrTnUeAJcA0sd73O/HEERlkFIviVKpYI+E4MLrF5p8Vq2zpfbIpmVsMVKT4qLBVsmt8fToexjrsy50uuJmzZNrqSuVMq3mzFG6sBAXMeMxqFv34Jsrouh8ez+9QJIYB/szLL8LC4m5hRsL9jemu6JShR6M9XrOdNnfOMiqfVF0Gth+7vy71OHN8sVy3kLs1nihaXH+OdyMvW8HVh/W3mBq6eT2bnkPMZ8E47u1vSZEIxzwkZY/4r85qeXyp0I/hiCZDKw3+p7zl33Q2WhZLmNjutKE1smt6eBj+PdB1AO9DExRA15GnNmJk5Dh+I4cIBsYdJqsWndmurzf0RpXXIC0KErKUxZcJRm+RY0NFnSrIcfrfuXfhxVFCGaqgAhmipOTFouT3y1F7NEicXUykSSIPOG7M5LjQR770KBZO9935ldlYXJLPHmqtOsPx2HhVLB3GeacTgyhV8PX8fFVs3WyR3wcrz/H/17QZIk3lgpj83NTsPWye3xKOcJ6M8fT5MRloYJE0PcPsLH4jwEdoXhf5aaAXntbApb5smZQd1G16/6elrl4cRS2PQaOPrB5FP3HPyemZzHxm9DyUqXsFZm0Kv9Ff4+204O/u4dQEi/opXJ8/Qmnl98hGPX0nF11GLy/RyTZGJZ72U0cm9018+RJIkDsQdYGLaQsGR5ri2UFvQP7M+YhmPwc5Bbv+gMJnZdSGTNiRv8czkZb4OSETlqFCho4PUXnfhD3mCTZ6HP18W+V0mS2DIvjOvhqVQLUNKvQziKuONw4wRkRhcdlEIJ3WbIWYcliIJ/Lifz/OKjWCoVfFW9GrFnUrDUqBj4ZjNy0nXsXHIeg05ui9T1xQa8tCmMiwnlF/rlQTKZSJgxg4zVawCwadkSlzGjsXviiWINYo0mMytWXSBzfyIARzUGjjtItKnlxtnLKQxMs8ReUhCnMhMdbMvAltXp2dALG/Udx47ZLMcWXbzZlqnFi3JrqVKEk8FkJuxGBoeupLL/cjLHr6ejsZDLC9z5O5pyI5stP8rB8moN9LD7HD/LY9DxHejyvrxS+FpOLN1MaM6zgETPlxvxzfkYtp5NKNPyXF5yDhwkZtw4kCQUajWSXo9NSAjVF8y/q2DK1RvpOfsA0Wm5PNfan2mdZTfj3cpY3CtCNFUBQjTdGy//fpwd5xJ5JsSPzwfe/STwuGIyS7y1+gzrTsXeFExN6dnQG53BxKAfD3E+PouQGi4sG9sa1UOI57mT3w5f46MN51ApFSx7KYSQmuWvXG02mZn/aSjE69ArDIz2fBMnYuS4ruHLQV086y0zOY/Vs46Rn2uk4RO+PDGi4i1GHjqSBPNCIOUS9Phcjge7D7SZ+Wz+aj8pKYUnT3snFSNmtMXythglvdHM2N+Os/9yMvZWFqwc14Y/r37BxsiNdPXryuzOs4tt2yyZ2XV9Fz+d/YmLaRcB0Kg0DK49mNENR+Nle3eBmpilY/2pWBK2nsEt0wlrZTqDXd9krnoEli1HMbh5NQLdi1oEr55KZtvCs7KV7MMQnDxv+85zkmRr8I3jcgB59E0LVP3+crmK2xp1m80SfeYc5EJ8FmPa1eD9XnXZNOcMsZfSUVtboM+TLT4+teW2SDYOaq6laOk79yDZurJdyhVBkiRy9uzBwsMT60YNiy03mMysOxnLj/uucC01l8b5KrrnydanBt2q0ap7AGu+PE52ig6tlYLF6lx0N/WWrVpFn2BvBjerRqsaLrLbcdcMOPgdKC1vxlpKxYST2SxxMSGbQ5EpHIpM5cjVVLT6wmwzhQJmDWzE8FZ+Je5Tbpae7T+eIP5aHgpMtKt1jOA3pqJQyReWl0Lj2bX0AgAd7H8ieFh39tr1YfTSY7jYqgl9t2tBNfb7IWXBQpJnzwZkQVp94QKUNnfPjv1083l+PhiFj6MVO97oiL3Vg3HhC9FUBQjRdG8cuZrKsEWhWFkqOTytK84lNJN8XDGZJd5efYa1p2JRKRXMHdGUXo0Ki1teTc6h75yDaPUmXutamzeevP/2DBXhxPV0hi86jMEk8UGferzUoXj/tbLIzzMw94N/sdKaybPQMcFzItamVAjoAM+sLNIax6A38deXJ0i9kYNnDQcGvtms1OrK94ReKwf9e9SrkIujVCJ2yQVY1fZyMcvb4nQkSWLHuQQ2h8UzoIkvXet5lCurJz/PyNbPNxOXLG+rl9MsajpelIOrvRtj9m7CJyfULL2kxMrSkj9eakVzfxciMyIZsGEAABv6b6Cmk/ydGc1GtkVt4+ezP3M1U45jsbawZnjQcJ5v8Dxu1m5l76ckwYmlGLa+z+rEWaSbqpNklc2vt2VNNfVz4oM+9Wju74JeZ2T5zCN3tZIV2/bxxbBtqhxv6BYkWyTd5IDxv07c4M3VZ7C3suCftzvjbKsmP8/Iuq9Pkhoru7xKCmi+vbfaDyOa0q+E5IXKIt9oYvXxG8zfF0lshhxT5WxjyUsdatLSYMGxtTfn3d6SvGwD9q5WDHqrOemSibUnY/nr5A2i0wobcPu52PCB7ym6R8yUXxj0kyya1o8HJLIajWKT7xQOXU3jcGQqaVp9kfE421jSJtCVtoFudKjthr9rKZmDhjxMvzzFvkutuZgnN0yu386bjiOCiIvIYPOcM5jNEk2DYmmbOREUSkyDl9Jmgy1J2fkseLY5PRvev0VYkiSSZ3+PKS0Nz3enlSqYTkWnM3j+IczSgw9jEKKpChCi6d6QJImn5hzkXFwW7/QMYnynkpu4Pm6YzBJvrznD2pMlC6ZbbDgdy2srTqNQwJ8vhdA2sBwnt0ogJSefp344SEKWjt6NvJj3TLN7TuFNSszh90+OYmWEXBstU1zHozJkye1DRq4CjT2SJLHn1wtcDE3A2t6Soe+1xM65kl2Sl3fA5jfk7Mi2k+HJjytHOP0+UG6Fc0ern8QsHR+uD+fv84kFrzWp7sRb3YNoV8u1zPk06o0cW7gGy/RzNGchCnN+sXWyJWtMno1wCmwF3k3Apwmvhc1hT8xe+gf2Z3qb6WyM3MjPZ3/mRs4NQG5LM7LeSEbWHVn+kg36XHnuwlbI++b9PH+dGYhkBo8evmzPzGLf5WRMZgkbtYrfxrRCfzKNU39H4+BmxYiPCksklErMMdkVlR0vi9CBC9DV6kWXr/cRl6ljWq+6vPJEYbyKNiOf0A2RVKvrclc37pfbL/LjzTIZGya0q7ibvwx0BhPLj0azcP9VErLksg5udhrGdazByBB/bDWyqDy9K5p/11wBZOE06K3mRSxvkiRx7Fo6a07EsCUsngaGcP5Qf45aYeIvu2cwd3oPS5US3fHfGRr7P5RI/GZ8ko+MLwAKbNQqWtVwoV2gG21ruVLPy6F8QeWSBGvHwtnVSFYunK6/jkPb0kACr5oOpMZpMehM1G7hwZOj66PY8jqc/BVUGv6sM5v3TznSrZ4HP49qWanzWhp6o5mn5hzgcmIOg5r68u2wJg/084RoqgKEaLp3bl1lejlYcWBq5webhv8QMJkl3lkTxl8nb6BSKpgzoim9SxBMt3hnzRlWHb+Bh72Gra91wK0cWVH3g9Fk5rlfjnL4aiqB7rZsmNgeO839Fag8F57MznlhWEoKDO5aJluPR6nPgmqtYOACws9ZsX/ZJRQK6PdaE6rVrYR6W7fQpsD2aXB2ddHX206CJz+5P+GUdAF+bC3H40w+Bc4BmM0SK47FMGvrBbLzjVgoFXRv4Mmei0noDHKWZUgNF97qEUTLgHLup8kAyReR4k5xMnQfyoQz1FNcx0pRPAv0jK0Tz3o4YIECN7UDCXo5k81Z48zzDZ5nWNAw7NUVEA4pV2Qhk3Re3s+u06Hda4RuvMqJbdexsrNkxEch5CjMvLnqDAciUqiusmBEuiWSGfqMDyYguAJiPycJVr8A1+Xq3if9xzDkUhe8HG3Y81YnrCwrFotoNJlLLMh6v2jzjfx55DqL/okiJUcWtF4OVrzyRE2Gt/IrcZxn993gyokk2g+tjXv1u38HeQmXUf3SDbUhk82m1kwyTESi8HdvsPIfvrJciFIhccb7aQzdvyC4uvO9ucgOzoZd0+WCrM+vhxoduXY2hb9/OYdBJ7v4fGo70W9yEzlQ3WSE1aPg4mZManueyn6Py4oADr/bBQ/7hxN7+d3Oy3y/OwJXWzW7pjwheyCyE+Ssv5BXoHrlCjghmqoAIZrunXyjiXb/20tKTj7fD29C/yZ3Kcb2GGAyS0z9K4w1J2TB9MPwpvQJLr3fXK7eSP+5/xKRlEPHOu4sfaHlA0lLvsX/tl1kwX75ynzjxHbl7ktVFjt2XCViXRQKFNjWy+eFvFdAl0GCsS7rUj/DLClpMzCQZj3K39ahVCQJzq6B7VPlrDaFUrYGOfjAjvfkde5XOG2cBCd/g3r9YNjvXE3O4d21ZzkSJff6alzdiS8GN6KulwNJ2Tp+3BvJsiPR6E2yeOpYx503n6xD4+pO5fq4eXuv8NWOSwB8NbAeT/vnQvxpiDst3yecBaOOMV4eHLOWT2AeRiMv6C0Y7N4SG/+2UL217J4sTyLEufWwYSLos+Visk8vgYD2gJzyvXrWcVJjc6jR2I1erzRCZzAzZslR/MJy8DOpcK3jyPApxQt1lonJADunQ+g8AP4xNSKrzwKeal08hqg83G457dXQix9H3pvl1GyWuJCQxc7zifx66BrpubJoreZszaudAhnSvFq5Gt+WSl46/PwkpEaAb3NiB65hXVgqm8PisVQpaVtLdrm1ztqGZvNkQIKWY6H3VxU/ji/vgGXD5G30/hpajS1YlBan5e/F51BrVPSZEFy05IchD34fBNGHSFM60y9vOs/36si4jpWbtVYSFxOy6DvnIAaTxNxnmvJUQ0849gvs+QTys2SL67h9led+R4imKkGIpvvj+10RfLfrMnW97Fk3vh3W5TH1P2KYbwqm1TcF0/fDm/BUcPliLC4lZNNv7kHyjeZiLorK5FZDTkD+QSrn+MrL4l/OkHcsFQmJml3UdNYuYtWxvuSY3ahpfYyeffQo2k+Wq0rfD5k3ZHdSxM16SR4NoP+cwkrbx36GLTf7vLWZCN0/rfiPbE6yXGbAlI/xhW0sjPLg+90R6I1mrC1VvN0jiFFtA4oF8Mdl5DFnzxVWH4/BeLO0xJP1PZnyZB3qed/9t+H3w9f4cMM5gLvHmJmMkHKJyKu7mHNtI21zsugfF4Hmzp9xjQNUayk3wK4eIhehvC2+7E7Rgn87uSq0fVEXWMqNbFbPOo7ZJBVkOoYdjOXAH5cwILHGw8RPr7Yudb9K469fZ9P76mdYK/RITn4ohv0hx3XdAyej0xm2UI7Re793PcZ2LF+MXmKWjgMRKRyISOZgRAqpt8UO1XCzZXynQAY09a0cC7jJAH8Ogav7wKEajN0D9p53X//Un7BhAvcknJIvwU9dZUHcfDQ89V2J7729DlYR8jJgSW9IOkeU2ZOpjl+xckq/Sq3EfScms8SgH//lzI1MnqzvyaKuShSb35AvGECu/fXUt3J3iEpEiKYqQIim+yM1J58u3+wnM89A5yB3Fj3f4rFy05nNEtPWhrHquCyYZg9rUmJF7dJYcTSaaWvPYqFUsPLlNjT3d67UMV5NzqH/3H/JzjfyYvsafPhU5ffFkySJrz8/jE2MDgMSrr52ZMVqcdKk8rTjJNTKPLkuTIc35ZNAOWo6FcFshuO/yBlH+hy5SGnHt6Hd68Urkt+vcNr3Bez7nFz3xgw2fMqFhGwAOtR24/OBjajucvcgVoDrqVq+3x3B+lOx3Ora8lSwN693q0Mtj6JZaOtPxfL6ytMATO5SiyndK5BVqMuCG8cg5ghEh8rZarcq1d9CoZKr5/u1lusmHftJXh/k+K+u0+9aRuH41iiObIxCY2PBoLeas/67k+RlG4jwVLE+PwcXWzUrx7WucCzR9VQt3b7dT6D5Outcf8Q6JxosrOCp2dBkRJnvL4nyZIPm6U0ciUrlQEQKByNSuJSYXWS5jVpFm5qu9GviQ59G3lhU1u+QJMlC/8QSuUjoizvk76QsTv0hWwORoNU46PVl2cdxXjr81EUuCOzXFp7fUOGK/QBkxWP+pTvKzGjCzDVQjNpMo8C791u8X34+cJVPt1zAxyqfXY0PYHNmCSCBxhG6fggtxjyQUjJCNFUBQjTdPyeupzHy5yPoDGYGNvXlm6cbP1A3VWVhNku8u/YsK4/HoFTA98NLbkFSFpIkMXnFaTadicPXyZqtkzvgWElVsm8vYNkywJllYyunrk1J5OuNfPX+QZyzZReVwkJBvzebUk13EHZ/LKfug3yl3fldaDyifD+EKRGyu+xWK49qraDfHPAopaXCsV9gyxT5cUWEkzEf6buGKLRJTDZMZKOpLU42lnz0VH0GNvWt0NX2laRsvtsVwZaweACUChjYtBqvd6tNdRcbdp1P5OU/TmAyS7zQNoDpfevf39W8yQiJ4YUiKuaIHBx/JxpHGPAj1Cu9Qa/ZZOavL0+QdD0bC40KY74JZy8ber7ZlFFLj3E2NhM3Ow0rX25drCRBaUxcdpLNYfFycdsRtWHtuELLYcuxcnmHCp7o76w7tmVye9ztNJyPz+LgFdmadCwqvcB9CvLhEOzrSIfa7nSo7UZTv3uMHSqL0Ply7B0KGLEcgnqV/70VEU4m401r1l65rti4vWB7HwkmKVfImd8FO1MmV+xaUOv1bfcmwMrgeqqWHrP30910kC/sVmCtT5UXNBoq/92WZpG7T4RoqgKEaKoc9l5M4qXfjmMySwXtAB6kOfh+MZsl3lt3lhXHZME0e/j9pT1n6ww8Necg11Nz6dHAkwXPNr/v/b+anMP0jec4EJGCu72GLZPKX8DyXklIyeWXmaE4GCS22Bi4bgvd6nvSv5EHnXS7sfjnf4Uncve6ct++oN4lnwhMBvj3e9j/JZjy5av0btOh5UvlE1vHF8tX+ACtJ0CPz8oUTpe3L6BO6FTiJBc65s+mTxM/Pnyq/n0F6Z+Ly+S7nZfZdUGuYm+hVPBUsDdbwxPQG80MaubL10Me0IVCRsxtlqijcvxS7y/lXo7lIC1Oy6rPj2G62ceu/+tyMH9Grp7hi0K5mJCNp4OGVS+3KT31/SanYzIYMO9fFArYOrmD7N4zm+U2NftuZihWD5GrrzuUHhN4J7dfIPi52KDNNxZxuQH4OFrJIqmOG+0C3YqXOsnLkN2Z99iIuhiXd8Dy4XJl9e6fQduJFd/Gyd/liwYkaPUy9Pqi5ON4+7sQ+qPcHurFv8tnzSqD06F7qL1tOLaKfEz1B6Ea8kv5OyqUA0mSeHP+GgbFf0d7leyixrUW9PkWaj5RaZ9zN4RoqgKEaKo81p68wZRVZwAeeBmC66larqXm4mKjxtnWEhdbNdaWqnIJFbNZ4v31Z1l+VBZM3w2rnCD2szcyGTT/XwwmiZn9GjCqbcA9bSchU8f3uyNYdTwGk1nCUqXgz5da06pGJWaulfb56bks23+NjRGJXEstrE/jaG1JvwbOvKjehf/5BSh0GfKCaq3kitEB7Qo3EncKNkyCRLmZLIFdoe9sufL7bZjNEtFpuYTHZRKbnoeDtSXONpY426hxtlXjFbEch11vyyu3Hi9bMUr4jjNy9Xy6+Twvhj9LPWUM81TPUe/pD+lSt/Kuck9Fp/PtzssciEgpeO3J+p7MH9ms8lxBD4Azu2M4uDqCoNZedHuh0LWbmpPP8EWhRCTl4OtkzcqXW1PN+e6uS0mSGLYolKNRaQxpXo2vn74jhunSdtnqlJ8ptzt6ein4t63QWG/vpQiFLrcOtd3oUMedmm62Jf+NS5Ls1t3+Lti4QsjL0GK07FK+VxLPwS/dZXdys1HQ9/t7D2IuSzid+uNmDBRyy5v6/e993LdhNku8MetbvtJ/hlphKl20VRRDHuErp1M74hc0CiNmlRXKjm9Bu8nl7mV5vwjRVAUI0VS53PJtA/xv0N0r3d4rkiSx6J+rfLnjUkEfuFtoLJS42KpxtlHL97ZqXGwscSryXM3msLgCC1NlCaZbLPk3ipmbzqNWKVk7vi0NfcsfOJ2Rq2f+/kiW/nuN/JuWga51PXi7ZxB1vR7+sSlJEmdjM9lwOo5NZ+JIyi6sRxRob+Rj1120SV6F0iTXwKF2d3hiKpzfIDcVlczySavn/yB4GCZJtp6Fx2USHptFeGwm5+OyCk6Qd2OEajezLH8BYKNVf/5yn4Czrbrge7VQKVh8MIo6uSdZpv6cfKU1hsnh2Dk9mNpZoVdTWbA/EmcbNbMGNapwqn1VkBanxcnTGuUd4i4pW8fwhaFcTdFS3cWaVS+3wdux5NYYu84n8tJvx9FYKNn3dqeS10uNhJXPQdI5uY9kt5my2K2AdeN0TAYHI5JpEeBCs/K43Aw62PqmLDxux9IWmj0PrV+VWzRVhJwkObYoMwZqdIRn196/9ep24RTyivx3oVBA9BH49Skw6eGJabLruxL55u9LXN/3Gz+o58ovdPkQOr51fxuN2IVx8xQsMq8DEOPajuoj54FLjfscbcUQoqkKEKKp8rlVtE6pgPnPNqdHg8rpUZalM/DWqjMFRQkDXG3IM5hI1xqKxDqUB6UCvh3ahAFNK7dMgiRJjPv9BDvPJxLgasPmyR3KrKWUqzey5N9rLNgfWdCEtWWAM1N71qVFeesFPWBMZokjUalsPB3H1rPxZN0cpzvpfGC3ib7GnSgxFXlPZs2+7Kv5FidSLQiPzeRCfDZ5BlOxbastlNTzssff1RZtvpH0XD3puQbSc/Vk5hmQpKLCabGxJx8bnwOKXi2vsPuW1sbjcuxI768ezET8PyQhU8ewRYe5nppLDTdbVo5rXcwNbDSZ6TH7HyKTtYzvFMg7PUuJR9NrYeNkCJf7wFGzEwyYL5eUqGyy4mDls3LbF4VSdhfbe8OhOXJ8GMjB9A0GyGUsypO9ZciDX/vKQfquteClXfdnsbqdO4VT20mwqLPcwLxeX3j6t0p1n4FslX/iq32MttjOdIvf5BedA2RroK27fG/nCXYet917yK7gOxM+suJka9759QDESy786vgKb7/2dpGK7w8LIZqqACGaKh9Jkpj2lxxgrbZQ8uvoVrQJLH9vtJI4H5fFq3+e4HpqLmqVko/61mdkiB8KhQJJksjVm0jT6knP1d92byD95uOC17UGzJLE5K617ynouzxk5Orp/f0B4jJ19G/iw+xhTUp0KRhMZlYci+GH3REk37Ti1PWyZ2rPunQKcn9kY8LyjSb+uZzCxjNx7DyfgM5gJkARz1sWq3lKFUqqwpX3DaPZbmxW7L02ahX1vR1o6OtIAx/5vpaH3V2D201miaw8A2m5eixO/Yr/IbmO09lqz7DVdzIZeQYycg10dE5nxLHBgAImnQDXB1+X5v8TsRl5DFt4mBvpeQS627JiXBvc7QtdLMuORPPeurM421iy/53OOJRVhFKS5Gyz7e+B8WbmZd/vK83tBMD1w7DqeVlwWDnJpRdqdS38/Mg9sni6urfwPQEd5KzDWt1KFieSBH+9COF/ydscu6fyj6WTv8PGm7FRVk6gywDPhjBmB2jKH5BfEYYuPMzRqDRWB+2l5fWfyv9GK0dZPNl5gq0rXNkN+hzMChWLDd2ZKz3NykndCfKq3Gru5UWIpipAiKYHg9FkZvyfJ/n7fCJ2GgtWjGtdIVfV7aw6HsOH68PJN5rxdbJm/rPNCK7mVLkDrmROXE9j6MJQTGaJLwcHM7Rl9YJlZrPEprA4vt15mes3Y4b8XGx4s3sd+gb7PBaZh7fQ5hvZeT6RjWfi+OdyMh7mZNKwR4cGBysLGvo6FgikBj6O1HCzvb8GxyeWwqbX5Me3uzg2vyEHjgf1ljOcBBUmJi2XoQsPE5+pI8jTnuXjWuNiq0abb6TT1/tIzs5net/6jG5XARdMSgT89VJhvZ4mI+WYGs19nGQL4pemyT3fPBrI/fDu5hpKOAuH5sqWL/NNV7B7XTkrM3ho0fibff+TA9qVFvDceqjR4d7HWRonf7tpcUKOwRq7t+IuxAqw6ngM76wJo4abLXvG1kKRGQs5iTdvSbLwzEkqfJ6TKLsLS8Do3YJnE4cTmuvD691q83q3h9t783aEaKoChGh6cOgMJkYtPsqRqDTc7NSseaUtAW5lZ+jc/v7pG86x8ngMAJ2C3PluaJPHpjnwj/uu8OX2S1hZKtk0sT21POzYdzmZL7df4kJ8FiD3wnqtay2GtfR7MOnSD5E0rZ59l5KwtlTR0NeRas7WD8ZaduJX2DRZftzqZeg0Db6tL1s0XthSUBlbUHGiUrQMW3iYpOx86ns7sHxsa5YcimL2rgj8XW3Y+cYTFT9OjXrY/z848C0gya6hQT9B9VYVH+Cd8UsNBkL/eUULgN6NzBtwZAEcXyoXjgTZghLyslxH6Mpu2coEckmMZs9XfHwV4cxK+SKg2wzwC3mgH6XNN9Lys13k6k2seaVN2W5/SQJd5m1C6qaYcvBhylk/1p6OJ8jTnk2T2lfp75YQTVWAEE0PliydgeELQzkfn4Wfiw1rXmlTrrT566laXv3jJOfjs1AoYEq3OkzoXOuxssKYzRKjlhzlQEQKtTzscLFVc/RmGw97jQUvP1GTMe1rYKO+v/5x/0luv1J3rS23tvAKhpf/qdQ2Df9FriTlMHzRYVJy9DT0deBqspZcvYl5zzQrs7VQqVw/BGtfhsxoOc6o49vy7S7FOYtRLH5J7rVX4e9blykL79D5kB0nv2ZpK1uhTPmy+677JxXb5mPAW6vPsObEDYa1qM4XQ4LvaRt7LyYxeukxlApYO74dTcrZZuhBIURTFSBE04MnOTufIQsOcT01l7pe9qx8uQ2O1nePifj7XAJvrj5Dts6Iq62a74c3pX3tB5MJ9aBJzs6n9w8HCmKW1BZKXmgbwKtPBD42FrNHlpO/yQHH3PwpHLgQGg+v0iH9f+FSQjbDFx0u6OHWpLoT68a3vX/LoS4Ttr4NYSvl574tYNCisuOGSotfuleMeji3Dg79UBg0HtQHhv3+QKpXVzVHrqYybFEodhoLjr7ftcIXa9k6A92/+4f4TB0vta/BBw+gM0FFEaKpChCi6eEQnZrL4AWHSM7Op1WAC7+92KpYqrbRZOarvy+xcP9VAJr5OTFvZLO7pkA/LhyNSmPaX2G0quHCa91qP/b780hxKxvJsRpMOvlAKh7/VzkXl8kzPx0hJ9/IinGtaVmZmZxn18DmKXJNJ0tb6PU/aPpccatRReOX7gVJkoPFE8/LtZ3K4+p7DJEkiSe+2kd0Wi7+rjbYqi2wUClQKRWoFPK9/FyJhVKBUqHAQqlApZLvY9JyORmdgZ+LDdtf7/BIWMiFaKoChGh6eJyPy2LYwsNk5xvpVs+TBc8WFgVMytYxadmpgi70o9sF8G6veo99nI/gIZB4Ts7OehAp7f9xUnPyScnRP5jsqIwYWP8qXDsgP6/XF/r+ADY3xdn9xC8JSmTRP5F8vvXifW1j2UshtK31aFj+hWiqAoRoergcuZrK84uPkm8083Tzanw5JJijUWlMXH6K5Ox8bNUqvhzS+P5iJwQCweOB2SSXBdjzKZgNYOcl99XzqFc58UuCIpjNcsFabb4Ro1nCZJZu3psLnhe+dvPeJC8zSxK1PezpXNejqnejACGaqgAhmh4+O88n8srNRqfta7lx+GoqJrNEHU875j/bvELNQwUCwf8D4s/AX2MLm0JrHGXXXWXFLwn+X1KR87fwWQgeW56s78msQXIzyoNXUjCZJQY08WH9hHZCMAkE/0W8G8O4fXI1d5AFk0cD+TUhmASVQNVHYAkE98HQFtXJzTfyy79RjOsYyLM3q3sLBIL/KGobuf1N3T5yw+dW40T8kqDSEO65SkK45wQCgUAgePwQ7jmBQCAQCASCSkaIJoFAIBAIBIJyIESTQCAQCAQCQTkQokkgEAgEAoGgHAjRJBAIBAKBQFAOhGgSCAQCgUAgKAdCNAkEAoFAIBCUAyGaBAKBQCAQCMqBEE0CgUAgEAgE5aBKRdOsWbNo2bIl9vb2eHh4MGDAAC5dulSwPC0tjUmTJhEUFIS1tTV+fn5MnjyZzMzMIttRKBTFbitWrCiyzr59+2jWrBkajYZatWqxdOnSYuOZN28eAQEBWFlZERISwtGjRx/IfgsEAoFAIHj8qFLRtH//fiZMmEBoaCg7d+7EYDDQvXt3tFotAHFxccTFxfH1118THh7O0qVL2b59Oy+++GKxbS1ZsoT4+PiC24ABAwqWRUVF0adPHzp37szp06d5/fXXeemll9ixY0fBOitXrmTKlClMnz6dkydP0rhxY3r06EFSUtIDnweBQCAQCASPPo9U77nk5GQ8PDzYv38/HTt2LHGd1atX8+yzz6LVarGwkPsNKxQK1q1bV0Qo3c7UqVPZsmUL4eHhBa8NHz6cjIwMtm/fDkBISAgtW7Zk7ty5AJjNZqpXr86kSZOYNm1amWMXvecEAoFAIHj8eGx7z91yu7m4uJS6joODQ4FgusWECRNwc3OjVatWLF68mNu14OHDh+nWrVuR9Xv06MHhw4cB0Ov1nDhxosg6SqWSbt26FaxzJ/n5+WRlZRW5CQQCgUAg+P+LRdmrPBzMZjOvv/467dq1o2HDhiWuk5KSwieffMK4ceOKvP7xxx/TpUsXbGxs+Pvvvxk/fjw5OTlMnjwZgISEBDw9PYu8x9PTk6ysLPLy8khPT8dkMpW4zsWLF0scy6xZs5g5c+a97q5AIBAIBILHjEdGNE2YMIHw8HAOHjxY4vKsrCz69OlD/fr1mTFjRpFlH374YcHjpk2botVq+eqrrwpE04Pg3XffZcqUKQXPMzMz8fPzExYngUAgEAgeI26dt8sTrfRIiKaJEyeyefNm/vnnH6pVq1ZseXZ2Nj179sTe3p5169ZhaWlZ6vZCQkL45JNPyM/PR6PR4OXlRWJiYpF1EhMTcXBwwNraGpVKhUqlKnEdLy+vEj9Do9Gg0WgKnt+a9OrVq5drnwUCgUAgEDw6ZGdn4+joWOo6VSqaJEli0qRJrFu3jn379lGjRo1i62RlZdGjRw80Gg0bN27EysqqzO2ePn0aZ2fnAlHTpk0btm7dWmSdnTt30qZNGwDUajXNmzdn9+7dBcHkZrOZ3bt3M3HixHLti4+PDzExMdjb26NQKMr1nvKSlZVF9erViYmJEUHmJSDmp2zEHJWNmKOyEXNUOmJ+yuZRnCNJksjOzsbHx6fMdatUNE2YMIFly5axYcMG7O3tSUhIAMDR0RFra2uysrLo3r07ubm5/PHHH0UCrt3d3VGpVGzatInExERat26NlZUVO3fu5PPPP+ett94q+JxXXnmFuXPn8s477zBmzBj27NnDqlWr2LJlS8E6U6ZMYdSoUbRo0YJWrVoxe/ZstFoto0ePLte+KJXKEq1klYmDg8Mjc5A9ioj5KRsxR2Uj5qhsxByVjpifsnnU5qgsC1MBUhUClHhbsmSJJEmStHfv3ruuExUVJUmSJG3btk1q0qSJZGdnJ9na2kqNGzeWFixYIJlMpiKftXfvXqlJkyaSWq2WatasWfAZtzNnzhzJz89PUqvVUqtWraTQ0NAHPAPlIzMzUwKkzMzMqh7KI4mYn7IRc1Q2Yo7KRsxR6Yj5KZvHfY4eqTpNgpIRNaBKR8xP2Yg5KhsxR2Uj5qh0xPyUzeM+R49UnSZByWg0GqZPn14k8FxQiJifshFzVDZijspGzFHpiPkpm8d9joSlSSAQCAQCgaAcCEuTQCAQCAQCQTkQokkgEAgEAoGgHAjRJBAIBAKBQFAOhGgSCAQCgUAgKAdCND3izJs3j4CAAKysrAgJCeHo0aNVPaRHhhkzZqBQKIrc6tatW9XDqlL++ecf+vbti4+PDwqFgvXr1xdZLkkSH330Ed7e3lhbW9OtWzciIiKqZrBVRFlz9MILLxQ7rnr27Fk1g60CZs2aRcuWLbG3t8fDw4MBAwZw6dKlIuvodDomTJiAq6srdnZ2DB48uFgbqv/PlGeOOnXqVOw4euWVV6poxA+X+fPnExwcXFDAsk2bNmzbtq1g+eN8/AjR9AizcuVKpkyZwvTp0zl58iSNGzemR48eJCUlVfXQHhkaNGhAfHx8we1uDZ//K2i1Who3bsy8efNKXP7ll1/yww8/sGDBAo4cOYKtrS09evRAp9M95JFWHWXNEUDPnj2LHFfLly9/iCOsWvbv38+ECRMIDQ1l586dGAwGunfvjlarLVjnjTfeYNOmTaxevZr9+/cTFxfHoEGDqnDUD5fyzBHA2LFjixxHX375ZRWN+OFSrVo1/ve//3HixAmOHz9Oly5d6N+/P+fOnQMe8+OnSktrCkqlVatW0oQJEwqem0wmycfHR5o1a1YVjurRYfr06VLjxo2rehiPLIC0bt26gudms1ny8vKSvvrqq4LXMjIyJI1GIy1fvrwKRlj13DlHkiRJo0aNkvr3718l43kUSUpKkgBp//79kiTJx4ylpaW0evXqgnUuXLggAdLhw4eraphVyp1zJEmS9MQTT0ivvfZa1Q3qEcPZ2Vn6+eefH/vjR1iaHlH0ej0nTpygW7duBa8plUq6devG4cOHq3BkjxYRERH4+PhQs2ZNRo4cSXR0dFUP6ZElKiqKhISEIseUo6MjISEh4pi6g3379uHh4UFQUBCvvvoqqampVT2kKiMzMxMAFxcXAE6cOIHBYChyHNWtWxc/P7//7HF05xzd4s8//8TNzY2GDRvy7rvvkpubWxXDq1JMJhMrVqxAq9XSpk2bx/74qdKGvYK7k5KSgslkwtPTs8jrnp6eXLx4sYpG9WgREhLC0qVLCQoKIj4+npkzZ9KhQwfCw8Oxt7ev6uE9ctxqiF3SMXVrmUB2zQ0aNIgaNWoQGRnJe++9R69evTh8+DAqlaqqh/dQMZvNvP7667Rr146GDRsC8nGkVqtxcnIqsu5/9TgqaY4AnnnmGfz9/fHx8SEsLIypU6dy6dIl1q5dW4WjfXicPXuWNm3aoNPpsLOzY926ddSvX5/Tp08/1sePEE2Cx5ZevXoVPA4ODiYkJAR/f39WrVrFiy++WIUjEzzODB8+vOBxo0aNCA4OJjAwkH379tG1a9cqHNnDZ8KECYSHh//nYwVL425zNG7cuILHjRo1wtvbm65duxIZGUlgYODDHuZDJygoiNOnT5OZmcmaNWsYNWoU+/fvr+ph3TfCPfeI4ubmhkqlKpZRkJiYiJeXVxWN6tHGycmJOnXqcOXKlaoeyiPJreNGHFMVo2bNmri5uf3njquJEyeyefNm9u7dS7Vq1Qpe9/LyQq/Xk5GRUWT9/+JxdLc5KomQkBCA/8xxpFarqVWrFs2bN2fWrFk0btyY77///rE/foRoekRRq9U0b96c3bt3F7xmNpvZvXs3bdq0qcKRPbrk5OQQGRmJt7d3VQ/lkaRGjRp4eXkVOaaysrI4cuSIOKZK4caNG6Smpv5njitJkpg4cSLr1q1jz5491KhRo8jy5s2bY2lpWeQ4unTpEtHR0f+Z46isOSqJ06dPA/xnjqM7MZvN5OfnP/bHj3DPPcJMmTKFUaNG0aJFC1q1asXs2bPRarWMHj26qof2SPDWW2/Rt29f/P39iYuLY/r06ahUKkaMGFHVQ6sycnJyilzJRkVFcfr0aVxcXPDz8+P111/n008/pXbt2tSoUYMPP/wQHx8fBgwYUHWDfsiUNkcuLi7MnDmTwYMH4+XlRWRkJO+88w61atWiR48eVTjqh8eECRNYtmwZGzZswN7eviDOxNHREWtraxwdHXnxxReZMmUKLi4uODg4MGnSJNq0aUPr1q2rePQPh7LmKDIykmXLltG7d29cXV0JCwvjjTfeoGPHjgQHB1fx6B887777Lr169cLPz4/s7GyWLVvGvn372LFjx+N//FR1+p6gdObMmSP5+flJarVaatWqlRQaGlrVQ3pkGDZsmOTt7S2p1WrJ19dXGjZsmHTlypWqHlaVsnfvXgkodhs1apQkSXLZgQ8//FDy9PSUNBqN1LVrV+nSpUtVO+iHTGlzlJubK3Xv3l1yd3eXLC0tJX9/f2ns2LFSQkJCVQ/7oVHS3ADSkiVLCtbJy8uTxo8fLzk7O0s2NjbSwIEDpfj4+Kob9EOmrDmKjo6WOnbsKLm4uEgajUaqVauW9Pbbb0uZmZlVO/CHxJgxYyR/f39JrVZL7u7uUteuXaW///67YPnjfPwoJEmSHqZIEwgEAoFAIHgcETFNAoFAIBAIBOVAiCaBQCAQCASCciBEk0AgEAgEAkE5EKJJIBAIBAKBoBwI0SQQCAQCgUBQDoRoEggEAoFAICgHQjQJBAKBQCAQlAMhmgQCgeABoVAoWL9+fVUPQyAQVBJCNAkEgv+XvPDCCygUimK3nj17VvXQBALBY4roPScQCP7f0rNnT5YsWVLkNY1GU0WjEQgEjzvC0iQQCP7fotFo8PLyKnJzdnYGZNfZ/Pnz6dWrF9bW1tSsWZM1a9YUef/Zs2fp0qUL1tbWuLq6Mm7cOHJycoqss3jxYho0aIBGo8Hb25uJEycWWZ6SksLAgQOxsbGhdu3abNy48cHutEAgeGAI0SQQCP6zfPjhhwwePJgzZ84wcuRIhg8fzoULFwDQarX06NEDZ2dnjh07xurVq9m1a1cRUTR//nwmTJjAuHHjOHv2LBs3bqRWrVpFPmPmzJkMHTqUsLAwevfuzciRI0lLS3uo+ykQCCqJqu4YLBAIBA+CUaNGSSqVSrK1tS1y++yzzyRJkjvVv/LKK0XeExISIr366quSJEnSokWLJGdnZyknJ6dg+ZYtWySlUiklJCRIkiRJPj4+0vvvv3/XMQDSBx98UPA8JydHAqRt27ZV2n4KBIKHh4hpEggE/2/p3Lkz8+fPL/Kai4tLweM2bdoUWdamTRtOnz4NwIULF2jcuDG2trYFy9u1a4fZbObSpUsoFAri4uLo2rVrqWMIDg4ueGxra4uDgwNJSUn3uksCgaAKEaJJIBD8v8XW1raYu6yysLa2Ltd6lpaWRZ4rFArMZvODGJJAIHjAiJgmgUDwnyU0NLTY83r16gFQr149zpw5g1arLVj+77//olQqCQoKwt7enoCAAHbv3v1QxywQCKoOYWkSCAT/b8nPzychIaHIaxYWFri5uQGwevVqWrRoQfv27fnzzz85evQov/zyCwAjR45k+vTpjBo1ihkzZpCcnMykSZN47rnn8PT0BGDGjBm88soreHh40KtXL7Kzs/n333+ZNGnSw91RgUDwUBCiSSAQ/L9l+/bteHt7F3ktKCiIixcvAnJm24oVKxg/fjze3t4sX76c+vXrA2BjY8OOHTt47bXXaNmyJTY2NgwePJhvv/22YFujRo1Cp9Px3Xff8dZbb+Hm5saQIUMe3g4KBIKHikKSJKmqByEQCAQPG4VCwbp16xgwYEBVD0UgEDwmiJgmgUAgEAgEgnIgRJNAIBAIBAJBORAxTQKB4D+JiEwQCAQVRViaBAKBQCAQCMqBEE0CgUAgEAgE5UCIJoFAIBAIBIJyIESTQCAQCAQCQTkQokkgEAgEAoGgHAjRJBAIBAKBQFAOhGgSCAQCgUAgKAdCNAkEAoFAIBCUAyGaBAKBQCAQCMrB/wEP01cQE8dpUQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_12/'\n", + "n_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "for n_state in n_states:\n", + " for i in range(1, 6):\n", + " # Load the numpy array from each file\n", + " file_path = f\"repeat_{i}/loss_history.npy\"\n", + " loss_history = np.load(f'{save_dir}HMM_ICA_25_state_{n_state}/repeat_{i}/loss_history.npy')\n", + "\n", + " # Plot the loss history\n", + " plt.plot(loss_history, label=f'Repeat {i}')\n", + "\n", + " # Add labels and title\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.title(f'Loss History Comparison,n_state={n_state}')\n", + " plt.legend() # Add legend to differentiate between repeat runs\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c505a66a", + "metadata": {}, + "source": [ + "#### Question 2: Do these states all look very similar to each other?" + ] + }, + { + "cell_type": "markdown", + "id": "9d08b7ca", + "metadata": {}, + "source": [ + "Note here we should make use of the original './results_simulation_202311_toy_6'" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "9d707d60", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import twopair_fisher_z_transformed_correlation, twopair_riemannian_distance" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "03c8fb24", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202311_toy_12/'\n", + "n_states = [16]#[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "for n_state in n_states:\n", + " fisher_z_correlation_cor = np.load(f'{save_dir}HMM_ICA_25_state_{n_state}/distance/fisher_z_correlation_cor.npy')\n", + " seaborn.heatmap(fisher_z_correlation_cor)\n", + " plt.title(f'Fisher_z_correlation, N_state={n_state}')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "db75003e", + "metadata": {}, + "source": [ + "Conclusion: These states do not look similar to each other. The model training across all n_states is successful." + ] + }, + { + "cell_type": "markdown", + "id": "828a496e", + "metadata": {}, + "source": [ + "#### Question 3: How does free energy change with respect to training data? (No validation involved)." + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "8900b6f0", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.analysis import comparison_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "1d0f70fe", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './results_HCP_202312_no_mean/HMM_ICA_25_state_2/cross_validation_0/validation/metrics.json'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[174], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m result_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./results_HCP_202312_no_mean/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 5\u001b[0m save_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./results_HCP_202312_no_mean/comparison/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mcomparison_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlist_channels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlist_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43mresult_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/analysis.py:1285\u001b[0m, in \u001b[0;36mcomparison_analysis\u001b[0;34m(models, list_channels, list_states, result_dir, save_dir, learn_mean)\u001b[0m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m5\u001b[39m):\n\u001b[1;32m 1284\u001b[0m temp \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m-> 1285\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mresult_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmodel\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_ICA_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_channel\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_state_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_state\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/cross_validation_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/validation/metrics.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1286\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m json_file:\n\u001b[1;32m 1287\u001b[0m \u001b[38;5;66;03m# Use json.load to load the data from the file\u001b[39;00m\n\u001b[1;32m 1288\u001b[0m temp\u001b[38;5;241m.\u001b[39mappend(json\u001b[38;5;241m.\u001b[39mload(json_file)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfree_energy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 1289\u001b[0m free_energy_cv[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mICA_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_channel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_state_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_state\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m temp\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './results_HCP_202312_no_mean/HMM_ICA_25_state_2/cross_validation_0/validation/metrics.json'" + ] + } + ], + "source": [ + "models = ['HMM']\n", + "list_channels = [25,100,200]\n", + "list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "result_dir = './results_HCP_202312_no_mean/'\n", + "save_dir = './results_HCP_202312_no_mean/comparison/'\n", + "comparison_analysis(models,list_channels,list_states,result_dir,save_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "589cb975", + "metadata": {}, + "source": [ + "#### Step 9: Simulate a slightly more complicated case." + ] + }, + { + "cell_type": "markdown", + "id": "54ed152a", + "metadata": {}, + "source": [ + "There are two subjects '10001.txt','10002.txt'. Their covariances are perturbation from the ground truth './results_HCP_202311_no_mean'. We aim to generate these two subjects first and compare their state covariances." + ] + }, + { + "cell_type": "markdown", + "id": "2d5e5ac1", + "metadata": {}, + "source": [ + "Ask ChatGPT how to do some perturbation on the covariance matrices first" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "1f9d0fe9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Covariance Matrix:\n", + "[[1. 0.5 0.3]\n", + " [0.5 2. 0.2]\n", + " [0.3 0.2 0.8]]\n", + "\n", + "Perturbed Covariance Matrix:\n", + "[[1.348 1.557 0.635]\n", + " [1.557 4.272 0.738]\n", + " [0.635 0.738 0.772]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Assuming cov_matrix is your original covariance matrix\n", + "cov_matrix = np.array([[1.0, 0.5, 0.3],\n", + " [0.5, 2.0, 0.2],\n", + " [0.3, 0.2, 0.8]])\n", + "\n", + "# Define a small perturbation factor (adjust as needed)\n", + "perturbation_factor = 0.01\n", + "\n", + "# Generate a random perturbation matrix\n", + "perturbation_matrix = np.random.normal(0, perturbation_factor, cov_matrix.shape)\n", + "\n", + "# Add the perturbation to the original covariance matrix\n", + "perturbed_cov_matrix = cov_matrix + perturbation_matrix\n", + "\n", + "# Ensure the resulting matrix is still positive definite (optional)\n", + "perturbed_cov_matrix = np.dot(perturbed_cov_matrix, perturbed_cov_matrix.T) \n", + "\n", + "print(\"Original Covariance Matrix:\")\n", + "print(cov_matrix)\n", + "print(\"\\nPerturbed Covariance Matrix:\")\n", + "print(perturbed_cov_matrix)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "8f709c5f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import cov2stdcor, twopair_riemannian_distance, twopair_fisher_z_transformed_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "b40cba0b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 426.60it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data_dir = './data/node_timeseries/simulation_toy_12/'\n", + "covariances = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy')\n", + "covariances_1 = np.load(f'{data_dir}10001_state_covariances.npy')\n", + "covariances_2 = np.load(f'{data_dir}10002_state_covariances.npy')\n", + "\n", + "#seaborn.heatmap(twopair_riemannian_distance(covariances,covariances))\n", + "#plt.show()\n", + "#seaborn.heatmap(twopair_riemannian_distance(covariances_1,covariances_1))\n", + "#plt.show()\n", + "#seaborn.heatmap(twopair_riemannian_distance(covariances_2,covariances_2))\n", + "#plt.show()\n", + "seaborn.heatmap(twopair_riemannian_distance(covariances_1,covariances_2))\n", + "plt.show()\n", + "#print(twopair_riemannian_distance(covariances_1,covariances_2))\n", + "#stds_1, correlations_1 = cov2stdcor(covariances_1)\n", + "#stds_2, correlations_2 = cov2stdcor(covariances_2)\n", + "#seaborn.heatmap(twopair_fisher_z_transformed_correlation(correlations_1,correlations_2))\n", + "#plt.show()\n", + "#print(f'stds_1: {stds_1}')\n", + "#print(f'stds_2: {stds_2}')" + ] + }, + { + "cell_type": "markdown", + "id": "405e2008", + "metadata": {}, + "source": [ + "#### Step 6: Understand different components of free energy " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fe80cc26", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-12 17:41:09 INFO osl-dynamics [mod_base.py:589:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read from directory: data/node_timeseries/simulation_toy_6\n", + "Number of subjects: 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading files: 100%|███████████████████████| 100/100 [00:00<00:00, 19400.11it/s]\n", + "2023-12-12 17:41:11 INFO osl-dynamics [hmm.py:1105:free_energy]: Getting free energy\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "batch size: 64\n", + "log likelihood: -1204573.6141823456\n", + "entropy: -3398.9551675731\n", + "prior: -7956.800133308694\n", + "batch size: 64\n", + "log likelihood: -1211393.6685237056\n", + "entropy: -3685.379136066971\n", + "prior: -8470.877153165018\n", + "batch size: 64\n", + "log likelihood: -1225209.2512716432\n", + "entropy: -3676.6515730986484\n", + "prior: -8587.43910372143\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-12 17:41:12 INFO osl-dynamics [hmm.py:1155:evidence]: Getting model evidence\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "batch size: 8\n", + "log likelihood: -152295.15767775595\n", + "entropy: -441.871216345985\n", + "prior: -1039.9540452404765\n", + "Batch 1/4\n", + "600/600 [==============================] - 14s 24ms/step\n", + "Batch 2/4\n", + "600/600 [==============================] - 13s 22ms/step\n", + "Batch 3/4\n", + "600/600 [==============================] - 13s 22ms/step\n", + "Batch 4/4\n", + "600/600 [==============================] - 13s 22ms/step\n", + "free energy: 19056.939799001382, evidence: -19055.842126217503\n" + ] + } + ], + "source": [ + "import pathlib\n", + "from osl_dynamics.models import load\n", + "from rotation.preprocessing import PrepareData\n", + "model = load('./results_simulation_202311_toy_6/HMM_ICA_25_state_2')\n", + "data_dir = pathlib.Path('./data/node_timeseries/simulation_toy_6/')\n", + "prepare_data = PrepareData(data_dir)\n", + "subj, dataset = prepare_data.load(z_score_data=False)\n", + "free_energy = model.free_energy(dataset)\n", + "evidence = model.evidence(dataset)\n", + "print(f'free energy: {free_energy}, evidence: {evidence}')" + ] + }, + { + "cell_type": "markdown", + "id": "8ef8ad1b", + "metadata": {}, + "source": [ + "This result is important because it tells us the main component of free energy should be log-likelihood. At least empirically." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "569c6d1d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.008569053719397767" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "10000 / 1166990" + ] + }, + { + "cell_type": "markdown", + "id": "a87174b4", + "metadata": {}, + "source": [ + "#### Step 3: Understand the intuitive relationship between free energy, temporal_dynamics and n_states" + ] + }, + { + "cell_type": "markdown", + "id": "745a9234", + "metadata": {}, + "source": [ + "Let's have a look at HMM_ICA_25_state_16 from './results_simulation_202311_toy_6', which have the largest number of states, smallest free energy and not very good split-half reproducibility." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "508223b3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "684f16ca", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir_truth = './results_HCP_202311_no_mean/HMM_ICA_25_state_8/'\n", + "save_dir_1 = './results_simulation_202311_toy_6/HMM_ICA_25_state_8/'\n", + "save_dir_2 = './results_simulation_202311_toy_6/HMM_ICA_25_state_16/'" + ] + }, + { + "cell_type": "markdown", + "id": "3d27064d", + "metadata": {}, + "source": [ + "Question 1: Does the split-half models recover the underlying ground truth successfully?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "78f4ddfa", + "metadata": {}, + "outputs": [], + "source": [ + "covariances_truth = np.load(f'{save_dir_truth}state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "80dedf33", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 527.17it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 734.46it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 675.36it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covariances_fit_1_first_half = np.load(f'{save_dir_1}split_1_first_half/state_covariances.npy')\n", + "covariances_fit_1_second_half = np.load(f'{save_dir_1}split_1_second_half/state_covariances.npy')\n", + "\n", + "riem_fit_1_first = twopair_riemannian_distance(covariances_truth, covariances_fit_1_first_half)\n", + "index_fit_1_first, riem_fit_1_first_reorder = hungarian_pair(riem_fit_1_first,distance=True)\n", + "seaborn.heatmap(riem_fit_1_first_reorder)\n", + "plt.title('Riemannian distance, 8 states first split')\n", + "plt.show()\n", + "\n", + "riem_fit_1_second = twopair_riemannian_distance(covariances_truth, covariances_fit_1_second_half)\n", + "index_fit_1_second, riem_fit_1_second_reorder = hungarian_pair(riem_fit_1_second,distance=True)\n", + "seaborn.heatmap(riem_fit_1_second_reorder)\n", + "plt.title('Riemannian distance, 8 states second split')\n", + "plt.show()\n", + "\n", + "riem_fit_1_first_second = twopair_riemannian_distance(covariances_fit_1_first_half, covariances_fit_1_second_half)\n", + "index_fit_1_first_second, riem_fit_1_first_second_reorder = hungarian_pair(riem_fit_1_first_second,distance=True)\n", + "seaborn.heatmap(riem_fit_1_first_second_reorder)\n", + "plt.title('Riemannian distance, 8 states first split vs second split')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b1070ec", + "metadata": {}, + "source": [ + "Conclusion: For HMM_ICA_25_state_8, 7/8 states are recovered in split 1, 8/8 states are recovered in split 1, which gives us further confidence that these states are recovered very successfully" + ] + }, + { + "cell_type": "markdown", + "id": "eb7ccbca", + "metadata": {}, + "source": [ + "The second question: what about state = 16 case?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "36a7b3ef", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 313.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "riem_fit_2_first shape: (8, 16)\n", + "index fit_2_first: {'row': [0, 1, 2, 3, 4, 5, 6, 7], 'col': [7, 8, 13, 11, 9, 0, 10, 4]}\n", + "riem_fit_2_first_reorder_shape: (8, 8)\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 370.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'row': [0, 1, 2, 3, 4, 5, 6, 7], 'col': [4, 5, 8, 13, 10, 11, 6, 1]}\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covariances_fit_2_first_half = np.load(f'{save_dir_2}split_1_first_half/state_covariances.npy')\n", + "covariances_fit_2_second_half = np.load(f'{save_dir_2}split_1_second_half/state_covariances.npy')\n", + "\n", + "riem_fit_2_first = twopair_riemannian_distance(covariances_truth, covariances_fit_2_first_half)\n", + "print(f'riem_fit_2_first shape: {riem_fit_2_first.shape}')\n", + "index_fit_2_first, riem_fit_2_first_reorder = hungarian_pair(riem_fit_2_first,distance=True)\n", + "print(f'index fit_2_first: {index_fit_2_first}')\n", + "print(f'riem_fit_2_first_reorder_shape: {riem_fit_2_first_reorder.shape}')\n", + "seaborn.heatmap(riem_fit_2_first_reorder)\n", + "plt.title('Riemannian distance, 16 states first split')\n", + "plt.show()\n", + "\n", + "riem_fit_2_second = twopair_riemannian_distance(covariances_truth, covariances_fit_2_second_half)\n", + "index_fit_2_second, riem_fit_2_second_reorder = hungarian_pair(riem_fit_2_second,distance=True)\n", + "print(index_fit_2_second)\n", + "seaborn.heatmap(riem_fit_2_second_reorder)\n", + "plt.title('Riemannian distance, 16 states second split')\n", + "plt.show()\n", + "\n", + "riem_fit_2_first_second = twopair_riemannian_distance(covariances_fit_2_first_half, covariances_fit_2_second_half)\n", + "index_fit_2_first_second, riem_fit_2_first_second_reorder = hungarian_pair(riem_fit_2_first_second,distance=True)\n", + "print(f'index_fit_2_first_second: {index_fit_2_first_second}')\n", + "seaborn.heatmap(riem_fit_2_first_second_reorder)\n", + "plt.title('Riemannian distance, 16 states first split vs second split')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1b000b7d", + "metadata": {}, + "source": [ + "Conclusion: When n_state = 16. It seems that these the original 8 states are recovered very successfully.\n", + "OK, please take a note on the state indexes:\n", + "HMM_ICA_25_state_16: first split (7,8,13,11,9,0,10,4)\n", + "HMM_ICA_25_state_16: second split (4,5,8,13,10,11,6,1)" + ] + }, + { + "cell_type": "markdown", + "id": "19bf37d3", + "metadata": {}, + "source": [ + "Question 2: temporal dynamics on these states?\n", + "Hypothesis: only the states \"matched\" well with ground truth should be activated." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a3ff4d42", + "metadata": {}, + "outputs": [], + "source": [ + "fo_fit_2_first_half = np.load(f'{save_dir_2}split_1_first_half/fo.npy')\n", + "fo_fit_2_second_half = np.load(f'{save_dir_2}split_1_second_half/fo.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d547a237", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.11555 , 0.00206667, 0.0066 , 0.0091 , 0.11955 ,\n", + " 0.00991667, 0.00348333, 0.17053333, 0.1189 , 0.12525 ,\n", + " 0.11045 , 0.07183333, 0.03326667, 0.10001667, 0.0017 ,\n", + " 0.00178333])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(fo_fit_2_first_half,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6240a00f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.001, 0.245, 0.001, 0.011, 0.18 , 0.076, 0.132, 0. , 0.1 ,\n", + " 0. , 0.089, 0.022, 0. , 0.139, 0.003, 0. ])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.set_printoptions(suppress=True,precision=3)\n", + "np.mean(fo_fit_2_second_half,axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "97feaeef", + "metadata": {}, + "source": [ + "Conclusion: states not paired well are not utilised. But please note that they contribute to the overall calculation of free energy even if they are not used." + ] + }, + { + "cell_type": "markdown", + "id": "2ed4031b", + "metadata": {}, + "source": [ + "Question -1: How do we implement cross-validation?\n", + "We can \"pair\" these states and pretend that our paired states are the same!" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7559e593", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-13 15:41:03.150356: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2023-12-13 15:41:04.347466: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2023-12-13 15:41:04.347495: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "import pathlib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.preprocessing import PrepareData\n", + "from osl_dynamics.models import load" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "ad288ad1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read from directory: data/node_timeseries/simulation_toy_6\n", + "Number of subjects: 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading files: 100%|█████████████████████████| 50/50 [00:00<00:00, 20476.00it/s]\n", + "Loading files: 100%|█████████████████████████| 50/50 [00:00<00:00, 14672.58it/s]\n" + ] + } + ], + "source": [ + "data_dir = pathlib.Path('./data/node_timeseries/simulation_toy_6/')\n", + "prepare_data = PrepareData(data_dir)\n", + "subj, dataset_1, dataset_2 = prepare_data.load(z_score_data=False,split_strategy='1')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6e23fc6a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-13 15:44:15 INFO osl-dynamics [mod_base.py:607:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_8/split_1_first_half/\n", + "2023-12-13 15:44:17.886558: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", + "2023-12-13 15:44:17.886614: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2023-12-13 15:44:17.886637: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (win002.hpc.in.bmrc.ox.ac.uk): /proc/driver/nvidia/version does not exist\n", + "2023-12-13 15:44:17.975773: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-12-13 15:44:24 INFO osl-dynamics [mod_base.py:607:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_8/split_1_second_half/\n" + ] + } + ], + "source": [ + "model_1_first_half = load(f'{save_dir_1}split_1_first_half/')\n", + "model_1_second_half = load(f'{save_dir_1}split_1_second_half/')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7653d749", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(covariances_fit_1_first_half!=model_1_first_half.get_covariances())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "14b5281a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'row': [0, 1, 2, 3, 4, 5, 6, 7], 'col': [2, 5, 6, 0, 3, 4, 1, 7]}\n" + ] + } + ], + "source": [ + "print(index_fit_1_first_second)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a81d75b3", + "metadata": {}, + "outputs": [], + "source": [ + "covariances_fit_1_second_half_pair = covariances_fit_1_second_half[index_fit_1_first_second['col']]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "54f54f66", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-13 15:55:30 INFO osl-dynamics [hmm.py:1109:free_energy]: Getting free energy\n", + "2023-12-13 15:55:34 INFO numba.core.transforms [transforms.py:58:_extract_loop_lifting_candidates]: finding looplift candidates\n", + "2023-12-13 15:55:38 INFO osl-dynamics [hmm.py:1109:free_energy]: Getting free energy\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18470.062918814518\n", + "18532.93638736665\n" + ] + } + ], + "source": [ + "print(model_1_first_half.free_energy(dataset_1))\n", + "print(model_1_first_half.free_energy(dataset_1,covariances=covariances_fit_1_second_half_pair))" + ] + }, + { + "cell_type": "markdown", + "id": "c954f3c4", + "metadata": {}, + "source": [ + "Maybe work on the model with more number of states?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4bd77009", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-13 15:58:04 INFO osl-dynamics [mod_base.py:607:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_16/split_1_first_half/\n", + "2023-12-13 15:58:05 INFO osl-dynamics [mod_base.py:607:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_16/split_1_second_half/\n", + "2023-12-13 15:58:06 INFO osl-dynamics [hmm.py:1109:free_energy]: Getting free energy\n", + "2023-12-13 15:58:09 INFO osl-dynamics [hmm.py:1109:free_energy]: Getting free energy\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18389.61165836891\n", + "18594.25057721719\n" + ] + } + ], + "source": [ + "model_2_first_half = load(f'{save_dir_2}split_1_first_half/')\n", + "model_2_second_half = load(f'{save_dir_2}split_1_second_half/')\n", + "covariances_fit_2_second_half_pair = covariances_fit_2_second_half[index_fit_2_first_second['col']]\n", + "print(model_2_first_half.free_energy(dataset_1))\n", + "print(model_2_first_half.free_energy(dataset_1,covariances=covariances_fit_2_second_half_pair))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "6425b905", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-13 16:31:11 INFO osl-dynamics [mod_base.py:607:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_14/split_1_first_half/\n", + "2023-12-13 16:31:12 INFO osl-dynamics [mod_base.py:607:load]: Loading model: ./results_simulation_202311_toy_6/HMM_ICA_25_state_14/split_1_second_half/\n", + "Computing Riemannian distances: 100%|██████████| 14/14 [00:00<00:00, 461.67it/s]\n", + "2023-12-13 16:31:13 INFO osl-dynamics [hmm.py:1109:free_energy]: Getting free energy\n", + "2023-12-13 16:31:16 INFO osl-dynamics [hmm.py:1109:free_energy]: Getting free energy\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18391.97746429379\n", + "18453.210000027982\n" + ] + } + ], + "source": [ + "save_dir_3 = './results_simulation_202311_toy_6/HMM_ICA_25_state_14/'\n", + "model_3_first_half = load(f'{save_dir_3}split_1_first_half/')\n", + "model_3_second_half = load(f'{save_dir_3}split_1_second_half/')\n", + "covariances_fit_3_first_half = np.load(f'{save_dir_3}split_1_first_half/state_covariances.npy')\n", + "covariances_fit_3_second_half = np.load(f'{save_dir_3}split_1_second_half/state_covariances.npy')\n", + "riem_fit_3_first_second = twopair_riemannian_distance(covariances_fit_3_first_half, covariances_fit_3_second_half)\n", + "index_fit_3_first_second, riem_fit_3_first_second_reorder = hungarian_pair(riem_fit_3_first_second,distance=True)\n", + "\n", + "\n", + "covariances_fit_3_second_half_pair = covariances_fit_3_second_half[index_fit_3_first_second['col']]\n", + "print(model_3_first_half.free_energy(dataset_1))\n", + "print(model_3_first_half.free_energy(dataset_1,covariances=covariances_fit_3_second_half_pair))" + ] + }, + { + "cell_type": "markdown", + "id": "f32fc7d3", + "metadata": {}, + "source": [ + "### Update 14th Dec 2023" + ] + }, + { + "cell_type": "markdown", + "id": "ff98d4f5", + "metadata": {}, + "source": [ + "### Task: visualisation of spatial maps (Using CIFTI data)" + ] + }, + { + "cell_type": "markdown", + "id": "39fe1f17", + "metadata": {}, + "source": [ + "First of all, have a try on Chet's code." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "f1e1e1f3", + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir visualisation" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "ec7b9197", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "^C\r\n", + "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\r\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install osfclient" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "9d5e65dd", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from osl_dynamics.analysis import power, workbench" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "184e5b59", + "metadata": {}, + "outputs": [], + "source": [ + "def get_data(name, output_dir):\n", + " if os.path.exists(output_dir):\n", + " print(f\"{output_dir} already downloaded. Skipping..\")\n", + " return\n", + " os.system(f\"osf -p by2tc fetch trained_models/{name}.zip\")\n", + " os.system(f\"unzip -o {name}.zip -d {output_dir}\")\n", + " os.remove(f\"{name}.zip\")\n", + " print(f\"Data downloaded to: {output_dir}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "e9c1a776", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sh: osf: command not found\n", + "unzip: cannot find or open ae-hmm_notts_rest_55_subj.zip, ae-hmm_notts_rest_55_subj.zip.zip or ae-hmm_notts_rest_55_subj.zip.ZIP.\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'ae-hmm_notts_rest_55_subj.zip'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mae-hmm_notts_rest_55_subj\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvisualisation/MEG\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[76], line 7\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(name, output_dir)\u001b[0m\n\u001b[1;32m 5\u001b[0m os\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mosf -p by2tc fetch trained_models/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.zip\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6\u001b[0m os\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munzip -o \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.zip -d \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.zip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData downloaded to: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'ae-hmm_notts_rest_55_subj.zip'" + ] + } + ], + "source": [ + "get_data(\"ae-hmm_notts_rest_55_subj\", output_dir='visualisation/MEG')" + ] + }, + { + "cell_type": "markdown", + "id": "99463799", + "metadata": {}, + "source": [ + "### Update 18th Dec 2023" + ] + }, + { + "cell_type": "markdown", + "id": "880cd48d", + "metadata": {}, + "source": [ + "In this part, we try direct rendering of volume-based results." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2d7c6a29", + "metadata": {}, + "outputs": [], + "source": [ + "!module load ConnectomeWorkbench" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b2f66901", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "from osl_dynamics.analysis.workbench import render\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0b90788b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/apps/eb/2020b/skylake/software/Anaconda3/2022.05:/apps/eb/2020b/skylake/software/Anaconda3/2022.05/sbin:/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/bin:/well/win/software/packages/fsl/5.0.11/bin:/mgmt/uge/8.6.8/bin/lx-amd64:/opt/TurboVNC/bin:/apps/eb/2020b/skylake/software/Anaconda3/2022.05/condabin:/mgmt/uge/8.6.8/bin/lx-amd64:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/lpp/mmfs/bin:/opt/ibutils/bin:/users/win-fmrib-analysis/uap971/.local/bin:/users/win-fmrib-analysis/uap971/bin:/usr/lpp/mmfs/bin:/users/win-fmrib-analysis/uap971/.local/bin:/users/win-fmrib-analysis/uap971/bin\n" + ] + } + ], + "source": [ + "print(os.environ[\"PATH\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "52feeb0f", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "process = subprocess.Popen(\"module load ConnectomeWorkbench\", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2fdcf521", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/apps/eb/2020b/skylake/software/Anaconda3/2022.05:/apps/eb/2020b/skylake/software/Anaconda3/2022.05/sbin:/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/bin:/well/win/software/packages/fsl/5.0.11/bin:/mgmt/uge/8.6.8/bin/lx-amd64:/opt/TurboVNC/bin:/apps/eb/2020b/skylake/software/Anaconda3/2022.05/condabin:/mgmt/uge/8.6.8/bin/lx-amd64:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/lpp/mmfs/bin:/opt/ibutils/bin:/users/win-fmrib-analysis/uap971/.local/bin:/users/win-fmrib-analysis/uap971/bin:/usr/lpp/mmfs/bin:/users/win-fmrib-analysis/uap971/.local/bin:/users/win-fmrib-analysis/uap971/bin\n" + ] + } + ], + "source": [ + "print(os.environ[\"PATH\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f9d59375", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving images: 100%|██████████████████████████████| 8/8 [00:13<00:00, 1.71s/it]\n", + "Saving images: 100%|██████████████████████████████| 8/8 [00:13<00:00, 1.69s/it]\n", + "Saving images: 100%|██████████████████████████████| 8/8 [00:09<00:00, 1.16s/it]\n" + ] + } + ], + "source": [ + "nii = './results_HCP_202311_no_mean/HMM_ICA_25_state_8/FC_map.nii.gz'\n", + "save_dir = './visualisation_1/'\n", + "render(nii,save_dir,gui=False,inflation=0,image_name='./visualisation_1/mid_thickness')\n", + "render(nii,save_dir,gui=False,inflation=1,image_name='./visualisation_1/inflated')\n", + "render(nii,save_dir,gui=False,inflation=2,image_name='./visualisation_1/very_inflated')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "082fa07a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving images: 100%|██████████████████████████████| 8/8 [00:13<00:00, 1.71s/it]\n", + "Saving images: 100%|██████████████████████████████| 8/8 [00:13<00:00, 1.74s/it]\n", + "Saving images: 100%|██████████████████████████████| 8/8 [00:09<00:00, 1.20s/it]\n" + ] + } + ], + "source": [ + "nii = './results_HCP_202311_no_mean/HMM_ICA_25_state_8/FC_surface_map.dscalar.nii'\n", + "save_dir = './visualisation_2/'\n", + "render(nii,save_dir,gui=False,inflation=0,image_name='./visualisation_2/mid_thickness',input_cifti=True)\n", + "render(nii,save_dir,gui=False,inflation=1,image_name='./visualisation_2/inflated',input_cifti=True)\n", + "render(nii,save_dir,gui=False,inflation=1,image_name='./visualisation_2/very_inflated',input_cifti=True)" + ] + }, + { + "cell_type": "markdown", + "id": "aa54d521", + "metadata": {}, + "source": [ + "## Update 20th January 2024" + ] + }, + { + "cell_type": "markdown", + "id": "4d62ea0b", + "metadata": {}, + "source": [ + "This week we implement ten runs of ten subjects with different subject variability level $\\sigma = \\{0.025,0.05,0.075,0.1\\}$. This would require a substantial amount of work! To start with, look at your data! i.e. do we have the expected subject variability level?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4886e6c6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "%matplotlib inline\n", + "from rotation.utils import twopair_riemannian_distance" + ] + }, + { + "cell_type": "markdown", + "id": "015a9ab7", + "metadata": {}, + "source": [ + "So first import the original unperturbed ground-truth Riemannian distance." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a07ccb6d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 498.17it/s]\n" + ] + }, + { + "data": { + "image/png": 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FYQIAAFyOygAAAGYMEwAA4G5uGyYgGQAAwMxllQHmDAAA4HJUBgAAMDFcVhloPsnAny84HUGjfVv0J6dDsEXZ42OcDsEWvmf3OB1Co1VMn+N0CLYIHyt0OoRGMy5963QItmjVO8npEFoGlyUDDBMAAOByzacyAABAM8EwAQAAbueyZIBhAgAAXI7KAAAAJgwTAADgciQDAAC4nNuSAeYMAADgclQGAAAwMzxOR3BVkQwAAGDCMAEAAHAVKgMAAJgYYYYJAABwNYYJAACAq1AZAADAxGA1AQAA7sYwAQAAcBUqAwAAmLCaAAAAlzMMpyO4ukgGAAAwcVtlgDkDAAC4nOVk4JNPPtGGDRt07NgxSdKxY8c0b948PfDAA/rwww/r1UcoFFJ5eXlEC31baTUUAACahBH22NasCAaDGjlypGJiYtSlSxelpKSosLCwzms2btwoj8cT0aKjoy3d11IysH37dg0ZMkSPPvqohg4dqu3bt2vMmDE6fvy4Tp48qbvvvrteCUEwGJTP54toz+ccsxQ4AABNxTDsa1bs3r1baWlp2rdvn7KysnT58mXdfffdqqioqPO62NhYFRcXV7WTJ09auq+lZODpp5/WokWL9L//+7/asGGD/vEf/1Fz585VVlaWdu7cqUWLFmnp0qVX7CcQCKisrCyiPZrc31LgAABca7Zv3677779ft9xyi5KSkrRx40YVFRUpPz+/zus8Ho/i4+OrWlxcnKX7WkoGjh49qvvvv1+SdO+99+rcuXP6h3/4h6rv77vvPv3P//zPFfvxer2KjY2NaN42rS0FDgBAU7FzmKDGofFQqF5xlJWVSZI6depU53nnz59Xr1691KNHD02bNk1Hjx619LyW5wx4PN+Nf7Rq1UrR0dHy+XxV38XExFQFDgBAS2UYHttaTUPjwWDwijGEw2EtXLhQo0eP1qBBg2o9r1+/flq/fr3eeecdbdq0SeFwWMnJyfrjH/9Y7+e1tLTwpptu0meffabvf//7kqTc3Fz17Nmz6vuioiIlJCRY6RIAgGtaIBBQenp6xDGv13vF69LS0nTkyBHt3bu3zvP8fr/8fn/V5+TkZA0YMEBr167VkiVL6hWjpWRg3rx5qqz866x/c6aybds23XnnnVa6BACg2bHz3QRer7deP/5/a/78+Xrvvfe0Z88ede/e3dK1bdu21dChQ3X8+PF6X2MpGXjooYfq/P7ZZ5+10h0AAM1S2KG3FhqGoYcfflibN29Wdna2evfubbmPyspKHT58WJMnT673NexACABAM5GWlqaMjAy98847iomJUUlJiSTJ5/PpuuuukySlpqaqW7duVfMOnn76ad12223q06ePvvnmGz333HM6efKk5syZU+/7kgwAAGBiOFQZWL16tSRp3LhxEcc3bNhQtZqvqKhIrVr9df7/119/rblz56qkpEQdO3bU8OHDlZOTo4EDB9b7viQDAACYOPVuAqMeuxRlZ2dHfF6+fLmWL1/eqPuSDAAAYOK2txbyoiIAAFyOygAAACZue4UxyQAAACZOLS10CsMEAAC4HJUBAABMnFpa6BSSAQAATFhNAAAAXIXKAAAAJm6bQEgyAACAidvmDDBMAACAy1EZAADAxG0TCEkGAAAwYc6AQ9rMWuB0CI3Wtp3P6RDwNyqm1/9d3s1V+6GpTodgiwtf/d7pEBrt2yPZTodgi8rszU6HYI9bxjdp98wZAAAArtJsKgMAADQXDBMAAOByLps/yDABAABuR2UAAAAThgkAAHA5VhMAAABXoTIAAIBJ2OkArjKSAQAATAwxTAAAAFyEygAAACZhl200QDIAAIBJ2GXDBCQDAACYMGcAAAC4CpUBAABMWFoIAIDLMUwAAAAcEQwGNXLkSMXExKhLly5KSUlRYWHhFa9766231L9/f0VHR2vw4MHaunWrpfuSDAAAYBK2sVmxe/dupaWlad++fcrKytLly5d19913q6KiotZrcnJyNHPmTD344IM6dOiQUlJSlJKSoiNHjtT7vh7DMJrFasrQp3udDqHRPO18ToeAv2GUnXY6hEZrPzTV6RBsceGr3zsdQqN9eyTb6RBsYRw96HQItmiXtrJJ+98a90Pb+ppcmtnga8+cOaMuXbpo9+7dGjNmTI3nzJgxQxUVFXrvvfeqjt12220aMmSI1qxZU6/72FIZaCb5BAAAzU4oFFJ5eXlEC4VC9bq2rKxMktSpU6daz8nNzdWECRMijk2cOFG5ubn1jtGWZMDr9eqTTz6xoysAABxnyGNbCwaD8vl8ES0YDF4xhnA4rIULF2r06NEaNGhQreeVlJQoLi4u4lhcXJxKSkrq/byWVhOkp6fXeLyyslJLly7VDTfcIEl64YUXrHQLAECzErZxMUEgEKj2++n1eq94XVpamo4cOaK9e5t+GN1SMvDiiy8qKSlJ119/fcRxwzD0ySefqH379vJ4rvxfMBQKVS+RXLokb1SUlXAAAGj2vF5vvX78/9b8+fP13nvvac+ePerevXud58bHx6u0tDTiWGlpqeLj4+t9P0vDBM8++6zKysr0b//2b9q1a1dVa926tTZu3Khdu3bpww8/vGI/NZVM/n3tJiuhAADQZMLy2NasMAxD8+fP1+bNm/Xhhx+qd+/eV7zG7/dr586dEceysrLk9/vrfV9LycBPf/pTvfHGG5o3b54effRRXb582crlVQKBgMrKyiLaYz/+UYP6AgDAboaNzYq0tDRt2rRJGRkZiomJUUlJiUpKSnThwoWqc1JTUxUIBKo+L1iwQNu3b9eyZct07NgxPfXUU8rLy9P8+fPrfV/LEwhHjhyp/Px8nTlzRiNGjNCRI0fqNTTwt7xer2JjYyMaQwQAgObCqX0GVq9erbKyMo0bN04JCQlV7Y033qg6p6ioSMXFxVWfk5OTlZGRoXXr1ikpKUlvv/22tmzZUuekQ7MGbUfcoUMHvf7668rMzNSECRNUWVnZkG4AAMDfqM9S/ezs7GrHpk+frunTpzf4vo16N8EPf/hD3X777crPz1evXr0a0xUAAM1G2GLFu6Vr9IuKunfvfsWZjgAAtCRu20qPdxMAAOByvMIYAAATqxP/WjqSAQAATOzcgbAlYJgAAACXozIAAICJ1Z0DWzqSAQAATFhNAAAAXIXKAAAAJm6bQEgyAACACUsLAQBwOeYMAAAAV6EyAACACXMGAABwObfNGWCYAAAAl6MyAACAidsqAyQDAACYGC6bM8AwAQAALtdsKgMXn1nsdAiN1qp9lNMh2KLN6BFOh2CL8LFCp0NotAtf/d7pEGxxXdc7nA6h0c7vXOp0CLao/LzI6RBaBIYJAABwObclAwwTAADgclQGAAAwcdt2xCQDAACYsAMhAAAux5wBAADgKlQGAAAwcVtlgGQAAAATt00gZJgAAACXozIAAICJ21YTUBkAAMAkbGOzYs+ePZo6daq6du0qj8ejLVu21Hl+dna2PB5PtVZSUmLpviQDAAA0ExUVFUpKStKqVassXVdYWKji4uKq1qVLF0vXM0wAAICJUxMIJ02apEmTJlm+rkuXLrr++usbfF8qAwAAmIRl2NZCoZDKy8sjWigUsjXeIUOGKCEhQXfddZf+8Ic/WL6eZAAAgCYUDAbl8/kiWjAYtKXvhIQErVmzRr/5zW/0m9/8Rj169NC4ceN08OBBS/0wTAAAgImdmw4FAgGlp6dHHPN6vbb03a9fP/Xr16/qc3Jysj7//HMtX75cv/rVr+rdD8kAAAAmds4Z8Hq9tv3418eoUaO0d+9eS9eQDAAAYNKStyMuKChQQkKCpWtIBgAAaCbOnz+v48ePV30+ceKECgoK1KlTJ/Xs2VOBQECnTp3SL3/5S0nSiy++qN69e+uWW27RxYsX9eqrr+rDDz/U+++/b+m+JAMAAJg4tQNhXl6efvCDH1R9/stcg1mzZmnjxo0qLi5WUVFR1feXLl3Sv/7rv+rUqVNq166dEhMT9cEHH0T0UR+NSgYqKir05ptv6vjx40pISNDMmTN1ww03NKZLAAAcF3Zop4Fx48bJMGq/98aNGyM+P/bYY3rssccafV9LycDAgQO1d+9ederUSV9++aXGjBmjr7/+WjfffLM+//xzLVmyRPv27VPv3r3r7CcUClVbYxmqDMvbmpWOAABcbZZ+fY8dO6Zvv/1W0ndLJbp27aqTJ09q//79OnnypBITE/XEE09csZ+a1ly+cPj/NugBAACwm2Fjawka/Kd4bm6unnrqKfl8PklShw4d9POf/7xeyxkCgYDKysoiWvrgmxoaCgAAtnLqRUVOsTxnwOP5blbFxYsXqy1d6Natm86cOXPFPmpac2kwRAAAgCMsJwPjx49XmzZtVF5ersLCQg0aNKjqu5MnTzKBEADQ4jk1gdAplpKBxYsXR3zu0KFDxOd3331Xd9xxR+OjAgDAQe5KBRqZDJg999xzjQoGAABcfWw6BACASUuZ+GcXkgEAAEyYMwAAgMu5KxVoxD4DAADg2kBlAAAAE+YMAADgcobLBgoYJgAAwOWoDAAAYMIwAQAALue2pYUMEwAA4HJUBgAAMHFXXYBkAACAahgmAAAArkJlAAAAE1YTAADgcm7bdIhkAAAAE7dVBpgzAACAyzWbykDUHUlOh9BoxvkKp0OwRbjwM6dDsIVx6VunQ2i0b49kOx2CLc7vXOp0CI3WYfxPnQ7BFufeDTgdQovAMAEAAC7HMAEAAHAVKgMAAJiEDYYJAABwNXelAgwTAADgeiQDAACYhGXY1qzYs2ePpk6dqq5du8rj8WjLli1XvCY7O1vDhg2T1+tVnz59tHHjRsvPSzIAAICJYeM/VlRUVCgpKUmrVq2q1/knTpzQlClT9IMf/EAFBQVauHCh5syZox07dli6L3MGAABoJiZNmqRJkybV+/w1a9aod+/eWrZsmSRpwIAB2rt3r5YvX66JEyfWux8qAwAAmIRtbKFQSOXl5REtFArZEmdubq4mTJgQcWzixInKzc211A/JAAAAJnbOGQgGg/L5fBEtGAzaEmdJSYni4uIijsXFxam8vFwXLlyodz8MEwAAYGLndsSBQEDp6ekRx7xer23924FkAACAJuT1epvsxz8+Pl6lpaURx0pLSxUbG6vrrruu3v2QDAAAYNJS3k3g9/u1devWiGNZWVny+/2W+mHOAAAAJoZh2NasOH/+vAoKClRQUCDpu6WDBQUFKioqkvTdkENqamrV+Q899JC++OILPfbYYzp27Jhefvllvfnmm3rkkUcs3ZdkAACAZiIvL09Dhw7V0KFDJUnp6ekaOnSonnzySUlScXFxVWIgSb1799Z//dd/KSsrS0lJSVq2bJleffVVS8sKJYYJAACoxurOgXYZN25cndWEmnYXHDdunA4dOtSo+5IMAABg0lLmDNiFYQIAAFyOygAAACZ27jPQEpAMAABg4tScAacwTAAAgMtZSgYOHjyoEydOVH3+1a9+pdGjR6tHjx66/fbblZmZWa9+anxpw+VvrUUOAEATcWqfAadYSgZmz56tzz//XJL06quv6sc//rFGjBihJ554QiNHjtTcuXO1fv36K/ZT00sbntt2oGFPAACAzex8a2FLYGnOwGeffaa+fftKkl5++WWtWLFCc+fOrfp+5MiReuaZZ/TAAw/U2U9NL20I//pnVkIBAKDJMIGwDu3atdPZs2fVq1cvnTp1SqNGjYr4/tZbb40YRqhNTS9tuNCWuYwAADjB0jDBpEmTtHr1aknS2LFj9fbbb0d8/+abb6pPnz72RQcAgAPCMmxrLYGlP8d/8YtfaPTo0Ro7dqxGjBihZcuWKTs7WwMGDFBhYaH27dunzZs3N1WsAABcFS1l4p9dLFUGunbtqkOHDsnv92v79u0yDEP79+/X+++/r+7du+sPf/iDJk+e3FSxAgCAJmB5oP7666/X0qVLtXTp0qaIBwAAx7WU8r5dmLUHAICJ21YTsAMhAAAuR2UAAACTsMsmEJIMAABg4q5UgGECAABcj8oAAAAmrCYAAMDlSAYAAHA5diAEAACuQmUAAAAThgkAAHA5diAEAACuQmUAAAATt00gJBkAAMDEbXMGGCYAAMDlqAwAAGDCMIFDjPJzTofQaG1S5jodgi0qP97rdAi2aNU7yekQGq0ye7PTIdii8vMip0NotHPvBpwOwRYxU4NOh2CLby/Nb9L+GSYAAACOWbVqlW666SZFR0fr1ltv1f79+2s9d+PGjfJ4PBEtOjra8j1JBgAAMDFs/MeKN954Q+np6Vq8eLEOHjyopKQkTZw4UadPn671mtjYWBUXF1e1kydPWn5ekgEAAEzChmFbs+KFF17Q3LlzNXv2bA0cOFBr1qxRu3bttH79+lqv8Xg8io+Pr2pxcXGWn5dkAAAAEzsrA6FQSOXl5REtFApVu+elS5eUn5+vCRMmVB1r1aqVJkyYoNzc3FpjPX/+vHr16qUePXpo2rRpOnr0qOXnJRkAAKAJBYNB+Xy+iBYMVp/IefbsWVVWVlb7yz4uLk4lJSU19t2vXz+tX79e77zzjjZt2qRwOKzk5GT98Y9/tBRjs1lNAABAc2G1vF+XQCCg9PT0iGNer9eWvv1+v/x+f9Xn5ORkDRgwQGvXrtWSJUvq3Q/JAAAAJna+qMjr9dbrx79z585q3bq1SktLI46XlpYqPj6+Xvdq27athg4dquPHj1uKkWECAACagaioKA0fPlw7d+6sOhYOh7Vz586Iv/7rUllZqcOHDyshIcHSvakMAABgYucwgRXp6emaNWuWRowYoVGjRunFF19URUWFZs+eLUlKTU1Vt27dquYcPP3007rtttvUp08fffPNN3ruued08uRJzZkzx9J9SQYAADCxc5jAihkzZujMmTN68sknVVJSoiFDhmj79u1VkwqLiorUqtVfi/pff/215s6dq5KSEnXs2FHDhw9XTk6OBg4caOm+HqOZbMD85xda/la+bEfcvLAdcfMRvga2I2498W6nQ7DFtbMd8akm7b/vjcNt6+uzM/m29dVUqAwAAGDi1DCBU0gGAAAwcWqYwCmsJgAAwOWoDAAAYGIYYadDuKpIBgAAMAm7bJiAZAAAAJNmstDuqmHOAAAALkdlAAAAE4YJAABwOYYJAACAq1hKBh5++GH9/ve/b/RNQ6GQysvLI1ro28pG9wsAgB3ChmFbawksJQOrVq3SuHHjdPPNN+sXv/iFSkpKGnTTYDAon88X0Z7fWdCgvgAAsJth4z8tgeVhgvfff1+TJ0/W888/r549e2ratGl67733FA7Xf4OGQCCgsrKyiPbo+CFWQwEAADawnAwMHjxYL774or766itt2rRJoVBIKSkp6tGjh5544gkdP378in14vV7FxsZGNG+b1g16AAAA7GYYhm2tJWjwBMK2bdvq3nvv1fbt2/XFF19o7ty5+vWvf61+/frZGR8AAFddWIZtrSWwZTVBz5499dRTT+nEiRPavn27HV0CAICrxNI+A7169VLr1rWX8z0ej+66665GBwUAgJNaSnnfLpaSgRMnTjRVHAAANBstZUmgXdiBEAAAE7dVBtiBEAAAl6MyAACASUtZBWAXkgEAAEwYJgAAAK5CZQAAABNWEwAA4HIt5QVDdmGYAAAAl6MyAACACcMEAAC4HKsJAACAq1AZAADAhAmEAAC4nGEYtjWrVq1apZtuuknR0dG69dZbtX///jrPf+utt9S/f39FR0dr8ODB2rp1q+V7kgwAAGDiVDLwxhtvKD09XYsXL9bBgweVlJSkiRMn6vTp0zWen5OTo5kzZ+rBBx/UoUOHlJKSopSUFB05csTSfUkGAABoJl544QXNnTtXs2fP1sCBA7VmzRq1a9dO69evr/H8FStW6J577tGiRYs0YMAALVmyRMOGDdPKlSst3ZdkAAAAE8PGFgqFVF5eHtFCoVC1e166dEn5+fmaMGFC1bFWrVppwoQJys3NrTHO3NzciPMlaeLEibWeX/sDu8TFixeNxYsXGxcvXnQ6lAa7Fp7BMK6N57gWnsEweI7m5Fp4BsO4dp7DTosXL66WIyxevLjaeadOnTIkGTk5ORHHFy1aZIwaNarGvtu2bWtkZGREHFu1apXRpUsXSzF6DMMdiynLy8vl8/lUVlam2NhYp8NpkGvhGaRr4zmuhWeQeI7m5Fp4BunaeQ47hUKhapUAr9crr9cbceyrr75St27dlJOTI7/fX3X8scce0+7du/XRRx9V6zsqKkqvv/66Zs6cWXXs5Zdf1s9//nOVlpbWO0aWFgIA0IRq+uGvSefOndW6detqP+KlpaWKj4+v8Zr4+HhL59eGOQMAADQDUVFRGj58uHbu3Fl1LBwOa+fOnRGVgr/l9/sjzpekrKysWs+vDZUBAACaifT0dM2aNUsjRozQqFGj9OKLL6qiokKzZ8+WJKWmpqpbt24KBoOSpAULFmjs2LFatmyZpkyZoszMTOXl5WndunWW7uuaZMDr9Wrx4sX1KtU0V9fCM0jXxnNcC88g8RzNybXwDNK18xxOmTFjhs6cOaMnn3xSJSUlGjJkiLZv3664uDhJUlFRkVq1+mtRPzk5WRkZGfrZz36mxx9/XH379tWWLVs0aNAgS/d1zQRCAABQM+YMAADgciQDAAC4HMkAAAAuRzIAAIDLuSIZsPo6yOZmz549mjp1qrp27SqPx6MtW7Y4HZJlwWBQI0eOVExMjLp06aKUlBQVFhY6HZZlq1evVmJiomJjYxUbGyu/369t27Y5HVajLF26VB6PRwsXLnQ6FEueeuopeTyeiNa/f3+nw2qQU6dO6Uc/+pFuuOEGXXfddRo8eLDy8vKcDqvebrrppmr/Lzwej9LS0pwODfV0zScDVl8H2RxVVFQoKSlJq1atcjqUBtu9e7fS0tK0b98+ZWVl6fLly7r77rtVUVHhdGiWdO/eXUuXLlV+fr7y8vJ05513atq0aTp69KjToTXIgQMHtHbtWiUmJjodSoPccsstKi4urmp79+51OiTLvv76a40ePVpt27bVtm3b9PHHH2vZsmXq2LGj06HV24EDByL+P2RlZUmSpk+f7nBkqDdLbzJogUaNGmWkpaVVfa6srDS6du1qBINBB6NqOEnG5s2bnQ6j0U6fPm1IMnbv3u10KI3WsWNH49VXX3U6DMvOnTtn9O3b18jKyjLGjh1rLFiwwOmQLFm8eLGRlJTkdBiN9pOf/MS4/fbbnQ7DVgsWLDC+//3vG+Fw2OlQUE/XdGWgIa+DxNVRVlYmSerUqZPDkTRcZWWlMjMzVVFRYXnrz+YgLS1NU6ZMqfb605bks88+U9euXfW9731P9913n4qKipwOybLf/e53GjFihKZPn64uXbpo6NCheuWVV5wOq8EuXbqkTZs26YEHHpDH43E6HNTTNZ0MnD17VpWVlVU7N/1FXFycSkpKHIoK4XBYCxcu1OjRoy3vktUcHD58WB06dJDX69VDDz2kzZs3a+DAgU6HZUlmZqYOHjxYtaVpS3Trrbdq48aN2r59u1avXq0TJ07ojjvu0Llz55wOzZIvvvhCq1evVt++fbVjxw7NmzdP//Iv/6LXX3/d6dAaZMuWLfrmm290//33Ox0KLHDNdsRoPtLS0nTkyJEWOb4rSf369VNBQYHKysr09ttva9asWdq9e3eLSQi+/PJLLViwQFlZWYqOjnY6nAabNGlS1b8nJibq1ltvVa9evfTmm2/qwQcfdDAya8LhsEaMGKFnn31WkjR06FAdOXJEa9as0axZsxyOzrrXXntNkyZNUteuXZ0OBRZc05WBhrwOEk1r/vz5eu+997Rr1y51797d6XAaJCoqSn369NHw4cMVDAaVlJSkFStWOB1WveXn5+v06dMaNmyY2rRpozZt2mj37t166aWX1KZNG1VWVjodYoNcf/31uvnmm3X8+HGnQ7EkISGhWiI5YMCAFjnkcfLkSX3wwQeaM2eO06HAoms6GWjI6yDRNAzD0Pz587V582Z9+OGH6t27t9Mh2SYcDisUCjkdRr2NHz9ehw8fVkFBQVUbMWKE7rvvPhUUFKh169ZOh9gg58+f1+eff66EhASnQ7Fk9OjR1ZbZfvrpp+rVq5dDETXchg0b1KVLF02ZMsXpUGDRNT9McKXXQbYE58+fj/hr58SJEyooKFCnTp3Us2dPByOrv7S0NGVkZOidd95RTExM1ZwNn8+n6667zuHo6i8QCGjSpEnq2bOnzp07p4yMDGVnZ2vHjh1Oh1ZvMTEx1eZqtG/fXjfccEOLmsPx6KOPaurUqerVq5e++uorLV68WK1bt9bMmTOdDs2SRx55RMnJyXr22Wd17733av/+/Vq3bp3lV9A6LRwOa8OGDZo1a5batLnmf1quPU4vZ7ga/uM//sPo2bOnERUVZYwaNcrYt2+f0yFZsmvXLkNStTZr1iynQ6u3muKXZGzYsMHp0Cx54IEHjF69ehlRUVHGjTfeaIwfP954//33nQ6r0Vri0sIZM2YYCQkJRlRUlNGtWzdjxowZxvHjx50Oq0HeffddY9CgQYbX6zX69+9vrFu3zumQLNuxY4chySgsLHQ6FDQArzAGAMDlruk5AwAA4MpIBgAAcDmSAQAAXI5kAAAAlyMZAADA5UgGAABwOZIBAABcjmQAAACXIxkAAMDlSAYAAHA5kgEAAFyOZAAAAJf7fyHO3uVLGjINAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "true_covariance = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(true_covariance,true_covariance))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "16f55aa1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 523.18it/s]\n" + ] + }, + { + "data": { + "image/png": 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j51RtOutmybk2+pCSgLpN2E3VY467hCq0hT2bdltK3lNJuFTw+S5OzUYymq1G8PIir+Lo74lCo0I2li5ma//mJALvac+eEa9bRqKqg1yQg5xhfaWCnJGEonGHysvkZ1sCAQApIwmFQlly5UB2KhTlY9jyCajUKBxdkPOz0fQaiZyTVl1duTVimqBiAQEBHDlyhGbNmtncf+TIEfz9b3wAnT9/PjNnWl9CN7j58Cq14diBEzxy91SrtLnvzSHuchxrPv2xXCAAoHPUlUs3X1u5r1AoLMHA9Deeocd9PXh+1CyS45Or1B57UTnpcKzvj37Dn5Xm8x/SFYVWTdKGiuft/qEL9ELj5YI+NctezayU2kmHaz0/ijZmVym/QqnAs1kdEq6dRV3e+BdXy3xR3vv9C0Rv/IvIdZX/X+xJ7aTD5Sb74dG8Dld3lfRDoVaj0qrLDTPKZgmFsnwgbS/mKxdQ+gZZpSl9g5Gzbu4gqwwKRc6zfs9oRzyOulVXij57CbmKixJvhTnmAkq/YOv2+Abd9HMqgxog5/477/uqUDnpcKnnT+yGv2zuTz96ibojwixn1gAuDQIoSs4qFwgE39+JvSPfoDC+er88pcQoFF7Wx3OFp3+5BYHXMydGoWrSETQ6MJaM/io9/ZElCTm/zOthNpUEDkoVqsYdMUcctXcXbo+YJqjY7Nmzefzxxzl27Bh333235Ys/JSWFXbt28cUXX/Duu+/esB6dTodOp7NKq8oUAUBRQVG5efziomJys3It6fOXziU9OZ0vlnwJwMGdhxj12EiizkZZpgkenTOZv3ccsgQJz7/5HPcM78dLj/6PovxCvHw9AcjPK8BQbKhS225Go1cnkL79GMUJ6ej8PQl9YRSyWSJlU8nBosVHT6NPzuTym9brGoLG9SX9j/ByiwJVTjpCZz9I6tYjGFKzS9YMvDKeoivJZOw5RXXo9MpY4necoCAhHccAT9rPegBZkojeXDJN1GPZExQmZXF8SckoRtvnh5N2PIrcmBS0bs60mjYI52AfLl2bFtFn5aMv0y/ZZKYoLZvcaryWuv3/xpK4vbQfbWaX9CNmU0k/ui17gqLkLE4uLulHqxnDST8eRf6VFDTuzrS41o+oa/0w5ReR8vcF2r8yFlOxkYKEdPy7NSP0wR4cX1B+WspejPt/xvGZt9D0exDTqQOo6jZB07U/+vWfWvJo738Yhbs3+rVLAdD0HIKUmYKUHIdCo0V9172oGrWmeMVrljK6B55A3b4XRasWgb4IhasHAHJRIZjs+9kw7tuC43Nvo7l71LU+NEbTdQD69Z+U9mHQRBRuXpZpDE2voUgZKUgpcSjUGtRd+6Nq3Jriz18trVjrgNIn0LKp9PJHDgpFLsxDzrb/CGCb/43j6o7jFMaXvKdazh6JLEnEXZvK6/zhkxQlZ3F20Y8AXP56J40e6U+71x8m6qvtuIQG0Py5YURet3am/eLJ1B0Rxl+PvI8xvxidr3vJ/yyvEKm4alOmN8N0bAe6sfNQdxmIOSIcZUB91G17Ydj+jSWPpucDKFw8MPz+FQDmC4eRuw5Ge98jGP/egsLRBU3vBzGfPWBZQKgMCEXh6omUGofCxRNN2FBQKDAe/cPufRCq7qaCgaeffhofHx8++OADPv30U8zXrptVqVR07NiR1atXM3r06Gpp6M3wD/ZDvi6q+3bZd8iyzKMvPIJPgA/ZGTn8veMgX779lSXP8ElDAVi24X2rupbMeJs/1t94JfPNcgjyptXy59B4umLIyCXnSAThA1/GmFFy9YJDsLdVHwCcGgbi0bU5J0a9Ua4+WZJwaVGPwId6o3ZzRp+cSea+00S/tQ65mu414BzoRe9Pnkbn6UJxZh6pRyLYOuQ19JklfXAJ8rEa9tR6OBP2zlQcfd0x5BSQfiaG34YtICfyarW0r6qcAr3o/mlJP/QZeaQejWDb4NJ+OAf7IF/fD3dnur4zFYdr/cg8HcP2YQvIva4fB6Z9TLsXH6L7x9PQerhQkJjOqbfWE/nNrmrrhxQfRfHqxWgHPoz23oeQM1PQb1mJ6UTpHLXCzROlp09pIZUa3ZApKNy9wKDHnBRL8eevYr5cejmtJmwgAE5PWd/Yp3jtsgrn8W+rD6sWoR00EW3/6/pw/Lo+uHqi9LzuqiWVGt2w6/sQQ/Hy/2GOKu2Dqk4jqxsT6YaXjC4aj+xCv7Z0WspeHAO96PrpM2ivvafSj0Swa9CrGK59vp2Cva0+G0VXM/lz7BLaLXiY/rsWU5ScReTKP0quJrim0eR7Aej70ytWz3Vk+ufEVsPImZQcg37Lp2h7PoCm2xDknHQMu9divlB6syaFszsKN+/SQkY9+g3vo+03DocJLyMXFWC+FG5ZdwCULPLsMRyFuy8YijFfOVNySaG+iDuJLNeu+wwo5Iru+nIDRqOR9PSSiNrHxweNRnNbDekTcs9tlb8TvGL0vnGm/4B49e29lncKjf0voPjXDRtXfZe2/quq4WqWf9vva1xrugl2MWiWY003wS6cZlfvPQmKT/5qt7oc2g22W13V5ZZvOqTRaAgMDLxxRkEQBEEQ7mj/2TsQCoIgCEK1EQsIBUEQBKGWE5cWCoIgCEItV8t+qOgOu/+jIAiCIAj/NjEyIAiCIAhliWkCQRAEQajlatkCQjFNIAiCIAi1nBgZEARBEISyxDSBIAiCINRyYppAEARBEITaRIwMCIIgCEJZtWxkQAQDgiAIglBGbfvVQjFNIAiCIAi1nBgZEARBEISyxDSBIAiCINRy4tJCQRAEQajlatnIgFgzIAiCIAi13B0zMvC6yaOmm3DbLmo0Nd0Eu+jplFnTTbCLnHyHmm7C7ZPkmm6BXUh5xTXdhNsWJGtrugl2oWjXs6ab8N8gpgkEQRAEoZYT0wSCIAiCINSE+vXro1Aoyj2efvrpCsusX7+eZs2a4eDgQOvWrfntt99u+nlFMCAIgiAIZcmS/R434ejRoyQlJVkeO3bsAGDUqFE28//999+MHTuWRx99lBMnTjB8+HCGDx/O2bNnb+p5RTAgCIIgCGVJkv0eN8HX15eAgADL49dff6Vhw4b07t3bZv5ly5Zx3333MWfOHJo3b87rr79Ohw4d+Pjjj2/qeUUwIAiCIAjVSK/Xk5uba/XQ6/U3LGcwGPjuu++YMmUKCoXCZp6DBw9yzz33WKUNGDCAgwcP3lQbRTAgCIIgCGXZcWRg8eLFuLu7Wz0WL158wyZs3ryZ7OxsJk+eXGGe5ORk/P39rdL8/f1JTk6+qe6KqwkEQRAEoSw7Xlo4f/58Zs6caZWm0+luWO7LL7/k/vvvJygoyG5tqYgIBgRBEAShGul0uip9+V8vNjaWnTt38tNPP1WaLyAggJSUFKu0lJQUAgICbur5xDSBIAiCIJRVQwsI/7Fq1Sr8/PwYNGhQpfm6devGrl27rNJ27NhBt27dbur5xMiAIAiCIJRVg3cglCSJVatWMWnSJNRq66/piRMnEhwcbFlzMH36dHr37s17773HoEGDWLt2LeHh4axYseKmnlOMDAiCIAhCWTU4MrBz507i4uKYMmVKuX1xcXEkJSVZtsPCwvjhhx9YsWIFbdu2ZcOGDWzevJlWrVrd1HOKkQFBEARBuIP0798fWbb9uyR79+4tlzZq1KgKb0pUVSIYEARBEISyxA8VCYIgCEItJ36oSBAEQRCE2kSMDAiCIAhCWbVsZOA/FwzUnT2aerNHW6UVRiZyrOf0CssEPTaIwEn90QX7YMrMI/3XQ1xZ9D2y3giAW9fmhDw1DJc2DdAFeHF+8ltk/HG0WvvRYeYDdJz5gFVadtRV1vd5wWb+xqN60ueDJ6zSTMUGVjUqXW1a//5ONJ9wNz5t6uPg6crG/i+SeT7O/o2/xufZ8fg8O94qTR8dz5X7nrBdQK3C+4nRuI+4B7W/N4YrCaS9s4qC/ccsWZTOjvhMfxjXe8NQebtTfP4yqW9+TvGZyGrrR/CshwiZ9ZBVWlFUAqd7PVdhmYCpg/GbNABdkA/GrDwyfz1I/OLvLO+pW6nTHhTuXmgHT0bdrANodUjpSejXfIiUEGUzv6phKxyfXlQuveDVich52ZZtTfeBaPqOQOHqiXT1CvpNK5Diquc1UXh4oxs5FXWrzii0OqTUqxStfhcp9sbPp2rYAqc57yFdjaFg4bTS9vcejLbPYJTeJbdtNV+NxfDr95jOVs/nvM7s0dS1cZw6UclxKvDacUp77TiV8eshYsocp4KvHae0AV5cmPwWmdV4nLr/lS9Jyswtlz66V1tefKifzTLbj1/i01//5mpGLnX9PJg+rCc9W4Va5YlOzmDZ5gMci0zAJEk0CPDmvccGE+jlVi39uGUVLOD7/+o/FwwAFFyM48yohZZt2WyuMK/viB6EvjSeSzM+JTc8AscGQTRZ9jSyLHPlta8BUDk5UHAuhpQ1u2mxyvaXcXXIvBjPb2OXWLYlU8X9ADDkFrKu95zShDJvVrWTjuSjEUT/ephe70y1a1sror8UQ9zkl0oTKnstnp+I27C+JL/8IYboBJx7dCD4k5eJfWgW+gvRAAS8OR1d43pcnfMuptQM3If1o87qRVwZ+CSmlIxq60fhxTguPvSaZbuy95T3iJ7UeXEC0bM+Ie/oRRwbBtHgg2dBlolbsPqW6rQLR2ccn30Lc9QZir5YgJyfi9InELko/4ZFCxY/CcWFlm05P8fyt7pdD7TDHkW//lPMcZfQ9hqK4+MLKFwyzSqfXTi54Dz3A0wRpyhc9hJyfg5Kv2Dkwhv3AUdnHKe8gPniCRRunla75Kx09Bu/REpNBIUCTbd7cXz6NQpefwrpaqx9+3BNwcU4zlXxOOUzogf1XxpP5IxPybt2nGp87TgVc+04pbzuONX8XzhOff/CWCSp9BgTlZTOkx/9xL3tG9vMfzL6KvNX/cazQ3vQq3Uovx+NYMaKn1k7bzyNgnwAiE/L5pH31zG8W0umDeqGs4OWy0kZ6DT/ya+i/1f+k6+AbDJjTMuuUl63zk3JPRpB2qYDAOjj00jbfADX697QWbtPkLX7RHU0tVKyWaIoreoHU1mWK80ftfEvAFxCfG67bVUlm82Y07OqlNdtWD8ylv9Iwb5wALLX/IZzWHu8pjxA0px3Uei0uPbvTsJTCykKL/kt7vSPvsel7114jB1E+tJvqrUfVX1PuXRqSt7Ri2Rs2g+AISGNjM0HcOlgfZC8mTrtQdtvJHJ2Ovq1H1rSzJkplZQoJeflQHGBzX2a3sMwHtqO6WjJXc70Gz5F1aIT6rvuwbh74+03/Dq6+0YjZaVRvPo9S5o5vWo/uOI4YTrGI3tAklC3D7PaZzp9yGpbv3k12j6DUTVoXm3BwK0cp9IrOU5l7z5B9r94nPJydbLa/mrHUer4uNOpcYjN/D/sOUFYi/pMvrcTAE8PCePQxVjW7jvJy2NLflXv41/+okeL+swY0ctSro6vR/V04HaJaYI7n2ODQO46uQJJbyQv/BIxi75Hn5huM2/u0Qj8RvbCpX0j8k9E4VDXD69+HUjd8Oe/3Ory3EL9GRf+EWa9kdTjkRxZvI6CqxWf/WqcHRhzaCkKpYL0MzGEv7WOrEuJ/2KLy9PWC6bh/m+RDQaKTlwk7b3VmJLSbOZVajXIeoNVmlSsx6ljSwAUahUKtap8Hr0Bp44tqqcD1ziEBtL++EokvYH8Y5eIX/wdhgreU/nhEfg80Bvndo0oOBmFrq4/Hnd3IH3Dvluu0x7ULe/CFHECh4lzUTZsiZyTifHv3zAd2n7Dsk6zl4Jag5QUh2HbGqSYCyU7VGqUIY0w7NpQmlmWMV86hap+M4z27kPbbpjOHcPxiZdRNWmDnJ2OYe8vGPf/Xmk5TVh/lL4BFH25BN2g8ZXmRaFE3akXaB0wXz5vx9Zbc2wQSOcyx6mKXv/coxH4Xnec0tX1w7NfB9LugOMUgNFk5rcjF5jQr2OFP6V7+koSE/p1sErr1rwee09fBkCSZPafvcLkezsx7eOfuBifSrC3O1MGdKZf20bV3oebJoKBO1ve8UguTf+EwqiraP09qDdrNG22vM7x3jMwFxSXy5+26QAaLzfabnkdFAqUGjVJX28j/sPKf/yhuqWeiGLfjBXkRCfh5OdBhxkjGPLTK2y8ex5GG/3IuZzEn7O+IONCHFo3J9o8MZChm19lw93zKEjKrIEeQNGpCJLmvY/hSgJqXy+8nxlHvR/e4crgaUgFReXy5x84jtcjIyg8ehZjXBJO3drh2j8MVCoApIIiCo+fx+epsVy9HI8pPRu3wb1xbNcMQ2xSufrsJf/4JaKf/4iiy1fR+nkSPGs0LTa9yem+05FsvBYZm/aj9nKlxeY3Le+plK//4OpHG2+5TntQeAegCbsf474tGHatR1mnMboRj4HJhCl8t80yUm4Wxes/QYqPArUGTZf+OD79JkVLZyMlRqNwdkOhUlmtHwCQ87JR+gXbvQ9K30C0fQZj2LER/W9rUNVvisOYp8Bkwnhwh+0yfkHoRj5K4dszKz2AK4Pr4zxvGWi0oC+i6NMFSEnVs6Ym73gkkdM/oejacarOrNG03vI6Jys4TqVfO061LnOcSqjh49Q/dp+KIq9Iz9CuFQfl6bkFeLtZjyZ4uzmTnlsy/ZSZV0ih3shX24/y9JDuTB/Wg78vxDDri1/4YvqoCkcchH+H3YOB+Ph4Xn31Vb766qsK8+j1evR6vVWaQTajVahuWP/1w/mFF2LJOx7JXeGf4TM0jJQ15Q947mEtqTN9BFHzVpJ3PBLH0AAavP4IdWY8SPwHG8rl/7ck7Dlt+TvzQjypJy4z9tBSGgzpQsTafeXypx6PIvV46SKwlPBIRu19m2bj+3Hs3ZrpR8Gf4Za/9RExFJ2KoOHe1bje35OcDeXPRlPfWE7Am9Np8MfnIIMhLomcn3biPvJeS56kOe8SuHgGjQ58h2wyU3w+itxf9+HQqvrOHHL2lL6nii7Ekn/iEu2OfI730O6krdlVLr9rt5YEPTuSmBe/IP/4JRzqB1Lv9SkEpYzi6tL1t1SnXSgUSPFRGH77FgApMRplYF00YfdVGAzIaYmY0kpHl/QxF1H6BKDpPQz9Dx9UTzsro1BgjrmEftMqAKT4yyiD66PpPch2MKBQ4vjYfPQ/f4OUUvkomZScQP7CaSgcndF07InDlDkUvjO7WgKCbBvHqU7hn+E9NIxUG8cpt7CWhEwfQfS145TDteOUYcaDJNTgceofmw+eo3uL+vh5uNxyHdK1NU592jTk4WsjCM3q+HEqOokN+0/fecGAuOnQ7cnMzOTrr7+uNBhYvHgxCxYssEqb7NycKS43PxRszi2kKDoJx1DbP9dY74UxpG74k5QfSg7AhRfjUDrpaPzOk8Qv3XjHrBg15BaSE52MW33/KuWXTWYyzsZUOf+/QcorwBCTiLae7d/eNmflkvjU6yi0GlSebphSMvCd/QjG+NI5YWN8MnET5qJw1KF0ccKclkXQ0nlWeaqbObeQ4ugkHOrbfk+FvDCW9I37SPthJwBF195Toe9M4+qyDTbfUzeq0x7k3CyklHirNCklAXWbsApK2GaOu4QqtOSzKBfkIpvNKFw9rPIoXD3KjRbYg5yTWe7LWUqKQ9Ohh+0CDo4lowd1GuEw9plrjVOgUCpxXf47hUvnY754siTdbEJOu4oM6OMiUdVvgvbuERR/t8zu/SjrRsepui+MIa3McUrlpKPhO0+SUMPHqasZuRy+GMd7jw2pNJ+PmzMZuYVWaRm5BfhcGy3wdHFErVTSMMDbKk9ogBcnLtfsdKdNYpqgcj///HOl+6Ojo29Yx/z585k5c6ZV2tHGk262KUDJCluHev4YUrJt73fUIktlPkjmay+yQnHHBANqJx2u9f0o/Cm7SvkVSgVezeoQv/tU9TbsJiicHNDWCSQ31fZZ6D9kg7HkygC1CtcB3cn9fX/5PEV6zEV6lG4uOPfoQOo7FQeX9vbPeyp9o+2FkSpHHZR9T0mVv6duVKc9mGMulBu6V/oGIWem3lQ9yqAGyLnX2mk2ISVEoWrcFvPZwyVpCgWqxm0wHthqj2ZbMUedQxlgfYao9A9ByqhgIWRxIfmvPm6VpO0zBFWzdhQtfx2pssWHSiVoNLfb5Cr55/VPq+A4pbJxnJLvkOPUlkPn8HJ1LHeJYFltQgM5EhFntW7g0MU42oQGAqBRq2hRz5+YFOtpzdjUrDvvskK4Y74b/i03HQwMHz4chUJR4Y8oABUuMPmHTqdDp9NZpVVligAg9NWJZG4PpzghDa2/F/XmjAZJIm1zySrcJh89iyEpg5hFPwCQueMYwU8MpuDMFXJPROJYP4B6c8eQuSPccgBXOjlYRey6uv44t6yPKTu/woWJt6vLy2OJ3XmC/IR0nPw96TjrAWSzxOXNBwHos/QJCpKzOLpkHQDtnx9O6vEocmNS0Lo50+bJQbiE+HBxzZ7Sdns44xzkjXNAyWVVHg1LPoRFaTk3ddVCVfnOfZT83YcxXU1F7eeNz3MTkCWJ3F/3AhD49ixMKRmkvbcaAIc2TVEHeKO/EI3a37vkHgVKBZlflA6DOvfoAAoFhisJaOoG4Td3CoboBHI22p4vtoe6/5tE1vaj6BPS0AZ4ETJ7DLIkkXFtZXeDZc9hTM4gfvH3AGTtCCfw8SEUnI2m4HgkutBAQuaMJfu699SN6qwOxn1bcHzubTR3j8J06gCquo3RdB2Afv0nljzaQRNRuHmhX7MUAE2voUgZKUgpcSjUGtRd+6Nq3Jriz1+1qlc39nmk+KiSSwt7D0WhdcB0xP7THfqdP+E8dynagWMwHv0TVWhTtL0GUvTtUkse3YgpKDy9Kf7qHZBlpKsxVnXIedlgMlil60ZMwXT2KFJmKgoHRzR39UPVpA36pS/avQ8A9a8dp/TXjlN1yxynGl87TsVed5wKunacyjsRiUP9AOrOHUNWJccph2vHKWN2frUtTJUkmZ8PnmNIlxaoVdY3rH356z/w83DhuWElozbj+rZn6gfr+WbnMXq2CuWPYxGcj0vhf+PusZSZfE8nXvhqKx0ah9C5cR3+Ph/Dn2eiWTn99n5kR7h9Nx0MBAYG8umnnzJs2DCb+0+ePEnHjh1vu2EV0QV60/Sz59F4umLMyCX3yEVODnwRY0bJzTF0wT5WwztxH5QM29abNwZtgBfGjFwydxwjZvEPljyu7RrS5qfSaYuGCycDkPLjHi5NLz2Q2pNzoBf9Pn4aB08XijLzSDkSwZahr1GcmVeyP9jH6kxB5+5Mz7en4uTrjj6ngPQzMfw8bAHZkVcteere28HqxkR3f/YsAMfe/4nj79t/IZImwIeg9+ei8nTDnJlD0bFzxI6agTmr5LXQBPpavRYKnQbf5yeiqROAVFhEwb5wkua8i5RXekmb0tUZ31mTUQf4IGXnkbf9L9Le/xpucA+G26EN9KbRpzNRe7piysgl7+gFzg2ehynT9nsqcel6kGXqvDCu5D2VmUv2jnDil3xf5TqrgxQfRfGqRWgHTUTb/yHkzBT0W1ZiOl66BkXh6onS07e0kEqNbtgUFO5eYNBjToqhePn/MEedsWQxnTyAwsUd7X3jULh5IiVGU7TiNeT8bPv3IeYSRZ8tQDdiCrrBE5DSkyn+8TNMh0tHmxQeXii9/G6qXoWbB45T5qBw90IuKkRKiKZw6YuYLxy3dxeAkte/6WfPo77uOHV64IuYrjtOyde9p+KvHafqXjtOma4dp2KvO065tGtI6+uOU6HXHaeiquk4dSgijqSsPIZ3K/9zuElZeVYnfu0aBLHokfv55Je/+eiXv6jr68EHjw+13GMAoF+7Rrw85m6+3H6Ut9fvoZ6fF+9OHUL7RvZfjHrbatk0gUKu7BTfhqFDh9KuXTsWLlxoc/+pU6do37490k3+I/cHPHhT+e9EF9UONd0Eu+jpVDNXJ9hbTv5///VoOcZU002wCymveq6g+Ded+eUOHMq+BR2+u+fGmf4DHO95slrrL/pytt3qcnz0XbvVVV1uemRgzpw5FBTYvjkJQKNGjdizZ0+F+wVBEARBuLPcdDDQs2fPSvc7OzvTu3fvW26QIAiCINQ4cWmhIAiCINRu5a5C+39OeeMsgiAIgiD8fyZGBgRBEAShrFp2NYEIBgRBEAShrFq2ZkBMEwiCIAhCLSdGBgRBEAShrFq2gFAEA4IgCIJQllgzIAiCIAi1XC0LBsSaAUEQBEGo5cTIgCAIgiCUJX7CWBAEQRBqOTFNIAiCIAhCbSJGBgRBEAShLHFpoSAIgiDUcuIOhIIgCIIg1CZiZEAQBEEQyhLTBDXD3zO/pptw2xp6ZtR0E+xifNwd87a4LWOUXjXdhNvWPC+1pptgF9rJk2q6Cbct/ZetNd0E+8j8//Geqm6yuJpAEARBEITa5P/HKaAgCIIg2JOYJhAEQRCEWq6WXU0gggFBEARBKKuWjQyINQOCIAiCUMuJkQFBEARBKKuWXU0gggFBEARBKEtMEwiCIAiCUJuIkQFBEARBKEtcTSAIgiAItZyYJhAEQRAEoTYRIwOCIAiCUEZt+20CEQwIgiAIQllimkAQBEEQhNpEBAOCIAiCUJYk2+9xkxITE5kwYQLe3t44OjrSunVrwsPDK8y/d+9eFApFuUdycnKVn1NMEwiCIAhCWTV0aWFWVhbdu3enb9++/P777/j6+hIZGYmnp+cNy0ZERODm5mbZ9vPzq/LzimBAEARBEMqqoTUDb731FnXq1GHVqlWWtNDQ0CqV9fPzw8PD45ae9z8ZDKj9vPGZ9SjOvTqhcNBhjLtK8ovvoz8XWWEZx85t8J33ONpGdTElpZO5fA25m3eU7u/UCs8pD+LQsjFqP28Sn1lAwa6D1doPpY8Pbk8/jkPXu1A4OGBKSCT7zbcwXrxkO7+3F27PPoW2WRNUIcEUrP+J3GWfWPdz4AA8X55nlSbrDST1HVBt/fjH2Kcf4vH5U9mw8ic+ee2zCvONfHQEQycOwT/Yj5zMHPZt3c8XS77EqDcCMPThwQydOISAEH8AYi7F8s3S7ziy52i1tLvjzAfoNPMBq7SsqKus6/PCDcs2HNqVez59hit/hLN96lJLuqOPG11eHENIr9Zo3Z1IPhzBgVe+JvdKir2bb0Xh4Y1u5FTUrTqj0OqQUq9StPpdpNiKPxv/UDVsgdOc95CuxlCwcJolXXv/GDQduqMMqINsMGC+fB79xpVIKQl2b//9c5ZxNSOnXPpDfTvx4sMDy6VHJaby6ea9XIhJ4mpGDnPG9GdC/65Web7ceoBdxy5yJSkdnVZNu0Z1eP7Bu6kf6GP39v+j6eyRNJs90iotL/Iqu3vOrrCM2s2JFvNHEziwMxoPF4oS0jnzv29J3XUSgHuPLsOpjm+5cldWbef0/NX2bL5FSm4hy7ad4K/IqxQbzdTxcmHBA91oGexdYZm1hyP48dAlrmYXEODuxNTerRjSvoFl/8bwKH49GU1USsnr3CLIi2fubUvrkOp7PWqaXq9Hr9dbpel0OnQ6Xbm8P//8MwMGDGDUqFHs27eP4OBgnnrqKR577LEbPk+7du3Q6/W0atWK1157je7du1e5jf+5YEDp5kKdH96n8PApEh9/GVNmDtp6wUi5+RWWUQf7E7x8Idk/biV5zls4dW2H/+vPY0rLpPCvYwAoHB3QR1wh96ftBH30v2rvh8LVBZ/PP8Jw/AQZM+chZWejrhOClFdxPxQaDVJ2Nnmrv8NlzIMV5pPy80kdM7E04V8IcJu2bcKQ8YO4fP5ypfnuHt6Xx+dP5e3Z73I2/Dx1GoQw9/05IMt8uvBzANKS0vli8ZckXElEAQwY1Z83vlzA4/dNI+ZSbLW0P/NiPL+OXWLZlk3mG5ZxCfGh6yvjSDp0sdy+AV/OQDKa2fboBxjyimjz+P0MXjOfdX3nYirS26jNDpxccJ77AaaIUxQuewk5PwelXzByYcXvKQtHZxynvID54gkUbtbDkeomrTHs+RlzzCVQqtCNeASnGYvJ/99jYCi2axe+f2Uqklz6ho1KSOWJ977j3s4tbOYvNhgJ8fXk3k4teHftdpt5wiNieahfJ1qGBmE2S3z0026efP97fnpjGk46rV3bf73ci/H8PWqRZVs2VzzsrNCoCFs3H316LkenLqMoOROnEB+MOYWWPPvuexmFsnSZl1uzOoStf5HEXw5XT/uL9Ez+YjudQ/35eGJfvJwdiM3Iw82x4v/ZuiOX+GjHSf43rAstg705m5DBwi2HcXPU0rtZCADhV1K4r3V92g7yQadWsWr/eaZ9vZuNzw7G382pWvpyK2Q7jgwsXryYBQsWWKW9+uqrvPbaa+XyRkdH89lnnzFz5kxefPFFjh49ynPPPYdWq2XSpEk26w8MDGT58uV06tQJvV7PypUr6dOnD4cPH6ZDhw5VauN/LhjwmjoKY1IaKS+9b0kzJVZ+tuUxZhDGxGTS3/4CAEN0PA4dW+I5aYQlGCjcH07h/ooXaNiby4SxmFNSyX7zbUuaOanyxR7m5BRyl34MgNPg+yvOKIOUmWWXdlaFg5MDL300n3df+ICHp4+vNG/LTi05G36OXZv3AJCSkMLuLXto3r6ZJc/BnYesynz59iqGThxMiw7Nqy0YkMwSRWnlz0grolAquPujpwh/byOBdzVFe91BzD00AP+OjVnXby5ZlxIB2D9/FRNPfEyj4d24uGavvZsPgO6+0UhZaRSvfs+SZk6v2gIixwnTMR7ZA5KEun2Y1b7CZS9ZbRevehfXD9ajqtcYc+SZ22/4dbzcnK22v9r6F3X8POnUtJ7N/K1Cg2kVGgzAhxt22czz2Uzr9+TCKcPo+/x7XIhJomMF9dqDbDKjr+J7qt7YPmg9XNg/+DVLIFoUn26Vx5CRZ7Xt/+xQ8q8kk/H3Bfs0uIxV+88T4O7Ewge6WdKCPV0qLfPrySuM7NSYAa3rAxDi5cq5xAxW7T9vCQYWj7I+W311eBd2nY/jyOVkqxGEGmfHYGD+/PnMnDnTKs3WqACAJEl06tSJRYtKAsn27dtz9uxZli9fXmEw0LRpU5o2bWrZDgsL4/Lly3zwwQd8++23VWrjf+5qAue+XdGfu0TgBy/R4MBa6m78GPdR91VaxqFdcwoPnrBKKzxwDId2zauzqZVy6BGG8WIEnm+8iv/Wn/BdvQKnoYPsUrfC0RG/n9bgv+lHPN96A3VofbvUW5Hn33yWQ7sOc/zAiRvmPRd+jiatG9OsXckbN7BuAF363cXh3Uds5lcqlfQd2gcHRwfOHTtv13Zfzz3UnwnhHzH2r/fp99E0XIIqHgYF6DhjBEUZuUSs3Vdun0pXEmObr017ACDLmA0mAjo3sWu7r6du2w1zTCSOT7yMy3vrcH7lUzQ9Kwkar9GE9UfpG4D+l6odNHAs+cKWC/JukPH2GE1mth46zfAe7VAoFHarN//ayIybs6Pd6rTFuUEAA05+wj2Hl9Lhk6dxrGRoPWBARzLDI2mz+BEGnPmMvnvfovFzw0Bpu98KjYqQkT2IW1P+/Wcv+y4m0CLIm9lr99N3yQYe+uQ3NoZHVVrGaJbQqVVWaTqNirOJGRgrGBkpNpoxmWXcnapvlKam6XQ63NzcrB4VBQOBgYG0aGE9Eta8eXPi4uJu6jnvuusuoqIqf72ud9MjA0VFRRw7dgwvL69yDS4uLmbdunVMnDixgtIlbM2fGCQJrfLGsYmmTiDuYwaTtfonMlesxaFVE3xfnIZsMJG7ZafNMmofTwrSs63SzBnZqFydUei0yHrDDZ/X3tRBQahHDCN/7XryvvkebfNmuM94Ftloouj3bbdcrykunuxFb2O8fBmlswsu40bj8/lHpI5/BCkt/cYV3KS+Q/vQuHVjnhz0dJXy79q8B3cvdz786QMUCgVqjZot3/zC9x+vscoX2qw+n2z5EK1OS1FBEf97bAGxkTf3Yaiq1BNR7J2xguzoJJz8POg4YwRDf3qF9XfPw1hQfhg8oHMTmo7pw8b+L9qsLzsqibyEdO6a9xB/zvsSU6Ge1o/dj0uQN05+HtXSBwClbyDaPoMx7NiI/rc1qOo3xWHMU2AyYTy4w3YZvyB0Ix+l8O2ZVfv9doUChzFPYoo8i3Q1xr4dKGP38YvkFRYztHs7u9UpSTJvr9lGu0Z1aBxS9ZXWNyvreBQnpn9OftRVdP6eNJv1AD22/I89vedisvGecqrrh0/3FiT89BeHxr+Nc6g/bRc/glKjIuK9n8rlD7y/Exp3J+J/rL5gICErn/VHLzEhrDlTe7XkbGIGb28NR6NSMrSCM/hujQLZdCyKvs1DaB7kxfmrmWw6dhmTWSK7UI+va/kAbOn2E/i6OtKlQWC19eWW1NAdCLt3705ERIRV2qVLl6hX7+ZGsU6ePElgYNX/pzcVDFy6dIn+/fsTFxeHQqGgR48erF271vKEOTk5PPLIIzcMBmzNnzzj3ZBnfRvdsA0KhYLic5FkLF0NgP7CZbSN6+M+ZlCFwcAdSanAeDGCvM9XAmC6FIW6QSjOI4bcVjBgPHse49nSM+jMM2fxW/M1zsOHkPfFqkpK3jzfQF+eWfAUc8bNtSz+u5G23dow/pmxLH3pIy6cuEBw/WCeWfAUD08fz7fLvrfki7+cwNQBT+Li6kyvQT2Z98Ecnn9wVrUEBPF7Tlv+zrwQT+qJy4w7tJQGQ7qUO/PXODvQd9mT/PnCSoqzbM/FSyYz2x9bSu93H+ORcyuQTGYSD5wjbvdJsOMZbjkKBeaYS+g3lbzOUvxllMH10fQeZDsYUChxfGw++p+/QUpJrNJTOIx7BlVQfQrennnjzLdp0/4TdG/dCD9PV7vVuei737icmMrq+Y/YrU5bUnefKt24EE/W8Sj6h39I0NCuxNmYJlIoFejTczk5eyVIMjmnr+AY4EWjpwbZDAbqje1L6u5TFKdkV1sfJLlkcd9z97YDoFmQF5dTc9hwNLLCYODxPq3IyC9i4optyICXswND2jVg9YHzNgc5vvrzHNvOxLJyyj3oNKryGWpSDV1NMGPGDMLCwli0aBGjR4/myJEjrFixghUrVljyzJ8/n8TERL755hsAli5dSmhoKC1btqS4uJiVK1eye/dutm+3vY7GlpsKBubOnUurVq0IDw8nOzub559/nu7du7N3717q1q1b5XpszZ/Eda54Qdz1TOmZGC5bfyEYouNw7V/xqklTehZqHw+rNJW3B+a8ghoZFQAwZ2RgvGI9/22KicWxT087P5EZ46VIVCHB9q0XaNKmMV6+nqz4vfTKAZVaRZsurRkxeRj9GwxEKhNdT5k9me0/7eS3Nb8DcOViDA5ODsx663m++/AH5GuLx0xGE1djrgJw6Uwkzdo2ZeSjI3h/3jK796MsQ24hOdHJuNf3L7fPrZ4fbnX9uG/VLEua4tpR7rGYr/mx9xxyY1NJPxPDxgEvoXV1RKlRU5yZx/BfXiP91JVqa7eck4mUZP3ZkJLi0HToYbuAg2PJ6EGdRjiMfeZaZxQolEpcl/9O4dL5mC+eLM0+9mnUbbpS8M4s5Cz7jzJd72p6NofPX+H9Z0bbrc5F3/3On6ci+WreJPy93G5cwI5MuYXkRyfhHFr+PQVQnJqNbDRbfQHlRSbi4O+JQqMq2XeNY4gPvr1acWTKB9XaZl8XBxr6uVulhfq6sfNcxQG5g0bNghHdeHloFzLzi/FxdWBjeBTOOjWeTg5Web8+cJ6v9p/j88l30yTgxtfQ1xadO3dm06ZNzJ8/n4ULFxIaGsrSpUsZP7507UtSUpLVtIHBYGDWrFkkJibi5OREmzZt2LlzJ3379q3y895UMPD333+zc+dOfHx88PHx4ZdffuGpp56iZ8+e7NmzB2dn5xtXgu1LKqoyRQBQdPw8mvoh1mXrB2O8mlphmeKTF3Du1dkqzSmsA8Unq2fhTVUYTp9DXbeOVZq6TgjmZDtfeqZUom7YAP1B+684Pn7gBI/cbX25y9z3ZhN3OZ41n/5YLhAAcHDUlVulK12bS1QoFJZgoCyFUoFG++/MKaqddLjV9yPyp+xy+7IvJ7HubutLNzvPeRCtiyN/vfot+VczrPYZ8ooAcAv1x7dNA8Lf2VBt7TZHnUMZYP3ZUPqHIGVU8J4qLiT/1cetkrR9hqBq1o6i5a8jXbf40GHs06jbd6fw3dnIVVyUeDu2HDiJl5szPds0vu26ZFlm8fd/sPv4Rb6cO5EQ33//i0flpMO5nj8JKQds7s88comQB8JKRo6ufQZcGgRSnJxlFQgA1B3TG316Dik7b7xG53a0retLTHquVVpseh6BHjc+zmtUSvzdSxbVbjsTS88mwSivGxpYtf8cX+47x6eT+lV6mWKNqsHfJhg8eDCDBw+ucP/q1auttl944QVeeOHGl0JX5qYWEBYVFaFWl8YPCoWCzz77jCFDhtC7d28uXbJ9fbw9ZX29Cce2zfB6/CE0dQNxHdQH91EDyf7hF0senxmPELCk9Hre7LVb0YQE4jP7UTShIbiPHYzrfb3I+npTaV+cHNA1a4CuWcnwlyYkAF2zBqgDy1/Xaw8FP65H26oFLhPHowoOwvHeu3EaNpiCjVsseVyfnIrHK/OtyqkbN0TduCEKR0eUHh4l2/VL55JcHpmI7q5OqIIC0TRpjMerL6IO8Kfw561270NRQRExETFWj+KiYnKzcomJiAFg/tIXmDpviqXM3zsPMfThwfQd2oeAOgF07NmBKXMmcXDHIUvwMHXeFNp0aY1/iD+hzeozdd4U2nVry85NtleL366uL48lsGszXEJ88O/YmAErn0c2S0RtLrnPRN+lT3DXvJIzVLPeSFZEgtXDkFuIIb+IrIgEpGsH7gaD7iKwW3Nc6/pSr38HBv8wj5ht4ST8ebZa+gCg3/kTqtDmaAeOQeEbhPquvmh7DcSwt/SzoRsxBYcpc0o2ZBnpaozVQ87LBpOhZD3AtcsGHcY9i6br3RStXIxcXITCzbPk8kNN9QRnkiSz5a9TDAlrg1plfYh66YvNLLvuqgGjyczFuGQuxiVjNJlJzc7jYlwycSmZljyLvvud3w6eZskTI3B20JGek096Tj7FhqpNbd2Klq+Ow7tbMxzr+ODZqTF3rZqJLEkkbP4bgA4fTaP5iw9Z8l/5egcaD2davzER5wYB+N/TjsbTh3FlVZlhXoWCumN6Eb9uf6WXKtrDhLDmnIlPZ+W+s8Rl5PHbqStsDI/koS6li2A/3H6Clzf8bdmOTc9l68krxGbkciYhnbk/HiAqJZtnr001AKz68xyf7jrNayO6EuThTHpeEel5RRRWcarx3yLLst0e/wU3NTLQrFkzwsPDad7cehX+xx+XXO42dOhQ+7WsAvqzl7j63EJ8ZjyC11PjMSYkk7ZkOXm/7rHkUfl6oQ4sXRxkSkwh8cn/4TvvcTweHoYpOZ2UV5ZaLisEcGjZhDrflF7m5zfvCQByNu0g5cXSS7XsxXghgsx5r+A27TFcH5mIKSmJ3GWfULS9dN2Dytsblb/1Iie/r1da/tY2b4rTgHswJSWTOnIsAEpXF9znzULl5YWUl48x4hJpTzyDKaZ6Lsm7Eb9gP6TrIuxvl32PLMs8+sJkfAJ8yM7I4eCOQ6x8+ytLHk8fD+YvfQEvPy8K8gqIvnCFF8bP59j+49XSRudAL+7++GkcPF0oyswj+UgEm4e+RnFmyWp5l2Cfm77m2Mnfg26vjsfRx53C1GwubTjA8WWbblzwNkgxlyj6bAG6EVPQDZ6AlJ5M8Y+fYTq825JH4eGF0uvmFs5p+w4BwHmO9eegaNU7GP+2vTDxdhw6H01SRg7De7Yvty85M8fqDDM1O4+HXiudS/36j4N8/cdBOjWtx5dzSy7DWren5JLhR9/6xqquhVOGMqxHO7u3H8Ah0JtOnz2LxtMFQ0YuGUcu8efA/1kuD3QM9rb6idziq5kcHPMWrRZOoO/uJRQnZxH9xR9EfvyzVb2+vVrhFOJLbDVdnnq9ViHevD+uFx9uP8mKvWcI9nBhzsBODGpbeje8tPxiknIKLNtmSeabvy4Qm5GLWqmkU6g/Xz82wOqSxHVHIzGaJWav3W/1fE/0bc20fm2qvV+CbQr5JsKWxYsXs3//fn777Teb+5966imWL19uc3j4Ri41r/zywP8CF89qupnMv2x83H/u9hM2jcH2/Ox/ydj7K57++i/RTrZ9ffR/ybaR9h9dqwn9P2x240z/AY6jq/fmcLmP9bdbXW5fVH0hX025qWmC+fPnVxgIAHz66ae3FAgIgiAIwh2lBn+1sCb8/zgFFARBEAQ7suftiP8L/nN3IBQEQRAEwb7EyIAgCIIglFXLRgZEMCAIgiAIZdWy5W9imkAQBEEQajkxMiAIgiAIZdS2BYQiGBAEQRCEsmpZMCCmCQRBEAShlhMjA4IgCIJQVi1bQCiCAUEQBEEoo7atGRDTBIIgCIJQy4mRAUEQBEEoS0wTCIIgCELtVtumCUQwIAiCIAhl1bKRAbFmQBAEQRBqOTEyIAiCIAhlyLVsZOCOCQbMpv/+IMXlaO+aboJdLEBR002wiwcKj9R0E27bsIigmm6CXaj2/F7TTbht2xxMNd0Eu+iv/O8fa/8VtSwYEO8KQRAEQajl7piRAUEQBEG4U4hpAkEQBEGo7WpZMCCmCQRBEAShlhMjA4IgCIJQhpgmEARBEIRaTgQDgiAIglDL1bZgQKwZEARBEIRaTowMCIIgCEJZ8v+Pm69VlQgGBEEQBKEMMU0gCIIgCEKtIkYGBEEQBKEMWRLTBIIgCIJQq4lpAkEQBEEQahUxMiAIgiAIZcjiagJBEARBqN3ENIEgCIIgCLXKf25kwOfZ8fg+N94qTX85nuj7nrBdQK3C58nRuI+4B7W/N4boBFLfWUXB/mOWLA33rEIb4l+uaOZ3v5Ky4FO7tv8fdWePpt7s0VZphZGJHOs5vcIyQY8NInBSf3TBPpgy80j/9RBXFn2PrDda8gQ+ch8hTw1F6+tB/vlYLr/0JfknoqqlD/Vmj7LZh/Cez9vM3+an1/AIa1kuPWPncc5NWAyAxsed0Fcm4Nm7DWo3Z3IOXSDqpS8pvpJs9/ZX5LkZj/O/BbNZ/ulqXp636Ib5R4wcxBerPuC3X3cycdxTlvSPPlvC2PEPWOXdtfNPHnpgqt3bDKD08cH1ySfQdbkLhYMDpsREcha/hSkiwnZ+by9cn3oKTbOmqIKDKdz4E3kffWyVx2vZUrTt25UrW3zwINlz51dHN1C4eqLtNwZVwzag0SFnpaD/ZQVS0pWKC6nUaHqOQN26Owpnd+T8bIz7N2E69WfJ7qad0HQfitLLH5QqpMwUTId/w3Tmr2rpw6DnRzHo+VFWacmXE1l49wyb+QMbhzB45kPUbR2Kd4gf6xeuZs9Xv91WnfaQklPIsm3H+etSIsVGM3W8XVnwQBgtQ7wrLLP2UAQ/HrrI1awCAjycmdqnFUPaN7Ts33Uuji/3niEuMw+TWaKutxsTe7RgcPsG1daPWyGuJvgPKL4UQ9ykl0oTzOYK8/rOmIj70L4kvfwhhugEnHt2IOTTl4l5aBb689EAxIycDkqVpYyuST3qfb2IvN/3V1sfAAouxnFm1ELLtlxZP0b0IPSl8Vya8Sm54RE4NgiiybKnkWWZK699DYDPsDAavDaJqLkryDseSdBjg2i15mWO9XgOY3putfXh9KjXq9SH81PeRaEpfctpvFzouOtd0n85aElrufoFZKOJc5PfxpxXRMgTg2mz/n+E95qBVKivlj5cr32H1kx65CHOnrlYpfx16gaz4I25HPzrqM39O3f8yXPT5lm29QaDXdpZlsLFBe9PPkZ/4gRZL8xFys5GFRKCnJdXcRmNFiknm/xvvsV51CibebJefgWFRmPZVrq54f3Vl+j37LN7HwBwcMJh0v8wx16geO07yIV5KL38kYsLKi2me+BZFM7u6H/9AjkzBYWLByhKD+ZycQHGv35GSr8KkglVo/ZohzyOXJCLOfpMtXTlakQcH04o/WyYTRWPO2sddaTHpXD8t4M8+Moku9R5u3KL9Exe8QedGwTw8aS78XLWEZuRh5ujtsIy6w5H8NH2E/xveFdahnhzNiGdhZsO4eagpXfzOgC4OWqZ2qc19X3d0aiU/BmRwKs//Y2XiwNhjYOqrT83S5ZrugX/rv9kMIDZjDk9q0pZ3Yf1I+OzHynYFw5A9g+/4RzWHu8pD3B19rsl1WVaf1G6PjEKQ+xVCo9Uz0HiH7LJjDEtu0p53To3JfdoBGmbDgCgj08jbfMBXNs3tuQJfmIIyd/vJGXtHgCiXliB1z0d8B/Tj4SPN9u7+QDIJqnKfTBl51tt+w0Pw1ykJ+1aMODYIBC3Tk0I7z2DwogEACLnfkHXM1/gN7w7yT/stmvby3J2dmL5yneZ8dwrzJoz7Yb5lUoly1e+y1uLPqRrWCfc3d3K5THoDaSmpldHc604jx+HOTWV3CVvWdLMSZWPppiTk8n7sGQkwGngQJt55Lw8rj8mOtzdD1lfTPHevbfbZJs03YYg52Zi+GVFaTuz0yoto2rQBlW9ZhR+PBOuBQ1yjvX/XIq9YLVtOroNdZueKOs0rbZgwGyWyE3LqVLe2NOXiT19GYDhc8fZpc7bterPcwS4O7NwZJglLdjLtdIyv568wsjOjRnQpj4AIV6unEvIYNX+c5ZgoHODAKsy48Oa88vxaE7EpN5ZwYAYGbjzaesF0+jAt8gGA0UnLpL67mpMSbYPGAqtBklvfTYmF+tx7Fh+uBoAjRq3oX3JXLXJ3s0ux7FBIHedXIGkN5IXfomYRd+jT7T9xZF7NAK/kb1wad+I/BNRONT1w6tfB1I3lAyDKjRqXNs0IOHDn0oLyTLZ+8/g1qlpNfYhgC4nP7f04cqiHyrsQ1kB4+4mbfPfljN+hbbkDFQqLp32QJaR9UbcujSv9mDgrfdeZce2vfy59+8qBQNz5j1Delom33+7ga5hnWzm6d7jLi5cPkhOdg779x1i0RtLycrMtnPLwaF7GPojR/FY8Bqadm2R0tIp3LyZol+32vV5HAcNpHjXbuTiYrvW+w91kw6Yo0+je+BZVPWaIeVlYTq2E9OJvRWWUTXpgJR0BU23wahbdwejHvOl4xj2bQCT0WYZZf2WKL0DMOyu2gjQrfCrH8Ciw8sx6Y1EH7/Elrd/IOtqxh1XZ0X2XUigW+NAZq/Zx7ErKfi5OTG6S1NGdm5cYRmjyYxOrbJK02lUnE3IwGiW0Kisl6nJssyR6GRi0nOYfl/7aumHUDU3HQxcuHCBQ4cO0a1bN5o1a8bFixdZtmwZer2eCRMm0K9fvxvWodfr0euth3wNshmtQlVBiVJFpyK4Ovd9DFcSUPt64fPsOOqveYfoQdOQCorK5S84cBzvKSMoPHoWY1wSzmHtcO0fBirbz+V6TzdUbi7k/LTzhm25HXnHI7k0/RMKo66i9feg3qzRtNnyOsd7z8BcUP5Am7bpABovN9pueR0UCpQaNUlfbyP+2pe/xssVhVqFocxZgyEtG8dGwdXSh9zjkURM/4SiqKto/T2pO2sUbbcs5FjvmTb7cD3X9o1wbl6XSzM/s6QVRSVSnJBG6EvjiJyzAnOhnuAnBqEL9kHr51EtffjHiJGDaNO2Bff2GVml/F26dmT8ww/Sp/uwCvPs3rmfrT9vJzY2gfqhdXn51Zn8uHEl9909Gkmy7/CuKjAIp2HDKFi3jvzvvkPTrBlu059DNpko/mObXZ5D07wZmgYNyH3rbbvUZ4vC0xd1x7sxHv4D418/owxqgLb/RDCbMZ22PW2n8PRDWacJssmIfsNSFI6uaO+fjNbJ1WqEAZ0jTtM/ApUaZAnD76uRrpytln5cORnJN7M/JTX6Km5+ngya/iAz1y3kjQGz0N/gs/Fv1lmZhKw81h/JY0L3Fkzt3ZqzCem8/etRNColQzs0tFmmW+MgNoVH0bdFHZoHeXE+MZNN4VGYzBLZBcX4ujkBkFdsoP9bGzGazCiVCl4c0oVuje6cUQEQIwOV+uOPPxg2bBguLi4UFhayadMmJk6cSNu2bZEkif79+7N9+/YbBgSLFy9mwYIFVmlPeTbiGe+KI85/FPwZbvlbHxFD0akIGu1bjev9PcnZsL1c/pQ3lhP4xnQabvscZDDEJZG9cSceD95rs36PUf3J/zMcU2rmDdtyO7J2n7D8XXghlrzjkdwV/hk+Q8NIWVP+DNg9rCV1po8gat5K8o5H4hgaQIPXH6HOjAeJ/2BDtba1Ilm7T1r+LrgQR+7xSLqEf4bv0DCSbfThegFj+5F/Ppa86xY3yiYz56e8S5P3pxEWsRrZZCbrzzNk7jpuNf9rb0HBAbz51ks8OOwR9Pobz+m7uDjz6Yq3mfHcy2RmVjxdtWlj6Vn5hfOXOH8ugmOnd9G9Zxf27ztYYblbolRgjIgg/4uVAJgio1CHhuI0dKjdggHHQQMxXr6M8UL1nU2jUCJdjca4Zx0AUkosSt8Q1B36VRwMKBQgg37zp6AvOSEw7Pge3YPPYfh9VenogL6Yoi9eQqHVoazfEu2945Gy08pNIdjD+b0nLX8nXowj5mQkbxz4lI6DuvH3uj13TJ2VkWRoEezNc/1LztibBXlxOTWbDUcuVRgMPN63NRl5RUxc/jsy4OXswJD2DVm9/xxKZeln2Fmr4cdnBlGoN3EkOpl3fw8n2Mul3BRCTRJrBiqxcOFC5syZwxtvvMHatWsZN24c06ZN48033wRg/vz5LFmy5IbBwPz585k5c6ZV2pUOthcw3YiUV4DhSiLaerajSnNmLglPvY5Cq0Hl6YYpJQPfOY9gjC8/n6oO8sM5rB0JT795S225HebcQoqik3AMtf1hqPfCGFI3/EnKD7sAKLwYh9JJR+N3niR+6UaMmXnIJjNaX3erclpfD4yp2dXdfOCfPlzFoYI+/EPppMN3eHdi3v6x3L7809Ecv2cOKlcnlFo1xoxc2v22iPxTl6ur2bRt1wo/Px927y+dGlKr1XTr3pmpj08gyKeV1Zl8/dC61Ktfh+9/XF7aJ2XJ8Gdy5nm6dhxAzJX4cs8TGxNPenomDRrUtXswIGVkYIqJtUozxcbi0LuXXepXODjg0K8f+V+tskt9FZHzs0sW+V1HSr+KqlnnCstI+dko8rIsgcA/ZRQKJQpXL+SslH9qR85KQQaklDiUPsFowoagr4ZgoKyi3EJSr1zFt779vuyqo87r+bo60rDM8STU152dZ+MqLOOgUbNgZBgvD+9KZn4RPq6ObDwaibNOg6eTgyWfUqmgrnfJGptmQV5cSc3hq31n76hgoLa5qfsMnDt3jsmTJwMwevRo8vLyePDBBy37x48fz+nTp29Yj06nw83NzepRlSkCWxRODmjrBmJKq/xMXjYYMaVkgFqF24Du5O08VC6Px8h7MWfkkL/3yC215XYonRxwqOePISXb9n5HLbJUJlQ1X/uCUiiQjSbyTkfj0bN16X6FAo8erckNt31pmb2V9CEAQ0rlizt9h3RDqVWTuvHPCvOY8woxZuTiEBqAa9uGZPxhe7W+Pezfd5AeXQbRp/swy+PE8TNsWPcLfboPKzekH3npcrn8f/y2mwN/HqZP92EkJtheuBcY5I+XlwcpyZUviLsVhjNnUdepY5WmrlMHc0pKBSVujkOfPig0Woq277BLfRWR4i+h9A60SlN6B5RbEFi2jMLVAzQ66zKShJxXyXFBoUCh1lS83450Tjp86gWQY8fAvDrqvF7bur7ElLkKKTY9l0BPlxuW1aiU+Ls7o1Iq2XY6hp5Ng61GBsqSZBmD+c66y48sKez2+C+46TUDimvDtUqlEgcHB9zdSyNHV1dXcnKqd6Wr39xHyd9zGGNiKmo/b3ymT0CWJHJ/3QtA4NuzMKVkkPbeagAc2jZF4+9N8YVo1P7e+D47HpQKMr4oM7SuUOAx8l6yN+0s/ZKtRqGvTiRzezjFCWlo/b2oN2c0SBJpm0uuFmjy0bMYkjKIWfQDAJk7jhH8xGAKzlwh90QkjvUDqDd3DJk7wuHal1Xi57/QdNkz5J26TN6JKIIfG4TSSWe5usD+fXiYzO3HKE5IQ+fvSb05DyFLEmmbS67dbvrRM+iTMi19+EfA2H6k/3EUU1Z+uTp9hnTFmJGLPiEd5+Z1afjGI6T/foSsfTcOMm9Vfn4BFy9EWqUVFhSSmZllSf/k87dJuprCGwveQ683lMufk1Ny0Pwn3dnZiTnznuGXn7eRmpJO/dC6vLZwDleiY9m9y/6XrBasX4/3p5/gPGE8xXv2omneDMchg8l99z1LHpfHH0Pl40POosWWNHWjRgAoHB1RerijbtQI2WjEHGs9yuA4aCDFBw4g51bPJar/MB7+A4fJ/0PTfSim84dRBjVA3b4v+t++suTR9B2NwtUTw8+fA2A6+zeansPRDXkcw58bUTi5or17LKZT+yxTBJqwIZiTrpSMEqg0qBq1Rd26O4bfV1dLPx548WHO7AonIzEdDz9PBs0YjWSWCP+55PM96b2nyU7JZMvbawBQaVQENg659rcaD38vQlrUQ19QTFpsSpXqtLcJ3Zsz+fM/WLn3DP1b1+NsQgYbj0byyvCuljwfbjtOam4Rb4zqDpQEC2cT0mkV4kNukYHv/jpPVEo2Cx/sbinz5b4ztAj2po6XKwaTxIFLiWw9Gc2LQ7tUSz9ulbgdcSXq169PZGQkDRuWzBcdPHiQunXrWvbHxcURGBhYUXG7UAf4EPT+XFSebpgzcygMP0fMqBmWywM1Qb5W95FU6jT4zpiIpk4AUkER+fvCuTrnXaQ86+uWnbu3QxPsR86G6j3z+Ycu0Jumnz2PxtMVY0YuuUcucnLgixgzSvqhC/axfMkDxH2wAWSZevPGoA3wwpiRS+aOY8QsLv2iTd/yNxpvN+q9MKbkpkPnYjg39k2M6dUToOkCvWn22XRLH3Js9KHsaIZjwyDcuzbn9OjXbVWJ1s+Thq9NQuPrgSE1i5R1+4j7YGO1tP9mhIQE3tSiP7PZTItWTXlo3Ajc3V1JTkpl7+6/WPzGUgwG2yvcb4fpYgTZL72CyxOP4TJpEubkJPI++pjiHaULYVXe3qj8rW+u5fPVSsvfmmZNcbz3XsxJyaQ9NKa0XJ06aNu2IXPmLLu3uywpKRr9+qVo+z2Epudw5Ow0DDu+w3z2b0sehYsHSnef0kJGPcXfL0E3YCKOj76OXJSP+fxhDHvXl+bR6tDdPxmFqxeYDEjpV9Fv+Qzz+cPV0g+PQC8e+XA6zh6u5Gfmcjn8Iu+MeIn8zJL7PngG+yBdNynt7u/Fi7+9Y9m+94mh3PvEUC4dOsfSMQuqVKe9tQrx4f3xffhw+wlW7DlNsKcLcwZ1ZlC70psDpeUVkZRTeiw1SzLfHDhPbHouaqWSTg38+fqJ+wi+bjShyGBi0c9HSM0pRKdRUd/XnTdH9bBcjihAYmIic+fO5ffff6ewsJBGjRqxatUqOnWyfdUSwN69e5k5cybnzp2jTp06vPzyy5aR/KpQyHLVl0ksX76cOnXqMGjQIJv7X3zxRVJTU1m5cqXN/ZW50Nj2dc7/Jel5TjXdBLuQ+f8RET9QeKqmm3Dbzra7s1ZY3yrX/iE13YTbNmdl9dww6t/23rttaroJduH44MvVWn9UiwF2q6vR+aov4s3KyqJ9+/b07duXadOm4evrazkJ/+dEvKwrV67QqlUrnnzySaZOncquXbt4/vnn2bp1KwMGVK0fNzUy8OSTT1a6f9GiG9+6VRAEQRDudFINTRO89dZb1KlTh1WrShfrhoaGVlpm+fLlhIaG8t57JdOCzZs358CBA3zwwQdVDgbEDxUJgiAIQjXS6/Xk5uZaPcrea+cfP//8M506dWLUqFH4+fnRvn17vvjii0rrP3jwIPfcc49V2oABAzh4sOpXLYlgQBAEQRDKkGWF3R6LFy/G3d3d6rF48WKbzxsdHc1nn31G48aN2bZtG9OmTeO5557j66+/rrCtycnJ+JdZD+Tv709ubi5FReVvxmfLf/J2xIIgCIJQnex5SaCte+vodDqbeSVJolOnTpZp9/bt23P27FmWL1/OpEkV/4jV7RLBgCAIgiCUYc87EOp0ugq//MsKDAykRYsWVmnNmzdn48aKr6oKCAggpcw9RVJSUnBzc8PR0bFKzyumCQRBEAThDtG9e3ciIqxvFHfp0iXq1atXYZlu3bqxa9cuq7QdO3bQrVu3Kj+vCAYEQRAEoYyaugPhjBkzOHToEIsWLSIqKooffviBFStW8PTTT1vyzJ8/n4kTJ1q2n3zySaKjo3nhhRe4ePEin376KevWrWPGjBlVfl4RDAiCIAhCGZKssNvjZnTu3JlNmzaxZs0aWrVqxeuvv87SpUsZP368JU9SUhJxcaW/EREaGsrWrVvZsWMHbdu25b333mPlypVVvqwQxJoBQRAEQbijDB48mMGDB1e4f/Xq1eXS+vTpw4kTJ8pnriIRDAiCIAhCGeK3CQRBEAShlrPn1QT/BWLNgCAIgiDUcmJkQBAEQRDKqKnfJqgpIhgQBEEQhDJq25oBMU0gCIIgCLWcGBkQBEEQhDJq2wJCEQwIgiAIQhlizUANqTfV/8aZ7nD1Gzas6SbYhZyYUNNNsIuI7YE13YTb9sxZj5pugl188+TCmm7CbZuyfOaNM/0HSBGXaroJ/wlizYAgCIIgCLXKHTMyIAiCIAh3CjFNIAiCIAi1XC1bPyimCQRBEAShthMjA4IgCIJQhpgmEARBEIRaTlxNIAiCIAhCrSJGBgRBEAShDKmmG/AvE8GAIAiCIJQhI6YJBEEQBEGoRcTIgCAIgiCUIdWyGw2IYEAQBEEQypBq2TSBCAYEQRAEoQyxZkAQBEEQhFpFjAwIgiAIQhni0kJBEARBqOXENIEgCIIgCLXKf3JkQOHigabPaFQNWoNai5ydiuG3L5GSYyoupFKjCRuKqmU3FM7uyAU5GP/6GfOZ/QDoxs5FVbdZuWLmy6fQb1haLf1IySlk2bbj/HUpkWKjmTrerix4IIyWId4Vlll7KIIfD13kalYBAR7OTO3TiiHtG1r27zoXx5d7zxCXmYfJLFHX242JPVowuH2DaumDwtkDTY8RqOq1BI0WOTsNw46vkVLjbObX3jsJdYtu5dKljKsUf7cQAHXrXqjb9ELhWvJ/kDKTMB7eihR7rlr6AKD09sHpkSfQdOyCQueAOSmR/A+WYI6KsN2PsJ7oBg5H3aARaDSYY2Mo+mEVxuNHb6ve2zHy+TE8OGOMVVpiVAKz736mwjJdBoYxatY4fEP8SI5JYs2Sbzi555hlf+f7unLP+PsIbd0AV0835t0/g9jzV+ze9n/0HzmJq8mp5dLHPDCYl2c9XWnZ33bu5YVX36Jfz258uOR/lvSX3niPLb/vtMrbvUtHPn//Dfs02oagmWMImmn9WhRFJXCuT8Wvhd+jQ/CbeB/aYB9MmXlkbf2bhCXfIuuNJRmUSoJmjsH7gd5o/DwwJGeRsX43ScvWVVs/FK6eaPuORtWwbcnnOysF/a8rkZIqeQ+o1Gh6DkfdKqzkWJufjfHAFkyn/gRA3aYHuiGPWxWRTQYK35pabf24FWKa4E6nc0I34SWkuAvo17+PXJiHwtMfubig0mLaYU+hcHbD8Psq5KwUFC4eoCgdBtJv+hhUKsu2wtEFh0cWYrp41EZtty+3SM/kFX/QuUEAH0+6Gy9nHbEZebg5aisss+5wBB9tP8H/hnelZYg3ZxPSWbjpEG4OWno3rwOAm6OWqX1aU9/XHY1KyZ8RCbz60994uTgQ1jjIvp3QOaEbPQcpIQL9lo+Ri/JQePgh6wsrLGLY9yOGvzZZthVKJQ7jXsYcedySJudnYfhrM3J2yZeCunk3dEOmUfzDm8iZSfbtA6BwccHtnY8xnj5J3qsvIOVkowoKQc7Pq7CMumVbjCfCKfz6C+SCPHT3DMT1f4vJmTkNc3TkLdd7u+IjYnlz/KuWbclkrjBv445NefajWax9+1uO7wqn+7BezFoxj/mDZpFwqSSY0zk6EHH0PIe2HuDxtyr+IrOXtSuXIUmlh+HI6Fgee/5F+vftWWm5xKQU3vt4JR3btrK5v0fXTrzx4gzLtkajsU+DK1F0MZaIsaWvBZW8Fl7DexEy/2FiZn9MfvhFHBoEUf/955BlmYSFqwAIeOoBfCfeR8zzyyi6FI9z24bUf+85zHkFpH611f4dcHDCYeLLmGMvUPzju8gFuSi9ApCLKj/W6h54BoWzG/pfv7R5rAWQiwspWj63dJs776J+EQzcAlmWUSj+nfkVTdeByLmZGH77qvT5c9IrLaMMbYWqTlOKPn8BrgUNcm6GdaYywYSqeRcwGjBHVE8wsOrPcwS4O7NwZJglLdjLtdIyv568wsjOjRnQpj4AIV6unEvIYNX+c5ZgoHODAKsy48Oa88vxaE7EpNo9GNB06o+cl4lhxzeWtHL/17IMxSWPa5QN2oKDE6bzf1vSzFfOWBUxHtyCuk0vlIGhmKshGHB8cBxSWhoFS5dY0qSU5ErLFH7xsdV20TdfoO3aHW2XMIquBQO3Uu/tMpskctKyq5T3/keGcGrfcX79fDMA69/7gdY92jJg0kC+fGk5AAc27QXAJ8SvGlpbnpenh9X2ym/XUSc4kM7tW1dYxmw2M3fB2zz16MMcP3WWvPzyX1ZajQYfby97N7dSslnCVMXXwqVTU/LDL5K5ueTs2ZCQSuaW/Ti3b2yVJ3v7EXJ2H7Pk8RrWC+d2jW3Webs03QaXHGt/XWlJM9/gWKtq0BpV3aYUfjK79Fhrs4yMXJBjz+YKt8kuwYBOp+PUqVM0b97cHtVVStWoHeYrZ9EOewpVnabI+VkYT+zGfG0IynaZ9kjJV9B0uR9VyzAw6jFHncS4/ycwGW2WUbfphfnCYTAaqqUf+y4k0K1xILPX7OPYlRT83JwY3aUpIztX/ME2mszo1CqrNJ1GxdmEDIxmCY3KegmILMsciU4mJj2H6fe1t3sfVKFtMcedRzvwMVTBjUuGA0//ifncgSrXoW7ZHSnuInJepu0MCgWqxh1Bra18aPI2aLp0x3j8CC7zF6Bp1RYpI53irZvRb/u16pUoFCgcnZDzcu1b700KCA3k0yNfYdAbiDwewdq3viXjqu0DeOMOTflt5c9Waaf/PEGn/l2qrX03w2g08uv2PUx8aESlJxufrfoBL093Rg4ZwPFTZ23mOXriNL0GjcHN1YW7Orbluccn4eHuVl1NB0AXGkib8K+Q9Qbyj0eQuPhbDBW8FvnhEXiN6INzu8YUnIxEW9cf934dyNi4zyqP7/j+6EKD0F+5imPz+rh0bk78wq9s1nm71I3bY44+g+6BZ1DVbYaUl4Xp2C5MJ/dWWEbVpANSUgyaboNQtw4DgwFz5HEM+zZaH2u1Djg+8z4oFEjJsRj2rEdOT6yWftyq2raA8KaCgZkzZ9pMN5vNLFmyBG/vkjne999///ZbVgGFhx/q9v0wHd1G8cFfUQaGor17PAazGfPZv2yWUXr4ogxpAiYjhk0fgaMr2v4Po3B0thphsOQPDEXpG4Lh9+r5kAEkZOWx/kgeE7q3YGrv1pxNSOftX4+iUSkZ2qGhzTLdGgexKTyKvi3q0DzIi/OJmWwKj8JklsguKMbXzQmAvGID/d/aiNFkRqlU8OKQLnRrZOcpAkDh7oO6dS9MJ3ZSfPQPlP710PYZjUEyYb5w6Mblnd1R1m+J4Y/y/2eFdxAOo18AtQaMevRbP6+WKQIAVUAgqoHDKNq0nqIfv0PdpBnOTzwHJiP6XduqVIfDA2NQODqi37/HrvXejKiTl1g+60OSohPx8PNk5PNjeHX9Il7o/xzFBcXl8nv4epCTnm2VlpOeg4evp93bdit2/XmQvPx8hg+8t8I8x0+dZdOv29iw+pMK83Tv2pF7encnOMif+MQkln2+midnvcL3n7+PSqWqsNztyD9xiaIZH1IcnYjGz5OgGWNo+tMizt39HJKN1yJz85+ovVxp+tMiUChQatSkfvM7yR9vsORJ/mQjKldHWu37GNksoVApSXzrezI3VXwidDsUnr6oO/bDePgPjH/9gjIoFG3/CWA2YTpjO+BXePiirNMY2WREv+FDFI4uaO+bhNbRxTLCIGUkY/h1JVJqPOgc0XQdiOOkVyhaMR85L6ta+nIrpNoVC9xcMLB06VLatm2Lh4eHVbosy1y4cAFnZ+cqTRfo9Xr0er1VmtnGWa9NCgVScgzGPzeWlEuNw+QTjLpdnwqDARQKkGX0v6wAQxEAxt1r0Q5/CrZ/W250QNWmF1JqfLWdiULJfa9bBHvzXP+SM/ZmQV5cTs1mw5FLFQYDj/dtTUZeEROX/44MeDk7MKR9Q1bvP4dSWfp/d9Zq+PGZQRTqTRyJTubd38MJ9nIpN4Vw2xQKpJRYjH9vAcCcFo/JOwh1615VCgZUzbuCvgjz5ZPl9slZKRT/8CboHFE36oDu3kkUb3y/egIChRJTVARF33wBgDk6ElW9UHT3D6vSl7a29z04jZtE3usvIedk263em3Vqb+m6i7iLsUSdjOSjv1bQdXAP9v64s5KSd6afft1Gj66d8PO1vaC2oKCQ+a+/y2tzp+Pp4V5hPQPv6WP5u0nDUJo0DOX+0VM4euI0XTvZf8QMIHdP6WtRdCGWghORtD60Aq8hPUhfW/61cO3WisBnHiTupc8pOBGJrn4AdRZMJXB6lmWBoOeQ7niP6E30M+9TfCkex5ah1H1tCsaUTDI27ClX521TKJGSrmDcWxKQSCmxKH1DUHfoV3EwoFCADPotn4G+5Fhr2LkG3chnMPzxNZiMSIlRSIlRljL6hCgcn1iCukM/jPs22r8fQpXcVDCwaNEiVqxYwXvvvUe/fv0s6RqNhtWrV9OiRYsq1bN48WIWLFhglfbi3W156d4bfzDl/Gzk9KvWaRlJKJp2qqRMDnJ+liUQgJLV6wqFEoWrF3JWSmlmjRZ187sw7t9cpb7cKl9XRxr6Wh/AQn3d2XnW9ip8AAeNmgUjw3h5eFcy84vwcXVk49FInHUaPJ0cLPmUSgV1vUuGQJsFeXElNYev9p21ezAgF+SU+3KWM5NRNOpQpfLqlt0xXTwMko2FVZIZOScNAGNqHEr/eqjb9cW4+4fbbne5p8rKwBwXY5Vmjo9FF9brhmW1vfrh8twc8pa8ivHkMat9t1OvPRTmFpB05SoB9Wy/7tlp2bj7eFilufu4k51W82dnV5NTOBR+kqWLXq4wT3xiEolJKTwz9zVLmnTt12Xa9hrELz98Qd2Q8iNidYID8fRwIy4hqdqCgbLMuQXoo6+iq2/7tQiaPY6Mn/aSvqYkUCi6GIvSyYF6bz1F0ofrQZap8/Jkkj7ZSNbPByx5dMG+BDwzslqCATk/G6nM0L2UfhVVs4qPtVJ+Doq8LEsg8E8Zm8daSwZzSaDh+e+sS6mq2vbbBDd1n4F58+bx448/Mm3aNGbPno3RaHu+/Ubmz59PTk6O1WN23zZVKislRqHwsv5AKbz8K124Zk6MLFnRqtFZ0pSeAciSVG6uWtW0M6g0mM79TXVqW9eXmPRcq7TY9FwCPV1uWFajUuLv7oxKqWTb6Rh6Ng22GhkoS5JlDGb7r42Vki6j8PS3SlN4Vv5a/EMZ3ASlhx+mcxWM5pSlUKBQVc8KcNP5s6iC61qlqYJDMKfZOHBdR9v7blyen0feOwsxHi0/EnKr9dqLzskB/3oBZKXa/nKPPB5By+7Wn7vWPdsRedz+lz3erE1bd+Dl6U6vbndVmCe0Xh02ffsZG1Z/Ynn07dGVuzq0YcPqTwj097VZLjk1jeycPHz/xQWFSicHdPUDMFbwWigddchlfybvn8/stdFWpaO23E/pyWYJRSWf/dshxUei9Aq0bqdXAHJOxZ9vKeESClcP62Ott+1jrYVCgdI3BDn/zlpQKNvx8V9w0zcd6ty5M8eOHSMtLY1OnTpx9uzZm76SQKfT4ebmZvWo0hQBYDq6HWVQA9RdB6Hw8EPVvCvqtn0wHd9lyaPp9SDaQaXXrJrPH0IuKkA78FEU3kEoQ5qg6Tu65B4DZaYI1G16lVzmdoNLFW/XhO7NOROfxsq9Z4jLyOW3U1fYeDSSh7o0seT5cNtxXl5f+mUZm57L1pPRxKbnciY+nblr/yQqJZtn+5ee3Xy57wwHo66SkJlHdGoO3xw4z9aT0QxqG2r3PphO7EIZ0AB15/tQuPuiatoZdasemE6XLnrShA1H239yubLqlmGYk6KRM66W26cJG44yqBEKV28U3kEl2yFNMEUcsXsfAIo2r0fdrAWOoyegDAxG2/seHO4bQvGvpZdAOk16DJeZL1q2tb3vwWXmixR8+SmmiAsoPL1KHk7ON1WvPY1/aTLNu7TEJ8SPxh2bMmvFPCSzxN8/l9xLY9r70xnzwgRL/t9X/ULb3u0Z9NgwghoGM/L5MTRo3ZBtX/9myePs7kK9FqGENC65WiWwQRD1WoTi7utRLX0AkCSJzVt3MOz+e1CXOS7Mf/1dPvis5FI7nU5L4wb1rR6uLs44OznRuEF9NBoNhYVFvPvxSk6dvUBiUgqHwk/w3LyF1A0JonuXqo1g3YqQlyfj0rUl2hA/nDs2pdHKechmiczNJa9F/aXTCZ5X+lrk7DyK38P34Tm0B9o6frj1bEvQnHHk7DgK1y61zN4RTuBzD+LeryPaED887uuC/+NDyfrjcLX0wXjkD5TBDdGEDUHh6YeqZTfU7ftiDC+d5tD0GYX2unsGmM4eRC7KRzfkMRQ+QSjrNEXbb0zJPQauHWs1PYahCm1Vsr4goB66YU+icPfBWMnCxJog2fHxX3BLVxO4uLjw9ddfs3btWu655x7M5oqvn7U3KfkK+k0fo+39IJruw5Bz0jDs/gHz+dIzM4WLOwq36+YZjXr0P76D9t4JOEz6H3JRAeaLR0quJriOwisAVZ0mFP/4TrX3o1WID++P78OH20+wYs9pgj1dmDOoM4Pald4cKC2viKSc0qDELMl8c+A8sem5qJVKOjXw5+sn7iP4utGEIoOJRT8fITWnEJ1GRX1fd94c1cNyOaI9SSmx6LcuRxs2HM1dg5Bz0zHsW4/5ui9thbM7CtcyZ2BaB1SNOmDYZ/tmKQonV7QDHkHh5AaGIqT0RPSbP0KKu2D3PgCYIy+S98bLOE1+HMexEzGnJFOw4mMMe0sPegovb5S+pcOYDvcNRqFW4/LUDHiq9Pr14p2/U/DBkirXa09eAd48+9EsXDxcyc3MIeLoBV4ZPpe8zJIRKJ8gX6uzz8hjEXz83PuMnj2eh+ZMIDnmKu89vsRyjwGAjvfexbT3nrNsT/9kDgAbPljLxqVrq6UfB4+eICkllRGD+pfbl5SSivImTj6UKiWXLl/h5993kptfgJ+PF2F3deCZxyai1VZ8T4/bpQ30psHHs1B7umLKzCH/yAUuDp2L6dproQv2tTrLv7psHbIsE/zCeLQBXhgzcsnZcZTEt7+35Il7ZQXBc8ZTd9ETaHzcMSRnkfbdNpKWVs9Nh6SkK+g3fIi27yg0PYchZ6dj2PE95nMHLXkULh4o3a2PtcU/vI2u/8M4TlmAXJSP+fwRDPtKF0IqHJzRDppSckOi4gKk5BiKv3693PSv8O9SyLJ8W6MYCQkJHDt2jHvuuQdnZ+cbF6hA4VuP3E4z7giKhrYX/v3XyIkJNd0EuyjaXj3Bw7/pmbN3xsr+2/XNseq7wujfcqqd7aup/muaT6veSyr/Lc4vfXPjTLdhQ+B4u9X1YNL3N85Uw277PgMhISGEhITYoy2CIAiCcEf4r8z124v4oSJBEARBqOX+e79NIAiCIAjV7L+y8M9eRDAgCIIgCGXUtjsQimkCQRAEQajlxMiAIAiCIJRR2+5AKIIBQRAEQShDXE0gCIIgCEKtIkYGBEEQBKGM2raAUAQDgiAIglCGuLRQEARBEGo5sWZAEARBEIQa8dprr6FQKKwezZo1qzD/6tWry+V3cHC46ecVIwOCIAiCUEZNrhlo2bIlO3eW/rqpWl35V7WbmxsRERGWbcVN/LKn5TluuoQgCIIg/D9Xk2sG1Go1AQEBVc6vUChuKr8tYppAEARBEKqRXq8nNzfX6qHX6yvMHxkZSVBQEA0aNGD8+PHExcVVWn9+fj716tWjTp06DBs2jHPnzt10G0UwIAiCIAhlSHZ8LF68GHd3d6vH4sWLbT5vly5dWL16NX/88QefffYZV65coWfPnuTl5dnM37RpU7766iu2bNnCd999hyRJhIWFkZCQcFP9FdMEgiAIglCGbMc1A/Pnz2fmzJlWaTqdzmbe+++/3/J3mzZt6NKlC/Xq1WPdunU8+uij5fJ369aNbt26WbbDwsJo3rw5n3/+Oa+//nqV2yiCAUEQBEGoRjqdrsIv/xvx8PCgSZMmREVFVSm/RqOhffv2Vc7/jzsmGDBejK/pJtw2w87omm6CXTiPbFfTTbCLonRVTTfhtn31v0Y13QS7yJ82paabcNuaPuBS002wD2Utu7XeLbpTbjqUn5/P5cuXefjhh6uU32w2c+bMGQYOHHhTzyPWDAiCIAhCGfZcM3AzZs+ezb59+4iJieHvv/9mxIgRqFQqxo4dC8DEiROZP3++Jf/ChQvZvn070dHRHD9+nAkTJhAbG8vUqVNv6nnvmJEBQRAEQajtEhISGDt2LBkZGfj6+tKjRw8OHTqEr68vAHFxcSiVpefxWVlZPPbYYyQnJ+Pp6UnHjh35+++/adGixU09rwgGBEEQBKGMmrod8dq1ayvdv3fvXqvtDz74gA8++OC2n1cEA4IgCIJQhvjVQkEQBEGo5e6UBYT/FrGAUBAEQRBqOTEyIAiCIAhl1LaRAREMCIIgCEIZNbWAsKaIaQJBEARBqOXEyIAgCIIglCGuJhAEQRCEWq62rRkQ0wSCIAiCUMuJkQFBEARBKKO2LSAUwYAgCIIglCHVsnBATBMIgiAIQi0nRgYEQRAEoYzatoDwPxkMKDy8cRj9GOrWd6HQ6pBSr1L05TuYYy7dsKyqUUuc572PlHiF/FeftNqn7TcU3f2jUbh7YY67TPH3H2O+ElFd3UDp7YPz1CfQ3tUFhc4B89VE8t5dgumS7efU9uiJ4+DhqBs2Ao0Gc2wMBd+uwhh+1JLHccx4dD16oapTF/R6jOfPUrDyc8wJ8dXSh9T8Ypbtj+CvmHSKjWbqeDjxWv/WtAxwt5n/f9tO88v5q+XSG3i5sHFSDwAGfrmXpNzicnlGt63L/H4397OcVaX09cH96cdwCLsLpc4BU0Iima+/jfGi7feU0tsLj+nT0DRvgjokmPx1m8j54BOrPA59euI2eRzqkGBQqzDFJ5L/w3oKf99RLX1IySti2b4L/BWdSrHJTB0PZxbc346WgR4Vltl6LoGvj1wmLisfF52G7g38mNGnBR6OWgA2norl13MJRKXlAdAiwJ1nejWjdaBntfQBQOHlg+P4J9C0vwuFzgEpOZGCT97CHG37c6Fq1hqn8Y+jDK5bkj8tBf2On9Fv3WDJ4zB8HJouvVAF10U26DFFnKPo+8+RrlbP5wJA4e6Nbuhk1C06gkaHlJ5E8fdLkeKjblhWFdocx+eWICXFUvj2c9dVqkR7/zg0nfugcPVEzs3EeHgXhm2V/9LdLffBxRNt39GoGrYBtRY5KwX91pVIyTGVNF6Npvsw1K3CUDi7I+dnY/xrC6bT+wFQt+6BbvBjVkVkk4HCdx6zVVuNqV2TBP/FYMDJBZeXlmG6cJLC9+cj5eWg8g9GLsi7cVlHZxwfm4vpwgmUbh5WuzR39cFhzJMUfbMMc/QFdPeOxHnWEvLmP4Kcl233bihcXPBY+jHGUyfJefEFpJxsVMEhSHkV90PTui2G4+EUfPUFckEeDgMG4r5wMdnPTsN0ORIAbZu2FP28CVPERVCpcJ7yGO5L3iVz6iQoLv8Feztyi41M/vEQnUO8+XhERzwdtcRlF+LmoKmwzJw+zXmuR1PLtlmSeei7v7i3ib8l7buxYUhy6UcxKj2PaT+Fc29jf6qDwtUFvxUfoj9+kvTn5yNlZaOuG4KUl19xGa0Gc3Y2xV99h8vYB23mkXNzyV31PabYOGSjCcceXfF8+QXMmVnoD4fbtQ+5xQYmf/8Xnev68PGoLng56ojNyq/0tTiRkMkrv51gdr+W9G4YQGp+EW9sP8PCP07x/ojOAITHZXBf82Da3u2JTq1k1eHLTFt3iI1T+uDv6mjXPgAonF1wff1jTOdOkL9oLnJuNsqAkMo/38VFFP+xCXNsNOiLUTdrjdPjM5H1xRh2/gqAumU79Ns2Y4q6iEKlwnHcVFxefofcGZNBb9/PBQCOzjg9/zbmyNMUfvYacn4OSr8g5KKK31PXl3V4eCbmS6dQuHpY7dLeMxJNj/sp/u4DpOQ4VHUb4zBuOnJRAcY/f7FvHxyccHj4JcxxFyn+8T3kwlyUXgHIxYWVFtMNfxqFsxv6375EzkpF4eJO2RlpubiQohXzSrflO++rV4wM3OF0A8cgZaZR9NW7ljRTenKVyjpOeh7jod0gSSg7hFnt0/YfieHP3zAe2AZA0TdLUbftgrbnfeh/s3/U7fTQOKS0NPLeXWJJk5Ir70fBZx9bb3/1Bdpu3dF2C7MEAzkvvmCVJ++dxfhs+BlN4yYYz5y2U+tLrDoaTYCLIwsGtLakBbs7VVrGVafBVVe6vScqhdxiI0NbhljSvJy05Z6njrsTHUO87NPwsm16eCzm1FSyXn/bkmZOqvy1MCelkPN+yUiA85D7bebRHz9ltZ3/4084DRyArl1ruwcDqw5fJsDNkYUD21nSgj0qfy1OX80iyN2JcR0bWPI/2LYeqw6XnrkuHtLBqsyr97Vl16UkjsSmM6RVHft14BqH4eOQMlIp/PQtS5qUeoPXIiYKc0xpmw1pyWi69ETdvI0lGMh/0/pzUfDJEjy+3IK6QRNMF+z7uQDQ3vMgUnY6xT8sK21nZkqVyjqMfhpj+D6QJdStu1rtU4U2x3TmMObzJe8fU2Yqpg69UNVrgtF+zQdA03UQcl4mhq0rLWnmnPRKy6gatEZVtymFn82B4gIAZJtlZOSCHHs2V7hN/7lgQNOuG6az4Tg99Qqqpm2QszLQ7/4Z45+/VV6uxwCUvoEUrViMbsgE650qNar6TdBvXVOaJsuYzh9H1ah6hqW13bpjCD+C2ysL0LRuizkjneKfN1P8+69Vr0ShQOHkhJSXW3EWZxeASkccbtW+6FTC6vkw59cTHEvIws9Fx+i2dXmgddW/JDafTaBLXW+C3GyfZRrNEr9duMqEjvVRKKrnlmCOvbpRfCgcr0WvomvfBnNaOgUbf6Zgy1a7Po+uU3vU9ULQf2L/L599Ucl0q+/L7C3hHIvPwM/FgdHt6zOybb0Ky7QJ8uSjPy+w/3IKPRr4kVloYGfEVXo08KuwTLHRjEmScHfQVpjndmg6hWE8eRTnma+hbtEWKTMd/bbNGHZV/bVQ1W+EumkritZ8WWEehdO1z0W+/T8XAOrWXTBfOI7DI/NQNWqFnJOBcf9vGA9uq7xcl3tQ+gRQ/O27aAeMKbfffOUCmrD7MPgGIaddRRkUiqpBC/SbK+7rLfehcXvM0WfRDX8aVd1mSHlZmI7vwnRqX4VlVI3bIyXFoOk6EHWr7mDUY448geHPjWC6LlzROuD41HugUCAlx2LYtwE5PdHufbgd4g6EN6GgoIB169YRFRVFYGAgY8eOxdvb215ts0npF4i23xD02zZQ/OsaVKFNcRz/NJiNGP+yPRer9A/G4cGpFCx+HqTygz8KV3cUKhVybpZVupyThTLA/mc/AKrAQByHDKNo43oKf/gOddNmuDz9HLLJiH5H5QeMfziOGoPCwRH9vj22MygUuEx7BuPZ05hjrtix9SUSc4pYfzqeCR3q8+hdDTmXnMPbey6gVioZ2jL4huVT84v5KyadRfe3qTDPnqgU8vQmhrS4cX23Sh0UhMsDQ8lbs5681d+jbdEUj5nPIBuNFP62/bbqVjg7E/jrOhRaDZglst5Ziv7IMTu1vFRCdiHrT8YyoXMDpnZtzNmkbN7edRaNSsnQCs7g24d4sWhwB+b+fAyDWcIkyfRu6M/8e1vbzA+wdN95fF0c6FLfx+59AFD6BaHrP4ziX9dR/NN3qBo1w2nKc2AyYdhX+efCffl6FG7uoFJRvG41ht0VBBAKBY6Tn8F08QxSvP0/FwBK7wCUPQZi2LMZw451qOo2RjfycWSzEdOR3bab5RuEbsgkCpfNtXmcAjDs3AAOTji/tBxkCRRKDFu/xRS+1+59UHj4ou7QF+ORbRgP/oIysAHaeyeAZMJ05q8KyyjrNEY2G9Fv/BCFkwva/hPROrpYRhikzCQMW79ESo0HnSOaLvfj+PDLFK18ETkvy2a9NaG2XVp4U8FAixYtOHDgAF5eXsTHx9OrVy+ysrJo0qQJly9f5vXXX+fQoUOEhoZWWo9er0ev11unmSV0qipc6ahQYI65hH7jVwBIcVGoguuj7TPEdjCgUOL4xIvoN3+NlHIHRZ4KJaZLERR89QUApsuRqOqH4jh4WJWCAV3fe3CeMImcV19Czs62mcfl2Rmo64eSPeNZe7bcQpJlWvi782yPJgA083MjKiOPDWfiqhQM/HI+EVedmr6NKl4LsPlcAt3r++Dn4mC3dpejVGC4cIncz0rOroyXolA3CMX5gSG3HQzIhYWkPPwYSkdHdJ074DH9KcyJSeWmEG6XJMu0CPDguV7NAWjm787l9Dw2nIytMBi4nJ7HO7vO8nhYE8JC/UgvKOaDved5c/tpXru/Xbn8Xx2KZNvFq6wcE4ZOrbJr+y2UCsyXIyheU/LFYY6JQlUnFF3/oTcMBvL+9yw4OKJu3ALH8Y9jTk7E+Ff5L16nqc+jqhNK3ivV87kASs5446Mw/PoNAFJCNMrAemi7D7QdDCiUOE6cjeH3H5DTyi+w/Ye6fU80nfpQ/M27SEmxKEMa4PDAY0g5GRUGGbfeByVS0hWM+0oWYkopcSh9g1G371dxMKBQggz6n5eDvggAw6416B54BsO2r8FkREq8jJR42VJGnxiF4+OLUbfvi/HPn+zbB6HKbuo+AxcvXsRkMgEwf/58goKCiI2N5ciRI8TGxtKmTRteeumlG9azePFi3N3drR7vn46pUhvk7Eykq7FWaVJSHErvCoY2HRxRhzbFYcKzuK3chtvKbeiGTkBVtxFuK7ehat4OOS8H2WxG4Wa9Qlrh7llutMBepMwMTHExVmnmuFiUfhUP0f5D16cfrjPnkPvGaxhP2D7LdHlmOtou3cie8zxSepo9mlyOj7OOBt4uVmmhXi4k27gSoCxZltlyLpFBzYPQVBAEXs0t4nBcBsNbh9jcby/m9ExMV2Ks0kwxcaj97bBgUZYxJ1zFGHmZ/B/WU7R7H66Txt1+vWX4ujjQ0NvVKi3U24Wk3KIKy3x1KJK2IV5M7tKIJn5uhIX68eK9rdl8Jp60fOvX8Osjl/nqcBSfjepKEz83u7f/H1JWBuYE68+3OTEWpc+NPxdSajJS3BUMu7ai/3UDjqMnl8vj+Oh0NB26kb/geeTM6vlcAMi5WZiT46zblxKPwtPXdgEHR1T1mqB78ElcPtiCywdb0A4YgyqkAS4fbEHVuGT0TDfsEQw7N2A6/idSUiymo3sw7NmC9t5R9u9DfjZSunVgIqUnoXCrePRXys9Gzs+yBAIAUsZVFAolCtcK1vxIZqTkWJSe1bNA+FbJdnz8F9zyNMHBgwdZvnw57u4ll5C5uLiwYMECxowpP89V1vz585k5c6ZVWvEzw6v0vKaoc+WG7pX+IUgZFSzOKS4k7+WpVknafkNRN29H4ScLkdKSwWzCHHMJdYsOmE78XZJJoUDdvD2GXVuq1K6bZTx3FnVIXas0VUgIUkrli4x0fe/GddZcct9cgOHIIZt5XJ6ZjrZ7T3JmT7/hosT/a+++w5us2geOf5/s7gGdjLI3ZcoqS6YswQGIIKCIoiCoL+iL4wfO4kBAUFRQUBFxMvQVEBEQFGSJbCyrBUoLLd1tkiZ5fn9UU1JaaCE1QO8PV66LnJxzcp/mSXPnnPM8vRbNI4OIv5DjUpaQlkNECev/F9t1+gKn0nMZ1KTkD/pVB04T7GWkU80SfoG6iXXvfnRRrseUrnpVbEml2/BVJhoNir7kHf5Xq1mVYE6mue5Uj7+QfdnXwmyzo1VcEzHN3/syLt7cvej3o3y4NY53h7S77GmK7mA/sh9NpOtroY2ohuN8GV8LjQI6130NXmMmYWjTkaxpj19xU+K1sh8/iCbU9djWhFRBTTtXfANzLjmx412K9B37oq0XjfmjGThSC+JVDMaC5YGLqY6Cb+Ru5jgdh6ZSuEuZJji8hA2BhW10DW4BvRHyLYVtHA7UrAvFN1IUNKFVsR9z/16aa1HRziYo8xH0zyYus9lMRESEy2NVqlTh/PkrZ9tGoxF/f3+XW6mWCADrj9+grdUQY79haEIj0bfrhqFrX5cPbePdY/B68OmCO6qK48xJl5uamQ75VhxnToLV7OzX0KUv+pieaCKqYxo5CcVowrplTaniKqu8b75C17AR3sNGoImsgvHWHnj1HUDequXOOj4PjMXvqWcKx3VrD/yeeobs998l//AhlKDggpu3j7OO72NPYOzek6zYl3Dk5jnrYHD/hq8RLWuwLymdD7cfIyE9h9WHE/lm32mGNitMct7ecoTn1lz6Jl+x/wxNwwOoU9nvksegYNp75YEz9G8UiU5TvhfKzPr8awxNGuE36l60VSPx6tUNn0H9yP56hbOO/6MPEjTtvy7t9HVro69bG8XbC21gAPq6tdHVLNyw5zdqGMY2rdBGRqCrUR3fewfj3acnOWt+cvsYRrSuxb7ENBZujSMhLYcfDp7mm70JDG1Rw1nn7U2HeO5/fzjvd64dzs9xZ/nyj5OcTs/hj9MXeG39fppEBBLqV7Ass+j3o7y75QjT+zQj0t+LlGwzKdlmcq02t48BwPz9V+jqNsJ0x3A04VXQd+yOsUd/LGtWOOuY7h2L94SpzvvG3oPQt2qPJrwKmvAqGLr1xTRgKNbNhcuGXg8+jqFTT3LmvIxqzkMJDEYJLJ/3BYB140q0Nepj6DkYpXIEulZdCjb+bS7cx2AYMArTiL+/FKkqjrPxLjc1OwPy83GcjQdrwQerbf92DL2Gom3UGiU4FF10e/S3DiJ/71a3jyF/x1o0kbXRt++PEhSKtlE7dM27kr97vbOOvstgDP0fct63HdiKmpeNsd+DKJUi0VSrj6HbPdj2/uLcQKiPGYi2ZpOC/QVhURhvH4fiX5n8PSVvTBTlr8wzA927d0en05GZmcmRI0do0qSJ87H4+Phy30BoP3GE3HnTMN39IMaB9+E4f5a8pfMLThn8myYguORlgxLkb9+I4heAadBolIAg7AnHyHlrakHiUA5sfx0mc/pz+Ix5CO8RI7EnJZE9fx6Wnws/KDSVKrksG5j69UfR6fCb+AR+E59wlpt/XE3WGwWnKHrdPgiAwJlvuzxf5huxWH50b2LTODyAmQNaMHfLX3yw7RhVAryY0rUBfRtGOuuk5FhIynKdqs6y5LP+aBJTujYsse/fE1JJyjJfdubAXfIPHSH1qf8j4NEH8R8zElviWTJmvUve2sJfetpKwejCXI+psCULnP83NKyP9209sCUmkXRHwTKAYvIi8KlJ6EJCUC0W8uNPcWHaq+T9tNHtY2gSEchbg27h7V8O8cFvf1ElwJsp3RrT76JTNs/nmF2WDQY2rUau1cay3Sd4a8MB/Ix6bomqzKQuha/Ll3+cJN/uYPJK1+WohzvU45GLrhfhLvZjR8h543m8ho/FdPcoHOfOkrt4HtYtF70vgiqhqXzRlLKi4HXvQ2hCw8Fhx56USO5n72NdV3jevan3IAD8Xig81Q8KTjG0bnR/wu9IiCNv4SsYB4zCcNswHKnJWL5d4LLRT+MfVPKyQQnMX7+Psd8ITEMeRfENKLjo0K+rsa5x/+nPjrMnsHz7NoYug9F3HIianoL1p8+wHyhMPBTfADT+F03/51swf/4Gxl4j8Lp/OmpeNvZD2wvOJvinjckHQ5/7Cy5IZM7BkXQS86cvoaaWvFfCEyraBkJFLcPVHl544QWX++3ataN3797O+1OmTOH06dN8/vnnRZteUcb9Pcrc5npjPe3uM309w+eu5p4OwS0uLNrn6RCuWaWHWl650g3AvMa911XwBF2E75Ur3QA0EeX7he3f4jP143Lt/6kaw9zW1+sny/6Z+G8r08zAtGnTLvv4G2+8cU3BCCGEEOLfd8NddEgIIYQobxVtA6EkA0IIIUQRFW3PgCQDQgghRBEVKxW4ilMLhRBCCHFzkZkBIYQQogjZMyCEEEJUcGoFWyiQZQIhhBCigpOZASGEEKIIWSYQQgghKriKdmqhLBMIIYQQFZzMDAghhBBFVKx5AUkGhBBCiEvIMoEQQgghKhSZGRBCCCGKkLMJhBBCiAquol10SJIBIYQQooiKNjMgewaEEEKICu66mRk4tcnk6RCuWWTTfE+H4BYbpp3zdAhuccAU6ekQrtmYFTs8HYJbePWN9nQI18x/0reeDsEt0sa18HQINwRZJhBCCCEqOFkmEEIIIUSFIjMDQgghRBEOVZYJhBBCiAqtYqUCskwghBBCXDemT5+OoigutwYNGly2zVdffUWDBg0wmUw0bdqUH374oczPK8mAEEIIUYQD1W23smrcuDFnz5513rZs2VJi3d9++41hw4YxZswY/vjjDwYNGsSgQYPYv39/mZ5TkgEhhBCiCNWN/8pKp9MRHh7uvFWuXLnEunPmzOG2225jypQpNGzYkJdeeomWLVsyb968Mj2nJANCCCFEObJYLGRmZrrcLBZLifXj4uKIjIykVq1aDB8+nISEhBLrbt26lR49eriU9e7dm61bt5YpRkkGhBBCiCIcbrzFxsYSEBDgcouNjS32edu2bcvixYtZs2YN8+fP58SJE3Tq1ImsrKxi6yclJREWFuZSFhYWRlJSUpnGK2cTCCGEEEVczVp/SaZOncqTTz7pUmY0Gout26dPH+f/o6Ojadu2LVFRUXz55ZeMGTPGbTEVJcmAEEIIUYQ7L0dsNBpL/PC/ksDAQOrVq8fRo0eLfTw8PJzk5GSXsuTkZMLDw8v0PLJMIIQQQlynsrOzOXbsGBEREcU+3r59e9avX+9Stm7dOtq3b1+m55FkQAghhCjCnXsGymLy5Mls2rSJkydP8ttvv3HHHXeg1WoZNmwYACNHjmTq1KnO+pMmTWLNmjXMnDmTw4cPM336dHbu3MmECRPK9LyyTCCEEEIUoXrocsSnT59m2LBhpKamEhISQseOHdm2bRshISEAJCQkoNEUfo/v0KEDS5cu5bnnnuOZZ56hbt26rFixgiZNmpTpeSUZEEIIIa4Ty5Ytu+zjGzduvKRs8ODBDB48+JqeV5IBIYQQogh3nk1wI7jhkoHQSfcSOulelzLLsVPE9Xyk+AY6LSGPDCbozu7owithOX6G5NcWkf3L7qvv002U4Mp4j3oYfcu2KEYT9rNnyJk7A/vRI8XW17frhKnPILQ166Do9dgTTpK3bBH5f+worOTlhfe9Y9C364QmIAjbiThyF8zFfvSw2+OvO/ku6k2526UsO+4MmzpOLrGNzt+b+lOHEt7vFvSBvuSdTuHg859wfv0eALQ+Jur/dwhhfVpjrBxA5v6THHjuYzL2HHd7/P+IefxOYp6406Us9WgiH3Z/qtj6dW9rTfvxtxMYFYZGryXtRDI7FvzAweW/OuvovY10+e9Q6vZqjSnIl4xT59m9aC17Pvu53MYBoAmujPfoh9G3KjymsueUfEwZ2nfC2GcQulp14J9jaqnrMRW4cBnasEs3L5n/t5yc92a7fQznss3M2RLHrydTMOfbqRbozfRejWkcFlBs/f9bu5/vDiVeUl4r2IdvRsYA0PfDXzibZb6kzpDoakzt1tC9AyjiqSnjefWVZ5jz9kL+M3lasXXGPHAv9424m8aN6wOwe/c+nnt+Bjt27nHWCQ2tTOyrz9KzR2cCAwPYvHkbk554nqNHT5Rb7EpAJYy3j0bXqBXojThSzmL+bDaOU8XvbL+YtmZDvCbOwHE2ntzXJxY+YPTC2G8Euuj2KL4BOM4cx/zNBzgS4sptHFejrGv9N7obLhkAMB+J5+R9zzrvq/aSX7aw/9xH4MBbOfPMXCzHTuHXuSXV33uW43dPwXyw8AOmLH26g+Lji/+Medj27yHrxadQM9LRRFZFzS7+whIA+sbNyN+zk9xPF6DmZGHs3hffZ2PJnPII9hMFbySfCU+hrV6TnFmv4LiQirFrT/xenEnGhFGoF1LcPo6sw6f4/e5XnPcdl/m5KXotbb98BmtKJrvHzMacdAGvqiHkZ+Q460TPegi/+tX4c8K7mJPSqHJ3R9p+9SybOk3GkpTm9vj/cf7IKb4cPqNwHDZ7iXXN6TlsnbeK1GOJOKw2andvQd83HyI3NZOTv+wDoNvzw6neoTHfPz6fjNPnqdmpKT1fHk12cjpHf9pdYt/XQvHxxf/1eeTv20PW9KdwZKajvcIxpbv4mMrOwtijL37Px5Ix+RHsxwuOqYwnHwaNtrBNVE38X34Ly5aNbh9Dpjmf0V9s55Zqwcwb1JIgLz0J6bn4G/UltpnStT4TO9Z13rc7VIZ+tpWedQsvxLJkWDuXP0l7NDWbR77d5VKnPLRu1YyxD47gz70HL1uvS5f2LPtiJVu37cRsNjNl8nhW/7CU6ObdSEwsuHjMt19/RH5+Pnfe9QCZWdk8Pukh1q5eRtNmXcnNzXN/8F4+eD/+Ova4veTOn46anYEmNBI1L7tUbU33PYn9rz9R/AJdHjINewxNRBTmT2fiyLiA/pZb8R7/MjmvPoqaker+cYhSuSGTAdVux5aSXqq6gYNu5fy7X5K9cScAFz5bjU9Mcyo/eAenn5x5VX26g+mue3GknCfn7Ys+gM5d/opRuR+6Xms6b8kC9G1j0LfpUJAMGAwY2ncm+5VnsR3cW1Bn2WL0t3TA1GcgeZ996PZxOGx2LOczSlW32rBb0Qf58lv/aah/f9jmnSpMUDQmPeH92rBr1EwubCuYyYh78xvCerUkanRP/prxpdvj/4fD5iCnlOM4te2Qy/1di9bS5O5OVL2lvjMZiGxVl/3fbHbW/fPzDTQb3o3w5rXKLRnwuvvvY2rORcdU8hWOqYVFjqlPF2BoF4OhTQfy/k4G1EzXn4v+7nuxJ57Gtn+PewK/yKKdJwj3M/FCr8LNT1UCvC/bxs+ox++iU7g3HD1Hpjmf2xtXcZYFextcn2fHCaoFeNGqapB7Ai+Gj483n3wyj3GPPMUzUydetu7IUY+53H/o4cnceUdfunXryJIlX1O3bi3atWtFdPNbOXjwLwDGT/gvZ07t4Z6hg/ho0eduj9/Q424c6SmYl85xltkvJF+mRSHTkPHk79wEqgNd03aFD+gN6JrFkLfgJezHDgBgXb0UXZM26Dv2wfq/JW4dw7Vw53UGbgQ35KmFxhqR1N/6MfU2LqTqrMnoI0NKrKsY9KgWq0uZarbi3brRVffpDoY2MdiPHcb3qRcI/HgF/rMWYuzZv2ydKAqKlzdqVmbBfa0WRatDzS8yXqsFXcOmborclU+tcLr/+S63bp9N83fHY6pSqcS6Yb1bkr4zjiYz7qfH/vfovOl1ak8aCBqlYDhaLRqdFnuR18tuthLcpn65xP+PoJphPLp9Lg9tfov+cx7BL7LkcRRVPaYxQbXCOfV74VJM4q446vRoiW9YwYdN9fYNCa4Z7kwWyoO+TQy2o4fxffoFgj5dQcDshRh7XeMxVZROh/HWnlh+Wn3tARdj0/HzNArzZ8r//qTb+xu457OtfLvvdJn6WHHgNG2rVyLS36vYx/PtDn44fJaBjaugKIo7wi7W3LdfZfUP61n/8+Yyt/X29kKv15F2IR0Ao7EgmTGbC69nr6oqFouVmJg2bom3KF3TtjgS4jDd/198XlmC91Nz0LfvfeV2bXugqRyOdc3SSx/UaFG0WrDluxSrVgvaWo3dFbpbePKvFnrCDTczkLvnCKenzMJy4gz6kGBCJw6j5hevcfS28ThyLp0qy968m0oPDCJn+wGs8WfxiWmGf+/2LtOeZe3THTRhERhvG4h55VfkfbUEXd0GeI+diGrLx7phban6MA26B8XkhfXXDQUFeXnkH96P15CRZJ+OR01Pw9CpO7r6jXEknXH7GNJ3H+XPie+Rc+wsxtBA6k2+i/Yrp/FLl6ew51y6PusdFYpXxxASv/2V7fe+hk/NMJrMeACNTkfczG+w55hJ2/EXdZ+4k+y/ErGcT6fKHTEEta5HzomyXWe7LBL3HGX1fz7gwvGz+IQGEvP4Hdz71fMs6vVfrMWMA8Dg58Wjv89Fa9Ch2h2se34x8VsK/2ToT9M+oXfsGB7dPhd7vg3VobL2vx9yenvxa/fuoA2PQNtnIHkrCo8pn4cmgi0fy8+lPKbuKDimLFs2FPu4oV0nFB9fzOvLJxk4k5HHV3tPM6JlFGNuqcmB5Exe33gYnVbh9kZVrtj+XLaZX0+m8mqfkpPfDcfOkWWxMaBRpDtDdzFkyO20aNGEdu37XVX72FefJTExmZ/WFyQShw8fJT7+NK+8PJVHHn2anJxcHp80lmrVIokID3Vn6E6aSuFoOvbFumEF1nVfoq1eF+NdD6Ha87FtL37vixISiXHAKHLnPA2OYpYMLXnYTxzC0PsezEmnULPS0bXqjLZmA9TzZ8tlHKJ0ypQM7N69m6CgIGrWrAnAp59+ynvvvUdCQgJRUVFMmDCBe+6554r9WCyWS/5ik1W1Y1C0JbQolL1pV2E/h0+Su+cI9bd8REC/jqR9ue6S+mdf/IAqrz5G3XXzQQVrwlnSvv6JoME9r7pPt1A02I4dIW/JAgDsJ+LQRtXEdNvAUiUDhs498LpnFFmvPouake4sz5n1Cj6PPU3Qom9R7Tbsx+Kwbl6Prrb7v1mf//lP5/+zDiaQvvso3XbNJXJgO04t3XhpA40Ga0ome/+zABwqmXtPYAoPptb4/sTN/AaAPePfIXr2OHrsfReHzU7mvhMkLv+NgOiabo//Hyc27i0c0+FTnN1zjHG/zqZ+/7bs+2JTsW2s2WYW93kWg4+RqJjG3PrccNITzjuXBVqO7kVkizp888BMMs+kULVtA3q+NIrs5DTifz1QPgNRNNiOHiHv07+PqeMFx5Sxz8BSJQOGLj3wHjaKrJddj6mLGXv2JX/XdtQL5bO261BVGoX581hMwR6ABqH+HE3N5uu9p0uVDHx3MBE/o45ba5f8Abli/xlialQi1NfktrgvVrVqJLNmvshtfYdd9i/TleSpKeMZOuR2uvcc7Gxvs9kYPORBPvhgJinnDmKz2Vi/fjOrV68vv9kNRcFx6ijW7z8BwHH6OJqIKAwxfYtPBhQNXiMnY129FPX8pRs6/5H36UxM907C9+VPUO12HKePYdv1C5pqdcpnHFfJU9cZ8JQyJQP3338/M2fOpGbNmixcuJCJEycyduxY7rvvPo4cOcLYsWPJzc3lgQceuGw/sbGxvPDCCy5ljwTW5dGgemUegCMrB8uJMxiiis/y7RcySRj3CopBjzbIH1tyKmFPj8aaUPI3zSv16Q6OtFTsp066xnoqHkP7zldsa+jUDZ8JU8h+bRq2P3e5POZISiTr2UlgNKF4e6OmXcBnyjTsySW/Od3FlplLzrGzeNcs/prYluR0VJsNHIVvsuy4M5jCglD0WtR8O7nx59h2x4tovY3ofL2wnEunxQcTyY0/V+7xO+PMzOXCiSSCoi6zuUxVSY8vWD89dzCBSnWq0O7RAZzadgidUU/nKUNY/vBsjv+8ByhIMsIaRXHLQ/3KLRko6ZgydijdMeX72BSyZkwjv8gx9Q9NSBj6Zq3Iin3eHeEWq7KPkVrBvi5lNYN8WB935bVqVVVZeTCRfg0j0GuLXwFNzMzj91OpvNm/uTvCLVbLlk0JCwthx+9rnGU6nY5Ondox/tHRePvWxFHct2bgySce5qkp4+l92z3s2+e6N2X3H/tofUsv/P39MBj0pKRc4Lct37Fz195i+7pWamYa9iTXP53rSD6FrllM8Q1MXmij6qGpWhvj3eMKyhQFRaPBd9ZK8t59HnvcXtSUJPLengoGI4rJGzUzDdPop1BTy2/272rI2QSXERcXR926BRn7u+++y5w5cxg7dqzz8VtuuYVXXnnlislAcX/B6WizoWUJxUnjbcJQPYL05cVPa/5DteZjS04FnRb/3h3I/GHLNfd5LWyH9qONrO5Spq1SFfv5y//SM3Tqjs9jT5P95gvk79pWckWLGdViRvHxRd/8FvI+ft8dYV+W1tuId40wLF8Xv0aatuMIkXfEgKLA31m3T+0IzElpqPmuu/ftuRbsuRZ0AT6EdI3m0EvFrD+WE723kcCoUA58m176RhoFraFgx7tGrytYPijyC99hd6Boym+N2nZoP9oqxRxT565wTHXuju/Ep8l64wXyd5Z8TBl79EHNSCd/x2WOu2vUPDKQ+LQcl7KE9Bwi/K/8LX7X6TROpecyqHHJMwirDpwh2MtAp5qVrznWkvz88xaatejmUrZwwVscOXKMN958p8REYPJ/HmHqfyfSt99wdu0u+QM+M7Pg7JA6dWrSqlUzpk1/w33BX8R+/CCa0KouZZqQKqhpJSTm5lxyYse7FOk79kVbLxrzRzNwFP2wt1pQrRbw8kHXoCWWVYvcGf41q2gbCMuUDHh7e5OSkkJUVBRnzpyhTRvXjStt27blxIkrn/Na3F9wKs0SAUD41AfIXL+d/DPn0IUFE/b4cLA7yPiuYDq3yptPYktOJfmNjwHwalYPfXgl8g4eRx9emdBJ96JoNJx//5tS91kezKu+wv+1dzDdPQLrlg3o6jXE2GsAOe++6azjdd9YNJVCyJn9KlCwNOAzaSq5C+di++sQSmBwQUWrBTW34BeovsUtgIL9TAKaiKp4jx6H40wClvU/uH0MDacNJ/nH3eSdPo8pLIi6Tw1GtTtIXP4bAM3mPoI5KY0jrxRcUSt+8TqiHuhF41dGcXLhGnxqRVBn0iBOLiz8BlW5azSKopB9LBGfGuE0mHYv2UcTOf15+b0WXZ8dxrGf/iDjTAq+YUF0fOJOVLuDQ6u2AtD3rYfJTkrjl9cLzmZo++gAkvaeID0+Ga1RT+1bm9H4jhjWPbcYAGt2HglbD9H1mWHYzPlknkmhWtsGNL6rIxte+qzcxpG38isCXn8Hr8EjsPx9TJl6DyB7XuEx5T2y4JjKnvX3MdWlB76PTyVnwVxsR4o/pgBQFIw9+mD5eQ04Sj7t8lqNaBHF6C+38+H24/SsF86BpAy+2Xea53sUbi57e0sc53LMvNzbdV/AigNnaBoeQJ3KfsX27fh75qB/o0h0mvLbO52dncOBA657Q3JzcklNTXOWL/poDomJZ3n2uYIzP6ZMfpTp0yYzYuQETsafIiwsxNlXTk4uAHfd1Z+U86kknDpDkyYNmDXzRVauWsO6n34pl3FYN67E+4k3MPQcTP4fW9BG1UPf4TbMXxSegWIYMApNQCXMS94CVcVxNt6lDzU7A/LzXcq1DVqCAo7kM2hCIjAOfADHudPkb/upXMYhSqdMyUCfPn2YP38+CxcupEuXLnz99dc0a9bM+fiXX35JnTrlu+6jC69MtTlT0Ab6Y7+QQc7Ogxy/6z/YLxTsfjZEhrhsXFGMBkKfvA9D9XAcOXlkbdzF6Sdn4sjKKXWf5cF+9DDZsc/hdd9DeA0diSM5idyF87BuKnxDaIIqoalcuPZp7NUfRafDZ9wT+Ix7wlluWb/aeYqi4u1bkERUDkHNysK6dRN5SxaC3f2/wE2RwbR47zH0Qb5YUzNJ236E3/o+jzW14JuLV5XKqBctCZgTL7D9nhk0evE+Om14DXNSGicWrObY3FXOOnp/b+o/ew+miGDy07NJ+n47R2K/cJ6KWB78woMZMHc8pkBf8i5kcXrHEZYMmk7ehYJx+Ee6jkPvbaTXy6PxjQjGZrZy4Vgi/3t8Poe//91Z57vH5tH5qaH0n/MIpkBfMk+nsPmNr9izZP0lz+8u9rjDZL36HN4jH8LrnpHYk5PIWeB6TCnBldCEFB5Tpt4Fx5TvI0/AI4XHlHn9anJmF56iqG/eCm1oOOZ17k8qL9Y4PICZ/Zsz99c4Pvj9OFX8vZjSpQF9GxRe9Cglx0JSpuvGzixLPuuPJjOlS4MS+/49IZWkLPNlZw7+LdWrRbrMEDz80EiMRiNffbHApd6LL83kxZfeAiAiPJQ3X59GWFhlzp49x5LPvublV2aXW4yOhDjyFr6CccAoDLcNw5GajOXbBdh2bnTW0fgHoQSV7cwrxcsb44BRKIGVUXOysP35G5bvPynXJPNq3ChnAbiLopZhl0RiYiIxMTFUr16d1q1bM3/+fFq1akXDhg05cuQI27ZtY/ny5fTt27fMgeyvVcZToK5DkU1LvrjLjWTrtvLbK/FvOmAq3WzT9WxM9ClPh+AWXn2jPR3CNfOf9K2nQ3CLtHEtPB2CW/i9/X259t+9ai+39bX+9I9u66u8lGmuLDIykj/++IP27duzZs0aVFVl+/bt/Pjjj1StWpVff/31qhIBIYQQQnhOma8zEBgYyIwZM5gxY8aVKwshhBA3oIq2THDDXXRICCGEKG8V7WyCG/JyxEIIIYRwH5kZEEIIIYpwyBUIhRBCiIqtYqUCskwghBBCVHgyMyCEEEIUIWcTCCGEEBWcJANCCCFEBVfR/oSx7BkQQgghKjiZGRBCCCGKkGUCIYQQooKTKxAKIYQQokKRmQEhhBCiiIq2gVCSASGEEKKIirZnQJYJhBBCiApOZgaEEEKIImSZwENqPRzi6RCumdJmkKdDcIv2sz/1dAhucWuXep4O4ZrZj3p7OgS30LTu6ukQrlnauBOeDsEtUjZZPB2CW/iVc/+yTCCEEEKICuW6mRkQQgghrhcV7ToDkgwIIYQQRThkz4AQQghRsVW0mQHZMyCEEEJUcDIzIIQQQhQhywRCCCFEBSfLBEIIIYSoUGRmQAghhChClgmEEEKICk6WCYQQQghRocjMgBBCCFGELBMIIYQQFZwsEwghhBCiQpGZASGEEKIIVXV4OoR/lSQDQgghRBGOCrZMcEMmA4pvIPquQ9DWago6A2r6Oaw/fIgj6WTJjbQ69B1uR9u4PYpPAGpOBvm/rsK+bzMAxmFPo63e4JJm9mN/Yvl6ttvH0OeZ9zl7IfOS8iFdmvPMsJ7Ftvlx1xHeXbWFxNQMqocGMemOLnRqWsv5eK7Zypzlv7DhzzgycsxUqRTAsG4tGdy5udvj/4cSXBnvUQ+jb9kWxWjCfvYMOXNnYD96pNj6+nadMPUZhLZmHRS9HnvCSfKWLSL/jx2FlTQavO4ZjaFrLzSBwTgupGD5eQ3mLz8pv3H4BqLvdBfaGk1A//cxtXYxjuT4Yusbet+PrnGHS8odKYmYP5lWMIwqddG37o0SFoXGNxDLynewH9tTfmMIqITxjgfQNWoNBiOO84mYP52FIyHuim21tRrh9cTrOBJPkhs7wW39llWfx18nMSX9kvKhPdryzOiBl5QfPZ3Mu9/8xKETZ0hMSWfKiH6MuC3mmvp0FyWgEsbbR6Nr1Ar0RhwpZzF/NhvHqaNXbKut2RCviTNwnI0n9/WJF3WqwdDnXvS3dEXxC0LNvED+7+uxrl1WLmPQhlYi+PEH8Yppg2IyYjuVyPn/exPrwb9KbGNqHU3w5HEYakdhSzpP+oKlZK/60fl41R8+RV8l/JJ2mctWkRo7t1zGcTVU2UB4nTN6YxzxLI6EQ1i+egs1NwslKAzVnHPZZoaBj6L4+GNdvQg1LRnFNxAUxfm4Zfk80Gqd9xUvX0z3v4jt8I5iert2n029D4ejcBrqaGIK4+Z8Rc+W9Yutv+fYGaZ++B2PDepM56a1Wb3jEE+8t5xlz4ykTpUQAN78egM7jiTwyv39iKwUwNZDJ4n9fB0hAb50bVbH7WNQfHzxnzEP2/49ZL34FGpGOprIqqjZWSW20TduRv6eneR+ugA1Jwtj9774PhtL5pRHsJ8o+HAx3Xkvxj4DyZkdi/3USbR16uM78b+ouTlYvv/G7ePA6I1x6NM4Th3BsnwOam42SlAoqjm3xCbWDcuwbi6MRdFoMd33f9jjdl40WCOO86dxHPgV4+2Puj/ui3n54j15Jva//iT3nedRszPQhFZBzc0uRVsfTKMmYz+yB8Uv0H39XoXPXnwUh6Pwl/DR08k8POMjerZpWmx9syWfqiHB9GzThDeX/OCWPt3Cywfvx1/HHreX3PnT//65RaLmlfL1uO9J7H/9ecnrYehxF/qOfTAvmYUjKQFt9bqY7p2EmpdD/i/fuXUIGj9fIhbPxrzzT5LGP4MjLQN99So4Mkt+f+uqhBM272Wyvvof56fOwNS2BZWnPYk95QJ5vxW8NxKHT0DRFG5X09epQcQHr5OzbpNb478ZzJgxg6lTpzJp0iRmz55dbJ3Fixdz//33u5QZjUbMZnOZnuuGSwb07fqiZl7A+sNHzjI1I+WybTQ1m6CtVp+895+Cv5MGNTPVtVKRZELbsC3kW7EfKZ9kINjP2+X+R2u3Uy0kkNb1qhVbf+nPu+jQuCaje7UBYPztHdl26CTLNv7Bc8N7AfDn8UQGtGvMLfWrA3B3p2Z8s/lP9p88Wy7JgOmue3GknCfn7RnOMse5pMu2yf1wnsv9vCUL0LeNQd+mgzMZ0DVoTP7vv5K/a5uzz/xO3dHVbYDFzWMA0N9yG2pWGtYfFzvL1MzLH1NY8wpuf9PUbg4mb2z7f3WWOU7ux3Fyv5ujLZ6h12AcaecxfzrLWWZPTS5VW9Owx8jfsQFUB7ro9m7r92oE+/u63P/ou01UCw2mdcOaxdZvUrsqTWpXBeDtL9a6pU93MPS4G0d6Cualc5xl9gulfD2GjCd/56aC16NpO5fHtDUbYtv3O/aDBR+stgvnsLXsjDaqHvnuCx+AgAeGYk8+T8r/vekss525/Pvbb3B/bGeSuDDzfQDyTyRgatEY/xF3OpMBR1pGkee5h/yEM5h37nXzCK6Np5cJduzYwfvvv090dPQV6/r7+3PkSOFsrHLRF93SuuHOJtDWaY4j6QSGgY/iNWEOptHT0TbrfIU2LXAknUDftg+mR9/CNDYW/a1DQacvsY0uujP2Q79DvtXdQ7hEvs3OD78fZGCHpiW+iHuPJ9K2QZRLWftGNdh7PNF5v1mtSDbuPUZyWhaqqrLjSALxyRdo36hGucRtaBOD/dhhfJ96gcCPV+A/ayHGnv3L1omioHh5o2YVLpnYDh9AF90STWTBL3ltjdroGjUlf/fv7gzfSVu7GY7kkxj6P4zXuJmYRjyPtmmnMvWha9IRR/wh1KwL5RLjFZ8/uh2O+DhMDz6Dz2uf4z11HvqY267crl1PNJXDsf7wmVv7dYd8m43//bqHQV1aX9Uvt3+rz+LomrbFkRCH6f7/4vPKEryfmoO+fe8rt2vbo+D1WLO02MftJw6hq9cMJSQSAE1kTbS1GmE7tMut8QN4d2mP5cBfhL7xPNU3fEnkF/Pxu7PPZduYohuSt+0Pl7K833Zhim5UfAOdDt9+3claUXwi50mqqrrtVlbZ2dkMHz6cBQsWEBQUdMX6iqIQHh7uvIWFhZX5OW+4mQElMBRdi27YdqzFvPV7NBE1MXQfjtVux37Rt7KLaQJD0FStB7Z8rMvngpcfhl73oXj5uMwwOOtH1EQTUhXr6ksfKw8/74kjK8/M7e2blFgnJTOHSv4+LmWV/HxIySyc0fjv0O68+NmP9J76HjqNBkWj8H8jetGqbvGzDddKExaB8baBmFd+Rd5XS9DVbYD32ImotnysG0r35jYNugfF5IX11w3OMvM3n6F4exPwzqfgcIBGQ96ShVg3/VQu41ACQtA164pt1zrMv/+AJrwGhlvvwWq3YT+49crtfQLQ1GyC9YeF5RJfaWgqh6Pp3A/r+m+xrvkCbVQ9jIPHodps2H4v/uemhERiHHQ/uW9NKfg5u6lfd/l550Gycs3c3rnldd1ncTSVwtF07It1wwqs675EW70uxrseQrXnY9v+c7FtlJBIjANGkTvn6RJfD+tPX4PJG59n3wPVAYoG6/8+xbZzo9vHoKsagd+QAWR++g3pHy7F2Lg+wU+PR823kf3dumLbaCsHY09Ncymzp6ah8fNBMRpQLa5frny6dUDj5+uyp+BmZLFYsFhc5zWNRiNGo7HY+uPHj6dfv3706NGDl19++Yr9Z2dnExUVhcPhoGXLlrz66qs0bty4TDGWKRl47LHHGDJkCJ06le1bU1HF/WDsNjtGnbaEFhdRFBxJJ8n/pWC91n4uAVvlKuiady0xGUBRQFWxfPeBc2o3/+dlGAY9Cj9+CjbXCTZtdGcc507hOHui7IO7Cit+20dM41qEBvpeufJlfL5hN/tOJDLn0TuICPZnd9xpYj//iZAAX9o1rOGeYC+maLAdO0LekgUA2E/EoY2qiem2gaVKBgyde+B1zyiyXn0WNSO9sLzjrRi69CTnrZewJ5xEW7MO3mMm4LiQUuoko2zjUHAknyT/1+UF4zh/quCYiu5SqmRA26gDWHKxH/3jinXLjaLgSIjDuupjABynj6GJjMLQqW/xH9qKBq8Hnsb6vyWo5864r183Wr5pFzHN6hEa5H9d91ksRcFx6ijW7ws2vTpOH0cTEYUhpm/xyYCiwWvkZKyrl6KeT7z08b/pWnRC37or5k/exHE2Hk3VWpjuHIsjI7XEJOOqh6BRsBz4i7S5BV+KrIePoa9TA7/B/UtMBsrK944+5P26Hfv51CtX/pe58wqEsbGxvPDCCy5l06ZNY/r06ZfUXbZsGbt372bHjtItUdevX5+PPvqI6OhoMjIyePPNN+nQoQMHDhygatWqpY6xTMsE77zzDl27dqVevXq89tprJCVdfv2oJLGxsQQEBLjc3txQuvUiNTsdNcX1zaKmnkXxr3SZNhmo2Wkua7yO1EQURYPiF+xaWW9A17ANtr2bSz+ga5CYmsHvh+K5I+bym5kq+/uQmum6ryE1K4fKf88WmK35zF25mf/cfStdoutQr2oo99zakt6tG/DJuvLZ9+BIS8V+6qRLmf1UPJqQ0Cu2NXTqhs+EKWS/Ph3bn65TnF6jH8H8zWdYN/+MPf441o0/Yl71FV53D3dn+E5qTgZq6lnXstSzKP7BJbRwpWsSg+3gNnDYyyO8UlEzLmA/m+BS5kg6hRIcUnwDk1fBt/whj+I793t8536Poc+9aKvVxnfu92jrNbu6ft0kMSWN3/cf5c6ura/rPkuiZqZhTyryc0s+hRJ0hdfj7nH4zlqJ76yVGHrfg7ZqLXxnrURbt2Dd2Djwfqw/fY1t9y84zsZj27EB64aVGHoOdvsY7OcvkH/cdQz5xxPQRZT8/ranXEBbyXVaW1spCEdWziWzArqIULzatiDr29XuC9qNVDf+mzp1KhkZGS63qVOnXvKcp06dYtKkSXz22WeYTKZSxdm+fXtGjhxJ8+bN6dKlC99++y0hISG8//77ZRpvmZcJfvzxR7777jvefPNNnn/+efr06cPYsWPp27cvGk3pcoupU6fy5JNPupTZ504oobYrx5mjKMGup6UowWGXbgi8uO8zcWgbtAa9EfILZiQ0QeGoDscla7za+reAVo/twG+liudarfxtP8F+3nRqWvuy9aJrRbL9cAIjuhf+Itt2KJ7oWgVrhza7A5vdgabIOqhGo5TbNbZth/ajjazuUqatUhX7+ctvlDJ06o7PY0+T/eYLzk2CF1MMRnAUidlRMCVaHhyJR1GCihxTQZc/pv6hqVoPTVAYlv1byiW20rIfP4gmzPVbgCa0CuqFc8U3MOeS89I4lyJ9l/5o6zXDvOAVHKlJV9evm6zctItgf186NS/+7Jrrpc+S2I8fRBNa5OcWUgU17TKvR+x4lyJ9x75o60Vj/miG8/VQDMaC5YGLqQ6UcnhvmPccQF/DdQz6qKrYEkt+f5v3HsK7YxuXMq92LTHvPXhJXd+BvbFfSCd3c/nsBbqeXG5J4GK7du3i3LlztGxZuIxlt9v55ZdfmDdvHhaLBa328jPoer2eFi1acPTolU9hvViZj6CmTZsye/ZsEhMTWbJkCRaLhUGDBlGtWjWeffbZUgVgNBrx9/d3uZVqiQCw7fgRTWQtdO36oQSGom3YrmC9d/d6Zx1957sx9HvQed9+cBtqXg6GvmNQKkWiqVoP/a1DCq4xUGSJQBfdGXvc7kvOLigPDofKqq37GdC+MTqt60vx3KL/8fbyX5z37+3Wit8OnOCTdTs4kZTK/O9+5WB8Evd0bQGAr5eRVnWrMevbTew4ksCZlHRW/raf77cdpFvzuuUSv3nVV+jqN8J09wg04VUwdO6BsdcALD8sd9bxum8sPo8/47xv6NwDn8efIXfRu9j+OoQSGFxw8y7cD5G/4ze8Bo9A36odmtDwgmsTDByCdVv5zNbYdv2EJqImujZ9UQJD0DZogy66M7Y9G5119B3vwHDbA5e01TXpiP3scdTUYqZ29UaUkGooIQV7NpSAygX3i85GuYH15xVoazbA0HsoSkgEutZd0Xfsg3XT9846hoGjMY36T8EdVcVxNt7lpmalQ74Vx9l4sFpK3a+7ORwOVv6ymwGdWqAr8ovv2fe+Ys5FZw3k22wcjk/kcHwi+TY75y5kcjg+kYSk1FL3WR6sG1eirVEfQ8/BKJUj0LXqgr7DbVg3/89ZxzBgFKYRf38pKu71yM6A/HyX18O2fzuGXkPRNmqNEhyKLro9+lsHkb/3ystZZZWx5BuMTRsSMGYYumqR+PS5Fb+7+5L5xSpnnaCJD1D55aec97O++h5d1XCCHn8QfY1q+A0ZgE+vLmQu+da1c0XBb2DvguUG+/V5pT9PbCDs3r07+/btY8+ePc5b69atGT58OHv27LliIgAFycO+ffuIiIgo03ivegOhXq9nyJAhDBkyhISEBD766CMWL17MjBkzsNvLb7rUkXQCy/J5GLrcjT5mIGrGeaw/L8V+sPAbpuIb4LpskG/B8sUbGHqOwDTq/1DzcrAf3k7+ZtcDVAkOR1utHuYv3ii3+C+27fBJzl7IZFCHS5cIzl7Ictnt3Lx2FV4d0593Vm1m7srNVA8NYta4O5zXGAB47cH+vL1iM8989D8yc81EBPszYWDHcrvokP3oYbJjn8PrvofwGjoSR3ISuQvnuWz00wRVQlO5cFrR2Ks/ik6Hz7gn8Bn3hLPcsn618xTFnAVz8L53DN7jnkATEFRw0aG1q8j74uNyGYcj+SSWVfMxdLoDfbv+qBkpWDd+gf1w4TcWxSfw0g9xgxfaui2xbvyi2H41YVGYhkwprN51KAC2A79hXbvIvWOI/4u891/COHA0hr734khNwvL1+9h2FG7M1PgHowRdeQmnrP2627YDxzibms6gLpdO5yelpLvMfp1Ly2Los4Wnq378w2Y+/mEzrRvU5MPnxpaqz/LgSIgjb+ErGAeMwnDbMBypyVi+XeCy0U/jH1TyskEJzF+/j7HfCExDHkXxDSi46NCvq7Gucf9Fh6wH/iL5yekETxxD4MMjCk4ZfH0+OT8U7k3QVq6ELrzwmLKdSSJ5wnMET3mEgOF3YEtOIeWFt5ynFf7Dq11LdJFhZK1Y4/a43cUTpxb6+fnRpInrRnIfHx8qVarkLB85ciRVqlQhNjYWgBdffJF27dpRp04d0tPTeeONN4iPj+fBBx+8pP/LUdQypC0ajYakpCRCQ4v/haKqKj/99BM9exZ/Bb3LyX3t/itXus4pbWKuXOkGkDf7U0+H4BamLvU8HcI1sx897ekQ3EJ/f9l+MV2P8j91bwLnKSmbyuNqHf++mn+6ZxNjSUIC3LecdD6j+CuylkbXrl1p3ry586JDXbt2pUaNGixevBiAJ554gm+//ZakpCSCgoJo1aoVL7/8Mi1atCjT85RpZiAqKuqy0xSKolxVIiCEEEJcT66XyxFv3LjxsvdnzZrFrFmzuFZlSgZOnPh3TrUTQgghPKm8Nl5fr264iw4JIYQQ5e16mRn4t9xwlyMWQgghhHvJzIAQQghRhKf/UNG/TZIBIYQQoghZJhBCCCFEhSIzA0IIIUQRcjaBEEIIUcGpFWzPgCwTCCGEEBWczAwIIYQQRcgygRBCCFHBydkEQgghhKhQZGZACCGEKKKibSCUZEAIIYQooqItE0gyIIQQQhRR0ZIB2TMghBBCVHAyMyCEEEIUUbHmBQC1gjCbzeq0adNUs9ns6VCu2s0wBlW9OcZxM4xBVWUc15ObYQyqevOMo6JRVLViLIxkZmYSEBBARkYG/v7+ng7nqtwMY4CbYxw3wxhAxnE9uRnGADfPOCoa2TMghBBCVHCSDAghhBAVnCQDQgghRAVXYZIBo9HItGnTMBqNng7lqt0MY4CbYxw3wxhAxnE9uRnGADfPOCqaCrOBUAghhBDFqzAzA0IIIYQoniQDQgghRAUnyYAQQghRwUkyIIQQQlRwFSIZeOedd6hRowYmk4m2bduyfft2T4dUJr/88gsDBgwgMjISRVFYsWKFp0Mqs9jYWG655Rb8/PwIDQ1l0KBBHDlyxNNhldn8+fOJjo7G398ff39/2rdvz+rVqz0d1jWZMWMGiqLw+OOPezqUMpk+fTqKorjcGjRo4OmwrsqZM2cYMWIElSpVwsvLi6ZNm7Jz505Ph1VqNWrUuOS1UBSF8ePHezo0UUo3fTLwxRdf8OSTTzJt2jR2795Ns2bN6N27N+fOnfN0aKWWk5NDs2bNeOeddzwdylXbtGkT48ePZ9u2baxbt478/Hx69epFTk6Op0Mrk6pVqzJjxgx27drFzp076datGwMHDuTAgQOeDu2q7Nixg/fff5/o6GhPh3JVGjduzNmzZ523LVu2eDqkMktLSyMmJga9Xs/q1as5ePAgM2fOJCgoyNOhldqOHTtcXod169YBMHjwYA9HJkrNs38aofy1adNGHT9+vPO+3W5XIyMj1djYWA9GdfUAdfny5Z4O45qdO3dOBdRNmzZ5OpRrFhQUpC5cuNDTYZRZVlaWWrduXXXdunVqly5d1EmTJnk6pDKZNm2a2qxZM0+Hcc2efvpptWPHjp4Ow60mTZqk1q5dW3U4HJ4ORZTSTT0zYLVa2bVrFz169HCWaTQaevTowdatWz0YmcjIyAAgODjYw5FcPbvdzrJly8jJyaF9+/aeDqfMxo8fT79+/VzeHzeauLg4IiMjqVWrFsOHDychIcHTIZXZqlWraN26NYMHDyY0NJQWLVqwYMECT4d11axWK0uWLOGBBx5AURRPhyNK6aZOBlJSUrDb7YSFhbmUh4WFkZSU5KGohMPh4PHHHycmJoYmTZp4Opwy27dvH76+vhiNRsaNG8fy5ctp1KiRp8Mqk2XLlrF7925iY2M9HcpVa9u2LYsXL2bNmjXMnz+fEydO0KlTJ7KysjwdWpkcP36c+fPnU7duXdauXcsjjzzCxIkT+fjjjz0d2lVZsWIF6enpjB492tOhiDLQeToAUfGMHz+e/fv335DruwD169dnz549ZGRk8PXXXzNq1Cg2bdp0wyQEp06dYtKkSaxbtw6TyeTpcK5anz59nP+Pjo6mbdu2REVF8eWXXzJmzBgPRlY2DoeD1q1b8+qrrwLQokUL9u/fz3vvvceoUaM8HF3Zffjhh/Tp04fIyEhPhyLK4KaeGahcuTJarZbk5GSX8uTkZMLDwz0UVcU2YcIEvv/+ezZs2EDVqlU9Hc5VMRgM1KlTh1atWhEbG0uzZs2YM2eOp8MqtV27dnHu3DlatmyJTqdDp9OxadMm3n77bXQ6HXa73dMhXpXAwEDq1avH0aNHPR1KmURERFySSDZs2PCGXPKIj4/np59+4sEHH/R0KKKMbupkwGAw0KpVK9avX+8sczgcrF+//oZc472RqarKhAkTWL58OT///DM1a9b0dEhu43A4sFgsng6j1Lp3786+ffvYs2eP89a6dWuGDx/Onj170Gq1ng7xqmRnZ3Ps2DEiIiI8HUqZxMTEXHKa7V9//UVUVJSHIrp6ixYtIjQ0lH79+nk6FFFGN/0ywZNPPsmoUaNo3bo1bdq0Yfbs2eTk5HD//fd7OrRSy87Odvm2c+LECfbs2UNwcDDVq1f3YGSlN378eJYuXcrKlSvx8/Nz7tkICAjAy8vLw9GV3tSpU+nTpw/Vq1cnKyuLpUuXsnHjRtauXevp0ErNz8/vkr0aPj4+VKpU6YbawzF58mQGDBhAVFQUiYmJTJs2Da1Wy7BhwzwdWpk88cQTdOjQgVdffZUhQ4awfft2PvjgAz744ANPh1YmDoeDRYsWMWrUKHS6m/6j5ebj6dMZ/g1z585Vq1evrhoMBrVNmzbqtm3bPB1SmWzYsEEFLrmNGjXK06GVWnHxA+qiRYs8HVqZPPDAA2pUVJRqMBjUkJAQtXv37uqPP/7o6bCu2Y14auHQoUPViIgI1WAwqFWqVFGHDh2qHj161NNhXZXvvvtObdKkiWo0GtUGDRqoH3zwgadDKrO1a9eqgHrkyBFPhyKugvwJYyGEEKKCu6n3DAghhBDiyiQZEEIIISo4SQaEEEKICk6SASGEEKKCk2RACCGEqOAkGRBCCCEqOEkGhBBCiApOkgEhhBCigpNkQAghhKjgJBkQQgghKjhJBoQQQogKTpIBIYQQooL7f88uzxpa6A+/AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data_dir = './data/node_timeseries/simulation_202401/sigma_0.1/run_1/'\n", + "for i in range(10001,10002):\n", + " for j in range(i+1,10003):\n", + " covariance_1 = np.load(f'{data_dir}{i}_state_covariances.npy')\n", + " covariance_2 = np.load(f'{data_dir}{j}_state_covariances.npy')\n", + " riem = twopair_riemannian_distance(covariance_1,covariance_2)\n", + " seaborn.heatmap(riem,annot=True,fmt='.2f')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ec7c7d0", + "metadata": {}, + "source": [ + "Now the second step is to see whether only run_1 works. So with run_1, have a look at the split-half reproducibility and goodness of fit. (Just to run comparison analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0b99be3a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from rotation.analysis import comparison_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5e5f0837", + "metadata": {}, + "outputs": [], + "source": [ + "models =['HMM']\n", + "list_channels = [25,200,300]\n", + "list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "result_dir = './results_simulation_202401/sigma_0.05/run_4/'\n", + "save_dir = f'{result_dir}/plot/'\n", + "comparison_analysis(models,list_channels,list_states,result_dir, save_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "77d58b6d", + "metadata": {}, + "source": [ + "Now we write the code to average across five runs (also add error bars)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9511f6b2", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import os\n", + "\n", + "save_dir = './results_simulation_202401/'\n", + "plot_dir = f'{save_dir}summary_plot/'\n", + "if not os.path.exists(plot_dir):\n", + " os.makedirs(plot_dir)\n", + " \n", + "list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "sigma = 0.1\n", + "split_half_correlation = {}\n", + "split_half_riem = {}\n", + "for state in list_states:\n", + " split_half_correlation[f'state_{state}'] = []\n", + " split_half_riem[f'state_{state}'] = []\n", + " for i in range(1,6):\n", + " correlation = np.load(f'{save_dir}sigma_{sigma}/run_{i}/HMM_ICA_25_state_{state}/reproduce_analysis/FCs_fisher_correlation_reorder_split_1.npy')\n", + " riem = np.load(f'{save_dir}sigma_{sigma}/run_{i}/HMM_ICA_25_state_{state}/reproduce_analysis/FCs_distance_reorder_split_1.npy')\n", + " split_half_correlation[f'state_{state}'].append(np.mean(np.diagonal(correlation)))\n", + " split_half_riem[f'state_{state}'].append(np.mean(np.diagonal(riem)))\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "bar_width = 0.15\n", + "colors = plt.cm.viridis(np.linspace(0, 1, len(list_states)))\n", + "\n", + "means = [np.mean(split_half_correlation[f'state_{state}']) for state in range(2, 17)]\n", + "stds = [np.std(split_half_correlation[f'state_{state}']) for state in range(2, 17)]\n", + "plt.bar(list_states, means, yerr=stds, capsize=5, align='center', alpha=0.7)\n", + "plt.xticks(list_states,fontsize=15)\n", + "plt.yticks(fontsize=15)\n", + "plt.xlabel('Number of states',fontsize=20)\n", + "plt.ylabel('Mean Split-Half Correlation',fontsize=20)\n", + "plt.title(f'Split-half correlation,sigma_{sigma}',fontsize=25)\n", + "plt.tight_layout()\n", + "plt.savefig(f'{plot_dir}/sigma_{sigma}_Fisher_z_transformed_correlation_average.jpg')\n", + "plt.savefig(f'{plot_dir}/sigma_{sigma}_Fisher_z_transformed_correlation_average.pdf')\n", + "plt.close()\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "bar_width = 0.15\n", + "colors = plt.cm.viridis(np.linspace(0, 1, len(list_states)))\n", + "\n", + "means = [np.mean(split_half_riem[f'state_{state}']) for state in range(2, 17)]\n", + "stds = [np.std(split_half_riem[f'state_{state}']) for state in range(2, 17)]\n", + "plt.bar(list_states, means, yerr=stds, capsize=5, align='center', alpha=0.7)\n", + "plt.xticks(list_states,fontsize=15)\n", + "plt.yticks(fontsize=15)\n", + "plt.xlabel('Number of states',fontsize=20)\n", + "plt.ylabel('Mean Split-Half Riemannian distance',fontsize=20)\n", + "plt.title(f'Split-half Riemannian_distance,sigma_{sigma}',fontsize=25)\n", + "plt.tight_layout()\n", + "plt.savefig(f'{plot_dir}/sigma_{sigma}_Riemannian_distance_average.jpg')\n", + "plt.savefig(f'{plot_dir}/sigma_{sigma}_Riemannian_distance_average.pdf')\n", + "plt.close()\n", + "\n", + "#plt.title(f'Comparison of {metric_name} by N_channels and N_states', fontsize=20)\n", + "#plt.xticks(np.arange(len(channel_keys)) + bar_width * (len(list_states) - 1) / 2,\n", + "# [str(N_channels) for N_channels in list_channels], fontsize=15)\n", + "#plt.yticks(fontsize=15)\n", + "#plt.legend(prop={'size': 15})\n", + "\n", + "#plt.savefig(f'{save_dir}{model_name}_{metric_name}_comparison.jpg')\n", + "#plt.savefig(f'{save_dir}{model_name}_{metric_name}_comparison.pdf')\n", + "#plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "e7ac8b4e", + "metadata": {}, + "source": [ + "# Look at your data!" + ] + }, + { + "cell_type": "markdown", + "id": "16a83d30", + "metadata": {}, + "source": [ + "So first look at these states obtained from split_1 and split_2, and then compare against the ground truth. We start from sigma=0.025, and run_1. We should expect only 8 states." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "efc8dbb1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "%matplotlib inline\n", + "from rotation.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b946dd76", + "metadata": {}, + "outputs": [], + "source": [ + "ground_truth_dir = './results_HCP_202311_no_mean/HMM_ICA_25_state_8/'\n", + "ground_truth_FC = np.load(f'{ground_truth_dir}state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "97b5ac7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8, 25, 25)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ground_truth_FC.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "022aa39d", + "metadata": {}, + "outputs": [], + "source": [ + "fit_dir = './results_simulation_202401/sigma_0.025/run_1/'\n", + "N_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "fit_FC = np.load(f'{fit_dir}HMM_ICA_25_state_{N_states[1]}/state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "90891f45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 25, 25)\n" + ] + } + ], + "source": [ + "print(fit_FC.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "3d845540", + "metadata": {}, + "outputs": [], + "source": [ + "fit_FC_first = np.load(f'{fit_dir}HMM_ICA_25_state_{N_states[1]}/split_1_first_half/state_correlations.npy')\n", + "fit_FC_second = np.load(f'{fit_dir}HMM_ICA_25_state_{N_states[1]}/split_1_second_half/state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "e1b20d5a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 3/3 [00:00<00:00, 11\n" + ] + }, + { + "data": { + "image/png": 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wwgtqaGjQc889p48++ki33nqrPvvsM1vfCXsgAAC4iEpKSuT3+6POud3umMy9Y8cOrVmzRs8++6x8Pp8OHDigpUuXauXKlVqxYoUkafr06ZHx48ePl8/n08iRI7Vx40YtXLiw3+9FgAAAwCSGN5Jyu939CgypqalyuVxqb2+POt/e3q709PReX7NixQrNmzdP999/vyRp3Lhx6urq0qJFi/Szn/1Mzl4eCjZ06FDdcMMNOnDggK3PwRIGAAAGYTlidvRXUlKScnJy1NjYGDkXCoXU2NioyZMn9/qaM2fOWEKCy+X6r88QDvf6mtOnT+vgwYMaPnx4v2uT6EAAAJCw/H6/FixYoIkTJ2rSpEmqrq5WV1eXioqKJEnz589XRkZGZA/FjBkzVFVVpZtvvjmyhLFixQrNmDEjEiQeeeQRzZgxQyNHjtTx48dVXl4ul8ulwsJCW7URIAAAMIjXszAKCgp04sQJlZWVqa2tTdnZ2WpoaIhsrGxtbY3qOJSWlsrhcKi0tFTHjh1TWlqaZsyYodWrV0fGHD16VIWFhTp58qTS0tI0depU7d69W2lpabZqc4T76mnE2dQZO+NdAhJIScOieJeABPLcfb+OdwlIML9dN/aCzv/pH3fEbK6h2dNiNlc8sQcCAADYxhIGAAAGYR6mZUGAAADAIF57IBIZAQIAABM6EBZEKgAAYBsdCAAADFjCsCJAAABgYOcOkpcLIhUAALCNDgQAAAYsYVgRIAAAMOEqDAsiFQAAsI0OBAAABmH+f9uCAAEAgAG3srYiUgEAANvoQAAAYMBVGFYECAAADLiRlBUBAgAAAzoQVnwjAADANjoQAAAYcBWGFQECAAAD9kBYsYQBAABsowMBAIABmyitCBAAABiwhGFFpAIAALbRgQAAwIAlDCsCBAAABixhWBGpAACAbXQgAAAwYAnD6oJ+Iy+99JK+//3vG8cFg0F1dnZGHaGe7gtZGgAA/RaWI2bHpeKCBogjR45o586dxnGBQEApKSlRx9EDv7yQpQEA0G9hhyNmx6UiIXoyJSUlOnXqVNTxF9fNjXdZAACgD7b3QGRlZfV77KlTp/o1zu12y+12R51zupJs1QUAwIUSDl86nYNYsR0gDh8+rCuvvFIjRowwjj1z5sx5FQUAQCIJJ0bDPqHYDhDXXnutrrvuOr3xxhvGsatWrVJ5efl5FQYAABKX7UiVk5OjPXv29Gus4xLaLAIAuHxxFYaV7QBx88036+TJkzp8+LBx7MiRI/VXf/VX51MXAAAJgwBhZTtAlJSUKBQKKTMz0zj23nvv1Ztvvnk+dQEAgATGnSgBADC4lDoHscK2UgAADOK5hFFTU6PMzEx5PB75fD41Nzd/5fjq6mqNHj1agwYN0jXXXKOHHnpIZ8+e/Vpz9oYAAQBAgtqwYYP8fr/Ky8u1Z88eTZgwQXl5efrkk096Hf/yyy/rscceU3l5ufbu3au6ujpt2LBBy5cvP+85+0KAAADAIBx2xOywo6qqSsXFxSoqKtLYsWNVW1ur5ORk1dfX9zp+165dmjJliubMmaPMzEzdfvvtKiwsjOow2J2zLwQIAAAMYrmE0dsDJIPBoOU9u7u71dLSotzc3Mg5p9Op3NxcNTU19VrnLbfcopaWlkhgOHTokLZu3ar8/PzznrMvBAgAAAxiGSB6e4BkIBCwvGdHR4d6enrk9Xqjznu9XrW1tfVa55w5c1RZWampU6dq4MCBGjVqlKZNmxZZwjifOftCgAAA4CLq7QGSJSUlMZl7x44dWrNmjZ599lnt2bNHmzdv1pYtW7Ry5cqYzP9FXMYJAIBBLC/j7O0Bkr1JTU2Vy+VSe3t71Pn29nalp6f3+poVK1Zo3rx5uv/++yVJ48aNU1dXlxYtWqSf/exn5zVnX+hAAABgEI9NlElJScrJyVFjY2PkXCgUUmNjoyZPntzra86cOSOnM/pPu8vl+u/PED6vOftCBwIAgATl9/u1YMECTZw4UZMmTVJ1dbW6urpUVFQkSZo/f74yMjIieyhmzJihqqoq3XzzzfL5fDpw4IBWrFihGTNmRIKEac7+IkAAAGAQitOdKAsKCnTixAmVlZWpra1N2dnZamhoiGyCbG1tjeo4lJaWyuFwqLS0VMeOHVNaWppmzJih1atX93vO/nKEw+FwbD5mbE2dsTPeJSCBlDQsincJSCDP3ffreJeABPPbdWMv6Px/2N8Rs7luvj41ZnPFE3sgAACAbSxhAABgYPcOkpcDAgQAAAY8jdOKJQwAAGAbHQgAAAxYwrAiQAAAYMAShhUBAgAAAzoQVuyBAAAAttGBAADAIBTvAhIQAQIAAAOWMKxYwgAAALbRgQAAwICrMKwIEAAAGLCEYcUSBgAAsI0OBAAABixhWBEgAAAwCIXjXUHiYQkDAADYRgcCAAADljCsCBAAABhwFYYVAQIAAIMweyAs2AMBAABsowMBAIBBiD0QFgQIAAAM2ANhxRIGAACwjQ4EAAAGbKK0IkAAAGDAfSCsWMIAAAC20YEAAMCAZ2FYESAAADDgKgwrljAAAIBtdCAAADDgKgwrAgQAAAbcidKKAAEAgAEdCCv2QAAAANvoQAAAYMBVGFYECAAADLgPhBVLGAAAwDY6EAAAGLCJ0ooOBAAABmE5YnbYVVNTo8zMTHk8Hvl8PjU3N/c5dtq0aXI4HJbjzjvvjIy57777LD+/4447bNdFBwIAgAS1YcMG+f1+1dbWyufzqbq6Wnl5edq3b5+GDRtmGb9582Z1d3dH/n3y5ElNmDBBs2fPjhp3xx136B//8R8j/3a73bZrowMBAIBBKBy7w46qqioVFxerqKhIY8eOVW1trZKTk1VfX9/r+Kuuukrp6emRY9u2bUpOTrYECLfbHTXuyiuvtP2dECAAADAIh2N39Fd3d7daWlqUm5sbOed0OpWbm6umpqZ+zVFXV6d77rlHV1xxRdT5HTt2aNiwYRo9erQWL16skydP9r+w/5awSxjbCnfFuwQkkNnpv453CUggi1+YFe8SkGjW7Yt3Bf0WDAYVDAajzrndbssyQkdHh3p6euT1eqPOe71effDBB8b3aW5u1vvvv6+6urqo83fccYf+5m/+Rtdee60OHjyo5cuXa/r06WpqapLL5er356ADAQCAQSw7EIFAQCkpKVFHIBCIec11dXUaN26cJk2aFHX+nnvu0V133aVx48bp7rvv1m9/+1u988472rFjh635CRAAABiEwo6YHSUlJTp16lTUUVJSYnnP1NRUuVwutbe3R51vb29Xenr6V9bb1dWlV199VQsXLjR+tqysLKWmpurAgQO2vhMCBAAABrHsQLjdbg0ZMiTq6O0qiKSkJOXk5KixsTFyLhQKqbGxUZMnT/7Kel977TUFg0Hde++9xs929OhRnTx5UsOHD7f1nRAgAABIUH6/X+vWrdOLL76ovXv3avHixerq6lJRUZEkaf78+b12L+rq6nT33Xfr6quvjjp/+vRp/d3f/Z12796tw4cPq7GxUTNnztR1112nvLw8W7Ul7CZKAAASRbzuRFlQUKATJ06orKxMbW1tys7OVkNDQ2RjZWtrq5zO6F7Avn379NZbb+l3v/udZT6Xy6X33ntPL774oj799FONGDFCt99+u1auXGn7XhCOcDgxb9D555djv6EE31yz35wZ7xKQQLgKA19257kLexXGS/8ndn8q77310niyJ0sYAADANpYwAAAwCIcvja5BLBEgAAAwSMzF/vhiCQMAANhGBwIAAAO7D8G6HBAgAAAwYAnDiiUMAABgGx0IAAAM6EBYESAAADBgD4QVAQIAAAM6EFbsgQAAALbRgQAAwCAUincFiYcAAQCAAUsYVixhAAAA2+hAAABgQAfCigABAIABl3FasYQBAABsowMBAIBBOKZrGI4YzhU/BAgAAAzYA2HFEgYAALCNDgQAAAbcSMqKAAEAgAFLGFYECAAADLiM04o9EAAAwDY6EAAAGLCEYUWAAADAIBzTNYxL4z4QLGEAAADb6EAAAGDAJkorAgQAAAbsgbBiCQMAANhGBwIAAIMQaxgWBAgAAAxYwrBiCQMAANhGBwIAAAM6EFYECAAADEIkCAsCBAAABmEe523BHggAAGAbHQgAAAzCLGFYECAAADAIsYRhwRIGAAAJrKamRpmZmfJ4PPL5fGpubu5z7LRp0+RwOCzHnXfeGRkTDodVVlam4cOHa9CgQcrNzdX+/ftt10WAAADAIBwOx+ywY8OGDfL7/SovL9eePXs0YcIE5eXl6ZNPPul1/ObNm/Xxxx9Hjvfff18ul0uzZ8+OjPn5z3+up59+WrW1tXr77bd1xRVXKC8vT2fPnrVVGwECAACDUDh2hx1VVVUqLi5WUVGRxo4dq9raWiUnJ6u+vr7X8VdddZXS09Mjx7Zt25ScnBwJEOFwWNXV1SotLdXMmTM1fvx4/dM//ZOOHz+u119/3VZtBAgAAC6iYDCozs7OqCMYDFrGdXd3q6WlRbm5uZFzTqdTubm5ampq6td71dXV6Z577tEVV1whSfroo4/U1tYWNWdKSop8Pl+/54zUYms0AACXoXAoHLMjEAgoJSUl6ggEApb37OjoUE9Pj7xeb9R5r9ertrY2Y83Nzc16//33df/990fO/c/rznfOL+IqDAAADGJ5FWdJSYn8fn/UObfbHbs3+G91dXUaN26cJk2aFPO5JToQAABcVG63W0OGDIk6egsQqampcrlcam9vjzrf3t6u9PT0r3yPrq4uvfrqq1q4cGHU+f953fnM+WUECAAADEKhcMyO/kpKSlJOTo4aGxu/UEdIjY2Nmjx58le+9rXXXlMwGNS9994bdf7aa69Venp61JydnZ16++23jXN+GUsYAAAYxOtOlH6/XwsWLNDEiRM1adIkVVdXq6urS0VFRZKk+fPnKyMjw7KHoq6uTnfffbeuvvrqqPMOh0PLli3TqlWrdP311+vaa6/VihUrNGLECN199922aiNAAABgEK+HaRUUFOjEiRMqKytTW1ubsrOz1dDQENkE2draKqczejFh3759euutt/S73/2u1zkfffRRdXV1adGiRfr00081depUNTQ0yOPx2KrNEU7QG3z/+WXrjlRcvma/OTPeJSCBLH5hVrxLQIK589y+Czr/o7V/jtlcP//RoJjNFU90IAAAMAgl5v9rxxUBAgAAgwRt1scVV2EAAADbbAeIYDCoZ599Vj/+8Y/1+OOP9/kEr+3bt+v73//+1y4QAIB4i8dlnInO1hLGmTNnNGXKFL333nuRds6aNWu0cuVKPfroo1Fj29vbtXPnzn7NGwwGLfcBD537XO6BrLAAAOKPFQwrWx2I6upq/fu//7uWL1+u9957T1u2bFFOTo5KSkr04x//+LyL6O2+4E/8Zsd5zwcAAC4sW/+L/9prr6mgoEArV66UJN10003Ky8vT0qVLVVNTo3PnzmndunW2i+jtvuChXz9lex4AAC6E8CW09BArtgLEwYMHtXjx4qhzTqdTa9eu1dChQ7V69WqFQiHV1dXZKsLtdlvuA/5nli8AAAmCyzitbP2V9ng8OnfuXK8/W7lypVwulyorKxUKhdhACQDAJcxWgBg1apR2796tJUuW9PrziooKSVJlZaV27NjxdWsDACAhsIRhZStA3HbbbXrqqaf02Wef6Vvf+lavYyoqKuR0OlVRUSGHwxGTIgEAiCcChJWtADFv3jydO3dO+/fv13e+850+x5WVlenqq6/Wu++++7ULBAAg3sgPVrYCxPXXX295ZGhf+lrmAAAA33xc6gAAgAFLGFYECAAADHiYlhUP0wIAALbRgQAAwOBSeghWrBAgAAAwYAnDiiUMAABgGx0IAAAMuArDigABAIABAcKKJQwAAGAbHQgAAAx4nLcVAQIAAAOWMKwIEAAAGHAZpxV7IAAAgG10IAAAMOBOlFYECAAADNgDYcUSBgAAsI0OBAAABmyitCJAAABgEA6F4l1CwmEJAwAA2EYHAgAAA67CsCJAAABgwB4IK5YwAACAbXQgAAAw4D4QVgQIAAAMCBBWBAgAAAxCYS7j/DL2QAAAkMBqamqUmZkpj8cjn8+n5ubmrxz/6aefasmSJRo+fLjcbrduuOEGbd26NfLziooKORyOqGPMmDG266IDAQCAQbyWMDZs2CC/36/a2lr5fD5VV1crLy9P+/bt07Bhwyzju7u7ddttt2nYsGH61a9+pYyMDB05ckRDhw6NGnfjjTdq+/btkX8PGGA/DhAgAAAwiFeAqKqqUnFxsYqKiiRJtbW12rJli+rr6/XYY49ZxtfX1+tPf/qTdu3apYEDB0qSMjMzLeMGDBig9PT0r1UbSxgAAFxEwWBQnZ2dUUcwGLSM6+7uVktLi3JzcyPnnE6ncnNz1dTU1Ovcv/nNbzR58mQtWbJEXq9XN910k9asWaOenp6ocfv379eIESOUlZWluXPnqrW11fbnIEAAAGAQDodjdgQCAaWkpEQdgUDA8p4dHR3q6emR1+uNOu/1etXW1tZrnYcOHdKvfvUr9fT0aOvWrVqxYoWefPJJrVq1KjLG5/PphRdeUENDg5577jl99NFHuvXWW/XZZ5/Z+k5YwgAAwCAUw4dplZSUyO/3R51zu90xmTsUCmnYsGF6/vnn5XK5lJOTo2PHjumJJ55QeXm5JGn69OmR8ePHj5fP59PIkSO1ceNGLVy4sN/vRYAAAOAicrvd/QoMqampcrlcam9vjzrf3t7e5/6F4cOHa+DAgXK5XJFz3/72t9XW1qbu7m4lJSVZXjN06FDdcMMNOnDggK3PwRIGAAAG4VA4Zkd/JSUlKScnR42NjZFzoVBIjY2Nmjx5cq+vmTJlig4cOBDVMfnwww81fPjwXsODJJ0+fVoHDx7U8OHD+12bRIAAAMAoHA7F7LDD7/dr3bp1evHFF7V3714tXrxYXV1dkasy5s+fr5KSksj4xYsX609/+pOWLl2qDz/8UFu2bNGaNWu0ZMmSyJhHHnlEO3fu1OHDh7Vr1y7NmjVLLpdLhYWFtmpjCQMAgARVUFCgEydOqKysTG1tbcrOzlZDQ0NkY2Vra6uczv/fC7jmmmv0xhtv6KGHHtL48eOVkZGhpUuX6qc//WlkzNGjR1VYWKiTJ08qLS1NU6dO1e7du5WWlmarNkc4QZ9R+ueXrTtScfma/ebMeJeABLL4hVnxLgEJ5s5z+y7o/Pn/6//GbK6t9eNiNlc80YEAAMCAh2lZESAAADDgYVpWbKIEAAC20YEAAMCAJQwrAgQAAAbhGN6J8lLBEgYAALCNDgQAAAYsYVgRIAAAMLB7B8nLAUsYAADANjoQAAAYhFjCsCBAAABgwFUYVixhAAAA2+hAAABgwFUYVgQIAAAMuArDigABAIABHQgr9kAAAADb6EAAAGDAVRhWjnA4TF8mQQWDQQUCAZWUlMjtdse7HMQZvw/4In4fEG8EiATW2dmplJQUnTp1SkOGDIl3OYgzfh/wRfw+IN7YAwEAAGwjQAAAANsIEAAAwDYCRAJzu90qLy9ngxQk8fuAaPw+IN7YRAkAAGyjAwEAAGwjQAAAANsIEAAAwDYCBAAAsI0AkWACgYBmz56trKwsORwOZWZmxrskxNGHH36osrIyfe9731NaWpq+9a1vKTs7W6tXr1ZXV1e8y0MchEIh/f3f/73GjBkjj8eja665Rg8//DC/D7jouAojwTgcDl111VX6zne+o5aWFg0ZMkSHDx+Od1mIk8cee0w1NTW666679L3vfU8DBw7Um2++qY0bN2r8+PHavXu3Bg0aFO8ycREtXbpUTz/9tGbNmqXp06dr7969Wrt2rW699VZt375dTif/X4iLgwCRYA4dOqSsrCxJ0k033aTTp08TIC5j7777rq6//nqlpKREnS8tLdXq1au1du1aPfDAA3GqDhfbf/zHf2jcuHGaNWuWNm3aFDm/du1a/eQnP9Evf/lLzZkzJ44V4nJCVE0w/xMeAEmaOHGiJTxIUkFBgSTp/fffv9glIY5eeeUVhcNhLVu2LOp8cXGxkpOT9dJLL8WnMFyWCBDAN9DRo0clSV6vN86V4GJ655135HQ6NWnSpKjzHo9H2dnZeuedd+JUGS5HBAjgG6anp0crV67UgAEDaFdfZo4fP67U1NReb1+dkZGhjo4OdXd3x6EyXI4IEMA3zLJly9TU1KTKykqNHj063uXgIjpz5kyfz77weDyRMcDFQIAAvkFWrFihZ555RosWLVJJSUm8y8FFlpycrGAw2OvPzp49GxkDXAwECOAboqKiQqtWrVJRUZFqa2vjXQ7iYMSIEero6Og1RBw7dkypqalKSkqKQ2W4HBEggG+AiooKPf7441qwYIF+8YtfyOFwxLskxMF3v/tdhUIhNTc3R50/e/as/vjHP2rixIlxqgyXIwIEkOAqKyv1+OOPa968eaqvr+dGQZexgoICORwOVVdXR51ft26dzpw5o7lz58anMFyWuJFUglm/fr2OHDki6b9uDtPd3a2HH35YkjRy5EjNmzcvnuXhIqupqdEDDzygv/zLv9TKlSst4cHr9eq2226LU3WIhwcffFDPPPOMZs2apfz8fO3du1dPP/20pkyZot///vcETFw0BIgEM23aNO3cubPXn/31X/+1duzYcXELQlzdd999evHFF/v8Ob8Tl5+enh5VV1fr+eef1+HDh5WamqqCggJVVlZq8ODB8S4PlxECBAAAsI1eFwAAsI0AAQAAbCNAAAAA2wgQAADANgIEAACwjQABAABsI0AAAADbCBAAAMA2AgQAALCNAAEAAGwjQAAAANsIEAAAwDYCBAAAsO3/AbOG2/G3QFoGAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.99998476 0.99999353 0.99998682]\n" + ] + } + ], + "source": [ + "# Fisher-z transformated correlation\n", + "fit_FC_first_second = twopair_fisher_z_transformed_correlation(fit_FC_first,fit_FC_second)\n", + "FC_first_second_indices, fit_FC_first_second_reordered = hungarian_pair(fit_FC_first_second)\n", + "seaborn.heatmap(fit_FC_first_second_reordered, cmap=\"coolwarm\")\n", + "plt.xticks(np.arange(len(FC_first_second_indices['col'])) + 0.5, FC_first_second_indices['col'], fontsize=13)\n", + "plt.yticks(np.arange(len(FC_first_second_indices['row'])) + 0.5, FC_first_second_indices['row'], fontsize=13)\n", + "plt.show()\n", + "print(np.diagonal(fit_FC_first_second_reordered))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "13cf3b26", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 47\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 29\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 7 1 6 4 5 3 0]\n" + ] + }, + { + "data": { + "image/png": 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v369Dhw7p+uuv1+9///vznicYDCo+Pj5iNJw50dZ7AADAVZbl3ugKHCUDS5Ys0X333ae//e1vWrdunW677Tbl5uZq69atKisr03333adly5ad9zwFBQWqq6uLGLExfdt6DwAAdBsrV65USkqK4uLilJGRocrKyrN+9pprrpHP52s2brzxRkfXdJQMvP/++7rjjjskSbfccotOnTqlf/7nf246fvvtt+svf/nLec/j9/vVp0+fiEGLAADQWVhhn2vDiU2bNikQCKiwsFA7d+5UamqqZsyYoWPHjrX4+S1btujo0aNNY/fu3YqOjtbNN9/s6LqOFxD+zy/tqKgoxcXFKT4+vunYxRdfrLq6OqenBACgU7Esn2vDiRUrVig3N1c5OTkaO3asiouL1bNnT5WUlLT4+UsvvVRJSUlNY+vWrerZs2fHJgMpKSn66KOPmn6uqKjQ0KFDm34+fPiwkpOTHQUAAEB3FgqFdPLkyYhhX0QvSQ0NDaqqqlJWVlbTXFRUlLKyslRRUdGqa61du1bf+c531KtXL0cxOkoG5s2bp8bGxqafx40bpx49/rEh4fXXX2/VbgIAADozK+zeaGnRfDAYbHbN2tpaNTY2KjExMWI+MTFR1dXV5425srJSu3fv1l133eX4fh1tLbz77rvPeXzp0qWOAwAAoLMJu/jWwoKCAgUCgYg5v9/v2vn/x9q1azV+/HhNmjTJ8Xd5AiEAAB3I7/e36pd/QkKCoqOjVVNTEzFfU1OjpKSkc363vr5eGzdu1JIlS9oUI08gBADAxosFhLGxsUpLS1NZWVnTXDgcVllZmTIzM8/53X//939vevhfW1AZAADAxqsnEAYCAc2dO1fp6emaNGmSioqKVF9fr5ycHEnSnDlzNGjQoGZrDtauXavZs2erX79+bbouyQAAADZePTkwOztbx48f1+LFi1VdXa2JEyeqtLS0aVHh4cOHFRUVWdTfu3evtm3bpjfffLPN1yUZAACgE8nPz1d+fn6Lx8rLy5vNjRo1SlY7sxeSAQAAbEx7URHJAAAANm5uLewK2E0AAIDhqAwAAGDj9J0CXR3JAAAANl7tJvAKbQIAAAxHZQAAABvTFhCSDAAAYGPamgHaBAAAGI7KAAAANqYtICQZAADAhjUDHtkxdKTXIbTbqi+9jsAdMWr0OgRX7D99sdchtNtvd//E6xBc8VD6Iq9DaLddV+Z5HYIrDjYe9zoEV8zq4POzZgAAABil01QGAADoLGgTAABgOMPWD9ImAADAdFQGAACwoU0AAIDh2E0AAACMQmUAAACbsNcBXGAkAwAA2FiiTQAAAAxCZQAAAJuwYQ8aIBkAAMAmbFibgGQAAAAb1gwAAACjUBkAAMCGrYUAABiONgEAADAKlQEAAGxoEwAAYDjTkgFX2gSWZdjTGQAA6EZcSQb8fr/27NnjxqkAAPCcJZ9roytw1CYIBAItzjc2NmrZsmXq16+fJGnFihXtjwwAAI+Eu8bvcNc4SgaKioqUmpqqvn37RsxblqU9e/aoV69e8vnO/99gKBRSKBSKmGsIhxUbxeYGAAAuNEfJwNKlS7V69WotX75c1157bdN8TEyM1q9fr7Fjx7bqPMFgUI8++mjEXH6/y3VP/xFOwgEAoEOY9m4CR3+KP/DAA9q0aZPmzZunhQsX6syZM226aEFBgerq6iLGD/sNb9O5AABwm+Xi6Aoc1+WvuuoqVVVV6fjx40pPT9fu3btb1Rr43/x+v/r06RMxaBEAADqLsIujK2jTcwZ69+6tDRs2aOPGjcrKylJjY6PbcQEAgAukXQ8d+s53vqOrr75aVVVVGjZsmFsxAQDgqbDDindX1+4nEA4ePFiDBw92IxYAADqFrtLrdwuNegAADMe7CQAAsOkqC//cQjIAAICNaU8gpE0AAIDhSAYAALAJy+facGrlypVKSUlRXFycMjIyVFlZec7PnzhxQnl5eUpOTpbf79fIkSP12muvObombQIAAGy82k2wadMmBQIBFRcXKyMjQ0VFRZoxY4b27t2rAQMGNPt8Q0ODvvnNb2rAgAHavHmzBg0apEOHDjV7h9D5kAwAANBJrFixQrm5ucrJyZEkFRcX69VXX1VJSYkeeOCBZp8vKSnRZ599pnfeeUcxMTGSpJSUFMfXpU0AAIBN2OfeCIVCOnnyZMSwv7lX+u+/8quqqpSVldU0FxUVpaysLFVUVLQY58svv6zMzEzl5eUpMTFR48aN09KlSx0/GZhkAAAAGzffTRAMBhUfHx8xgsFgs2vW1taqsbFRiYmJEfOJiYmqrq5uMc4DBw5o8+bNamxs1GuvvaaHH35Yy5cv109+8hNH90ubAAAAGzfXDBQUFCgQCETM+f1+V84dDoc1YMAArV69WtHR0UpLS9ORI0f0+OOPq7CwsNXnIRkAAKAD+f3+Vv3yT0hIUHR0tGpqaiLma2pqlJSU1OJ3kpOTFRMTo+jo6Ka5MWPGqLq6Wg0NDYqNjW1VjLQJAACwcXPNQGvFxsYqLS1NZWVl/4gjHFZZWZkyMzNb/M6UKVO0f/9+hcP/eGbivn37lJyc3OpEQCIZAACgGTfXDDgRCAS0Zs0abdiwQXv27NG8efNUX1/ftLtgzpw5KigoaPr8vHnz9Nlnn2n+/Pnat2+fXn31VS1dulR5eXmOrkubAACATiI7O1vHjx/X4sWLVV1drYkTJ6q0tLRpUeHhw4cVFfWPv+OHDBmiN954QwsWLNCECRM0aNAgzZ8/X/fff7+j65IMAABg4+WLivLz85Wfn9/isfLy8mZzmZmZ2r59e7uuSTIAAICNxYuKAACASTpNZWDj3/t5HUK7Ld1wndchuOKrjS94HYIrokcM9jqEdluUvsjrEFzx2I7HvA6h3U7+/wVcXV3chMu8DqFL8LJN4IVOkwwAANBZmJYM0CYAAMBwVAYAALDx6hXGXiEZAADAxsmTA7sDkgEAAGxYMwAAAIxCZQAAABvTKgMkAwAA2Ji2gJA2AQAAhqMyAACADbsJAAAwnGlrBmgTAABgOCoDAADYmLaAkGQAAACbsGHpAG0CAAAMR2UAAAAb0xYQkgwAAGBjVpOAZAAAgGZMqwywZgAAAMNRGQAAwIYnEDpQX1+vF154Qfv371dycrJuvfVW9evXz63YAADwhGlbCx0lA2PHjtW2bdt06aWX6pNPPtG0adP0+eefa+TIkfr444/14x//WNu3b9dll112zvOEQiGFQqGIua+sRvXwRTu/AwAA0C6O1gx8+OGH+uqrryRJBQUFGjhwoA4dOqTKykodOnRIEyZM0KJFi857nmAwqPj4+Ijxh7r323YHAAC4zHJxdAVtXkBYUVGhRx55RPHx8ZKk3r1769FHH9W2bdvO+92CggLV1dVFjKnxX2trKAAAuCrs4ugKHK8Z8Pn+e1XF6dOnlZycHHFs0KBBOn78+HnP4ff75ff7IwOhRQAAgCccJwPXXXedevTooZMnT2rv3r0aN25c07FDhw6xgBAA0OWxgPAcCgsLI37u3bt3xM+vvPKKpk6d2v6oAADwkFmpQDuTAbvHH3+8XcEAAIALj4cOAQBg01UW/rmFZAAAABvWDAAAYDizUgFeVAQAgPGoDAAAYMOaAQAADGcZ1iigTQAAgOGoDAAAYEObAAAAw5m2tZA2AQAAhqMyAACAjVl1AZIBAACaoU0AAACMQjIAAIBN2MXh1MqVK5WSkqK4uDhlZGSosrLyrJ9dv369fD5fxIiLi3N8TZIBAABsLBf/cWLTpk0KBAIqLCzUzp07lZqaqhkzZujYsWNn/U6fPn109OjRpnHo0CHH90syAACAjVeVgRUrVig3N1c5OTkaO3asiouL1bNnT5WUlJz1Oz6fT0lJSU0jMTHR4VVJBgAA6BQaGhpUVVWlrKysprmoqChlZWWpoqLirN/74osvNGzYMA0ZMkQ33XST3n//fcfX7jS7Caac/srrENrt5CO/9DoEV9TVOO83dUaX/eRBr0NotylPPOR1CK44s+4nXofQbp/8Jd7rEFxx6t3usUp+6mMde343300QCoUUCoUi5vx+v/x+f8RcbW2tGhsbm/1ln5iYqA8//LDFc48aNUolJSWaMGGC6urq9MQTT2jy5Ml6//33NXjw4FbHSGUAAAAbN9sEwWBQ8fHxESMYDLoSZ2ZmpubMmaOJEydq+vTp2rJli/r3769nn33W0Xk6TWUAAIDuqKCgQIFAIGLOXhWQpISEBEVHR6umpiZivqamRklJSa26VkxMjK688krt37/fUYxUBgAAsAlblmvD7/erT58+EaOlZCA2NlZpaWkqKyv7RxzhsMrKypSZmdmquBsbG/Xee+8pOTnZ0f1SGQAAwMarlRWBQEBz585Venq6Jk2apKKiItXX1ysnJ0eSNGfOHA0aNKipzbBkyRJ9/etf14gRI3TixAk9/vjjOnTokO666y5H1yUZAACgk8jOztbx48e1ePFiVVdXa+LEiSotLW1aVHj48GFFRf2jqP/5558rNzdX1dXVuuSSS5SWlqZ33nlHY8eOdXRdkgEAAGy8fDdBfn6+8vPzWzxWXl4e8fOTTz6pJ598st3XJBkAAMDGza2FXQELCAEAMByVAQAAbNrygqGujGQAAAAbL9cMeIFkAAAAG9YMAAAAo1AZAADAhjUDAAAYzrJoEwAAAINQGQAAwIbdBAAAGM60NQO0CQAAMByVAQAAbEx7zgDJAAAANqatGaBNAACA4RwlAzt37tTBgwebfn7uuec0ZcoUDRkyRFdffbU2btzYqvOEQiGdPHkyYjRYjc4iBwCgg1iW5droChwlAzk5Ofr4448lSb/4xS/0wx/+UOnp6Vq0aJGuuuoq5ebmqqSk5LznCQaDio+Pjxi/rt/TtjsAAMBlYRdHV+BozcBHH32kK664QpL0zDPP6KmnnlJubm7T8auuukqPPfaY7rzzznOep6CgQIFAIGLujyPO/R0AAC4UFhCeQ8+ePVVbW6thw4bpyJEjmjRpUsTxjIyMiDbC2fj9fvn9/oi5WF+0k1AAAIBLHLUJbrjhBq1atUqSNH36dG3evDni+AsvvKARI0a4Fx0AAB4Iy3JtdAWOKgM//elPNWXKFE2fPl3p6elavny5ysvLNWbMGO3du1fbt2/Xiy++2FGxAgBwQXSVhX9ucVQZGDhwoP70pz8pMzNTpaWlsixLlZWVevPNNzV48GD98Y9/1MyZMzsqVgAA0AEcP3Sob9++WrZsmZYtW9YR8QAA4LmuUt53C08gBADAxrTdBDyBEAAAw1EZAADAJmzYAkKSAQAAbMxKBWgTAABgPCoDAADYsJsAAADDkQwAAGA4nkAIAACMQmUAAAAb2gQAABiOJxACAACjUBkAAMDGtAWEJAMAANiYtmaANgEAAIajMgAAgI1pbQKf1Unu+EztAa9DaLct4x/2OgRXXJ1y1OsQXPHBgf5eh9Buw/uf8DoEV2w8leB1CO0WPP6O1yG44vRXDV6H4IqvGo506PlTkya7dq4/V3f+/+3QJgAAwHC0CQAAsDHtOQMkAwAA2IQ7Rwf9giEZAADAxrTKAGsGAAAwHMkAAAA2YctybTi1cuVKpaSkKC4uThkZGaqsrGzV9zZu3Cifz6fZs2c7vibJAAAANpaL/zixadMmBQIBFRYWaufOnUpNTdWMGTN07Nixc37vr3/9qxYuXKipU6e26X5JBgAA6CRWrFih3Nxc5eTkaOzYsSouLlbPnj1VUlJy1u80Njbq9ttv16OPPqrhw4e36bokAwAA2LjZJgiFQjp58mTECIVCza7Z0NCgqqoqZWVlNc1FRUUpKytLFRUVZ411yZIlGjBggL7//e+3+X5JBgAAsHGzTRAMBhUfHx8xgsFgs2vW1taqsbFRiYmJEfOJiYmqrq5uMc5t27Zp7dq1WrNmTbvul62FAAB0oIKCAgUCgYg5v9/f7vOeOnVK3/ve97RmzRolJLTvkd8kAwAA2Lj50CG/39+qX/4JCQmKjo5WTU1NxHxNTY2SkpKaff7jjz/WX//6V82aNatpLhwOS5J69OihvXv36vLLL29VjLQJAACw8WI3QWxsrNLS0lRWVtY0Fw6HVVZWpszMzGafHz16tN577z3t2rWraXzrW9/SN77xDe3atUtDhgxp9bWpDAAA0EkEAgHNnTtX6enpmjRpkoqKilRfX6+cnBxJ0pw5czRo0CAFg0HFxcVp3LhxEd/v27evJDWbPx+SAQAAbCwr7Ml1s7Ozdfz4cS1evFjV1dWaOHGiSktLmxYVHj58WFFR7hf1SQYAALAJe/hugvz8fOXn57d4rLy8/JzfXb9+fZuuSTIAAICNZdhbC1lACACA4agMAABg42WbwAskAwAA2NAmAAAARnGUDNxzzz36wx/+0O6LtvalDQAAeMHNFxV1BY6SgZUrV+qaa67RyJEj9dOf/vSsL044n5Ze2vDTp4rbdC4AANzmxRMIveS4TfDmm29q5syZeuKJJzR06FDddNNN+u1vf9v0POTWKCgoUF1dXcS4f/7dTkMBAAAucJwMjB8/XkVFRfr000/1q1/9SqFQSLNnz9aQIUO0aNEi7d+//7zn8Pv96tOnT8Rw4w1OAAC4wbIs10ZX0OYFhDExMbrllltUWlqqAwcOKDc3V//2b/+mUaNGuRkfAAAXXFiWa6MrcGU3wdChQ/XII4/o4MGDKi0tdeOUAADgAnH0nIFhw4YpOjr6rMd9Pp+++c1vtjsoAAC81FXK+25xlAwcPHiwo+IAAKDT6CpbAt3CEwgBALAxrTLAEwgBADAclQEAAGy6yi4At5AMAABgQ5sAAAAYhcoAAAA27CYAAMBwXeUFQ26hTQAAgOGoDAAAYEObAAAAw7GbAAAAGIXKAAAANqYtICQZAADAxrQ2AckAAAA2piUDrBkAAMBwVAYAALAxqy4gyTLE6dOnrcLCQuv06dNeh9Jm3eEeLKt73Ed3uAfL4j46k+5wD5bVfe7DND7LMqMxcvLkScXHx6uurk59+vTxOpw26Q73IHWP++gO9yBxH51Jd7gHqfvch2lYMwAAgOFIBgAAMBzJAAAAhjMmGfD7/SosLJTf7/c6lDbrDvcgdY/76A73IHEfnUl3uAep+9yHaYxZQAgAAFpmTGUAAAC0jGQAAADDkQwAAGA4kgEAAAxnRDKwcuVKpaSkKC4uThkZGaqsrPQ6JEf+8z//U7NmzdLAgQPl8/n00ksveR2SY8FgUFdddZUuvvhiDRgwQLNnz9bevXu9DsuxVatWacKECerTp4/69OmjzMxMvf76616H1S7Lli2Tz+fTvffe63UojjzyyCPy+XwRY/To0V6H1SZHjhzRd7/7XfXr108XXXSRxo8frx07dngdVqulpKQ0+3fh8/mUl5fndWhopW6fDGzatEmBQECFhYXauXOnUlNTNWPGDB07dszr0Fqtvr5eqampWrlypdehtNnbb7+tvLw8bd++XVu3btWZM2d0/fXXq76+3uvQHBk8eLCWLVumqqoq7dixQ9dee61uuukmvf/++16H1ibvvvuunn32WU2YMMHrUNrka1/7mo4ePdo0tm3b5nVIjn3++eeaMmWKYmJi9Prrr+uDDz7Q8uXLdckll3gdWqu9++67Ef8etm7dKkm6+eabPY4MrebtqxE63qRJk6y8vLymnxsbG62BAwdawWDQw6jaTpL14osveh1Gux07dsySZL399tteh9Jul1xyifWLX/zC6zAcO3XqlHXFFVdYW7dutaZPn27Nnz/f65AcKSwstFJTU70Oo93uv/9+6+qrr/Y6DFfNnz/fuvzyy61wOOx1KGilbl0ZaGhoUFVVlbKysprmoqKilJWVpYqKCg8jQ11dnSTp0ksv9TiStmtsbNTGjRtVX1+vzMxMr8NxLC8vTzfeeGPE/z+6mo8++kgDBw7U8OHDdfvtt+vw4cNeh+TYyy+/rPT0dN18880aMGCArrzySq1Zs8brsNqsoaFBv/rVr3TnnXfK5/N5HQ5aqVsnA7W1tWpsbFRiYmLEfGJioqqrqz2KCuFwWPfee6+mTJmicePGeR2OY++995569+4tv9+vu+++Wy+++KLGjh3rdViObNy4UTt37lQwGPQ6lDbLyMjQ+vXrVVpaqlWrVungwYOaOnWqTp065XVojhw4cECrVq3SFVdcoTfeeEPz5s3Tv/zLv2jDhg1eh9YmL730kk6cOKE77rjD61DgQA+vA4B58vLytHv37i7Z35WkUaNGadeuXaqrq9PmzZs1d+5cvf32210mIfjkk080f/58bd26VXFxcV6H02Y33HBD03+eMGGCMjIyNGzYML3wwgv6/ve/72FkzoTDYaWnp2vp0qWSpCuvvFK7d+9WcXGx5s6d63F0zq1du1Y33HCDBg4c6HUocKBbVwYSEhIUHR2tmpqaiPmamholJSV5FJXZ8vPz9dvf/lZvvfWWBg8e7HU4bRIbG6sRI0YoLS1NwWBQqampeuqpp7wOq9Wqqqp07Ngx/dM//ZN69OihHj166O2339bTTz+tHj16qLGx0esQ26Rv374aOXKk9u/f73UojiQnJzdLJMeMGdMlWx6HDh3S7373O911111ehwKHunUyEBsbq7S0NJWVlTXNhcNhlZWVdckeb1dmWZby8/P14osv6ve//70uu+wyr0NyTTgcVigU8jqMVrvuuuv03nvvadeuXU0jPT1dt99+u3bt2qXo6GivQ2yTL774Qh9//LGSk5O9DsWRKVOmNNtmu2/fPg0bNsyjiNpu3bp1GjBggG688UavQ4FD3b5NEAgENHfuXKWnp2vSpEkqKipSfX29cnJyvA6t1b744ouIv3YOHjyoXbt26dJLL9XQoUM9jKz18vLy9Pzzz+s//uM/dPHFFzet2YiPj9dFF13kcXStV1BQoBtuuEFDhw7VqVOn9Pzzz6u8vFxvvPGG16G12sUXX9xsrUavXr3Ur1+/LrWGY+HChZo1a5aGDRumTz/9VIWFhYqOjtatt97qdWiOLFiwQJMnT9bSpUt1yy23qLKyUqtXr9bq1au9Ds2RcDisdevWae7cuerRo9v/aul+vN7OcCH87Gc/s4YOHWrFxsZakyZNsrZv3+51SI689dZblqRmY+7cuV6H1motxS/JWrdundehOXLnnXdaw4YNs2JjY63+/ftb1113nfXmm296HVa7dcWthdnZ2VZycrIVGxtrDRo0yMrOzrb279/vdVht8sorr1jjxo2z/H6/NXr0aGv16tVeh+TYG2+8YUmy9u7d63UoaANeYQwAgOG69ZoBAABwfiQDAAAYjmQAAADDkQwAAGA4kgEAAAxHMgAAgOFIBgAAMBzJAAAAhiMZAADAcCQDAAAYjmQAAADDkQwAAGC4/weLJWJBC+wDrAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(ground_truth_FC,fit_FC_second))\n", + "print(np.argmax(twopair_fisher_z_transformed_correlation(ground_truth_FC,fit_FC_second),axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "55335ec0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 698.58it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.03947350199527376\n" + ] + } + ], + "source": [ + "# Riemannian distance\n", + "fit_riem_first_second = twopair_riemannian_distance(fit_FC_first,fit_FC_second)\n", + "riem_first_second_indices, fit_riem_first_second_reordered = hungarian_pair(fit_riem_first_second,distance=True)\n", + "seaborn.heatmap(fit_riem_first_second_reordered, cmap=\"coolwarm\")\n", + "plt.xticks(np.arange(len(riem_first_second_indices['col'])) + 0.5, riem_first_second_indices['col'], fontsize=13)\n", + "plt.yticks(np.arange(len(riem_first_second_indices['row'])) + 0.5, riem_first_second_indices['row'], fontsize=13)\n", + "plt.show()\n", + "print(np.mean(np.diagonal(fit_riem_first_second_reordered)))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f7d88778", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 698.58it/s]\n", + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 746.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 7 1 6 4 5 3 0]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_riemannian_distance(ground_truth_FC,fit_FC_second))\n", + "print(np.argmin(twopair_riemannian_distance(ground_truth_FC,fit_FC_second),axis=1))" + ] + }, + { + "cell_type": "markdown", + "id": "3e1abd54", + "metadata": {}, + "source": [ + "The take-home message: there are actually 7/16 states fitted well when N_state = 16, and 8/8 states fitted well when N_state = 8. These states (7/16 and 8/8) correspond well with the ground truth. Now maybe look at the fractional occupancy?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "b502fa40", + "metadata": {}, + "outputs": [], + "source": [ + "fo = np.load(f'{fit_dir}HMM_ICA_25_state_{N_states[-1]}/fo.npy')\n", + "import pickle\n", + "with open(f'{fit_dir}HMM_ICA_25_state_{N_states[-1]}/alpha.pkl','rb') as file:\n", + " alpha = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "37690d43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000\n", + "1000\n" + ] + } + ], + "source": [ + "print(len(alpha))\n", + "print(len(fo))" + ] + }, + { + "cell_type": "markdown", + "id": "59bbbed1", + "metadata": {}, + "source": [ + "Now answer: What's happending when the subject variability level is higher?\n", + "Short Answer: The optimal number of states still occur. But what happens to real data?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "42cfe06c", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './results_HCP_202401/HMM_ICA_25_state_4/metrics_repeat.json'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m result_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./results_HCP_202401/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 5\u001b[0m save_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/plot/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mcomparison_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlist_channels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlist_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43mresult_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/analysis.py:1269\u001b[0m, in \u001b[0;36mcomparison_analysis\u001b[0;34m(models, list_channels, list_states, result_dir, save_dir, learn_mean)\u001b[0m\n\u001b[1;32m 1267\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m N_channel \u001b[38;5;129;01min\u001b[39;00m list_channels:\n\u001b[1;32m 1268\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m N_state \u001b[38;5;129;01min\u001b[39;00m list_states:\n\u001b[0;32m-> 1269\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mresult_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmodel\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_ICA_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_channel\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_state_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_state\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/metrics_repeat.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m json_file:\n\u001b[1;32m 1270\u001b[0m \u001b[38;5;66;03m# Use json.load to load the data from the file\u001b[39;00m\n\u001b[1;32m 1271\u001b[0m metrics \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(json_file)\n\u001b[1;32m 1272\u001b[0m free_energy[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mICA_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_channel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_state_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_state\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfree_energy\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './results_HCP_202401/HMM_ICA_25_state_4/metrics_repeat.json'" + ] + } + ], + "source": [ + "models =['HMM']\n", + "list_channels = [25,200,300]\n", + "list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14]#,15,16,17,18,19,20]\n", + "result_dir = './results_HCP_202401/'\n", + "save_dir = f'{result_dir}/plot/'\n", + "comparison_analysis(models,list_channels,list_states,result_dir, save_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "19fb3ff8", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "41c9dd86", + "metadata": {}, + "outputs": [], + "source": [ + "models = ['HMM','Dynemo','MAGE','SWC']\n", + "list_channels = [15, 25, 50, 100, 200, 300]\n", + "list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n", + "index = 16\n", + "model,n_channels, n_states = parse_index(index,models,list_channels,list_states,training=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c1faeca0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_channels" + ] + }, + { + "cell_type": "markdown", + "id": "2f3ef7b1", + "metadata": {}, + "source": [ + "### Now let's look at the temporal dynamics of our data" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "bbb724ba", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn" + ] + }, + { + "cell_type": "markdown", + "id": "768887e1", + "metadata": {}, + "source": [ + "## Update 31th January 2024" + ] + }, + { + "cell_type": "markdown", + "id": "e85adf1e", + "metadata": {}, + "source": [ + "### Task 1: Understand the best training configurations of HMM model on real HCP data" + ] + }, + { + "cell_type": "markdown", + "id": "2f3bc185", + "metadata": {}, + "source": [ + "Just check the loss function to see whether it converges." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "07328efc", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8f711be2", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_training_config_202402/'\n", + "sequence_lengths = [600,400,200,100,50,25]\n", + "batch_sizes = [1024,512,256,128,64,32]\n", + "learning_rates = [0.0005,0.001,0.005,0.01]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "9dcf2bde", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a directory to save the plots\n", + "plot_dir = f'{save_dir}/loss_plots/'\n", + "os.makedirs(plot_dir, exist_ok=True)\n", + "\n", + "for sl in sequence_lengths:\n", + " n_cols = 2\n", + " n_rows = (len(learning_rates) // n_cols) # Ceiling division to get the number of rows\n", + " \n", + " plt.figure(figsize=(15, 6 * n_rows))\n", + "\n", + " for i, lr in enumerate(learning_rates, start=1):\n", + " plt.subplot(n_rows, n_cols, i)\n", + " \n", + " # Iterate over each combination of batch size\n", + " for bs in batch_sizes:\n", + " # Skip the case where sequence_length=25 and batch_size=32\n", + " if sl == 25 and bs == 32:\n", + " continue\n", + " \n", + " # Load the loss history\n", + " file_path = f'{save_dir}sl_{sl}_bs_{bs}_lr_{lr}/loss_history.npy'\n", + " if os.path.exists(file_path):\n", + " loss_history = np.load(file_path)\n", + " epochs = np.arange(1, len(loss_history) + 1)\n", + "\n", + " # Plot the loss history\n", + " plt.plot(epochs, loss_history, label=f'BS={bs}')\n", + "\n", + " # Customize subplot\n", + " plt.title(f'Loss History for SL={sl}, LR={lr}')\n", + " plt.xlabel('Epochs')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " \n", + " # Adjust layout\n", + " plt.tight_layout()\n", + "\n", + " # Save the plot\n", + " plt.savefig(f'{plot_dir}loss_plots_sl_{sl}.jpg')\n", + " plt.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4a5eb7c", + "metadata": {}, + "source": [ + "Now choose the best ten training configurations." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "b98e9c67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Training Configurations:\n", + "1. Sequence Length: 400, Batch Size: 256, Learning Rate: 0.001, Normalized Loss: 58.891545\n", + "2. Sequence Length: 400, Batch Size: 256, Learning Rate: 0.01, Normalized Loss: 58.901418\n", + "3. Sequence Length: 100, Batch Size: 1024, Learning Rate: 0.001, Normalized Loss: 58.911931\n", + "4. Sequence Length: 200, Batch Size: 512, Learning Rate: 0.005, Normalized Loss: 58.911981\n", + "5. Sequence Length: 100, Batch Size: 1024, Learning Rate: 0.0005, Normalized Loss: 58.912000\n", + "6. Sequence Length: 400, Batch Size: 256, Learning Rate: 0.005, Normalized Loss: 58.912208\n", + "7. Sequence Length: 200, Batch Size: 512, Learning Rate: 0.01, Normalized Loss: 58.914890\n", + "8. Sequence Length: 400, Batch Size: 128, Learning Rate: 0.0005, Normalized Loss: 58.918577\n", + "9. Sequence Length: 400, Batch Size: 128, Learning Rate: 0.001, Normalized Loss: 58.927658\n", + "10. Sequence Length: 100, Batch Size: 512, Learning Rate: 0.001, Normalized Loss: 58.928611\n" + ] + } + ], + "source": [ + "results = []\n", + "\n", + "# Iterate over each configuration\n", + "for sl in sequence_lengths:\n", + " for lr in learning_rates:\n", + " for bs in batch_sizes:\n", + " \n", + " # Load the loss history if available\n", + " file_path = f'{save_dir}sl_{sl}_bs_{bs}_lr_{lr}/loss_history.npy'\n", + " try:\n", + " if os.path.exists(file_path):\n", + " loss_history = np.load(file_path)\n", + " normalized_loss = np.min(loss_history) / sl # Normalize by sequence length\n", + "\n", + " # Store the configuration and normalized loss\n", + " results.append({\n", + " 'sequence_length': sl,\n", + " 'batch_size': bs,\n", + " 'learning_rate': lr,\n", + " 'normalized_loss': normalized_loss\n", + " })\n", + " except Exception as e:\n", + " print(f\"Error loading file {file_path}: {e}\")\n", + " continue\n", + "\n", + "# Sort results by normalized loss in ascending order\n", + "results.sort(key=lambda x: x['normalized_loss'])\n", + "\n", + "# Print the top 10 configurations\n", + "print(\"Top 10 Training Configurations:\")\n", + "for i, result in enumerate(results[:10], start=1):\n", + " print(f\"{i}. Sequence Length: {result['sequence_length']}, Batch Size: {result['batch_size']}, \"\n", + " f\"Learning Rate: {result['learning_rate']}, Normalized Loss: {result['normalized_loss']:.6f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "fdccb614", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Save the results to a file\n", + "with open(f'{plot_dir}loss_comparison.pkl', 'wb') as f:\n", + " pickle.dump(results, f)" + ] + }, + { + "cell_type": "markdown", + "id": "f692a125", + "metadata": {}, + "source": [ + "We do not need complicated post analysis in this case. But please just extract the state correlations, covariances, etc" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "782c25a2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-02-01 14:48:09.346043: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-02-01 14:48:10.412825: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /apps/eb/2020b/skylake/software/Qt5/5.15.2-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/JasPer/2.0.28-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/snappy/1.1.8-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/NSS/3.65-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/NSPR/4.30-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libjpeg-turbo/2.0.6-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libGLU/9.0.1-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/Mesa/21.1.1-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/LLVM/11.1.0-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libunwind/1.4.0-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libglvnd/1.3.3-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libdrm/2.4.106-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/zstd/1.4.9-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/lz4/1.9.3-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libevent/2.1.12-GCCcore-10.3.0/lib:/apps/eb/2022b/skylake/software/OpenSSL/1.1/lib:/apps/eb/2020b/skylake/software/DBus/1.13.18-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/X11/20210518-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libpciaccess/0.16-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/fontconfig/2.13.93-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/freetype/2.10.4-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/expat/2.2.9-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/bzip2/1.0.8-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libpng/1.6.37-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/GLib/2.68.2-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/util-linux/2.36-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/PCRE2/10.36-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/gettext/0.21-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/ncurses/6.2-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libxml2/2.9.10-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/zlib/1.2.11-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/XZ/5.2.5-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/libffi/3.3-GCCcore-10.3.0/lib64:/apps/eb/2020b/skylake/software/libffi/3.3-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/double-conversion/3.1.5-GCCcore-10.3.0/lib:/apps/eb/2020b/skylake/software/GCCcore/10.3.0/lib64:/apps/eb/2020b/skylake/software/GCCcore/10.3.0/lib\n", + "2024-02-01 14:48:10.412856: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "from osl_dynamics.models import load\n", + "from rotation." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9be0213d", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_training_config_202402/'\n", + "sequence_lengths = [400,400,400,400,200,200,200,200,100,100,100,100]\n", + "batch_sizes = [256,256,256,256,512,512,512,512,1024,1024,1024,1024]\n", + "learning_rates = [0.0005,0.001,0.005,0.01,0.0005,0.001,0.005,0.01,0.0005,0.001,0.005,0.01,0.0005,0.001,0.005,0.01]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3cdc216", + "metadata": {}, + "outputs": [], + "source": [ + "for sl in sequence_lengths:\n", + " for bs in batch_sizes:\n", + " for lr in learning_rates:\n", + " model = load(f'{save_dir}sl_{sl}_bs_{bs}_lr_{lr}')\n", + " means, covariances = model.get_means_covariances()\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "31cb0dbc", + "metadata": {}, + "source": [ + "# Update 5th February 2024" + ] + }, + { + "cell_type": "markdown", + "id": "82c9654c", + "metadata": {}, + "source": [ + "This week we have received feedbacks from all supervisors. We are advised to run the simulation with 500 subjects (each with only one session), and conduct the split-half reproducibility analysis for five times (to estimate the average). Also, check the sigma = 0 case for free energy comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6a94dda1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "67830035", + "metadata": {}, + "outputs": [], + "source": [ + "result_dir = './results_HCP_202402_single_session/'\n", + "N_states = list(range(2,37))\n", + "for N_state in N_states:\n", + " for i in range(5):\n", + " if not os.path.exists(f'{result_dir}/HMM_ICA_25_state_{N_state}/reproduce_analysis/FCs_distance_reorder_split_{i+1}.npy'):\n", + " print(f'N_state={N_state},i={i}')" + ] + }, + { + "cell_type": "markdown", + "id": "106d6e05", + "metadata": {}, + "source": [ + "All necessary files seem to exist! So we can revise the code in comparison_analysis to cater to our needs!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b4d5b62d", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './results_HCP_202402_single_session/HMM_ICA_25_state_2/cross_validation_0/validation/metrics.json'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m list_channels \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m25\u001b[39m,\u001b[38;5;241m200\u001b[39m,\u001b[38;5;241m300\u001b[39m]\n\u001b[1;32m 4\u001b[0m save_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/plot/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[43mcomparison_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlist_channels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mN_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43mresult_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/analysis.py:1292\u001b[0m, in \u001b[0;36mcomparison_analysis\u001b[0;34m(models, list_channels, list_states, result_dir, save_dir, learn_mean)\u001b[0m\n\u001b[1;32m 1290\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m5\u001b[39m):\n\u001b[1;32m 1291\u001b[0m temp \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m-> 1292\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mresult_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmodel\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_ICA_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_channel\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_state_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_state\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/cross_validation_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/validation/metrics.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m json_file:\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;66;03m# Use json.load to load the data from the file\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m temp\u001b[38;5;241m.\u001b[39mappend(json\u001b[38;5;241m.\u001b[39mload(json_file)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfree_energy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 1296\u001b[0m free_energy_cv[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mICA_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_channel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_state_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_state\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m temp\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './results_HCP_202402_single_session/HMM_ICA_25_state_2/cross_validation_0/validation/metrics.json'" + ] + } + ], + "source": [ + "from rotation.analysis import comparison_analysis\n", + "models = ['HMM']\n", + "list_channels = [25,200,300]\n", + "save_dir = f'{result_dir}/plot/'\n", + "comparison_analysis(models,list_channels,N_states,result_dir,save_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "66d03ffc", + "metadata": {}, + "source": [ + "# Update 12th February 2024" + ] + }, + { + "cell_type": "markdown", + "id": "86cdfaa3", + "metadata": {}, + "source": [ + "Check the new covariance structure of simulated covariance matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "73f75f33", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import twopair_riemannian_distance" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "aed70bd5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 267.67it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './data/node_timeseries/simulation_202402_se/3_group/'\n", + "group_covariances = np.load(f'{save_dir}truth/group_state_covariances.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(group_covariances,group_covariances))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3dd1eaa3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 439.41it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 366.51it/s]\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 472.21it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 169.05it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 452.51it/s]\n" + ] + }, + { + "data": { + "image/png": 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2Dx8+XPHx8dq2bVvztsrKSh05ckR5eXmtPg+VPwAAMaKgoEDr1q3Tyy+/rKSkpOZ5/EAgoO7duysQCOiuu+5SYWGhUlJSlJycrEWLFikvL6/Vi/0kkj8AAGcxIva1/a0oKiqSJI0fPz5qe3FxsebOnStJeuKJJ9SlSxfNmDFD4XBYU6ZM0Zo1ayydh+QPAICJU/f5G4bR4ne6deum1atXa/Xq1W0+D3P+AAB4DJU/AAAmho2r/WMRyR8AABPe6gcAAFyFyh8AABOnVvt3FpI/AAAmrVh0f1Ej+QMAYOL2yp85fwAAPMZy5f/ee++prKxMeXl5GjhwoP74xz/qySefVDgc1h133KEJEya0eIxzvds4HInI34XfRQAAzqPy/5KSkhJde+21Wrp0qa677jqVlJRo7Nixqqqq0uHDhzV58mS9/vrrLR7nXO82XvWnI22+CAAA7GQY9o1YZCn5P/zww7rvvvv0ySefqLi4WN/+9rc1f/58bd26Vdu2bdN9992nFStWtHicYDCourq6qPG9K7LbfBEAAKD1LCX/d955p/nFAjNnztSpU6f0T//0T837b7/9dr311lstHsfv9ys5OTlq0PIHAMQKI+KzbcQiy3P+Pt8XF9KlSxd169ZNgUCgeV9SUpLq6ursiw4AAAe4/fG+lsrtK664QgcPHmz+vHv3bmVn/6Ndf+TIEWVkZNgXHQAAsJ2lyv+ee+5RU1NT8+chQ4ZE7d+8eXOrVvsDABDL3P5sf0vJf8GCBRfc/8gjj7QrGAAAYkGEtj8AAHATHu8LAICJ2xf8kfwBADCJ1Vv07ELyBwDAJFafzGcX5vwBAPAYKn8AAExo+wMA4DHc6gcAAFyFyh8AABNu9QMAwGNY7Q8AAFyFyh8AABO3L/gj+QMAYOL2OX/a/gAAeAyVPwAAJm5f8EfyBwDAhDn/TvLZp/FOh9BuiYk9nA7BFr74i//fQpLey81wOoR2y/1DvdMh2OKNwQGnQ2i3kzWXOB2CLdISL/5/i87AnD8AAHCVmKn8AQCIFbT9AQDwGJev96PtDwCA11D5AwBgQtsfAACPYbU/AABwFSp/AABMIk4H0MFI/gAAmBii7Q8AAFyEyh8AAJOIy2/0J/kDAGAScXnbn+QPAIAJc/4AAMBVqPwBADDhVj8AADyGtj8AAHAVKn8AAExo+wMA4DFuT/62tP0Nw+VPQwAAwEVsSf5+v1/vvfeeHYcCAMBxhny2jVhkqe1fWFh4zu1NTU1asWKFUlNTJUn/9m//1v7IAABwSCQ2c7ZtLCX/lStXatiwYerZs2fUdsMw9N5776lHjx7y+Vr+GwuHwwqHw9HbIhH5u3DzAQAAHc1S8n/kkUf0zDPP6PHHH9eECROat8fHx2vt2rUaPHhwq44TCoX00EMPRW27N72flmRcaSUcAAA6hNuf7W+p1P7+97+v3/zmN7rnnnu0dOlSNTY2tumkwWBQdXV1UeO7fa5o07EAALCbYeOIRZb77CNHjlR5ebk+/vhjjRgxQgcOHGhVq//L/H6/kpOTowYtfwBArIjYOKzYuXOnpk2bpszMTPl8Pm3cuDFq/9y5c+Xz+aLG1KlTLV9fm+7zT0xM1HPPPaf169dr0qRJampqasthAADAlzQ0NGjYsGGaN2+ebrnllnN+Z+rUqSouLm7+7Pf7LZ+nXQ/5ue222zR69GiVl5erb9++7TkUAAAxI2Kxo22X/Px85efnX/A7fr9f6enp7TpPu5/wl5WVpaysrPYeBgCAmGHnXP257nDz+/1tqtglaceOHerdu7cuvfRSTZgwQT/+8Y+bb7VvLSbaAQDoQKFQSIFAIGqEQqE2HWvq1Kl6/vnntW3bNj366KMqLS1Vfn6+5el3nu0PAICJnc/2DwaDZz0kr61V/2233db856FDhyonJ0dXXXWVduzYoYkTJ7b6OCR/AABM7HzCX3ta/C258sorlZaWpqqqKkvJn7Y/AAAXqT//+c/65JNPlJGRYennqPwBADBx6gl/p0+fVlVVVfPn6upqVVRUKCUlRSkpKXrooYc0Y8YMpaen69ChQ7r//vvVv39/TZkyxdJ5SP4AAJg49WS+/fv36xvf+Ebz57+vFZgzZ46Kior01ltv6bnnntPJkyeVmZmpyZMn60c/+pHlaQWSPwAAMWL8+PEyjPP/6rFlyxZbzkPyBwDAhFf6AgDgMXbe6heLSP4AAJjE6tv47MKtfgAAeAyVPwAAJsz5AwDgMW6f86ftDwCAx1D5AwBg4vbKn+QPAICJ4fI5f9r+AAB4TMxU/l3jXdBkCYedjsAWkaO1Todgi1M1HfMKzc70xuCA0yHYIqvsoNMhtFv1sIFOh2CPTz92OoKLggsy0gXFTPIHACBWuD350/YHAMBjqPwBADBx++N9Sf4AAJjwhD8AADyGOX8AAOAqVP4AAJi4vfIn+QMAYOL2BX+0/QEA8BgqfwAATFjtDwCAx7h9zp+2PwAAHkPlDwCAidsX/JH8AQAwibg8/dP2BwDAY6j8AQAwcfuCP5I/AAAm7m76k/wBADiL2yt/5vwBAPAYKn8AAEx4wt8FNDQ06KWXXlJVVZUyMjI0e/Zspaam2hUbAACOcPutfpaS/+DBg7Vr1y6lpKToww8/1NixY/Xpp59qwIABOnTokH70ox+prKxM/fr1u+BxwuGwwuFw9LZIRP4uzEIAANDRLGXbP/7xj/rb3/4mSQoGg8rMzNThw4e1d+9eHT58WDk5OXrwwQdbPE4oFFIgEIgaq48ebtsVAABgM8PGEYvaXGrv3r1by5cvVyAQkCQlJibqoYce0q5du1r82WAwqLq6uqhRkNm3raEAAGCriI0jFlme8/f5vlgFcebMGWVkZETtu+yyy/Txxx+3eAy/3y+/3x+1rZ6WPwAAncJy8p84caLi4uJUX1+vyspKDRkypHnf4cOHWfAHALjoseDvS5YtWxb1OTExMerzpk2bNGbMmPZHBQCAg9yd+tuZ/M1+9rOftSsYAADQ8XjIDwAAJrG6UM8uJH8AAEyY8wcAwGPcnfp5sQ8AAJ5D5Q8AgAlz/gAAeIzh8sY/bX8AADyGyh8AABPa/gAAeIzbb/Wj7Q8AgMdQ+QMAYOLuup/kDwDAWWj7AwAAV6HyBwDAhNX+AAB4jNsf8kPyBwDAxO2VP3P+AAB4TMxU/j43/BoSFzN/ne3iSwk4HYItEi457nQI7VZ3vLvTIdjiT9dd43QI7Tb1Tw1Oh2CLfal9nA7hokDbHwAAj6HtDwAAOsXOnTs1bdo0ZWZmyufzaePGjVH7DcPQD3/4Q2VkZKh79+6aNGmSDh48aPk8JH8AAEwihmHbsKKhoUHDhg3T6tWrz7n/pz/9qVatWqWnn35ae/bsUY8ePTRlyhSdOXPG0nlo+wMAYOLUjH9+fr7y8/PPuc8wDK1cuVI/+MEPdPPNN0uSnn/+efXp00cbN27Ubbfd1urzUPkDANCBwuGw6uvro0Y4HLZ8nOrqatXU1GjSpEnN2wKBgHJzc7V7925LxyL5AwBgEpFh2wiFQgoEAlEjFApZjqmmpkaS1KdP9B0bffr0ad7XWrT9AQAwsfNWv2AwqMLCwqhtfr/ftuO3BckfAIAO5Pf7bUn26enpkqTa2lplZGQ0b6+trdW1115r6Vi0/QEAMInYOOzSr18/paena9u2bc3b6uvrtWfPHuXl5Vk6FpU/AAAmEYfW+58+fVpVVVXNn6urq1VRUaGUlBRlZ2dr8eLF+vGPf6yrr75a/fr107/+678qMzNT06dPt3Qekj8AACZOPd53//79+sY3vtH8+e9rBebMmaO1a9fq/vvvV0NDg+6++26dPHlSo0ePVklJibp162bpPCR/AABixPjx42Vc4MFAPp9PDz/8sB5++OF2nYfkDwCAiduf7U/yBwDA5ELVtxuw2h8AAI+h8gcAwMSp1f6dheQPAICJ2+f8afsDAOAxVP4AAJg4dZ9/ZyH5AwBg4vY5f9r+AAB4jKXk/+abb6q6urr58wsvvKBRo0bp8ssv1+jRo7V+/fpWHSccDqu+vj5qhCNuX14BALhYGIZh24hFlpL/nXfeqUOHDkmSfvnLX+o73/mORowYoQcffFAjR47U/Pnz9atf/arF44RCIQUCgajx1EeH23YFAADYLBbf6mcnS3P+Bw8e1NVXXy1JWrNmjZ588knNnz+/ef/IkSP1k5/8RPPmzbvgcYLBYPPLCv7uxA3ftBIKAAAdhgV/X3LJJZfoxIkT6tu3rz766CNdf/31Uftzc3OjpgXOx+/3y+/3R2071YXlBwAAdAZLGTc/P19FRUWSpHHjxul3v/td1P6XXnpJ/fv3ty86AAAcEJFh24hFlir/Rx99VKNGjdK4ceM0YsQIPf7449qxY4cGDRqkyspKlZWVacOGDR0VKwAAnSJWF+rZxVLln5mZqT/84Q/Ky8tTSUmJDMPQ3r179eqrryorK0v/8z//oxtvvLGjYgUAADaw/JCfnj17asWKFVqxYkVHxAMAgONitV1vF57wBwCAidtX+7PEHgAAj6HyBwDAJOLyBX8kfwAATNyd+mn7AwDgOVT+AACYsNofAACPIfkDAOAxPOEPAAC4CpU/AAAmtP0BAPAYnvAHAABchcofAAATty/4I/kDAGDi9jl/2v4AAHgMlT8AACa0/TtJypLxTofQbpGDVU6HYIuE7yx3OgRbpGa84HQI7ZbaI8npEOzxl4+djqDd9qX2cToEW/xswR6nQ7DFDw937PFp+wMAAFeJmcofAIBY4fb7/En+AACYRJjzBwDAW9xe+TPnDwCAx1D5AwBgQtsfAACPoe0PAABchcofAAAT2v4AAHgMbX8AAOAqVP4AAJjQ9gcAwGNo+wMAAFeh8gcAwMQwIk6H0KFI/gAAmERc3vYn+QMAYGK4fMEfc/4AAHgMlT8AACa0/QEA8Bja/gAAwFUsJf9Fixbpv//7v9t90nA4rPr6+qgRbvxbu48LAIAdIoZh24hFlpL/6tWrNX78eA0YMECPPvqoampq2nTSUCikQCAQNX62sf2/VAAAYAfDxv9ikeW2/6uvvqobb7xRjz32mLKzs3XzzTfrlVdeUSTS+gciBINB1dXVRY37po+xGgoAAGgDy8l/6NChWrlypY4ePaoXX3xR4XBY06dP1+WXX64HH3xQVVVVLR7D7/crOTk5avjjWXsIAIgNhmHYNqxYvny5fD5f1Bg4cKDt19fmBX/x8fGaOXOmSkpK9MEHH2j+/Pn69a9/rWuuucbO+AAA6HQRGbYNq77yla/o2LFjzWPXrl22X58tq/2zs7O1fPlyVVdXq6SkxI5DAgDgSXFxcUpPT28eaWlptp/DUvLv27evunbtet79Pp9PN9xwQ7uDAgDASXa2/c95h1s4fN5zHzx4UJmZmbryyit1++2368iRI7Zfn6XkX11drdTUVNuDAAAglth5q9+57nALhULnPG9ubq7Wrl2rkpISFRUVqbq6WmPGjNGpU6dsvT5W2QEAYGLnE/6CwaAKCwujtvn9/nN+Nz8/v/nPOTk5ys3NVd++ffXSSy/prrvusi0mkj8AAB3I7/efN9m3pGfPnhowYECr7qSzgsf7AgBg4uRq/y87ffq0Dh06pIyMDJuu7AskfwAATJy6z3/p0qUqLS3Vn/70J73xxhv61re+pa5du2r27Nm2Xh9tfwAAYsSf//xnzZ49W5988ol69eql0aNHq6ysTL169bL1PCR/AABMnHohz/r16zvlPCR/AABMYvWFPHZhzh8AAI+h8gcAwMSptn9nIfkDAGBi50N+YhFtfwAAPIbKHwAAE7cv+CP5AwBg4va2P8kfAAATtyd/5vwBAPAYKn8AAEzcXfdLMjzizJkzxrJly4wzZ844HUqbueEaDMMd1+GGazAMriOWuOEaDMM91+F2PsNw+cTG/1dfX69AIKC6ujolJyc7HU6buOEaJHdchxuuQeI6YokbrkFyz3W4HXP+AAB4DMkfAACPIfkDAOAxnkn+fr9fy5Ytk9/vdzqUNnPDNUjuuA43XIPEdcQSN1yD5J7rcDvPLPgDAABf8EzlDwAAvkDyBwDAY0j+AAB4DMkfAACP8UTyX716ta644gp169ZNubm52rt3r9MhWbJz505NmzZNmZmZ8vl82rhxo9MhWRYKhTRy5EglJSWpd+/emj59uiorK50Oy7KioiLl5OQoOTlZycnJysvL0+bNm50Oq11WrFghn8+nxYsXOx2KJcuXL5fP54saAwcOdDqsNvnoo490xx13KDU1Vd27d9fQoUO1f/9+p8NqtSuuuOKsfwufz6eCggKnQ8N5uD75/+Y3v1FhYaGWLVumN998U8OGDdOUKVN0/Phxp0NrtYaGBg0bNkyrV692OpQ2Ky0tVUFBgcrKyrR161Y1NjZq8uTJamhocDo0S7KysrRixQqVl5dr//79mjBhgm6++Wa98847TofWJvv27dO///u/Kycnx+lQ2uQrX/mKjh071jx27drldEiWffrppxo1apTi4+O1efNmvfvuu3r88cd16aWXOh1aq+3bty/q32Hr1q2SpFtvvdXhyHBezr5aoONdf/31RkFBQfPnpqYmIzMz0wiFQg5G1XaSjA0bNjgdRrsdP37ckGSUlpY6HUq7XXrppcYvf/lLp8Ow7NSpU8bVV19tbN261Rg3bpxx7733Oh2SJcuWLTOGDRvmdBjt9sADDxijR492Ogxb3XvvvcZVV11lRCIRp0PBebi68v/8889VXl6uSZMmNW/r0qWLJk2apN27dzsYGerq6iRJKSkpDkfSdk1NTVq/fr0aGhqUl5fndDiWFRQU6Kabbor6/+Nic/DgQWVmZurKK6/U7bffriNHjjgdkmW///3vNWLECN16663q3bu3rrvuOv3iF79wOqw2+/zzz/Xiiy9q3rx58vl8ToeD83B18j9x4oSamprUp0+fqO19+vRRTU2NQ1EhEolo8eLFGjVqlIYMGeJ0OJa9/fbbSkxMlN/v14IFC7RhwwYNHjzY6bAsWb9+vd58802FQiGnQ2mz3NxcrV27ViUlJSoqKlJ1dbXGjBmjU6dOOR2aJR988IGKiop09dVXa8uWLbrnnnv0ve99T88995zTobXJxo0bdfLkSc2dO9fpUHABcU4HAO8pKCjQgQMHLsr5WUm65pprVFFRobq6Ov3ud7/TnDlzVFpaetH8AvDhhx/q3nvv1datW9WtWzenw2mz/Pz85j/n5OQoNzdXffv21UsvvaS77rrLwcisiUQiGjFihB555BFJ0nXXXacDBw7o6aef1pw5cxyOzrpnn31W+fn5yszMdDoUXICrK/+0tDR17dpVtbW1Udtra2uVnp7uUFTetnDhQr3yyivavn27srKynA6nTRISEtS/f38NHz5coVBIw4YN05NPPul0WK1WXl6u48eP66tf/ari4uIUFxen0tJSrVq1SnFxcWpqanI6xDbp2bOnBgwYoKqqKqdDsSQjI+OsXxwHDRp0UU5hHD58WK+99pr+5V/+xelQ0AJXJ/+EhAQNHz5c27Zta94WiUS0bdu2i3KO9mJmGIYWLlyoDRs26PXXX1e/fv2cDsk2kUhE4XDY6TBabeLEiXr77bdVUVHRPEaMGKHbb79dFRUV6tq1q9Mhtsnp06d16NAhZWRkOB2KJaNGjTrrttf3339fffv2dSiitisuLlbv3r110003OR0KWuD6tn9hYaHmzJmjESNG6Prrr9fKlSvV0NCgO++80+nQWu306dNR1Ux1dbUqKiqUkpKi7OxsByNrvYKCAq1bt04vv/yykpKSmtdcBAIBde/e3eHoWi8YDCo/P1/Z2dk6deqU1q1bpx07dmjLli1Oh9ZqSUlJZ6216NGjh1JTUy+qNRhLly7VtGnT1LdvXx09elTLli1T165dNXv2bKdDs2TJkiX6+te/rkceeUQzZ87U3r179cwzz+iZZ55xOjRLIpGIiouLNWfOHMXFuT61XPycvt2gM/z85z83srOzjYSEBOP66683ysrKnA7Jku3btxuSzhpz5sxxOrRWO1f8kozi4mKnQ7Nk3rx5Rt++fY2EhASjV69exsSJE41XX33V6bDa7WK81W/WrFlGRkaGkZCQYFx22WXGrFmzjKqqKqfDapNNmzYZQ4YMMfx+vzFw4EDjmWeecToky7Zs2WJIMiorK50OBa3AK30BAPAYV8/5AwCAs5H8AQDwGJI/AAAeQ/IHAMBjSP4AAHgMyR8AAI8h+QMA4DEkfwAAPIbkDwCAx5D8AQDwGJI/AAAeQ/IHAMBj/h+mMuO5NcL+XAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 554.46it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 467.54it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 537.96it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 441.35it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 280.39it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 700.54it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 817.20it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 589.38it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 560.51it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 740.57it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 341.31it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 419.02it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 560.32it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 450.62it/s]\n" + ] + }, + { + "data": { + "image/png": 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06VJlZ2fr+9//vtatW6cPPvhAL730UszXIfkDABAnSkpKJEkTJ06M2l5aWqq5c+dKkhYuXKgzZ85o0aJF+vzzz5Wdna3KykpdeeWVMV+H5A8AgInhUOVvGLFNEyxZsiTqOX+rfEasV+pk/512u9MhdFh238+cDsEWJz7r5XQItriz+bDTIXRYTs/Y5/DiWR9fotMhdFj22Uv/HiTp1tEfOR2CLVI2VnXq+T+7eYJt5+rzfzs31vZgwR8AAB5D2x8AABOn2v4XC8kfAAAzlyd/2v4AAHgMlT8AACa0/QEA8BiSPwAAHuP25M+cPwAAHkPlDwCAmeFzOoJORfIHAMCEtj8AAHAVKn8AAEyMCG1/AAA8hbY/AABwFSp/AABMDFb7AwDgLbT9AQCAq1D5AwBgwmp/AAA8xjCcjqBzkfwBADBxe+XPnD8AAB5jufJ///33VV1drdzcXA0bNkwffPCBnnzySYXDYd15552aNGlSm+cIh8MKh8NR25qNFiX4uloNBwAA21H5f0NFRYW+//3v68EHH9Q111yjiooK5efn6+DBgzp69KgKCgq0ffv2Ns8TCoUUCASixn83vdfumwAAwE6GYd+IR5aS/y9+8QstXrxYn332mUpLS3X77bdr/vz5qqys1LZt27R48WItX768zfMUFxeroaEhatzSc0S7bwIAAMTOUvJ/9913NXfuXEnSrFmzdPr0af393/996/477rhD//u//9vmefx+v5KTk6MGLX8AQLwwIj7bRjyyPOfv8/3lRrp06aLu3bsrEAi07ktKSlJDQ4N90QEA4AC3v97XUuU/cOBAHThwoPXn3bt3KzMzs/XnY8eOKT093b7oAACA7SxV/vfdd59aWlpaf87Kyora/8orr8S02h8AgHjm9nf7W0r+99577wX3P/roox0KBgCAeBCh7Q8AANyE1/sCAGDi9gV/JH8AAEzi9RE9u5D8AQAwidc389mFOX8AADyGyh8AABPa/gAAeAyP+gEAAFeh8gcAwIRH/QAA8BhW+wMAAFeh8gcAwMTtC/5I/gAAmLh9zp+2PwAAHkPlDwCAidsX/JH8AQAwYc7/IuluRJwOocN69Q07HYIt6k8lOR2CLeb1HOF0CB32Lyd2Oh2CLZ7pM8HpEDqsySWTpAmDkp0O4ZLAnD8AAHCVuKn8AQCIF25v+1P5AwBgYtg4rAiFQsrJyVFSUpJSU1M1Y8YM1dXVnT9Gw9DUqVPl8/m0YcMGS9ch+QMAECeqqqoUDAZVXV2tyspKNTc3q6CgQE1NTecc+8QTT8jna1+HgrY/AAAmTrX9Kyoqon5eu3atUlNTVVNTo/z8/Nbt+/fv12OPPaZ9+/YpPT3d8nVI/gAAmNi52j8cDiscjn4azO/3y+/3t/m7DQ0NkqSUlJTWbX/+8591++23a+XKlUpLS2tXTLT9AQDoRKFQSIFAIGqEQqE2fy8SiWjhwoXKy8tTVlZW6/ZFixZp3Lhxmj59ertjovIHAMDEzjfPFBcXq6ioKGpbLFV/MBhUbW2tdu3a1bpt06ZN2r59u95+++0OxUTyBwDAxJB9bf9YW/zfVFhYqC1btmjnzp3KyMho3b59+3YdOnRIvXv3jjp+5syZGj9+vHbs2BHT+Un+AADECcMwtGDBApWXl2vHjh0aNGhQ1P4lS5boJz/5SdS2kSNH6vHHH9e0adNivg7JHwAAk4hDH/YJBoMqKyvTxo0blZSUpPr6eklSIBBQjx49lJaWdt5FfpmZmef8oXAhJH8AAEwiNrb9rSgpKZEkTZw4MWp7aWmp5s6da9t1SP4AAJjYOedv6brt+JZwe36HR/0AAPAYKn8AAEwu/Y/MXxjJHwAAE6fa/hcLbX8AADyGyh8AABPa/gAAeIzbk78tbf/2PGYAAACcYUvy9/v9ev/99+04FQAAjjPks23EI0ttf/NXif6qpaVFy5cvV58+fSRJv/nNbzoeGQAADonEZ862jaXk/8QTTyg7O/ucrwkZhqH3339fPXv2lM/X9j+xcDiscDgcta3ZaFGCr6uVcAAAQDtYSv6PPvqoVq9erccee0yTJk1q3Z6QkKC1a9dqxIgRMZ0nFArp4Ycfjtp2+2VX645eWVbCAQCgUzj1bv+LxdKc/5IlS/TCCy/ovvvu04MPPqjm5uZ2XbS4uFgNDQ1RY1bP4e06FwAAdjNsHPHI8oK/nJwc1dTU6NNPP9WYMWNUW1sbU6v/m/x+v5KTk6MGLX8AQLyI2DjiUbue8+/Vq5fWrVun9evXa8qUKWppabE7LgAA0Ek69JKff/iHf9ANN9ygmpoaDRgwwK6YAABwVMRiR/tS0+E3/GVkZCgjI8OOWAAAiAvxOldvFz7sAwCAx/BufwAATOJ1oZ5dSP4AAJi4/Q1/tP0BAPAYKn8AAEzc/oY/kj8AACas9gcAAK5C5Q8AgInbF/yR/AEAMOFRPwAAPIY5fwAA4CpU/gAAmDDnDwCAx7h9zp+2PwAAHkPlDwCAidsrf5I/AAAmhsvn/Gn7AwDgMXFT+df54yaUdss81svpEGzxcvdEp0Owxd6Wz5wOocP690xxOgRbzP/0dadD6LB5/cc5HYItvvjDn50OwRZJnXx+2v4AAHiM25M/bX8AADyGyh8AABO3v96X5A8AgAlv+AMAwGOY8wcAAK5C5Q8AgInbK3+SPwAAJm5f8EfbHwAAj6HyBwDAhNX+AAB4jNvn/Gn7AwDgMVT+AACYuH3BH8kfAACTiMvTP21/AAA8huQPAIBJxMZhRSgUUk5OjpKSkpSamqoZM2aorq6udf/nn3+uBQsWaOjQoerRo4cyMzN1//33q6GhwdJ1SP4AAJgYNg4rqqqqFAwGVV1drcrKSjU3N6ugoEBNTU2SpOPHj+v48eNasWKFamtrtXbtWlVUVOjuu++2dB3m/AEAMHHqUb+Kioqon9euXavU1FTV1NQoPz9fWVlZ+v3vf9+6/8orr9QjjzyiO++8U2fPnlW3brGldZI/AACdKBwOKxwOR23z+/3y+/1t/u5f2/kpKSkXPCY5OTnmxC/R9gcA4BwRn30jFAopEAhEjVAo1HYMkYgWLlyovLw8ZWVlnfeYU6dO6Ze//KV++tOfWrq/DlX+TU1NevHFF3Xw4EGlp6frtttuU58+fTpySgAAHGfno34PFRerqKgoalssVX8wGFRtba127dp13v2NjY26+eabNWLECC1btsxSTJaS/4gRI7Rr1y6lpKToo48+Un5+vv70pz/pqquu0qFDh/TLX/5S1dXVGjRo0AXPc74WyFmjRd18XS0FDwBAvIu1xf9NhYWF2rJli3bu3KmMjIxz9p8+fVo//OEPlZSUpPLyciUkJFg6v6W2/wcffKCzZ89KkoqLi9W/f38dPXpUe/bs0dGjRzVq1Cg99NBDbZ7nfC2Q1xrftRQ4AACdxanV/oZhqLCwUOXl5dq+fft5i+nGxkYVFBQoMTFRmzZtUvfu3S3fX7vn/Hfv3q1ly5YpEAhIknr16qWHH374W9sT31RcXKyGhoaoMSX56vaGAgCArZx6zj8YDOr5559XWVmZkpKSVF9fr/r6en311VeS/pb4m5qatGbNGjU2NrYe09LSEvN1LM/5+3x/+c7hmTNnlJ6eHrXvu9/9rj799NM2z3G+FggtfwCA15WUlEiSJk6cGLW9tLRUc+fO1VtvvaU333xTkjR48OCoY44cOaKBAwfGdB3LyX/y5Mnq1q2bGhsbVVdXF7UC8ejRoyz4AwBc8px6t79hXPi6EydObPOYWFhK/kuXLo36uVevXlE/b968WePHj+9wUAAAOMndn/XpYPI3+/Wvf92hYAAAQOfjDX8AAJg49Xrfi4XkDwCAiVNz/hcLyR8AABN3p37e7Q8AgOdQ+QMAYMKcPwAAHmO4vPFP2x8AAI+h8gcAwIS2PwAAHuP2R/1o+wMA4DFU/gAAmLi77if5AwBwDtr+AADAVaj8AQAwYbU/AAAe4/aX/JD8AQAwcXvlz5w/AAAeEzeVv98FHZa+GV86HYItrv8wyekQbPFl995Oh9BhX3VvdjoEW9zXa6TTIXTYtpbPnA7BFpeP7+l0CJcE2v4AAHgMbX8AAOAqVP4AAJhEDNr+AAB4irtTP21/AAA8h8ofAAATt7/bn+QPAICJ2x/1o+0PAIDHUPkDAGDi9uf8Sf4AAJgw5w8AgMcw5w8AAFyFyh8AABPm/AEA8BjD5a/3pe0PAIDHUPkDAGDCan8AADzG7XP+tP0BAPAYKn8AAEzc/pw/yR8AABO3z/nT9gcAwGMsJf+33npLR44caf35P/7jP5SXl6fvfe97uuGGG7R+/fqYzhMOh9XY2Bg1mo0Wa5EDANBJDMOwbcQjS8l/3rx5OnTokCTp2Wef1T333KMxY8booYceUk5OjubPn6/nnnuuzfOEQiEFAoGo8Wrju+27AwAAbBaxccQjS3P+Bw4c0JAhQyRJq1at0pNPPqn58+e37s/JydEjjzyiu+6664LnKS4uVlFRUdS20hH3WAkFAIBOw4K/b7jssst06tQpDRgwQB9//LHGjh0btf+6666Lmhb4Nn6/X36/P2pbgq+rlVAAAEA7WWr7T506VSUlJZKkCRMm6KWXXora/+KLL2rw4MH2RQcAgAMiMmwb8chS5f+rX/1KeXl5mjBhgsaMGaPHHntMO3bs0PDhw1VXV6fq6mqVl5d3VqwAAFwU8bpQzy6WKv/+/fvr7bffVm5urioqKmQYhvbs2aNXX31VGRkZ+sMf/qCbbrqps2IFAAA2sPySn969e2v58uVavnx5Z8QDAIDj4rVdbxfe8AcAgInbV/vzhj8AAOJEKBRSTk6OkpKSlJqaqhkzZqiuri7qmDNnzigYDKpPnz7q1auXZs6cqRMnTli6DskfAACTiGHYNqyoqqpSMBhUdXW1Kisr1dzcrIKCAjU1NbUes2jRIm3evFm/+93vVFVVpePHj+uWW26xdB3a/gAAmDjV9K+oqIj6ee3atUpNTVVNTY3y8/PV0NCgNWvWqKysTJMmTZIklZaWavjw4aqurtb1118f03Wo/AEA6ETn+55NOByO6XcbGhokSSkpKZKkmpoaNTc3a8qUKa3HDBs2TJmZmdq9e3fMMZH8AQAwsfMlP+f7nk0oFGo7hkhECxcuVF5enrKysiRJ9fX1SkxMVO/evaOO7devn+rr62O+P9r+AACY2Pmo3/m+Z2N+xf35BINB1dbWateuXbbF8lckfwAATOx8w9/5vmfTlsLCQm3ZskU7d+5URkZG6/a0tDR9/fXX+uKLL6Kq/xMnTigtLS3m89P2BwAgThiGocLCQpWXl2v79u0aNGhQ1P7Ro0crISFB27Zta91WV1enY8eOKTc3N+brUPkDAGDi1Bv+gsGgysrKtHHjRiUlJbXO4wcCAfXo0UOBQEB33323ioqKlJKSouTkZC1YsEC5ubkxr/SXSP4AAJzDqTf8/fXLuRMnTozaXlpaqrlz50qSHn/8cXXp0kUzZ85UOBzWjTfeqFWrVlm6DskfAIA4Ectag+7du2vlypVauXJlu69D8gcAwMTtn/Ql+QMAYOL2r/qx2h8AAI+h8gcAwIS2/0Uyb/VYp0PosLNbG5wOwRYFq+91OgRbTCorcTqEDmv52B3NuW5X93A6hA5bMPRGp0OwxaEFrzkdgi2yftO556ftDwAAXCVuKn8AAOKFU8/5XywkfwAATCLM+QMA4C1ur/yZ8wcAwGOo/AEAMKHtDwCAx9D2BwAArkLlDwCACW1/AAA8hrY/AABwFSp/AABMaPsDAOAxtP0BAICrUPkDAGBiGBGnQ+hUJH8AAEwiLm/7k/wBADAxXL7gjzl/AAA8hsofAAAT2v4AAHgMbX8AAOAqlpL/ggUL9MYbb3T4ouFwWI2NjVEj3Hy2w+cFAMAOEcOwbcQjS8l/5cqVmjhxoq666ir96le/Un19fbsuGgqFFAgEosavX6hs17kAALCbYeP/4pHltv+rr76qm266SStWrFBmZqamT5+uLVu2KBKJ/YUIxcXFamhoiBqLZ//AaigAAKAdLCf/kSNH6oknntDx48f1/PPPKxwOa8aMGfre976nhx56SAcPHmzzHH6/X8nJyVHDn8DaQwBAfDAMw7YRj9q94C8hIUGzZs1SRUWFDh8+rPnz5+s///M/NXToUDvjAwDgoovIsG3EI1tW+2dmZmrZsmU6cuSIKioq7DglAADoJJZ67QMGDFDXrl2/db/P59MPfsDcPQDg0hav7Xq7WEr+R44c6aw4AACIG/H6iJ5dWGUHAICJ2yt/3vAHAIDHUPkDAGASr6v07ULyBwDAhLY/AABwFSp/AABMWO0PAIDHxOsHeexC2x8AAI+h8gcAwIS2PwAAHsNqfwAA4CpU/gAAmLh9wR/JHwAAE7e3/Un+AACYuD35M+cPAIDHUPkDAGDi7rpfkuERZ86cMZYuXWqcOXPG6VDazQ33YBjuuA833INhcB/xxA33YBjuuQ+38xmGyyc2/r/GxkYFAgE1NDQoOTnZ6XDaxQ33ILnjPtxwDxL3EU/ccA+Se+7D7ZjzBwDAY0j+AAB4DMkfAACP8Uzy9/v9Wrp0qfx+v9OhtJsb7kFyx3244R4k7iOeuOEeJPfch9t5ZsEfAAD4C89U/gAA4C9I/gAAeAzJHwAAjyH5AwDgMZ5I/itXrtTAgQPVvXt3XXfdddqzZ4/TIVmyc+dOTZs2Tf3795fP59OGDRucDsmyUCiknJwcJSUlKTU1VTNmzFBdXZ3TYVlWUlKiUaNGKTk5WcnJycrNzdUrr7zidFgdsnz5cvl8Pi1cuNDpUCxZtmyZfD5f1Bg2bJjTYbXLxx9/rDvvvFN9+vRRjx49NHLkSO3bt8/psGI2cODAc/5d+Hw+BYNBp0PDt3B98n/hhRdUVFSkpUuX6q233lJ2drZuvPFGnTx50unQYtbU1KTs7GytXLnS6VDaraqqSsFgUNXV1aqsrFRzc7MKCgrU1NTkdGiWZGRkaPny5aqpqdG+ffs0adIkTZ8+Xe+++67TobXL3r179W//9m8aNWqU06G0y9VXX61PPvmkdezatcvpkCz705/+pLy8PCUkJOiVV17Re++9p8cee0yXX36506HFbO/evVH/HiorKyVJt956q8OR4Vs5+2mBzjd27FgjGAy2/tzS0mL079/fCIVCDkbVfpKM8vJyp8PosJMnTxqSjKqqKqdD6bDLL7/cePbZZ50Ow7LTp08bQ4YMMSorK40JEyYYDzzwgNMhWbJ06VIjOzvb6TA67Gc/+5lxww03OB2GrR544AHjyiuvNCKRiNOh4Fu4uvL/+uuvVVNToylTprRu69Kli6ZMmaLdu3c7GBkaGhokSSkpKQ5H0n4tLS1av369mpqalJub63Q4lgWDQd18881R/31cag4cOKD+/fvriiuu0B133KFjx445HZJlmzZt0pgxY3TrrbcqNTVV11xzjX772986HVa7ff3113r++ed11113yefzOR0OvoWrk/+pU6fU0tKifv36RW3v16+f6uvrHYoKkUhECxcuVF5enrKyspwOx7J33nlHvXr1kt/v17333qvy8nKNGDHC6bAsWb9+vd566y2FQiGnQ2m36667TmvXrlVFRYVKSkp05MgRjR8/XqdPn3Y6NEsOHz6skpISDRkyRFu3btV9992n+++/X+vWrXM6tHbZsGGDvvjiC82dO9fpUHAB3ZwOAN4TDAZVW1t7Sc7PStLQoUO1f/9+NTQ06KWXXtKcOXNUVVV1yfwB8NFHH+mBBx5QZWWlunfv7nQ47TZ16tTW/z9q1Chdd911GjBggF588UXdfffdDkZmTSQS0ZgxY/Too49Kkq655hrV1tbqmWee0Zw5cxyOzro1a9Zo6tSp6t+/v9Oh4AJcXfn37dtXXbt21YkTJ6K2nzhxQmlpaQ5F5W2FhYXasmWLXn/9dWVkZDgdTrskJiZq8ODBGj16tEKhkLKzs/Xkk086HVbMampqdPLkSV177bXq1q2bunXrpqqqKj311FPq1q2bWlpanA6xXXr37q2rrrpKBw8edDoUS9LT08/5w3H48OGX5BTG0aNH9dprr+knP/mJ06GgDa5O/omJiRo9erS2bdvWui0SiWjbtm2X5BztpcwwDBUWFqq8vFzbt2/XoEGDnA7JNpFIROFw2OkwYjZ58mS988472r9/f+sYM2aM7rjjDu3fv19du3Z1OsR2+fLLL3Xo0CGlp6c7HYoleXl55zz2+uGHH2rAgAEORdR+paWlSk1N1c033+x0KGiD69v+RUVFmjNnjsaMGaOxY8fqiSeeUFNTk+bNm+d0aDH78ssvo6qZI0eOaP/+/UpJSVFmZqaDkcUuGAyqrKxMGzduVFJSUuuai0AgoB49ejgcXeyKi4s1depUZWZm6vTp0yorK9OOHTu0detWp0OLWVJS0jlrLXr27Kk+ffpcUmswHnzwQU2bNk0DBgzQ8ePHtXTpUnXt2lW33Xab06FZsmjRIo0bN06PPvqoZs2apT179mj16tVavXq106FZEolEVFpaqjlz5qhbN9enlkuf048bXAz/+q//amRmZhqJiYnG2LFjjerqaqdDsuT11183JJ0z5syZ43RoMTtf/JKM0tJSp0Oz5K677jIGDBhgJCYmGt/5zneMyZMnG6+++qrTYXXYpfio3+zZs4309HQjMTHR+O53v2vMnj3bOHjwoNNhtcvmzZuNrKwsw+/3G8OGDTNWr17tdEiWbd261ZBk1NXVOR0KYsAnfQEA8BhXz/kDAIBzkfwBAPAYkj8AAB5D8gcAwGNI/gAAeAzJHwAAjyH5AwDgMSR/AAA8huQPAIDHkPwBAPAYkj8AAB5D8gcAwGP+H7/mQVG4mVurAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 477.29it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 295.02it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 482.43it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 212.51it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 421.53it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 374.31it/s]\n" + ] + }, + { + "data": { + "image/png": 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0efNmPfroo1q4cGGDx8nLy1NlZWXUmN07vdEXAQAAYmcp+X/44YeaNm2aJGny5Mk6e/as/vEf/7F+/1133aU///nPDR7H7/crOTk5atDyBwDECyPis21YEQqFNGzYMCUlJSklJUWTJk1SeXn5OZ/buXOnxowZo7Zt2yo5OVnZ2dn69ttvYz6P5Yzr8/1wIS1atFDr1q0VCATq9yUlJamystLqIQEAiCuG4bNtWFFcXKxgMKiSkhIVFRWptrZWOTk5qq6urv/Mzp07deuttyonJ0e7du3S7t27NXPmTLVoEXtKt7Tgr3v37jp48KCuvfba+gDS0//Wrj9+/LhSU1OtHBIAAPyvwsLCqJ+XL1+ulJQUlZaWKjs7W5I0Z84cPfzww3r88cfrP9enTx9L57FU+T/00EOqq6ur/zkjI0MtW/7t+8PGjRtjWu0PAEA8MyL2jXA4rKqqqqhhvuPtQv7aTe/QoYMk6dSpU3r//feVkpKiESNGqHPnzho1apR27Nhh6fosJf8HH3xQ48ePv+D+BQsW6NVXX7UUAAAA8SZi+Gwb57vDLRQKNRxDJKLZs2crKytLGRkZkqQjR45IkubPn68ZM2aosLBQ119/vcaOHauDBw/GfH084Q8AgGaUl5en3NzcqG1+v7/B3wsGgyorK4uq6iORH143+MADD+iee+6RJA0ePFibN2/W66+/HtOXConkDwDAOawu1LsYv98fU7L/sZkzZ2rDhg3avn270tLS6rf/dV1d//79oz7fr18/HT8e+/NyuL8OAAATp271MwxDM2fOVEFBgbZs2aIePXpE7e/evbu6du16zu1/Bw4cULdu3WI+D5U/AAAmTj2ZLxgMauXKlVq7dq2SkpJUUVEhSQoEAmrTpo18Pp8effRRzZs3T4MGDdJPf/pTrVixQp988oneeuutmM9D8gcAIE7k5+dLkkaPHh21fdmyZfUP2Zs9e7Zqamo0Z84cnTlzRoMGDVJRUVH9bfixIPkDAGDi1It9jBhbDo8//njUff5WkfwBADCJ2LjgLx6x4A8AAI+h8gcAwMTOW/3iEckfAAATp1b7Xyq0/QEA8BgqfwAATNy+4I/kDwCAidvn/Gn7AwDgMVT+AACYuH3BH8kfAAAT5vwvka8/jZtQGq3N/75n+XKX0M4ds0GPtb7O6RCa7JGT250OwRb/N5DpdAhN9mWkldMh2MLXuq3TIVwWmPMHAACucvmX2wAA2Iy2PwAAHuPy9X60/QEA8BoqfwAATGj7AwDgMaz2BwAArkLlDwCAiTue2nJhJH8AAEwM0fYHAAAuQuUPAIBJxOU3+pP8AQAwibi87U/yBwDAhDl/AADgKlT+AACYcKsfAAAeQ9sfAAC4CpU/AAAmtP0BAPAYtyd/W9r+huHypyEAAOAitiR/v9+vjz/+2I5DAQDgOEM+20Y8stT2z83NPe/2uro6LVy4UB07dpQk/fa3v216ZAAAOCQSnznbNpaS/+LFizVo0CC1b98+arthGPr444/Vtm1b+XwN/4mFw2GFw+HobZGI/C24+QAAgOZmKfkvWLBAL7/8shYtWqQxY8bUb09MTNTy5cvVv3//mI4TCoX05JNPRm2bndpdc7peYyUcAACahduf7W+p1H788ce1evVqPfTQQ5o7d65qa2sbddK8vDxVVlZGjV936d6oYwEAYDfDxhGPLPfZhw0bptLSUn3xxRcaOnSoysrKYmr1/5jf71dycnLUoOUPAIgXERtHPGrUff7t2rXTihUrtGrVKt10002qq6uzOy4AANBMmvSQn3/6p3/SjTfeqNLSUnXr1s2umAAAcFTEYkf7ctPkJ/ylpaUpLS3NjlgAAIgL8TpXbxcm2gEA8Bie7Q8AgEm8LtSzC8kfAAATtz/hj7Y/AAAeQ+UPAICJ25/wR/IHAMCE1f4AAMBVqPwBADBx+4I/kj8AACbc6gcAgMcw5w8AAFyFyh8AABPm/AEA8Bi3z/nT9gcAwGOo/AEAMHF75U/yBwDAxHD5nD9tfwAAPCZuKv+2qd87HULTpXR1OgJbRGrc0fCqccFX23/ucqPTIdjiydqTTofQZItbtHM6BFv4rnDH31PNzR1/C15Y3CR/AADihduTvwtqIwAAYAWVPwAAJm5/vC/JHwAAE7c/4Y+2PwAAJhEbhxWhUEjDhg1TUlKSUlJSNGnSJJWXl5/3s4ZhaNy4cfL5fFqzZo2l85D8AQCIE8XFxQoGgyopKVFRUZFqa2uVk5Oj6urqcz67ePFi+XyNa1HQ9gcAwMSp1f6FhYVRPy9fvlwpKSkqLS1VdnZ2/fZ9+/Zp0aJF2rNnj1JTUy2fh+QPAIBJvCz4q6yslCR16NChfts333yjX/ziF1q6dKm6dOnSqOOS/AEAaEbhcFjhcDhqm9/vl9/vv+jvRSIRzZ49W1lZWcrIyKjfPmfOHI0YMUITJ05sdEzM+QMAYBLx2TdCoZACgUDUCIVCDcYQDAZVVlamVatW1W9bt26dtmzZosWLFzfp+qj8AQAwsXPOPy8vT7m5uVHbGqr6Z86cqQ0bNmj79u1KS0ur375lyxYdPnxY7du3j/r87bffrpEjR2rbtm0xxUTyBwCgGcXS4v8rwzA0a9YsFRQUaNu2berRo0fU/scff1z33Xdf1LYBAwboueee04QJE2KOieQPAICJUwv+gsGgVq5cqbVr1yopKUkVFRWSpEAgoDZt2qhLly7nXeSXnp5+zheFiyH5AwBgEnEo/efn50uSRo8eHbV92bJlmjZtmm3nIfkDABAnDMP6l47G/A7JHwAAE7e/0pfkDwCASbw85Ke5kPwBADBxe+XPQ34AAPAYKn8AAEwijXtZ3mWjScm/urpab775pg4dOqTU1FTdeeed6tixo12xAQDgCKdu9btULCX//v37a8eOHerQoYM+/fRTZWdn68svv1Tv3r11+PBhPfXUUyopKWnwQQPne8lBuC4ifwKzEAAANDdL2faTTz7R999/L+mHZxV37dpVx44d065du3Ts2DENHDhQTzzxRIPHOd9LDhYfOt64KwAAwGaGjSMeNbrU3rlzp+bPn69AICBJateunZ588knt2LGjwd/Ny8tTZWVl1JjdM72xoQAAYKuIjSMeWZ7z9/l+WAVRU1Oj1NTUqH1XXXWVvvjiiwaPcb6XHNTS8gcA4JKwnPzHjh2rli1bqqqqSuXl5crIyKjfd+zYMRb8AQAueyz4+5F58+ZF/dyuXbuon9evX6+RI0c2PSoAABzk7tTfxORv9pvf/KZJwQAAgObHQ34AADCJ14V6diH5AwBgwpw/AAAe4+7Uz4t9AADwHCp/AABMmPMHAMBjDJc3/mn7AwDgMVT+AACY0PYHAMBj3H6rH21/AAA8hsofAAATd9f9JH8AAM5B2x8AALgKlT8AACas9gcAwGPc/pAfkj8AACZur/yZ8wcAwGPipvJ/b/dVTofQZDk5B5wOwRZnDrZ2OgRbfNaizukQmmxf3ZdOh2CL+5TqdAhNNq32sNMh2OL9zw86HYI9+o5q1sPT9gcAwGNo+wMAAFeh8gcAwCRi0PYHAMBT3J36afsDAOA5VP4AAJi4/dn+JH8AAEzcfqsfbX8AADyGyh8AABO33+dP8gcAwIQ5fwAAPIY5fwAA4CpU/gAAmDDnDwCAxxguf7wvbX8AADyGyh8AABNW+wMA4DFun/On7Q8AgMdQ+QMAYOL2+/xJ/gAAmLh9zp+2PwAAHmMp+e/du1dHjx6t//n3v/+9srKydPXVV+vGG2/UqlWrYjpOOBxWVVVV1Kg16qxFDgBAMzEMw7YRjywl/3vuuUeHDx+WJL366qt64IEHNHToUD3xxBMaNmyYZsyYoddff73B44RCIQUCgajxdvVHjbsCAABsFrFxxCNLc/4HDx5Ur169JEkvvviilixZohkzZtTvHzZsmJ5++mlNnz79osfJy8tTbm5u1LZNvWZc4NMAAFxaLPj7kZ/85Cc6ffq0unXrps8++0w33HBD1P7hw4dHTQtciN/vl9/vj9qW6EuwEgoAAGgkS23/cePGKT8/X5I0atQovfXWW1H733zzTfXs2dO+6AAAcEBEhm0jHlmq/J955hllZWVp1KhRGjp0qBYtWqRt27apX79+Ki8vV0lJiQoKCporVgAALol4XahnF0uVf9euXfXBBx8oMzNThYWFMgxDu3bt0jvvvKO0tDT9z//8j2677bbmihUAANjA8kN+2rdvr4ULF2rhwoXNEQ8AAI6L13a9XXjCHwAAJm5f7c8T/gAA8BgqfwAATCIs+AMAwFsMG4cVoVBIw4YNU1JSklJSUjRp0iSVl5fX7z9z5oxmzZqlPn36qE2bNkpPT9fDDz+syspKS+ch+QMAECeKi4sVDAZVUlKioqIi1dbWKicnR9XV1ZKkEydO6MSJE3r22WdVVlam5cuXq7CwUPfee6+l89D2BwDAxKnV/oWFhVE/L1++XCkpKSotLVV2drYyMjL09ttv1++/9tpr9fTTT+vuu+/W999/r5YtY0vrJH8AAEzsTP7hcFjhcDhq2/kec38+f23nd+jQ4aKfSU5OjjnxS7T9AQA4h52v9D3fm2xDoVCDMUQiEc2ePVtZWVnKyMg472dOnz6tp556Svfff7+l66PyBwCgGZ3vTbaxVP3BYFBlZWXasWPHefdXVVVp/Pjx6t+/v+bPn28pJpI/AAAmdrb9Y23x/9jMmTO1YcMGbd++XWlpaefsP3v2rG699VYlJSWpoKBAiYmJlo5P8gcAwMSpJ/wZhqFZs2apoKBA27ZtU48ePc75TFVVlW655Rb5/X6tW7dOrVu3tnwekj8AAHEiGAxq5cqVWrt2rZKSklRRUSFJCgQCatOmjaqqqpSTk6NvvvlGb7zxhqqqqlRVVSVJuvLKK5WQkBDTeUj+AACYOPVK3/z8fEnS6NGjo7YvW7ZM06ZN0969e/X+++9Lknr27Bn1maNHj6p79+4xnYfkDwCAiVP3+Tf0pWP06NG2fDHhVj8AADyGyh8AABOn2v6Xis+Ikys8++CtTofQZJvWdnQ6BFuMf3240yHY4tCvi5wOocm++KaN0yHYolf6X5wOocnaZ/3E6RBskbXqtNMh2OLPFTub9fiDuoyw7Vh/qvijbceyC21/AAA8hrY/AAAmTt3nf6mQ/AEAMInEx4x4syH5AwBg4vbKnzl/AAA8hsofAAAT2v4AAHgMbX8AAOAqVP4AAJjQ9gcAwGNo+wMAAFeh8gcAwIS2PwAAHkPbHwAAuAqVPwAAJoYRcTqEZkXyBwDAJOLytj/JHwAAE8PlC/6Y8wcAwGOo/AEAMKHtDwCAx9D2BwAArmIp+c+aNUvvvfdek08aDodVVVUVNcJ17r6tAgBw+YgYhm0jHllK/kuXLtXo0aPVu3dvPfPMM6qoqGjUSUOhkAKBQNRY9MGRRh0LAAC7GTb+E48st/3feecd3XbbbXr22WeVnp6uiRMnasOGDYpEYq/c8/LyVFlZGTX+efA1VkMBAACNYDn5DxgwQIsXL9aJEyf0xhtvKBwOa9KkSbr66qv1xBNP6NChQw0ew+/3Kzk5OWr4E1h+AACID4Zh2DbiUaMzbmJioiZPnqzCwkIdOXJEM2bM0H/8x3+oT58+dsYHAMAlF5Fh24hHtpTb6enpmj9/vo4eParCwkI7DgkAAJqJpfv8u3XrpoSEhAvu9/l8uvnmm5scFAAATorXdr1dLCX/o0ePNlccAADEjXi9Rc8uPOEPAAATt1f+LLEHAMBjqPwBADCJ11X6diH5AwBgQtsfAAC4CpU/AAAmrPYHAMBj4vWFPHah7Q8AgMdQ+QMAYELbHwAAj2G1PwAAcBUqfwAATNy+4I/kDwCAidvb/iR/AABM3J78mfMHAMBjqPwBADBxd90vyfCImpoaY968eUZNTY3ToTSaG67BMNxxHW64BsPgOuKJG67BMNxzHW7nMwyXT2z8r6qqKgUCAVVWVio5OdnpcBrFDdcgueM63HANEtcRT9xwDZJ7rsPtmPMHAMBjSP4AAHgMyR8AAI/xTPL3+/2aN2+e/H6/06E0mhuuQXLHdbjhGiSuI5644Rok91yH23lmwR8AAPiBZyp/AADwA5I/AAAeQ/IHAMBjSP4AAHiMJ5L/0qVL1b17d7Vu3VrDhw/Xrl27nA7Jku3bt2vChAnq2rWrfD6f1qxZ43RIloVCIQ0bNkxJSUlKSUnRpEmTVF5e7nRYluXn52vgwIFKTk5WcnKyMjMztXHjRqfDapKFCxfK5/Np9uzZTodiyfz58+Xz+aJG3759nQ6rUT777DPdfffd6tixo9q0aaMBAwZoz549TocVs+7du5/z38Ln8ykYDDodGi7A9cl/9erVys3N1bx587R3714NGjRIt9xyi06dOuV0aDGrrq7WoEGDtHTpUqdDabTi4mIFg0GVlJSoqKhItbW1ysnJUXV1tdOhWZKWlqaFCxeqtLRUe/bs0ZgxYzRx4kR9+OGHTofWKLt379a///u/a+DAgU6H0ijXXXedPv/88/qxY8cOp0Oy7Msvv1RWVpYSExO1ceNGffTRR1q0aJGuuOIKp0OL2e7du6P+OxQVFUmS7rjjDocjwwU5+2qB5nfDDTcYwWCw/ue6ujqja9euRigUcjCqxpNkFBQUOB1Gk506dcqQZBQXFzsdSpNdccUVxquvvup0GJadPXvW6NWrl1FUVGSMGjXKeOSRR5wOyZJ58+YZgwYNcjqMJnvssceMG2+80ekwbPXII48Y1157rRGJRJwOBRfg6sr/u+++U2lpqW666ab6bS1atNBNN92knTt3OhgZKisrJUkdOnRwOJLGq6ur06pVq1RdXa3MzEynw7EsGAxq/PjxUf9/XG4OHjyorl276pprrtFdd92l48ePOx2SZevWrdPQoUN1xx13KCUlRYMHD9Yrr7zidFiN9t133+mNN97Q9OnT5fP5nA4HF+Dq5H/69GnV1dWpc+fOUds7d+6siooKh6JCJBLR7NmzlZWVpYyMDKfDsWz//v1q166d/H6/HnzwQRUUFKh///5Oh2XJqlWrtHfvXoVCIadDabThw4dr+fLlKiwsVH5+vo4ePaqRI0fq7NmzTodmyZEjR5Sfn69evXpp06ZNeuihh/Twww9rxYoVTofWKGvWrNFXX32ladOmOR0KLqKl0wHAe4LBoMrKyi7L+VlJ6tOnj/bt26fKykq99dZbmjp1qoqLiy+bLwCffvqpHnnkERUVFal169ZOh9No48aNq//3gQMHavjw4erWrZvefPNN3XvvvQ5GZk0kEtHQoUO1YMECSdLgwYNVVlaml156SVOnTnU4Outee+01jRs3Tl27dnU6FFyEqyv/Tp06KSEhQSdPnozafvLkSXXp0sWhqLxt5syZ2rBhg7Zu3aq0tDSnw2mUVq1aqWfPnhoyZIhCoZAGDRqkJUuWOB1WzEpLS3Xq1Cldf/31atmypVq2bKni4mI9//zzatmyperq6pwOsVHat2+v3r1769ChQ06HYklqauo5Xxz79et3WU5hHDt2TO+++67uu+8+p0NBA1yd/Fu1aqUhQ4Zo8+bN9dsikYg2b958Wc7RXs4Mw9DMmTNVUFCgLVu2qEePHk6HZJtIJKJwOOx0GDEbO3as9u/fr3379tWPoUOH6q677tK+ffuUkJDgdIiN8vXXX+vw4cNKTU11OhRLsrKyzrnt9cCBA+rWrZtDETXesmXLlJKSovHjxzsdChrg+rZ/bm6upk6dqqFDh+qGG27Q4sWLVV1drXvuucfp0GL29ddfR1UzR48e1b59+9ShQwelp6c7GFnsgsGgVq5cqbVr1yopKal+zUUgEFCbNm0cji52eXl5GjdunNLT03X27FmtXLlS27Zt06ZNm5wOLWZJSUnnrLVo27atOnbseFmtwZg7d64mTJigbt266cSJE5o3b54SEhJ05513Oh2aJXPmzNGIESO0YMECTZ48Wbt27dLLL7+sl19+2enQLIlEIlq2bJmmTp2qli1dn1ouf07fbnAp/Nu//ZuRnp5utGrVyrjhhhuMkpISp0OyZOvWrYakc8bUqVOdDi1m54tfkrFs2TKnQ7Nk+vTpRrdu3YxWrVoZV155pTF27FjjnXfecTqsJrscb/WbMmWKkZqaarRq1cq46qqrjClTphiHDh1yOqxGWb9+vZGRkWH4/X6jb9++xssvv+x0SJZt2rTJkGSUl5c7HQpiwCt9AQDwGFfP+QMAgHOR/AEA8BiSPwAAHkPyBwDAY0j+AAB4DMkfAACPIfkDAOAxJH8AADyG5A8AgMeQ/AEA8BiSPwAAHkPyBwDAY/4/ebQt3r1jHSMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 421.81it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 674.69it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 677.10it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 800.31it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 466.18it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 351.00it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 317.00it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 679.18it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 307.09it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 456.48it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 362.03it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 368.10it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 710.85it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 665.87it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 447.52it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 397.81it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 415.30it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 372.21it/s]\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 488.68it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 684.04it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(10):\n", + " for j in range(i+1,10):\n", + " covariances_1 = np.load(f'{save_dir}truth/{10001+i}_state_covariances.npy')\n", + " covariances_2 = np.load(f'{save_dir}truth/{10001+j}_state_covariances.npy')\n", + " seaborn.heatmap(twopair_riemannian_distance(covariances_1,covariances_2))\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b9ffeb6b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 540.61it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 270.12it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result_dir = './results_simulation_202402_se/small/HMM_ICA_25_state_8/'\n", + "state_covariances = np.load(f'{result_dir}state_covariances.npy')\n", + "state_correlations = np.load(f'{result_dir}state_correlations.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(state_covariances,state_covariances))\n", + "plt.show()\n", + "seaborn.heatmap(twopair_riemannian_distance(state_correlations,state_correlations))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af7fe28e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from rotation.analysis import comparison_analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "25dc08e8", + "metadata": {}, + "outputs": [], + "source": [ + "models = ['HMM']\n", + "list_channels = [25,100,200]\n", + "list_states = list(range(2,36))\n", + "result_dir = './results_HCP_202402_single_session/'\n", + "save_dir = f'{result_dir}plot/'\n", + "\n", + "comparison_analysis(models,list_channels,list_states,result_dir, save_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "dab6a145", + "metadata": {}, + "source": [ + "## Update 13th February 2024" + ] + }, + { + "cell_type": "markdown", + "id": "a8d5115b", + "metadata": {}, + "source": [ + "Plot the fisher-z transformed correlation between ground truth and states from the first-split and second split" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c148dd84", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1ae66605", + "metadata": {}, + "outputs": [], + "source": [ + "ground_truth_dir = './results_HCP_202311_no_mean/HMM_ICA_25_state_8/'\n", + "first_half_dir = './results_simulation_202402/sigma_0/HMM_ICA_25_state_16/split_1/first_half/'\n", + "second_half_dir = './results_simulation_202402/sigma_0/HMM_ICA_25_state_16/split_1/second_half/'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "98b700ee", + "metadata": {}, + "outputs": [], + "source": [ + "FC_ground_truth = np.load(f'{ground_truth_dir}state_correlations.npy')\n", + "FC_first_half = np.load(f'{first_half_dir}state_correlations.npy')\n", + "FC_second_half = np.load(f'{second_half_dir}state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c8a1bc7c", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.utils import twopair_fisher_z_transformed_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c70df9a2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 37\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(FC_ground_truth,FC_first_half),cmap=\"coolwarm\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5c9237fa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 16/16 [00:00<00:00, \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(FC_first_half,FC_first_half),cmap=\"coolwarm\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0ea222a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 38\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(FC_ground_truth,FC_second_half),cmap=\"coolwarm\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "fdd90520", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 16/16 [00:00<00:00, \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(FC_second_half,FC_second_half),cmap=\"coolwarm\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7afbada0", + "metadata": {}, + "outputs": [], + "source": [ + "first_half_HCP_dir = './results_HCP_202402_single_session/HMM_ICA_25_state_25/split_1/first_half/'\n", + "second_half_HCP_dir = './results_HCP_202402_single_session/HMM_ICA_25_state_25/split_1/second_half/'\n", + "FC_first_half_HCP = np.load(f'{first_half_HCP_dir}state_correlations.npy')\n", + "FC_second_half_HCP = np.load(f'{second_half_HCP_dir}state_correlations.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "10c59c61", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 25/25 [00:00<00:00, \n" + ] + }, + { + "data": { + "image/png": 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cHfvfZ6Xafqv9Kqn8uNhLUvm8Ug+pfNi3L0nllz5h3KnDx8X7rZHKv9tR9ySpykg+LleyeELeDKm8plCuZPOV7mOl8g6QKzUfnxoplZ855IZU3uVubsU76TFsaWOptte2lYpXikplmc/SCQkJGDFiBNq2bYv27dtj/vz5KCgowKhRowAAw4cPR926dcvmLOzduxeXLl1CmzZtcOnSJcyYMQMajQavv/66Ue1a5eCAiIjIqlioJsLAgQNx7do1TJs2DTk5OWjTpg1SU1PLJilmZ2dD/af5EIWFhXj77bdx5swZ1KxZEz169MDy5ctRq1Yto9pVfHAwY8YMvPPOO1qPNW3aFMeOyX0aJyIishRLllqOj49HfHy8zucen/DfpUsX/PHHH9JtmuXMQYsWLbB58+b/NWLPExRERES2wix/te3t7fXOpCQiIrI1tlJqWSlmOU9y8uRJBAQEoEGDBhgyZAiys7PN0QwREVHVUKmV22yA4mcOIiMjsXTpUjRt2hRXrlzBO++8g7/97W84cuQI3Nzcyu2vaxnJ4uISODk6KN01IiIiqgTFhzBxcXF47rnn0Lp1a8TGxuKnn37C7du38e233+rcX9cykh8u070vERGRJajUKsU2W2D2mYK1atVCkyZNcOrUKZ3PJyaWX0ay+NAWc3eLiIio8ix4t4IlmP3V3r17F6dPn4a/v7/O53WVbOYlBSIiIstRfHDwr3/9C9u2bcO5c+ewa9cu9OvXD3Z2dhg8eLDSTREREVUJlUql2GYLFL+scPHiRQwePBg3btyAt7c3OnXqhD179sDb21vppoiIiKpGNbusoPjgYOXKlUofkoiIiKoQly4kIiKqgK3cZaAUDg6IiIgqYiOLFynFKgcHDtcuSOVVteRKJj/7olzJ56becqVPbwXXkcrLllzemZgqlW8f97zJWVWgr1TbdevWkMoHXJIsF16zn1S+UW25ks8+XnK/wB4415TKO94wveR10yAh1bb6VohUXnU9Ryrvk3tIKn+odoxU3sGxRCpfoHaXyte5e9PkbPc+TaTarhLV7MxB9RoKERERUYXMMji4dOkShg4dCi8vL7i4uKBVq1bIyMgwR1NERERmp1KpFdtsgeKXFW7duoWOHTuia9eu+Pnnn+Ht7Y2TJ0+idu3aSjdFRERUNarZZQXFBwdz585F/fr1sWTJkrLHQkLkrgUSERFR1VH8/Mb69evRtm1bPPfcc/Dx8UF4eDgWLlyodDNERERVRqVWK7bZAsV7eebMGSQnJ6Nx48bYuHEjxo0bh4kTJ2LZsmU69y8qKkJ+fr7WVlQiN+uWiIhIUSqVcpsNUHxwoNFo8MQTT2D27NkIDw/H2LFjMWbMGKSkpOjcX1fJ5g++3ah0t4iIiKiSFB8c+Pv7IzQ0VOux5s2bIzs7W+f+iYmJyMvL09peGxCrdLeIiIhMp1Yrt9kAxSckduzYEcePH9d67MSJEwgKCtK5v5OTE5ycnLQeK3RgyWYiIrIiNnI5QCmKD2EmT56MPXv2YPbs2Th16hRWrFiBL7/8EuPHj1e6KSIiIjIDxc8ctGvXDmvWrEFiYiLeffddhISEYP78+RgyZIjSTREREVUJW7nLQClmqa3wj3/8A//4xz/McWgiIqKqZyMrGyrFKgsvERERWZVqtkKiSgghVwrNDL74RS4/Aksq3skAjatcdTJZ6ltXpfJnmvWRynsXyVXF3NdmuMnZyINLpdoWajup/Lfn20vl2ze4LZVvkSv35lc9KJLKP/hdrrLgD+Hvm5yNrbVbqm3XS0el8hpnV7n871lSeVW4XDXZ4hpyS9Tfc/GUynudM71+zp16LaTa9g6V+7mtjPsrkhQ7lsvziYody1x45oCIiKgCtlIwSSmKv9rg4GCoVKpyG+9WICIim6VWKbfZAMXPHOzfvx+lpaVlXx85cgRPP/00nnvuOaWbIiIiIjNQfHDg7e2t9fWcOXPQsGFDdOnSRemmiIiIqgYvKyinuLgYX331FV544QWoqtnqUkRE9BfCwkvKWbt2LW7fvo2RI0easxkiIiJSkFnvVli0aBHi4uIQEBCgd5+ioiIUFWnfflVS7AQHRyc9CSIioipWzVZINNurPX/+PDZv3ozRo0cb3E9XyebUVcrdT0pERCRNpVZuswFm6+WSJUvg4+ODnj17GtxPV8nm7gOtf4EIIiKivyqzXFbQaDRYsmQJRowYAXt7w03oKtns4GiOXhEREZnIRtYnUIpZBgebN29GdnY2XnjhBXMcnoiIqGrZyOUApZhlcNCtWzdYYckGIiIi09jILYhKqV5DISIiIqoQCy8RERFVpJrdymiVg4P+AXKlWw879JPKz1uUL5V/caivVH7zH3KXZMb+91mpvCpQrv8yZZf3ho+UanvLxwek8uNiz0rld14Ikcr7f/9fqXz6gFVS+WfrfiKVf9pzv8nZdTkdpNrutWOhVL5Gff3rsVRKREep+GnPSKn83MUlUvmxQ+VKPrvXyjY5O39PmFTbs0Kl4pXDywpERERUnSk+OCgtLcXUqVMREhICFxcXNGzYEDNnzuQERSIisl3VbBEkxS8rzJ07F8nJyVi2bBlatGiBjIwMjBo1Ch4eHpg4caLSzREREZkf5xzI2bVrF/r06VO2MmJwcDC++eYb7Nu3T+mmiIiIyAwUHwp16NABaWlpOHHiBADgt99+w86dOxEXF6d0U0RERFWjmpVsVvzMwZQpU5Cfn49mzZrBzs4OpaWlmDVrFoYMGaJ0U0RERFXDRuYKKEXxwcG3336Lr7/+GitWrECLFi2QlZWFSZMmISAgACNGjCi3v66SzUXFxXByZIEFIiIiS1B8KPTaa69hypQpGDRoEFq1aoVhw4Zh8uTJSErSXYZZV8nmj//vP0p3i4iIyHS8rCDn3r17UD82q9POzg4ajUbn/omJiUhISNB67M6pg0p3i4iIyHS8W0FOr169MGvWLAQGBqJFixY4ePAg5s2bp7dCo66SzcW8pEBERFZE2MgnfqUoPjj49NNPMXXqVLzyyiu4evUqAgIC8NJLL2HatGlKN0VERERmoPjgwM3NDfPnz8f8+fOVPjQREZFl8G4FIiIi0lLNBgfV69USERFRhVTCCisiDX7d9NKfAJD8/BmpvMZO7oSKc36uVF598bRU/q0b46XydevWkMoPa2x62d456S2k2n7q1Sek8hlLjkjlX4g8KZVfsq+JVP6fIeul8ld8w6XyH60xvexvUte9Um3vd+wilVdD7ldhE6dTUnn3/EtSeftjcuXKT0aOlsoHFJn+e8uhuECqbY8nYqTylXFv20rFjlWjyyDFjmUuvKxARERUEV5WkHfnzh1MmjQJQUFBcHFxQYcOHbB/v+mfJomIiKjqmGVwMHr0aGzatAnLly/H4cOH0a1bN8TExODSJbnTZkRERBZhwRUSFyxYgODgYDg7OyMyMrLCKsfz589H06ZN4eLigvr162Py5MkoLCw0qk3FBwf379/H999/j/fffx+dO3dGo0aNMGPGDDRq1AjJyclKN0dERGR+arVymxFWrVqFhIQETJ8+HQcOHEBYWBhiY2Nx9epVnfuvWLECU6ZMwfTp03H06FEsWrQIq1atwptvvmncyzVq70p48OABSktL4ezsrPW4i4sLdu7cqXRzREREf1nz5s3DmDFjMGrUKISGhiIlJQU1atTA4sWLde6/a9cudOzYEc8//zyCg4PRrVs3DB48uMKzDY9TfHDg5uaGqKgozJw5E5cvX0ZpaSm++uor7N69G1euXCm3f1FREfLz87W20gdFOo5MRERkGUKlUmzT9Xfv8erEAFBcXIzMzEzExPzvbgy1Wo2YmBjs3r1bZz87dOiAzMzMssHAmTNn8NNPP6FHjx5GvV6zzDlYvnw5hBCoW7cunJyc8Mknn2Dw4MHlCjIBuqsy/rH3c3N0i4iIyDQqtWKbrr97uioXX79+HaWlpfD19dV63NfXFzk5OTq7+fzzz+Pdd99Fp06d4ODggIYNGyI6OtrylxUAoGHDhti2bRvu3r2LCxcuYN++fSgpKUGDBg3K7ZuYmIi8vDytLTTyFXN0i4iIyCRCpVZs0/V3LzExUZF+pqenY/bs2fj8889x4MABrF69Ghs2bMDMmTONOo5Z1zlwdXWFq6srbt26hY0bN+L9998vt4+uqox29rfN2S0iIiKL0fV3T5c6derAzs4OubnaC+vl5ubCz89PZ2bq1KkYNmwYRo9+uKhVq1atUFBQgLFjx+Ktt97SeQZfF7OcOdi4cSNSU1Nx9uxZbNq0CV27dkWzZs0watQoczRHRERkXha4ldHR0RERERFIS0sre0yj0SAtLQ1RUVE6M/fu3Ss3ALCzswMAGLMgslnOHDw6RXLx4kV4enqif//+mDVrFhwcHMzRHBERkVkJC62QmJCQgBEjRqBt27Zo37495s+fj4KCgrIP28OHD0fdunXL5iz06tUL8+bNQ3h4OCIjI3Hq1ClMnToVvXr1KhskVIZZBgcDBgzAgAEDzHFoIiKiamPgwIG4du0apk2bhpycHLRp0wapqallkxSzs7O1zhS8/fbbUKlUePvtt3Hp0iV4e3ujV69emDVrllHtsrYCERFRRUxY2VAp8fHxiI+P1/lcenq61tf29vaYPn06pk+fLtUmBwdEREQVqWaFl6xycJDi/YFU/lfNPKn8/y0+L5UfPkz3RJHKOlBYKpUfFytXwyLgklyRrCXnTb+kNC72rFTbX0uWXG47qqVUPueIXNnhke3lSj5/uK+3VL6No9yno7k13jI5m/TbO1JtR7aW+3UWWluuVPzu63Llxk9fkMvv3ddIKv/vqDtS+Qf2zhXvpMcnmRFSbb8rV6mddLDKwQEREZE1ERa8rGAJRp8n2b59O3r16oWAgACoVCqsXbtW63khBKZNmwZ/f3+4uLggJiYGJ0/KfRoiIiKyKAVXSLQFRveyoKAAYWFhWLBggc7n33//fXzyySdISUnB3r174erqitjYWKPLRRIREZFlGH1ZIS4uDnFxcTqfE0Jg/vz5ePvtt9GnTx8AwH/+8x/4+vpi7dq1GDRokFxviYiILECAlxVMdvbsWeTk5GhVkPLw8EBkZKTeClJERETWTsnaCrZA0QmJj6pEGVNBqqioqFypyqIHpXCyr/xKTkRERGZlI3/UlWLxV6urdOW8LRmW7hYREVG1pejg4FGVKGMqSOkqXZnwVFslu0VERCRFqFSKbbZA0cFBSEgI/Pz8tCpI5efnY+/evXorSDk5OcHd3V1r4yUFIiKyJpxzUIG7d+/i1KlTZV+fPXsWWVlZ8PT0RGBgICZNmoT33nsPjRs3RkhICKZOnYqAgAD07dtXyX4TERGRmRg9OMjIyEDXrl3Lvk5ISAAAjBgxAkuXLsXrr7+OgoICjB07Frdv30anTp2QmpoKZ2fTl9YkIiKyKBu5HKAUowcH0dHREELofV6lUuHdd9/Fu+++K9UxIiIia2ErlwOUUr1eLREREVWIhZeIiIgqUN1WSFQJQ9cILCTtsFwdhno1rknl00/Xl8q3D7kulVdB7r+kVMjd7ZF7100q7+1aYHL29yu1pNr+e90/pPI5mgCp/NWWkVJ5t6wDUnkX+xKpfKmQ+wXooDa93PjJ67Wl2vZweSCVb+4mVy78hqaOVN7T7qZU3itPrv8yJZcBwO5BUcU76bEmP6binQwYGS0Vr5Rrv8uVY/8z7xZyvyeqAi8rEBERkRbFSzavXr0a3bp1g5eXF1QqFbKyshTqKhERkYWoVMptNkDxks0FBQXo1KkT5s6dK905IiIiayCgVmyzBYqWbAaAYcOGAQDOnTtncqeIiIisia0se6wU2xjCEBERUZWx+K2Muko2FxcLODo6WahHRERE2rgIUhXTVbL5m//7wNLdIiIiKiOgUmyzBRY/c5CYmFhWn+GRX09a3dILRERE1YbFBwdOTk5wctK+hODoKLcIEhERkZKq22UFxUs237x5E9nZ2bh8+TIA4Pjx4wAAPz8/+Pn5KdRtIiKiqsO7FSqQkZGB8PBwhIeHA3hYsjk8PBzTpk0DAKxfvx7h4eHo2bMnAGDQoEEIDw9HSkqKgt0mIiIic1G8ZPPIkSMxcuRImT4RERFZFVuZSKgUi885ICIisnacc2AFWjgclcoXiRpS+cNH8qTyDf3kqhrK1skM+/YlqXyj2h5SeYf2HU3O+n//X6m2Fz+5Qio/sv1Jqfx9yaqKd9o8IZW/uUuuKmX3sx9J5e+07Gxy9rx9W6m2n0x9VSpvX9NVKi87p+pYm+FS+QV7Tf+5A4DEv2VJ5R0Kbpmczbsje4da9fpUXxWscnBARERkTarbZQVFqzKWlJTgjTfeQKtWreDq6oqAgAAMHz687M4FIiIiWyRUasU2W6BoVcZ79+7hwIEDmDp1Kg4cOIDVq1fj+PHj6N27tyKdJSIisgSukFgBQ1UZPTw8sGnTJq3HPvvsM7Rv3x7Z2dkIDAw0rZdERERUZcw+5yAvLw8qlQq1atUyd1NERERmYSuXA5Ri1sFBYWEh3njjDQwePBju7u7mbIqIiMhsbOVygFLMNjgoKSnBgAEDIIRAcnKy3v10lWwuKi6Gk6OjubpGREREBpjlPMmjgcH58+exadMmg2cNdJVs/vTLxeboFhERkUmESqXYZgsUP3PwaGBw8uRJbN26FV5eXgb311Wy+dY5uUWQiIiIlCSEbfxRV4qiVRn9/f3x7LPP4sCBA/jxxx9RWlqKnJwcAICnpyccdVwq0FWy+R4vKRAREVmM0YODjIwMdO3atezrR5/6R4wYgRkzZmD9+vUAgDZt2mjltm7diujoaNN7SkREZCHCPFfhrZbiVRkNPUdERGSLqtvdCtVrKEREREQVYuElIiKiClS3MwcqYYXXAf6VfE8q/8+uJ6TyBQ5yJYvrXtgjlc/3bSKVX/ZHuFTex0vuhNJAzXKTs987DJVqu5/dGqn8h2fl6oDEtb0rlb+UL1fu26FDqFQ+8uBSqfzXZ580Odsz9JxU25ZW5855qfzdGnWk8l6XD0vlj9V9Wirf+OZuk7PFNWpLte3ZqpNUvjKOnb6o2LGaNayn2LHMhWcOiIiIKlDdzhwoWrIZAGbMmIFmzZrB1dUVtWvXRkxMDPbu3atUf4mIiMjMFC3ZDABNmjTBZ599hsOHD2Pnzp0IDg5Gt27dcO3aNenOEhERWYIQKsU2W6BoyWYAeP7557W+njdvHhYtWoRDhw7h73//u/E9JCIisjBeVlBQcXExvvzyS3h4eCAsLMycTREREZFCzDIh8ccff8SgQYNw7949+Pv7Y9OmTahTR24mLhERkaVUtzMHZhkcdO3aFVlZWbh+/ToWLlyIAQMGYO/evfDx8Sm3r66SzQ9KSmHv4FRuXyIiIkuoboMDs1xWcHV1RaNGjfDkk09i0aJFsLe3x6JFi3Tuq6tk876N/zZHt4iIiKgSqmT5ZI1GU+7swCOJiYnIy8vT2trH/qsqukVERFQpvFuhAoZKNnt5eWHWrFno3bs3/P39cf36dSxYsACXLl3Cc889p/N4uko22zvIrZBIRESkJE01u6ygaMnmlJQUHDt2DMuWLcP169fh5eWFdu3aYceOHWjRooVyvSYiIqpC1W3OgeIlm1evXi3VISIiIrIs1lYgIiKqgK3MFVAKBwdEREQV4GUFK/DkE85SeY/cY1L5y/7dpPK16wRJ5R2L5cr+xvvJlS1+4FxTLr/rkMnZZ+t+ItX2xTC5ksttHOV+AZRKfrrofvYjqfxdyZLLe8NHSuWbZ5heNtizQK4kbuGSz6Xynp3aS+U1ebfl2m/YXCp/OTBSKu/z4IpU3v7cUZOzJ8JflGrbUypt/RYsWIAPPvgAOTk5CAsLw6effor27XW/X6Ojo7Ft27Zyj/fo0QMbNmyodJtVcisjERGRLbPUrYyrVq1CQkICpk+fjgMHDiAsLAyxsbG4evWqzv1Xr16NK1eulG1HjhyBnZ2d3jsG9VG8ZPOfvfzyy1CpVJg/f76xzRAREVkNAZVimzHmzZuHMWPGYNSoUQgNDUVKSgpq1KiBxYsX69zf09MTfn5+ZdumTZtQo0YN8w8OKirZ/MiaNWuwZ88eBAQEGNsEERHRX1ZRURHy8/O1Nl0LBRYXFyMzMxMxMTFlj6nVasTExGD37t2VamvRokUYNGgQXF1djeqj0YODuLg4vPfee+jXr5/efS5duoQJEybg66+/hoODg7FNEBERWRUlLyvoKhuQlJRUrs3r16+jtLQUvr6+Wo/7+voiJyenwj7v27cPR44cwejRo41+vYpPSNRoNBg2bBhee+01LnxERER/CRoFj5WYmFi2gOAjj68UrIRFixahVatWeicvGqL44GDu3Lmwt7fHxIkTK7W/rqqMJcUOcHBkVUYiIvrr0VU2QJc6derAzs4Oubm5Wo/n5ubCz8/PYLagoAArV67Eu+++a1IfFb1bITMzEx9//DGWLl0Klapyky50nV5Zs2yOkt0iIiKSYom7FRwdHREREYG0tLSyxzQaDdLS0hAVFWUw+91336GoqAhDhw416fUqOjjYsWMHrl69isDAQNjb28Pe3h7nz5/HP//5TwQHB+vM6KrK2G/EFCW7RUREJMVSdyskJCRg4cKFWLZsGY4ePYpx48ahoKAAo0aNAgAMHz4ciYmJ5XKLFi1C37594eXlZdLrVfSywrBhw7RmVQJAbGwshg0bVvZCHqfr9IqDo5JXd4iIiORYavnkgQMH4tq1a5g2bRpycnLQpk0bpKamlk1SzM7Ohlqt/Tn/+PHj2LlzJ3755ReT21W0ZHNgYGC5UYqDgwP8/PzQtGlTkztJRERUXcXHxyM+Pl7nc+np6eUea9q0qcECiZWhaMnmpUuXSnWGiIjIGrG2QgUqKtn8uHPnzhnbBBERkVXRyH0QtzmsrUBERERarLIqIxERkTWpbpcVVEJ21oIZXJ8mV75zc/f/k8qv/OqkVH706AZS+c2/Fkrl3+1YuTW39XG8cUkqv9plhMnZpz33S7U946cmUvm5NeTW2Dge87pUPvDBqYp3MmDF6XZS+eaBJVL5kratTM5u/ihTqu3eXeyk8o2dTkvl99+WK7l89bZc/39ac1wqnxBfXyofcTet4p30eG3736TaTn6tllS+MtKP3FfsWNEtXRQ7lrnwsgIRERFpUbxk88iRI6FSqbS27t27K9VfIiKiKieEcpstMHrOwaOSzS+88AKeeeYZnft0794dS5YsKfvaHAUliIiIqoqmms05MHpwEBcXh7i4OIP7ODk5VVgUgoiIiKyTWeYcpKenw8fHB02bNsW4ceNw48YNczRDRERUJSxReMmSFL+VsXv37njmmWcQEhKC06dP480330RcXBx2794NO7vys3F1lWwuelAKJ3u5mbtERERKsZW5AkpRfHAwaNCgsn+3atUKrVu3RsOGDZGeno6///3v5fZPSkrCO++8o/XYa53b4PUuTyjdNSIiIpNUt3UOzH4rY4MGDVCnTh2tYk1/pqtk86sdw8zdLSIiItLD7CskXrx4ETdu3IC/v7/O53WVbC7mJQUiIrIi1a22gqIlmz09PfHOO++gf//+8PPzw+nTp/H666+jUaNGiI2NVbTjREREVcVWJhIqRdGSzcnJyTh06BCWLVuG27dvIyAgAN26dcPMmTO51gEREZGNULxk88aNG6U6REREZG14twIRERFpqW4rJFplVcZnXz0jlf9iTI5UPt/FRyrvdv+aVF4tSqXyC493kMo3DZJ7S/zNZa/J2XVX5fr+rIfcmau5v0VL5Vs1c5TKO9rLfe9be52XynsWXJTKz9webnI2ZnKEVNuhxzZI5e+LGlL54EvbpfKFnvWk8kIld/PZOcdmUvmgYtOrQjoXXJdqu2ZkL6l8Zfx44IFix/rHE9b/udz6e0hERGRh1vcx2rwUr8oIAEePHkXv3r3h4eEBV1dXtGvXDtnZ2Ur0l4iIqMpVt+WTjR4cPKrKuGDBAp3Pnz59Gp06dUKzZs2Qnp6OQ4cOYerUqXB2dpbuLBEREZmf4lUZ33rrLfTo0QPvv/9+2WMNGzY0rXdERERWoLotgqTo8skajQYbNmxAkyZNEBsbCx8fH0RGRuq89EBERGQrhFBuswWKDg6uXr2Ku3fvYs6cOejevTt++eUX9OvXD8888wy2bdumZFNERERVRkCl2GYLFL1bQaPRAAD69OmDyZMnAwDatGmDXbt2ISUlBV26dCmX0VWyufRBEezsuaIiERGRJSh65qBOnTqwt7dHaGio1uPNmzfXe7dCUlISPDw8tLbjGSlKdouIiEiKRii32QJFBweOjo5o164djh/XXgzjxIkTCAoK0pnRVbK5aduXlewWERGRlOo250DRqoyBgYF47bXXMHDgQHTu3Bldu3ZFamoqfvjhB6Snp+s8nq6SzXb2cqtlERERkekUrcq4dOlS9OvXDykpKUhKSsLEiRPRtGlTfP/99+jUqZNyvSYiIqpCtvKJXymKV2UEgBdeeAEvvPCCyZ0iIiKyJhobWdlQKYrOOSAiIiLbx8JLREREFeBlBSswckSgVF5dekEqvzIzRCr/Qqs8qfwfGrnSqRPyZkjl1bfkXj9uakyO9tqxUKrp/QOTpfKRreV+JBzs5Mq6Ppn6qlT+xoDXpPKFSz6Xyvd+YbHJ2WDJkst/NOsple+aMlAqr3KSrB8jWbJ54+0oqXwT79tSedfr50zObnV/VqrtHlLpyqlugwNeViAiIiItipdsVqlUOrcPPvhAqT4TERFVKS6CVIGKSjZfuXJFa1u8eDFUKhX69+8v3VkiIiJLEEKl2GYLFC/Z7Ofnp/X1unXr0LVrVzRo0MD43hEREVmB6jbnwKwTEnNzc7FhwwYsW7bMnM0QERGRgsw6OFi2bBnc3NzwzDPPmLMZIiIis7KVuQJKMevgYPHixRgyZAicnfXf4qOrZHNJsR0cHFmymYiIrEN1u6xgtlsZd+zYgePHj2P06NEG99NVsvm7JXPN1S0iIiKqgNnOHCxatAgREREICwszuF9iYmJZ8aZH0v6wM1e3iIiIjFbdzhwoXrIZAPLz8/Hdd9/hww8/rPB4uko2OzjKrTJHRESkJM45qEBFJZsBYOXKlRBCYPDgwcr0koiIiKqMWUo2jx07FmPHjjW5U0RERNaElxWIiIhIi8b0enI2iYWXiIiISItVnjkIczshlXe+dFkqHxQgN2YSKrl8TYdCqbymUC6vup4jlRd1TS/5XKN+gFTbasid+wutnS2VtxclcvmarlJ5WZ6d2kvlGzudNjmbLzyl2pYtubz15VVS+b+vnSyVd7l2VirftF6oVL55zmapvKronslZd+diqbYBB8l8xXhZgYiIiLRUt8GB4iWb7969i/j4eNSrVw8uLi4IDQ1FSkqKUv0lIiKqcizZXIGKSjYnJCQgNTUVX331FY4ePYpJkyYhPj4e69evl+4sERERmZ/iJZt37dqFESNGIDo6GsDD2xq/+OIL7Nu3D7179za5o0RERJZS0S38xlEpeCzzUPxuhQ4dOmD9+vW4dOkShBDYunUrTpw4gW7duindFBERUZUQQrnNFig+IfHTTz/F2LFjUa9ePdjb20OtVmPhwoXo3Lmzzv11VWUsKi6Gk6Oj0l0jIiKiSlD8zMGnn36KPXv2YP369cjMzMSHH36I8ePHY/Nm3bfJ6KrKuCBlodLdIiIiMplGo9xmCxQ9c3D//n28+eabWLNmDXr27AkAaN26NbKysvDvf/8bMTEx5TK6qjJevXBGyW4RERFJsZXLAUpRdHBQUlKCkpISqNXaJyTs7Oyg0TNc0lWVMY+XFIiIiCxG8ZLNXbp0wWuvvQYXFxcEBQVh27Zt+M9//oN58+Yp2nEiIqKqYivrEyjF6DkHGRkZCA8PR3h4OICH6xqEh4dj2rRpAB6Wa27Xrh2GDBmC0NBQzJkzB7NmzcLLL7+sbM+JiIiqiCXvVliwYAGCg4Ph7OyMyMhI7Nu3z+D+t2/fxvjx4+Hv7w8nJyc0adIEP/30k1FtKl6y2c/PD0uWLDH2sERERPSYVatWISEhASkpKYiMjMT8+fMRGxuL48ePw8fHp9z+xcXFePrpp+Hj44P//ve/qFu3Ls6fP49atWoZ1S5rKxAREVVAKHpdofKLIM2bNw9jxozBqFGjAAApKSnYsGEDFi9ejClTppTbf/Hixbh58yZ27doFB4eHBamCg4ON7iFLNhMREVVAydoKRUVFyM/P19oeX+8HeHgWIDMzU+tOP7VajZiYGOzevVtnP9evX4+oqCiMHz8evr6+aNmyJWbPno3S0lKjXq9VnjnYdEGu9Oggh9+l8k/VyZLKq4Tcjayh53+Qyl/pPlYq75N7SCovsgxfDzMooqNU202cTlW8kwG7r7eQyofUvi2V9/Pzk8rXuXNeKq/Juy2V33+7ucnZ2PvfSbWtcnKWysuWXE7r+5FUvvPuj6XyddTXpPIFXkFSebdzB03OtslPk2obMP/S/EreypiUlIR33nlH67Hp06djxowZWo9dv34dpaWl8PX11Xrc19cXx44d03nsM2fOYMuWLRgyZAh++uknnDp1Cq+88gpKSkowffr0SvfRKgcHREREf1W61vd5/JZ+U2k0Gvj4+ODLL7+EnZ0dIiIicOnSJXzwwQdGDQ4UL9mcm5uLkSNHIiAgADVq1ED37t1x8uRJY5shIiKyGhqNUGxzcnKCu7u71qZrcFCnTh3Y2dkhNzdX6/Hc3Fy9Zxn9/f3RpEkT2NnZlT3WvHlz5OTkoLi4uNKvV9GSzUII9O3bF2fOnMG6detw8OBBBAUFISYmBgUFBcY2RUREZBUscSujo6MjIiIikJb2v8suGo0GaWlpiIqK0pnp2LEjTp06pbXw4IkTJ+Dv7w9HIxYYVLRk88mTJ7Fnzx4cOXIELVo8vHabnJwMPz8/fPPNNxg9erSxzREREVVbCQkJGDFiBNq2bYv27dtj/vz5KCgoKLt7Yfjw4ahbty6SkpIAAOPGjcNnn32GV199FRMmTMDJkycxe/ZsTJw40ah2FZ1z8Gi2pbPz/yYGqdVqODk5YefOnRwcEBGRTbJUbYWBAwfi2rVrmDZtGnJyctCmTRukpqaWTVLMzs7WKllQv359bNy4EZMnT0br1q1Rt25dvPrqq3jjjTeMalfRwUGzZs0QGBiIxMREfPHFF3B1dcVHH32Eixcv4sqVKzozuko2lxQ7wcFRmckZREREsjQWrLwUHx+P+Ph4nc+lp6eXeywqKgp79uyRalPRdQ4cHBywevVqnDhxAp6enqhRowa2bt2KuLi4csWYHtFVsvmnb5KU7BYREREZQfFbGSMiIpCVlYW8vDwUFxfD29sbkZGRaNu2rc79dd3S8c0unjUgIiLrIbl8jc0x2zoHHh4eAB5OUszIyMDMmTN17qerZLMDKzYTEZEVMVRT6K9I8ZLN3333Hby9vREYGIjDhw/j1VdfRd++fdGtWzdFO05ERETmYfTgICMjA127di37+tElgREjRmDp0qW4cuUKEhISkJubC39/fwwfPhxTp05VrsdERERVTMPLCoZVVLJ54sSJRt9PSUREZM14WYGIiIi0KFqx2QawZDMRERFpscozB2eyK18cQhd1wF2pvEZlV/FOBtxzqCWVv93g71J5B8h9/w7Vjql4JwNah5s+5jztGSnVdkjeAan86QtyJZsj6tyUyh9rM1wqX0fkVryTAZ4NTS+5DABXb5v+s1PoX0+qbXjK5V2unZXKy5Zc3h71qlS+4/4vpPLOV+Vef5F/I5Ozl9xDpdqWS1eOqGanDqxycEBERGRNqtmUA+MuKyQlJaFdu3Zwc3ODj48P+vbti+PHj2vtU1hYiPHjx8PLyws1a9ZE//79y5WbJCIiIutl1OBg27ZtGD9+PPbs2YNNmzahpKQE3bp10yrHPHnyZPzwww/47rvvsG3bNly+fBnPPPOM4h0nIiKqKhqNUGyzBUZdVkhNTdX6eunSpfDx8UFmZiY6d+6MvLw8LFq0CCtWrMBTTz0FAFiyZAmaN2+OPXv24Mknn1Su50RERFWkut3KKHW3Ql5eHgDA09MTAJCZmYmSkhLExPxvQtujSo27d++WaYqIiIiqiMkTEjUaDSZNmoSOHTuiZcuWAICcnBw4OjqiVq1aWvv6+voiJydH53F0lWx+UKKCvQOLLxERkXWoboWXTD5zMH78eBw5cgQrV66U6oCuks27NrwvdUwiIiIlaYRQbLMFJg0O4uPj8eOPP2Lr1q2oV+9/9xb7+fmhuLgYt2/f1to/NzcXfn5+Oo+VmJiIvLw8ra1Dz9dN6RYREREpwKjBgRAC8fHxWLNmDbZs2YKQkBCt5yMiIuDg4IC0tLSyx44fP47s7GxERUXpPKaTkxPc3d21Nl5SICIiayKEUGyzBUbNORg/fjxWrFiBdevWwc3NrWwegYeHB1xcXODh4YEXX3wRCQkJ8PT0hLu7OyZMmICoqCjeqUBERDbLVm5BVIpRg4Pk5GQADysz/tmSJUswcuRIAMBHH30EtVqN/v37o6ioCLGxsfj8888V6SwREZEl2MgHfsUYNTiozOkQZ2dnLFiwAAsWLDC5U0RERGQ5rK1ARERUARZesgLp38stmDQ5qalUvsi+hlReVt1rB6Xy8alylQ0dHEuk8u/3rG1ydu5iubb/r4VcVca9+0yvLAcA4xrIVbZbsLejVH5m88NS+cuBcu+dnz44XvFOejw7Xq6C/Mbbuic9V1bTenK1/eqor0nlZasq/truJam8/++7pPK+uGxy1l71QKrtqmArtyAqRe6nkYiIiP5yFK/K+OWXXyI6Ohru7u5QqVTl1jwgIiKyNUIjFNtsgeJVGe/du4fu3bvjzTffVLyzREREllDdBgeKVmUEgEmTJgEA0tPTFekgERERVS2pCYmPV2UkIiL6K7KRD/yKUbQqIxER0V+RrVwOUIrJg4NHVRl37twp1QFdJZs1pcVQ2zlKHZeIiIhMo2hVRlPoKtl88dTXUsckIiJSUnUrvKRoVUZT6CrZXK/REOnjEhERKUWjEYpttkDRqowAkJOTg5ycHJw6dQoAcPjwYbi5uSEwMFDnxEUnJyc4OWmXaOYlBSIisia28olfKUadOUhOTkZeXh6io6Ph7+9ftq1atapsn5SUFISHh2PMmDEAgM6dOyM8PBzr169XtudERERkFopXZZwxYwZmzJhhan+IiIisDu9WICIiIi3VbXDAwktERESkRSWscJbFwZPXpfJNr2yRyqe5PiOV96t5VyrvZi+Xd9XkS+UL1O5SebfSWyZnT5U0lGq7tqPc966m+o5UvlaB6WVrAUCo7aTy2U5NpPI+4opUXub/r6ZDoVTbQqik8qG5m6TyBV5BUnm3S39I5f8I7i2Vv9Kig1S+y85/m5zNcI2Raju6pYtUvjJGzshV7FhLZ/gqdixz4WUFIiKiCvCyggEVlWy+efMmJkyYgKZNm8LFxQWBgYGYOHFiWQ0GIiIisn6Klmy+fPkyLl++jH//+984cuQIli5ditTUVLz44otm6TwREVFVqG4rJCpasrlly5b4/vvvy55v2LAhZs2ahaFDh+LBgwewt+dVDCIisj22srKhUqTuVqhMyea8vDy4u7tzYEBERGQjzFqy+fr165g5cybGjh1rcgeJiIgsrbpNSDRbyeb8/Hz07NkToaGhBldM1FWyubi4CI6OTnoSREREVctW5gooxSwlm+/cuYPu3bvDzc0Na9asgYODg95j6SrZvDjlY1O6RUREZBZCo1FsswVG11aYMGEC1qxZg/T0dJ0lm/Pz8xEbGwsnJyesX78ezs7OBo+ZmJiIhIQErceOXpBbiIaIiIhMp2jJ5vz8fHTr1g337t3DV199hfz8fOTnP1ytz9vbG3Z25Vd/01Wy2dGx2NTXQ0REpLjqdreCUYOD5ORkAEB0dLTW40uWLMHIkSNx4MAB7N27FwDQqFEjrX3Onj2L4OBg03tKRERkIdVtzoGiJZujo6Or3TeQiIjor4aLDxAREVWAtzISERGRFg4OrIAXrskdQCVXurWhx1WpvE/heam8plT/rZ+V4XJXrrRonbs3pfKqBw9MzrrXypZqu0joX62zMh7YG767piJ2D4oq3skAhwLTy10DQGPHG1J5+3NHpfIewc1Nzha61pFq2/X6Oam8quieVN7t3EGpfJF/o4p3MsAXcuXCm0iUXAaAbZ3+ZXK28TG5ctlAoGSeHmeVgwMiIiJrohG2sT6BUhQt2QwAL730Eho2bAgXFxd4e3ujT58+OHbsmKKdJiIiqkpCIxTbbIGiJZsBICIiAkuWLMHRo0exceNGCCHQrVs3lJaWKt55IiIiUp6iJZsBaBVZCg4OxnvvvYewsDCcO3cODRs2VKDLREREVctWPvErRWrOQUUlmwsKCrBkyRKEhISgfv36Mk0RERFZTHVbw8csJZs///xzvP766ygoKEDTpk2xadMmODo66jyOrqqMRcXFcNKzPxERUVXT2EjBJKWYVJUR+F/J5pUrV5Z7bsiQITh48CC2bduGJk2aYMCAASgsLNR5HF1VGT9P+dLUbhEREf2lLFiwAMHBwXB2dkZkZCT27dund9+lS5dCpVJpbRUVQNTFpDMHj0o2b9++XWfJ5kd/5Bs3bownn3wStWvXxpo1azB48OBy++qqyph74awp3SIiIjILS805WLVqFRISEpCSkoLIyEjMnz8fsbGxOH78OHx8fHRm3N3dte4kVJmw9o9RZw6EEIiPj8eaNWuwZcsWnSWbdWWEEOUuHTzi5OQEd3d3rY2XFIiIyJoIoVFsM8a8efMwZswYjBo1CqGhoUhJSUGNGjWwePFivRmVSgU/P7+yzdfX1+jXa9TgYPz48fjqq6+wYsWKspLNOTk5uH//PgDgzJkzSEpKQmZmJrKzs7Fr1y4899xzcHFxQY8ePYzuHBER0V9NUVER8vPztTZdH6CLi4uRmZmJmJiYssfUajViYmKwe/duvce/e/cugoKCUL9+ffTp0we///670X00anCQnJyMvLw8REdHw9/fv2xbtWoVAMDZ2Rk7duxAjx490KhRIwwcOBBubm7YtWuX3tMfRERE1k7JRZB0zbVLSkoq1+b169dRWlpa7pO/r68vcnJydPazadOmWLx4MdatW4evvvoKGo0GHTp0wMWLF416vYqWbA4ICMBPP/1kVAeIiIisnZJzDnTNtXNyclLk2FFRUYiKiir7ukOHDmjevDm++OILzJw5s9LHYW0FIiKiKuTk5FSpwUCdOnVgZ2eH3FztYnq5ubnw8/OrVFsODg4IDw/HqVOnjOqjybcyEhERVRcaoVFsqyxHR0dEREQgLS3tf/3QaJCWlqZ1dsCQ0tJSHD58GP7+/ka9Xqs8c/DaFzWk8nNfDpPKB9w9IZVXa0qk8iqNXB2KYUsbS+W792kile9fX/89uBWZv0fu/+71Njuk8p9kRkjlGwTJ3WmTd0fu1OWw4F+l8ifCX5TKL1xnZ3L2g85y/3db3Z+Vyrs7F0vl2+SnVbyTAZfcQ6Xy9irTS6UDwEnXmIp3MkCm7PLJZk9Ltd2k5HjFO0my1K2MCQkJGDFiBNq2bYv27dtj/vz5KCgowKhRowAAw4cPR926dcvmLLz77rt48skn0ahRI9y+fRsffPABzp8/j9GjRxvVrlUODoiIiAgYOHAgrl27hmnTpiEnJwdt2rRBampq2STF7OxsqNX/uwhw69YtjBkzBjk5OahduzYiIiKwa9cuhIYaN/hUvGTzI0IIxMXFQaVSYe3atUZ1ioiIyJoIjUaxzVjx8fE4f/48ioqKsHfvXkRGRpY9l56ejqVLl5Z9/dFHH5Xtm5OTgw0bNiA8PNzoNhUv2fzI/PnzTVqViYiIyNooeSujLVC8ZDMAZGVl4cMPP0RGRobRkyCIiIisjbErG9o6qbsVdJVsvnfvHp5//nksWLCg0rdaEBERkfVQvGTz5MmT0aFDB/Tp06dSx9FVsrn0QRHs7JVZEIKIiEiWxkYuByhF0ZLN69evx5YtWzB//vxKH0fXMpJH931uareIiIgUZ8kJiZZg0uDgUcnmrVu3apVs3rJlC06fPo1atWrB3t4e9vYPT0z0798f0dHROo+VmJiIvLw8ra15+1dM6RYREREpwOjaChMmTMCaNWuQnp5ermTzlClTyi200KpVK3z00Ufo1auXzmPqWkbSzv6WMd0iIiIyK1u5y0ApRg0Oxo8fjxUrVmDdunVlJZsBwMPDAy4uLmW1ox8XGBhYbiBBRERkK3i3ggEVlWwmIiIi26doyWalMkRERNaElxWIiIhIi63cZaAUlmwmIiIibcLGFBYWiunTp4vCwkLmq1nelvte3fO23Hfmbfv/nkxjc4ODvLw8AUDk5eUxX83yttz36p635b4zb9v/92QaXlYgIiIiLRwcEBERkRYODoiIiEiLzQ0OnJycMH369HJLLjP/18/bct+re96W+868bf/fk2lUQnCVIiIiIvofmztzQERERObFwQERERFp4eCAiIiItHBwQERERFo4OLBBnENKRETmZPVVGa9fv47Fixdj9+7dyMnJAQD4+fmhQ4cOGDlyJLy9vS3cw6rn5OSE3377Dc2bN7d0V6zalStXkJycjJ07d+LKlStQq9Vo0KAB+vbti5EjR8LOzs7SXSTSa9++feV+70VFRaF9+/ZSx7116xZ++OEHDB8+3OB+Go0GanX5z48ajQYXL15EYGCgzpwQAufOnUP9+vVhb2+P4uJirFmzBkVFRejRowfq1KljdJ+feuopLFmyBEFBQUZnyTRWfSvj/v37ERsbixo1aiAmJga+vr4AgNzcXKSlpeHevXvYuHEj2rZta3IbFy5cwPTp07F48WKdz9+/fx+ZmZnw9PREaGio1nOFhYX49ttvDf6QHT16FHv27EFUVBSaNWuGY8eO4eOPP0ZRURGGDh2Kp556Sm82ISFB5+Mff/wxhg4dCi8vLwDAvHnzKnqZAICCggJ8++23OHXqFPz9/TF48OCyY+hy4MAB1K5dGyEhIQCA5cuXIyUlBdnZ2QgKCkJ8fDwGDRqkNz9hwgQMGDAAf/vb3yrVv8d99tln2LdvH3r06IFBgwZh+fLlSEpKgkajwTPPPIN3330X9va6x7cZGRmIiYlBo0aN4OLigt27d+P5559HcXExNm7ciNDQUKSmpsLNzc1gH4qLi7F27Vqdg9M+ffrA0dHRpNdWWRcvXkStWrVQs2ZNrcdLSkqwe/dudO7cWW/2xo0bOHToEMLCwuDp6Ynr169j0aJFKCoqwnPPPWfS4LJBgwbYuHEjGjdubFROCIH09PSy915sbCwcHByMbr+yPvzwQzz77LM2+cfk6tWr6N+/P3799VcEBgZq/d7Lzs5Gx44d8f3338PHx8ek4//222944oknUFpaqvP5/Px8jB49Gj/88APc3d3x0ksvYfr06WWD6dzcXAQEBOjMHz9+HLGxsbhw4QIaNGiAX375Bc899xyOHTsGIQRq1KiBXbt26X3/rF+/XufjzzzzDD7++GPUr18fANC7d29TXjoZw3JlHSoWGRkpxo4dKzQaTbnnNBqNGDt2rHjyySel2sjKyhJqtVrnc8ePHxdBQUFCpVIJtVotOnfuLC5fvlz2fE5Ojt6sEEL8/PPPwtHRUXh6egpnZ2fx888/C29vbxETEyOeeuopYWdnJ9LS0vTmVSqVaNOmjYiOjtbaVCqVaNeunYiOjhZdu3bVm2/evLm4ceOGEEKI7OxsERwcLDw8PES7du2Ep6en8PHxEWfOnNGbb926tdi0aZMQQoiFCxcKFxcXMXHiRJGcnCwmTZokatasKRYtWmSw/2q1WjRu3FjMmTNHXLlyRe++j5s5c6Zwc3MT/fv3F35+fmLOnDnCy8tLvPfee2L27NnC29tbTJs2TW++Y8eOYsaMGWVfL1++XERGRgohhLh586Zo06aNmDhxosE+nDx5UjRo0EA4OzuLLl26iAEDBogBAwaILl26CGdnZ9GoUSNx8uTJSr+mx+Xk5Ih33nlH53OXL18W7dq1E2q1WtjZ2Ylhw4aJO3fuaGUNvff27t0rPDw8hEqlErVr1xYZGRkiJCRENG7cWDRs2FC4uLiIzMxMvfmPP/5Y52ZnZycSExPLvtYnLi5O3L59WwghxI0bN0RkZKRQqVTC29tbqNVq0axZM3H16lW9+QsXLohr166Vfb19+3bx/PPPi06dOokhQ4aIXbt26c0K8fC9Z2dnJ2JiYsTKlStFUVGRwf11+eGHH8TUqVPFzp07hRBCpKWlibi4OBEbGyu++OKLCvP37t0TixYtEqNGjRLdu3cXPXr0EPHx8WLz5s0Gc/379xdRUVHi2LFj5Z47duyY6NChg3j22Wf15vPy8gxuO3bsMPjemThxomjSpIn47rvvxMKFC0VQUJDo2bNn2fcwJydHqFQqndk+ffqI3r17i0OHDolJkyaJ5s2biz59+oji4mJRWFgoevXqJYYOHaq37Ue/M1Qqld7NUN9JOVY9OHB2dhZHjx7V+/zRo0eFs7OzwWOsW7fO4PbRRx/pfbP17dtX9OzZU1y7dk2cPHlS9OzZU4SEhIjz588LISr+BR0VFSXeeustIYQQ33zzjahdu7Z48803y56fMmWKePrpp/Xmk5KSREhISLkBhL29vfj9998Nvm4hHv6g5ebmCiGEGDJkiOjQoUPZL+w7d+6ImJgYMXjwYL15FxcXce7cOSGEEOHh4eLLL7/Uev7rr78WoaGhBtvfvHmzePXVV0WdOnWEg4OD6N27t/jhhx9EaWmpwb43bNhQfP/990KIhwM4Ozs78dVXX5U9v3r1atGoUSODfT99+nTZ16WlpcLBwUHk5OQIIYT45ZdfREBAgME+xMTEiD59+uisBpeXlyf69OkjunXrZvAYhhgamA4fPlxERkaK/fv3i02bNomIiAjRtm1bcfPmTSGE4V/Qj/o+evRokZ+fLz744ANRr149MXr06LLnR40aJfr27as3r1KpRL169URwcLDWplKpRN26dUVwcLAICQkxmH/03hs3bpwIDQ0tG4heuHBBREREiJdffllvvn379uKHH34QQgixdu1aoVarRe/evcUbb7wh+vXrJxwcHMqe19f+kiVLRJ8+fYSDg4Pw8vISr776qjh8+LDezJ+lpKQIe3t7ERERIdzd3cXy5cuFm5ubGD16tHjppZeEi4uLmD9/vt78yZMnRVBQkPDx8RH169cXKpVK9OzZU0RGRgo7Ozvx3HPPiZKSEp3ZmjVrigMHDug9dkZGhqhZs6bB165Wq/VuFf2BDQwMFFu3bi37+tq1a6J9+/aiW7duorCw0ODvPW9vb3Hw4EEhhBB3794VKpVK7Nixo+z5X3/9VQQGBuptu3v37qJnz55l751HKvs7j5Rj1YOD4OBgsWzZMr3PL1u2TAQFBRk8hsxI1MfHRxw6dKjsa41GI15++WURGBgoTp8+XeHgwN3dveyTZWlpqbC3t9f6oT98+LDw9fU12P99+/aJJk2aiH/+85+iuLhYCGHa4KBBgwbil19+0Xr+119/FfXr19eb9/LyEhkZGUKIh9+LrKwsredPnTolXFxcKtV+cXGxWLVqlYiNjRV2dnYiICBAvPnmm3o/ebu4uJQNwoQQwsHBQRw5cqTs63PnzokaNWrobTsoKKjsE58QDz+Jq1Qqce/ePSGEEGfPnq1wYOni4mLwj8mhQ4cMvv7ffvvN4LZq1Sq975+AgACxd+/esq8ffepq06aNuHHjRoXvvdq1a4s//vhDCPHwe69Wq7WOl5mZKerWras3/9JLL4k2bdqUHeMRU957TZs2FevWrdN6fvPmzQYHF66urmWDicjISDFnzhyt5z/99FMRHh5eqfZzc3PF3LlzRbNmzYRarRbt2rUTX375pcjPz9ebDw0NLRsMb9myRTg7O4sFCxaUPb9kyRLRvHlzvfm4uDjx0ksvlZ31nDNnjoiLixNCCHHixAkRHBwspk+frjPr5eUl0tPT9R5769atwsvLS+/z7u7uYu7cuSI9PV3ntnDhQoPvHRcXl3JnFPPz80VUVJR46qmnxJkzZ/TmH/+5rVmzpjh16lTZ19nZ2cLJyUlv20IIMW/ePFG/fn2twR8HB1XPqgcHn332mXBychITJ04U69atE3v27BF79uwR69atExMnThQuLi5aP7C6BAQEiLVr1+p9/uDBg3rf6G5ubuV+OQohxPjx40W9evXE9u3bKxwc/PkHo2bNmlqfZs+dO1fhHyghHn7KHz58uGjdurU4fPiwcHBwqPQv6EenbgMCAsr9oauo/aFDh4oXX3xRCCHEc889J95++22t52fPni1atWplsP3HPwEIIcT58+fF9OnTRVBQkN7vX0hIiPj555+FEA9/marVavHtt9+WPb9hwwYRHByst+1XX31VtGzZUvz8889iy5YtomvXriI6Orrs+dTUVNGwYUO9eSGE8Pf3N/jpdP369cLf31/v84YGphV9gnN1dRUnTpzQeqykpET07dtXtG7dWhw6dMjge8/V1VWcPXu27OvH33vnz5+v8L23evVqUb9+ffHpp5+WPWbM4ODRe8/Hx0drYCfEw/eeoT8SHh4e4rfffivLP/r3I6dOnTI4ONT33tu+fbsYMWKEcHV1Fa6urnrzuganf/75OXv2rMH2a9SoofX/V1RUJBwcHMT169eFEA/Phuh7/77yyisiKChIrF69WuusVV5enli9erUIDg4W8fHxetuOjo4Wc+fO1ft8VlaWwbNOTZs2FRs2bCj3+J07d0RUVJQICwvT+95r2LCh1pmCzz//XGsQlpmZKfz8/PS2/cjBgwdFaGioGDt2rCgoKODgwAKsenAghBArV64UkZGRwt7evuwXq729vYiMjBSrVq2qMN+rVy8xdepUvc8b+kFp166d+M9//qPzufHjx4tatWoZ/AXdunXrsj9wQjw8U/DnU4nbt283+Onpcd98843w9fUVarW60r+gW7VqJcLDw0XNmjXFf//7X63nt23bZvDT46VLl0RwcLDo3LmzSEhIEC4uLqJTp05izJgxonPnzsLR0VHnL5E/t6/rF/QjGo2m3NmMR95++23h7e0tRo8eLUJCQsSUKVNEYGCgSE5OFikpKaJ+/fpi8uTJeo99584dMWDAgLL3TYcOHbQ+DW3cuFFrsKHL1KlTRe3atcW8efPEb7/9JnJyckROTo747bffxLx584Snp6feT39CPPwEuGjRInHu3Dmd24YNG/S+f1q1alXu/0uI/w0QAgMDDb73mjVrpnU56scffyw7ayKEEHv27BH16tUz+PqFEOLixYviqaeeEt27dxdXrlwxanDQo0cP0a9fP1G7du1yg6w9e/YYPGvWu3dvMWXKFCGEELGxseXmNyxcuFA0btxYb16tVht87+Xl5ZW7TPZnjwb/Qjz8OVCpVFrv9fT0dIPfv4CAAK05Hbdu3RIqlarsD+WZM2f0Do4KCwvFyy+/LBwdHYVarRbOzs7C2dlZqNVq4ejoKMaNGycKCwv1tv3ll18anA+Sk5OjNR/ncRMmTNA7pyE/P19ERkbqfe+99NJLYuHChXqPnZSUJHr06KH3+T+7d++eeOmll0Tjxo2FnZ0dBwdVzOoHB48UFxeLy5cvi8uXL5edXq+M7du3a/2Bftzdu3f1nsKbPXt22alAXcaNG2dwBJ6cnCx+/PFHvc8nJiaWfTKvrAsXLoi1a9eKu3fvVrjvjBkztLbU1FSt5//1r3+JQYMGGTzGrVu3xBtvvCFCQ0OFs7OzcHR0FEFBQeL5558X+/fvN5gNDg4u+6RkrNLSUjFr1izxj3/8Q8yePVtoNBrxzTffiPr16wsvLy8xcuTISn0P7t+/rzWRz1hz5swR/v7+WtdxVSqV8Pf3N/jpTAghunXrJmbOnKn3eUMD09dff13vfIaSkhLRu3dvg++9GTNmiG+++Ubv82+++aZ45pln9D7/ZxqNRsyePVv4+flV+pf0yJEjtbbHB/KvvfaaiI2N1Zv/448/hJeXlxg+fLiYOXOmqFmzphg6dKiYNWuWGD58uHBychJLlizRm69oYFqR8ePHi8aNG4v33ntPtG/fXowYMUI0a9ZM/PzzzyI1NVW0atVKvPDCC3rzI0aMEF26dBFHjx4VZ86cEQMHDtS6DJKenm7wkp4QDwcwW7ZsEStWrBArVqwQW7Zs0Tn/RWk3b94sd6bnz/Lz8w1e9jDkzJkzWpO6K2PdunVi0qRJUv+fZDybGRwQWdKZM2fErl27xK5duwze4fFnq1evFsuXL9f7/M2bN8XSpUt1PldSUmLwD0FJSUnZZFFTFBQUGPz0qUtGRoaYP39+2aRIGXfv3hX37983uM+pU6fEoEGDhJubW9lZQwcHB9GhQwexZs0a6T5U1L8xY8aIli1birFjx4qioiLxwQcfCEdHR6FSqUR0dLTBP1a5ubniySefLBtUBgUFac03+u6778Qnn3xi1tdAJIODAyITZWdni1GjRtlk27aU12g0Iicnx+izhkq1/2f37983OJHxcSdOnCh3ObEy7t27J3bs2KHzLM39+/cNTtS2dN7SfSdlcHBAZCJDtyJWhswfWNm2bT0vOzixdPuG8rrWV7l06VLZ8xXdqSK7PotM3pJtk7KsfvlkIkvRt1rbI2fOnJE6/s2bN7Fs2TKdq3PKtm3r+YoY+t5ZQ/sy+TfeeAMtW7ZERkYGbt++jUmTJqFTp05IT0/Xu2RxRfmOHTtWSd6SbZOyrHr5ZCJLUqvVUKlUBgtdqVQqvcvQVuYP1D//+U+dedm2bT0v872zhvZl8r6+vti8eTNatWoF4OHS06+88gp++uknbN26Fa6urnqXL7Z03tJ9JwVZ5HwFkQ2QWSNDCLkFuGTbtvW87DK6lm5fJi+7vool85buOymHJZuJ9IiIiEBmZqbe5yv6ZOrv74/Vq1dDo9Ho3A4cOGC2tm09L/O9s4b2ZfLNmjVDRkZGucc/++wz9OnTp8KiQ5bMW7rvpBwODoj0eO2119ChQwe9zzdq1Ahbt27V+7zMHyjZtm09L/vH3dLty+T79euHb775Rudzn332GQYPHmywbUvmLd13Ug7nHBCZyY4dO1BQUIDu3bvrfL6goAAZGRno0qVLFffM+ln6eyfbvqX7TySLgwMiIiLSwssKREREpIWDAyIiItLCwQERERFp4eCAiIiItHBwQERERFo4OCAiIiItHBwQERGRlv8H8mACG3CbILkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_fisher_z_transformed_correlation(FC_second_half_HCP,FC_second_half_HCP),cmap=\"coolwarm\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e33f599f", + "metadata": {}, + "source": [ + "## Update 19th February 2024" + ] + }, + { + "cell_type": "markdown", + "id": "eca575a0", + "metadata": {}, + "source": [ + "Now we explore the idea of: reproducibility of temporal dynamics. This is something we've never touched upon." + ] + }, + { + "cell_type": "markdown", + "id": "31504b1e", + "metadata": {}, + "source": [ + "Steve has some thoughts that the uncertainty of temporal dynamics will be very high when you have very large number of states.\n", + "Maybe have a look at the perfect model first." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "783c07d3", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "import pickle\n", + "from scipy.stats import multivariate_normal\n", + "from rotation.utils import twopair_riemannian_distance,twopair_fisher_z_transformed_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "0d88006e", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202402/sigma_0.1/HMM_ICA_25_state_16/'\n", + "data_dir = './data/node_timeseries/simulation_202402/sigma_0.1/'" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "81a07ca6", + "metadata": {}, + "outputs": [], + "source": [ + "# Open the pickle file for reading\n", + "with open(f'{save_dir}alpha.pkl', 'rb') as f:\n", + " # Load the object from the pickle file\n", + " alpha = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "79939ee0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1200" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(alpha[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "09f29219", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200, 8)\n" + ] + } + ], + "source": [ + "ground_truth = np.load(f'{data_dir}truth/10001_state_time_course.npy')\n", + "print(ground_truth.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "92e3aefb", + "metadata": {}, + "outputs": [], + "source": [ + "posterior = alpha[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "f0d57716", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0, 0],\n", + " [1, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " 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7.28608143e-08,\n", + " 1.18289969e-03, 1.59002680e-04, 6.15865865e-05, 7.66190886e-03,\n", + " 1.21444069e-01, 3.66124503e-13, 8.56715500e-01, 9.19596234e-04,\n", + " 2.68110174e-08, 8.62679008e-05, 4.25013917e-04, 1.03140958e-02],\n", + " [1.15946687e-15, 4.85925623e-12, 3.11087690e-13, 5.62655466e-17,\n", + " 3.52807616e-10, 2.49118759e-13, 1.44165483e-07, 5.70455194e-01,\n", + " 2.31573232e-08, 7.65167964e-12, 9.12592441e-06, 9.47606638e-02,\n", + " 3.12093995e-09, 2.62845945e-09, 5.27053044e-06, 3.34769607e-01],\n", + " [7.66458863e-04, 7.65254488e-03, 9.41250205e-01, 1.05828571e-03,\n", + " 8.61869659e-04, 3.94771574e-03, 1.68752231e-04, 1.61132496e-02,\n", + " 7.62103475e-04, 7.08330958e-07, 2.95762019e-03, 4.50424943e-03,\n", + " 1.42953277e-05, 6.91038882e-03, 2.17490410e-03, 1.08566498e-02],\n", + " [2.63690425e-04, 7.59128295e-03, 9.63834405e-01, 6.00141415e-04,\n", + " 9.40173981e-04, 5.12760319e-03, 3.37446807e-04, 1.59384939e-03,\n", + " 8.99844221e-04, 3.03626002e-05, 3.47985653e-03, 1.75311510e-03,\n", + " 2.87380158e-06, 1.04150595e-02, 1.21160422e-03, 1.91868446e-03],\n", + " [6.00130646e-04, 8.60018563e-03, 9.59564328e-01, 1.27493043e-03,\n", + " 1.93174870e-03, 6.89545739e-03, 4.87849553e-04, 7.55628454e-04,\n", + " 8.45833099e-04, 2.13339376e-06, 4.69791796e-03, 6.46910106e-04,\n", + " 6.34415255e-06, 1.11761978e-02, 1.23586925e-03, 1.27850706e-03],\n", + " [5.52557234e-04, 2.69020274e-02, 9.10004497e-01, 9.58592223e-04,\n", + " 3.53473239e-03, 2.28542071e-02, 1.05167994e-04, 6.76665397e-04,\n", + " 2.03147391e-03, 1.19981978e-05, 1.43060936e-02, 5.75516606e-04,\n", + " 2.70785986e-05, 1.56438239e-02, 6.95131894e-04, 1.12045615e-03],\n", + " [2.48434226e-04, 1.11740793e-03, 9.91419792e-01, 3.24006309e-04,\n", + " 2.28772569e-03, 2.11015576e-03, 7.08575171e-05, 6.94348855e-05,\n", + " 1.47322964e-04, 2.45877970e-07, 1.40645460e-03, 3.30453295e-05,\n", + " 5.08789810e-07, 5.64089220e-04, 8.95868870e-05, 1.10932531e-04],\n", + " [1.78606104e-04, 2.91215605e-04, 9.98323023e-01, 1.49040483e-04,\n", + " 3.85437277e-04, 2.81101093e-04, 1.08909726e-05, 1.81608066e-05,\n", + " 2.28804693e-05, 4.24642678e-07, 1.40155447e-04, 9.26070061e-06,\n", + " 2.36886351e-07, 1.42455567e-04, 3.51672679e-05, 1.19507577e-05],\n", + " [1.36381379e-04, 6.77534335e-05, 9.99401867e-01, 1.30702756e-04,\n", + " 5.44012983e-05, 3.42165695e-05, 3.05069329e-06, 4.80740937e-06,\n", + " 4.16297153e-05, 1.40163070e-06, 6.10464194e-05, 3.03930756e-06,\n", + " 7.28243378e-07, 4.05345381e-05, 1.53547808e-05, 3.09238476e-06],\n", + " [7.42119664e-05, 3.41092004e-04, 9.94976759e-01, 1.50099571e-04,\n", + " 1.21169462e-04, 1.44061240e-04, 6.27153349e-05, 1.45041140e-05,\n", + " 1.91531377e-03, 5.36734717e-07, 1.84217049e-03, 1.22272604e-05,\n", + " 3.98368286e-07, 3.11567623e-04, 1.73812023e-05, 1.57809391e-05],\n", + " [3.72435134e-05, 1.10545859e-03, 9.45490420e-01, 1.08361877e-04,\n", + " 4.68290644e-04, 8.33240338e-04, 1.56409654e-03, 5.82089589e-04,\n", + " 2.81573925e-02, 1.78393456e-09, 1.87372714e-02, 5.94917801e-04,\n", + " 1.83939380e-06, 1.08733331e-03, 1.80430492e-04, 1.05161965e-03],\n", + " [1.28493397e-04, 4.28756839e-03, 4.08544272e-01, 7.65305012e-04,\n", + " 1.04128774e-02, 2.13986193e-03, 3.05033047e-02, 1.02207422e-01,\n", + " 9.80060026e-02, 5.95890004e-10, 1.13910086e-01, 5.00371158e-02,\n", + " 2.06148752e-06, 1.32157644e-02, 1.96144707e-03, 1.63878396e-01],\n", + " [7.13096524e-05, 4.38900997e-06, 2.75474940e-05, 6.47819325e-05,\n", + " 3.16880061e-04, 3.83108636e-06, 9.77869425e-03, 2.65486419e-01,\n", + " 9.63409402e-05, 6.81333034e-08, 7.28257175e-04, 1.14052549e-01,\n", + " 6.75894626e-05, 1.28685217e-03, 3.62075662e-05, 6.07978284e-01],\n", + " [2.92446574e-15, 3.21488523e-16, 3.45888310e-14, 1.08405924e-16,\n", + " 1.72995143e-11, 4.12900610e-16, 6.80918340e-03, 2.54305005e-01,\n", + " 1.19253052e-10, 9.89668156e-12, 4.45051995e-09, 1.07120544e-01,\n", + " 1.91247533e-08, 2.78809068e-07, 8.59641659e-07, 6.31764114e-01],\n", + " [3.35948812e-07, 5.70147165e-07, 4.07425809e-08, 1.33086314e-06,\n", + " 8.93374545e-06, 7.46584703e-07, 2.46851181e-04, 2.56831080e-01,\n", + " 4.64427039e-08, 1.48444981e-08, 5.11751296e-07, 1.04274929e-01,\n", + " 1.73012031e-05, 4.65251333e-06, 7.79522490e-03, 6.30817413e-01]],\n", + " dtype=float32)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "posterior[240:300,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "43182419", + "metadata": {}, + "outputs": [], + "source": [ + "covariance_fit = np.load(f'{save_dir}state_covariances.npy')\n", + "covariance_truth = np.load(f'{data_dir}truth/10001_state_covariances.npy')\n", + "covariance_fit = np.load(f'{save_dir}state_covariances.npy')\n", + "covariance_truth = np.load(f'{data_dir}truth/10001_state_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "f39431bf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 216.18it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 0%| | 0/8 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.heatmap(twopair_riemannian_distance(covariance_truth,covariance_fit),annot=True,fmt=\".2f\")\n", + "plt.show()\n", + "#seaborn.heatmap(twopair_fisher_z_transformed_correlation(covariance_truth,covariance_fit),annot=True,fmt=\".2f\")\n", + "#plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8a50bc44", + "metadata": {}, + "source": [ + "It seems to me that even the riemannian distance is very close, the model still have a preference over one state. Maybe look at your data to confirm that!" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "2792a33e", + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(f'{data_dir}10001.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "aed6fb48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200, 25)\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "23b4d91e", + "metadata": {}, + "outputs": [], + "source": [ + "logpdf_baseline = multivariate_normal(cov=covariance_truth[-1]).logpdf(data[250:275,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "ddc99907", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-29.95887397038275\n" + ] + } + ], + "source": [ + "print(np.mean(logpdf_baseline))" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "0f1de6f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-30.533795785938437\n" + ] + } + ], + "source": [ + "logpdf_0 = multivariate_normal(cov=covariance_fit[0]).logpdf(data[250:275,:])\n", + "print(np.mean(logpdf_0))" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "ffbe3f43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-31.66624565540816\n" + ] + } + ], + "source": [ + "logpdf_1 = multivariate_normal(cov=covariance_fit[1]).logpdf(data[250:275,:])\n", + "print(np.mean(logpdf_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "98c6279f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-30.447577862777038\n" + ] + } + ], + "source": [ + "logpdf_3 = multivariate_normal(cov=covariance_fit[3]).logpdf(data[250:275,:])\n", + "print(np.mean(logpdf_3))" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "50e90e66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-32.15634421065483\n" + ] + } + ], + "source": [ + "logpdf_4 = multivariate_normal(cov=covariance_fit[4]).logpdf(data[250:275,:])\n", + "print(np.mean(logpdf_4))" + ] + }, + { + "cell_type": "markdown", + "id": "9de67a49", + "metadata": {}, + "source": [ + "## Update 24th Feb 2024" + ] + }, + { + "cell_type": "markdown", + "id": "c78e948f", + "metadata": {}, + "source": [ + "Let's try to revise the Config so that the initial means and covariances could be read in from specific file." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1da6bdeb", + "metadata": {}, + "outputs": [], + "source": [ + "from dataclasses import dataclass\n", + "from typing import Union\n", + "\n", + "@dataclass\n", + "class Point:\n", + " x: Union[float, str]\n", + " #x: float\n", + " y: float\n", + " \n", + " def __post_init__(self):\n", + " # Check if x is a string, then convert it to float\n", + " if isinstance(self.x, str):\n", + " self.x = float(self.x)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "59cb58e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Whether None is a instance of string?:False\n" + ] + } + ], + "source": [ + "# Test cases\n", + "point_float = Point(2.0, 3.5) # Initialize with float\n", + "#print(point_float) # Output: Point(x=2.0, y=3.5)\n", + "\n", + "point_str = Point(\"2.5\", 4.5) # Initialize with str\n", + "print(type(point_str.x)) # Output: Point(x=2.5, y=4.5)\n", + "temp = None\n", + "print(f'Whether None is a instance of string?:{isinstance(temp,str)}')" + ] + }, + { + "cell_type": "markdown", + "id": "acba028e", + "metadata": {}, + "source": [ + "The code shows that we can actually revise the config class so that a string can be passed in, i.e., read in that file to initialise the TPM/means/covariances" + ] + }, + { + "cell_type": "markdown", + "id": "4d9959ab", + "metadata": {}, + "source": [ + "The training has finished. So let's check the results" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "28455133", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import numpy.testing as npt\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "89ee330c", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_0/sigma_0/hmm_ICA_25_state_16/'\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']\n", + "covariance_truth = np.load('./results_simulation_202402_repeat/fixed_covariances.npy')\n", + "# Check the mean activation are all zero, and covariance matrices are all identical.\n", + "for repeat in repeats:\n", + " means = np.load(f'{save_dir}{repeat}/inf_params/means.npy')\n", + " assert np.sum(np.abs(means)) == 0\n", + " covariances = np.load(f'{save_dir}{repeat}/inf_params/covs.npy')\n", + " npt.assert_almost_equal(covariances,covariance_truth,decimal=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "0bfbd9b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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QtWbNmo73jIiIqLN42DSBpGJg7dq1iImJQWBgoN1xURRx6NAh+Pn5QdWGLVotFgssFkujGG15LhEREclLUjGwfPlybNy4ES+99BLuvPNO23Fvb29s3boVgwcPblMco9GIJUuW2B1TqbtBpfGX0h0iIiJF8N4ELZg/fz5ycnIwY8YMPP3006ivr29XUoPBgMrKSrumUndvVywiIiLZCaJ8zQ1IXkB4yy23wGQy4fz584iPj8eBAwckD+9rtVr4+/vbNU4REBERdY527TPQrVs3bNu2Ddu3b0dSUhKsVqvc/SIiIuo8bvKJXi4d2nRo8uTJuPXWW2EymRARESFXn4iIiDqXm1wSKJcO70B4/fXX4/rrr5ejL0RERK7Bw0YGeKMiIiIiD8d7ExARETkQPWxkgMUAERGRIw8rBjhNQERE5OFYDBARETkSBPmaROvXr0dkZCR8fX2RkJCAwsLCFh9/6dIlzJw5EzqdDlqtFgMGDMDHH38sKafLTBOonbDpkFqlbO0jOOFSFGdsztQlbJTiOU7ERCueo//3JYrGd8b3QqtR/i1qaWjfTqJSOGNLMaW/H6cvX1A0PgC83et2xXOkXfxc8RzO+F2ouE6aJsjJyYFer0dWVhYSEhKwdu1aJCcno6SkBMHBwY0eX1dXhz/+8Y8IDg7G+++/j/DwcJw8ebLRPYRa4zLFABERkadbs2YNMjIykJ6eDgDIysrCRx99hOzsbMyfP7/R47Ozs1FRUYEvv/wS3t7eAIDIyEjJeTlNQERE5EjGexNYLBZUVVXZNcc79wK/fco3mUxISkqyHVOr1UhKSkJBQUGT3dy1axcSExMxc+ZMhISEYMiQIVi+fLnknYFZDBARETkQRVG2ZjQaERAQYNeMRmOjnOXl5bBarQgJCbE7HhISgrKysib7efz4cbz//vuwWq34+OOPsWjRIrz00kt4/vnnJb1eThMQEREpyGAwQK/X2x3TarWyxBYEAcHBwdi4cSM0Gg3i4uJw+vRprFq1CpmZmW2Ow2KAiIjIkYwLCLVabZv++AcFBUGj0cBsNtsdN5vNCA0NbfI5Op0O3t7e0Gg0tmODBg1CWVkZ6urq4OPj06Y+cpqAiIjIkYxrBtrKx8cHcXFxyMvL+283BAF5eXlITExs8jkjR47E0aNHIfzuEsbDhw9Dp9O1uRAAWAwQERE1IgqibE0KvV6PTZs2Ydu2bTh06BBmzJiB6upq29UFqampMBgMtsfPmDEDFRUVmDVrFg4fPoyPPvoIy5cvx8yZMyXl5TQBERGRi5g0aRLOnz+PxYsXo6ysDLGxscjNzbUtKiwtLYVa/d/P8b1798bu3bsxZ84cDBs2DOHh4Zg1axbmzZsnKa9KFEWX2IDZR6v8bZC56VDbWNuxY5ZU3HSobbzUmtYf1EHcdKhtBCf8quSmQ21XZ/lF0fiVaWNkixWwLa/1B3WyDo0MVFdX47333sPRo0eh0+kwZcoU9OrVS66+ERERdY5rYBNFKSQVA4MHD8b+/fvRs2dPnDp1CrfddhsuXryIAQMG4NixY1i2bBm++uor9O3bt8U4Foul0YYLoig65ZMWERER2ZM0bv7TTz+hoaEBwG/XTYaFheHkyZMoLCzEyZMnMWzYMCxcuLDVOE1twCBYL7fvFRAREcmssxYQdpZ2T6IXFBTgueeeQ0BAAACgW7duWLJkCfbv39/qcw0GAyorK+2aWtO9vV0hIiKSVydcWtiZJK8ZuDqUX1tbC51OZ3cuPDwc58+fbzVGUxswcIqAiIioc0guBsaMGQMvLy9UVVWhpKQEQ4YMsZ07efIkFxASEZH74wLC5jnuc9ytWze7rz/44AOMGjWq470iIiLqRO4y1y+XDhUDjlatWtWhzhAREZHzcQdCIiIiR5wmICIi8mycJiAiIvJ0HjYywLsWEhEReTiODBARETlwwr2WXIrLFAPOuCOY0uM+ztg4Sek7LwKA1QnjY4MOHlc8x6nEKEXjh395RNH4AKBywv3+roU7CgK/3d/E3aVe/EzxHOuCRiue48ly5V+H4jysGOA0ARERkYdzmZEBIiIiV8FpAiIiIk/nYcUApwmIiIg8HEcGiIiIHHCagIiIyMOxGCAiIvJwnlYMcM0AERGRh+PIABERkSPRGdtxuQ4WA0RERA44TdCCoqIinDhxwvb1m2++iZEjR6J379649dZbsX379jbFsVgsqKqqsmvXwlaiRERE7khSMZCeno5jx44BAN544w383//9H+Lj47Fw4ULccsstyMjIQHZ2dqtxjEYjAgIC7JooXG7fKyAiIpKZKKhka+5A0jTBkSNHcMMNNwAAXn/9dbzyyivIyMiwnb/lllvwwgsvYPr06S3GMRgM0Ov1dsd69IqW0hUiIiLFeNo0gaRioGvXrigvL0dERAROnz6N4cOH251PSEiwm0ZojlarhVartTvmjLuaERERUWOSpgnGjRuHDRs2AABGjx6N999/3+78e++9h6goZW8bS0REpDRRVMnW3IGkkYGVK1di5MiRGD16NOLj4/HSSy9h3759GDRoEEpKSvDVV19h586dSvWViIjIKTxtmkDSyEBYWBi+++47JCYmIjc3F6IoorCwEJ988gmuv/56fPHFF7j77ruV6isREREpQPIOhIGBgVixYgUOHjyIX3/9FRaLBT///DPefvttxMfHK9FHIiIip+rMqwnWr1+PyMhI+Pr6IiEhAYWFhc0+duvWrVCpVHbN19dXck5uR0xERORAFOVrUuTk5ECv1yMzMxNFRUWIiYlBcnIyzp071+xz/P39cfbsWVs7efKk5NfLYoCIiMhBZ40MrFmzBhkZGUhPT8fgwYORlZWFrl27triHj0qlQmhoqK2FhIRIfr0sBoiIiBTU1K67Foul0ePq6upgMpmQlJRkO6ZWq5GUlISCgoJm41+5cgURERHo3bs3JkyYgIMHD0ruI4sBIiIiB3KODDS1667RaGyUs7y8HFartdEn+5CQEJSVlTXZz4EDByI7Oxv//Oc/8dZbb0EQBIwYMQK//PKLpNfrMjcq8vXyUTxHbUOdsgmccH8FK5S/3sVbo/yPhQjl/60ivjquaPwbAsMVjQ8ARy6dVjyHMzjjSmulf6Kc8Rq81cq/92acy1c8x+WPFymeQ2ly/jpvatddx4332isxMRGJiYm2r0eMGIFBgwbhr3/9K5YtW9bmOC5TDBAREV2Lmtp1tylBQUHQaDQwm812x81mM0JDQ9uUy9vbGzfddBOOHj0qqY+cJiAiInLQGQsIfXx8EBcXh7y8PNsxQRCQl5dn9+m/JVarFT/88AN0Op2k18uRASIiIgedtY2wXq9HWloa4uPjMXz4cKxduxbV1dVIT08HAKSmpiI8PNy25mDp0qX4wx/+gKioKFy6dAmrVq3CyZMn8cgjj0jKy2KAiIjIRUyaNAnnz5/H4sWLUVZWhtjYWOTm5toWFZaWlkKt/u+g/sWLF5GRkYGysjL06NEDcXFx+PLLLzF48GBJeVWi6IRVb23QrWtfxXMovoDwGuGMBYRqJ9yl0ioou9iyr3/b5vA64lpZQOiM77eg8K8yZ3xO1F4LC6nhnAWEXZIeUzT+0cHJssWK+nG3bLGUwpEBIiIiB4Kb3G1QLlxASERE5OE4MkBEROSgsxYQdhYWA0RERA7ac7dBd8ZigIiIyIFrLK13HklrBp588kl8/vnnHU7a1E0bXOSiBiIiIo8jqRhYv349br/9dgwYMAArV65s9sYJrWnqpg31DZfaFYuIiEhunXUL484i+WqCTz75BHfffTdWr16NPn36YMKECfjwww8hSLim22AwoLKy0q55ewVK7QoREZEiBFElW3MHkouBoUOHYu3atThz5gzeeustWCwWpKSkoHfv3li4cGGbbo6g1Wrh7+9v11RO2JSEiIiIGmv3PgPe3t544IEHkJubi+PHjyMjIwNvv/02Bg4cKGf/iIiInE4UVbI1dyDLpkN9+vTBc889hxMnTiA3N1eOkERERJ1GFOVr7kBSMRAREQGNRtPseZVKhT/+8Y8d7hQRERE5j6R9Bk6cOKFUP4iIiFyGuyz8kws3HSIiInLgLnP9cuGNioiIiDwcRwaIiIgcuMvCP7mwGCAiInLgaWsGVKKL3BTgxYg/K55jkflTxXMozSphp0dX1tVbq3iO2oY6xXMo7fxk5fft6PXuT4rnuBZ+rTrjF+W18O8EwCmbyNVZflE0/jfh98kW65bTO2WLpRSuGSAiIvJwnCYgIiJy4GnTBCwGiIiIHLjE/LkTcZqAiIjIw3FkgIiIyAGnCYiIiDwcdyAkIiIij8KRASIiIgfXxo4ubcdigIiIyIF4zWwB1TacJiAiIvJwkouBdevWITU1Fdu3bwcAvPnmmxg8eDCio6OxYMECNDQ0tBrDYrGgqqrKrjWIVum9JyIiUoAgytfcgaRi4Pnnn8eCBQtQU1ODOXPmYOXKlZgzZw6mTp2KtLQ0vPHGG1i2bFmrcYxGIwICAuxafuXBdr8IIiIiOQlQydbcgaQ1A1u3bsXWrVtx//334z//+Q/i4uKwbds2TJ06FQAQHR2NZ555BkuWLGkxjsFggF6vtzu2bsj/Sew6ERGRMrhmoAVnzpxBfHw8ACAmJgZqtRqxsbG28zfffDPOnDnTahytVgt/f3+75qXSSOs5ERHRNWj9+vWIjIyEr68vEhISUFhY2Kbnbd++HSqVCikpKZJzSioGQkND8eOPPwIAjhw5AqvVavsaAA4ePIjg4GDJnSAiInIlgoxNipycHOj1emRmZqKoqAgxMTFITk7GuXPnWnzezz//jKeffhqjRo2SmPE3kqYJpk6ditTUVEyYMAF5eXl45pln8PTTT+PChQtQqVR44YUX8Kc//aldHSEiInIVnTVNsGbNGmRkZCA9PR0AkJWVhY8++gjZ2dmYP39+k8+xWq2YOnUqlixZgs8//xyXLl2SnFdSMbBkyRJ06dIFBQUFyMjIwPz58xETE4NnnnkGNTU1GD9+fJsWEBIREXkKi8UCi8Vid0yr1UKr1dodq6urg8lkgsFgsB1Tq9VISkpCQUFBs/GXLl2K4OBgPPzww/j888/b1UdJxYBarcaCBQvsjk2ePBmTJ09uV3IiIiJXJOcOhEajsdHC+szMTDz33HN2x8rLy2G1WhESEmJ3PCQkBD/99FOTsffv34/NmzejuLi4Q33kDoREREQO5CwGmrqCznFUoD0uX76Mhx56CJs2bUJQUFCHYrEYICIiUlBTUwJNCQoKgkajgdlstjtuNpsRGhra6PHHjh3Dzz//jPHjx9uOCcJvZYyXlxdKSkrQv3//NvWR2xETERE5EKGSrbWVj48P4uLikJeXZzsmCALy8vKQmJjY6PHR0dH44YcfUFxcbGv33nsv7rjjDhQXF6N3795tzs2RASIiIgdCJ+05pNfrkZaWhvj4eAwfPhxr165FdXW17eqC1NRUhIeHw2g0wtfXF0OGDLF7fmBgIAA0Ot4aFgNEREQuYtKkSTh//jwWL16MsrIyxMbGIjc317aosLS0FGq1/IP6KlEUXeI2Clrftg9ntNfgwD6Kxj948aSi8QFArVJ+ZqdBUP6mUV5q5XectCr8OlzijSODK5+tUTxH99v0rT+og66F74dGgV/yjpzxK19wQo6GutOKxv9n6IOyxZpQ9o5ssZTCkQEiIiIH10JxKQWLASIiIgdyXlroDng1ARERkYfjyAAREZEDQeVZtzBmMUBEROTA09YMcJqAiIjIw3FkgIiIyIGnLSBkMUBEROSgs3Yg7CySi4GzZ89iw4YN2L9/P86ePQu1Wo1+/fohJSUF06ZNg0aj/GYyREREJB9Jawa+/fZbDBo0CB9//DHq6+tx5MgRxMXFwc/PD08//TRuu+02XL58udU4FosFVVVVds1FNkIkIiKCAJVszR1IKgZmz56NOXPm4Ntvv8Xnn3+OrVu34vDhw9i+fTuOHz+OmpoaPPvss63GMRqNCAgIsGtWa1W7XwQREZGcRBmbO5BUDBQVFeGhhx6yff3ggw+iqKgIZrMZPXr0wIsvvoj333+/1TgGgwGVlZV2TaPxl957IiIi6jBJawaCg4Nx9uxZ9OvXDwBgNpvR0NAAf//f/pDfcMMNqKioaDWOVquFVqu1O6bysA0eiIjIdXnaAkJJIwMpKSl47LHHkJubi/z8fEydOhWjR49Gly5dAAAlJSUIDw9XpKNERETOIsjY3IGkkYHnn38eZ8+exfjx42G1WpGYmIi33nrLdl6lUsFoNMreSSIiImdyl7l+uUgqBrp164acnBzU1taioaEB3bp1szs/duxYWTtHREREymvXpkO+vr5y94OIiMhleNqaAe5ASERE5MBd5vrlwhsVEREReTiODBARETnwtJEBFgNEREQORA9bM8BpAiIiIg/nMiMDKifczOGHip8VjX/li1cVjQ8A/rfOUjyHMwriLl4+iueoExqUjd9Qr2h8wDnXOve44xnFc3T1Uf4KpJq6WkXjO+N7oVYp//msQeH3BQBo1O7/OZPTBERERB7O04oB9y/fiIiIqEM4MkBEROSA2xETERF5OO5ASERE5OE8bc1Au4qBuro6/OMf/0BBQQHKysoAAKGhoRgxYgQmTJgAHx/lV4oTERGRPCQvIDx69CgGDRqEtLQ0fPfddxAEAYIg4LvvvkNqaipuvPFGHD16VIm+EhEROYUgY3MHkouBGTNmYOjQoTCbzdi3bx9ycnKQk5ODffv2wWw248Ybb8TMmTOV6CsREZFTiDI2qdavX4/IyEj4+voiISEBhYWFzT52x44diI+PR2BgIPz8/BAbG4s333xTck7J0wRffPEFCgsL4e/v3+icv78/li1bhoSEBMkdISIi8nQ5OTnQ6/XIyspCQkIC1q5di+TkZJSUlCA4OLjR43v27ImFCxciOjoaPj4++PDDD5Geno7g4GAkJye3Oa/kkYHAwED8/PPPzZ7/+eefERgY2GIMi8WCqqoquyaKnnYhBxERuSpBJV+TYs2aNcjIyEB6ejoGDx6MrKwsdO3aFdnZ2U0+/vbbb8d9992HQYMGoX///pg1axaGDRuG/fv3S8oruRh45JFHkJqaipdffhnff/89zGYzzGYzvv/+e7z88suYNm0aHn300RZjGI1GBAQE2DWrtUpqV4iIiBTRGWsG6urqYDKZkJSUZDumVquRlJSEgoKCVp8viiLy8vJQUlKC2267TULmdkwTLF26FH5+fli1ahX+8pe/QKVS2ToRGhqKefPm4ZlnWt7r3GAwQK/X2x277robpXaFiIjI5VksFlgsFrtjWq0WWq3W7lh5eTmsVitCQkLsjoeEhOCnn35qNn5lZSXCw8NhsVig0Wjw+uuv449//KOkPrbr0sJ58+Zh3rx5OHHihN2lhX379m3T85v6R7haVBAREXU2OSeujUYjlixZYncsMzMTzz33nCzxu3fvjuLiYly5cgV5eXnQ6/Xo168fbr/99jbH6NCmQ3379m1UAJw6dQqZmZnNzm8QERG5OkHGcqCp0XDHD8QAEBQUBI1GA7PZbHfcbDYjNDS02fhqtRpRUVEAgNjYWBw6dAhGo1FSMSD7jYoqKiqwbds2ucMSERG5Ja1WC39/f7vWVDHg4+ODuLg45OXl2Y4JgoC8vDwkJia2OZ8gCI2mJVojeWRg165dLZ4/fvy41JBEREQupbM2C9Lr9UhLS0N8fDyGDx+OtWvXorq6Gunp6QCA1NRUhIeHw2g0AvhtCiI+Ph79+/eHxWLBxx9/jDfffBMbNmyQlFdyMZCSkgKVStXipYCc/yciInfWWRe7T5o0CefPn8fixYtRVlaG2NhY5Obm2hYVlpaWQq3+76B+dXU1Hn/8cfzyyy/o0qULoqOj8dZbb2HSpEmS8qpEiRf4h4eH4/XXX8eECROaPF9cXIy4uDhYrVZJHfH17SPp8e3RIEjrk1RXvnhV0fgA4H/rLMVzOGPPh24+XRTPUSc0KBu/oV7R+IBzfiF5a5S/X5mPE3LU1NUqGv9a+V40WJV9XwCw+2OlFEvtKUXjPxcxVb5YJ9+WLZZSJH/H4uLiYDKZmj3f2qgBERERuRbJZejcuXNRXV3d7PmoqCjk5+d3qFNERESdSerOge5OcjEwatSoFs/7+flh9OjR7e4QERFRZ5Pz0kJ3oPzEDhEREbk05VertJFV4cV9AODr5aNo/IBRsxWNDwBld/dXPEfox8cUz1FrVX7xnSAqe3GQMz43KP0zCwDdfHwVz3GptvmpRbk8EjZS0fibznyhaPxriVXorAvz5ONZ4wIuVAwQERG5CvcvZ6ThNAEREZGH48gAERGRA09bQMhigIiIyIFnlQKcJiAiIvJ4HBkgIiJywAWEHWQ2m7F06VK5wxIRETmNAFG25g5kLwbKysqwZMkSucMSERE5jShjcweSpwm+//77Fs+XlJS0uzNERETkfJKLgdjY2GbvTHj1uErV8h0eLBYLLBaL3bG2PI+IiMgZPG3NgORioGfPnnjxxRcxZsyYJs8fPHgQ48ePbzGG0WhsNJWgUneDRuMvtTtERESyE91mgF8ekouBuLg4nDlzBhEREU2ev3TpUpOjBr9nMBig1+vtjvXsFS21K0RERCQDycXAY489hurq5m860qdPH2zZsqXFGFqtFlqt1u4YpwiIiMhVcJqgFffdd1+L53v06IG0tLR2d4iIiKizucslgXKR/dLCU6dOYfr06XKHJSIiIoXIXgxUVFRg27ZtcoclIiJyGu4z0Ipdu3a1eP748ePt7gwREZEr8LRpAsnFQEpKSrP7DFzFxYBERETuQ/I0gU6nw44dOyAIQpOtqKhIiX4SERE5jSBjcweSi4G4uDiYTKZmz7c2akBEROTqRBn/cweSpwnmzp3b4j4DUVFRyM/P71CniIiIOpO7fKKXi+RiYNSoUS2e9/Pzw+jRo9vdISIiInIuycWAO6ttqFM0vjOWTQZ/dFTxHBenD1U8R4/sHxTPoVHLfuWsU+MDyv/MAoDFCTmcYdOZLxSN/3DYCEXjA0D2mS8Vz+EMA3tc39ld6DB3Gd6Xi0cVA0RERG3hadMEyn+0ISIiIpfGkQEiIiIHgoddFceRASIiIgeduR3x+vXrERkZCV9fXyQkJKCwsLDZx27atAmjRo1Cjx490KNHDyQlJbX4+OawGCAiInIROTk50Ov1yMzMRFFREWJiYpCcnIxz5841+fh9+/ZhypQpyM/PR0FBAXr37o2xY8fi9OnTkvKyGCAiInIgQJStSbFmzRpkZGQgPT0dgwcPRlZWFrp27Yrs7OwmH//222/j8ccfR2xsLKKjo/HGG29AEATk5eVJytvuYuCXX37BlStXGh2vr6/HZ5991t6wREREnU7OHQgtFguqqqrsmsViaZSzrq4OJpMJSUlJtmNqtRpJSUkoKChoU79rampQX1+Pnj17Snq9kouBs2fPYvjw4YiIiEBgYCBSU1PtioKKigrccccdUsMSERFdk4xGIwICAuya0Whs9Ljy8nJYrVaEhITYHQ8JCUFZWVmbcs2bNw9hYWF2BUVbSC4G5s+fD7Vaja+//hq5ubn48ccfcccdd+DixYu2x/DeBERE5M7kvFGRwWBAZWWlXTMYDLL3ecWKFdi+fTt27twJX19fSc+VfGnh3r17sXPnTsTHxwMAvvjiC0ycOBF33nmnbY6itVsYWyyWRkMkoijy1sdEROQSpM71t0Sr1UKr1bb6uKCgIGg0GpjNZrvjZrMZoaGhLT539erVWLFiBfbu3Ythw4ZJ7qPkkYHKykr06NHD9rVWq8WOHTsQGRmJO+64o9kVj7/X1JCJIFyW2hUiIiJFdMZdC318fBAXF2e3+O/qYsDExMRmn/fiiy9i2bJlyM3NtX1Ql0pyMdCvXz98//33dse8vLzw97//Hf369cP//M//tBqjqSETtbq71K4QERFdU/R6PTZt2oRt27bh0KFDmDFjBqqrq5Geng4ASE1NtZtiWLlyJRYtWoTs7GxERkairKwMZWVlTS7wb4nkYmDcuHHYuHFjo+NXC4LY2NhW1wxotVr4+/vbNU4REBGRq5BzzYAUkyZNwurVq7F48WLExsaiuLgYubm5tkWFpaWlOHv2rO3xGzZsQF1dHf70pz9Bp9PZ2urVqyXlVYkSV/s1NDSgpqYG/v7+zZ4/ffo0IiIiJHXE2ydc0uPbQ+lljc4oZ5yxNJN3LXQdVkH526VcK2W40u8N3rWw7QY44a6FB81fKxr/vj7jZYu1s/QD2WIpRfJvSy8vr2YLAeC3Sw+XLFnSoU4RERGR88j+0amiogLbtm2TOywREZHTdNYOhJ1F8qWFu3btavH88ePH290ZIiIiV6D8BJ1rkVwMpKSkQKVStbhIkIsBiYiI3IfkaQKdTocdO3ZAEIQmW1FRkRL9JCIicprO2GegM0kuBuLi4mAymZo939qoARERkavjmoFWzJ07F9XV1c2ej4qKQn5+foc6RURERM4juRgYNWpUi+f9/PwwevTodneIiIios3naCLfkYkApzlh0qFY4hzN+eDQq5TfSccaGQI+H3ap4jg1n9iueQ2lK/8wCgLdG+V8DDYJV8RxKv/82O2FDoEt/+YPiOQJf+krxHEcunVY8h9J4NQEREZGHc5eFf3Jx//1aiYiIqEM4MkBEROTAXa4CkAuLASIiIgeetoCQ0wREREQejiMDREREDjhN0AYXLlzA999/j5iYGPTs2RPl5eXYvHkzLBYLJk6ciEGDBsndTyIiIqfxtKsJJBcDhYWFGDt2LKqqqhAYGIg9e/Zg4sSJ8PLygiAIWLFiBfbv34+bb75Zif4SERGRzCSvGVi4cCEmTpyIyspKLFiwACkpKRgzZgwOHz6Mo0ePYvLkyVi2bJkSfSUiInIKQRRla+5AcjFgMpmg1+vRvXt3zJo1C2fOnEFGRobt/BNPPIFvvvlG1k4SERE5kyhjcweSpwnq6urQpUsXAIC3tze6du2KoKAg2/mgoCBcuHChxRgWiwUWi8XumCiKTtmSmIiIiOxJHhno3bs3jh8/bvt6+/bt0Ol0tq/Pnj1rVxw0xWg0IiAgwK4J1stSu0JERKQIT7uFseRiYPLkyTh37pzt63vuucc2UgAAu3btwvDhw1uMYTAYUFlZadfUmu5Su0JERKQITysGJE8TZGZmtnh+4cKF0Gg0LT5Gq9VCq9XaHeMUARERuQruQNhBFy5cwIwZM+QOS0RERAqRvRioqKjAtm3b5A5LRETkNJwmaMWuXbtaPP/7xYVERETuiDsQtiIlJQUqlarF+RTO/xMREbkPydMEOp0OO3bsgCAITbaioiIl+klEROQ0oijK1tyB5GIgLi4OJpOp2fOtjRoQERG5Oq4ZaMXcuXNRXV3d7PmoqCjk5+d3qFNERETkPJKLgVGjRrV43s/PD6NHj253h4iIiDqbp41wSy4GlOKMRYdKf3Od8RoaBKviOZyx/HNjWYHiOUqHD1A0fu/Cw4rGB5zzvVA5IYtVEBTP4aVuebOzjhJE5d97gS99pXiOdSF3KJ7jmYovFc+htM4c3l+/fj1WrVqFsrIyxMTE4LXXXmt2Z9+DBw9i8eLFMJlMOHnyJF5++WXMnj1bck7Z9xkgIiKi9snJyYFer0dmZiaKiooQExOD5ORku9sA/F5NTQ369euHFStWIDQ0tN15WQwQERE5EGX8T4o1a9YgIyMD6enpGDx4MLKystC1a1dkZ2c3+fhbbrkFq1atwuTJkxtt8y+Fy0wTEBERuQpBxmlli8UCi8Vid6ype/TU1dXBZDLBYDDYjqnVaiQlJaGgQNmpVY4MEBEROZBzZMBoNCIgIMCuGY3GRjnLy8thtVoREhJidzwkJARlZWWKvl6ODBARESnIYDBAr9fbHevIkL4SWAwQERE5kHOaoKkpgaYEBQVBo9HAbDbbHTebzR1aHNgWsk0T9OvXD0eOHJErHBERUafpjAWEPj4+iIuLQ15enu2YIAjIy8tDYmKiEi/TRvLIwKuvvtrk8dLSUmzZssVWvTz11FMd6xkREZGH0ev1SEtLQ3x8PIYPH461a9eiuroa6enpAIDU1FSEh4fb1hzU1dXhxx9/tP3/6dOnUVxcjG7duiEqKqrNeSUXA7Nnz0Z4eDi8vOyfKggC/va3v8Hb2xsqlYrFABERuS05pwmkmDRpEs6fP4/FixejrKwMsbGxyM3NtS0qLC0thVr930H9M2fO4KabbrJ9vXr1aqxevRqjR4/Gvn372pxXcjHw6KOP4uuvv8Y777yDQYMG2Y57e3vjk08+weDBg6WGJCIicilS9weQ0xNPPIEnnniiyXOOf+AjIyNl2V1X8pqBrKwsLF68GMnJyVi3bl27klosFlRVVdk1T9sHmoiIyFW0awHhfffdh4KCAuzcuRPjxo2TfP1jU9dcWq1V7ekKERGR7ARRlK25g3ZfTRAeHo69e/fitttuw0033STpk73BYEBlZaVd02j829sVIiIiWXXWdsSdpUP7DKhUKhgMBowdOxb79++HTqdr0/OauubSGXf8IyIiosZk2WcgLi4Os2bNQo8ePXDq1ClMnz5djrBERESdQhQF2Zo7kP3eBBUVFdi2bZvcYYmIiJxGgChbcweSpwl27drV4vnjx4+3uzNERESuwNOucJNcDKSkpEClUrX4D8X5fyIiIvcheZpAp9Nhx44dEAShyVZUVKREP4mIiJzG06YJJBcDcXFxMJlMzZ5vbdSAiIjI1YmiKFtzB5KnCebOnYvq6upmz0dFRSE/P79DnSIiIiLnkVwMjBo1qsXzfn5+GD16dLs7RERE1NncZedAuahEFxnD8PIJ7+wudJgzlk0645vlrenQXlRtYhWsiudQeiFrT9/uisYHgIray4rnsArKXwd9rbw3lKZ2wuJrZ/yR+/XkXsVzeOsGtf6gDggNlC9+2aVDssVSiuz7DBAREZF7Uf4jIBERkZtxkUFzp2ExQERE5MBdLgmUC6cJiIiIPBxHBoiIiBxwmoCIiMjDedqlhR0uBkRRxL59+3D06FHodDokJyfD29tbjr4RERF1Co4MtOLuu+/Gu+++i4CAAFRUVODuu+9GYWEhgoKCcOHCBQwYMACfffYZrrvuOiX6S0RERDKTvIAwNzcXFosFAPDss8/i8uXLOHbsGM6dO4eTJ0/Cz88Pixcvlr2jREREzsIbFUnw73//G0ajEX379gUAXH/99Vi5ciV2794tS+eIiIg6A29U1AZXt3m9ePEi+vfvb3cuKioKZ86cafH5FovFNrpwlSiKim8fS0RERI21a2Rg2rRpuP/++1FfX48TJ07YnSsrK0NgYGCLzzcajQgICLBroqD8HuxERERtIYiibM0dSC4G0tLSEBwcjICAAEyYMAE1NTV25//f//t/iI2NbTGGwWBAZWWlXVOplb/pCxERUVuIMv7nDmS/a2F1dTU0Gg18fX0lPY93LWwb3rWw7XjXwrbhXQtdB+9a2HZK37XQr2ukbLGqa36WLZZSZN+OuKKiAo8//rjcYYmIiJyG0wQdVFFRgW3btskdloiIyGl4NUErdu3a1eL548ePt7szRERE5HySi4GUlBSoVKoWqx1eIkhERO7MXRb+yUXyNIFOp8OOHTsgCEKTraioSIl+EhEROY2nTRNILgbi4uJgMpmaPd/aqAEREZGr87RiQPI0wdy5c1FdXd3s+aioKOTn53eoU0REROQ8kouBUaNGtXjez88Po0ePbneHiIiIOpt7fJ6XkeiGamtrxczMTLG2tpY5OjnHtfAamMN14jOHa+W4Fl4DtY3sOxA6Q1VVFQICAlBZWQl/f3/m6MQc18JrYA7Xic8crpXjWngN1DaybzpERERE7oXFABERkYdjMUBEROTh3LIY0Gq1yMzMhFarZY5OznEtvAbmcJ34zOFaOa6F10Bt45YLCImIiEg+bjkyQERERPJhMUBEROThWAwQERF5OBYDREREHs4ti4H169cjMjISvr6+SEhIQGFhoWyxP/vsM4wfPx5hYWFQqVT4xz/+IVtsADAajbjlllvQvXt3BAcHIyUlBSUlJbLm2LBhA4YNGwZ/f3/4+/sjMTER//rXv2TN4WjFihVQqVSYPXu2bDGfe+45qFQquxYdHS1bfAA4ffo0/vznP6NXr17o0qULhg4dim+//Va2+JGRkY1eg0qlwsyZM2XLYbVasWjRIvTt2xddunRB//79sWzZMtnvlnb58mXMnj0bERER6NKlC0aMGIFvvvmm3fFae6+JoojFixdDp9OhS5cuSEpKwpEjR2TNsWPHDowdOxa9evWCSqVCcXGxbPHr6+sxb948DB06FH5+fggLC0NqairOnDkj62t47rnnEB0dDT8/P/To0QNJSUn4+uuvZc3xe4899hhUKhXWrl0ra45p06Y1ep/cddddknJQ+7ldMZCTkwO9Xo/MzEwUFRUhJiYGycnJOHfunCzxq6urERMTg/Xr18sSz9Gnn36KmTNn4quvvsKePXtQX1+PsWPHtngnSKmuv/56rFixAiaTCd9++y3uvPNOTJgwAQcPHpQtx+998803+Otf/4phw4bJHvvGG2/E2bNnbW3//v2yxb548SJGjhwJb29v/Otf/8KPP/6Il156CT169JAtxzfffGPX/z179gAAJk6cKFuOlStXYsOGDVi3bh0OHTqElStX4sUXX8Rrr70mWw4AeOSRR7Bnzx68+eab+OGHHzB27FgkJSXh9OnT7YrX2nvtxRdfxKuvvoqsrCx8/fXX8PPzQ3JyMmpra2XLUV1djVtvvRUrV66U/TXU1NSgqKgIixYtQlFREXbs2IGSkhLce++9suUAgAEDBmDdunX44YcfsH//fkRGRmLs2LE4f/68bDmu2rlzJ7766iuEhYVJeg1tzXHXXXfZvV/effddyXmonTrzxgjtMXz4cHHmzJm2r61WqxgWFiYajUbZcwEQd+7cKXvc3zt37pwIQPz0008VzdOjRw/xjTfekD3u5cuXxRtuuEHcs2ePOHr0aHHWrFmyxc7MzBRjYmJki+do3rx54q233qpY/KbMmjVL7N+/vygIgmwx77nnHnH69Ol2x+6//35x6tSpsuWoqakRNRqN+OGHH9odv/nmm8WFCxd2OL7je00QBDE0NFRctWqV7dilS5dErVYrvvvuu7Lk+L0TJ06IAMTvvvuuXbFbi39VYWGhCEA8efKkYjkqKytFAOLevXtlzfHLL7+I4eHh4oEDB8SIiAjx5Zdfblf85nKkpaWJEyZMaHdM6hi3Ghmoq6uDyWRCUlKS7ZharUZSUhIKCgo6sWftV1lZCQDo2bOnIvGtViu2b9+O6upqJCYmyh5/5syZuOeee+y+J3I6cuQIwsLC0K9fP0ydOhWlpaWyxd61axfi4+MxceJEBAcH46abbsKmTZtki++orq4Ob731FqZPnw6VSiVb3BEjRiAvLw+HDx8GAPznP//B/v37MW7cONlyNDQ0wGq1wtfX1+54ly5dZB2tuerEiRMoKyuz+7kKCAhAQkKC277Xgd/e7yqVCoGBgYrEr6urw8aNGxEQEICYmBjZ4gqCgIceeghz587FjTfeKFtcR/v27UNwcDAGDhyIGTNm4MKFC4rlIntend0BKcrLy2G1WhESEmJ3PCQkBD/99FMn9ar9BEHA7NmzMXLkSAwZMkTW2D/88AMSExNRW1uLbt26YefOnRg8eLCsObZv346ioqIOzRu3JCEhAVu3bsXAgQNx9uxZLFmyBKNGjcKBAwfQvXv3Dsc/fvw4NmzYAL1ejwULFuCbb77BU089BR8fH6SlpcnwCuz94x//wKVLlzBt2jRZ486fPx9VVVWIjo6GRqOB1WrFCy+8gKlTp8qWo3v37khMTMSyZcswaNAghISE4N1330VBQQGioqJky3NVWVkZADT5Xr96zt3U1tZi3rx5mDJliux35/vwww8xefJk1NTUQKfTYc+ePQgKCpIt/sqVK+Hl5YWnnnpKtpiO7rrrLtx///3o27cvjh07hgULFmDcuHEoKCiARqNRLC/9xq2KgWvNzJkzceDAAUU+WQ0cOBDFxcWorKzE+++/j7S0NHz66aeyFQSnTp3CrFmzsGfPnkafFuXy+0+2w4YNQ0JCAiIiIvDee+/h4Ycf7nB8QRAQHx+P5cuXAwBuuukmHDhwAFlZWYoUA5s3b8a4cePaNd/akvfeew9vv/023nnnHdx4440oLi7G7NmzERYWJuvrePPNNzF9+nSEh4dDo9Hg5ptvxpQpU2AymWTLca2qr6/HAw88AFEUsWHDBtnj33HHHSguLkZ5eTk2bdqEBx54AF9//TWCg4M7HNtkMuGVV15BUVGRrCNajiZPnmz7/6FDh2LYsGHo378/9u3bhzFjxiiWl37jVtMEQUFB0Gg0MJvNdsfNZjNCQ0M7qVft88QTT+DDDz9Efn4+rr/+etnj+/j4ICoqCnFxcTAajYiJicErr7wiW3yTyYRz587h5ptvhpeXF7y8vPDpp5/i1VdfhZeXF6xWq2y5rgoMDMSAAQNw9OhRWeLpdLpGxdGgQYNknYq46uTJk9i7dy8eeeQR2WPPnTsX8+fPx+TJkzF06FA89NBDmDNnDoxGo6x5+vfvj08//RRXrlzBqVOnUFhYiPr6evTr10/WPABs7+dr4b1+tRA4efIk9uzZI/uoAAD4+fkhKioKf/jDH7B582Z4eXlh8+bNssT+/PPPce7cOfTp08f2Xj958iT+8pe/IDIyUpYcTenXrx+CgoJke79Ty9yqGPDx8UFcXBzy8vJsxwRBQF5eniLz4UoQRRFPPPEEdu7ciX//+9/o27evU/IKggCLxSJbvDFjxuCHH35AcXGxrcXHx2Pq1KkoLi5WZFjvypUrOHbsGHQ6nSzxRo4c2eiyzsOHDyMiIkKW+L+3ZcsWBAcH45577pE9dk1NDdRq+7eyRqOBIAiy5wJ++8Oj0+lw8eJF7N69GxMmTJA9R9++fREaGmr3Xq+qqsLXX3/tNu914L+FwJEjR7B371706tXLKXnlfL8/9NBD+P777+3e62FhYZg7dy52794tS46m/PLLL7hw4YJs73dqmdtNE+j1eqSlpSE+Ph7Dhw/H2rVrUV1djfT0dFniX7lyxa4SPXHiBIqLi9GzZ0/06dOnw/FnzpyJd955B//85z/RvXt32/xnQEAAunTp0uH4AGAwGDBu3Dj06dMHly9fxjvvvIN9+/bJ+sbt3r17o3UOfn5+6NWrl2zrH55++mmMHz8eEREROHPmDDIzM6HRaDBlyhRZ4s+ZMwcjRozA8uXL8cADD6CwsBAbN27Exo0bZYl/lSAI2LJlC9LS0uDlJf9bbvz48XjhhRfQp08f3Hjjjfjuu++wZs0aTJ8+XdY8u3fvhiiKGDhwII4ePYq5c+ciOjq63e+91t5rs2fPxvPPP48bbrgBffv2xaJFixAWFoaUlBTZclRUVKC0tNR27f/V4jA0NLRNIxAtxdfpdPjTn/6EoqIifPjhh7Barbb3e8+ePeHj49Ph19CrVy+88MILuPfee6HT6VBeXo7169fj9OnTki5fbe3fybGI8fb2RmhoKAYOHChLjp49e2LJkiX43//9X4SGhuLYsWN45plnEBUVheTk5DbnoA7o5KsZ2uW1114T+/TpI/r4+IjDhw8Xv/rqK9li5+fniwAatbS0NFniNxUbgLhlyxZZ4ouiKE6fPl2MiIgQfXx8xOuuu04cM2aM+Mknn8gWvzlyX1o4adIkUafTiT4+PmJ4eLg4adIk8ejRo7LFF0VR/OCDD8QhQ4aIWq1WjI6OFjdu3ChrfFEUxd27d4sAxJKSEtlji6IoVlVVibNmzRL79Okj+vr6iv369RMXLlwoWiwWWfPk5OSI/fr1E318fMTQ0FBx5syZ4qVLl9odr7X3miAI4qJFi8SQkBBRq9WKY8aMkfxv2FqOLVu2NHk+MzOzw/GvXq7YVMvPz5flNfz666/ifffdJ4aFhYk+Pj6iTqcT7733XrGwsFDWfydH7bm0sKUcNTU14tixY8XrrrtO9Pb2FiMiIsSMjAyxrKxMUg5qP97CmIiIyMO51ZoBIiIikh+LASIiIg/HYoCIiMjDsRggIiLycCwGiIiIPByLASIiIg/HYoCIiMjDsRggIiLycCwGiIiIPByLASIiIg/HYoCIiMjDsRggIiLycP8ftXTJ1wJ0ifUAAAAASUVORK5CYII=", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for repeat in repeats:\n", + " tpm = np.load(f'{save_dir}{repeat}/model/trans_prob.npy')\n", + " seaborn.heatmap(tpm)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e868e877", + "metadata": {}, + "source": [ + "This is important. It tells us that with identical states, you will see the random switching between them. Now have a look at the alpha." + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "e1c36a72", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "983bb0a0", + "metadata": {}, + "outputs": [], + "source": [ + "with open(f'{save_dir}train/inf_params/alp.pkl','rb') as file:\n", + " alpha = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "d35490c6", + "metadata": {}, + "outputs": [], + "source": [ + "with open(f'{save_dir}train/inf_params/alp.pkl','rb') as file:\n", + " alpha = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "936997e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(alpha))" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "e572cfa2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.81349826e-01 5.16284083e-04 3.40275379e-04 ... 8.43076705e-05\n", + " 3.41130544e-05 2.35170864e-05]\n", + " [2.89944887e-01 4.54870635e-04 2.70671462e-05 ... 6.73804534e-05\n", + " 2.65584094e-05 1.54978716e-05]\n", + " [3.55618507e-01 9.12061005e-05 1.09882035e-06 ... 1.80180173e-03\n", + " 9.72664384e-06 3.35799741e-05]\n", + " ...\n", + " [2.80968758e-04 1.73095271e-09 1.00545531e-05 ... 3.25659057e-05\n", + " 2.45787687e-05 6.99273944e-01]\n", + " [2.19925332e-05 3.96189179e-08 1.20662498e-05 ... 1.09363555e-05\n", + " 1.02338972e-05 7.00931847e-01]\n", + " [4.42815275e-04 7.30826832e-06 1.97023503e-04 ... 4.09335247e-04\n", + " 6.14416553e-04 6.67781532e-01]]\n" + ] + } + ], + "source": [ + "print(alpha[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "d5469895", + "metadata": {}, + "outputs": [], + "source": [ + "ground_truth = np.load('./data/node_timeseries/simulation_202402/sigma_0/truth/10001_state_time_course.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "3fc509bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 1 0 0 0 0 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 1 0 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 1 0 0 0 0 0]\n", + " [0 0 0 0 0 0 1 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0]]\n" + ] + } + ], + "source": [ + "print(ground_truth[:100])" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "7128b1f6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_states = 16\n", + "# Plot the time course for each state\n", + "for state in range(n_states):\n", + " plt.plot(alpha[0][100:200, state], label=f'State {state + 1}')\n", + "\n", + "# Add labels and legend\n", + "plt.xlabel('Timepoints')\n", + "plt.ylabel('Value')\n", + "plt.title('Time Course for Each State')\n", + "#plt.legend()\n", + "\n", + "# Add a caption\n", + "#plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0eda729", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d5314e3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9ead816f", + "metadata": {}, + "source": [ + "This confirms Steve and Rukuang's guess that the \"uncertainty\" in the state time course will be high. But it's not necessarily in a \"0.5-0.5\" split." + ] + }, + { + "cell_type": "markdown", + "id": "dc5efe3a", + "metadata": {}, + "source": [ + "What happens when there's subject variablity in the \"ground truth\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "f2259bf3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_0/sigma_0.025/hmm_ICA_25_state_16/'\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']\n", + "covariance_truth = np.load('./results_simulation_202402_repeat/fixed_covariances.npy')\n", + "for repeat in repeats:\n", + " tpm = np.load(f'{save_dir}{repeat}/model/trans_prob.npy')\n", + " seaborn.heatmap(tpm)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "89cdc843", + "metadata": {}, + "outputs": [], + "source": [ + "with open(f'{save_dir}train/inf_params/alp.pkl','rb') as file:\n", + " alpha = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "2db9a2ce", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IrUwh/9vf/mazn2Vq9c6dO222W6YD3zhl+Msvv5QjIyNlDw8P2dnZWQ4ODpZjY2Plb775plFtvnLlijx37lz5zjvvlJ2dnWUXFxd5wIAB8osvvigXFBTY7Ltp0ya5d+/eskqlkv38/ORZs2bJeXl5tc65du1auWvXrrJGo5GHDx8uf/PNN/VOIb/xccqyLK9cuVIePHiw7OnpKWu1Wrl3797yiy++KBsMBpv90tLS5GnTpsn+/v6ySqWSu3btKv/2t7+VP/zww5s+7rqmkMuyLGdnZ8tPPfWU3KlTJ1mtVst33323vGXLFpt96vvb3ex61DOFvObr4pdffpEnTZoke3p6yh4eHvJDDz0kX7lypc4p2hcvXpSnTZsm+/j4yBqNRu7Zs6f89NNPy+Xl5bIsN/yaAeQvv/yywTa/99578qOPPioHBwfLWq1WdnZ2lvv27Sv/+c9/tk6ntzh27Jg8YMAAWa1W27S1KY9nxYoVcteuXWWFQlFrOvlHH30k33vvvbKrq6vs6uoq9+7dW3766aflM2fO3PS5F4TGkmS5mfpBBUEQBEEQHIjIyREEQRAEoV0SQY4gCIIgCO2SCHIEQRAEQWiXRJAjCIIgCEK7JIIcQRAEQRDapQ5XJ8dkMnHlyhXc3NzqrcgpCIIgCIJjkWWZoqIiunTpUms9uvp0uCDnypUrtRZKFARBEAShbfj555/p1q1bo/btcEGOpcLpzz//fNNVnQVBEARBcAyFhYUEBAQ0qVJ5hwtyLENU7u7uIsgRBEEQhDamKakmIvFYEARBEIR2SQQ5giAIgiC0SyLIEQRBEAShXepwOTmCINgyGo1UVFTYuxlCA1QqFUql0t7NEIQ2RwQ5gtBBybJMVlYW+fn59m6K0Aienp74+/uL+l6C0AQiyBGEDsoS4Pj6+uLi4iI+PB2ULMuUlJRw9epVADp37mznFglC2yGCHEHogIxGozXAueOOO+zdHOEmtFotAFevXsXX11cMXQlCI4nEY0HogCw5OC4uLnZuidBYlr+VyJ8ShMYTQY4gdGBiiKrtEH8rQWg6EeQIgiAIgtAuiSBHEARBEIR2SQQ5giAIgiC0SyLIEQShzcjJyWHWrFkEBgai0Wjw9/cnMjKSo0ePWveRJIndu3c3+dxBQUFs2LDhttuYmZnJlClTuPPOO1EoFMyZM+e2zykIwq0RQY4gCG1GTEwMp06dYtu2bZw9e5a9e/cyevRocnNz7d00q/Lycnx8fPjLX/5CWFiYvZsj2MmVsz/y/pIF/Hz6O3s3pUOTZFmW7d2I1lRYWIiHhwcFBQW4u7vbuzmCYBdlZWWkp6fTo0cPnJ2dkWWZ0gqjXdqiVSkbNXMoPz8fLy8vDhw4wKhRo+rcJygoiIsXL1pvd+/enYyMDNLS0pg3bx7Hjx+nuLiYPn36kJCQwNixYwEYPXo0Bw8etDmX5a3xyJEjLFy4kG+++YZOnToxadIkEhIScHV1vWmbR48eTf/+/Zulh+jGv5nguEoKC9j+/56hOO86vkHBPP7XDWJ2XDO4lc9vUQxQEARKK4z0Xfxfu1z79PJIXNQ3fyvS6XTodDp2797N0KFD0Wg0tfY5ceIEvr6+bNmyhaioKGvRPL1ez/jx43nxxRfRaDRs376d6Ohozpw5Q2BgILt27SIsLIwZM2YQFxdnPV9aWhpRUVGsXLmSzZs3k5OTQ3x8PPHx8WzZsqX5ngSh3ZBNJj57ZR3FedcBuJqRxuUff6Bb31A7t6xjEsNVgiC0CU5OTmzdupVt27bh6enJ8OHDeeGFF0hNTbXu4+PjA1Sv82S5HRYWxsyZMwkNDSUkJIQVK1YQHBzM3r17AfD29kapVOLm5oa/vz/+/v4AJCQkMHXqVObMmUNISAjDhg1j48aNbN++nbKyslZ+BoS24OS+3aQnn8RJpaZ7v3Dztk9227dRHZjoyREEAa1KyenlkXa7dmPFxMQwYcIEDh8+zPHjx/n0009ZvXo1b775JrGxsfUep9frWbp0Kfv27SMzM5PKykpKS0u5dOlSg9dLSUkhNTWVd955x7pNlmVMJhPp6en06dOn0W0X2r+s82c5/N42AEY/GUe3PqFs/dMszn/zP/Kzs/D087dzCzseEeQIgoAkSY0aMnIEzs7OjBs3jnHjxrFo0SKmT5/OkiVLGgxy5s+fT2JiImvWrKFXr15otVomT56MwWBo8Fp6vZ6ZM2cye/bsWvcFBgbe7kMR2pHykmL+s3E1JqORO4cMp9/YKCRJIqj/ADKST3Lqs39z35NxNz+R0KzaxruaIAhCPfr27WszZVylUmE02iZRHz16lNjYWCZNmgSYg5eMjAybfdRqda3jIiIiOH36NL169WqRtgvtgyzLJL6+iYLsLNx9fBk38xlrovGA3zxARvJJvv/yc4Y9NBWNWC+uVYmcHEEQ2oTc3FzGjBnDjh07SE1NJT09nZ07d7J69WomTpxo3S8oKIikpCSysrLIy8sDICQkhF27dpGcnExKSgpTpkzBZDLZnD8oKIhDhw5x+fJlrl27BsCCBQs4duwY8fHxJCcnc+7cOfbs2UN8fHyDbU1OTiY5ORm9Xk9OTg7JycmcPn26mZ8RwVF898XnnPnqMAqlkgmz/x/Orjrrfd3DIvDuGoChtJQfDiTasZUdkwhyBEFoE3Q6HUOGDGH9+vWMHDmS0NBQFi1aRFxcHJs2bbLut3btWhITEwkICCA83Jz4uW7dOry8vBg2bBjR0dFERkYSERFhc/7ly5eTkZFBcHCwNWG5X79+HDx4kLNnzzJixAjCw8NZvHgxXbp0abCt4eHhhIeHc/LkSd59913Cw8MZP358Mz8jgiO49vNFvtz6OgDDH3mCLnf2trlfkiQifvMAAN9+uheTyT6lGjoqUSdHEDogUXOl7RF/M8dTYSjnnYVzyf3lEt37hROzcBmSonbfQUV5Ga//8SnK9EU8MP/PhAy6xw6tbftu5fNb9OQIgiAIwi04sO0Ncn+5hIuHJ795el6dAQ6ASuNMv7FRAHz7yZ7WbGKHJ4IcQRAEQWiiM18dIXX/ZyBJjI+fj6unV4P7979/Agqlkl9Of092elortVIQs6uE21ZWXEF+dgmySUaWzTMNLL8x2d6WZZBNMtywn0IpoVAqqn5LKG+4rVDU+HfN7UoJSSFZz2mq8VuW6/73jdtkWUY23fC7qp11PRZTHe23HNeo7TWeJyeVApWzErXGCZVGaf63c/W/VWolkkKUg28OsixjrDRhMlaN0Fv+roB10F6WkS333bCPddsNav11bvLnkiQJpKrdJJCqblir/ktS9X1V/5AkqKwwYjKayMsuRqWsQDZVvY4tP1WvN5Ns2WZ+rVle684uKjqHeKIQr6fbVnA1i8//uRGAwRMn071f/5se43ZHJ+4cei8/HT3IqU/3EvXHuS3cSgEcJMh55ZVX+Nvf/kZWVhZhYWG8/PLLDB48uM5961pjBmD8+PHs27evpZsq3MBkNPHByq/R55XbuyntlkqjrDMAUjs7oXVToXVT4+Kmtv5bW/VvlaZxa0K1BpsAzySDhDlYbYEPXFmWMVaYqKww2f6uNNWIZtqeikoDxfkGjr/9HWUFppsfUAcPHy39xwZw1z2dUakbX4RRqGasrOQ/f1+NobSELnf2YdhDUxt9bMT4B/jp6EF+OnqQEVNib9r7I9w+uwc5H3zwAfPmzeO1115jyJAhbNiwgcjISM6cOYOvr2+t/Xft2mVTwCs3N5ewsDAeeuih1my2UKXoehn6vHIkCTx8zfUfJAkkhfnbJ5L5t2T5rai+jeU35g8/o1HGZDR/0zbV+vcNt003/7CyXkcBCkmCqmsrFOZrWz5gzW2q3b46H4PC9rFA9flq3W/+T61tNc9ZWWGiorySijIjFeVGDGVGKsoqqSg3Wj+PK8rN91HYtL+NUqVA66aqCoBsgyCNG1RqjZSXVICx6sNOsvl1U+ZeAkuPlVx9u6rnwFSjN6G++Q2SJNXZQ6eso7fuxoBNNpl7ZqqDGaP13/WSzL2E1T0pEtZfNXtVrE+EVOs+88XrfEZqb65jP7mqi0iWq++36VGSq85UozcJWTa/3hTg7OqEQq56rSrMr7+ar1fzdqlqO9Z/X88spiCnlIPvneV/e9MJHdWVu0d3w8VdXf/zJdRy9IO3yTp/Fo2rKxNmP4fSqfEfo5173UXnO3uTefYnkj//hOEPNz5AEm6N3WdXDRkyhEGDBlmngJpMJgICAnjmmWd4/vnnb3r8hg0bWLx4MZmZmY1aFVjMrmpeF7/P5T+bUrijqyuPLhrSate1fIiajOYPV8sbvTWgqRFAtUWyLJsDoDIjFeWVVcGPJRAyB0GG0kpKiyooLTJQWmSgxPLvQgOVDX3QA84eCu6O9qBr5wBUTq37IScpJNthoEYdZBnCNH+AGytMGCvrf4ySJKFUKXBSKWr8VqJ0qh0stRW3O7vKUFbJT19lkpL0M4XXzOtuKZwk7hriT/9fB+Ld5ebvnx1dRvJJPkpYAsAD814gZMiwJp/jzFdH+M+Gv6J192DGK1twUosgs7Ha3CrkBoOBkydPsnDhQus2hULB2LFj+eqrrxp1jrfeeotHH3203gCnvLyc8vLqoZTCwiZ+HRYalJ9dAoCnb+tW8ZSqPvSU7bTHXZIkVGpl1ZBC098EK8qNVcFPRVUAZLC5XVFZbv7wVytQNWLtqLriEUVVj4Glp8rmtkJCIdW+XTP4NAep9ffcGatum/OtZIyVMsbK2s+Tk9o2kHFSmXuB2mow01LUzk70uy+A0FHduHAqh+T9l8hOL+THo5n8eDSTwF/dQf9xAXS7y0s8d3XQ513nk1fWARB2/4RbCnAAQgbfg9sdPhTl5vDT0YOE3jeuOZsp3MCuQc61a9cwGo34+fnZbPfz8+Onn3666fFff/0133//PW+99Va9+yQkJLBs2bLbbqtQt/yr5iDHw0+UKnck5jweLe6dtHXeb+kV8PBxsVvNFYVCQqFQgqrh/WRT7aBH6WQObEQw03QKhUSvAb70GuBLZloByYmXuJCSw6Ufcrn0Qy6dAnT0HxtIr4G+KJViAi6AyWTk001rKS0swCcwiNFP/OGWz6VQKgmP+i2H3tnCt5/s4Vejx4rXcAtq06/gt956i7vvvrveJGWAhQsXUlBQYP35+eefW7GF7Z+9enKEjkNSmIee1M5OOLua84rUWieUTgrx4XCbOgd78Jv/u5upy4Zy96iuOKkVXPtZz/4tp3n7z1/x7ecXzXlbHdyJPR9x6fsUnDQaJsxZcNtDTHePicRJoyHnUgY//5DaTK0U6mLXIKdTp04olUqys7NttmdnZ+Pv3/CS9MXFxbz//vv84Q8NR9QajQZ3d3ebH6H5FFwtBcDTt+4eA0EQHJ+nrwsjH7uLJ18azpAHeqJ1V1OcX85Xu9LYtvAYR/51rsPOoLz802mO/msHAL/+/Szu6Bpw2+d01un41aixAJwUxQFblF2DHLVazYABA0hKSrJuM5lMJCUlcc89DZe93rlzJ+Xl5Tz++OMt3UyhHpUGI0V55gRGTzFcJbSCnJwcZs2aRWBgIBqNBn9/fyIjIzl69Kh1H0mSbFYlb6ygoCA2bNhw223ctWsX48aNw8fHB3d3d+655x7++9//3vZ5W4OzTsXA8UE8+eIw7nuiN95dXKkoN5Lyxc/8K+EE134psncTW1Wpvoh9L/8N2WSiz72j+dWoXzfbuS3rWV349gR5mZeb7byCLbsPV82bN4833niDbdu28eOPPzJr1iyKi4t56qmnAJg2bZpNYrLFW2+9xYMPPsgdd9zR2k0WqhTklIIMGhcnnHU3SawQhGYQExPDqVOn2LZtG2fPnmXv3r2MHj2a3NxcezfN6tChQ4wbN45PPvmEkydPct999xEdHc2pU6fs3bRGU6oU9B3ehUcXDea3z4RxR1dXSgsNfLz2FFfO59u7ea0m8fWXKbqWg6d/Z8ZO/2OzDo96d+lKz4hBIMuc+uw/zXZe4QayA3j55ZflwMBAWa1Wy4MHD5aPHz9uvW/UqFHyk08+abP/Tz/9JAPy559/3uRrFRQUyIBcUFBwu83u8M5/my1vmpkk/yvhhL2bIjRRaWmpfPr0abm0tNS8wWSS5XK9fX5Mpka1OS8vTwbkAwcO1LtP9+7dLdVnZEDu3r27LMuyfP78efmBBx6QfX19ZVdXV3ngwIFyYmKi9bhRo0bZHFfzrfHw4cPyvffeKzs7O8vdunWTn3nmGVmv1zfp+e7bt6+8bNmyJh1zo1p/s1ZUVmyQP/rbN/KmmUnya/FfyumpOa3ehtZWnJ8nr3l4grzmkd/KWWnnWuQaGSmn5DUPT5D//kSMXKovapFrtCe38vlt92KAAPHx8cTHx9d534EDB2ptu+uuu5pWY0NoEdakYz+Rj9PmVZTAS13sc+0XroD65jVadDodOp2O3bt3M3ToUDQaTa19Tpw4ga+vL1u2bCEqKgplVY0BvV7P+PHjefHFF9FoNGzfvp3o6GjOnDlDYGAgu3btIiwsjBkzZhAXF2c9X1paGlFRUaxcuZLNmzeTk5Njfb/asmVLox6eyWSiqKgIb2/vRj4hjkfjoiJ6dn/++8b3XPwul09f/Y5fx/bhzsEN5062Zdcv/wKAh48vfj17tcg1Au8O445ugeT+convv/icgdG/a5HrdGR2H64S2q58a9KxyMcRWp6TkxNbt25l27ZteHp6Mnz4cF544QVSU6tnp/j4+ADg6emJv7+/9XZYWBgzZ84kNDSUkJAQVqxYQXBwMHv37gXA29sbpVKJm5sb/v7+1okPCQkJTJ06lTlz5hASEsKwYcPYuHEj27dvp6ysrFHtXrNmDXq9nocffrg5n45Wp1Ir+c3/3U3IID9MJpnELaf57sAv9m5Wi7lelSfj1aVbi11DkiQixk8E4NR//4PJaGyxa3VUDtGTI7RNBdaeHBHktHkqF3OPir2u3UgxMTFMmDCBw4cPc/z4cT799FNWr17Nm2++SWxsbL3H6fV6li5dyr59+8jMzKSyspLS0lIuXbrU4PVSUlJITU3lnXfesW6TZRmTyUR6ejp9+vRp8Ph3332XZcuWsWfPnjqXqWlrlEoF457qi7Oriu8O/MKh989SVlzBwPFB7W46vyUZ2Ktzy/Zw9hkxmiPvbaMw5yrnvznOnUOGt+j1OhoR5Ai3zFIIUPTktAOS1KghI0fg7OzMuHHjGDduHIsWLWL69OksWbKkwSBn/vz5JCYmsmbNGnr16oVWq2Xy5Mk26+DVRa/XM3PmTGbPnl3rvsDAwAaPff/995k+fTo7d+5k7NixjXpsbYGkkBjxSAjOrk6c2JfB1/9Op0xfwb0PhVjXc2sPrl8x91J5d265nhwAlVpD2LjfcHzXB3z7yR4R5DQzEeQIt6S8pILSInORMA9RI0ewo759+9pMGVepVBhv6PY/evQosbGxTJo0CTAHLxkZGTb7qNXqWsdFRERw+vRpevVqWk7Ge++9x+9//3vef/99JkyY0KRj2wJJkhgc3RONq4oj/zpH6pe/UF5SyX3TerebKsl5VyzDVV1b/Fph48bz9Z6PuPzTabLSzuEfHNLi1+wo2serUWh1lnwcFw81amcRKwstLzc3lzFjxrBjxw5SU1NJT09n586drF69mokTJ1r3CwoKIikpiaysLPLy8gAICQlh165dJCcnk5KSwpQpUzCZbBf4DAoK4tChQ1y+fJlr164BsGDBAo4dO0Z8fDzJycmcO3eOPXv21DtRAsxDVNOmTWPt2rUMGTKErKwssrKyKCgoaIFnxb7CxgQw9qm+SAqJM//L4rN/fk+loe3nlRgrKym4mgWAdwvm5FjovO/grmEjAPj2070tfr2ORAQ5wi0RyzkIrU2n0zFkyBDWr1/PyJEjCQ0NZdGiRcTFxbFp0ybrfmvXriUxMZGAgADCw8MBWLduHV5eXgwbNozo6GgiIyOJiIiwOf/y5cvJyMggODjYmrDcr18/Dh48yNmzZxkxYgTh4eEsXryYLl3qz9N4/fXXqays5Omnn6Zz587Wn2effbYFnhX7u2uIP+P/726UKgUZqdf498splJdW3vxAB1ZwNQuT0YhK44zOu3VqsQ2oSkA+c+wQ+uuOU/eprZPkDjYX+1aWahdq+9+/L/DNvgz63tuF+x7vbe/mCE1kWaCzR48edlugU2gaR/+bXTmXx75XUjGUGekUoCP6mf64uN/eGk/2cv6b/7HnbyvwDQrmiVV/b7Xrvr9kAZd/+oEhkx7h3kefaLXrthW38vktenKEW1IgenIEQaihS4gXD86LQOum4trPej5e+y2FuaX2btYtsc6saoV8nJosvTkp+z+lwtAx1wprbiLIEW6JtUaOKAQoCEIVn0A3fjd/AG7ezuRnl/Dxmm+5nlls72Y1WV7VzCqvzq0b5AQPGoK7jx9lRYX8ePhAq167vRJBjtBksizXqHYsenIEQajm6efC756LwMvfBX1eOR+v+ZbsjEJ7N6tJrlfNrPJu5Z4chUJJeNRvAfj2kz2isn8zENNihCYrKTRQUW5EksC9k+jJEQTBls7LmUnzI/jPyylcvVjEnvWneGjhQLz820YtJstwVWvMrLrR3WPu59jOd8n95RIXv0smqF94nfvJskx5cTElhQWUFhZQUlT1u8D821BWSv/7J7TYkhRthQhyhCaz9OK4ddKidBKdgYIg1KbVqZk4N5w9G5K5mlHI+ZNXGTShh72bdVNlxXpKCvKBlq92XBeNiyuh943l1Kf/5tgHO8g89xOlhYXVwUzV79KiwpsuA5GdnsYTf/17u6tG3RQiyBGarHr6uOjFEQShfmpnJ+4c7MfVjEKy09vGkJWlCKDOyxu11j7D8eFR0Zz67D9knj9D5vkzDe6r1mpxcfdE6+6O1t0DF3cPtO4enPpkLzkZF7hy5ke69u7bSi13PCLIEZqsQCzMKQhCI/n1ME/1zc4oRJZlh+9VsCzn0JILc96Ml38XxsTO4NL3KWjdLMGLJy5VgUzNYMZJparzHKWFhXz/5eec+u9/RJAjCE1hXbNKJB0LgnATPt3cUDhJlOkrKLxWhoePY/cA52WaF6pt7aTjG4VHRRMeFX3Lx/ePnMD3X37Ouf8dRZ93HZ2XdzO2ru0QCRVCk4lqx4IgNJZSpaBTNzcAsjMcf2kLe00fb25+PYLpcldfTEYjqfs/s3dz7EYEOUKTmEwyBTnm4SoPUSNHEIRG8AuqGrJqA3k51+1UCLAlhEeaF4dN3f8pxsoKO7fGPkSQIzRJUW4ZJqOM0kmBm5fjlZYX2recnBxmzZpFYGAgGo0Gf39/IiMjOXr0qHUfSZJsViVvrKCgIDZs2HDbbTxy5AjDhw/njjvuQKvV0rt3b9avX3/b523LrHk5Dh7kyCYT+Zbhqs72y8lpLiFDhuHq6UVxfh7nvv7K3s2xC5GTIzSJJR/Hw1eLpHDsBEKh/YmJicFgMLBt2zZ69uxJdnY2SUlJ5OY6zoKGrq6uxMfH069fP1xdXTly5AgzZ87E1dWVGTNm2Lt5dmHpybn2sx5jpclhS08UXsuhssKA0skJd19fezfntimdVPQb+xu++vBdkv/7H3oPG2nvJrU6x3ylCQ5LVDpun2RZpqSixC4/ja3qmp+fz+HDh1m1ahX33Xcf3bt3Z/DgwSxcuJAHHngAMPfGAEyaNAlJkqy309LSmDhxIn5+fuh0OgYNGsT+/fut5x49ejQXL15k7ty5SJJkMwPoyJEjjBgxAq1WS0BAALNnz6a4uP6lCsLDw3nsscf41a9+RVBQEI8//jiRkZEcPny4iX+V9sPDV4vG1QljpYncy3p7N6delnwcT/8uKBRKO7emefQbG4VCqeTyT6e5mnHB3s1pdaInR2iSAlEjp10qrSxlyLtD7HLt/035Hy6qmwfNOp0OnU7H7t27GTp0KBqNptY+J06cwNfXly1bthAVFYVSaf6g0uv1jB8/nhdffBGNRsP27duJjo7mzJkzBAYGsmvXLsLCwpgxYwZxcXHW86WlpREVFcXKlSvZvHkzOTk5xMfHEx8fz5YtWxr1+E6dOsWxY8dYuXJlI5+R9keSJPyC3Ln0w3Wy0wvx7d64FaRbmzUfp40nHdek8/ImZMhwzhw7RPJ//8P9M2fbu0mtSvTkCE2Sb0k6FjOrhFbm5OTE1q1b2bZtG56engwfPpwXXniB1NRU6z4+Pj4AeHp64u/vb70dFhbGzJkzCQ0NJSQkhBUrVhAcHMzevXsB8Pb2RqlU4ubmhr+/P/7+/gAkJCQwdepU5syZQ0hICMOGDWPjxo1s376dsrKyBtvbrVs3NBoNAwcO5Omnn2b69Okt8bS0GW0h+dheq4+3tPBI83pYPx45SKm+yM6taV2iJ0doEjFc1T5pnbT8b8r/7HbtxoqJiWHChAkcPnyY48eP8+mnn7J69WrefPNNYmNj6z1Or9ezdOlS9u3bR2ZmJpWVlZSWlnLp0qUGr5eSkkJqairvvPOOdZssy5hMJtLT0+nTp0+9xx4+fBi9Xs/x48d5/vnn6dWrF4899lijH2t74xtUXRTQUVkX5mxHPTkAXe7qg09QT3IyLvDDl4kMjP6dvZvUakSQIzRaZYWRouvmb6+iRk77IklSo4aMHIGzszPjxo1j3LhxLFq0iOnTp7NkyZIGg5z58+eTmJjImjVr6NWrF1qtlsmTJ2MwGBq8ll6vZ+bMmcyeXbuLPzAwsMFje/Qwr9N09913k52dzdKlSzt0kGOZYZWfXUJZcQXOrnVX6rUny5IO9qx23BIkSSI88rd8/s+NJCd+QsSEie0m5+hmxHCV0GgFOaUgg1rrhNbN8d6ghI6pb9++NonAKpUK4w0LFx49epTY2FgmTZrE3Xffjb+/PxkZGTb7qNXqWsdFRERw+vRpevXqVetHrVY3uo0mk4ny8vKmP7h2RKtT415V7fjqRcfrzakoK6MoNwewf7XjltB7+EicXXUUZGeRkfytvZvTakSQIzRaQbZlzSqtw68/I7Q/ubm5jBkzhh07dpCamkp6ejo7d+5k9erVTJw40bpfUFAQSUlJZGVlkZeXB0BISAi7du0iOTmZlJQUpkyZgslksjl/UFAQhw4d4vLly1y7dg2ABQsWcOzYMeLj40lOTubcuXPs2bOH+Pj4etv5yiuv8O9//5tz585x7tw53nrrLdasWcPjjz/eAs9K2+LIeTl5Web6OM5u7mjdHDMx+naoNM6EjrkfgFP//Y+dW9N6RJAjNJpYs0qwJ51Ox5AhQ1i/fj0jR44kNDSURYsWERcXx6ZNm6z7rV27lsTERAICAggPDwdg3bp1eHl5MWzYMKKjo4mMjCQiIsLm/MuXLycjI4Pg4GBrwnK/fv04ePAgZ8+eZcSIEYSHh7N48WK6dOlSbztNJhMLFy6kf//+DBw4kFdeeYVVq1axfPnyFnhW2paai3U6GsvCnO0tH6emsHHjQZLISD5pTbJu7yS5sUUq2onCwkI8PDwoKCjA3b39Rest6YvtP/LjsUwGR/dg0IQe9m6OcBvKyspIT0+nR48eODuLytVtQXv4m2WlF/DRqpM461T8/m/3OlSP8Fcfvsexne/wq9FjiZo1x97NaTEfr1rGhW9PEDF+Ivc9GXfzAxzIrXx+i54codGsPTki6VgQhFvQqZsOhbJ6RXJHktcOa+TUxTKd/IcD+zGUldq5NS1PBDlCo1mmj3uIQoCCINwCJ5WSTt10gOOtSG6dPt4Ok45r6t4vHK/OXSgvKebHw1/auzktTgQ5QqOUl1ZSWmRexVb05AiCcKv8engAcDXdcYrSybJMXmZVTk47mz5+I0mhoP/95tXJT332n0Yvq9JWiSBHaJSCqqEqF3c1aq0oryQIwq2pTj52nJ6c4vw8DKWlSJICD7/O9m5Oi+s76teoNM7k/nKJX05/Z+/mtCgR5AiNIiodC4LQHCzTyHMumVckdwSWhTk9fP1wUrX/GmDOrjr6jrwPaP/TyUWQIzRKvliYUxCEZuDhq0Xj4lgrkl+/0j7XrGqIZcjq/InjFF7LsXNrWo4IcoRGyb9atTCn6MkRBOE2WFYkB8cpClidj9NxgpxOgUEE9L0b2WQidf9n9m5Oi7F7kPPKK68QFBSEs7MzQ4YM4euvv25w//z8fJ5++mk6d+6MRqPhzjvv5JNPPmml1nZc1T05IsgRBOH2+PZwtCDHXO24vU8fv1H/KPN08tSkz6isqLBza1qGXYOcDz74gHnz5rFkyRK+/fZbwsLCiIyM5OrVq3XubzAYGDduHBkZGXz44YecOXOGN954g65dO9YLs7XJsiyqHQuC0Gz8HGxFcku1Y6/O7Xtm1Y16DRyK7o5OlBYWcPb4EXs3p0XYNchZt24dcXFxPPXUU/Tt25fXXnsNFxcXNm/eXOf+mzdv5vr16+zevZvhw4cTFBTEqFGjCAsLq/ca5eXlFBYW2vwITVNSaKCizIgkgUcnkZMjCMLtuXFFcnsyVlZQcDUb6FjDVQAKpZKwsb8BIPmz9pmAbLcgx2AwcPLkScaOHVvdGIWCsWPH8tVXX9V5zN69e7nnnnt4+umn8fPzIzQ0lJdeeqnWysE1JSQk4OHhYf0JCAho9sfS3hVU5eO43eGMUmX3EU6hA8vJyWHWrFkEBgai0Wjw9/cnMjKSo0ePWveRJIndu3c3+dxBQUFs2LCh+RqLefVzJycn+vfv36znbescaUXy/KwsZJMJlbMWVy9vu7bFHvr9OhKlkxOZ58+Qdf6svZvT7Oz2iXXt2jWMRiN+fn422/38/MjKyqrzmAsXLvDhhx9iNBr55JNPWLRoEWvXrmXlypX1XmfhwoUUFBRYf37++edmfRwdgVjOQXAUMTExnDp1im3btnH27Fn27t3L6NGjyc3NtXfTasnPz2fatGn8+te/tndTHJKjJB9fr5F07EhrabUWFw9P7rxnBADJn++zc2uaX5v6Wm4ymfD19eX1119nwIABPPLII/z5z3/mtddeq/cYjUaDu7u7zY/QNNblHEQ+TrslyzKmkhK7/DS24mp+fj6HDx9m1apV3HfffXTv3p3BgwezcOFCHnjgAcDcGwMwadIkJEmy3k5LS2PixIn4+fmh0+kYNGgQ+/fvt5579OjRXLx4kblz5yJJks2H3ZEjRxgxYgRarZaAgABmz55NcXHxTdv7f//3f0yZMoV77rmnkX+FjsVR8nLyrnSMNasaYlnP6qdjhygpdJwijc3BbqVrO3XqhFKpJDs722Z7dnY2/v7+dR7TuXNnVCoVSqXSuq1Pnz5kZWVhMBhQq9Ut2uaOSsysav/k0lLORAywy7Xv+vYkksvNX1s6nQ6dTsfu3bsZOnQoGo2m1j4nTpzA19eXLVu2EBUVZX2v0Ov1jB8/nhdffBGNRsP27duJjo7mzJkzBAYGsmvXLsLCwpgxYwZxcdUrM6elpREVFcXKlSvZvHkzOTk5xMfHEx8fz5YtW+pt65YtW7hw4QI7duxosKe5I/OrMcNKlmW79aJUJx133CDHv9ed+PUMIfvCOb774nOGPPiQvZvUbOzWk6NWqxkwYABJSUnWbSaTiaSkpHq/+QwfPpzz589jMlVXyTx79iydO3cWAU4LstTI8fQTSceC/Tg5ObF161a2bduGp6cnw4cP54UXXiA1NdW6j4+PDwCenp74+/tbb4eFhTFz5kxCQ0MJCQlhxYoVBAcHs3fvXgC8vb1RKpW4ubnh7+9v/aKVkJDA1KlTmTNnDiEhIQwbNoyNGzeyfft2ysrqXkX73LlzPP/88+zYsQMnJ7EESn06BVSvSF6Ua78VyS3Txzta0nFNkiQRXjWdPCXxE0wN5Lm2NXb9P3DevHk8+eSTDBw4kMGDB7NhwwaKi4t56qmnAJg2bRpdu3YlISEBgFmzZrFp0yaeffZZnnnmGc6dO8dLL73E7Nmz7fkw2jWTSaYgR/TktHeSVstd356027UbKyYmhgkTJnD48GGOHz/Op59+yurVq3nzzTeJjY2t9zi9Xs/SpUvZt28fmZmZVFZWUlpayqVLlxq8XkpKCqmpqbzzzjvWbbIsYzKZSE9Pp0+fPjb7G41GpkyZwrJly7jzzjsb/bg6IsuK5FcvFpGdXoi7nWZuWpZ08GrnC3PezF33jODA229RdC2HtG+/JmRQ+xhmtWuQ88gjj5CTk8PixYvJysqif//+fPbZZ9Zk5EuXLqFQVHc2BQQE8N///pe5c+fSr18/unbtyrPPPsuCBQvs9RDaPf31MkyVMkonBTpvZ3s3R2ghkiQ1asjIETg7OzNu3DjGjRvHokWLmD59OkuWLGkwyJk/fz6JiYmsWbOGXr16odVqmTx5MgaDocFr6fV6Zs6cWecXqcDAwFrbioqK+Oabbzh16hTx8fGAuYdalmWcnJz4/PPPGTNmTNMecDvm18PDGuSEDPK7+QHNrFRfRGmROSfIuwMPVwE4qdX0G3M/X+/5kOTP/iOCnOZiGd+uy4EDB2ptu+eeezh+/HgLt0qwsCYd+2pRKDrezAPB8fXt29dmyrhKpapVVuLo0aPExsYyadIkwBy8ZGRk2OyjVqtrHRcREcHp06fp1atXo9ri7u7Od9/Zrur8j3/8gy+++IIPP/yQHj16NPJRdQx+Pdz57oD9ViS39OLo7uiEyll8iQsbN54Te3dx6fsUcn/5mTu6tf2SK21qdpXQ+izTxz18RD6OYF+5ubmMGTOGHTt2kJqaSnp6Ojt37mT16tVMnDjRul9QUBBJSUlkZWWRl5cHQEhICLt27SI5OZmUlBSmTJlik9tnOe7QoUNcvnyZa9euAbBgwQKOHTtGfHw8ycnJnDt3jj179tT7xUyhUBAaGmrz4+vri7OzM6Ghobi6urbQs9M22XtFcsvCnB29F8fC3ceX4IGDAUj+vH0UBxRBjtCg6qTjtjGUIbRfOp2OIUOGsH79ekaOHEloaCiLFi0iLi6OTZs2Wfdbu3YtiYmJBAQEEB4eDpirq3t5eTFs2DCio6OJjIwkIiLC5vzLly8nIyOD4OBga8Jyv379OHjwIGfPnmXEiBGEh4ezePFiunTp0noPvB2z94rkIh+ntv5V08l/OPgF5SUldm7N7ZPkxhapaCcKCwvx8PCgoKBA1MxphH9vTObS6evc90Rv+g4Xb+ztRVlZGenp6fTo0QNn0U3fJrTXv5nlPWbko3dy9+jWDTb2rHmR8ye+4r4n44gYP/HmB3QAsiyz+dkZ5Gdn8uD/W0TwgCH2bpLVrXx+i54coUGi2rEgCC3JuiK5HYoC5mVWFQIUPTlWkiTR5S7zrMHsC+ft3JrbJ4IcoV7GChOFVfUrxHCVIAgtwV7LO5hMRvKzM4GOXSOnLn49zYn2IsgR2rWCnFKQQe2sROumsndzBEFoh+y1InlhTg7GigqUKhVunXxa7bptgV+PqiAnPc3OLbl9IsgR6mUdqvJz6ZAL1wmC0PK0OjXuncw5Rq25Irk16di/CwqF8iZ7dyy+QT2RJAXFedfRX3e8xW+bQgQ5Qr2qa+SIoSpBEFqOXw8PoHWHrCzTx73EUFUtKmdnvLua85Sy09v2kJUIcoR61ezJEQRBaCmWvJyrrZh8nJdp7snxFknHdWoveTkiyBHqVWCpkeMrCgEKgtBy/GrMsGqtqibWmVWiEGCdRJAjtHuW4SrRkyMIQkuyrEheWtR6K5Jbh6tEkFMnv54hgAhyhHbKUFpJSaF58UKRkyMIQkuyrEgOrZOXYygrtSbUiuGquvkG9TAnH+fntenkYxHkCHWy5ONo3dVotHZfx1UQhHauNevl5FX14mjdPXDW6Vr8em2RStM+ko9FkCPUqbrSscjHERxHTk4Os2bNIjAwEI1Gg7+/P5GRkRw9etS6jyRJNquSN1ZQUBAbNmy47TYeOHAASZJq/WRlZd32uduz6rycll+R/HpVPo4oAtgw/+C2P2QlvqILdcrPFgtzCo4nJiYGg8HAtm3b6NmzJ9nZ2SQlJZGb63jd6WfOnLFZX8fX19eOrXF8lmnkOZf0GI0mlMqW+w5urZHTWQxVNcS3Ry9+OJjUpoMc0ZMj1MmadCzycToEWZapKDfa5aexs2ny8/M5fPgwq1at4r777qN79+4MHjyYhQsX8sADDwDm3hiASZMmIUmS9XZaWhoTJ07Ez88PnU7HoEGD2L9/v/Xco0eP5uLFi8ydO9fa82Jx5MgRRowYgVarJSAggNmzZ1NcXHzT9vr6+uLv72/9USjE221DbFYk/6VlVyS3JB2LnpyGtYcZVqInR6hTgaiR06FUGky8/uxBu1x7xt9HodLcvOKsTqdDp9Oxe/duhg4dikajqbXPiRMn8PX1ZcuWLURFRaFUms+r1+sZP348L774IhqNhu3btxMdHc2ZM2cIDAxk165dhIWFMWPGDOLi4qznS0tLIyoqipUrV7J582ZycnKIj48nPj6eLVu2NNje/v37U15eTmhoKEuXLmX48OFNfGY6FkmS8Aty59Lp62SnF+LbvXGrTN8KMX28cW5MPtZ532HvJjWZ+Goh1CLLsujJERyOk5MTW7duZdu2bXh6ejJ8+HBeeOEFUlNTrfv4+JjXIPL09MTf3996OywsjJkzZxIaGkpISAgrVqwgODiYvXv3AuDt7Y1SqcTNzc3a8wKQkJDA1KlTmTNnDiEhIQwbNoyNGzeyfft2ysrqnurcuXNnXnvtNT766CM++ugjAgICGD16NN9++21LPj3tQmusSC7LMnmZVwBR7fhmVBpn7ugWALTd5GPRkyPUUlpUgaHMCBK4+zjbuzlCK3BSK5jx91F2u3ZjxcTEMGHCBA4fPszx48f59NNPWb16NW+++SaxsbH1HqfX61m6dCn79u0jMzOTyspKSktLuXTpUoPXS0lJITU1lXfeece6TZZlTCYT6enp9OnTp9Yxd911F3fddZf19rBhw0hLS2P9+vW8/fbbjX6sHVFrzLDS5+VSUVaKpFDg6effYtdpL/x69uLazxfJSjtP8IAh9m5Ok4kgR6jFMrPKzdsZJ5VYuK4jkCSpUUNGjsDZ2Zlx48Yxbtw4Fi1axPTp01myZEmDQc78+fNJTExkzZo19OrVC61Wy+TJkzEYDA1eS6/XM3PmTGbPnl3rvsDAwEa3efDgwRw5cqTR+3dUliDHsiK5s6uq2a9hmT7u6eeP0qn5z9/eWJKPr4qeHKG9EJWOhbakb9++NlPGVSoVRqPRZp+jR48SGxvLpEmTAHPwkpGRYbOPWq2udVxERASnT5+mV69et9XG5ORkOnfufFvn6Ai0buYVyQuvlZFzsYiAvt7Nfg1R6bhp2nryscjJEWqxJh2LfBzBgeTm5jJmzBh27NhBamoq6enp7Ny5k9WrVzNx4kTrfkFBQSQlJZGVlUVeXh4AISEh7Nq1i+TkZFJSUpgyZQomk8nm/EFBQRw6dIjLly9z7do1ABYsWMCxY8eIj48nOTmZc+fOsWfPHuLj4+tt54YNG9izZw/nz5/n+++/Z86cOXzxxRc8/fTTLfCstD/WFclbqF6Odfq4qHTcKG298rEIcoRaqmvkiEKAguPQ6XQMGTKE9evXM3LkSEJDQ1m0aBFxcXFs2rTJut/atWtJTEwkICCA8PBwANatW4eXlxfDhg0jOjqayMhIIiIibM6/fPlyMjIyCA4OtiYs9+vXj4MHD3L27FlGjBhBeHg4ixcvpkuXLvW202Aw8Kc//Ym7776bUaNGkZKSwv79+/n1r3/dAs9K+9PSeTnWQoCiJ6dRaiYfZ7XB3hxJbq0lXx1EYWEhHh4eFBQU2BTqEqq9t/x/XL9STPQzYQT+qu1NGRRurqysjPT0dHr06IGzs0gubws6yt8s60IBH60+idZNxVOr77WpWdQc3pw9nYLsLB5ekkBA37ub9dzt1Wf/WM8PB5MYGvMYwx+eard23Mrnt+jJEWyYTDIFV0W1Y0EQ7KMlVySvrKig8OpVQCzM2RS+Pcx5OW0x+VgEOYIN/fUyjJUmFE4SOu/2+21REATH1JIrkudnXUGWTai1Lrh4eDbrudsz/+Dq5OO2NvgjghzBhqUXx6OTFoWiebuJBUEQGsOal9PMRQHzaizn0NzDYO2ZT/caycd5bSv5WAQ5go18sZyDIAh2Zl2RvJl7cq6LmVW3xKby8YU0O7emaUSQI9gQyzkIgmBv1hXJfy7CaDTdZO/GyxMzq25ZW62XI4IcwYboyREEwd6sK5JXNO+K5NU9OSLIaSpLkNPWko9FkCPYqK52LGrkCIJgH5Ik4dsC9XKsC3OKnpwmswQ5WWnn2lTysQhyBCtjhck6ZdNDDFcJgmBHzZ18XFJYQJm+CACvzvUXcxTqZkk+LinIb1PJxyLIEawKrpUiy6ByVuLirrZ3cwRB6MAsycdXmynIscyscuvkg0ojymM0VVtNPhZBjmBVM+lYTK8UBMGeLD05eVkllJdU3Pb5rmea83FEEcBb59czBGhbycciyBGsRNKx4OhycnKYNWsWgYGBaDQa/P39iYyM5OjRo9Z9JEmyWZW8sYKCgtiwYUOztLO8vJw///nPdO/eHY1GQ1BQEJs3b26Wc3cUlhXJAa5mFN32+fLE6uO3za9nMADZF87ZuSWN5xBBziuvvEJQUBDOzs4MGTKEr7/+ut59t27diiRJNj/teR2X1mQtBOgrko4FxxQTE8OpU6fYtm0bZ8+eZe/evYwePZrcXMfKEXj44YdJSkrirbfe4syZM7z33nvcdddd9m5Wm9OcK5Jfr1EIULg1NaeRt5XkYyd7N+CDDz5g3rx5vPbaawwZMoQNGzYQGRnJmTNn8PX1rfMYd3d3zpw5Y70thlaah6iR03HJskxlebldru2k0TTq/+H8/HwOHz7MgQMHGDVqFADdu3dn8ODB1n2CgoIAmDRpkvX+jIwM0tLSmDdvHsePH6e4uJg+ffqQkJDA2LFjARg9ejQXL15k7ty5zJ07F8D6Jn7kyBEWLlzIN998Q6dOnZg0aRIJCQm4urrW2c7PPvuMgwcPcuHCBby9vW3aJTSNX5A7505kN8sMqzzL9HHRk3PLbkw+dvPuZO8m3ZTdg5x169YRFxfHU089BcBrr73Gvn372Lx5M88//3ydx0iShL+/f6POX15eTnmNN+/CwuatoNmeiOGqjquyvJyNT062y7Vnb/sQVSN6Y3U6HTqdjt27dzN06FA0Gk2tfU6cOIGvry9btmwhKioKpVIJgF6vZ/z48bz44otoNBq2b99OdHQ0Z86cITAwkF27dhEWFsaMGTOIi4uzni8tLY2oqChWrlzJ5s2bycnJIT4+nvj4eLZs2VJnO/fu3cvAgQNZvXo1b7/9Nq6urjzwwAOsWLECrVb0kjaFNfn44u0NV5mMRvKzswCRk3M7VBpn7ggI5NqlDLIvpLWJIMeuw1UGg4GTJ09av00BKBQKxo4dy1dffVXvcXq9nu7duxMQEMDEiRP54Ycf6t03ISEBDw8P609AQECzPob2wlBWSUmBAQBPMVwlOCAnJye2bt3Ktm3b8PT0ZPjw4bzwwgukpqZa9/Hx8QHA09MTf39/6+2wsDBmzpxJaGgoISEhrFixguDgYPbu3QuAt7c3SqUSNzc3/P39rV+iEhISmDp1KnPmzCEkJIRhw4axceNGtm/fTllZ3StkX7hwgSNHjvD999/z8ccfs2HDBj788EP++Mc/tuTT0y65dzK/F5UUGm6r8nFBTjYmYyVOag1udzj+B7Mj8+thGbJqG3k5du3JuXbtGkajET8/P5vtfn5+/PTTT3Uec9ddd7F582b69etHQUEBa9asYdiwYfzwww9061Y7Ql+4cCHz5s2z3i4sLBSBTh0s+ThaNxUaF5WdWyO0NieNhtnbPrTbtRsrJiaGCRMmcPjwYY4fP86nn37K6tWrefPNN4mNja33OL1ez9KlS9m3bx+ZmZlUVlZSWlrKpUuXGrxeSkoKqampvPPOO9ZtsixjMplIT0+nT58+tY4xmUxIksQ777yDh4c5p2TdunVMnjyZf/zjH6I3pwmcdSokhYRskiktrEDn1fjXSk3WpGP/zkgKh0hFbbP8egbzw8H9bWaGld2Hq5rqnnvu4Z577rHeHjZsGH369OGf//wnK1asqLW/RqOps1tbsFVd6VgMVXVEkiQ1asjIETg7OzNu3DjGjRvHokWLmD59OkuWLGkwyJk/fz6JiYmsWbOGXr16odVqmTx5MgaDocFr6fV6Zs6cyezZs2vdFxgYWOcxnTt3pmvXrtYAB6BPnz7Isswvv/xCSEhI4x6ogEIhoXVTUVJgoLTIcMtBjliYs/ncmHzs6Dmxdg1yOnXqhFKpJDs722Z7dnZ2o3NuVCoV4eHhnD/fNqJKR2XNxxFJx0Ib07dvX5sp4yqVCqPRaLPP0aNHiY2NtSYk6/V6MjIybPZRq9W1jouIiOD06dP06tWr0e0ZPnw4O3fuRK/Xo9PpADh79iwKhaLO3mahYS7uakoKDBQXlOOD2y2dI0/MrGo2Pt17ICnaTvKxXfvt1Go1AwYMICkpybrNZDKRlJRk01vTEKPRyHfffUfnzp1bqpkdgujJERxdbm4uY8aMYceOHaSmppKens7OnTtZvXo1EydOtO4XFBREUlISWVlZ5OXlARASEsKuXbtITk4mJSWFKVOmYDLZ5ngEBQVx6NAhLl++zLVr1wBYsGABx44dIz4+nuTkZM6dO8eePXuIj4+vt51Tpkzhjjvu4KmnnuL06dMcOnSI5557jt///vdiqOoWWKqvlxQ23OvWEEshQDGz6vaZKx+bezGz0xy/c8Hug5Pz5s3jjTfeYNu2bfz444/MmjWL4uJi62yradOmsXDhQuv+y5cv5/PPP+fChQt8++23PP7441y8eJHp06fb6yG0C/miRo7g4HQ6HUOGDGH9+vWMHDmS0NBQFi1aRFxcHJs2bbLut3btWhITEwkICCA8PBww58R4eXkxbNgwoqOjiYyMJCIiwub8y5cvJyMjg+DgYGvCcr9+/Th48CBnz55lxIgRhIeHs3jxYrp0qX/tI51OR2JiIvn5+QwcOJCpU6cSHR3Nxo0bW+BZaf8sQU5p0a0HOdacHNGT0yysycdtYEVyu+fkPPLII+Tk5LB48WKysrLo378/n332mTUZ+dKlSyhqJIrl5eURFxdHVlYWXl5eDBgwgGPHjtG3b197PYQ2T5ZlCsRwleDgNBoNCQkJJCQkNLhfdHQ00dHRNtuCgoL44osvbLY9/fTTNreHDh1KSkpKrfMNGjSIzz//vElt7d27N4mJiU06Rqibi7s5D8cy+7OpyktKKM439+iJ6ePNoy0lH9s9yAGsdSfqcuDAAZvb69evZ/369a3Qqo6jTF9BeUklSODhI3pyBEFwHLc7XJWXae7FcfHwRONSdwFHoWlqrmHl6MnHdh+uEuzPko/j5uWMk1pp59YIgiBUu+0g54pYmLO5+QTVSD6+7lhLqtxIBDlCjUrHohdHEATHcrtBzvVMkY/T3FRqTXXysYMPWYkgRyA/25x0LPJxBEFwNC4etxnkWKaPi5lVzaqtJB+LIEegpMC8tpfrLRbaEgRBaCmWnhxDaSWVBuNN9q7NujCn6MlpVn7B1UUBHZkIcgRKiysA0Lqp7dwSQRAEW2qtEwonc2JrU3tzZJOJvMwrAHh1Fjk5zal6DStz8rGjEkGOQGlRVZCjE2tWCYLgWCRJqs7LaWKtnKLruVQaylEolXj4+t38AKHR2krysQhyBMr05jcOZ53oyREEwfHcaq0cSxFAD7/OKJ0comJKu9FWko9FkCNQphc9OYIgOK5bnWFlWc5BrFnVMqyLdTpw8rEIcjo4Y6UJQ5k5mc9ZBDmCg8vJyWHWrFkEBgai0Wjw9/cnMjKSo0ePWveRJMlmwc7GCgoKYsOGDbfdxtjYWCRJqvXzq1/96rbP3VHdapBjKQQo1qxqGdYgJ+2cnVtSP9F/18GVVSUdSwoJjVa8HATHFhMTg8FgYNu2bfTs2ZPs7GySkpLIzXWcnIC///3v/PWvf7XerqysJCwsjIceesiOrWrbrOtXNTHIKa5aoNXtDsdeKbutqp5GnuawlY9FT04HZ0k6dnZ1QlI43gtUECzy8/M5fPgwq1at4r777qN79+4MHjyYhQsX8sADDwDm3hiASZMmIUmS9XZaWhoTJ07Ez88PnU7HoEGD2L9/v/Xco0eP5uLFi8ydO9fa82Jx5MgRRowYgVarJSAggNmzZ1NcXFxvOz08PPD397f+fPPNN+Tl5VkXHRaa7lZ7ckoLCwDQuns0e5uEtpF8LIKcDk4kHQtgXqTVZDDa5aex0091Oh06nY7du3dTXl5e5z4nTpwAYMuWLWRmZlpv6/V6xo8fT1JSEqdOnSIqKoro6GguXboEwK5du+jWrRvLly8nMzOTzMxMwBwcRUVFERMTQ2pqKh988AFHjhypd629urz11luMHTuW7t27N/oYwVZ1kFP3370+JVVBjoubCHJagkqtoZODJx+L8YkOrlQkHQuAXGHiyuJjdrl2l+XDkBqxZpqTkxNbt24lLi6O1157jYiICEaNGsWjjz5Kv379APDx8QHA09MTf39/67FhYWGEhYVZb69YsYKPP/6YvXv3Eh8fj7e3N0qlEjc3N5vjEhISmDp1KnPmzAEgJCSEjRs3MmrUKF599VWcnZ0bbPOVK1f49NNPeffddxv9fAi13XJPTlEhAFp392Zvk2Dm27MXOZcyyL5wjl6Dhtq7ObWInpwOzjKzSiQdC21BTEwMV65cYe/evURFRXHgwAEiIiLYunVrg8fp9Xrmz59Pnz598PT0RKfT8eOPP1p7cuqTkpLC1q1brb1IOp2OyMhITCYT6enpN23vtm3b8PT05MEHH2zCoxRuVHNph8b2/MkmkzXIcRHDVS3GmnwsenIERyR6cgQASaWgy/Jhdrt2Uzg7OzNu3DjGjRvHokWLmD59OkuWLCE2NrbeY+bPn09iYiJr1qyhV69eaLVaJk+ejMHQcM+AXq9n5syZzJ49u9Z9gYGBDR4ryzKbN2/miSeeQK0Ww8G3w1KNvdJgoqLciNr55h9dZcV6ZJPJfLzoyWkxjp58LIKcDk705AhgnnbdmCEjR9S3b1+bKeMqlQqj0XaNo6NHjxIbG8ukSZMAc/CSkZFhs49ara51XEREBKdPn6ZXr15NbtfBgwc5f/48f/jDH5p8rGBL7eyEk0ZJZbmRkgJDo4IcSz6OxsUVpZN4f2spNyYfO9pMNjFc1cFZEo+1IvFYcHC5ubmMGTOGHTt2kJqaSnp6Ojt37mT16tVMnDjRul9QUBBJSUlkZWWRVzWFOCQkhF27dpGcnExKSgpTpkzBVPUtv+Zxhw4d4vLly1y7dg2ABQsWcOzYMeLj40lOTubcuXPs2bOnUYnHb731FkOGDCE0NLQZn4WOq6lLO1TPrBK9OC2pZvJx1gXHq5cjgpwOrlT05AhthE6nY8iQIaxfv56RI0cSGhrKokWLiIuLY9OmTdb91q5dS2JiIgEBAYSHhwOwbt06vLy8GDZsGNHR0URGRhIREWFz/uXLl5ORkUFwcLA1gblfv34cPHiQs2fPMmLECMLDw1m8eDFdunRpsK0FBQV89NFHohenGblagpxGLu1QWmhJOhb5OC3Ntyov56oD5uWI4aoOzlIMUOTkCI5Oo9GQkJBAQkJCg/tFR0cTHR1tsy0oKIgvvvjCZtvTTz9tc3vo0KGkpKTUOt+gQYP4/PPPm9RWDw8PSkpKmnSM0LCmzrCyTh8XQU6L8+vZix8O7HfI5GPRk9PBWYsBiiBHEAQHpm1irRzrcJWokdPi/HuGAJB14XyjZ7+1FhHkdGCyLIvEY0EQ2oSmLu1QUmTpyRE5OS2tU/cgJIWC0sICinKv2bs5NkSQ04FVlBsxVlZNsRSJx4IgOLCmDleJnJzWY1P52MFWJBdBTgdm6cVRqhQ4qcVLQRAEx+XioQFETo6jctTkY/HJ1oHVLAToaAWcBEEQanJxa2JPTkE+IHpyWkvNvBxHIoKcDkzk4wiC0FY0dWmHErGkQ6uqubyDIyUfiyCnA6suBCiCHEEQHJulJ8dklCkvqWxwX1mWa+TkiMTj1uCoycciyOnAqgsBiqRjQRAcm1KlQONiLu12s4KA5SXFmIzmQEgMV7UOR00+FkFOB1YmFucUBKENaezSDpYaOSpnLSq1psXbJZj5BZvzcrLTRJAjOACxpIMgCG2JSyMLApYUWvJxxFBVa6pekVwEOYIDED05QluTk5PDrFmzCAwMRKPR4O/vT2RkJEePHrXuI0mSzarkjRUUFMSGDRuapZ3vvPMOYWFhuLi40LlzZ37/+9+Tm5vbLOfuyFwauX5V9eKcYqiqNTli8rEIcjqw0qrEY5GTI7QVMTExnDp1im3btnH27Fn27t3L6NGjHSqAOHr0KNOmTeMPf/gDP/zwAzt37uTrr78mLi7O3k1r81zcG1crR9TIsQ9HTD4WQU4HJqaQC21Jfn4+hw8fZtWqVdx33310796dwYMHs3DhQh544AHA3BsDMGnSJCRJst5OS0tj4sSJ+Pn5odPpGDRoEPv377eee/To0Vy8eJG5c+ciSZJN3agjR44wYsQItFotAQEBzJ49m+Li4nrb+dVXXxEUFMTs2bPp0aMH9957LzNnzuTrr79u/ielg9G6m9+rbra0g1i3yj5Uag2dAroDkH3hnJ1bYyaCnA6sVAxXCVVkWcZgMNjlp7Hd2jqdDp1Ox+7duykvrzsn48SJEwBs2bKFzMxM6229Xs/48eNJSkri1KlTREVFER0dzaVLlwDYtWsX3bp1Y/ny5WRmZpKZmQmYg6OoqChiYmJITU3lgw8+4MiRI8THx9fbznvuuYeff/6ZTz75BFmWyc7O5sMPP2T8+PGN/nsIdWtsT05pkWW4SuTktLbqIas0O7fEzMneDRDsw2SSKS8WPTmCWUVFBS+99JJdrv3CCy+gVt98yNTJyYmtW7cSFxfHa6+9RkREBKNGjeLRRx+lX79+APj4+ADg6emJv7+/9diwsDDCwsKst1esWMHHH3/M3r17iY+Px9vbG6VSiZubm81xCQkJTJ06lTlz5gAQEhLCxo0bGTVqFK+++irOzs612jl8+HDeeecdHnnkEcrKyqisrCQ6OppXXnnllp4foZqlIGDxTYerRCFAe/Hr0Yvvv0x0mORjh+jJeeWVVwgKCsLZ2ZkhQ4Y0ulv3/fffR5IkHnzwwZZtYDtkKKnE8gVaBDlCWxETE8OVK1fYu3cvUVFRHDhwgIiICLZu3drgcXq9nvnz59OnTx88PT3R6XT8+OOP1p6c+qSkpLB161ZrL5JOpyMyMhKTyUR6enqdx5w+fZpnn32WxYsXc/LkST777DMyMjL4v//7v1t92EKVxi7tIBKP7cevZy8USidwkMRju/fkfPDBB8ybN4/XXnuNIUOGsGHDBiIjIzlz5gy+vr71HpeRkcH8+fMZMWJEK7a2/bAkHau1TiiVDhHrCnakUql44YUX7HbtpnB2dmbcuHGMGzeORYsWMX36dJYsWUJsbGy9x8yfP5/ExETWrFlDr1690Gq1TJ48GYOh4Q9LvV7PzJkzmT17dq37AgMD6zwmISGB4cOH89xzzwHQr18/XF1dGTFiBCtXrqRz586Nf7CCDUtPTlmRAZNJRqGoe809kXhsP37BvXhm206cmvj/dUuxe5Czbt064uLieOqppwB47bXX2LdvH5s3b+b555+v8xij0cjUqVNZtmwZhw8fJj8/vxVb3D6I6eNCTZIkNWrIyBH17dvXZsq4SqXCaDTa7HP06FFiY2OZNGkSYA5eMjIybPZRq9W1jouIiOD06dP06tWr0e0pKSnBycn2rVWpVAI4zLTatkqrU4Fk7iQo01dYp5TfSCzpYD8KhRKFQmnvZljd0lf4yspK9u/fzz//+U+KiooAuHLlCnq9vknnMRgMnDx5krFjx1Y3SKFg7NixfPXVV/Uet3z5cnx9ffnDH/5w02uUl5dTWFho8yOIQoBC25Obm8uYMWPYsWMHqamppKens3PnTlavXs3EiROt+wUFBZGUlERWVhZ5eXmAOZdm165dJCcnk5KSwpQpUzCZTDbnDwoK4tChQ1y+fJlr18zTXxcsWMCxY8eIj48nOTmZc+fOsWfPngYTj6Ojo9m1axevvvoqFy5c4OjRo8yePZvBgwfTpUuXFnhmOg6FUmH9YlZfQUDzulWiJ0cwa3KQc/HiRe6++24mTpzI008/TU5ODgCrVq1i/vz5TTrXtWvXMBqN+Pn52Wz38/MjKyurzmOOHDnCW2+9xRtvvNGoayQkJODh4WH9CQgIaFIb2yvRkyO0NTqdjiFDhrB+/XpGjhxJaGgoixYtIi4ujk2bNln3W7t2LYmJiQQEBBAeHg6Ye4y9vLwYNmwY0dHRREZGEhERYXP+5cuXk5GRQXBwsDWBuV+/fhw8eJCzZ88yYsQIwsPDWbx4cYPBSmxsLOvWrWPTpk2Ehoby0EMPcdddd7Fr164WeFY6npsVBKwoL6OyomrxYRHkdHhNHq569tlnGThwICkpKdxxxx3W7ZMmTWrxYldFRUU88cQTvPHGG3Tq1KlRxyxcuJB58+ZZbxcWFopAh5qFAEWQI7QNGo2GhIQEEhISGtwvOjqa6Ohom21BQUF88cUXNtuefvppm9tDhw4lJSWl1vkGDRrE559/3qS2PvPMMzzzzDNNOkZoHBd3NbmXi+tdv8rSi+OkUqPS1J79JnQsTQ5yDh8+zLFjx2qN3wcFBXH58uUmnatTp04olUqys7NttmdnZ9tM47RIS0sjIyPD5g3M0uXs5OTEmTNnCA4OtjlGo9Gg0YgF2m5UJlYgFwShDbLWyqmnJ6ekxsyqmkUdhY6pycNVJpOpVnIewC+//IKbm1uTzqVWqxkwYABJSUk2509KSuKee+6ptX/v3r357rvvSE5Otv488MAD3HfffSQnJ4semiYQhQAFQWiLqhfprK8nRyQdC9Wa3JNz//33s2HDBl5//XXAPCtDr9ezZMmSW6roOW/ePJ588kkGDhzI4MGD2bBhA8XFxdbZVtOmTaNr164kJCTg7OxMaGiozfGenp4AtbYLDRNLOgiC0BZpbxLkiOnjQk1NDnLWrl1LZGQkffv2paysjClTpnDu3Dk6derEe++91+QGPPLII+Tk5LB48WKysrLo378/n332mTUZ+dKlSygUoo5LcxM9OYIgtEU378kRhQCFak0Ocrp160ZKSgrvv/8+qamp6PV6/vCHPzB16lS0Wu0tNSI+Pr7eKZkHDhxo8NibVToV6lYmViAXBKENshQEvHlPjhiuEm6xGKCTkxOPP/54c7dFaEViCrkgCG1RdU9O3XVyrDk5YgVygVsIcrZv397g/dOmTbvlxgitw1hhwlBmTh4XOTmCILQlliCnvLgSY6UJpZNtOkP1CuQiyBFusU5OTRUVFZSUlKBWq3FxcRFBThtQVrX6uKSQ0GjtvrKHIAhCozm7qFAoJEwmmdIiAzov21o4IvFYqKnJGb15eXk2P3q9njNnznDvvffeUuKx0PqsSzq4OiHVs8CdIAiCvckmmYLPMij+prqWmqSQGpxhJRKPhZqaZdpSSEgIf/3rX2v18giOyZJ0rHUTSceCIDiusjPXKTrwM/l7ziObqhc3bWhph5KqnByReCxAMwU5YE5GvnLlSnOdTmhB1T05Ih9HaFtycnKYNWsWgYGBaDQa/P39iYyM5OjRo9Z9JEmyWZW8sYKCgtiwYUOztPOVV16hT58+aLVa7rrrrpvmMgp1K6nqwZErTBhrLONgDXJuWNqh0mCgoqwUED05glmTEzL27t1rc1uWZTIzM9m0aRPDhw9vtoYJLUfMrBLaqpiYGAwGA9u2baNnz55kZ2eTlJREbm6uvZtm9eqrr7Jw4ULeeOMNBg0axNdff01cXBxeXl611tQS6mcsrqD0p+vVt3NLcfIwL+lQX0+OJR9HoXRC4+LaSi0VHFmTg5wHH3zQ5rYkSfj4+DBmzBjWrl3bXO0SWlCpqHYstEH5+fkcPnyYAwcOMGrUKAC6d+/O4MGDrfsEBQUB5gWDLfdnZGSQlpbGvHnzOH78OMXFxfTp04eEhATGjh0LwOjRo7l48SJz585l7ty5gPkLHMCRI0dYuHAh33zzDZ06dWLSpEkkJCTg6lr3h+jbb7/NzJkzeeSRRwDo2bMnJ06cYNWqVSLIaYKSU1fBWD1EVZlbhqan+d/1FQSszsdxF+tWCcAtrl1V88doNJKVlcW7775L586dW6KNQjMTSzoIN5JlGaOxxC4/lmDiZnQ6HTqdjt27d1NeXneNlBMnTgCwZcsWMjMzrbf1ej3jx48nKSmJU6dOERUVRXR0NJcuXQJg165ddOvWjeXLl5OZmUlmZiZgXhQ4KiqKmJgYUlNT+eCDDzhy5Ei9xUsBysvLcXa2nfGj1Wr5+uuvqaioaNRjFaDkpHmoSnJWAuYgx0JbT60cS5Dj4ibycQQzMX+4A7ImHotqx0IVk6mUAwfvtsu1R4/6DqXS5ab7OTk5sXXrVuLi4njttdeIiIhg1KhRPProo/Tr1w8AHx8fwLymnb+/v/XYsLAwwsLCrLdXrFjBxx9/zN69e4mPj8fb2xulUombm5vNcQkJCUydOpU5c+YA5kkWGzduZNSoUbz66qu1ghmAyMhI3nzzTR588EEiIiI4efIkb775JhUVFVy7dk18GWwEw2U9FZnFoJTQ3dOFoi9/pjK31Hp/fT05JUWWxTlFPo5g1qggZ968eY0+4bp16265MULrEMNVQlsVExPDhAkTOHz4MMePH+fTTz9l9erVvPnmm8TGxtZ7nF6vZ+nSpezbt4/MzEwqKyspLS219uTUJyUlhdTUVN555x3rNlmWMZlMpKen06dPn1rHLFq0iKysLIYOHYosy/j5+fHkk0+yevVqsQ5fI1l6cbS/ugN1NzcAKq9X9+S41rO0g5g+LtyoUUHOqVOnGnUyMQbaNojFOYUbKRRaRo/6zm7XbgpnZ2fGjRvHuHHjWLRoEdOnT2fJkiUNBjnz588nMTGRNWvW0KtXL7RaLZMnT8ZgqHv9Iwu9Xs/MmTOZPXt2rfsCAwPrPEar1bJ582b++c9/kp2dTefOnXn99ddxc3Oz9jQJ9ZMrTZQkXwXAdYAfyqpk48rcUmRZRpIkXNzN22r15IhCgMINGhXkfPnlly3dDqEViZwc4UaSJDVqyMgR9e3b12bKuEqlwmg02uxz9OhRYmNjrQnJer2ejIwMm33UanWt4yIiIjh9+jS9evVqcrtUKhXdunUD4P333+e3v/2t6MlphNIfr2MqqUThrkYT4oVcaQJALjNiKqlE6aqy5uRUlBmpMBhRqc15OzUTjwUBmrFOjtA2yLJcPYVcFAMU2pDc3FzGjBnDjh07SE1NJT09nZ07d7J69WomTpxo3S8oKIikpCSysrLIy8sDzLk0u3btIjk5mZSUFKZMmYLJZLI5f1BQEIcOHeLy5ctcu3YNgAULFnDs2DHi4+NJTk7m3Llz7Nmzp8HE47Nnz7Jjxw7OnTvH119/zaOPPsr333/PSy+91ALPSvtjGapyjfBFUkgo1EoUVUGNJS9H7axEqTJ/fJXW6M2pLgQoenIEs1tKPP7mm2/417/+xaVLl2p19+7atatZGia0jIpyI8aqb0aiJ0doS3Q6HUOGDGH9+vWkpaVRUVFBQEAAcXFxvPDCC9b91q5dy7x583jjjTfo2rUrGRkZrFu3jt///vcMGzaMTp06sWDBAgqrPhAtli9fzsyZMwkODqa8vBxZlunXrx8HDx7kz3/+MyNGjECWZYKDg63Tw+tiNBpZu3YtZ86cQaVScd9993Hs2DHr9HahfsZCA2VnzLVxXAb4Wbc7eTtjKDRgvF4Gge5VQ1ZqinLLKCk04N7JPOQpcnKEGzU5yHn//feZNm0akZGRfP7559x///2cPXuW7Oxsa1ew4LgsvThOKoW1i1cQ2gKNRkNCQgIJCQkN7hcdHV2rHk1QUBBffPGFzbann37a5vbQoUNJSUmpdb5Bgwbx+eefN7qdffr0aXQeo2Cr5FQ2yKDu7o7Kp3r41OkOLYaMQiqv2c6wKsotsykIaFmB3MVNBDmCWZOHq1566SXWr1/Pv//9b9RqNX//+9/56aefePjhh+tNxBMch5hZJQiCI5Jl2boQp2uNXhwApzvMU/VrzrCqa2mHEtGTI9ygyUFOWloaEyZMAMyJesXFxUiSxNy5c3n99debvYFC8xJJx4IgOCLDz0VU5pQiqRRo+3Wyuc/pDvNwVM2CgNVLO5gLAhorKykvLgZE4rFQrclBjpeXF0VFRQB07dqV77//HjCXXC8pKWne1gnNTqxALgiCI7LWxgnthMLZNpPC2pPTQEHA0qpCgJKkQKtza/H2Cm1Do4McSzAzcuRIEhMTAXjooYd49tlniYuL47HHHuPXv/51y7RSaDZiBXJBEByNXGGkJCUHsE04tnDyNgc5Jn0FpnLzNH8XD9taOZakY2c3NyQxVV+o0uhXQr9+/RgyZAh33303Dz30EAB//vOfmTdvHtnZ2cTExPDWW2+1WEOF5iEKAQqC4GhKf8hFLjOi9NKg6Vk7n0bhokLhYu7dsfTmuLjZ9uSIQoBCXRo9u+rgwYNs2bKFhIQEXnzxRWJiYpg+fTrPP/98S7ZPaGYiJ0cQBEdjSTh2ifBDUtRdOV/p7YypRG+eRt5Fh8sNSzuIQoBCXRrdkzNixAg2b95MZmYmL7/8MhkZGYwaNYo777yTVatWkZWV1ZLtFJqJKAQoCIIjqcwvozwtH6g9q6qmG5OPa+bkyLJcXQhQTB8XamjywKWrqytPPfUUBw8e5OzZszz00EO88sorBAYG8sADD7REG4VmVFqVeCxycgRBcAQlJ6+CDJqeHtbcm7rcmHxsWdrBWGHCUGa01sgR08eFmm4rO6tXr1688MIL/OUvf8HNzY19+/Y1V7uEFlImcnIEQXAQskmmuGpWlcvA+ntxAJy8q3pyqmrlqNRKVM6WNasMotqxUKdbDnIOHTpEbGws/v7+PPfcc/zud7/j6NGjzdk2oQWIYoCCIDgKQ0YhxutlSBol2tBODe7r1KmhaeTlNRKPRU6OUK1JQc6VK1d46aWXuPPOOxk9ejTnz59n48aNXLlyhTfeeIOhQ4e2VDuFZmAyyZQXiyBHaLtycnKYNWsWgYGBaDQa/P39iYyMtPmCJUmSzarkjRUUFMSGDRtuu42ZmZlMmTKFO++8E4VCwZw5c+rcb+fOnfTu3RtnZ2fuvvtuPvnkk9u+dltj6cXR3t0JxU2WmbH05Bjzy60rk1uCnOICA6VVOTmiJ0eoqdGzq37zm9+wf/9+OnXqxLRp0/j973/PXXfd1ZJtE5qZoaQSWTb/WwQ5QlsUExODwWBg27Zt9OzZk+zsbJKSksjNzbV306zKy8vx8fHhL3/5C+vXr69zn2PHjvHYY4+RkJDAb3/7W959910efPBBvv32W0JDQ1u5xfZhKjdS+p25No7rTYaqABRuKiSVArnCRGVeGSofF5vkYzGFXKhLo3tyVCoVH374Ib/88gurVq0SAU4bZEk61rg4oVSKYllC25Kfn8/hw4dZtWoV9913H927d2fw4MEsXLjQOunBstL3pEmTkCTJejstLY2JEyfi5+eHTqdj0KBB7N+/33ru0aNHc/HiRebOnYskSUhS9TTmI0eOMGLECLRaLQEBAcyePZviquUD6hIUFMTf//53pk2bhodH3R+4f//734mKiuK5556jT58+rFixgoiICDZt2nSbz1LbUfpdDrLBhFMnLeruNx9ikiSp1hpWLu7mgoAiJ0eoT6M/6fbu3cvEiRNRKsXK1W2VqHYs1EeWZYqNRrv8yJbuxZvQ6XTodDp2795NeXl5nfucOHECgC1btpCZmWm9rdfrGT9+PElJSZw6dYqoqCiio6O5dOkSALt27aJbt24sX76czMxMMjMzAXNwFBUVRUxMDKmpqXzwwQccOXKE+Pj423q+v/rqK8aOHWuzLTIykq+++uq2ztuWWGvjDPCzCSoborQMWVWtRl49XFVOqb6oapsIcoRqjR6uEtq++goBmgxGyi8UoOnpcdNxcaF9KjGZCD70nV2unTbyblwb8eXJycmJrVu3EhcXx2uvvUZERASjRo3i0UcfpV+/fgD4+PgA4Onpib+/v/XYsLAwwsLCrLdXrFjBxx9/zN69e4mPj8fb2xulUombm5vNcQkJCUydOtWaVxMSEsLGjRsZNWoUr776Ks7O9U95bkhWVhZ+frZDNH5+fh2m3ljltVIMGYUggWuEb6OPq9WTU1UQUJ9XgmUs3lmsWyXUIMYsOpD6CgEWHfiZ3K0/kL3hW8rO59uhZYLQODExMVy5coW9e/cSFRXFgQMHiIiIYOvWrQ0ep9frmT9/Pn369MHT0xOdTsePP/5o7cmpT0pKClu3brX2Iul0OiIjIzGZTKSnpzfjI+tYLAnHmhAvlFVrUDVGrYKAVe9lxfnmnj1nnRtKJ/HdXagmXg0diLUQ4A09ORXZ5tXjjdfLuPbmd7gO9sdjfI9aKwEL7ZeLQkHayLvtdu2mcHZ2Zty4cYwbN45FixYxffp0lixZQmxsbL3HzJ8/n8TERNasWUOvXr3QarVMnjwZg8HQ4LX0ej0zZ85k9uzZte4LDAxsUrtr8vf3Jzs722Zbdna2TS9SeyWbZEq+NT/2hioc1+XGgoCWnpzSoqovcGKoSriB+BTrQKyLc96Qk2MsMH8LUnd3x3CxkOKvsyj76TqevwtB29u71dsptD5Jkho1ZOSI+vbtazNlXKVSYTQabfY5evQosbGxTJo0CTAHLxkZGTb7qNXqWsdFRERw+vRpevXq1axtvueee0hKSrKZXp6YmMg999zTrNdxROVp+RgLDEjOTmj73tGkY609OdfLkE2yNSenvMQypVzUyBFsieGqDqS+nBxjVVev58RgfGbcjfIOZ4yFBnK3/sD1D85grKqtIwj2lJuby5gxY9ixYwepqamkp6ezc+dOVq9ezcSJE637BQUFkZSURFZWFnl5eYA5l2bXrl0kJyeTkpLClClTMJlMNucPCgri0KFDXL58mWvXrgGwYMECjh07Rnx8PMnJyZw7d449e/bcNPE4OTmZ5ORk9Ho9OTk5JCcnc/r0aev9zz77LJ999hlr167lp59+YunSpXzzzTe3ndDcFlgTjvv7IKma9hGk9NCAQgKjjLGw3Dr0LsuA5IxWrFsl3MAhgpxXXnmFoKAgnJ2dGTJkCF9//XW9++7atYuBAwfi6emJq6sr/fv35+23327F1rZddQU5coUJU9V2J08Nmp6e+D0bgW5EV5Cg5NRVstefpKSqnoUg2ItOp2PIkCGsX7+ekSNHEhoayqJFi4iLi7OZer127VoSExMJCAggPDwcgHXr1uHl5cWwYcOIjo4mMjKSiIgIm/MvX76cjIwMgoODrQnM/fr1s67TN2LECMLDw1m8eDFdunRpsK3h4eGEh4dz8uRJ3n33XcLDwxk/frz1/mHDhvHuu+/y+uuvExYWxocffsju3bvbfY0cU2klpT+Yaxo1pjbOjSSlhJOXOYenMrcMpZMCjat5QEKSXMXMKqEWSW7s/M0W8sEHHzBt2jRee+01hgwZwoYNG9i5cydnzpzB17d21v2BAwfIy8ujd+/eqNVq/vOf//CnP/2Jffv2ERkZedPrFRYW4uHhQUFBAe4drGtz51+/4WpGIeP/2I8e/cwl1CuvlZK15hsklYIuy4fZTOUsv1RI3ofnqLxqztnR/uoOPB/shVKsYN7mlZWVkZ6eTo8ePW55hpDQutrD30x/PJP83edx8nPBb05Eo6eO15Sz+XvKz+bh9bsQXAf78+6y/5GXWYyhaCeDoodx76NPtEDLBUdwK5/fdu/JWbduHXFxcTz11FP07duX1157DRcXFzZv3lzn/qNHj2bSpEn06dOH4OBgnn32Wfr168eRI0daueVtT1lV4nHNxTkrq4aqlJ6aWm84mkB3/GaH4zYmABQSpT/kkrXuJMUnsxtd20QQhLZNNslU5pUhVxhvvvNNWGZVuTahNs6NaiUfV+XloHAVOTlCLXZNPDYYDJw8eZKFCxdatykUCsaOHduooliyLPPFF19w5swZVq1aVec+5eXlNoXDCqvWN+mI6ioGaEk6VnrWPY1TclLgcX8Q2tBO5H10jorLevJ2nqU0NQfPSSE41XOcIAhtjyzLGAvKMfysp+JyEYZf9Bh+KUIuMyI5K3Ed6I9uaGecOmmbfO6K7GIqfi4CBbiEN742zo1uXI3cEuRIkquYXSXUYtcg59q1axiNxjqLYv3000/1HldQUEDXrl0pLy9HqVTyj3/8g3HjxtW5b0JCAsuWLWvWdrdFxgoTFWXmb2I1c3IsScc3q1Wh7qLD94/9KTr8C4X7L1J2Jo/s9SfxGN8D10H+SIpb+1YmdAyyLINJRjbKYJRBwpx0qpBu+Ru9cPuMeoM5kPm5iIpfijBc1ltz9GxIIJcZ0R+5jP7IZZzv8sL1ni443+nV6P/3i09eBcD5Lu/bGvK29uTcUPVYUriIIEeopU1OIXdzc7POXEhKSmLevHn07NmT0aNH19p34cKFzJs3z3q7sLCQgICAVmytYyirmiElKSQ0LtV/dktPTmN6ZCSlhPvoALR97yDvo3MYLhaS//F5SlNycB8biKRSQs33u5ofXjdst/lckwClAkkpISkVoJRs/32LAZT1g7VSNq9abDRZ/235sX7gKqs+cJ0k8+8a15aUUtW/G98e2SSbp3zINf5tst2OScZkMCIbTMjllZjKjcjlRvM2y7/LjcgGo/U+m/uNMkqdCoVObf7tpq6+7aZCqVObFzVUK1sskLAJXqxBjAnZWHNb1fNcF4WE5KRAUimqfzdz8CNb/g6WdshUvx6lqv9I1dus17XuI9W+rw0ylVZiqApkKn4299JY/v+3oZBQ+bug7uaGqpvO/NvXhbK0fIqPXaHsbB5lZ8w/Sm9ndEM74zrQD4VL/cvFyEaZklNVQ1W3kHBcU82qx7Is2/TkiMRj4UZ2DXI6deqEUqlsclEshUJhrVvRv39/fvzxRxISEuoMcjQaDRqNGFKpWQiw5ht1zZycxlL5uuAzsx/6r65Q+FkG5RcKyHm9BZcEUGAT/NwYEAFgrBm8VH3QVprMH2jNTaIqEJLMH4B1BDOtxVjVZd8QSaWoFQBVuisweVdSqTdQWV71HMpV/5Gpft6qcq/kG26bgwYZTE18ji3BolwVeJpkZIM5kLNRV/DjVPUaqHr93hi8yCZzL5H1do1ttGYOWaPiIKnBm3VtN1QYMBaUc/WfKTjpqQ6Wsbz+qv9tfU3W+DemOp4DCZx8XFBbgpluOtSdXc1fWG6gvcsb7V3eVF4rRX88k+JvsjFeL6Pgk3QKEy/i0t8X13s6o+6iq3Vs2dnrmIoqULiqcL7N2ltO3lpzz1K5EVNxBVq3quBK4YJW5OQIN7BrkKNWqxkwYABJSUk8+OCDAJhMJpKSkppUL8JkMtW7YJ9gZi0EWE+NnKaUVgdzj5Db8K5oe3uTvy+diit68x3W91HZ+m/Z9j+2H4pVb8I2H1A3BgkmkE0mqLjNmMUSLDlVfXA6VQVKsu0H4o0fmrXIWD9Eb5mEuUdLpUChUSJV/SjUVb9rbXOq2qYw/1utBAWYiisw6iswFRlsfhv1BkxFFeYAosKE8XqZTUBU6SZhus8Vk74Ck1Mz9E4oagSgCql2b5xSqtU7I5tqBKYVVUGp5XdDwY9SYQ5wmhq8KKp64RSSbbAGNQIGbIO9W9Go4+QGb9bJZH5NmvQVmIpurXFKb2drQKPupkPVVYdC07SPAadOWjx/2xP3+7tTmpyD/qsrVGQWU3wii+ITWai7u6Mb1hntrzqZA1OgxFIbJ9zX/Jq4DZJKgdJdjbHAQGVuGSqN+Q1DklxFnRyhFrsPV82bN48nn3ySgQMHMnjwYDZs2EBxcTFPPfUUANOmTaNr164kJCQA5hybgQMHEhwcTHl5OZ988glvv/02r776qj0fhsMrqyPpWJbl6iDnFhOIne7Q0mla39tvYA3WYMNk6ZUxBx41h0Ks2yrN3Q6Sk6JGAGP+IJRU5h4fa1BzC8NesqVnxnTjEExVW+SqUTmFuVdHUmDeoJDq3Y7UesMeJoOxVgBk0hsoLS2lWGNAoXVCoVFhHbLBMsooYTukww3DNoBCUT2UdwuPR1JISGol3LAo7E2DH1MdgY81sKoxpFgz0FI0bpjRph01AqGqDY04qEmXsB4j13dgjc1OZQqURWq8Hw/EWaWpfjxVAbM1cK7591KYd5AkkNRKFNrme8tXqJW4DvbHZZAfhouF6I9dofT7XAwXC7l+sRCF2wVcB3dGG9qJ0p+uA+YVx5uD0ltrDnKulyFJ5vcwSemKk6r+ITOhY7J7kPPII4+Qk5PD4sWLycrKon///nz22WfWZORLly6hqLG2TXFxMX/84x/55Zdf0Gq19O7dmx07dvDII4/Y6yG0CdWLc9YIckorrd+UHWmWlOXbtoQC7NwsSZJACSiVSG3w/VOhVqK4Q2sth2+hLivjeno6Th4anBys5kq9wY9liKtSru6Vu4XgpdHtuDE3p3HjULd2rUbsozApkZwUqP1cUTvQ30ySJDRBHmiCPDAWlqP/XxbFX2diKqqgKOkSRUnmRVBVXc1DYc3B6Q5nDOkFGHNLkb2Kq9qhxWQ0objNniLh9hSUVLA7+TIRgV7c3c3+PWt2D3IA4uPj6x2eOnDggM3tlStXsnLlylZoVftinT6uq57VUFlgztNRuDrVOQYvCI5EkiTz67QNBpsdhdJdg8e47rjfF0DpD7nov7qCIcNctsN1UPP04oDtauQmXRGybEKSFJTqK3Bt4tC70LxOXrrOkr0/EOzjStKfRtu7OY4R5Agtr6yodiFAY745R0Pp6TjfCgVBaPskJwUuYT64hPlguKKn8lop2tBOzXb+mgUBy+4oArkCJFdKCgwiyLGzU5fyAQgP9LJvQ6qIfr0OorS4gUKA4k1BaCNycnKYNWsWgYGBaDQa/P39iYyM5OjRo9Z9JEmyWZW8sYKCgtiwYcNttzEzM5MpU6Zw5513olAobFYat/jhhx+IiYkhKCgISZKa5bqOSt1Fh0s/n2YdVqy5GnlJYQGyyTxkVVL1ZU6wn+ogx9Ou7bAQQU4HUdfinJakY0fKxxGEhsTExHDq1Cm2bdvG2bNn2bt3L6NHjyY3N9feTbMqLy/Hx8eHv/zlL4SFhdW5T0lJCT179uSvf/1rg+UyhLpZenJM+grK8oqQZfP6eiUFIsixJ5NJJuXnfADCA0RPjtCKSutIPL7V6eNC+yPLMiWGSrv8NHYdtPz8fA4fPsyqVau477776N69O4MHD2bhwoU88MADgLk3BmDSpElIkmS9nZaWxsSJE/Hz80On0zFo0CD2799vPffo0aO5ePEic+fONef+1JgtduTIEUaMGIFWqyUgIIDZs2dTXFxcbzuDgoL4+9//zrRp0/DwqDvxctCgQfztb3/j0UcfFXW8boHC2QlF1erjxvxysPTkFIpSIvaUlqOnqLwSF7WSO/1q10uyB5GT00FU5+TUSDy+zenjQvtRWmGk7+L/2uXap5dH4qK++VuRTqdDp9Oxe/duhg4dWmdwcOLECXx9fdmyZQtRUVEoleaEer1ez/jx43nxxRfRaDRs376d6Ohozpw5Q2BgILt27SIsLIwZM2YQFxdnPV9aWhpRUVGsXLmSzZs3k5OTY50osWXLluZ7EoQmc/LWYiguQiqSrT05pYV1LEkhtBrLUNXdXT1wcpBZbo7RCqFFybJcnZNT17pVIsgR2gAnJye2bt3Ktm3b8PT0ZPjw4bzwwgukpqZa9/Hx8QHA09MTf39/6+2wsDBmzpxJaGgoISEhrFixguDgYPbu3QuAt7c3SqUSNzc3/P39rUNICQkJTJ06lTlz5hASEsKwYcPYuHEj27dvp6zs5tWmhZajrBqyUpRK1Tk5zdyTI1c2vqdRgFM/5wGOk3QMoienQ6goN2KqNP+PaglyZJOMsdDcuyOCHEGrUnJ6eaTdrt1YMTExTJgwgcOHD3P8+HE+/fRTVq9ezZtvvklsbGy9x+n1epYuXcq+ffvIzMyksrKS0tJSLl261OD1UlJSSE1N5Z133rFuk2UZk8lEeno6ffr0aXTbheZlST5WV6hrBDnNl5NjLCoi49HHMJWU4Lfwedzvv7/Zzt1eOVrSMYggp0OwJB07qRWoqgqsmYoM5rVsFNzWisBC+yBJUqOGjByBs7Mz48aNY9y4cSxatIjp06ezZMmSBoOc+fPnk5iYyJo1a+jVqxdarZbJkydjMDT8oajX65k5cyazZ8+udV9gYODtPhThNjh5m3tynE0uYEk8bsYg5+q6dRjS0gC4PPtZCsb+Gv9Fi1D5NV+9n/ZEX17JmewiAMIDPO3bmBraxruacFtK65hZVWmZPu6uabGKsYLQGvr27WszZVylUmE02i79cPToUWJjY5k0aRJgDl4yMjJs9lGr1bWOi4iI4PTp09YFgQXHYZlh5erk0ew9OSXffkv+e+8D4DFxIgX79qHfn8SFr47j86d5eD36qHmhWcEq9ed8ZBm6emrxdXec2mvir9QBlNaRdCzycYS2Jjc3lzFjxrBjxw5SU1NJT09n586drF69mokTJ1r3CwoKIikpiaysLPLyzDkCISEh7Nq1i+TkZFJSUpgyZQomk+1KsEFBQRw6dIjLly9z7do1ABYsWMCxY8eIj48nOTmZc+fOsWfPnpsuIJycnExycjJ6vZ6cnBySk5M5ffq09X6DwWDdx2AwcPnyZZKTkzl//nxzPV3tnmW4ykXpjkplHo4vL6nEWHHjCr9NIxsMZC5eDIBHzO/osuqv9PjoI5zD+mEqLiZ7+QouTn2ccvG3snHKMnXcgYaqQAQ5HUJZQ0nHYvq40EbodDqGDBnC+vXrGTlyJKGhoSxatIi4uDg2bdpk3W/t2rUkJiYSEBBAeHg4AOvWrcPLy4thw4YRHR1NZGQkERERNudfvnw5GRkZBAcHWxOW+/Xrx8GDBzl79iwjRowgPDycxYsX06VLlwbbGh4eTnh4OCdPnuTdd98lPDyc8ePHW++/cuWKdZ/MzEzWrFlDeHg406dPb66nq91T6FTgZB5q9XbvhEJp7pG+3YKAuW+9heF8Gkpvb/yeew4A57vuJOjdd/H7y19QuLhQeuoUFyb9jpyNL2O6yZBnR3HqkuMlHQNIcgdLHS8sLMTDw4OCggLc3d3t3ZxWkbz/Ekc/PE/IID/u/8OvAMjfm4b+2BXcRnXD4zc97NxCobWVlZWRnp5Ojx49cHagxR6F+om/WW2XEo6gKJBJ5Rg50r3o88qZvGAgfj1u7b29/EI66RMnIldU0GXNGjx+O6HWPhWZmWQtX4H+yy8BUPfoQecVy3EZOPC2HktbJssyA1fuJ7fYwK4/DiOihQKdW/n8Fj05HUBdhQCtOTliuEoQhDaqUlMJgIfzHbi4m4fjb7UnR5ZlspYsQa6owHXECNwnjK9zP1XnznT7xyt03bABpU8nDOnpXHz8CTIXL8FYWHhrD6SN+/l6KbnFBtRKBb/q4lidByLI6QDqXpxTDFcJgtC2GZzMtYpcnTyrg5yCW6uVU/DRR5ScOIGk1eK/ZLFN1esbSZKEe1Qkwf/5D54PPQRA/r/+RdqECRR+9t8OV1vHUh+nbxd3NE6NLwnRGkSQ0wFUz64SiceCILQfJbIeAC2u1UHOLcywqrx2jezVfwPA55lnUHfr1qjjlB4edF6xnMDt21AHBWHMucblOXP45el4KjIzm9yOtsoR6+NYiCCnAyi7YQVyucKIqWqbWJxTEIS2Sl9p7kHQVDrjUtUrfStBTvZLCZgKC9H07YP3tCeafLzr4MH02LObTn+cBSoV+i++4MKE33J9xzvIN5QlaI+qZ1Y5VtIxiCCnQyi7ISensmqlXkmtQNKKUkmCILRNhWXmqf7KcqV1OL60iUGO/uBBCj/5BBQKOi9fgeR0a++JCo0Gn9mz6bnrI7T9+2MqKSF75UouxT5FxZUrt3TOtqCswsjpKwWAYxUBtBBBTgdwYzHAmvk4DY07C4IgOLL8oqsYZSOSLOHsVDWFvAlBjqm4mKxlywHwnjYNbeivbrtNmpAQur/7Dn6LFyG5uFBy4gQXJj5Iwb//c9vndkQ/XCmkwijTSaemm5fW3s2pRQQ57ZzJJFuHqyzFAEU+jiAI7UGpvoDiinwA1CZzsm9xE4KcnJc3UXHlCqouXfB5puECj00hKRR4T5lCz90fow0Lw1RUxJXnnuPyn+a3uxlYlvo4/QO8HPJLswhy2rnykgqoSvTXuJq7YY0FYmaVIAhtX0lhAfrKfADUVZWOGztcVfr9D1zfvh0A/yWLUbi6Nnv71IGBdH9nB52eiQelksJ9+7gw8UGK//d1s1/LXhy10rGFCHLaOUs+jsbFCaXS/Oe29OSIpGNBENqqivIyKsvL0VeYexJUpZVV240YyiobPFaurCRz8SIwmXAfPx7dqFEt1k7JyQmfp58m6J0dqAIDqczM5FJsLFfXrEFuB9WSkx14ZhWIIKfda3BxThHkCILQRpVWDfuUmMy/pfwynNTmj7TSmxQEvL79bcpP/4jC3R2/Fxa2bEOraPv3p+fHu/B8aDLIMrlvvkX6I4+26TWwsgvLuJxfikKCft087d2cOokgp50rK7Lk49QsBGguoCWCHKGtycnJYdasWQQGBqLRaPD39ycyMpKjR49a95EkyWZV8sYKCgpiw4YNt93GzMxMpkyZwp133olCoWDOnDm19nnjjTcYMWIEXl5eeHl5MXbsWL7+uv0MYbSGkkLzjJ4Ktfk9zphXXqMgYP1BjuGXX8h5+WUA/P7fczh16tTCLa2mcHWl84oVdNv0MkpPT8p//JH0mMnmqeZtsICgpT7OnX5u6DSOOVNXBDntXKne/D+7pRCgLMui2rHQZsXExHDq1Cm2bdvG2bNn2bt3L6NHjyY3N9feTbMqLy/Hx8eHv/zlL4SFhdW5z4EDB3jsscf48ssv+eqrrwgICOD+++/n8uXLrdzatqu0KsgxupiDg8rcMrQ3WdpBlmWyli1HLi3FZdAgPGJiWqexN3AbO5Yee/fgOmIEcnk52StX8vOMmVRcvWqX9twqS6VjR6yPYyGCnHbuxhXI5dJKZIM5QU/k5AhWsgyGYvv8NPIbbH5+PocPH2bVqlXcd999dO/encGDB7Nw4UIeeOABwNwbAzBp0iQkSbLeTktLY+LEifj5+aHT6Rg0aBD79++3nnv06NFcvHiRuXPnIkmSzSyRI0eOMGLECLRaLQEBAcyePZvi4uJ62xkUFMTf//53pk2bhoeHR537vPPOO/zxj3+kf//+9O7dmzfffBOTyURSUlKjnguhuidHclOCBLLBiIuL+X2uvp6cwn2fUHz4MJJKhf+yZXadDaTy9SXg9X/i95e/IGk0FB8+TPrEBymq8bp0dI5c6djCMfuXhGZjXZyzKsiprOrFUbiqkFSOtcaIYEcVJfBSF/tc+4UroL75zBadTodOp2P37t0MHToUjaZ2kH7ixAl8fX3ZsmULUVFRKJXm17her2f8+PG8+OKLaDQatm/fTnR0NGfOnCEwMJBdu3YRFhbGjBkziIuLs54vLS2NqKgoVq5cyebNm8nJySE+Pp74+Hi2bNnSbE9BSUkJFRUVeHt7N9s52ztLT46zuxtKJw3G/HKcVebv7XXVyjHm55P90ksA3DHr/9D07NF6ja2HJEl4Pz4V16FDuPzc/6P8xx/5Jf4ZPB+ajN/zz7fIjK/mUmk0kfpLPgARDhzkiJ6cdq7sxkKAIulYaKOcnJzYunUr27Ztw9PTk+HDh/PCCy+Qmppq3cfHxwcAT09P/P39rbfDwsKYOXMmoaGhhISEsGLFCoKDg9m7dy8A3t7eKJVK3Nzc8Pf3x9/fH4CEhASmTp3KnDlzCAkJYdiwYWzcuJHt27dTVlbWbI9twYIFdOnShbFjxzbbOdu7kiJzwrGLuwdO3s4AaKo+0eoKcrL/9jeM16+j7hVMp+nTW62djaHp1YseH7zPHdP/AJJE/s4PufC731Hy7bf2blq9fsoqoqzChJuzEz076ezdnHqJnpx2rvSGxGORjyPUSeVi7lGx17UbKSYmhgkTJnD48GGOHz/Op59+yurVq3nzzTeJjY2t9zi9Xs/SpUvZt28fmZmZVFZWUlpayqVLlxq8XkpKCqmpqbzzzjvWbbIsYzKZSE9Pp0+fPo1ue33++te/8v7773PgwAGcnZ1v+3wdhaUnR+vugRNayi8UWAsC3hjkFB//HwUf7QKg8/LlSGo1jkZSq/GdPx/XESO58vzzVFy8xMUpU9H07YPngw/i/tvf4uRAPX2W+jj9AzxRKByvCKCFCHIcxNWrV/n+++8ZOnQoLi6Nf9O/mbIbEo8tPTkiH0ewIUmNGjJyBM7OzowbN45x48axaNEipk+fzpIlSxoMcubPn09iYiJr1qyhV69eaLVaJk+ejOEmdUr0ej0zZ85k9uzZte4LDAy83YfCmjVr+Otf/8r+/fvp16/fbZ+vI7Hk5Li4e6B0qurJqTBW3Vf9dzWVl5O1ZAkAno8+gktERCu3tGlchwym557dZP91FQX//jflp38k+/SPZK/+G7qRI/F4cCJuo0fbPVCzVDp2xPWqahJBjoP47LPPuHDhApcvX2bq1KkoFM0zkli9pINtTo4YrhLai759+9pMGVepVBhvWPn56NGjxMbGMmnSJMAcvGRkZNjso1arax0XERHB6dOn6dWrV7O3e/Xq1bz44ov897//ZeDAgc1+/vauuifHHSetOchRlVqCnHLrftdeew3DxYs4+fjgO29e6zf0Fijd3eny0ov4Pjefwn2fULB7N2Xff4/+iy/Qf/EFSg8P3CdMwGPSgziHhtolgTrZgVcer0nk5DgAg8HAxYsXAXOi4+HDh5vt3A0tzikIbUlubi5jxoxhx44dpKamkp6ezs6dO1m9ejUTJ0607hcUFERSUhJZWVnk5Zm/bYaEhLBr1y6Sk5NJSUlhypQpmEwmm/MHBQVx6NAhLl++zLVr5tWtFyxYwLFjx4iPjyc5OZlz586xZ88e4uMbXucoOTmZ5ORk9Ho9OTk5JCcnc/r0aev9q1atYtGiRWze/P/ZO+/wKKq1gf9m+yab3kMahNCkBaSKggpSI2IXC1iwgQWuXsWG7YrXinotn4ViVxQRERBEQLq00AkQUkjvZTfJtjnfH5ssCUkgQCAB5vc88+zOzJkz75yd3X3nPW+ZTUxMDDk5OeTk5GA2m5truC54apIBGr190AS4CkNqza7ft4oyG0IIrIcOUfjZ5wCEPPccam/vlhH2NNH4+eF/x+20/Wk+7Rb/RsB996IJDsZZWkrxt9+SetPNHBmTQMFnn2HPzT1ncpVU2DiS74ow7NnKLTmKktMKSE1Nxel0otG4DGurVq3iyJEjZ9yv0y5jr3I92dTzyVEsOQrnGSaTiX79+vHuu+9yxRVX0LVrV55//nkmTZrE//73P3e7t99+mxUrVhAZGUl8fDwA77zzDn5+fgwcOJCEhASGDx9Or+OmLV5++WVSU1OJjY11Oyx3796dNWvWcPDgQS6//HLi4+N54YUXCA8/cSRafHw88fHxbNu2jW+//Zb4+HhGjRrl3v/xxx9js9m48cYbCQsLcy9vvfVWcw3XBU/t6SpNQLUlp/r3TnYIbJUOir78ChwOTFdeidc1w1pM1uZA3749wU88QftVfxH5+ed4jxmDpNdjS04m/+13OHzlVaTfex+lvy1Grqw8q7LUWHHaBnri51l32kwImaLijcjyiUtrnCuU6apWQHJyMuD6QRVCsGPHDn7++WcefPBBvLy8TrvfGiuOpJLQGTUIWeAsU5QchfMTvV7PzJkzmTlz5gnbJSQkkJCQUGdbTEwMf/31V51tkydPrrPev39/du7cWa+/Pn36sHz58lOS9WTZa4+fKlM4NRx2O7bKCsBlyVHpNag8tWCxo9OrsVmdVJTZqDpwAACfsWNbZYXs00FSqzENugzToMtwms2UL1tGycKFVG7dhmX9eizr16Py9MRr5AiCJk9GGxbW7DK48+M0YMUpLd3Ojh13YDRGM6D/n0hSy9pSFEtOK+Bwde2S9u3bM3LkSIKDg7FYLPz000/1fAROhSpLjdOxFkmSkMttIAMqUHu1vugCBQUFhaZQWV6dCFClwuDhcpivseYYja5nd0tJlbsulL5DhxaQ8uyjNpnwvfFGYr7+mtgVywmcPBltRASyxULpTz9z9IEHEXZ7s5/3RJXHc/MWA+DjE9/iCg4oSk6LU1JSQmFhIZIk0bZtW3Q6HTfffDM6nY60tDRWr1592n03lghQ7a1HasUhfwoKCgonoqK02unYyxupOkijxi+nJiFgeUoOorISSadDFxXZMoKeQ3SRkQQ9MoXY5X8Q9eU8V22sgwcp/GJ2s55HlgWJ6Q2XcxDCSV7eMgBCgsc063lPl1ah5Hz44YfExMRgMBjo16/fCQvVXWiF7WqsOBERERiNri9pYGCgO0392rVrOXTo0Gn1XXWckqMkAlRQULgQqKzlj1NDjSWnJiFgeWo2ALr2sUiai8czQ1Kp8Ozbl5DpTwNQ8NFHWFNSmq3/IwUWyqocGLQqOobWdacoLvkHmy0fjcYHf//Lmu2cZ0KLKzk//PAD06ZNY8aMGWzfvp0ePXowfPhw8hopVHahFbarPVVVm65du9KnTx8AFixYQGn1k8upUJMIsF5klaLkKCgonMe4lZxatcHU1ZYcfXVCQHO2y9pgiLswp6pOhve11+I5cCDCZiNnxovNVuW8Jj9O9za+aNV1VYjcXNdUVXDQcFSq1uES0eJKzjvvvMOkSZO4++676dKlC5988gkeHh7Mnt2wie1CKmzndDpJqdawY2Nj6+0fPnw4YWFhVFZWMn/+/FP2z6mXCLBaydEo4eMKCgrnMRU14eNetSw51aUddNUJAc2FrhBnfYe4cyxd60CSJEJfehHJYKDin38oXbCgWfptzB9Hlu3k5/8BQEhI65iqghZWcmw2G9u2batTr0WlUjF06FA2btzYpD5OVtjOarVSVlZWZ2ktZGRkYLVaMRqNDYakajQabrrpJvR6PRkZGXWqJjeF46erlESACgoKFwI1jsfGBqartDZX/qNKs0vZuVCdjpuCy0/nEQBy33gTR3X+pzOhscrjxcUbsNuL0WoD8PXtd8bnaS5aVMkpKCjA6XQSEhJSZ3tISAg5OTlN6uNkhe1mzpyJj4+Pe4mMbD0OaDWh4+3atWs0w7G/vz/XXXcdABs3buRAdUhkU6i0NFKcU7HkKCgonMdUNOCTo/LUIunVGKpjKqqcLj8cfdzFacmpwX/CXRi6dEEuLXVXYT9dLFYHSTkuQ8HxTse5ub8DEBw8EpWq9fhAtfh01ZlQU9jul19+abSw3fTp0yktLXUvR48ePcdSNk5j/jjH07lzZ/r37w/AwoUL3Vlca1OUlUl5YV0tvdHinIolR0FB4TymdnHOGiRJQuNvQF+dD8em9ULl7Y3muIfoiw1JoyH0lZdBraZsyVLKzyBid1dGKbKAcB8DId7H/nNl2Up+gSuXVGuaqoIWVnICAwNRq9XkHpeOOjc3l9DQ0BMeW1PYbvny5ScsbKfX6/H29q6ztAYsFgtZWa6qzw354xzP0KFDiYiIoKqqivnz5+NwHMsmWZyTxVf/foQfXnyqjnNZVa2SDsLuRK627CjFORUUFM5nanxyPI77PdcEGt3RVXadF7q4uAsmCeCZYLzkEvwnTAAg56WXcZotp9XPjqOuB+yex01VFRauxeEoR68Pxden9xnJ2ty0qJKj0+no3bt3HafhGifiAQMGNHrcG2+8wSuvvMKyZcvO28J2NWUbgoODm6R4aTQabrzxRoxGI1lZWXUysG77/VccdhulebmUF+a7t9c4HhtNOhylrveSToVkbD2mRAUFBYVTpSFLDlBtyXG9F5IaKbbLuRat1RL0yBS0ERE4srPJf++90+rjWKbj46aqqhMABgePahUJAGvT4tJMmzaNzz77jHnz5rF//34eeughLBYLd999NwB33XUX06dPd7e/UArb1fjjnEp1Y19fX3cV5X/++Yc9e/ZQUVbK3tXHHJLz01IBV1r52j45zpIqwDVVpTzZKJyv5Ofn89BDDxEVFYVeryc0NJThw4ezfv16dxtJkupUJW8qMTExzJo164xlzM7OZvz48XTo0AGVSsXjjz9er82CBQu49NJL8fX1xdPTk549e/LVV1+d8bkvFhrKkwOuhIAqSULrdNVukiObv3r8+YrKaCT0pRcBKP76ayobKGFyIoQQtSqP+7q3O52VFBS4DBUhwaObQ9RmpcWVnFtuuYW33nqLF154gZ49e5KYmMiyZcvczsjp6elkZ2e7218Ihe2EEG5/nKZMVdWmQ4cODBo0CIBFixaxceFPOGxW9/6C9FQA7FYnssM1deVSclyWHMXpWOF85oYbbmDHjh3MmzePgwcPsmjRIoYMGUJhYWFLi+bGarUSFBTEc889R48ePRps4+/vz7PPPsvGjRvZtWsXd999N3fffTd//PHHOZb2/MPpcFBlcT3UHm/JUVdHWOmsruksZ1DUuRWulWO67DJ8xl4LQpD9/AunVPIhs6SS/HIrGpVE1zbHxr2gcBVOZwUGQwTe3g3f7y1Jq5i3mDJlClOmTGlw3/FlDS6Ewna5ubmYzWY0Gg1RUaf+Jbzyyis5evQoaSkpJC53ebSHtGtP7pHD5FcrOTVOxxqdCq1OTWW1JUfj27CDtsLFjRCCSsfZrVzcGEaNsUnWxZKSEtauXcvq1asZPHgwANHR0fTt29fdJiYmBsBt8YyOjiY1NZXk5GSmTZvGpk2bsFgsdO7cmZkzZ7qjMocMGUJaWhpTp05l6tSpwLEim+vWrWP69Ols3bqVwMBAxo0bx8yZM/H09GxQzpiYGN6rng5oLN/XkCFD6qw/9thjzJs3j3Xr1jF8+PCTjsXFTJW53PVGkjCYTHX2aQIMCHsluqoiLB4hOHyCW0DC1k3w009j/nutu+RD4IMPNOm4mqmqLuHeGLRq9/aaqKqQ4NGtcpagVSg5Fxs1U1UxMTFotdpTPl6tVnPDDTfwf6/OALsNjYeJATfexsI3XnFbcmo7HQM4S2ssOa0jC6VC66LSUUm/b1smt8Xm8Zvx0HqctJ3JZMJkMrFw4UL69++PXl/fKrllyxaCg4OZM2cOI0aMQK12/RibzWZGjRrFf/7zH/R6PV9++SUJCQkkJSURFRXFggUL6NGjB/fffz+TJk1y95ecnMyIESN49dVXmT17Nvn5+e6Hsjlz5jTL9Qsh+Ouvv0hKSuK///1vs/R5IVMTPm40eaFSqevsU3vrkS1Z6OwuRajKrvzFHY/Gz4+Q6U+T9e+nKPjoI7yGX4O+bduTHtdQ5XGHw0xh4Sqg9UVV1dDi01UXI00NHT8RXiYT3hbXl93s5UdBpWvKqigrA4fdTmUtp2MAh9snR7HkKJyfaDQa5s6dy7x58/D19eWyyy7jmWeeYdeuXe42QUFBgMt/LTQ01L3eo0cPHnjgAbp27UpcXByvvPIKsbGxLFq0CHBNH6nVary8vAgNDXVHd86cOZPbb7+dxx9/nLi4OAYOHMj777/Pl19+SVVV1RldT2lpKSaTCZ1Ox+jRo/nggw8YNmzYGfV5MdCY0zHgKjzsyENnc01XVZRZ67VRAO+EBDwHDTqlkg81kVW18+MUFKxElq14eLTFZOp81uQ9ExQ19xxjs9lIT08HTt0fpzbJ27dgLshDrdNj9wnkz9V/4+3hia3CQlHmUaosrjou9XPkKJYchfoYNUY2j9/cYuduKjfccAOjR49m7dq1bNq0iaVLl/LGG2/w+eefM3HixEaPM5vNvPjii/z+++9kZ2fjcDiorKx0fxcbY+fOnezatYtvvvnGvU0IgSzLpKSk0Lnz6f+we3l5kZiYiNlsZuXKlUybNo127drVm8pSqEtDiQBrI1fkoLfZqtvazplc5xOSJBH64gyOJFzrKvnw88/43nhjo+2tDid7M2uSAPq6t9fUqgoJHtMqp6pAUXLOOampqTidTnx8fAgMDDztfrb+9jMAvUaMIdWp5siRIzh0BqiwkJ+Wgs3myvJpMGkRQtSqQK5YchTqI0lSk6aMWgMGg4Fhw4YxbNgwnn/+ee677z5mzJhxQiXniSeeYMWKFbz11lu0b98eo9HIjTfeiM124j9Bs9nMAw88wKOPPlpv3+n409VGpVK5rbk9e/Zk//79zJw5U1FyTsIxS07DqTfk0gx0NtfvnKLkNI4uIoKgRx4h7403yH3jTUyDB6Optnwez76sMmxOGX9PHVH+rt8Ju72UwqK1AASHtL6oqhqU6apzTI0/Tmxs7GlrvlkHD5B5YB8qtYZeI691O1lWSa756fz01LqJACsdiOp6LhrFJ0fhAqNLly5YLMeSm2m12nrFbNevX8/EiRMZN24c3bp1IzQ0tF4Qg06nq3dcr1692LdvH+3bt6+36HTN+12SZRmrVZleORnHEgHWt+QIIXDkpqGzlVe3VZScE+F/150YLrkEuayMnBOUfKjtj1Pzv5Wfvxwh7Jg8O2LybL2lMxQl5xzTHP442xb/AkDnQUMw+Qfg5eVFYGAgst5l9i9IT62VCFDrLsyp8tQiadUNd6qg0MopLCzkqquu4uuvv2bXrl2kpKQwf/583njjDcaOHetuFxMTw8qVK8nJyXGXQImLi2PBggUkJiayc+dOxo8fjyzLdfqPiYnh77//JjMzk4LqQoZPPfUUGzZsYMqUKSQmJnLo0CF+/fXXRqNBa0hMTHRPReXn55OYmMi+ffvc+2fOnMmKFSs4cuQI+/fv5+233+arr77ijjvuaK7humA5kU+Os6AA2VymKDlNRNJoCKsu+VC+dBnlq1Y12K6hyuO5edW1qlqxFQeU6apzSnFxMYWFhUiSRLt27U6rj5KcbA7946rQfumY69zbw8LCKDqaCriUHA//GkuOTqlZpXBBYDKZ6NevH++++y7JycnY7XYiIyOZNGkSzzzzjLvd22+/7U4y2qZNG1JTU3nnnXe45557GDhwIIGBgTz11FOUVVsEanj55Zd54IEHiI2NxWq1IoSge/furFmzhmeffZbLL78cIQSxsbHccsstJ5Q1Pj7e/X7btm18++237nB2cJV1efjhh8nIyMBoNNKpUye+/vrrk/arUEvJ8aqv5FQdPAiAXuuyslWZ7TidMmq18jzfGIYuXfCfMIGi2bPJeellPPr0RW2qmx5hR3pdp2ObrZDi4g1A60wAWBtFyTmH1ExVRURENFpQ9GRsW/IrQsjE9OxNYFSMe3tYWBi7qy05lpJiLCUlgMuSo1QfV7gQ0Ov1zJw5k5kzZ56wXUJCAgkJCXW2xcTE8Ndff9XZNnny5Drr/fv3Z2cDWWD79OlTp4xKUzhZtMqrr77Kq6++ekp9KrioKK9xPK7vk2M9dAgAg4cfEiCAqnI7nsoD3gkJemQK5cuXY8/IIH/WLEKfe9a9L6+8ioziSiQJuke4FMu8/D8QwomXV1c8PGJaSOqmoai355AznaqqLC9jz+oVAPRJuL7OvrCwMFCpweBSdMoLM4GabMcuJUcpzKmgoHC+U1ltgWtousp60KXkaIKj0FW7PCpTVienTsmHb76hMjHRvS+x2h8nLtiEl8EVreuOqmqluXFqoyg55win00lKSgpw+qHjO5cvwWG1EhwTS+QldSuvh4WFAWDXuhSZyjJXKQxDLZ8cZbpKQUHhfOdEIeTW6ukqXVQ7DNX/bpZSxZm7KTRW8sHtj1NdlNNqzaWk5B+g9U9VgaLknDMyMjKwWq0YjUbCw8NP+XiHzcaOP1za86UJ4+pFZhkMBvz9/d3Ox/bKXMCVDPBY+Lii5CgoKJy/CFmmqtzlVHy8JUfIMtZqa7m+fRz66t9IxZLTdIKffhq1nx/WQ4co/MJVkuSYP44vAHl5SwGBj08vDIZT/y871yhKzjmixh+nXbt2qFSnPuz71q6iorQEr4AgOvQf1GCbsLAwZL0rh4HsdEWHGDw1xxyPFZ8cBQWF85hKczlCuKLijF51fXLsR48iqqqQ9Hr0cW3dlpzKckXJaSo1JR8ACj76iKrMLHZluCxnNU7HxxIAtn4rDihKzjnjTPxxhCy7w8Z7jboWtaZhf/GwsDCc1T45wlmAzqhCkiScZYpPjoKCwvlPjT+O3tOz3u9gjdOxPjYWbZDnMUtOqaLknAreCQkYe/VC2GykfP8zFTYnJr2G9sEmKiszKS3bAUgEB49qaVGbhKLknAMsFgtZWVnA6fnjHNmxlaKsDPQennS/uvEKxWFhYQit3uWAjBOt3oyz3AYyoJJQeSmJABUUFM5fKk/gj+MOH4+LQx1gRF/976ZMV50akiThM+46ACqXLgWgR6QPapVEXnVuHF/fvuj150eFd0XJOQccOXIEgODgYLwbSUV+Irb+tgCA7kNHoDM2nno/LCwMJAlZawJAJRUem6ry1rmK1ykoKCicp9SEjzeUI8dtyenQAY2/AUO1JcdSojgenyre11wDWi3GjBSiy7LdTsc1CQDPh6iqGhQl5xxwJlNV2YeTyNi/B5VaTfzIhBO29fDwwNfXF7QuRUp2FCiJABUUFC4YTpTtuCZ8XN8hDpVejcHDNZ1VoSg5p4zaxwfTFVcAMCQjkfgoXyoqUikv34MkqQkOGtHCEjYdRclpJmwOmT2ZpZRV2etsF0LUqVd1qmxdvBCATpcNxsv/5AU9w8LCEBqX1u2w5imRVQoKChcMx8LH61rEZZsNW3U2aX2HDgB4Brj8ExXH49NDO8zlGjE4Ywc9InzcVhw/v4HodP4tKdopoWQ8biaOFJgZ88E6APw8tET5exAV4EmE3kql2YxKrUHvG4IsC1RNnDYqzcvh0Kb1AFx6XPK/GsoLC9i5Yil71/xJSLv2hPUbTLrmKABV5hwlEaCCgsIFQ2OJAG1HjoDTicrbG02wy1fEFGyEpBJsNhmHzYlGp9TtOxUOxvbEpNYRVlGEZ/IBDltroqrOn6kqUCw5zUZphZ1Ak8uxt7jCzs6MUn7bmcXf2/YAkG7zZNCba+j8wjKGvbOGe+du4aXf9jJ3fQqrDuSRnG/G6qhbAdldwqFHL4JqlXAQQpCxfw+/vfs6n025h82//IC5qJDkrZsx6bSo1C6Lj9VSSGWB68lHCR9XuBDIz8/noYceIioqCr1eT2hoKMOHD2f9+vXuNpIksXDhwlPuOyYmhlmzZp2xjNnZ2YwfP54OHTqgUql4/PHHT9j++++/R5IkrrvuujM+94VOY4kAj/njxLlziBmDje4/OMX5+NTZkVvFxrCuABT88jUWy0EkSUtQ0LAWluzUUCw5zUS/dgFsfW4YZquD9MIK0osqSC+ykPFPKpihwhCI2ilhdcgcyjNzKM9crw8PnZqv7u1H72g/Ks3l7P7LVS/n0jEuK47dZuXA+jXsWLaY/NQj7uMiunTFajaTn56KJSMNCRNC8gRhoTD7KL74K9NVChcEN9xwAzabjXnz5tGuXTtyc3NZuXIlhYWFLS2aG6vVSlBQEM899xzvvvvuCdumpqbyxBNPcPnll58j6c5vGvPJsdaKrKpBG+SBXoJKARXlNrwDjedO0AuAHUeLsUTEc1XGdszL/4JBEBByBVptfX+o1oyi5DQzJr2GLuHedAn3xmaz8d81RTiB9+4fga9/AFkllaQVVpBWVEF6oYW0GoWocjuy6R9mrc/ly6i72LViKQ6rlaDotviGhbH227ns+ms5VeUuc61Gp6fzoMHEj0ggKLoticuXsPKLjzi6cytaaTAOdRCyw0JRQQa+en/FkqNwQoQQiMrKFjm3ZDTWy+DdECUlJaxdu5bVq1czePBgAKKjo+nbt6+7TUxMDADjxo1z709NTSU5OZlp06axadMmLBYLnTt3ZubMmQwdOhSAIUOGkJaWxtSpU5k6dSpwrMjmunXrmD59Olu3biUwMJBx48Yxc+ZMPD3rVmquLcN7770HwOzZsxu9HqfTye23385LL73E2rVrKakuqqvQOO4Q8uMSAdaEjxuq/XEA1P4G9CqJSqdQcuWcIkIIdqSXYA7ugPD2QSouRX9QIqTb+ZEAsDaKknMWSU1Nxel04uPjQ2BgIJIkER3gSXRA3R/HzdmbeXDFlziEnUT7bu7+/S96/+7ypVGpNXzxyCR3lk/voGB6XjOarlddg9Hk5e4jtndfVn7xEdmHkjCGXINTHQiOVIrLc0Gv+OQonBhRWUlSr94tcu6O27cheTSeGqEGk8mEyWRi4cKF9O/fH72+/j29ZcsWgoODmTNnDiNGjECtdvlhmM1mRo0axX/+8x/0ej1ffvklCQkJJCUlERUVxYIFC+jRowf3338/kyZNcveXnJzMiBEjePXVV5k9ezb5+flMmTKFKVOmMGfOnDO67pdffpng4GDuvfde1q5de0Z9XSxUlDfsk1M7fLwGTYARQ02RzuKqcyPgBUJKgYXSSjs6nRaPq/tS+csKjFu1BD5wdUuLdsooSk4zIYQgN/c3NBoTgYFXAcdKObRv377RJ9V9hft49K9HcQg7KnsbIA/DX2nYyl25bnKPuL68kZd0J35kArG9+6JS1Xeg8woIJLhtLHkpyWBXoVIH4gRKbXlIOhWSUfmoFc5vNBoNc+fOZdKkSXzyySf06tWLwYMHc+utt9K9u6tgbVBQEAC+vr6Ehoa6j+3Rowc9evRwr7/yyiv88ssvLFq0iClTpuDv749arcbLy6vOcTNnzuT22293+9XExcXx/vvvM3jwYD7++GMMBsNpXcu6dev44osvSKxV7VnhxAghGnQ8dpaX48hyFSTW10rTofLQYNSpwOGk+Gj5uRX2PGdHdeXxbm18sIVp4BcwJqpRObXnndZwnonbesnOns/+A9PR60Pp79sPjcbTnR+nsdDx9LJ0HvrzISocFQzwuZS2Ozsi79+MQbjC0J2SzOHICiIu78cVVz9KqGdog/3U0P7S/uSlpCJkFZLa9WNfYs9H5aNr0nSAwsWLZDTScfu2Fjt3U7nhhhsYPXo0a9euZdOmTSxdupQ33niDzz//nIkTJzZ6nNls5sUXX+T3338nOzsbh8NBZWUl6enpJzzfzp072bVrF9988417mxACWZZJSUmhc+fOTZa9hvLycu68804+++wzAgNPnhZCwYW1woLsdAB1HY+th1y/s5rQUNQ+x7ZLkkSQv4GUCguZh0rPrbDnOTuOVhfljPQh33crPr4CdYkT85o1rkSB5xGKktNMhIRcS0rqR1RVHSU19UMCAiZRWFiIJEm0a9euXvv8inzuX3E/RVVFdDd0pO8qNaU569z7VRoNGTeHsbFkHRQvYdEvKxnfaTz3drsXH33Djl+xl/Zjw0+/ulbUvggk7LKVKr1iqlU4MZIkNWnKqDVgMBgYNmwYw4YN4/nnn+e+++5jxowZJ1RynnjiCVasWMFbb71F+/btMRqN3HjjjdhsJ/bVMJvNPPDAAzz66KP19kVFRZ2W/MnJyaSmppKQcCy5pyy7pqM1Gg1JSUmnlVPrQqfGH0drMKLRHStR05DTcQ3hEZ6QYaEwv5JKsw2jSSltczLsTpn1h12O/L3CsqkqzUTfV4/nckHZ70vOOyVHCSFvJtRqAx06PA9A+tHZHDzommOPiIioZ9Ius5Xx4J8PkmnOpL06ksHrvCnNycE7MJgyg+vJzqf3Vbw79mO+GvkVvYJ7YXVambN3DiMXjOSL3V9Q6ajvJBoU3RaTf7hrRSOQ9a7zlovWE3mioNDcdOnSBYvF4l7XarU4nXXTMaxfv56JEycybtw4unXrRmhoKKnVyeNq0Ol09Y7r1asX+/bto3379vUWne70/jA7derE7t27SUxMdC/XXnstV155JYmJiURGRp5Wvxc6FdVTVccnAqwdPn48XjE+eFX/y2UmlZxV+S4U/m9NMikFFnyMWiKNGwAwDBsAgHnVKpzm+pHBrRlFyWlGggKvJiDgSoSwU1T8ISDqlXKoclTx6F+PcrD4IBEiiOGbginPy8MnOISr7n4A76oCnKjYZroEgJ7BPZk7Yi4fXv0hcX5xlNvKmbV9FmMWjOGngz/hkB3uviVJIiyup+u9yoGsd00DlFjzzsn1KyicTQoLC7nqqqv4+uuv2bVrFykpKcyfP5833niDsWPHutvFxMSwcuVKcnJyKC52md3j4uJYsGABiYmJ7Ny5k/Hjx7utJ7WP+/vvv8nMzKSgoACAp556ig0bNjBlyhQSExM5dOgQv/76K1OmTDmhrDXKi9lsJj8/n8TERPbt2we4LFFdu3ats/j6+uLl5UXXrl1PW3m60DmV8PEaDHF+BGlcf3NH9ykPeyfjYG457690Tf+9mNCJ0uI/AAjudyu6du0QNhvlf/7ZkiKeMoqS08x0iHseSdKh0x0mMDC9jtnZITv499//ZlvuNoLt3iT8E4ElvwCfkFBunjGT/evXAHDI1J4V6TbKq0tESJLEFRFXMH/MfF4b9BrhnuHkVebx0saXGPfrOFakrXCHuwZGdQRAdlrcSk5xec65HAIFhbOCyWSiX79+vPvuu1xxxRV07dqV559/nkmTJvG///3P3e7tt99mxYoVREZGEh8fD8A777yDn58fAwcOJCEhgeHDh9OrV686/b/88sukpqYSGxvrdmDu3r07a9as4eDBg1x++eXEx8fzwgsvEB4efkJZ4+PjiY+PZ9u2bXz77bfEx8czatSoZh6Ri4uGEgEKIdyWnNrh4zVowzwJNrm8MjL2Fp0DKc9fHE6ZJ+fvxOaUuapTMEPa5mC15qDReBEYeAXeY1zh42WLf29hSU8NxSenmfHwiMbX91aKi7+kXew2QkJ8AdeX8ZVNr7Dq6Cr8qoyM2xFDZXERvqFh3PzCTGSnk4ObXD45eTH9sVlkVuzL5fpeEe6+1So1CbEJDI8Zzo9JP/Lprk9JLUvlX6um8sg/AfTtPhLPuNuAZGSHGae3y8eiqCjrXA+DgkKzo9frmTlzJjNnzjxhu4SEhDr+LuCy0vz11191tk2ePLnOev/+/dm5c2e9/vr06cPy5ctPSdaah46mMnfu3FNqfzHituTUqkDuyM/HWVICKhW6BnwfJZVEm87+SOtzKCuxUl5UhZf/6UXEXeh8sS6FnRmleBk0vDauG7k5ru9ZUOA1qFR6fEaPpuD9D7Bs3IijsBBNQEALS9w0FEvOWaC4uB9VVZ7o9RbS0j8B4IMdH7Dg0AK8K7TcuD0Wa3EpfmFtuHnGTKwWM0v+9zZClonuHs+Q/j0BWLwru8H+dWodd3S5gyXXL+HBHg9yxUEtg/7KQzNrHuv3uKxBOMqPWXIKsnDY7Q32paCgoHA+UFleM111zCenxoqji45G1Ug4v1fnAHzVrujSjAOKNachkvPNvL3CNe33/OguBHtpyMtbCkBIiKtWlS46GkO3buB0UrZsWYvJeqooSs5ZIPlwOsmH+wCQnv453+2axWe7P8PLouGm7e2xl5TjHx7Btf96hn8WzufLfz9KVtI+NFodA28aT0KPMADWHsqnpKLx6A+TzsTD3R7k0URXaLkKMO9zlXtQy1b0ag+ESo2QZYoyj57di1ZQUFA4ixxzPK4VPn6wfhLA4zHE+RKkcSk5R/cofjnH45QF//5pFzaHzBUdgrjp0ghKSjZjtxei1frh5zfA3dZ7tGvK9XyaslKUnGbGYrGQlZVFUVEEPj6DXE7IRz/E26zmxm2xOMsq8G8TSefLr+SHGU+R+MfvCCET128gE9/5iPAOnWkf7EWnUC/sTsEfe0/sT1P+xx84j6S610NLXUkEtSoIFN44DS5rTkF6agNHKygoKJwfNOR4fCKn4xrUPnpCgl2/gxkHik95KvFCZ+6GVLalFWPSa5h5fTckSSI311VxPDhoBCqV1t3We+QokCQqd+zAlpHZUiKfEoqS00w4iovJefllUt5+B4Dg4BAq/a/FLqCTUWZinj/CXIVXYBBClln/w1dUWcwERcVw8wuvce20Z/AJPpbsb0x3lzWnsSkrAOF0kv/RRwD433sPktGIRrhKRjiNTgKEN7Le5ZeTryg5CgoK5zENOR6fKHy8Nm0uCUAFVFY4KM6uOGsynm+kFlh4848DAEwf1Yk2vkYcjnJy85YAEBxSt1aVNiQYj+pacWVLlpxbYU8TRclpJio2bqT42++Q5s/HWFGBT7gP/9rwX9YXuOrrBLXPQ+dhoLwgn+LsTIxe3gy9bzJ3/Pc9Ii/pXq+/Md1d0RsbkgspNFsbPGf5H39gO5yMytubwAcfxPuaYdi1LiUnICiEQNnL7ZdzNHn/2bhsBQUFhXPCsZIOLp8c4XRirc4qfyJLDoBHZ38CaqasFL8cAGRZ8NTPu6iyywyMDWB8X1dyy6ysH3E6zXh6xuHn27/ece4oq9/PjykrRclpJrxGjsTYqxcqu53uO3fxQ+EP6IsdeC9rx9G/QzmyJBpbRRUqtZreo8dyz3uf0mPYyAbrUAHEBHrSrY0PTlmwdE/9KSvhdJL/YbUVZ+IE1F5e+Fx3HTata7qqZ3g3AoWXe7oqNXkvS46cH5q3goKCQm1cdavqWnLsGRmIqiokvR7dSbJP69v6EKirzpezu+DsCnue8M3mNDanFGHUqnn9+u5IkoQsOzh6dC4AkZF3N1gOyPuaa0CrxZqU5LaktWZaXMn58MMPiYmJwWAw0K9fP/75559G2+7du5cbbriBmJgYJEli1qxZ507QkyBJEpoH7kcAMWlp+B8+ypiN4agqoHC/H0KW8I60cPMrTzPkrkkYPE0n7bNmyuq3nfVDwMuWLcOW7LLi+N95JwDGvn2x61yVydUp+zAJA5LWpeQYrSpe+HM6MzbMaDBbsoKCgkJrxW6twmF3BWHU+ORU1fjjxMYiqRt+WKxBpVPTJtL1m5t1uBTZKZ+w/YXO0aIKZi51TVM9NaIjUQHVbg35y6iyZqHV+hMacl2Dx6p9fDBdfjkApeeBNadFlZwffviBadOmMWPGDLZv306PHj0YPnw4eXkNZ+itqKigXbt2vP7663UqBbcWDmAnpW1bijwN9N8XhKY6GbFPSBg9bvah3ah08sr/r8mOb6OrlZx/UovILTtWf0o4nRR89DFwzIoD4LAJhOT6sss7/kRCwmAXyFrXlJl/uY4FhxZw2+LbOFTc+jVwBQUFBTjmdKzR6tBWl6s55o/TeGRVbUJ6BKGVwG6XyU8/v0oTNCdCCKYv2E2FzUnfGH/uGhDj3p6e/gUAERF3olbrG+3Dp1ZiwNbuyN2iSs4777zDpEmTuPvuu+nSpQuffPIJHh4ezJ49u8H2ffr04c033+TWW29Fr2/8A6iN1WqlrKysznI2sNgsvLdiN9tDA9nUvg0Wgw4VcNktd3L3Ox8xcOR/kCQdRUVryc9vWmKxCD8PekX5IgQs2X3MAbkhKw5ApdmVC0fttCKnbUeuKkFtrXRPWT0YfieBxkCSS5O57ffb+OngT63+BlVQUFCoqBVZVTOF4g4fP4k/Tg0eHWv55ey/eEPJv99ylHWHC9BrVPz3xu6oVK4xKS3dRln5LlQqPRFtbj9hH6Yrr0Ty8MCekUFVAwk0WxMtpuTYbDa2bdvG0KFDjwmjUjF06FA2btzYbOeZOXMmPj4+7uVsFb977dNPGXR4NR4VRSAgsqCUodll9B0+BrVGi4dHDNHRkwA4dOhVnM6mTRnVOCDXRFk1ZsUBqKpWcnSSDYTAcfQf9DjdzseepYKfEn7isvDLsDqtvLTxJf79978pt5U3zyAoKCgonAUqShtPBNhUS442zJNgD1eS/6M7L06/nKySSv7zuysI5cnhHWkb6Onel57+OQChoePQ6U6czVhlNOJ19dUAlP7eun09W0zJKSgowOl0EhISUmd7SEgIOTnNV2tp+vTplJaWupejR89OUryrol0pxTMM4cwPu55YqwZNXj4Fn37mbhMT/RAGQxuqrFmkpn7UpH5Hdw9DkmBbWjGZJZWNWnEAKs2uOWuDl8vKZT+6gci4mGNh5GkpBBgD+GjoR0zrPQ2NpGFZ6jJu/u1m9hbsPeMxUFA42+Tn5/PQQw8RFRWFXq8nNDSU4cOHs379encbSZJYuHDhKfcdExPTLH5+2dnZjB8/ng4dOqBSqXj88cfrtZk7dy6SJNVZDI1k7FWgntOxbLNhq64if7Lw8RoklUSbOF8Aco+acdidJz7gAkMIwTO/7MZsddArype7L2vr3ldRkUp+gavwZlTkPU3qzz1ltXQpwuE4SeuWo8Udj882er0eb2/vOsvZICY6CuHjz9Hw3uQYQ3gjdiQARXPmYMvIAECtNtIh7jkA0tI/p6Ii5aT9hngb6BPjD8DviUcbteLAMUuOR4AvqLTIZVl0DgnDqXVVNS44moYsO1FJKu7uejdzR86ljakNGeYM7lh6Bxuzms+CpqBwNrjhhhvYsWMH8+bN4+DBgyxatIghQ4ZQWNh6ph+sVitBQUE899xz9OjRo9F23t7eZGdnu5e0tLRzKOX5xfE5cmxHjoDTicrHB01wcJP7Ce4RiF5yZfnNSS49K7K2Vn7ensnqpHx0GhVv3NgDtepY5JQrokoQEHAlnp6xjfZRG8+BA1H7+uIsKKDiBAFDLU2LKTmBgYGo1Wpyc3PrbM/NzW2VTsUnI7Z7PHc/9SzfvnAnz4/pwj9hXdgeFIew2ch+/Q13u8DAYQT4X4EQNpIOvtQkn5iEHq4pq6MLFtez4gghKC7ezO49j5CZvgoAg0GHJqwnAJ57D6AWMkJS4bTbKallJesR1IMfE37kiogrcMgO5u6d20yjoXC+IYTAbnW2yNJUv7CSkhLWrl3Lf//7X6688kqio6Pp27cv06dP59prrwVc1hiAcePGIUmSez05OZmxY8cSEhKCyWSiT58+/Pnnn+6+hwwZQlpaGlOnTnVbVmpYt24dl19+OUajkcjISB599FEsFkujcsbExPDee+9x11134ePj02g7SZIIDQ11L8dbtRWOUVlekyPHNZ41mY4NcXENhjk3hqGD/0VZ4iG3rIqXf3NZ66cO7UD74GPRvXZ7CVnZPwFNt+IASFotXiOGA1Daiss8tFgVcp1OR+/evVm5ciXXXXcdALIss3LlSqZMmdJSYp0RIe3aA3DvoLaE+xh4x3IdPf58m4o/V5C1eh3hQwYhSRIdOrzAps2jXE7IBcsJDhp+wn5Hdg3lpYW7GLTxV8BlxVGZTBQU/EVq6keUlu0AIC9bD4xCJ4E2aiCOzC2YlywlYPRwyvUG1FUVFKSn4B/ext23t86bp/s8zd8Zf7MpexP5FfkEeQSdnQFSaLU4bDKfPramRc59/3uD0epPHAIMYDKZMJlMLFy4kP79+zcYfLBlyxaCg4OZM2cOI0aMQF0dWmw2mxk1ahT/+c9/0Ov1fPnllyQkJJCUlERUVBQLFiygR48e3H///UyaNMndX3JyMiNGjODVV19l9uzZ5OfnM2XKFKZMmcKcOXPO6LrNZjPR0dHIskyvXr147bXXuOSSS86ozwuV40s6uMs5NHGqqgaNj54Qfz0ZuVUc3V3IgBtP7fjzESEEz/6yh7IqB90jfJh0eds6+zMzv0OWKzGZutSpU9UUfEaPpuT7HyhfsQJ5xguomhgQdC5p0emqadOm8dlnnzFv3jz279/PQw89hMVi4e677wbgrrvuYvr06e72NpuNxMREEhMTsdlsZGZmkpiYyOHqrJetiZHdwnj9iev4s/1lAOyZ/iLJOdUmV4+2REfdB8Chgyd3Qg406bnHcYSo8jzsRk8c1wTwzz+j2blrEqVlO1CpdBgMbXBaq+tWyQJ1cGdUPgE4S0vpIgucJyjvEOkdSc+gnshCZklK63YiU7h40Wg0zJ07l3nz5uHr68tll13GM888w65du9xtgoJcCrqvry+hoaHu9R49evDAAw/QtWtX4uLieOWVV4iNjWXRokUA+Pv7o1ar8fLycltWwBW4cPvtt/P4448TFxfHwIEDef/99/nyyy+pqqridOnYsSOzZ8/m119/5euvv0aWZQYOHEhG9dS2Ql2On66qOkWn49pEXuJyqi3IrcBa2Xp9SZqLRTuz+HN/Llq1xJs39kCjPva3L8s2jmZ8CbisOKdiFQMw9u6NJjQUubwc899/N6vczUWLWXIAbrnlFvLz83nhhRfIycmhZ8+eLFu2zG22TU9PR6U69oFkZWURHx/vXn/rrbd46623GDx4MKtXrz7X4p+U3tH++L77AsXXjyWyOJOPnnyH8a8+Ru9of2JiHiYnZ6HLCTntY2LbTWu0H+F0MnK7yxxYPMRJfvozAKjVJiLajCcy8h7M5v0c/nMLABrZhiSp8LxsGOVLvics9SiytxFKIevggQbPMabdGBLzE/n9yO9MuGRCM4+EQmtHo1Nx/3uDW+zcTeWGG25g9OjRrF27lk2bNrF06VLeeOMNPv/8cyZOnNjocWazmRdffJHff/+d7OxsHA4HlZWVpKenn/B8O3fuZNeuXXzzzTfubUIIZFkmJSWFzp07N1n22gwYMIABA449NQ8cOJDOnTvzf//3f7zyyiun1eeFTH1LzqmFj9fGv3sQnqszsciQmVREu55N9+k538gvtzJjkWua6pGr4ugYWtePMzd3MTZbHnpdCCHH1alqCpJKhfeoURTNnk3Z4t/xHjasWeRuTlrc8XjKlCmkpaVhtVrZvHkz/fr1c+9bvXo1c+fOda/HxMQghKi3tEYFp4bY9hGEPPYoADftXMykD1exbE82arWRuBon5LTPGnVCdjgspHz9NIaso8hGgXNoGSq1L+3aTuWygX/Tvv1T6PVB+PsPQjhcTyg2zR4AvEaMAcC5dStaXGb73CMNW72GxwxHo9Kwv2g/h4tbn2VM4ewiSRJavbpFllN9ejQYDAwbNoznn3+eDRs2MHHiRGbMmHHCY5544gl++eUXXnvtNdauXUtiYiLdunXDZrOd8Diz2cwDDzzgtiAnJiayc+dODh06RGxs0xw0m4JWqyU+Pr5VWqVbAxXV+c08vL1xlpfjyHal1DgdJUff1ocgbXWJh8QLO5R8xqI9lFTY6RLmzUND6t6vQgjSj9Yk/7sLlUp3WueoibIyr16N09z6kiy2uJJzMRAx4Xa0sbH42Cq4ce8fPPTNdmavSyEo6Br8/S9HCBsHD71SxwHTbi/hSMr7rF93OebZvwFQfKWeb9PGscs+h7Ztp6DVHnNqlCQVOFwm9kq/9QgExm6dMHTrBg4H7arzTFgtZmyV9avw+hp8ubyNK1X34iOLz9pYKCg0N126dKnjCKzVanE664YHr1+/nokTJzJu3Di6detGaGgoqdUhyDXodLp6x/Xq1Yt9+/bRvn37eotOd3p/Cg3hdDrZvXs3YWFhzdbnhURtS05NfhxNaCjq04iWVenVhIW78sNcyMU6l+zOZsnuHDQqiTdv6o5WXffvvrh4A2bzAVQqI23a3Hba59F37oyuXTuE1Up5LWf+1oKi5JwDJI2G0GdcvkVjUzbQpiyPlxfv45XF+4mLewFJ0lJYuIaCghVYrXkcOjyT9RuuICXlPbSby9HmSkheRvKv+5o/069k0c6iBiNSHFZX0j/Z5zBVPkfQ+OjwGXcdALFpGcgaLQBZh5IalHNMO5fl5/eU35HFxV3bRaH1UVhYyFVXXcXXX3/Nrl27SElJYf78+bzxxhuMHTvW3S4mJoaVK1eSk5NDcXExAHFxcSxYsMBtiRk/fjyyXPcej4mJ4e+//yYzM5OCAtcT/lNPPcWGDRuYMmUKiYmJHDp0iF9//fWkwRE1Vh+z2Ux+fj6JiYns27fPvf/ll19m+fLlHDlyhO3bt3PHHXeQlpbGfffd11zDdcFgt1mxW13+Tx7ePsemqk7R6bg2kT0CASgtsWEptZ65kK2MQrOV5xe6LPoPD4nlkvD6UX41Vpzw8BvrPDCfKpIk4T16FABlrTAxoKLknCNMl12G6aqrUMlOXs9dCcDs9Sk8+UsxoeGuaI59+59m/YbBpKd/jtNpweTRiYCVrvDxwHsmcXXfTujUKpLzLRzIqZulWJYF1grXU6hab6YkehWSUYPPqFFIWi2GnBxUKpcL1sGNaxuUcXDkYLy0XuRYctiWu+2sjIOCwuliMpno168f7777LldccQVdu3bl+eefZ9KkSfzvf/9zt3v77bdZsWIFkZGRbh++d955Bz8/PwYOHEhCQgLDhw+nV69edfp/+eWXSU1NJTY21u2w3L17d9asWcPBgwe5/PLLiY+P54UXXiA8PPyEssbHxxMfH8+2bdv49ttviY+PZ9SoUe79xcXFTJo0ic6dOzNq1CjKysrYsGEDXbp0aa7humCoseKo1Bp0Ro9jkVWnMVVVg0/XQHyqA/oy9l1Y1pyyKjsT52yh0GKjY4gXU66qP05myyEKC9cAEpERd5/xOX1Gu6asLBs24ChqXePZoo7HFxsh/34S89q1BOzZxuejr+fhw0aW7skht6wvkzovA8cRAHx8ehMT/RDaTRay0p9E5eOD3x13oDZoGdIxiOX7clm8K4vOYbVSnFvsUG3cUesslAdvxuEoQevrh+mqqyj/4w98K6oo0cDRfXsalE+v1nNNzDX8fOhnFh9ZTJ/QPmd9TBQUmoper2fmzJnMnDnzhO0SEhJISEiosy0mJoa//vqrzrbJkyfXWe/fvz87G6jD06dPH5Yvb1q9uRpOlvvn3Xff5d133z2lPi9WKmv540iS5J6uMpxGZFUN2nATwQYNpRYH6Tvy6DjgwpgmrLA5uHfuFnZnluLvqePD23uh09S3ZRxNd9WHDAoahodH9BmfVxcTg6FrV6r27KFs2TL8x48/4z6bC8WScw7RxcTgf5criV/bHz/ny7vi8TZo2J5exutbnkDlfS+94r/j0t4/EuB3hTu7cUCt7MZjehyrZVX7h7SmOKder8ZgjkSo7O4ETz7XuUz54Xmu5Fcl+Xk4G0nDPbqdSyNfnrocq/PCM+MqKCicX9QuzimEaBZLjqSSCG/rekjMPFx6QRQqrrI7eeCrbWxJLcbLoOHLe/rWSfpXg9VWQE7uQgCiIu9ttvN716pM3ppQlJxzTOBDD6EOCMCWkkLcxmX8/NBA2vgaSSty8MTSPmRWdASgbOkybEeOuK04NVzdKRiDVkVaYQW7M4+lJa8p6aDXqvA9ehUAmZnfIoSMadAg1IGB+Je7nDOF08nRfbsblK93SG/CPMMw282sPrr6LIyAgoKCQtOp7XTsyM/HWVoKajW6M4xui+gVjARYKhyU5jetYHJrxe6UeeS7Haw9VICHTs3cu/vStU3DfjaZGd8gyza8vXvg49O72WTwHjkKJInK7duxZ2Y2W79niqLknGPUJhPBUx8HoODDj2irtfPLwwO5JNybQouN8Z9tIjG1kIKPXAU8A46rUeWp13B1Z1ceoZrK5FCrArlawjunP2rhSWVlOkVF65C0WnzGjMFkdVUnlxAcWN9wdluVpHJbc5QoKwUFhZamdiLAGqdjXXT0GWfXNXXxx19dXeJh9/kbSu6UBf/6cScr9uWi06j4/K5L6R3t13BbZxUZmV8DLivOqaZvOBHakGA8+vYFoHRJ63FAVpScFsBn3DgMXbogl5eT/977BHsb+P7+/lwa7UdZlYOPX/y/Bq04NSR0d80f/15ryqqmArlOApVTT5De5eSYkflN9TmvQyXAZHUpQ0e2b23URFsTZbUuYx3FVcXNeOUKCgoKp8YxS453s0xV1aDxNRDi40oDkL4j/4z7awlcJRt2s2hnFhqVxCd39GJg+8BG2+fkLMRuL8JgaEPQScoJnQ6tccpKUXJaAEmtJqQ6pLzkxx+p2r8fL4OWeff0ZUCML9fv+QOAimtvqldpHGBIx2A8dWoySyrZnl4CHPPJ0ckuxSUs4BYACgr+oqoqC0PHjqjj4vCudPnZVJSVUNBAiQeAWN9YOvt3xiEc/JH6R7Ndt4KCgsKp4k4E6HUsR86ZhI/Xpk0HXwCy08oR8vnllyOE4JXF+/l+y1FUErx3azxXdWq8yKsQMulHXQ7HkRET3dG2zYn3NdeAVos1Kcn9WbU0ipLTQnhceineo0aCEOS+NhMhBJ56De+HFhFlzqNca+Te8rasP1zfjGrQqhnWxXUz/7YzCzg2XaW1u3J/eAd1ws+3PyCTmfU9AP43XI9XVXWGV0lF8tbNjcpXY81RpqwUFBRaksryWokAm9GSA9Dm0hDUgNUuU5DZ+rL1noh3Vxxk9npXpvz/3tCd0d1PHCFWWLiGiopk1GoT4eE3nRWZ1D4+mC53JZUt/b11WHMUJacFCX7iCSS9nootWyhfvgLhdFL26ScA7Og/kiL03DN3C2sO1jelJlRHWS3ZnY1TFsd8cqqnoNQ+etpE3A5AVtYPyLIN32uvdU9XqYQgeVvjSs6odqNQSSp25u8kvezE9X0UFBQUzhbu6CqTF9bkZODMwsdrY4zzJVDr8ktJ357XLH2eCz5Zk8z7f7lKgLx07SXcdGnkSY+pSf7XJvwWNJr6MwTNRe3EgK0hak1RcloQbXg4Afe6Qvjy3niD0oUL3b44d73xJEM7h2B1yEyat5U/9+XWOfbyuCC8DRryyq1sSS2q5ZMjoTJpkbQqggKHodMFYbMVkJ+/Ao2/P5oIV04EIWRykg9hLipsULZAYyADwlwFBH8/0jo0cgUFhYuPGp8cQ0UloqoKyWBAG3nyP/WmoNJrCA12ZYo/uuv8cD7+amMqry91FVr+94iOTBgYc9Jjysv3UVy8EUlSExk58azK53XllfiNv42w/7x6Vs/TVBQlp4UJuO9eNKGh2DMzyZ7xomvbxAkY/Xz56PZejOwais0p8+DX21i251g0lU6jYvglrlpVv+3MOmbJUbmsOAAqlZbwcJdvTo0DssfQoWgdTpAkEILkbf80KlvtKKvWoJErKChcfNQkA9RUl9rQx8YiqdXN1n/EJa7CxrnZFTgdrbuczU/bMnj+V1dV8SlXtufhIe2bdFyNFSc4aCQGw4mzdZ8pKg8PQl94Ac++fZs1euu05WlpAS52VB4eBD/xhGvF4agTUaXTqPjgtniu7RGOQxZM/naH2wcHjiUGXLYn55jjsQRq32OhlW3Cb0WS1JSUbMZsOUTIqFF4Vk9ZmapsJ5yyujrqaowaI+nl6ewq2NWs162goKBwMpwOO9YKV34vKfP0K4+fiLBLQ9BJ4JAFucmlJz/gHCCEzP4Dz7Bt+23k5CxClu0s2Z3Nv39yZeSeODCGf13TtCm7KmsOubku38qoqOZL/ne+oCg5rQDv0aMwVtfRCbh7Yp2IKo1axbu39OT6Xm1wyoLHvt/Bgu0ZAAyMDcDfU0ehxYalzDVdpZckND7HlByDIYzAgJrkgN8Q0qYNQu8yzxrtDtL37MRW1XAiLA+tB1dHXQ3A4mTFAVmh5cnPz+ehhx4iKioKvV5PaGgow4cPZ/369e42kiSxcOHCU+47JiaGWbNmnbGM2dnZjB8/ng4dOqBSqXj88ccbbFdSUsLkyZMJCwtDr9fToUMHlrSi/CKtgbwUV6kbSaXCmebyDdQ3kz9ODboIL4L0LstQ2pbck7Q+NxzNmEdW1g+UlPzD3n1TWbV2ML9teAu9upKbL43ghTFdmmwlycj4CiEc+Pr0wdu7+1mWvPWhKDmtAEmSiPjwf7R5520CJk2qt1+tknjrxh7c2icSWcC/5u/kxy1H0apVjOgailqAXB1VpVPVteQAtIlwWYays38BrMiRLr8cWZKQKqtI27mjUdlqoqyWpS7D7rQ3x+UqKJw2N9xwAzt27GDevHkcPHiQRYsWMWTIEAoLG/YtawmsVitBQUE899xz9OjRo8E2NpuNYcOGkZqayk8//URSUhKfffYZbdq0OcfStk5k2ck/v/7EDy8+BUBobFyt8PHmVXIklURYhKv8QcaBli8uWW4+wOHDbwAQHDwKVP7gzOXGuIW8O+RF7u2+GJst+yS9uHA4LGRmfgtcnFYcUAp0tho0fn5416pSfDwqlcRr47qhUUt8vSmdf/+8C5tT5pq2AZT95brhJcn1gR6v5Pj7DcRojKayMo3c3EX4dO1OeXYqZoOOsFIzyds2E9dvYIPn7RfWj0BjIAWVBazPWs+QyCHNdckKrQghBA5ry9Qq0+j1TXoqLSkpYe3ataxevZrBgwcDEB0dTd/qLKvgssYAjBs3zr0/NTWV5ORkpk2bxqZNm7BYLHTu3JmZM2cydOhQAIYMGUJaWhpTp05l6tSpwLEim+vWrWP69Ols3bqVwMBAxo0bx8yZM/H09GxQzpiYGN577z0AZs+e3WCb2bNnU1RUxIYNG9BqtXVkv9gpyspk2UfvkH0oCYC28ZcybML9ZAy9Bmj+6SqAyJ6BbD1cSn5hFbYqBzpDy/w1Op1V7N37OELYCAy4CrvXS0z7aT3dAjZzfYe/8dFmkpExm8zMeQQHjyIq8l68vbs12l92zs84HGUYjdEEBl51Dq+k9aAoOecRKpXEK2O7olWr+HJdKr99f4Ar7Do6OzUIBO1NWiRJqqfkSJKKiDa3c+jwa2RkfktUp6fIWLEIq1ZDSKmFPdu3IDudqBpw5tOoNIxsO5Kv9n3F4iOLFSXnAsVhtfL+hBtb5NyPzvsJrcFw0nYmkwmTycTChQvp378/+gbS+m/ZsoXg4GDmzJnDiBEjUFff02azmVGjRvGf//wHvV7Pl19+SUJCAklJSURFRbFgwQJ69OjB/fffz6Ra1tTk5GRGjBjBq6++yuzZs8nPz2fKlClMmTKFOXPmnPY1L1q0iAEDBjB58mR+/fVXgoKCGD9+PE899ZRb5osNIctsX/ob676bh8NuQ2c0cuWE+7lkyFCsBw6A04naxwdNcFCznzsoPhjjz8lUCsjcV0TbXsHNfo6mcDj5v1gsh9DpAlH5Pcuds7dQVqVCGMcw8soZWMrWkX70C4qLN5Kb+xu5ub/h69uP6Kj7CAgYgiQdm5wRwsnRdNc9GhV5D5J0cd5XynTVeYYkSdwVHczjDhNXVGnBKSjxkPjaZKVzdR0WjW/9H/+wsBtQqfSYzfsIDLEgNK505ioB6vwCsg7ub/ScCe0SAFiVvopyW/lZuCoFhZOj0WiYO3cu8+bNw9fXl8suu4xnnnmGXbuOOcUHBbn+AH19fQkNDXWv9+jRgwceeICuXbsSFxfHK6+8QmxsLIsWLQLA398ftVqNl5cXoaGhhIa6IhdnzpzJ7bffzuOPP05cXBwDBw7k/fff58svv6Sqquq0r+XIkSP89NNPOJ1OlixZwvPPP8/bb7/Nq6+2jrDbc01Jbg4/vvwMq7/8DIfdRlS3nkx460O6XjkMSZLqJAE8GxE72gAjISaXRS19S06z998UCgpWkZHxJQC+YS8zcd4hyqoc9I7247O7LsWo0xIYeCW94r+mb59FhIaMdQeV7Nw1iU2bR5CZ+T1Op8sim5//J5VV6Wg0PoSFXd8i19QaUCw55xEluRWsm3+ItD2FqABhUPG7qor9WieekktjFRKoTLp6x2q1voQEjyE752eqrH/gNHqgKbdRbtDRpricw1s3E9G5a4Pn7eTfiVifWJJLk/kz7U/GxY07uxeqcM7R6PU8Ou+nFjt3U7nhhhsYPXo0a9euZdOmTSxdupQ33niDzz//nIkTJzZ6nNls5sUXX+T3338nOzsbh8NBZWUl6eknTnS5c+dOdu3axTfffOPeJoRAlmVSUlLo3Llzk2WvjSzLBAcH8+mnn6JWq+nduzeZmZm8+eabzJgx47T6PB8RQrDrz6Ws+Wo2dmsVWr2BK+64hx7DRtZRZs6WP05twtt5k7qzkMzD5z7CymYrYN9+l/+Rb+Dt3PeDmkKLlUvCvZk9sQ8eurp/1V5el3DJJe8QG/sERzPmkZn5PRUVyRxIepbkI+8QEXEnhYWrAYhoMx612uNcX1KrQVFyzgNsVQ62LU0l8c+jyE6BSi3R4+pILh0Vg7w+hf0rDmIBXqOSrkJNr9Qi+rcLqNdPm4jbyc75mYKCpRgDrsJeXkK5QUdcbhE7tmxk8B33NPiUJEkSY2LH8N7291h8ZLGi5FyASJLUpCmj1oDBYGDYsGEMGzaM559/nvvuu48ZM2acUMl54oknWLFiBW+99Rbt27fHaDRy4403YrPZTngus9nMAw88wKOPPlpvX1RU1GlfQ1hYGFqtts7UVOfOncnJycFms6HT1X9QudAoK8hn+f+9T9ouV+BDROeuDH/ocXxDQuu1raqx5DRTzaqGiLo0hA07Cykut1NptmFs4GHxbCCEYP/+6djthegNcTzxx2XkllmJCzbx1b398DFqGz3WYAgnrv102sZMISvrR9KPzsFqzSYlZRYAkqQlIuLOc3IdrRVFyWlO1s2C9ldDaOOOYKeCEIJDW3LZ8PNhLKWuH+OoS/wZdFMcfqEup8dHr45Dp1Hx+tIDLMHOEuzw6SaiAzwYGBtI37Z+XBrtT4SfEW+v7nh5daW8fA9B0ZVkpUKZpwGj3Ynm8BGKsjIIaNNwJtHRbUfz3vb32JKzhRxLDqGe9X+IFBRagi5dutQJGddqtTidzjpt1q9fz8SJE90OyWazmdTU1DptdDpdveN69erFvn37aN++aUnXmspll13Gt99+iyzLqFQur4GDBw8SFhZ2wSs4Qgj2rF7B6nmfY6usQKPVcfn4CcSPSEBSNexBYT3kKmFwNi05ft0C8VJLlDsF6dvy6Dg44qydqzaZmd9SUPgXkqTjgx13kFLoIDrAg2/u64e/Z9PuBY3Gi6ioe4mIuIu8vKWkH/2c8vK9tGlzK3p940U7LwYUJae5SPwO/pwBa9+B8d9DdMPRSk2lIKOcv78/SHa16dQ70MCgmzsQ0y2gnrXlwcGxROdbWbo1g02Skzwhk1ZYQVphOt/94zLHh/kY6BPjTwe/CXhZ3yEwLIks/DAbdAigTVE5G+d/y6hHn0Clqu+gFmYK49KQS9mau5Xfj/zOvd0uznBEhZajsLCQm266iXvuuYfu3bvj5eXF1q1beeONNxg7dqy7XUxMDCtXruSyyy5Dr9fj5+dHXFwcCxYsICEhAUmSeP7555HlutltY2Ji+Pvvv7n11lvR6/UEBgby1FNP0b9/f6ZMmcJ9992Hp6cn+/btY8WKFfzvf/9rVNbExETApUzl5+eTmJiITqejS5cuADz00EP873//47HHHuORRx7h0KFDvPbaaw1ajC4kzMVFrPj0A45s3wJAWFxHRjw8Df/wxkPnnWVlOLKrEwE2s7JZG5VBQ4ifnvKCKtK3nxslx2I5zKHD/wHgz4wb2HzUj3AfA9/c149g71O3rKpUWkJDryUkJAGrNfuiV3BAUXKaj44jIWoApG+Er8bBzV9Ch+Gn3E2V2c7mRUfYuzYTIUCjU9F7RAw9h0Wi0TbuHd9fq6MbRlT9Q7lhXzrZpVUEmHSE+xjYn11OdmkVi3ZmAXpgOp7qciZI34FwYtFpCS21sHLdGpap1Yx4eGqDkVZj2o1ha+5WFh9ZzD1dG57aUlA4W5hMJvr168e7775LcnIydrudyMhIJk2axDPPPONu9/bbbzNt2jR33pnU1FTeeecd7rnnHgYOHOhWXsqqywXU8PLLL/PAAw8QGxuL1WpFCEH37t1Zs2YNzz77LJdffjlCCGJjY7nllltOKGt8fLz7/bZt2/j222/d4ewAkZGR/PHHH0ydOpXu3bvTpk0bHnvsMZ566qnmG7BWhBCCA+vX8NfsT6iymFFrNAy8+Q4uTRjX4ENVbayHXVYcTVgYam/vsypnREc/Dhdkk5V29gMsZNnKnr1TkWUraeaufLe3H0Feer6Z1J8IvzPzoZEk6ayXbzhfkMRFVpSorKwMHx8fSktL8W7uL4ytAn66Gw4uA0kN130EPW494SFCFtisTmyVDtL2FLLp12SsFgcA7S8NZuD17fHyP7lGXzBvL1X7i/C9Lpb89j7c+PEGCi02BsYG8OHt8ezPKuef1CK2pBaxNTUPq0PNzZk/E2LLIzKnkm65WWyKjKPIz0nHywYzasq/6ik6ZbYyrvzhSmyyjfkJ8+nk3+n0x0qhRamqqiIlJYW2bdtiOE98cS52ztfPrKKslD8/+5BD/2wAIKRde0Y8PJXA6qSkJ6P4++/JefElPK+4nKhPPz2bolJ+sIgv30kE4M5XB+AdaDxr5zp0eCbp6Z9T6TTx7NqnUGkC+f7+AXQMPXsVws93Tuf/W7HkNBNVFjv5aVXYOszCWjgXe/oerF+txPa3BpvvJdgqndiqHNgqHdiqnNWvDuxVznp9BbTx5PKbO9Cmo1+Tz+8sdYUNqn0NtA30ZO7dfbn1041sSC7k2V/28MFtvRjYPhCAkrLD/LRyEjlrAyET9oa3o1tuFv2PHmKbI5YD6/5GCMHoR56oo+h467wZHDmYFWkrWJy8WFFyFBQUTkjO4YP8+s5rmAsLUKnV9L/+VvpedxNqTdP/eqwHXZFVhrPoj1ODqb0fflqJYrsgbWM23RLanZXzFBWtJz39cwA+33UrshTA1/f0UxScs4Ci5DQThZlmFr2fWL3Wr3oB9gKcPAW3Si3h4aMjflgUXa9og0p9aimMnCXVSk513apuET58etel3D1nC0t25+DnsYdXr+uKJEn4erenV0wbDqYfIDMzlKhoT5I6Pkj7Hz6ld3YyB6vakLRuPcgyox59ss4PUkK7BFakrWBJyhKm9p6K+iSmZgUFhYuTPav/5M/PP8Rpt+MXHsHoR58kpG3sKffjzpFzDpQcSSURFuJBcYaFo7sLzoqSY7cXs3ffkwCsPnoZSSXxfHVvH7pF+DT7uRQUJafZ0FmdeKtAI0lo9Sr0Jh0GVR6Gsk0YpDz0Md3R9b4RnYcOnUGNzqhBZ9C4Xo3qE/rbNIZc5cCWYcaWUY5c4Zriqp0I8LL2gbx7S0+mfLedbzanE2jSM3WY64eiTZvbSff/NwDledncO3sWq9u3xfT6DDoUZ+Jt9WOr+Ach3mD0Y/92KzqD2gzCR+9DfmU+m3M2MzD8zBysFRQULiycDgerv/ycxD9cRX1jL+3HyMnT0Hs0XAbjRAghjuXIOQvlHBoi4pIA9mVYyM6yIIRoVt9DIQT79j+LzZZLtiWYX5Kv5/MJl3JpjH+znUOhLoqS00x4m7Rc09EXe44FBGBzAP6gG4WKEnTZ+9Bv/w79mHvQRvoinaKlRrY6sWeZXUpNZjn2DDOOgrrVw1XeOiRDXWVpdPcwiiq68vzCPby38hCBJh13DoghKHAopiCXc5vTYsFWWcGQO65ld0wk+Y9PIdRcxJCDlaxxqhHidcY8/hRqjRatWsuImBH8kPQDvx/5XVFyFBQU3FhKivnt3dfJPLAXgIE33U7/629pNDT8eJzl5VTt3k3lrt1U7t5N1a5dOEtLQa1G1+7sTB0dT+TAcFR/pFPlEBSmlREY03wWlqzs+RQU/IFDVvPFnom8d9sALqt2I1A4OyhKTjOhb+tDyGO9kKscWNPKsKWWYU0txXa0HNnhS5U8kKoM4JO9SFoVuigvdDE+6GO80UV5o9IfU06E3Ykt24K92kpjyzDjyK9wKU/HofbTo4vwQhdhwnBJYINPHXf2j6ag3Mp7Kw/xwqK9+HnqGNM9nKh2N7HXYwWOCi0Zh5Jo1z2eboPiyZo/n513TyIm5wjDk/bzl13iN/E6CVOfRq3RMqbdGH5I+oEVaSt4tt+zeGgv3mya5zsXWdzBeU1r/6yyDyex6O3XMBcVojN6MOqRfxHbu1+j7WWbDWtSEpW7dlFVrdTYjhyp31Ctxvfmm1CdQmbsM8EQ4kGAUU1+pZO0jdnNpuRYLCns3f8SagkWHh7Nv0YncHVnJcT7bKMoOc2ExeHkjt1H6O9jYmCQid7to/BRqxAOGVumGdvWbVi3J2J1dkTYvbAml2JNLqUcQAXacBPaQCP23ArsuRaQ659D7aND28al0OgivNC2MaH2bDwbZm0eHxpHocXK15vSmfpDIn4eOnpH3Y7RfzHlFVoO7lxNu+6usNfwtuH4/PYjyyc+Qqe9G7kmeQ/rrA4WOv7D2CeepUdQDyJMEWSYM1h1dBWj241uvoFUOCfUVL6uqKjAaDx7ESQKzUdFRQVw7LNrTexZtYI/v/gIp92Of3gEY598Dv/wY3lmhCxjS0s7ZqXZtQvr/v0Iu71eX9rISIzdumHo3g1j9+4YOndGdRr3aFlZGfv37yc7O5uoqCg6duzYaOX44wmPMJF/qJSM/cX0PuUz10eW7azc+DCeqir2F8VxVe/HGdP9wgvxtjlk1h3O57ed2bQL9OSRq8/NFOOJUJScZuKfUgsbS1zLu2m5aCWJeG8PBvqaGOBr4tLrriCwrwnx9U04Kr2wel6FLXwi1kw7zhIr9gwz9gyzuz+VSetWZGqUGrXX6WdClSSJl67tSpHFxpLdOdz/5Va+v38AOl8TZEBe6kZgqru9p5cn1/74Ob9Ne5mOf/zAoIwD7FxSxQL7K1z/9HOMiR3DJzs/4bcjvylKznmIWq3G19eXvLw8ADw8PJS8R60UIQQVFRXk5eXh6+vbqqqUOx12Vs37nJ3Lfwcg9tL+1f43Hi65t2yh9OefKV+1Gvm4vEQAal9flzLTrTvG7t0wdO+Oxu9YVKlVlllbYkFUltHF00iQTnPC+7SkpIR9+/Zw4MAeMjOPIkkyQkgkJiYiSRIxMTF06dKFTp064eXVeCRTVHwwOw+VkltQieyUTzkQ5Hjmr36JQNVBLHYP/MJe5qY+TQufPx9wyoLNRwpZtDOLpXtyKK10Ka4RfkamXNW+xX9XlDw5zUSe1c6KwjI2lJjZUGIm21r3CUUjQU8vDwborAzc+Ap9c/7G0ysI7lqIQx2BLbUUR1EV2hAPtBFeqL11Z+XmsDqc3D1nCxuSCwnw1PGvgFVkr9mFZ2gl9729CI2m/pPO8vfnEfrJm2hlJ6neIRQOG8jAafcwdvE4VJKKlTetJNCozCufbwghyMnJoaSkpKVFUWgCNZXVW/pPowaX/81MMg/sA0li4E3j6T/uFhz5+ZT+spCSXxZgTztWAFXS6zF06eJWZozdu6ONiKh3PRank5X5hfyanc6qUqgQx57FvSknWsokSsogiqNEkUq4nI5GVCKEA0mq+3cmZHBUakCnw2LxosLii6XClwqLL37+XencqRedO3fGx8c1JSULQa7NTkpxBTte3g4yqG+L5spL29DBQ39aY//d2l8JtP0LlSTIVj3HHUPuPuU+WhtCCLanl/Dbzix+351NfrnVvS/IS8/obmEk9AinV5Rvs96vp/P/rSg5zYSzooKklT/Rtl1vdNHRHFVrXQpPsZmNJWYyj1N61MJJj/IDDLAcZGCfcfRt2w0vzbl5QiuvsnPbZ5vYk1lGf20GfQ7+hlrnpO/DDgIDB+Pvdxl+fv3qVK7dung1jmefxMdqpkjvRdKVV/DXlVnsLNnNU32e4o4ud5wT2RWaH6fTib2BaQOF1sPxxTxbmuxDSSx655j/zciHHie4zELpTz9jXrsWqktmqDw98R41Cp9x12Hs1g2pkam2YmsFizL2sLSglI0Vvlg51s5PFGKgihxCEVL9MZCEkzCyiSSNtpbDtMlLxzuvGJEnqMw3INvVqPVOPIIr8QipRISoKQ/2pUAXRI6jDTnONhSp21CiDaJQ8sAuXH/KN60rp1Omnb+6GVnfxUi4Xstgfy8G+3lxhb8X/tqTT4TM/2cfIv8uAozFFMnDuGnoJ6cz3K0CIQR7s8r4bVcWi3dmk1lyLPDF10PLyK6hJPQIp1/bANSqs6OIK0pOEzhbSs7htV8z7sh/8XLIdCx30KlcopPNRGdNOMEBcRRFdmFHaAfWeoSy3mznaFXd6scqBF1MHoTrtQTqNARqNQRUvwbqjm3z12rQNsMNVGC2cuPHG8gpKGZS2mwQ0OX2Q+hMrlB0SdLi49OLAP9B+PsPwsvrElJ2J5My6QHCS3OoUmtZfemlzBu8lY7BnflhzA9nLJOCgkLrQgiBzZZHRUUqkqRGpwvk0KadrPpiNk6Hk3AffwaGxGBduRJnUZH7OOOlvfG94UZMw4ZRggZzlQOjTo2HTo2nTgPYSC3cyeLsFJaXakh0ROGopdgEiVz6s41hVYkMzN2Jp11FvmcH1qm6sk2Ec0TypVjnhcpSRUBhHmF5RwnLPYq3pf6UWIPXhUSBfzBZwRFkhUaRHRxJoV8gSCpUwkkARQw4aKVHogdF/jJfDfalTHdMqZEQxGocdFbbaCvb8bPZKa+QKKmEogpBoUWmwCIYGj6b/mHbqJJDuWbwMrTaY1NksixTVVXlXiorK+u9lyQJLy8v9+Lt7Y2np+fZUXjtVVCcCkXJUJgMlcWgN3HY5s9v2b78dlTHkdJjzT11Kq7pEsK1PSO4rH0gapWgxFpCibWEoqoitCotPYN7NquI562S8+GHH/Lmm2+Sk5NDjx49+OCDD+jbt2+j7efPn8/zzz9PamoqcXFx/Pe//2XUqFFNOtfZUnJW/T6Df+X/jL0B01yAw0lXm41LrFYuqbLRySLwxJNiYxAZWk8Omtpw2BRFkdYHJyef+/VUSXipJEwqCZMkYZLASwIPrQEPkx9eJj+8fAPx9wnCy9MHSecFDYRwHi2q4PqPN3DDgU/R2ioIvfISYnrYkJ1bsdmy6rTVaHzx9xuARlzKoWkLictIQkZiacfOfJVwkF9u/JV2vucmxFOhZRFCIAPqczRtIoTAYreQX5lPQWUBpdZSfPQ+BBoDCTQGYtKaWs0UzvmK01lJRUUqFRVHqKg4gqX6taIiFafTXKetVAX6f1R4btSiTzv292Hz1pPZK5a9XbtyyNCGzDIDGaUGyqwGjJKVLt6HiPVPR+ercVlN5GA8ZCuezkpMzgpCHTl0qkimXdlR/MrMCKcaJBVCBVaVjkKniYJKA0WVBoordZRbtQjqf+4aTzVOXyNOPw/sfp5UmTwxV2pwlDrRFFvwLijAy1xfGRI60Ac58A0twRRSgcboTfpfLx/br6/A7G0ny8tIupcPBd5qCr3UlHiq0EuVdGEP3UmkGzsJIcctmRASJcX3YzYH11FgrFbrcQKI6kVGEgLZKXAiIavUyCoVSGqEJCFJEkYPT0wmE56eJjxN1Yunp3u70cMTbXU1e1m4/GZkIRAOO/bSLMqLsygpzaPEXExJpYXSqipKnYIStYkytYlytSclViMlhToqK475gkqSjLdPMd5+eei983CorFThoFJyUIkdhA1J2JGEja4aHT/d1LwPv+elkvPDDz9w11138cknn9CvXz9mzZrF/PnzSUpKIjg4uF77DRs2cMUVVzBz5kzGjBnDt99+y3//+1+2b99O165dT3q+s6XklGXlsXfhMgrVR0jnCMlkclhdQprGhrOB399wu4NLqhWfrlYbXaw2vM7iR2FRGaiSDFjR40CHEx0IHU5Zy9oMT4rKaz2lSKDWgEYno/ewYTBVovOwo/ZwoPZwgl6mdGk0PfenoHXKbIhsT/nzg5l6xRPNJq8QAocAmyxjEwKbLLDKMnb3e4FduLYJARqVhE6S0KgktFL1opJcyRmr32ulY+tqiRP+MQohEIBTgIxAFq75ehnXqxPXjwe4/K00koS6un+1BKpz/KcrHDYslhIqywupLM3Dai7GZi7CXlmKXFWGbC0HaznYLWjsZtSOCmTAodJgkzTYVRpsKi02lQab5HqtkrRYVRqskmupUmmplNRYJS12lQZUOtQaAzqtEb3OiEFnxGgw4WnwwMvDC5PRC1+jJz4GT/x0Wvy1Goy1HDidspOiqiJyyvNIL8giLzebgoI8youLqSwxYy23YLdYEZV2NDaB3q5C5wCdU2BTS9g0YNXKOHQSklGD1tOAzssDo48X3n6+BAQFExISTmRgGGFeIfgZ/NCoLt5YCyEEVVXZlJYfprgsGbPlCFWVR7BbUxHOnEaPk4UKs9UP76Ry/LY5MOxUI9ld97eQBCJORurqRB0lo5VltA6B1iGjszvROxzoHQ40wvU9dQgVVqcam6zBJquxyhpsTjVW2bXN6qzedtx6kdUDq1z/szNprIQaywkzlBNmLCfEWI5O1UBo6nGY7VqOmn3JrPAhp8pEgc2zwQdMlc4DCAChhToKleR+FRLYNCpsGgmrVsKqVSG0dozaErxVJdgtBqxVBnDKqGQnatn16lpkJNmJSshI4uRyC0ly1UWUVKCSEJIKJAmhqnmtXiQJVK72QlLVe0WSkGuOlVTIkgSSCrm6jWufCiFUrms0aREmPcJLD5LK9dspJBAqJAEICUnUbJOQBKgcNmY+OvVkl3RKnJdKTr9+/ejTpw//+9//AJcJLzIykkceeYSnn366XvtbbrkFi8XC4sWL3dv69+9Pz549+eST+vOdVqu1jsZcVlZGZGRksys52cmlLHhzW73tdpWNQo9M8kxpFJjSyTelUWwsaLCPYJuEpvrTELW+T6LWgtTweyG5vnYSAgmBSgik6jj0Y1/HY9T450mAX5GBzvuDMFRpUMunFkUgCYHO4UQSch2ZzyfOU7ERNCB7g9/m07nCBjpqpoGq6VkSINXcxNX9i1onqrmfjkkiubdJ7h2igfta1Hp/bEft70zLIdW6rtrXKR0n37H1Y+8l15N+zWYhav/VgnCPXK3vd+2xOHbH1P+uSo2Oi5BcQtX+ZZAlsGvAoW6orwauWoDaqUJ1Bj8SDpVMoY+dQh87BT42Cn1sVBnk6t891zlUNe8BlXBdlQpQI6PGWf0q17uVJRk8zXq8S/V4lxrwKTVgrDz9aNbmoUapc7SoFI0j1VqOW5ckhM6HJ76c3axnPO8KdNpsNrZt28b06dPd21QqFUOHDmXjxo0NHrNx40amTZtWZ9vw4cNZuHBhg+1nzpzJSy+91GwyN4aXv57LbmyP3erEXuV0vbqXEOzWHtgrndhLnJjtZrLUqWQb0sg3pZPnmY7ZUESerrl+ft1f8ya1zgxxsCfEVV9L45AwWtUYbKrqV3Xddasao02NwapC73CZT61NcMBTUFBoBqQaNcb1viUVNkmA9jT+f2UEdo2MXeN6tWld720auXq7jE0rjr3XyJiNDoq97IgziuSWAHX10gAGGQIrAZdDrd6qIqhEj9FW3V7U+juvGXghYXB4YLR74WE3YbSZMNq9MNpN6J01uX3UIGmQJDWgAUkDqJElgVMNThU41QKHSsapBodaxqly4FQ5EZKMyqlGLYNallA7BWoZVLKMyglqIVA5XQ+ZKrnaGiQcIOyAA4QDgROEE6pfBY5a6w7AiThuvWmPAu7H7QZ3OalqQh9nnxb9dyooKMDpdBISUjfrY0hICAcOHGjwmJycnAbb5+Q0bG6dPn16HaWoxpLT3Jj8DPQcGnVKx8iywFGtCOWVF5BakuZ6Uqv+IXOpKjXv69pjJGpPt9SYdGRAgOysntt1uqZdhIyQZcDpSjIonED1tuo5YFl2Vk/PCJwInEIgql9r1l0LyA4rsr0Cu6WcqtISKvJzUBfU8khrgBNaHKRjT5uScFmiJFH7uaBO4yaOrjjulWobl6sP0egT/XGWAVxDpGqwdS0TRO3+pbq9iFq9CtcH16AvwenglNTIkoQsqREqtWu8VCpQqVzy1FgCVKrqx1y12zrgfgartgi4fiCF6zMQEjUWAal6G9Vtao5BuA0LiOqrFFRn5hXC9cMrnNV9OxEIl1WAajO6TgUaNWg1oFUhtBqEptoUL6kRSEhuk7oaOGZer5EVXOZ+cN3nLouG0+XX4HCCw4Fkk8HuAIcTySa7r+V0OGOloubrXf0ZUOvF9V6q8/nUvU1clhhRe7X2PuGy9BwzZtW6B2s+J6Gp3lw9HVFzkhqFqXraw/UdOfZULksS5sBAnAYP1GoVGpUKNQKNJKGSXP5ZtX+vVCrX9IfreiU0Wi1BgW3w8/fDYDCiVqtRqVSoVSrXq1qFWqVGrVIhqVz9qzRq1JKEqtqfUJZlnEIgO53IssApO3HKrs/d6ZSRhRMhCxyyjCzLru3V7eqOlzhu/dgYOYWM1eHA5nRgddipsltx2qwgO12/kbITZCdCdiCEEyHLCNnhiiYTMsLpUjKELBBWC2qzGp3RgN7XE08/H/SeHmj1Hqi0WtcY1f6Ujn9T+7OvZaE8Xnxx3BtZlhE2gWyV65p73a9S3VO577lj21zX5URSU/37LKp/C2v8hqrfI1z/MdVjTvXnIYSMl6cPrYEL/hFcr9ejP0fpwE8VlUqqLtCpoa1vG9pGtmlpkRQUFBQUFC4YziyN4xkSGBiIWq0mNze3zvbc3FxCQ0MbPCY0NPSU2isoKCgoKChcnLSokqPT6ejduzcrV650b5NlmZUrVzJgwIAGjxkwYECd9gArVqxotL2CgoKCgoLCxUmLT1dNmzaNCRMmcOmll9K3b19mzZqFxWLh7rvvBuCuu+6iTZs2zJw5E4DHHnuMwYMH8/bbbzN69Gi+//57tm7dyqefftqSl6GgoKCgoKDQymhxJeeWW24hPz+fF154gZycHHr27MmyZcvczsXp6eluxzOAgQMH8u233/Lcc8/xzDPPEBcXx8KFC5uUI0dBQUFBQUHh4qHF8+Sca85WMkAFBQUFBQWFs8fp/H+3qE+OgoKCgoKCgsLZQlFyFBQUFBQUFC5IFCVHQUFBQUFB4YJEUXIUFBQUFBQULkgUJUdBQUFBQUHhgkRRchQUFBQUFBQuSBQlR0FBQUFBQeGCRFFyFBQUFBQUFC5IWjzj8bmmJvdhWVlZC0uioKCgoKCg0FRq/rdPJYfxRafklJeXAxAZGdnCkigoKCgoKCicKuXl5fj4+DSp7UVX1kGWZbKysvDy8kKSpGbtu6ysjMjISI4ePaqUjDgFlHE7dZQxOz2UcTs9lHE7PZRxO3VONGZCCMrLywkPD69T0/JEXHSWHJVKRURExFk9h7e3t3JDnwbKuJ06ypidHsq4nR7KuJ0eyridOo2NWVMtODUojscKCgoKCgoKFySKkqOgoKCgoKBwQaIoOc2IXq9nxowZ6PX6lhblvEIZt1NHGbPTQxm300MZt9NDGbdTp7nH7KJzPFZQUFBQUFC4OFAsOQoKCgoKCgoXJIqSo6CgoKCgoHBBoig5CgoKCgoKChckipKjoKCgoKCgcEGiKDkKCgoKCgoKFySKktNMfPjhh8TExGAwGOjXrx///PNPS4vUqnnxxReRJKnO0qlTp5YWq9Xx999/k5CQQHh4OJIksXDhwjr7hRC88MILhIWFYTQaGTp0KIcOHWoZYVsRJxu3iRMn1rv/RowY0TLCthJmzpxJnz598PLyIjg4mOuuu46kpKQ6baqqqpg8eTIBAQGYTCZuuOEGcnNzW0ji1kFTxm3IkCH17rcHH3ywhSRuHXz88cd0797dndl4wIABLF261L2/ue41RclpBn744QemTZvGjBkz2L59Oz169GD48OHk5eW1tGitmksuuYTs7Gz3sm7dupYWqdVhsVjo0aMHH374YYP733jjDd5//30++eQTNm/ejKenJ8OHD6eqquocS9q6ONm4AYwYMaLO/ffdd9+dQwlbH2vWrGHy5Mls2rSJFStWYLfbueaaa7BYLO42U6dO5bfffmP+/PmsWbOGrKwsrr/++haUuuVpyrgBTJo0qc799sYbb7SQxK2DiIgIXn/9dbZt28bWrVu56qqrGDt2LHv37gWa8V4TCmdM3759xeTJk93rTqdThIeHi5kzZ7agVK2bGTNmiB49erS0GOcVgPjll1/c67Isi9DQUPHmm2+6t5WUlAi9Xi++++67FpCwdXL8uAkhxIQJE8TYsWNbRJ7zhby8PAGINWvWCCFc95ZWqxXz5893t9m/f78AxMaNG1tKzFbH8eMmhBCDBw8Wjz32WMsJdZ7g5+cnPv/882a91xRLzhlis9nYtm0bQ4cOdW9TqVQMHTqUjRs3tqBkrZ9Dhw4RHh5Ou3btuP3220lPT29pkc4rUlJSyMnJqXPv+fj40K9fP+XeawKrV68mODiYjh078tBDD1FYWNjSIrUqSktLAfD39wdg27Zt2O32Ovdbp06diIqKUu63Whw/bjV88803BAYG0rVrV6ZPn05FRUVLiNcqcTqdfP/991gsFgYMGNCs99pFV4W8uSkoKMDpdBISElJne0hICAcOHGghqVo//fr1Y+7cuXTs2JHs7GxeeuklLr/8cvbs2YOXl1dLi3dekJOTA9DgvVezT6FhRowYwfXXX0/btm1JTk7mmWeeYeTIkWzcuBG1Wt3S4rU4sizz+OOPc9lll9G1a1fAdb/pdDp8fX3rtFXut2M0NG4A48ePJzo6mvDwcHbt2sVTTz1FUlISCxYsaEFpW57du3czYMAAqqqqMJlM/PLLL3Tp0oXExMRmu9cUJUehRRg5cqT7fffu3enXrx/R0dH8+OOP3HvvvS0omcLFwK233up+361bN7p3705sbCyrV6/m6quvbkHJWgeTJ09mz549ip/cKdLYuN1///3u9926dSMsLIyrr76a5ORkYmNjz7WYrYaOHTuSmJhIaWkpP/30ExMmTGDNmjXNeg5luuoMCQwMRK1W1/P6zs3NJTQ0tIWkOv/w9fWlQ4cOHD58uKVFOW+oub+Ue+/MadeuHYGBgcr9B0yZMoXFixezatUqIiIi3NtDQ0Ox2WyUlJTUaa/cby4aG7eG6NevH8BFf7/pdDrat29P7969mTlzJj169OC9995r1ntNUXLOEJ1OR+/evVm5cqV7myzLrFy5kgEDBrSgZOcXZrOZ5ORkwsLCWlqU84a2bdsSGhpa594rKytj8+bNyr13imRkZFBYWHhR339CCKZMmcIvv/zCX3/9Rdu2bevs7927N1qtts79lpSURHp6+kV9v51s3BoiMTER4KK+3xpClmWsVmvz3mvN6xt9cfL9998LvV4v5s6dK/bt2yfuv/9+4evrK3JyclpatFbLv/71L7F69WqRkpIi1q9fL4YOHSoCAwNFXl5eS4vWqigvLxc7duwQO3bsEIB45513xI4dO0RaWpoQQojXX39d+Pr6il9//VXs2rVLjB07VrRt21ZUVla2sOQty4nGrby8XDzxxBNi48aNIiUlRfz555+iV69eIi4uTlRVVbW06C3GQw89JHx8fMTq1atFdna2e6moqHC3efDBB0VUVJT466+/xNatW8WAAQPEgAEDWlDqludk43b48GHx8ssvi61bt4qUlBTx66+/inbt2okrrriihSVvWZ5++mmxZs0akZKSInbt2iWefvppIUmSWL58uRCi+e41RclpJj744AMRFRUldDqd6Nu3r9i0aVNLi9SqueWWW0RYWJjQ6XSiTZs24pZbbhGHDx9uabFaHatWrRJAvWXChAlCCFcY+fPPPy9CQkKEXq8XV199tUhKSmpZoVsBJxq3iooKcc0114igoCCh1WpFdHS0mDRp0kX/UNLQeAFizpw57jaVlZXi4YcfFn5+fsLDw0OMGzdOZGdnt5zQrYCTjVt6erq44oorhL+/v9Dr9aJ9+/biySefFKWlpS0reAtzzz33iOjoaKHT6URQUJC4+uqr3QqOEM13r0lCCHGaliUFBQUFBQUFhVaL4pOjoKCgoKCgcEGiKDkKCgoKCgoKFySKkqOgoKCgoKBwQaIoOQoKCgoKCgoXJIqSo6CgoKCgoHBBoig5CgoKCgoKChckipKjoKCgoKCgcEGiKDkKCgpnjYkTJ3Lddde1tBhNYu7cufWqHisoKJzfKMkAFRQUTgtJkk64f8aMGUydOhUhxHmhPFRWVlJeXk5wcHCTjxkyZAg9e/Zk1qxZZ08wBQWF00bT0gIoKCicn2RnZ7vf//DDD7zwwgskJSW5t5lMJkwmU0uIdloYjUaMRmNLi6GgoNCMKNNVCgoKp0VoaKh78fHxQZKkOttMJlO96aohQ4bwyCOP8Pjjj+Pn50dISAifffYZFouFu+++Gy8vL9q3b8/SpUvrnGvPnj2MHDkSk8lESEgId955JwUFBXX6nTJlClOmTMHHx4fAwECef/55ahuqi4uLueuuu/Dz88PDw4ORI0dy6NAh9/7jp6tefPFFevbsyVdffUVMTAw+Pj7ceuutlJeXA66puDVr1vDee+8hSRKSJJGamkpxcTG33347QUFBGI1G4uLimDNnTjOPvoKCQlNQlBwFBYVzyrx58wgMDOSff/7hkUce4aGHHuKmm25i4MCBbN++nWuuuYY777yTiooKAEpKSrjqqquIj49n69atLFu2jNzcXG6++eZ6/Wo0Gv755x/ee+893nnnHT7//HP3/okTJ7J161YWLVrExo0bEUIwatQo7HZ7o7ImJyezcOFCFi9ezOLFi1mzZg2vv/46AO+99x4DBgxg0qRJZGdnk52dTWRkJM8//zz79u1j6dKl7N+/n48//pjAwMCzMJIKCgonpVnKiSooKFzUzJkzR/j4+NTbPmHCBDF27Fj3+uDBg8WgQYPc6w6HQ3h6eoo777zTvS07O1sAYuPGjUIIIV555RVxzTXX1On36NGjAnBXXB88eLDo3LmzkGXZ3eapp54SnTt3FkIIcfDgQQGI9evXu/cXFBQIo9EofvzxxwavYcaMGcLDw0OUlZW5tz355JOiX79+da7nscceqyNbQkKCuPvuuxscHw2apQAAEnlJREFUJwUFhXOLYslRUFA4p3Tv3t39Xq1WExAQQLdu3dzbQkJCAMjLywNg586drFq1yu3jYzKZ6NSpE+CytNTQv3//Os7QAwYM4NChQzidTvbv349Go6Ffv37u/QEBAXTs2JH9+/c3KmtMTAxeXl7u9bCwMLdcjfHQQw/x/fff07NnT/7973+zYcOGE7ZXUFA4eyiOxwoKCucUrVZbZ12SpDrbahQVWZYBMJvNJCQk8N///rdeX2FhYWdR0oZlrZGrMUaOHElaWhpLlixhxYoVXH311UyePJm33nrrbIqqoKDQAIolR+G0GDJkCI8//nhLi9HixMTEKOHDZ4jT6eTjjz/G09OzwVDzXr16sXfvXmJiYmjfvn2dxdPT091u8+bNdY7btGkTcXFxqNVqOnfujMPhqNOmsLCQpKQkunTpctqy63Q6/vjjj3q5gIKCgpgwYQJff/01s2bN4tNPPz3tc7QEZ+u+Ppe5iJTfKAVQlByFEzBx4kR31Ejt5fDhwyxYsIBXXnmlpUVslAULFnDNNdcQEBCAJEkkJiae0vGrV69u8NprL6tXr2bLli3cf//9Z+ciLhLKysooKysjMTGRgwcP1ts/efJkioqKuO2229iyZQvJycn88ccf3H333TidTne79PR0pk2bRlJSEt999x0ffPABjz32GABxcXGMHTuWSZMmsW7dOnbu3Mkdd9xBmzZtGDt27GnLHhMTQ35+PhUVFRQUFCDLMi+88AK//vorhw8fZu/evSxevJjOnTs3uc/TVQRq7tmSkpJTPvZ4muO+Pt8eAJpz/BRaD8p0lcIJGTFiRL3w16CgINRq9Vk/txACp9OJRnPqt6nFYmHQoEHcfPPNTJo06ZSPHzhwYJ08MI899hhlZWV1xsLf3x+dTnfKfZ+v2Gy2s3K9DoeDiIgI4uLiGtwfHh7O+vXreeqpp7jmmmuwWq1ER0czYsQIVCrXc5osy9x1111UVlbSt29f1Go1jz32WJ0/6jlz5vDYY48xZswYbDYbV1xxBUuWLKk3JXUqPPHEE/zyyy+sXLmSoKAgUlJS0Ol0TJ8+ndTUVIxGI5dffjnff//9aZ+jJQgKCmppERQUmoeW9nxWaL0cHxlTm+OjSrKyssSoUaOEwWAQMTEx4ptvvhHR0dHi3XffFUIIkZKSIgCxY8cO9zHFxcUCEKtWrRJCCLFq1SoBiCVLlohevXoJrVYrVq1aJZxOp3jttddETEyMMBgMonv37mL+/PlNuoaGzns6NDYWta9RCCEA8cknn4jRo0cLo9EoOnXqJDZs2CAOHTokBg8eLDw8PMSAAQPE4cOH6/SzcOFCER8fL/R6vWjbtq148cUXhd1uP6FMX3zxhejSpYvQ6XQiNDRUTJ482b0vLS1NXHvttcLT01N4eXmJm266SeTk5Jzweh577DExePBg9/rgwYPF5MmTxWOPPSYCAgLEkCFDhCzLYsaMGSIyMlLodDoRFhYmHnnkEfcxVVVV4l//+pcIDw8XHh4eom/fvu7PtyGio6MF4F4mTJjQJPlnzJghevToIT777DMRExMjgHpRTjWsXbtWDBo0SBgMBhERESEeeeQRYTab3fu//PJL0bt3b2EymURISIi47bbbRG5ubp0+9uzZI0aPHi28vLyEyWQSgwYNcn+GNWP55ptvitDQUOHv7y8efvhhYbPZGr3uxMREMWTIEGEymYSXl5fo1auX2LJli/s7UHuZMWPGSeWsuc8bGsvT+f40dF9/9tln4rrrrhNGo1G0b99e/Prrr40eP3jw4HryCHEsgm3ZsmWiU6dOwtPTUwwfPlxkZWXVOf6zzz4TnTp1Enq9XnTs2FF8+OGHJ5TXbDaLO++8U3h6eorQ0FDx1ltv1fuNOt3xW7p0qbjsssuEj4+P8Pf3F6NHj673/VVovShKjkKjnIqSM3ToUNGzZ0+xadMmsW3bNjF48GBhNBpPS8np3r27WL58uTh8+LAoLCwUr776qujUqZNYtmyZSE5OFnPmzBF6vV6sXr36pNdwIiVnwoQJdf7UT2csGvozaNOmjfjhhx9EUlKSuO6660RMTIy46qqrxLJly8S+fftE//79xYgRI9zH/P3338Lb21vMnTtXJCcni+XLl4uYmBjx4osvNirPRx99JAwGg5g1a5ZISkoS//zzj1sOp9MpevbsKQYNGiS2bt0qNm3aJHr37l3nWpuq5JhMJvHkk0+KAwcOiAMHDoj58+cLb29vsWTJEpGWliY2b94sPv30U/cx9913nxg4cKD4+++/xeHDh8Wbb74p9Hq9OHjwYIPXkZeXJ0aMGCFuvvlmkZ2dLUpKSpok/4wZM4Snp6cYMWKE2L59u+jdu3eDSs7hw4eFp6enePfdd8XBgwfF+vXrRXx8vJg4caK7zRdffCGWLFkikpOTxcaNG8WAAQPEyJEj3fszMjKEv7+/uP7668WWLVtEUlKSmD17tjhw4IB7LL29vcWDDz4o9u/fL3777Tfh4eFRZ1yO55JLLhF33HGH2L9/vzh48KD48ccfRWJiorBarWLWrFnC29tbZGdni+zsbFFeXn5SOR0Oh/j555/dYfU1YymEOK3vT0P3dUREhPj222/FoUOHxKOPPipMJpMoLCxs8PjCwkIREREhXn75Zfd1COFScrRarRg6dKjYsmWL2LZtm+jcubMYP368+9ivv/5ahIWFiZ9//lkcOXJE/Pzzz8Lf31/MnTu3UXkfeughERUVJf7880+xa9cuMWbMGOHl5VXnnjjd8fvpp5/Ezz//LA4dOiR27NghEhISRLdu3YTT6WxUHoXWg6LkKDTKhAkThFqtFp6enu7lxhtvFELUVXL2798vALFlyxb3sYcOHRLAaSk5CxcudLepqqoSHh4eYsOGDXVku/fee8Vtt9120ms4kZLz9NNP18nPciJORcl57rnn3OsbN24UgPjiiy/c27777jthMBjc61dffbV47bXX6vT71VdfibCwsEblCQ8PF88++2yD+5YvXy7UarVIT093b9u7d68AxD///NPo9TSk5MTHx9dp8/bbb4sOHTo0aKVIS0sTarVaZGZm1tl+9dVXi+nTpzd6LWPHjnU/NTdV/hkzZgitVivy8vLcsjak5Nx7773i/vvvr7Nt7dq1QqVSicrKygbl2bJliwDcysX06dNF27ZtG7XMTJgwQURHRwuHw+HedtNNN4lbbrml0Wv28vJq9E+7sZxDJ5Oz5vtTXFzsbnO635+T3ddms1kAYunSpU3uo+bagDqWkA8//FCEhIS412NjY8W3335b57hXXnlFDBgwoMHzlJeXC51O5853JIRLyTIajY1a94Ro2vg1RH5+vgDE7t27T9hOoXWg+OQonJArr7ySjz/+2L1eO5qlhqSkJDQaDb169XJva9++PX5+fqd1zksvvdT9/vDhw1RUVDBs2LA6bWw2G/Hx8afVfw0zZ848o+Mbo3YemJqcL8fngamqqqKsrAxvb2927tzJ+vXr+c9//uNu43Q6qaqqoqKiAg8Pjzr95+XlkZWVxdVXX93g+ffv309kZCSRkZHubV26dMHX15f9+/fTp0+fJl9L796966zfdNNNzJo1i3bt2jFixAhGjRpFQkICGo2G3bt343Q66dChQ51jrFYrAQEBTT5nU+WPjo52+46sXr26wb527tzJrl27+Oabb9zbhBDIskxKSgqdO3dm27ZtvPjii+zcuZPi4mJ3iHh6ejpdunQhMTGRyy+//IS+O5dcckkdP7WwsDB2797daPtp06Zx33338dVXXzF06FBuuukmYmNjTzguJ5OzIZrz+1P7vvb09MTb2/ukOYMawsPDo8611s49ZLFYSE5O5t57763jS+dwOPDx8Wmwv+TkZGw2W50cSP7+/nTs2LFOu9MZP4BDhw7xwgsvsHnzZrdzec1xXbt2PcWrVzjXKEqOwgnx9PSkffv2Z9xPjYOoqFVLqLF0+rUVKbPZDMDvv/9OmzZt6rTT6/VnLNfZoKGcLyfLA/PSSy9x/fXX1+vLYDDU29YcRSRVKlWdzwIa/jyOV2ojIyNJSkrizz//ZMWKFTz88MO8+eabrFmzBrPZjFqtZtu2bfUc089Goc6GFO7jMZvNPPDAAzz66KP19kVFRWGxWBg+fDjDhw/nm2++ISgoiPT0dIYPH47NZgOaNt6nmk/nxRdfZPz48fz+++8sXbqUGTNm8P333zNu3LgG2zdFzsauH5rn+3M6OYOa2k/NvVgj72effVZHaQHOKNjhdMcPICEhgejoaD777DPCw8ORZZmuXbue9DiF1oGi5CicMR07dsThcLBjxw73k//hw4cpLi52t6l54s7OznY/QTYlrLtLly7o9XrS09MZPHhw8wvfCujVqxdJSUlNVia9vLyIiYlh5cqVXHnllfX2d+7cmaNHj3L06FG3NWTfvn2UlJS4n1iDgoLYs2dPneMSExObFGlkNBpJSEggISGByZMn06lTJ3bv3k18fDxOp5O8vDwuv/zyJl1LQzRF/qbSq1cv9u3b1+jY7t69m8LCQl5//XX3ubZu3VqnTffu3Zk3bx52u/2MIrGOp0OHDnTo0IGpU6dy2223MWfOHMaNG4dOp6sTGg9w4MCBk8pZE/lW+9iW/P40dB0nIyQkhPDwcI4cOcLtt9/epGNiY2PRarVs3ryZqKgowFWM9eDBg+5rPt3xq8ml9Nlnn7nv6XXr1p3SNSm0LIqSo3DGdOrUiaFDh3L//ffz8ccfo9Vq+de//oXRaHRbLYxGI/379+f111+nbdu25OXl8dxzz520by8vL5544gmmTp2KLMsMGjSI0tJS1q9fj7e3NxMmTGjwuKKiItLT08nKygJcU2pwrHI2wPTp08nMzOTLL79sjmE4bV544QXGjBlDVFQUN954IyqVip07d7Jnzx5effXVBo958cUXefDBBwkODmbkyJGUl5ezfv16HnnkEYYOHUq3bt24/fbbmTVrFg6Hg4cffpjBgwe7pwKvuuoq3nzzTb788ksGDBjA119/zZ49e046hTF37lycTif9+vXDw8ODr7/+GqPRSHR0NAEBAdx+++3cddddvP3228THx5Ofn8/KlSvp3r07o0ePbtJ4NEX+pvLUU0/Rv39/pkyZwn333Yenpyf79u1jxYoV/O9//yMqKgqdTscHH3zAgw8+yJ49e+rlf5oyZQoffPABt956K9OnT8fHx4dNmzbRt2/felMiTaGyspInn3ySG2+8kbZt25KRkcGWLVu44YYbAFd+GbPZzMqVK+nRowceHh5NkjM6OhpJkli8eDGjRo3CaDSe9venOYiJieHvv//m1ltvRa/XN7lI6UsvvcSjjz6Kj48PI0aMwGq1snXrVoqLi5k2bVq99iaTiXvvvZcnn3ySgIAAgoODefbZZ93WY+C0x8/Pz4+AgAA+/fRTwsLCSE9P5+mnn64nw9VXX824ceOYMmXKKY6SwlmnZV2CFFozpxpCPnLkSKHX60V0dLT49ttvRXBwsPjkk0/cbfbt2ycGDBggjEaj6Nmzp1i+fHmDjsfHO/7JsixmzZolOnbsKLT/394dsyQXxXEcPw510ZuKRBiC5NrQpclMyE2b3Rx6AQ4uLQ0ODkJLr6CtyUYvOAWK2BS01SjoZPQCatGG3zN5Sc0nCeN5uHw/cAe5cD3neNQ/198fNza0s7Oj09NT3d/fLx37NOA4f0zbcafz+43uKtd1vcdfBZ+/mufd3Z2y2ayCwaAikYjS6fRfu3Mk6fr62luT+Vbu71qwJalWqykejysajer8/FyVSmUheDwf3HRdV0dHR4pEIrJtW5lMRp1Oxzs/mUxUq9WUSqW8cRWLRT0/Py+dx3zweJXxT1vIV/H4+Kh8Pq+trS3Zti3HcXR5eemdv729VSqVkmVZOj4+VqvVWnjNnp6eVCgUFAqFFA6HdXJyosFgIGm1EPdn4/FYpVLJa8NPJBKqVCozQehyuazt7e2ZPbvKOOv1unZ3dxUIBLw1/cn757t9LUnRaFQ3NzdLr/Hw8CDHcWRZ1kIL+Weu62r+q6jRaOjw8FCbm5uKxWLK5XJqNptLn+vt7U1nZ2cKhUKKx+O6urpa2L8/Xb92u639/X1ZliXHcdTr9RbWY29vb+azBf+PgDT3wzywBqPRyCSTSdPpdJYGZAEA+E0UOViLbrdr3t/fzcHBgXl9fTUXFxfm5eXF9Pv9teYYAABYFZkcrMXHx4epVqtmOByacDhsstmsaTQaFDgAgH+GOzkAAMCX+BdyAADgSxQ5AADAlyhyAACAL1HkAAAAX6LIAQAAvkSRAwAAfIkiBwAA+BJFDgAA8KU/Ibec9nqrGgAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_states = 16\n", + "# Plot the time course for each state\n", + "for state in range(n_states):\n", + " plt.plot(alpha[0][:30, state], label=f'State {state + 1}')\n", + "\n", + "# Add labels and legend\n", + "plt.xlabel('Timepoints')\n", + "plt.ylabel('Value')\n", + "plt.title('Time Course for Each State')\n", + "plt.legend()\n", + "\n", + "# Add a caption\n", + "plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aaacc891", + "metadata": {}, + "source": [ + "## Update 24th Feb 2024" + ] + }, + { + "cell_type": "markdown", + "id": "4b765b6f", + "metadata": {}, + "source": [ + "Also, we're interested in giving some \"perturbation\" to the \"fixed covariances\"!" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "81790ab4", + "metadata": {}, + "outputs": [], + "source": [ + "fixed_covariances = np.load('./results_simulation_202402_repeat/fixed_covariances.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "9d8c9ebe", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-02-24 17:04:48.812257: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-02-24 17:04:48.817010: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2024-02-24 17:04:48.817026: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "from rotation.simulation import perturb_covariances" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "9cf8a801", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 731.16it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 455.90it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 404.87it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 333.14it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sigmas = [0.025,0.05,0.075,0.1]\n", + "for sigma in sigmas:\n", + " covariances = np.concatenate((perturb_covariances(fixed_covariances[:8],perturbation_factor=sigma),\n", + " perturb_covariances(fixed_covariances[:8],perturbation_factor=sigma)),axis=0)\n", + " seaborn.heatmap(twopair_riemannian_distance(covariances[:8],covariances[8:]))\n", + " plt.show()\n", + " np.save(f'./results_simulation_202402_repeat/fixed_covariances_{sigma}.npy',covariances)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "8beb3537", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 717.91it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fixed_matrices = np.load(f'./results_simulation_202402_repeat/fixed_covariances_0.025.npy')\n", + "seaborn.heatmap(twopair_riemannian_distance(fixed_matrices[:8],fixed_matrices[8:]))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8f8d86ad", + "metadata": {}, + "source": [ + "Now let's check the posterior \"uncertainty\" again." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "716cd681", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "import numpy as np\n", + "import numpy.testing as npt\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "8e525b46", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_0.025/sigma_0/hmm_ICA_25_state_16/'\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']\n", + "covariance_truth = np.load('./results_simulation_202402_repeat/fixed_covariances_0.025.npy')\n", + "for repeat in repeats:\n", + " tpm = np.load(f'{save_dir}{repeat}/model/trans_prob.npy')\n", + " covariances = np.load(f'{save_dir}{repeat}/inf_params/covs.npy')\n", + " means = np.load(f'{save_dir}{repeat}/inf_params/means.npy')\n", + " npt.assert_almost_equal(means,0)\n", + " npt.assert_almost_equal(covariance_truth,covariances,decimal=6)\n", + " seaborn.heatmap(tpm)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "44b84d9a", + "metadata": {}, + "source": [ + "There are still structures in the transition probability matrix (which is good). And we still see run-to-run variability." + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "f6d63805", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for repeat in ['train']:#repeats:\n", + " with open(f'{save_dir}{repeat}/inf_params/alp.pkl','rb') as file:\n", + " data = pickle.load(file)\n", + " n_states = 16\n", + " # Plot the time course for each state\n", + " for state in range(n_states):\n", + " plt.plot(alpha[0][200:300, state], label=f'State {state + 1}')\n", + "\n", + " # Add labels and legend\n", + " plt.xlabel('Timepoints')\n", + " plt.ylabel('Value')\n", + " plt.title('Time Course for Each State')\n", + " #plt.legend()\n", + "\n", + " # Add a caption\n", + " #plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fd428fc1", + "metadata": {}, + "source": [ + "It's very clear that you can definitely see the \"uncertainty\" in the state time courses even when these repeated \"states\" are slightly different. What if when you have larger \"fixed\" state variability?" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "677e135d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_0.1/sigma_0/hmm_ICA_25_state_16/'\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']\n", + "covariance_truth = np.load('./results_simulation_202402_repeat/fixed_covariances_0.1.npy')\n", + "for repeat in repeats:\n", + " tpm = np.load(f'{save_dir}{repeat}/model/trans_prob.npy')\n", + " covariances = np.load(f'{save_dir}{repeat}/inf_params/covs.npy')\n", + " means = np.load(f'{save_dir}{repeat}/inf_params/means.npy')\n", + " npt.assert_almost_equal(means,0)\n", + " npt.assert_almost_equal(covariance_truth,covariances,decimal=6)\n", + " seaborn.heatmap(tpm)\n", + " plt.show()\n", + "\n", + "for repeat in repeats:\n", + " with open(f'{save_dir}{repeat}/inf_params/alp.pkl','rb') as file:\n", + " data = pickle.load(file)\n", + " n_states = 16\n", + " # Plot the time course for each state\n", + " for state in range(n_states):\n", + " plt.plot(alpha[0][200:300, state], label=f'State {state + 1}')\n", + "\n", + " # Add labels and legend\n", + " plt.xlabel('Timepoints')\n", + " plt.ylabel('Value')\n", + " plt.title('Time Course for Each State')\n", + " plt.legend()\n", + "\n", + " # Add a caption\n", + " plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + " # Show the plot\n", + " plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "bbe792f0", + "metadata": {}, + "source": [ + "So the same results still holds. What happens if you only perturb only the second eight states? While fix the first eight states?" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "77e9be58", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 492.74it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 241.42it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 192.85it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 8/8 [00:00<00:00, 375.41it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fixed_covariances = np.load('./results_simulation_202402_repeat/fixed_covariances.npy')\n", + "sigmas = [0.025,0.05,0.075,0.1]\n", + "for sigma in sigmas:\n", + " covariances = np.concatenate((fixed_covariances[:8],\n", + " perturb_covariances(fixed_covariances[:8],perturbation_factor=sigma)),axis=0)\n", + " seaborn.heatmap(twopair_riemannian_distance(covariances[:8],covariances[8:]))\n", + " plt.show()\n", + " np.save(f'./results_simulation_202402_repeat/fixed_covariances_one_{sigma}.npy',covariances)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "a56b11f2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_one_0.05/sigma_0/hmm_ICA_25_state_16/'\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']\n", + "covariance_truth = np.load('./results_simulation_202402_repeat/fixed_covariances_one_0.05.npy')\n", + "for repeat in repeats:\n", + " tpm = np.load(f'{save_dir}{repeat}/model/trans_prob.npy')\n", + " covariances = np.load(f'{save_dir}{repeat}/inf_params/covs.npy')\n", + " means = np.load(f'{save_dir}{repeat}/inf_params/means.npy')\n", + " npt.assert_almost_equal(means,0)\n", + " npt.assert_almost_equal(covariance_truth,covariances,decimal=6)\n", + " seaborn.heatmap(tpm)\n", + " plt.show()\n", + "\n", + "for repeat in repeats:\n", + " with open(f'{save_dir}{repeat}/inf_params/alp.pkl','rb') as file:\n", + " data = pickle.load(file)\n", + " n_states = 16\n", + " # Plot the time course for each state\n", + " for state in range(n_states):\n", + " plt.plot(alpha[0][300:400, state], label=f'State {state + 1}')\n", + "\n", + " # Add labels and legend\n", + " plt.xlabel('Timepoints')\n", + " plt.ylabel('Value')\n", + " plt.title('Time Course for Each State')\n", + " plt.legend()\n", + "\n", + " # Add a caption\n", + " plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + " # Show the plot\n", + " plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "5d2332c0", + "metadata": {}, + "source": [ + "But the question is: in real data, we never see identical states (at least states are slightly different). Is there large uncertainty of posterior on real data" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "efa54349", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_HCP_202402_single_session/HMM_ICA_25_state_6/'\n", + "with open(f'{save_dir}alpha.pkl','rb') as file:\n", + " alpha = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "396073c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1200, 6)" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alpha[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "2b77e619", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_,n_states = alpha[0].shape\n", + "# Plot the time course for each state\n", + "for state in range(n_states):\n", + " plt.plot(alpha[0][300:400, state], label=f'State {state + 1}')\n", + "\n", + "# Add labels and legend\n", + "plt.xlabel('Timepoints')\n", + "plt.ylabel('Value')\n", + "plt.title(f'Time Course for {n_states} States',fontsize=20)\n", + "#plt.legend()\n", + "\n", + "# Add a caption\n", + "#plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + "# Show the plot\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "30bb7e8a", + "metadata": {}, + "source": [ + "Maybe have a look at the free training on simulation data (with eight states as the ground truth, but you feed in 16 states)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "05c15410", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202402/sigma_0/HMM_ICA_25_state_16/'\n", + "with open(f'{save_dir}alpha.pkl','rb') as file:\n", + " alpha = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "eca9743a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_,n_states = alpha[0].shape\n", + "# Plot the time course for each state\n", + "for state in range(n_states):\n", + " plt.plot(alpha[0][500:700, state], label=f'State {state + 1}')\n", + "\n", + "# Add labels and legend\n", + "plt.xlabel('Timepoints')\n", + "plt.ylabel('Value')\n", + "plt.title(f'Time Course for {n_states} States')\n", + "#plt.legend()\n", + "\n", + "# Add a caption\n", + "#plt.figtext(0.5, 0.01, 'Figure 1: Time course for each state in the data.', ha='center')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e9515e69", + "metadata": {}, + "source": [ + "Now let's plot the temporal correlation of different models, which should be very interesting." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "137061e7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import calculate_temporal_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "071bc3ad", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_0/sigma_0/hmm_ICA_25_state_16/'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b106a240", + "metadata": {}, + "outputs": [], + "source": [ + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8e9af5ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9580851516103333\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTemporal Correlation \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepeats[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m vs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepeats[j]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 11\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: " + ] + } + ], + "source": [ + "for i in range(len(repeats)):\n", + " with open(f'{save_dir}{repeats[i]}/inf_params/alp.pkl','rb') as file:\n", + " alpha_1 = pickle.load(file)\n", + " for j in range(i+1,len(repeats)):\n", + " with open(f'{save_dir}{repeats[j]}/inf_params/alp.pkl','rb') as file:\n", + " alpha_2 = pickle.load(file)\n", + " correlation = calculate_temporal_correlation(alpha_1,alpha_2)\n", + " seaborn.heatmap(correlation)\n", + " print(np.mean(np.diagonal(correlation)))\n", + " plt.title(f'Temporal Correlation {repeats[i]} vs {repeats[j]}')\n", + " plt.show()\n", + " raise ValueError('')" + ] + }, + { + "cell_type": "markdown", + "id": "00e4e3d7", + "metadata": {}, + "source": [ + "## Update 27th February 2024" + ] + }, + { + "cell_type": "markdown", + "id": "3b27e4aa", + "metadata": {}, + "source": [ + "Task1: Check the temporal reproducibility, does it give you any kind of information?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d54fed5f", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "import pickle\n", + "from rotation.utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4c2287d3", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_HCP_yaml/'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "84286e8c", + "metadata": {}, + "outputs": [], + "source": [ + "N_states = list(range(2,37))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "aa6e1478", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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Wqn///qqqqjrleyKRiObMmaPly5frkksuOSd1OgoPR48eVZ8+f15j6fP59Nhjj2nmzJmaNm2a9uzZ063z+P1+paWlxW1JPq4aBQB4QyzqS9jW1ag+HA6f9Ds7OjpUX1+vQCDQuS8pKUmBQEC1tbWnrPV73/uehg0bpgULFvTId9EVR3+xx44dq9dff/2k/WvWrNGNN96or371qwkrDAAAt8Riidu6GtUHg8GTfmdLS4sikYgyMzPj9mdmZioUCnVZ5yuvvKKf/exnWrduXY98D6fiKDx87Wtf07/+6792+dqaNWs0e/ZsxWKxhBQGAIBbEtl56GpUX15eftY1Hj58WN/85je1bt06ZWRkJOBTd5+j+zyUl5dbP/Cjjz6qRx999KyLAgDgfOH3++X3+097XEZGhpKTk9XU1BS3v6mpSVlZWScd/8477+i9997TzJkzO/dFoydWaPbp00e7d+/WX//1X59l9V1joQEAAIZEdh66KyUlRfn5+aqpqencF41GVVNTo6KiopOOHzt2rN58801t3769c/vqV7+qr3zlK9q+fbsuuuiihHwXXeEOkwAAGNyawJeVlWnevHkqKCjQpEmTtHr1arW3t3feQ2nu3LkaMWKEgsGgUlNTNX78+Lj3Dx48WJJO2p9ohAcAADyipKREBw8e1LJlyxQKhTRx4kRVV1d3LqJsbGxUUpL7QwPCAwAABjcfjLVkyRItWbKky9defPFF63vXr1+f+IK6QHgAAMBwJreV7k3c730AAIDPFToPAAAYeFajHeEBAABDlLGFFWMLAADgCJ0HAAAMLJi0IzwAAGBw81LNzwPCAwAABp7xaMeaBwAA4AidBwAADIwt7AgPAAAYuFTTjrEFAABwhM4DAAAGLtW0IzwAAGDgags7xhYAAMAROg8AABhYMGlHeAAAwMCaBzvGFgAAwBE6DwAAGFgwaUd4AADAwJoHO8+Eh/V9hrtdgifM6fjA7RI84d5nMt0uwRO+7Tvqdgme8Iv2Q26X4AmrUj90uwTP+EYPn581D3aseQAAAI54pvMAAIBXMLawIzwAAGBgvaQdYwsAAOAInQcAAAyMLewIDwAAGLjawo6xBQAAcITOAwAAhqjbBXgc4QEAAENMjC1sGFsAAABH6DwAAGCIcqMHK8IDAACGKGMLK8IDAAAG1jzYseYBAAA4QucBAAADl2raER4AADAwtrBjbAEAAByh8wAAgIGxhR3hAQAAA+HBjrEFAABwhM4DAAAGFkzaER4AADBEyQ5WjC0AAIAjdB4AADDwbAs7wgMAAAYeqmlHeAAAwMClmnaseQAAwEPWrl2rnJwcpaamqrCwUHV1dac8dt26dZo6daouuOACXXDBBQoEAtbjE4XwAACAIerzJWxzYvPmzSorK1NFRYUaGhqUl5en4uJiNTc3d3n8iy++qNmzZ+uFF15QbW2tLrroIs2YMUP79+9PxNdwSoQHAAAMsQRuTqxatUoLFy5UaWmpcnNzVVlZqf79+6uqqqrL43/5y1/qW9/6liZOnKixY8fqpz/9qaLRqGpqapx+ZEccr3nYuXOnXnvtNRUVFWns2LHatWuXHnnkEYXDYd166626+uqrT3uOcDiscDgct68jFlGKL9lpOQAAeFpXf/P8fr/8fn/cvo6ODtXX16u8vLxzX1JSkgKBgGpra7v1uz755BMdP35cQ4YMOfvCLRx1HqqrqzVx4kTdfffduvzyy1VdXa0vfelL2rt3r95//33NmDFDzz///GnPEwwGlZ6eHretP7znjD8EAACJFE3g1tXfvGAweNLvbGlpUSQSUWZmZtz+zMxMhUKhbtV97733avjw4QoEAs4/tAOOwsP3vvc93XPPPfroo4/085//XLfccosWLlyorVu3qqamRvfcc49Wrlx52vOUl5ertbU1bps/6NIz/hAAACRS1Je4rau/eX/ZXUiUlStXatOmTdqyZYtSU1MTfv6/5Cg8vPXWW5o/f74k6aabbtLhw4f1t3/7t52vz5kzR//zP/9z2vP4/X6lpaXFbYwsAADno67+5pkjC0nKyMhQcnKympqa4vY3NTUpKyvL+jsefvhhrVy5Uv/+7/+uCRMmJLT+rjheMOn7v5WjSUlJSk1NVXp6eudrgwYNUmtra+KqAwDABVH5ErZ1V0pKivLz8+MWO362+LGoqOiU7/vBD36ghx56SNXV1SooKDirz91djsJDTk6O3n777c6fa2trdfHFF3f+3NjYqOzs7MRVBwCAC9y62qKsrEzr1q3Thg0btHPnTi1atEjt7e0qLS2VJM2dOzdu5PGP//iPeuCBB1RVVaWcnByFQiGFQiEdOXLkjD97dzi62mLRokWKRCKdP48fPz7u9d///vfdutoCAACcrKSkRAcPHtSyZcsUCoU0ceJEVVdXdy6ibGxsVFLSn/+7/7HHHlNHR0fcEgJJqqio0IMPPthjdfpisZgnbuG9bcTX3C7BE+Z0fOB2CZ4QGHCJ2yV4wiLfUbdL8ISCA9vdLsETrrhwtNsleEbt/hd69Py/GHFrws41d/+/JOxcXsGzLQAAMPBsCzvCAwAABk+05D2M21MDAABH6DwAAGCIOnueVa9DeAAAwMCaBzvGFgAAwBE6DwAAGOg82BEeAAAwxFjzYMXYAgAAOELnAQAAA2MLO8IDAAAGwoMdYwsAAOAInQcAAAzcntqO8AAAgIE7TNoRHgAAMLDmwY41DwAAwBE6DwAAGOg82BEeAAAwsGDSjrEFAABwhM4DAAAGrrawIzwAAGBgzYMdYwsAAOAInQcAAAwsmLQjPAAAYIgSH6w8Ex6KDta5XYInXJd1udsleEJd+IDbJXjCU+H/dbsET2jdusLtEjzhmpvXu10CIMlD4QEAAK9gwaQd4QEAAANDCzvCAwAABjoPdlyqCQAAHKHzAACAgTtM2hEeAAAwcKmmHWMLAADgCJ0HAAAM9B3sCA8AABi42sKOsQUAAHCEzgMAAAYWTNoRHgAAMBAd7BhbAAAAR+g8AABgYMGkHeEBAAADax7sCA8AABiIDnaseQAAAI7QeQAAwMCaBzvCAwAAhhiDCyvGFgAAwBE6DwAAGBhb2BEeAAAwcKmmHWMLAAA8ZO3atcrJyVFqaqoKCwtVV1dnPf7Xv/61xo4dq9TUVF122WV69tlne7xGwgMAAIZYAjcnNm/erLKyMlVUVKihoUF5eXkqLi5Wc3Nzl8e/+uqrmj17thYsWKA//OEPmjVrlmbNmqUdO3Y4/ciOEB4AADBEFUvY5sSqVau0cOFClZaWKjc3V5WVlerfv7+qqqq6PP6RRx7Rtddeq3vuuUfjxo3TQw89pCuuuEJr1qxJxNdwSoQHAAB6UDgcVltbW9wWDodPOq6jo0P19fUKBAKd+5KSkhQIBFRbW9vluWtra+OOl6Ti4uJTHp8ohAcAAAzRBG7BYFDp6elxWzAYPOl3trS0KBKJKDMzM25/ZmamQqFQl3WGQiFHxydKQq62iMVi8vl8iTgVAACuS+RNosrLy1VWVha3z+/3J+z8bkhIePD7/XrjjTc0bty4RJwOAABXJfI+D36/v1thISMjQ8nJyWpqaorb39TUpKysrC7fk5WV5ej4RHEUHszk9JlIJKKVK1fqwgsvlHRiwYdNOBw+ad5D9wIA0JulpKQoPz9fNTU1mjVrliQpGo2qpqZGS5Ys6fI9RUVFqqmp0Z133tm5b+vWrSoqKurRWh2Fh9WrVysvL0+DBw+O2x+LxbRz504NGDCgWwEgGAxq+fLlcft8SQPlS05zUg4AAD3CrWdblJWVad68eSooKNCkSZO0evVqtbe3q7S0VJI0d+5cjRgxonPNxNKlSzVt2jT96Ec/0g033KBNmzbp9ddf1+OPP96jdToKDytWrNDjjz+uH/3oR7r66qs79/ft21fr169Xbm5ut87T1fznggvHOikFAIAe49btqUtKSnTw4EEtW7ZMoVBIEydOVHV1deeiyMbGRiUl/flah8mTJ2vjxo26//779d3vfldf+MIX9OSTT2r8+PE9WqcvFos5ilfbtm3TrbfeqpkzZyoYDKpv377q27ev3njjjW6Hh670SRlxxu89n1yXdbnbJXjCgeOtbpfgCaHw/7pdgifs/e29bpfgCdfcvN7tEjzjP/fX9Oj55+V8PWHn2vDevyXsXF7h+FLNK6+8UvX19Tp48KAKCgq0Y8cO1ioAAM4r0VgsYdv56Iyuthg4cKA2bNigTZs2KRAIKBKJJLouAABcc37+yU+cs7pU8+abb9ZVV12l+vp6jRo1KlE1AQAADzvr+zyMHDlSI0eOTEQtAAB4Ao/ktkvITaIAADifuHWp5ucFz7YAAACO0HkAAMDg1n0ePi8IDwAAGFjzYEd4AADAwJoHO9Y8AAAAR+g8AABgYM2DHeEBAACDw8c+9TqMLQAAgCN0HgAAMHC1hR3hAQAAA2se7BhbAAAAR+g8AABg4D4PdoQHAAAMrHmwY2wBAAAcofMAAICB+zzYER4AADBwtYUd4QEAAAMLJu1Y8wAAAByh8wAAgIGrLewIDwAAGFgwacfYAgAAOELnAQAAA2MLO8IDAAAGrraw80x4GNo/3e0SPGH/8Y/dLsETpvlHul2CJ8xJvtDtEjzh0ht/4HYJnrB96hC3SwAkeSg8AADgFVEWTFoRHgAAMBAd7LjaAgAAOELnAQAAA1db2BEeAAAwEB7sCA8AABi4w6Qdax4AAIAjdB4AADAwtrAjPAAAYOAOk3aMLQAAgCN0HgAAMLBg0o7wAACAgTUPdowtAACAI3QeAAAwMLawIzwAAGBgbGHH2AIAADhC5wEAAAP3ebAjPAAAYIiy5sGKsQUAAIZYAv/XUw4dOqQ5c+YoLS1NgwcP1oIFC3TkyBHr8XfccYfGjBmjfv366eKLL9a3v/1ttba2Ov7dhAcAAD6H5syZo7feektbt27VM888o5dfflm33XbbKY//8MMP9eGHH+rhhx/Wjh07tH79elVXV2vBggWOf7cv5pHrUbIH57pdgidk9xvidgmeMM0/0u0SPGHOp8fcLsET/uaTd9wuwRO2T+XfD58Z8tuXevT844ZNSti5djbXJexcnefcuVO5ubnatm2bCgoKJEnV1dW6/vrr9ac//UnDhw/v1nl+/etf69Zbb1V7e7v69On+SgY6DwAAGBI5tgiHw2pra4vbwuHwWdVXW1urwYMHdwYHSQoEAkpKStJ///d/d/s8ra2tSktLcxQcJMIDAAA9KhgMKj09PW4LBoNndc5QKKRhw4bF7evTp4+GDBmiUCjUrXO0tLTooYceso46ToXwAACAIRqLJWwrLy9Xa2tr3FZeXt7l7/3Od74jn89n3Xbt2nXWn6+trU033HCDcnNz9eCDDzp+P5dqAgBgSORVEn6/X36/v1vH/sM//IPmz59vPeaSSy5RVlaWmpub4/Z/+umnOnTokLKysqzvP3z4sK699loNGjRIW7ZsUd++fbtV218iPAAA4BFDhw7V0KFDT3tcUVGRPv74Y9XX1ys/P1+S9PzzzysajaqwsPCU72tra1NxcbH8fr+eeuoppaamnlGdjC0AADAkcmzRE8aNG6drr71WCxcuVF1dnf7rv/5LS5Ys0c0339x5pcX+/fs1duxY1dWduNqjra1NM2bMUHt7u372s5+pra1NoVBIoVBIkUjE0e+n8wAAgOHzcHvqX/7yl1qyZImmT5+upKQkff3rX9ePf/zjztePHz+u3bt365NPPpEkNTQ0dF6JMXr06Lhzvfvuu8rJyen27z6r8NDe3q5f/epX2rt3r7KzszV79mxdeOGFp31fOBw+6TKVWCwqn49GCAAA3TFkyBBt3LjxlK/n5OTEPVr8y1/+csIeNe7or3Vubq4OHTokSfrggw80fvx43XXXXdq6dasqKiqUm5urd99997Tn6eqylSPhj87sEwAAkGCxWDRh2/nIUXjYtWuXPv30U0lSeXm5hg8frvfff191dXV6//33NWHCBN13332nPU9Xl60M9J++YwEAwLkQVSxh2/nojMcWtbW1qqysVHp6uiRp4MCBWr58uW6++ebTvrery1YYWQAAvMIjT27wLMd/sX0+nyTp2LFjys7OjnttxIgROnjwYGIqAwAAnuS48zB9+nT16dNHbW1t2r17t8aPH9/52vvvv9+tBZMAAHjZ+TpuSBRH4aGioiLu54EDB8b9/PTTT2vq1KlnXxUAAC5ibGF3VuHB9MMf/vCsigEAAN7HTaIAADD01J0hzxeEBwAADJ+HO0y6iesjAQCAI3QeAAAwsGDSjvAAAICBSzXtGFsAAABH6DwAAGBgbGFHeAAAwMClmnaEBwAADHQe7FjzAAAAHKHzAACAgast7AgPAAAYGFvYMbYAAACO0HkAAMDA1RZ2hAcAAAw8GMuOsQUAAHCEzgMAAAbGFnaEBwAADFxtYcfYAgAAOELnAQAAAwsm7QgPAAAYGFvYER4AADAQHuxY8wAAAByh8wAAgIG+g50vRm9GkhQOhxUMBlVeXi6/3+92Oa7heziB7+EEvocT+B5O4HvAZwgP/6etrU3p6elqbW1VWlqa2+W4hu/hBL6HE/geTuB7OIHvAZ9hzQMAAHCE8AAAABwhPAAAAEcID//H7/eroqKi1y8C4ns4ge/hBL6HE/geTuB7wGdYMAkAAByh8wAAABwhPAAAAEcIDwAAwBHCAwAAcITwIGnt2rXKyclRamqqCgsLVVdX53ZJ59zLL7+smTNnavjw4fL5fHryySfdLskVwWBQV155pQYNGqRhw4Zp1qxZ2r17t9tlnXOPPfaYJkyYoLS0NKWlpamoqEi///3v3S7LdStXrpTP59Odd97pdinn1IMPPiifzxe3jR071u2y4KJeHx42b96ssrIyVVRUqKGhQXl5eSouLlZzc7PbpZ1T7e3tysvL09q1a90uxVUvvfSSFi9erNdee01bt27V8ePHNWPGDLW3t7td2jk1cuRIrVy5UvX19Xr99dd19dVX68Ybb9Rbb73ldmmu2bZtm37yk59owoQJbpfiii9+8Ys6cOBA5/bKK6+4XRLcFOvlJk2aFFu8eHHnz5FIJDZ8+PBYMBh0sSp3SYpt2bLF7TI8obm5OSYp9tJLL7ldiusuuOCC2E9/+lO3y3DF4cOHY1/4whdiW7dujU2bNi22dOlSt0s6pyoqKmJ5eXlulwEP6dWdh46ODtXX1ysQCHTuS0pKUiAQUG1trYuVwStaW1slSUOGDHG5EvdEIhFt2rRJ7e3tKioqcrscVyxevFg33HBD3L8repu3335bw4cP1yWXXKI5c+aosbHR7ZLgoj5uF+CmlpYWRSIRZWZmxu3PzMzUrl27XKoKXhGNRnXnnXdqypQpGj9+vNvlnHNvvvmmioqKdOzYMQ0cOFBbtmxRbm6u22Wdc5s2bVJDQ4O2bdvmdimuKSws1Pr16zVmzBgdOHBAy5cv19SpU7Vjxw4NGjTI7fLggl4dHgCbxYsXa8eOHb12tjtmzBht375dra2t+s1vfqN58+bppZde6lUB4oMPPtDSpUu1detWpaamul2Oa6677rrOf54wYYIKCws1atQo/epXv9KCBQtcrAxu6dXhISMjQ8nJyWpqaorb39TUpKysLJeqghcsWbJEzzzzjF5++WWNHDnS7XJckZKSotGjR0uS8vPztW3bNj3yyCP6yU9+4nJl5059fb2am5t1xRVXdO6LRCJ6+eWXtWbNGoXDYSUnJ7tYoTsGDx6sSy+9VHv37nW7FLikV695SElJUX5+vmpqajr3RaNR1dTU9NrZbm8Xi8W0ZMkSbdmyRc8//7z+6q/+yu2SPCMajSocDrtdxjk1ffp0vfnmm9q+fXvnVlBQoDlz5mj79u29MjhI0pEjR/TOO+8oOzvb7VLgkl7deZCksrIyzZs3TwUFBZo0aZJWr16t9vZ2lZaWul3aOXXkyJG4/4p49913tX37dg0ZMkQXX3yxi5WdW4sXL9bGjRv129/+VoMGDVIoFJIkpaenq1+/fi5Xd+6Ul5fruuuu08UXX6zDhw9r48aNevHFF/Xcc8+5Xdo5NWjQoJPWuwwYMEAXXnhhr1oHc/fdd2vmzJkaNWqUPvzwQ1VUVCg5OVmzZ892uzS4pNeHh5KSEh08eFDLli1TKBTSxIkTVV1dfdIiyvPd66+/rq985SudP5eVlUmS5s2bp/Xr17tU1bn32GOPSZK+/OUvx+3/+c9/rvnz55/7glzS3NysuXPn6sCBA0pPT9eECRP03HPP6ZprrnG7NLjgT3/6k2bPnq2PPvpIQ4cO1VVXXaXXXntNQ4cOdbs0uIRHcgMAAEd69ZoHAADgHOEBAAA4QngAAACOEB4AAIAjhAcAAOAI4QEAADhCeAAAAI4QHgAAgCOEBwAA4AjhAQAAOEJ4AAAAjhAeAACAI/8ftTWFuhXvAaEAAAAASUVORK5CYII=", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temporal_indice:{'row': [0, 1, 2, 3, 4, 5, 6, 7, 8], 'col': [4, 7, 0, 3, 5, 1, 6, 2, 8]}\n", + "temporal_correlation:0.5666194336670576\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Riemannian distances: 100%|████████████| 9/9 [00:00<00:00, 682.32it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spatial_indice:{'row': [0, 1, 2, 3, 4, 5, 6, 7, 8], 'col': [3, 1, 2, 5, 8, 6, 0, 7, 4]}\n", + "spatial_correlation:2.7591610805691387\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './results_HCP_yaml/hmm_ICA_25_state_11/repeat_1/inf_params/alp.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (N_state \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m8\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (N_state \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m10\u001b[39m):\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msave_dir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43mhmm_ICA_25_state_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mN_state\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/repeat_1/inf_params/alp.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[1;32m 5\u001b[0m alpha_1 \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(file)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msave_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mhmm_ICA_25_state_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mN_state\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/repeat_2/inf_params/alp.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m file:\n", + "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/IPython/core/interactiveshell.py:284\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 279\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 282\u001b[0m )\n\u001b[0;32m--> 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './results_HCP_yaml/hmm_ICA_25_state_11/repeat_1/inf_params/alp.pkl'" + ] + } + ], + "source": [ + "for N_state in N_states:\n", + " if (N_state == 8) or (N_state == 10):\n", + " continue\n", + " with open(f'{save_dir}hmm_ICA_25_state_{N_state}/repeat_1/inf_params/alp.pkl','rb') as file:\n", + " alpha_1 = pickle.load(file)\n", + " with open(f'{save_dir}hmm_ICA_25_state_{N_state}/repeat_2/inf_params/alp.pkl','rb') as file:\n", + " alpha_2 = pickle.load(file)\n", + " correlation = calculate_temporal_correlation(alpha_1,alpha_2)\n", + " temporal_indice, correlation_pair = hungarian_pair(correlation)\n", + " seaborn.heatmap(correlation_pair)\n", + " plt.show()\n", + " print(f'temporal_indice:{temporal_indice}')\n", + " print(f'temporal_correlation:{np.mean(np.diagonal(correlation_pair))}')\n", + " cov_1 = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/repeat_4/inf_params/covs.npy')\n", + " cov_2 = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/repeat_5/inf_params/covs.npy')\n", + " riem_distance = twopair_riemannian_distance(cov_1,cov_2)\n", + " spatial_indice, riem_distance_pair = hungarian_pair(riem_distance,distance=True)\n", + " print(f'spatial_indice:{spatial_indice}')\n", + " print(f'spatial_correlation:{np.mean(np.diagonal(riem_distance))}')\n", + " seaborn.heatmap(riem_distance_pair)\n", + " plt.show()\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "65296f1b", + "metadata": {}, + "source": [ + "## Update 4th March 2024" + ] + }, + { + "cell_type": "markdown", + "id": "f38dfa9e", + "metadata": {}, + "source": [ + "Now we're going to analysis the results obtained previously. There are three folders as follows:\n", + "- Simulation data with covariance fixed: `results_simulation_202402_repeat`\n", + "- Simulation data with free covariance: `results_simulation_202403`\n", + "- HCP data with free covariance: `results_HCP_yaml_202403`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cdb80a50", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import yaml\n", + "import pickle\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn\n", + "from rotation.utils import *" + ] + }, + { + "cell_type": "markdown", + "id": "ef45fa12", + "metadata": {}, + "source": [ + "### Question 1: When you have repeated states, there are run-to-run variability." + ] + }, + { + "cell_type": "markdown", + "id": "1722c4c4", + "metadata": {}, + "source": [ + "Does it impact the run-to-run variability? Maybe some states are silenced." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a740cfbe", + "metadata": {}, + "outputs": [], + "source": [ + "from osl_dynamics.utils.plotting import plot_matrices\n", + "from osl_dynamics.analysis.modes import fractional_occupancies\n", + "from osl_dynamics.utils.plotting import plot_violin" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "972fd982", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202402_repeat/cov_0/sigma_0/hmm_ICA_25_state_16/'\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2bf0f852", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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oqalBZGQksrOzcfvtt7c7p6RiIDs7GzExMejVq5fNcVEUcfToUQQEBEClanvjUbPZDLPZ3CxGe55LRETUVe3atQt6vR45OTlITExEdnY2UlJSUFZWhpCQkGaPb2xsxO9//3uEhITgrbfeQkREBMrLy5v9nW6LpGJg9erV2LRpE5599lnceuut1uO+vr7Yvn07hg0b1q44RqMRK1assDmmUveAShMopTtERESKcNW9CdavX4/MzExkZGQAAHJycvDuu+9i69atWLJkSbPHb926FTU1Nfjkk0/g6+sLAIiKipKcV9KagSVLlmDXrl2YPXs2Hn30UTQ1NUlOCAAGgwG1tbU2TaXu2aFYREREshNE2ZrZbEZdXZ1Nsx8dB379lF9SUoLk5GTrMbVajeTkZBQVFTns5p49ezB69GjMmTMHoaGhGDFiBFavXg2LxSLp5UpeQHjDDTegpKQE58+fR3x8PI4cOSJ5eF+r1SIwMNCmcYqAiIi6IqPRiKCgIJtmNBqbPa66uhoWiwWhoaE2x0NDQ2EymRzGPnnyJN566y1YLBa89957WLZsGZ599lk89dRTkvrYoX0GevTogR07dmDnzp1ITk6WXIEQERG5NRkXEBoMhmYL8LVarSyxBUFASEgINm3aBI1Gg7i4OJw5cwbr1q1DVlZWu+N0atOhadOm4cYbb0RJSQkiIyM7E4qIiMh9yHhJoFarbdcf/+DgYGg0GlRVVdkcr6qqQlhYmMPn6HQ6+Pr6QqPRWI8NHToUJpMJjY2N8PPza1cfO73PwNVXX43JkycjICCgs6GIiIjcg4xrBtrLz88PcXFxyM/P/283BAH5+fkYPXq0w+eMHTsWx48fh/CbBY/Hjh2DTqdrdyEA8EZFREREbkOv12Pz5s3YsWMHjh49itmzZ6O+vt56dUFaWhoMBoP18bNnz0ZNTQ3mzZuHY8eO4d1338Xq1asxZ84cSXl5bwIiIiI7oos2HZo6dSrOnz+P5cuXw2QyITY2Fnl5edZFhRUVFVCr//s5vl+/fti7dy8WLFiAUaNGISIiAvPmzcPixYsl5VWJbnJjAR+/CFd3odOccT2EM/6xkkKGK57j05pjiucQFP7RtgjKL5x1xtvTGT9TXeFaoYsFTyueo+ctixTP4Qw+GuU/Z/7yS7mi8S/+5Q+yxer5/DuyxVIKpwmIiIi8HKcJiIiI7LloB0JXcZtiwEetaftBnaT18VU0fkPjJUXjA84Zbt1/7mvFc/y0IFHxHH2yixWN74whfGcMtzZZLiuewxmbiik9LdTDCUP4WbqbFc/xZGWh4jncZPa5c1x4oyJX4DQBERGRl3ObkQEiIiK34WUjAywGiIiI7HSJqQ4JOE1ARETk5TgyQEREZI/TBERERF6OxQAREZF3c9V2xK7CNQNERERejiMDRERE9rxsZKBTxUB9fT3efPNNHD9+HDqdDtOnT0ffvn3l6hsREZFreNduxNKKgWHDhuHAgQPo06cPTp8+jZtuugk//vgjrr32Wpw4cQIrV67Ep59+igEDBrQax2w2w2w22xwTRdEpW5YSERGRLUlrBr799ltcvvzrPuYGgwHh4eEoLy9HcXExysvLMWrUKCxdurTNOEajEUFBQTbNYqnr2CsgIiKSmSiIsjVP0OEFhEVFRXjiiScQFBQEAOjRowdWrFiBAwcOtPlcg8GA2tpam6bRBHa0K0RERPISRPmaB5C8ZuDKUP6lS5eg0+lszkVEROD8+fNtxtBqtdBqtQ7jEhERkXNJLgbGjx8PHx8f1NXVoaysDCNGjLCeKy8v5wJCIiLyfFxA2LKsrCybr3v06GHz9dtvv41x48Z1vldEREQu5Clz/XLpVDFgb926dZ3qDBERETkfNx0iIiKyx2kCIiIi78ZpAiIiIm/nZSMDvFERERGRl+PIABERkR3Ry0YGVKIousXEiJ/2asVzKL2xkTO+lWqV8oM5lwWL4jk0auVfx88/7Fc0fo+rkxSNDzjnZ8oZG365ya+ZThGc8Bp8Ncp/PtM44XdIk3BZ8RzmS6cVjX/hDvne333fVfZ3kRw4TUBEROTlOE1ARERkx9umCVgMEBER2fOyYoDTBERERF6OIwNERER2OE1ARETk5VgMEBEReTlvKwa4ZoCIiMiNbNiwAVFRUfD390diYiKKi4tbfOz27duhUqlsmr+/v+ScLAaIiIjsiSr5mgS7du2CXq9HVlYWSktLERMTg5SUFJw7d67F5wQGBqKystLaysvLJb9cFgNERER2REG+JsX69euRmZmJjIwMDBs2DDk5OejevTu2bt3a4nNUKhXCwsKsLTQ0VPLrlVQMlJaW4tSpU9avX3nlFYwdOxb9+vXDjTfeiJ07d7YrjtlsRl1dnU3rCtuVEhER2XP0N89sNjd7XGNjI0pKSpCcnGw9plarkZycjKKiohbj//zzz4iMjES/fv0wefJkfP3115L7KKkYyMjIwIkTJwAAf/vb3/A///M/iI+Px9KlS3HDDTcgMzOz1erlCqPRiKCgIJsmWC5K7jwREZESREElW3P0N89oNDbLWV1dDYvF0uyTfWhoKEwmk8N+Dh48GFu3bsW//vUvvPrqqxAEAWPGjMEPP/wg6fVKulFR9+7dcfToUURGRuL666/H7NmzkZmZaT3/+uuvY9WqVW1WJWazuVlV1Dd4qOI3TOGNitqHNypqH96oqP26wsgfb1TUfl3hRkVnx9wiW6y+BXnN/uZptVpotVrbnGfPIiIiAp988glGjx5tPb5o0SLs378fBw8ebDNXU1MThg4diunTp2PlypXt7qOkn7zu3bujuroakZGROHPmDBISEmzOJyYm2kwjtMTRN8EZv5CIiIiczdHfPEeCg4Oh0WhQVVVlc7yqqgphYWHtyuXr64vrrrsOx48fl9RHSSXixIkTsXHjRgBAUlIS3nrrLZvzb775JqKjoyV1gIiIyN2Iokq21l5+fn6Ii4tDfn6+9ZggCMjPz7cZKWiNxWLBV199BZ1OJ+n1ShoZWLt2LcaOHYukpCTEx8fj2WefRWFhIYYOHYqysjJ8+umn2L17t6QOEBERuRtXbTqk1+uRnp6O+Ph4JCQkIDs7G/X19cjIyAAApKWlISIiwrrm4Mknn8Tvfvc7REdH46effsK6detQXl6OBx54QFJeScVAeHg4Pv/8c6xZswZvv/02RFFEcXExTp8+jbFjx+I///kP4uPjJXWAiIiIfjV16lScP38ey5cvh8lkQmxsLPLy8qyLCisqKqD+zZqrH3/8EZmZmTCZTOjduzfi4uLwySefYNiwYZLySlpAqCQ/7dWK5+ACwvbhAsL24QLC9nOTXzOdwgWE7dcVFhCevmG8bLH6Hcpv+0EuxnsTEBER2ekC9askLAaIiIjsiIJ3XeHG7YiJiIi8HEcGiIiI7HjbyIBXFQMWwfNvUC2IXWNxn+CEf4uQqAmKxo/sKf1mIFKVX6xq+0Gd5Iz3hY9ao3gOpRe+OmNxnzMWWpotjYrn+PnzlxXPoTRvWzPAaQIiIiIv51UjA0RERO3BaQIiIiIvJ2Ub4a6A0wRERERejiMDREREdlx1bwJXYTFARERkR+A0AREREXkTjgwQERHZ8bYFhCwGiIiI7PDSQiIiIi/HHQhb8cgjj+Df//53p5OazWbU1dXZtK5wv3MiIiJPJKkY2LBhA26++WZce+21WLt2LUwmU4eSGo1GBAUF2TTBcrFDsYiIiOQmCirZmieQfDXBBx98gNtvvx3PPPMM+vfvj8mTJ+Odd96RdOMZg8GA2tpam6bW9JTaFSIiIkUIokq25gkkFwMjR45EdnY2zp49i1dffRVmsxmpqano168fli5diuPHj7cZQ6vVIjAw0KapVJ7xDSMiIupqOrzPgK+vL+69917k5eXh5MmTyMzMxGuvvYbBgwfL2T8iIiKnE0WVbM0TyLLpUP/+/fHEE0/g1KlTyMvLkyMkERGRy4iifM0TSCoGIiMjodFoWjyvUqnw+9//vtOdIiIiIueRtM/AqVOnlOoHERGR2/CUhX9y4aZDREREdjxlrl8uvFERERGRl+PIABERkR1PWfgnFxYDREREdrhmwEXqDuYoniN4zCOKxm8SLisaH4CknR47yuKEHN19tYrnaLhsVjT+93Ud245bisjAUMVzVFw8p3gOQVT+Z0qjVnbWs8mi/Ptb7YTN15zxgbfn9emK52g0/6BofK4ZICIiIq/iNiMDRERE7oLTBERERF7Oy9YPcpqAiIjI23FkgIiIyI63TRNwZICIiMiOK+9auGHDBkRFRcHf3x+JiYkoLi5u1/N27twJlUqF1NRUyTlZDBAREbmJXbt2Qa/XIysrC6WlpYiJiUFKSgrOnWv9EuDvv/8ejz76KMaNG9ehvCwGiIiI7AgyNinWr1+PzMxMZGRkYNiwYcjJyUH37t2xdevWFp9jsVgwY8YMrFixAgMHDpSY8VcsBoiIiOyIUMnWzGYz6urqbJrZ3HxTtMbGRpSUlCA5Odl6TK1WIzk5GUVFRS329cknn0RISAj+/Oc/d/j1shggIiJSkNFoRFBQkE0zGo3NHlddXQ2LxYLQUNudR0NDQ2EyOd7x9MCBA9iyZQs2b97cqT5KLgZefPFFpKWlYefOnQCAV155BcOGDcOQIUPw2GOP4fLltrfsdFglNTZJ7z0REZECBFG+ZjAYUFtba9MMBkOn+3jx4kXcf//92Lx5M4KDgzsVS9KlhU899RSefvppTJgwAQsWLEB5eTnWrVuHBQsWQK1W469//St8fX2xYsWKVuMYjcZmj1n6P9Px+EP3SX8FREREMhMg36WFWq0WWm3b92MJDg6GRqNBVVWVzfGqqiqEhYU1e/yJEyfw/fffY9KkSdZjV+5f4+Pjg7KyMgwaNKhdfZRUDGzfvh3bt2/H3XffjS+++AJxcXHYsWMHZsyYAQAYMmQIFi1a1GYxYDAYoNfrbY6J3+ZL6QoREZFiRBmLgfby8/NDXFwc8vPzrZcHCoKA/Px8zJ07t9njhwwZgq+++srm2OOPP46LFy/iueeeQ79+/dqdW1IxcPbsWcTHxwMAYmJioFarERsbaz1//fXX4+zZs23GcVQlXfLzldIVIiKiLkev1yM9PR3x8fFISEhAdnY26uvrkZGRAQBIS0tDREQEjEYj/P39MWLECJvn9+rVCwCaHW+LpGIgLCwM33zzDfr374/vvvsOFosF33zzDYYPHw4A+PrrrxESEiKpA0RERO5G+ZtuOzZ16lScP38ey5cvh8lkQmxsLPLy8qyLCisqKqBW4HbdkoqBGTNmIC0tDZMnT0Z+fj4WLVqERx99FBcuXIBKpcKqVatwzz33yN5JIiIiZ3LFNMEVc+fOdTgtAACFhYWtPnf79u0dyimpGFixYgW6deuGoqIiZGZmYsmSJYiJicGiRYvQ0NCASZMmYeXKlR3qCBEREbmGpGJArVbjscceszk2bdo0TJs2TdZOERERuZKrpglchXctJCIisuNtxQB3ICQiIvJyHBkgIiKy48oFhK7AYoCIiMiO4F21AKcJiIiIvJ3bjAz0+t3Diueo++5tReMHXXunovEBOKV8swjKL50RRFHxHL5qZX+8L11uVDQ+AFRcPKd4jgBff8Vz/Nz4i+I51Cpl3xwWJywpc8b7whkfeJ3xOpQm570JPIHbFANERETuwvPLGWlYDBAREdnhpYVERETkVTgyQEREZEdQcc0AERGRV/O2NQOcJiAiIvJyHBkgIiKy420LCFkMEBER2fG2HQglFwOVlZXYuHEjDhw4gMrKSqjVagwcOBCpqamYOXMmNBqNEv0kIiIihUhaM/DZZ59h6NCheO+999DU1ITvvvsOcXFxCAgIwKOPPoqbbroJFy9ebDOO2WxGXV2dTRO7wI5VRETUNQhQydY8gaRiYP78+ViwYAE+++wz/Pvf/8b27dtx7Ngx7Ny5EydPnkRDQwMef/zxNuMYjUYEBQXZNIulrsMvgoiISE6ijM0TqEQJH8m7d++OI0eOYODAgQAAQRDg7++P06dPIzQ0FPv27cPMmTNx5syZVuOYzWaYzWabY1ddNRwqha/r7Ar3JhBE5Ze1OOPeBP4+fornUJoz7k2gUSt/wU9XuTeBRq3sFGWT5bKi8Z3FGZ9TnfEH8HJj639nOuvV8D/JFutPZ1+VLZZSJK0ZCAkJQWVlpbUYqKqqwuXLlxEYGAgAuOaaa1BTU9NmHK1WC61Wa3NM6UKAiIiovbxtAaGkjx2pqal46KGHkJeXh4KCAsyYMQNJSUno1q0bAKCsrAwRERGKdJSIiMhZBBmbJ5A0MvDUU0+hsrISkyZNgsViwejRo/Hqq/8d/lCpVDAajbJ3koiIyJk8Za5fLpKKgR49emDXrl24dOkSLl++jB49eticnzBhgqydIyIiIuV1aNMhf3/lFxwRERG5iretGeAOhERERHY8Za5fLrxRERERkZfjyAAREZEdbxsZYDFARERkR/SyNQOcJiAiIvJybjMyYBEsiufoec0fFI1/fsYQReMDQMhrZYrn6CrUCu9q6YytglVO2Dz2orlB8RzLdTcrnmOlab/iOZTmjA+jSm/bDABiF7hKn9MEREREXs7bigFOExAREbmRDRs2ICoqCv7+/khMTERxcXGLj83NzUV8fDx69eqFgIAAxMbG4pVXXpGck8UAERGRHVfdwnjXrl3Q6/XIyspCaWkpYmJikJKSgnPnzjl8fJ8+fbB06VIUFRXhyy+/REZGBjIyMrB3715JeVkMEBER2RFU8jUp1q9fj8zMTGRkZGDYsGHIyclB9+7dsXXrVoePv/nmm3HXXXdh6NChGDRoEObNm4dRo0bhwIEDkvKyGCAiIrLjirsWNjY2oqSkBMnJydZjarUaycnJKCoqavP5oigiPz8fZWVluOmmmyRk7uACwsbGRvzzn/9EUVERTCYTACAsLAxjxozB5MmT4efn15GwREREXY7ZbIbZbLY5ptVqodVqbY5VV1fDYrEgNDTU5nhoaCi+/fbbFuPX1tYiIiICZrMZGo0GL730En7/+99L6qPkkYHjx49j6NChSE9Px+effw5BECAIAj7//HOkpaVh+PDhOH78uNSwREREbkPOkQGj0YigoCCbZjQaZetrz549cfjwYRw6dAirVq2CXq9HYWGhpBiSRwZmz56NkSNH4vPPP0dgYKDNubq6OqSlpWHOnDmSFy8QERG5Czl3SjAYDNDr9TbH7EcFACA4OBgajQZVVVU2x6uqqhAWFtZifLVajejoaABAbGwsjh49CqPRiJtvvrndfZRcDPznP/9BcXFxs0IAAAIDA7Fy5UokJiZKDUtERNQlOZoScMTPzw9xcXHIz89HamoqAEAQBOTn52Pu3LntzicIQrNpibZILgZ69eqF77//HiNGjHB4/vvvv0evXr1ajeFo/kQURagU3jGOiIioPaReBSAXvV6P9PR0xMfHIyEhAdnZ2aivr0dGRgYAIC0tDREREdZpBqPRiPj4eAwaNAhmsxnvvfceXnnlFWzcuFFSXsnFwAMPPIC0tDQsW7YM48ePty50qKqqQn5+Pp566ik88sgjrcYwGo1YsWKFzTGVugc0muajDURERM7mqh0Ip06divPnz2P58uUwmUyIjY1FXl6e9W9tRUUF1L/ZCr2+vh4PP/wwfvjhB3Tr1g1DhgzBq6++iqlTp0rKqxJFUfLUyNq1a/Hcc8/BZDJZP82LooiwsDDMnz8fixYtavX5jkYG+vQdovjIgFrhveS7yr0JLjvhPhH+PspfcaL0vQnMliZF4wPOuTeBM+4L0hXuTSBI/1UpGe9N0H7mS6cVjb8m8k+yxVpS/qpssZTSoUsLFy9ejMWLF+PUqVM2lxYOGDCgXc93NH/CKQIiInIXnn+rJWk6daOiAQMGNCsATp8+jaysrBZ3SyIiInJ3gpeVA7KPm9fU1GDHjh1yhyUiIiKFSB4Z2LNnT6vnT5482eHOEBERuQNvu4Wx5GIgNTUVKpUKra075Pw/ERF5Mu+aJOjANIFOp0Nubq51G2L7VlpaqkQ/iYiInMYVNypyJcnFQFxcHEpKSlo839aoAREREbkXydMECxcuRH19fYvno6OjUVBQ0KlOERERuZKrdiB0FcnFwLhx41o9HxAQgKSkpA53iIiIyNV4aSERERF5lU5tOiSnrrBFpjO2Cq55aoLiOQIfe1/xHObLjYrn8NEo++NtEZRfGuTjhPeF0tt0A8CKykLFc2SEj1E0/raznygaHwCC/AMUz1F7qeVpXrl0hc/UXeE1SOE2xQAREZG78JSrAOTCaQIiIiIvx5EBIiIiO962gJDFABERkR3vKgU4TUBEROT1ODJARERkhwsIO6mqqgpPPvmk3GGJiIicRoAoW/MEshcDJpMJK1askDssERGR04gyNk8geZrgyy+/bPV8WZnyG+8QERGRfCQXA7GxsS3emfDKcZWq9Ts8mM1mmM1mm2PteR4REZEzeNuaAcnFQJ8+ffD0009j/PjxDs9//fXXmDRpUqsxjEZjs6kEjSYQPj5BUrtDREQkO6W3r3c3kouBuLg4nD17FpGRkQ7P//TTTw5HDX7LYDBAr9fbHLvqquFSu0JEREQykFwMPPTQQ6ivb/lGF/3798e2bdtajaHVaqHVam2OcYqAiIjcBacJ2nDXXXe1er53795IT0/vcIeIiIhczVMuCZSL7JcWnj59GrNmzZI7LBERESlE9mKgpqYGO3bskDssERGR03CfgTbs2bOn1fMnT57scGeIiIjcgbdNE0guBlJTU1vcZ+AKLgYkIiLyHJKnCXQ6HXJzcyEIgsNWWlqqRD+JiIicRpCxeQLJxUBcXBxKSkpaPN/WqAEREZG7E2X8zxNIniZYuHBhq/sMREdHo6CgoFOdIiIiciVP+UQvF8nFwLhx41o9HxAQgKSkpA53iIiIiJxLcjGglMuCxdVd8AiBj72veI4bQ4YqnuPAuaOK52iyXFY0vo9ao2h8gO8LKbad/UTR+PVH/6FofAAIGPpHxXM4g6/Gbf60dJgrh/c3bNiAdevWwWQyISYmBi+88AISEhIcPnbz5s14+eWXceTIEQC/TuWvXr26xce3RPZ9BoiIiDydqxYQ7tq1C3q9HllZWSgtLUVMTAxSUlJw7tw5h48vLCzE9OnTUVBQgKKiIvTr1w8TJkzAmTNnJOVlMUBEROQm1q9fj8zMTGRkZGDYsGHIyclB9+7dsXXrVoePf+211/Dwww8jNjYWQ4YMwd/+9jcIgoD8/HxJeVkMEBER2RFEUbbWXo2NjSgpKUFycrL1mFqtRnJyMoqKitoVo6GhAU1NTejTp4+k1+v5EztEREQyk3PFgNlshtlstjnm6O691dXVsFgsCA0NtTkeGhqKb7/9tl25Fi9ejPDwcJuCoj04MkBERKQgo9GIoKAgm2Y0GmXPs2bNGuzcuRO7d++Gv7+/pOdyZICIiMiOnPcmMBgM0Ov1NsfsRwUAIDg4GBqNBlVVVTbHq6qqEBYW1mqOZ555BmvWrMGHH36IUaNGSe5jh0cGfvjhB/z888/Njjc1NeHjjz/uaFgiIiKXk3MHQq1Wi8DAQJvmqBjw8/NDXFyczeK/K4sBR48e3WJfn376aaxcuRJ5eXmIj4/v0OuVXAxUVlYiISEBkZGR6NWrF9LS0myKgpqaGtxyyy0d6gwREZE30+v12Lx5M3bs2IGjR49i9uzZqK+vR0ZGBgAgLS0NBoPB+vi1a9di2bJl2Lp1K6KiomAymWAymRx+WG+N5GJgyZIlUKvVOHjwIPLy8vDNN9/glltuwY8//mh9DO9NQEREnsxV+wxMnToVzzzzDJYvX47Y2FgcPnwYeXl51kWFFRUVqKystD5+48aNaGxsxD333AOdTmdtzzzzjKS8KlHiX+6IiAjs3r3buruR2WzGlClTcPr0aeTn56OpqQnh4eGwWFreOc3RysrefYfw1sduoqvsQKg07kDoXbgDYfs5YwfCX34pVzT+lMjJssX6e/m/ZIulFMkjA7W1tejdu7f1a61Wi9zcXERFReGWW25pcZek33K0slIULkrtChERkSK87a6FkouBgQMH4ssvv7Q55uPjg7///e8YOHAg/vCHP7QZw2AwoLa21qap1D2ldoWIiIhkILkYmDhxIjZt2tTs+JWCIDY2ts01A45WVnKKgIiI3IWr1gy4iuSJnVWrVqGhocFxMB8f/OMf/5B8gwQiIiJ34m0L4SWPDPj4+CAwMLDF85WVlVixYkWnOkVERETOI/t2xDU1NdixY4fcYYmIiJxGgChb8wSSpwn27NnT6vmTJ092uDNERETuwFPm+uUiuRhITU2FSqVqdT6FiwGJiIg8h+RpAp1Oh9zcXAiC4LCVlpYq0U8iIiKn4T4DbYiLi0NJSUmL59saNSAiInJ3XDPQhoULF6K+vr7F89HR0SgoKOhUp4iIiMh5JBcD48aNa/V8QEAAkpKSOtwhIiIiV/O2EW7l7ybRTs5Ycqj0P62/j5/CGeCU+Sdn3ETooz5jFM8xvuYTReNbnHATIbUTFuM6Y8GvICi/NttH4ZvjOOMmQidGKH+TsOgjyr+/L1suK55DabyagIiIyMt5ysI/uci+6RARERF5Fo4MEBER2fGUqwDkwmKAiIjIjrctIOQ0ARERkZfjyAAREZEdThO0w4ULF/Dll18iJiYGffr0QXV1NbZs2QKz2YwpU6Zg6FDlL48hIiJSirddTSC5GCguLsaECRNQV1eHXr16Yd++fZgyZQp8fHwgCALWrFmDAwcO4Prrr1eiv0RERCQzyWsGli5diilTpqC2thaPPfYYUlNTMX78eBw7dgzHjx/HtGnTsHLlSiX6SkRE5BSCKMrWPIHkYqCkpAR6vR49e/bEvHnzcPbsWWRmZlrPz507F4cOHZK1k0RERM4kytg8geRpgsbGRnTr1g0A4Ovri+7duyM4ONh6Pjg4GBcuXGg1htlshtlstjkmiqJTtkUlIiIiW5JHBvr164eTJ09av965cyd0Op3168rKSpviwBGj0YigoCCbJggXpXaFiIhIEd52C2PJxcC0adNw7tw569d33HGHdaQAAPbs2YOEhIRWYxgMBtTW1to0tbqn1K4QEREpwtuKAcnTBFlZWa2eX7p0KTQaTauP0Wq10Gq1Nsc4RUBERO6COxB20oULFzB79my5wxIREZFCZC8GampqsGPHDrnDEhEROQ2nCdqwZ8+eVs//dnEhERGRJ+IOhG1ITU2FSqVqdT6F8/9ERESeQ/I0gU6nQ25uLgRBcNhKS0uV6CcREZHTiKIoW/MEkouBuLg4lJSUtHi+rVEDIiIid+dtawYkFwMLFy7EmDFjWjwfHR2NgoKCTnWKiIjIW23YsAFRUVHw9/dHYmIiiouLW3zs119/jT/+8Y+IioqCSqVCdnZ2h3JKLgbGjRuH2267rcXzAQEBSEpK6lBniIiI3IGrpgl27doFvV6PrKwslJaWIiYmBikpKTab/f1WQ0MDBg4ciDVr1iAsLKzDr1clusmYvta/n+I51CrZr6S0YREsisYHus5GGGq1sv8WAPB9fLSi8SMPfadofGdxxl3VtD6+iudoslxWPIfSAnz9Fc/xQuDvFM8x60Kh4jkazT8oGj8mrOURcKm+MH3S7scmJibihhtuwIsvvggAEAQB/fr1wyOPPIIlS5a0+tyoqCjMnz8f8+fPl9xH5X8jExEReTGz2Yy6ujqbZn+zPuDXGwGWlJQgOTnZekytViM5ORlFRUWK9pHFABERkR1Rxv8c3ZzPaDQ2y1ldXQ2LxYLQ0FCb46GhoTCZTIq+Xsn7DBAREXV1ck6fGQwG6PV6m2P29+dxNRYDREREduTcgdDRzfkcCQ4OhkajQVVVlc3xqqqqTi0ObA9OExAREbkBPz8/xMXFIT8/33pMEATk5+dj9OjRiubmyAAREZEdZ1xl44her0d6ejri4+ORkJCA7Oxs1NfXIyMjAwCQlpaGiIgI65qDxsZGfPPNN9b/P3PmDA4fPowePXogOrr9V1TJVgwMHDgQe/fuxTXXXCNXSCIiIpdw1Y2Kpk6divPnz2P58uUwmUyIjY1FXl6edVFhRUWFzaXZZ8+exXXXXWf9+plnnsEzzzyDpKQkFBYWtjuv5GLg+eefd3i8oqIC27Zts85r/OUvf5EamoiIyOvNnTsXc+fOdXjO/g98VFSULPvPSC4G5s+fj4iICPj42D5VEAS8/PLL8PX1hUqlYjFAREQey1XTBK4iuRh48MEHcfDgQbz++usYOnSo9bivry8++OADDBs2TNYOEhEROZurpglcRfLVBDk5OVi+fDlSUlKs2yVK5Wg3pq6yzS4REZGn6dClhXfddReKioqwe/duTJw4UfLOSI52Y7JY6jrSFSIiItkJoihb8wQd3mcgIiICH374IW666SZcd911kj7ZGwwG1NbW2jSNJrCjXSEiIpKVnNsRe4JOXVqoUqlgMBgwYcIEHDhwADqdrl3Pc7Qbk0ql6kxXiIiIqINk2YEwLi4O8+bNQ+/evXH69GnMmjVLjrBEREQuIYqCbM0TyL4dcU1NDXbs2CF3WCIiIqcRIMrWPIHkaYI9e/a0ev7kyZMd7gwREZE78LYr3CQXA6mpqVCpVK1+ozj/T0RE5DkkTxPodDrk5uZCEASHrbS0VIl+EhEROY23TRNILgbi4uJQUlLS4vm2Rg2IiIjcnSiKsjVPIHmaYOHChaivr2/xfHR0NAoKCjrVKSIiInIeycXAuHHjWj0fEBCApKSkDneIiIjI1Txl50C5dGrTITk5YyilSbisaHy1ExZOOuPH0xnLP1VOyNKv+Jii8eeH36RofADIPvux4jmcwXy5ydVd6DQftUbxHBcbf1E8R0a18iO3dVtnKp5DaZ6yc6BcZN9ngIiIiDyL24wMEBERuQtPWfgnFxYDREREdjzlkkC5cJqAiIjIy3FkgIiIyA6nCYiIiLwcLy2USBRFFBYW4vjx49DpdEhJSYGvr68cfSMiInIJjgy04fbbb8cbb7yBoKAg1NTU4Pbbb0dxcTGCg4Nx4cIFXHvttfj4449x1VVXKdFfIiIikpnkBYR5eXkwm80AgMcffxwXL17EiRMncO7cOZSXlyMgIADLly+XvaNERETOwhsVSfDRRx/BaDRiwIABAICrr74aa9euxd69e2XpHBERkSvwRkXtoPr/2+7++OOPGDRokM256OhonD17ttXnm81m6+jCFaIoWuMSERGR83RoZGDmzJm4++670dTUhFOnTtmcM5lM6NWrV6vPNxqNCAoKsmmC5WJHukJERCQ7QRRla55AcjGQnp6OkJAQBAUFYfLkyWhoaLA5/49//AOxsbGtxjAYDKitrbVpak1PqV0hIiJShCjjf55A8jTBtm3bWj2flZUFjab1u3tptVpotVqbY5wiICIicg3ZtyOuqanBww8/LHdYIiIip+E0QSfV1NRgx44dcoclIiJyGl5N0IY9e/a0ev7kyZMd7gwRERE5n+RiIDU1FSqVqtVqh/P/RETkyTxl4Z9cJE8T6HQ65ObmQhAEh620tFSJfhIRETmNt00TSC4G4uLiUFJS0uL5tkYNiIiI3J23FQOSpwkWLlyI+vr6Fs9HR0ejoKCgU50iIiIi55FcDIwbN67V8wEBAUhKSupwh4iIiFzNMz7Py0j0QJcuXRKzsrLES5cuMYeLc3SF18Ac7hOfOdwrR1d4DdQ+KlH0kAmN36irq0NQUBBqa2sRGBjIHC7M0RVeA3O4T3zmcK8cXeE1UPvIvukQEREReRYWA0RERF6OxQAREZGX88hiQKvVIisrq9mdD5nD+Tm6wmtgDveJzxzulaMrvAZqH49cQEhERETy8ciRASIiIpIPiwEiIiIvx2KAiIjIy7EYICIi8nIeWQxs2LABUVFR8Pf3R2JiIoqLi2WL/fHHH2PSpEkIDw+HSqXCP//5T9liA4DRaMQNN9yAnj17IiQkBKmpqSgrK5M1x8aNGzFq1CgEBgYiMDAQo0ePxvvvvy9rDntr1qyBSqXC/PnzZYv5xBNPQKVS2bQhQ4bIFh8Azpw5gz/96U/o27cvunXrhpEjR+Kzzz6TLX5UVFSz16BSqTBnzhzZclgsFixbtgwDBgxAt27dMGjQIKxcuVL2u6VdvHgR8+fPR2RkJLp164YxY8bg0KFDHY7X1ntNFEUsX74cOp0O3bp1Q3JyMr777jtZc+Tm5mLChAno27cvVCoVDh8+LFv8pqYmLF68GCNHjkRAQADCw8ORlpaGs2fPyvoannjiCQwZMgQBAQHo3bs3kpOTcfDgQVlz/NZDDz0ElUqF7OxsWXPMnDmz2fvktttuk5SDOs7jioFdu3ZBr9cjKysLpaWliImJQUpKCs6dOydL/Pr6esTExGDDhg2yxLO3f/9+zJkzB59++in27duHpqYmTJgwodU7QUp19dVXY82aNSgpKcFnn32GW2+9FZMnT8bXX38tW47fOnToEP7v//4Po0aNkj328OHDUVlZaW0HDhyQLfaPP/6IsWPHwtfXF++//z6++eYbPPvss+jdu7dsOQ4dOmTT/3379gEApkyZIluOtWvXYuPGjXjxxRdx9OhRrF27Fk8//TReeOEF2XIAwAMPPIB9+/bhlVdewVdffYUJEyYgOTkZZ86c6VC8tt5rTz/9NJ5//nnk5OTg4MGDCAgIQEpKCi5duiRbjvr6etx4441Yu3at7K+hoaEBpaWlWLZsGUpLS5Gbm4uysjLceeedsuUAgGuvvRYvvvgivvrqKxw4cABRUVGYMGECzp8/L1uOK3bv3o1PP/0U4eHhkl5De3PcdtttNu+XN954Q3Ie6iBX3hihIxISEsQ5c+ZYv7ZYLGJ4eLhoNBplzwVA3L17t+xxf+vcuXMiAHH//v2K5undu7f4t7/9Tfa4Fy9eFK+55hpx3759YlJSkjhv3jzZYmdlZYkxMTGyxbO3ePFi8cYbb1QsviPz5s0TBw0aJAqCIFvMO+64Q5w1a5bNsbvvvlucMWOGbDkaGhpEjUYjvvPOOzbHr7/+enHp0qWdjm//XhMEQQwLCxPXrVtnPfbTTz+JWq1WfOONN2TJ8VunTp0SAYiff/55h2K3Ff+K4uJiEYBYXl6uWI7a2loRgPjhhx/KmuOHH34QIyIixCNHjoiRkZHiX//61w7FbylHenq6OHny5A7HpM7xqJGBxsZGlJSUIDk52XpMrVYjOTkZRUVFLuxZx9XW1gIA+vTpo0h8i8WCnTt3or6+HqNHj5Y9/pw5c3DHHXfY/JvI6bvvvkN4eDgGDhyIGTNmoKKiQrbYe/bsQXx8PKZMmYKQkBBcd9112Lx5s2zx7TU2NuLVV1/FrFmzoFKpZIs7ZswY5Ofn49ixYwCAL774AgcOHMDEiRNly3H58mVYLBb4+/vbHO/WrZusozVXnDp1CiaTyebnKigoCImJiR77Xgd+fb+rVCr06tVLkfiNjY3YtGkTgoKCEBMTI1tcQRBw//33Y+HChRg+fLhsce0VFhYiJCQEgwcPxuzZs3HhwgXFcpEtH1d3QIrq6mpYLBaEhobaHA8NDcW3337rol51nCAImD9/PsaOHYsRI0bIGvurr77C6NGjcenSJfTo0QO7d+/GsGHDZM2xc+dOlJaWdmreuDWJiYnYvn07Bg8ejMrKSqxYsQLjxo3DkSNH0LNnz07HP3nyJDZu3Ai9Xo/HHnsMhw4dwl/+8hf4+fkhPT1dhldg65///Cd++uknzJw5U9a4S5YsQV1dHYYMGQKNRgOLxYJVq1ZhxowZsuXo2bMnRo8ejZUrV2Lo0KEIDQ3FG2+8gaKiIkRHR8uW5wqTyQQADt/rV855mkuXLmHx4sWYPn267Hfne+eddzBt2jQ0NDRAp9Nh3759CA4Oli3+2rVr4ePjg7/85S+yxbR322234e6778aAAQNw4sQJPPbYY5g4cSKKioqg0WgUy0u/8qhioKuZM2cOjhw5osgnq8GDB+Pw4cOora3FW2+9hfT0dOzfv1+2guD06dOYN28e9u3b1+zTolx++8l21KhRSExMRGRkJN588038+c9/7nR8QRAQHx+P1atXAwCuu+46HDlyBDk5OYoUA1u2bMHEiRM7NN/amjfffBOvvfYaXn/9dQwfPhyHDx/G/PnzER4eLuvreOWVVzBr1ixERERAo9Hg+uuvx/Tp01FSUiJbjq6qqakJ9957L0RRxMaNG2WPf8stt+Dw4cOorq7G5s2bce+99+LgwYMICQnpdOySkhI899xzKC0tlXVEy960adOs/z9y5EiMGjUKgwYNQmFhIcaPH69YXvqVR00TBAcHQ6PRoKqqyuZ4VVUVwsLCXNSrjpk7dy7eeecdFBQU4Oqrr5Y9vp+fH6KjoxEXFwej0YiYmBg899xzssUvKSnBuXPncP3118PHxwc+Pj7Yv38/nn/+efj4+MBisciW64pevXrh2muvxfHjx2WJp9PpmhVHQ4cOlXUq4ory8nJ8+OGHeOCBB2SPvXDhQixZsgTTpk3DyJEjcf/992PBggUwGo2y5hk0aBD279+Pn3/+GadPn0ZxcTGampowcOBAWfMAsL6fu8J7/UohUF5ejn379sk+KgAAAQEBiI6Oxu9+9zts2bIFPj4+2LJliyyx//3vf+PcuXPo37+/9b1eXl6O//3f/0VUVJQsORwZOHAggoODZXu/U+s8qhjw8/NDXFwc8vPzrccEQUB+fr4i8+FKEEURc+fOxe7du/HRRx9hwIABTskrCALMZrNs8caPH4+vvvoKhw8ftrb4+HjMmDEDhw8fVmRY7+eff8aJEyeg0+lkiTd27Nhml3UeO3YMkZGRssT/rW3btiEkJAR33HGH7LEbGhqgVtu+lTUaDQRBkD0X8OsfHp1Ohx9//BF79+7F5MmTZc8xYMAAhIWF2bzX6+rqcPDgQY95rwP/LQS+++47fPjhh+jbt69T8sr5fr///vvx5Zdf2rzXw8PDsXDhQuzdu1eWHI788MMPuHDhgmzvd2qdx00T6PV6pKenIz4+HgkJCcjOzkZ9fT0yMjJkif/zzz/bVKKnTp3C4cOH0adPH/Tv37/T8efMmYPXX38d//rXv9CzZ0/r/GdQUBC6devW6fgAYDAYMHHiRPTv3x8XL17E66+/jsLCQlnfuD179my2ziEgIAB9+/aVbf3Do48+ikmTJiEyMhJnz55FVlYWNBoNpk+fLkv8BQsWYMyYMVi9ejXuvfdeFBcXY9OmTdi0aZMs8a8QBAHbtm1Deno6fHzkf8tNmjQJq1atQv/+/TF8+HB8/vnnWL9+PWbNmiVrnr1790IURQwePBjHjx/HwoULMWTIkA6/99p6r82fPx9PPfUUrrnmGgwYMADLli1DeHg4UlNTZctRU1ODiooK67X/V4rDsLCwdo1AtBZfp9PhnnvuQWlpKd555x1YLBbr+71Pnz7w8/Pr9Gvo27cvVq1ahTvvvBM6nQ7V1dXYsGEDzpw5I+ny1ba+T/ZFjK+vL8LCwjB48GBZcvTp0wcrVqzAH//4R4SFheHEiRNYtGgRoqOjkZKS0u4c1AkuvpqhQ1544QWxf//+op+fn5iQkCB++umnssUuKCgQATRr6enpssR3FBuAuG3bNlnii6Iozpo1S4yMjBT9/PzEq666Shw/frz4wQcfyBa/JXJfWjh16lRRp9OJfn5+YkREhDh16lTx+PHjssUXRVF8++23xREjRoharVYcMmSIuGnTJlnji6Io7t27VwQglpWVyR5bFEWxrq5OnDdvnti/f3/R399fHDhwoLh06VLRbDbLmmfXrl3iwIEDRT8/PzEsLEycM2eO+NNPP3U4XlvvNUEQxGXLlomhoaGiVqsVx48fL/l72FaObdu2OTyflZXV6fhXLld01AoKCmR5Db/88ot41113ieHh4aKfn5+o0+nEO++8UywuLpb1+2SvI5cWtpajoaFBnDBhgnjVVVeJvr6+YmRkpJiZmSmaTCZJOajjeAtjIiIiL+dRawaIiIhIfiwGiIiIvByLASIiIi/HYoCIiMjLsRggIiLyciwGiIiIvByLASIiIi/HYoCIiMjLsRggIiLyciwGiIiIvByLASIiIi/HYoCIiMjL/T/cuTgHJOcr2wAAAABJRU5ErkJggg==", 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", 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", 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", 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YJSUl+vnPf64WLVrotddeU6tWrXTw4EE1adLEVF6KAQAAgtTWdsSLFy/WxIkTNX78eEnS8uXL9eabb2rVqlWaNWvWOa9ftWqVTp06pX/84x+Kjo6WJLVr1850XqYJAACwkdfrVWFhYUDzer3nvK6kpEQ5OTlKTk4uOxYREaHk5GRlZWWVG3vjxo3q37+/Jk+erISEBHXv3l2PPfaYfD5ze3tQDAAAEMzCNQPp6emKi4sLaOnp527+dOLECfl8PiUkJAQcT0hIUF5eXrnd3L9/v1577TX5fD699dZbmjNnjp566ik98sgjpj5ujaYJioqK9Oqrr2rv3r3yeDy6/fbb1bx585qEBACg9lm4iWJaWppSU1MDjrndbkti+/1+tWjRQitWrFBkZKSSkpJ05MgRPfHEE5o3b16V45gqBrp166Zt27apWbNmOnz4sK6++mp9++23uvTSS7Vv3z4tWLBAH3zwgdq3b19hHK/Xe84QiWEYcrlCsfkqAACh43a7q/TLPz4+XpGRkcrPzw84np+fr8TExHLf4/F4FB0drcjIf2/H3bVrV+Xl5amkpEQxMTFV6qOpaYIvv/xSZ8+elfSvSqdly5Y6ePCgsrOzdfDgQfXs2VOzZ8+uNE55QyY+X6GZrgAAYBvDb1jWqiomJkZJSUnKyMgoO+b3+5WRkaH+/fuX+56BAwdq79698v/keRC7d++Wx+OpciEg1WDNQFZWlh566CHFxcVJkho2bKj58+dr27Ztlb43LS1NBQUFAS0ysnF1uwIAgLVqaZ+B1NRUrVy5UmvWrNEXX3yhSZMmqaioqOzugjFjxigt7d8PtJo0aZJOnTqlqVOnavfu3XrzzTf12GOPafLkyabyml4z8ONQ/pkzZ+TxeALOtWrVSt98802lMcobMmGKAADgdKNGjdI333yjuXPnKi8vT71799amTZvKFhUeOnRIET95ym/r1q21efNmTZ8+XT179lSrVq00depUzZw501Re08XAkCFDFBUVpcLCQu3atUvdu3cvO3fw4EEWEAIAwl8tPoV5ypQpmjJlSrnntm7des6x/v3764MPPqhRTlPFQPDKxIYNGwZ8/frrr2vQoEE16hAAALWttjYdqi01KgaCPfHEEzXqDAAACD22IwYAIFgtThPUBooBAACCME0AAIDTOWxkgGcTAADgcIwMAAAQxHDYyICjigGf397vriH755hcsn9zplBs/3So8LjtOQa3uMzW+O9/87mt8SWF4CdK8vnNPeq0OqIiIit/UQ3Zff2F4rpI/K+nbc9R8MQI23M0nvG67Tls57BigGkCAAAczlEjAwAAVAXTBAAAOJ3DigGmCQAAcDhGBgAACMI0AQAADkcxAACAwzmtGGDNAAAADsfIAAAAwYxQbDNVd1AMAAAQhGmCCuTm5urAgQNlX7/00ksaOHCgWrdurauuukpr166tUhyv16vCwsKAZhjOelwkAAB1haliYPz48dq3b58k6fnnn9evfvUr9enTR7Nnz9aVV16piRMnatWqVZXGSU9PV1xcXEDz+Qqr9wkAALCY4XdZ1sKBqWmCPXv26JJLLpEkPffcc/rd736niRMnlp2/8sor9eijj2rChAkVxklLS1NqamrAsfiLupnpCgAAtnHaNIGpYqBBgwY6ceKE2rZtqyNHjqhv374B5/v16xcwjXA+brdbbrc74JjLFR7VEwAAFxpT0wTDhw/XsmXLJEmDBw/Wa6+9FnD+1VdfVadOnazrHQAAtcAwXJa1cGBqZGDRokUaOHCgBg8erD59+uipp57S1q1b1bVrV+3atUsffPCBNmzYYFdfAQAICadNE5gaGWjZsqW2b9+u/v37a9OmTTIMQ9nZ2Xr77bd18cUX6+9//7uuu+46u/oKAABsYHqfgSZNmmjhwoVauHChHf0BAKDWhctdAFZh0yEAAII4besbigEAAII4bWSABxUBAOBwjAwAABDEaSMDdaYYCMWzCXxOu1fE4d7/5nNb4xcdec/W+JLUoOUg23OEYmrU5/fZnsPuz+GOirY5g+Q9W2p7jib3v2F7jqiISNtz2M1pawaYJgAAwOHqzMgAAAB1BdMEAAA4XLhsI2wVpgkAAHA4RgYAAAjitPXmFAMAAATxM00AAACchJEBAACCOG0BIcUAAABBuLUQAACHYwfCCvz617/W+++/X+OkXq9XhYWFAS0U2xEDAIBzmSoGli5dqp/97Ge69NJLtWjRIuXl5VUraXp6uuLi4gKa33e6WrEAALCa4XdZ1sKB6bsJ3n77bV133XV68skn1aZNG40cOVJvvPGG/P6q35SZlpamgoKCgBYR2chsVwAAsIXfcFnWwoHpYqBHjx5asmSJjh49qj/+8Y/yer1KSUlR69atNXv2bO3du7fSGG63W40bNw5oLld4/AcDAOBCU+19BqKjo3Xrrbdq06ZN2r9/vyZOnKg//elP6ty5s5X9AwAg5AzDZVkLB5ZsOtSmTRs99NBDOnDggDZt2mRFSAAAao1hWNfCgalioG3btoqMjDzveZfLpZ///Oc17hQAAAgdU/sMHDhwwK5+AABQZ4TLwj+rsOkQAABBwmWu3yo8qAgAAIdjZAAAgCDhsvDPKhQDAAAEYc1ALVl20c9sz5H6XZat8c+cLbE1viRTOz1WVygK4sbuBrbnKPQW2xo/ttXVtsaXpN1du9meo/OXX9iewx+CP7Ps/qfbe7bU5gz2fwZJIdngzef32Z7DbqwZAAAAjlJnRgYAAKgrmCYAAMDhHLZ+kGkCAACcjpEBAACCME0AAIDDcTcBAABwFEYGAAAIYv+OLnULxQAAAEGMkGwBVXcwTQAAgMOZLgaeffZZjRkzRmvXrpUkvfTSS+rWrZu6dOmiBx54QGfPnq00htfrVWFhYUArNcJ/+0oAwIXBb1jXwoGpYuCRRx7RAw88oOLiYk2fPl2LFi3S9OnTNXr0aI0dO1bPP/+8FixYUGmc9PR0xcXFBbS3Tn9W7Q8BAICV/HJZ1sKBqTUDL774ol588UXddNNN+uc//6mkpCStWbNGo0ePliR16dJF999/v+bPn19hnLS0NKWmpgYce7nrr0x2HQAAezhtzYCpYuDo0aPq06ePJKlXr16KiIhQ7969y85fccUVOnr0aKVx3G633G53wLFoV6SZrgAAAIuYmiZITEzU559/Lknas2ePfD5f2deS9Nlnn6lFixbW9hAAgBDzW9jCgamRgdGjR2vMmDEaOXKkMjIydP/99+u+++7TyZMn5XK59Oijj+rmm2+2q68AAIQE0wQVmD9/vurXr6+srCxNnDhRs2bNUq9evXT//feruLhYI0aMqNICQgAAUHeYmiaIiIjQAw88oNdff11paWlyuVy67bbbdOjQIZ04cUKrV69WbGysXX0FACAkanOaYOnSpWrXrp3q1aunfv36KTs7u0rvW7t2rVwul1JSUkznZNMhAACC1FYxsG7dOqWmpmrevHnKzc1Vr169NGzYMB0/frzC93311Ve67777NGjQIJMZ/4ViAACAOmLx4sWaOHGixo8fr27dumn58uVq0KCBVq1add73+Hw+jR49WvPnz1eHDh2qlZdiAACAIIZclrXydt31er3n5CwpKVFOTo6Sk5PLjkVERCg5OVlZWVnn7evDDz+sFi1a6Je//GW1Py/FAAAAQfwu61p5u+6mp6efk/PEiRPy+XxKSEgIOJ6QkKC8vLxy+7lt2za98MILWrlyZY0+L08tBADARuXtuhu88V51nD59WnfeeadWrlyp+Pj4GsWqM8XApG+22p5jhudqW+M/mfe+rfGlfw0Z2c3nt3+bjO9LfrA9h913CfsN+59A0mXXl7bnOPnLHrbnaPb8Tttz2H1thOK6iIywfydWn9/+h8KFybN5KmTlMwXK23W3PPHx8YqMjFR+fn7A8fz8fCUmJp7z+n379umrr77SiBEjyo75///PaVRUlHbt2qWOHTtWqY9MEwAAEMSwsFVVTEyMkpKSlJGRUXbM7/crIyND/fv3P+f1Xbp00SeffKIdO3aUtRtuuEHXXHONduzYodatW1c5d50ZGQAAoK6orW2EU1NTNXbsWPXp00d9+/bVkiVLVFRUpPHjx0uSxowZo1atWik9PV316tVT9+7dA97fpEkTSTrneGUoBgAAqCNGjRqlb775RnPnzlVeXp569+6tTZs2lS0qPHTokC1TYhQDAAAE8btq79kEU6ZM0ZQpU8o9t3Xr1grf++KLL1YrJ8UAAABBLoRFkGawgBAAAIdjZAAAgCC1tYCwtlAMAAAQxF97SwZqheli4NixY1q2bJm2bdumY8eOKSIiQh06dFBKSorGjRunyEj7N80AAADWMbVm4OOPP1bXrl311ltvqbS0VHv27FFSUpJiY2N133336eqrr9bp06crjVPeQxuMEOzmBgBAVfjlsqyFA1PFwLRp0zR9+nR9/PHHev/99/Xiiy9q9+7dWrt2rfbv36/i4mI9+OCDlcYp76ENfl/lRQQAAKFQGzsQ1iZTxUBubq7uvPPOsq/vuOMO5ebmKj8/X02bNtXjjz+u1157rdI4aWlpKigoCGgRkY3M9x4AANSYqTUDLVq00LFjx9ShQwdJ/3p4wtmzZ9W4cWNJ0iWXXKJTp05VGqe8hza4anGDBwAAfsppCwhNjQykpKTonnvu0aZNm5SZmanRo0dr8ODBql+/viRp165datWqlS0dBQAgVPwWtnBgamTgkUce0bFjxzRixAj5fD71799ff/zjH8vOu1wupaenW95JAABCKVzm+q1iqhho2LCh1q1bpzNnzujs2bNq2LBhwPmhQ4da2jkAAGC/am06VK9ePav7AQBAneG0NQPsQAgAQJBwmeu3Cg8qAgDA4RgZAAAgiNNGBigGAAAIYjhszQDTBAAAOFydGRmIjrS/K0/mvW9r/KNDOtgaX5JaZuy3PUcoRLjsr0N9hs/W+FER9j+h02/YP1jZ9Pmdtud4xHON7TnmHMu0PYfdzvrt/ZmVQvNvbanvrO057MY0AQAADue0YoBpAgAAHI6RAQAAgrAdMQAADscOhAAAOJzT1gxUqxgoKSnRX/7yF2VlZSkvL0+SlJiYqAEDBmjkyJGKiYmxtJMAAMA+phcQ7t27V127dtXYsWO1fft2+f1++f1+bd++XWPGjNFll12mvXv32tFXAABCwm9hCwemRwYmTZqkHj16aPv27WrcuHHAucLCQo0ZM0aTJ0/W5s2bLeskAAChxALCSvz9739Xdnb2OYWAJDVu3FgLFixQv379LOkcAACwn+lioEmTJvrqq6/UvXv3cs9/9dVXatKkSYUxvF6vvF5vwDHDMORyOWz5JgCgTnLa3QSm1wzcddddGjNmjJ5++mnt3LlT+fn5ys/P186dO/X0009r3LhxuvvuuyuMkZ6erri4uIBWerag2h8CAAArsWagEg8//LBiY2P1xBNP6Le//W3ZX/OGYSgxMVEzZ87U/fffX2GMtLQ0paamBhxLTOhhtisAAMAC1bq1cObMmZo5c6YOHDgQcGth+/btq/R+t9stt9sdcIwpAgBAXcECQhPat29/TgFw+PBhzZs3T6tWrapRxwAAqC1+h5UDlj+o6NSpU1qzZo3VYQEAgE1Mjwxs3LixwvP79++vdmcAAKgLwmXhn1VMFwMpKSlyuVwyjPMPoTD/DwAIZ86aJKjGNIHH49H69evLtiEObrm5uXb0EwCAkHHarYWmi4GkpCTl5OSc93xlowYAAKBuMT1NMGPGDBUVFZ33fKdOnZSZmVmjTgEAUJuctgOh6WJg0KBBFZ6PjY3V4MGDq90hAABqG7cWAgAAR6nRpkNWOuv32Z7D57d3KUfLDPtvq3zqIvtHXabl2z/NUz8qxvYc35f8YGv8UPzMRkbYX69HhuDunznH7P+ZOjakk63xEzP22ho/VEp9Z2u7C2HBWeMCdagYAACgrgiXuwCswjQBAAAOx8gAAABBnLaAkGIAAIAgzioFmCYAAMDxGBkAACAICwhrKD8/Xw8//LDVYQEACBm/DMtaOLC8GMjLy9P8+fOtDgsAQMgYFrZwYHqaYOfOnRWe37VrV7U7AwAAQs90MdC7d+/zPpnwx+OuSnY083q98nq9Aceq8j4AAELBaWsGTBcDzZo10+OPP64hQ4aUe/6zzz7TiBEjKoyRnp5+zlRCRGQjRUXFme0OAACWM8JmgN8apouBpKQkHT16VG3bti33/HfffVfuqMFPpaWlKTU1NeBY/EXdzHYFAABYwHQxcM8996ioqOi859u0aaPVq1dXGMPtdsvtdgccY4oAAFBXME1QiRtvvLHC802bNtXYsWOr3SEAAGpbuNwSaBXLby08fPiwJkyYYHVYAABgE8uLgVOnTmnNmjVWhwUAIGTYZ6ASGzdurPD8/v37q90ZAADqAqdNE5guBlJSUs67z8CPWAwIAED4MD1N4PF4tH79evn9/nJbbm6uHf0EACBk/Ba2cGC6GEhKSlJOTs55z1c2agAAQF1nWPi/cGB6mmDGjBkV7jPQqVMnZWZm1qhTAADUpnD5i94qpouBQYMGVXg+NjZWgwcPrnaHAABAaJkuBuzi84d/HeYPwWeYlm//qEvRzpdtzxHb8w7bc9i9jLWxu4HNGaRCb7HtOS4UiRl7bY0f36CxrfEl6WRxoe05QrHA+0JYRB4uw/tWqTPFAAAAdUX4/3lqjuWbDgEAgPDCyAAAAEH8DrsrjmIAAIAgzioFmCYAAMDxKAYAAAjil2FZM2vp0qVq166d6tWrp379+ik7O/u8r125cqUGDRqkpk2bqmnTpkpOTq7w9edT7WLg66+/1vfff3/O8dLSUr333nvVDQsAQK2rrR0I161bp9TUVM2bN0+5ubnq1auXhg0bpuPHj5f7+q1bt+r2229XZmamsrKy1Lp1aw0dOlRHjhwxldd0MXDs2DH17dtXbdu2VZMmTTRmzJiAouDUqVO65pprzIYFAMDxFi9erIkTJ2r8+PHq1q2bli9frgYNGmjVqlXlvv5Pf/qT7r33XvXu3VtdunTR888/L7/fr4yMDFN5TRcDs2bNUkREhD788ENt2rRJn3/+ua655hp9++23Za/h2QQAgHBWGw8qKikpUU5OjpKTk8uORUREKDk5WVlZWVWKUVxcrNLSUjVr1sxE5mrcTfDOO+9ow4YN6tOnjyTp73//u2655RZde+21ZZVIZbtPeb1eeb3egGOGYVwQu1YBAMJfdeb6z6e833lut1tutzvg2IkTJ+Tz+ZSQkBBwPCEhQV9++WWVcs2cOVMtW7YMKCiqwvTIQEFBgZo2bVr2tdvt1vr169WuXTtdc801553X+Kn09HTFxcUFNMN/2mxXAACwhZVrBsr7nZeenm55nxcuXKi1a9dqw4YNqlevnqn3mi4GOnTooJ07dwYci4qK0v/+7/+qQ4cO+s///M9KY6SlpamgoCCguSIame0KAAB1Xnm/89LS0s55XXx8vCIjI5Wfnx9wPD8/X4mJiRXmePLJJ7Vw4UK9/fbb6tmzp+k+mi4Ghg8frhUrVpxz/MeCoHfv3pWuGXC73WrcuHFAY4oAAFBXWLlmoLzfecFTBJIUExOjpKSkgMV/Py4G7N+//3n7+vjjj2vBggXatGlT2RS+WabXDDz66KMqLi7/SWpRUVH685//bPqWBgAA6pLaWgifmpqqsWPHqk+fPurbt6+WLFmioqIijR8/XpI0ZswYtWrVqmyaYdGiRZo7d65efvlltWvXTnl5eZKkhg0bqmHDhlXOa3pkICoqSo0bn/9RnseOHdP8+fPNhgUAwPFGjRqlJ598UnPnzlXv3r21Y8cObdq0qWxR4aFDh3Ts2LGy1y9btkwlJSW6+eab5fF4ytqTTz5pKq/LsLj8+ec//6krrrhCPp/P1PuiYlpZ2Y1aEYqJjlDUqkU7X7Y9R2zPO2zPYff3o5G7gc0ZpEJv+aNwCL34Buf/I8gqJ4sLbc8RiinZUOTwnjlsa/yRbSpf/1ZV/3foDcti2cX0NMHGjRsrPL9///5qdwYAgLrAzP4AFwLTxUBKSopcLleF8yksBgQAIHyYXjPg8Xi0fv16+f3+cltubq4d/QQAIGRq69kEtcV0MZCUlKScnJzznq9s1AAAgLquNp9aWBtMTxPMmDFDRUVF5z3fqVMnZWZm1qhTAAAgdEwXA4MGDarwfGxsrAYPHlztDgEAUNucNsJtuhiwS3Sk/V0p9Z21Nf6F8qMTitv+vs/+H9tzNOz7K1vjF5d6K39RDUVcIItxQ7Go2O+3d/33iRDc9ndRgzjbc3x75vvKX1RDZ/3mbi2vi7ibAAAAhwuXhX9WMb2AEAAAXFgYGQAAIEi43AVgFYoBAACCOG0BIdMEAAA4HCMDAAAEYZqgCk6ePKmdO3eqV69eatasmU6cOKEXXnhBXq9Xt9xyi7p27Wp1PwEACBmn3U1guhjIzs7W0KFDVVhYqCZNmmjLli265ZZbFBUVJb/fr4ULF2rbtm264oor7OgvAACwmOk1A7Nnz9Ytt9yigoICPfDAA0pJSdGQIUO0e/du7d27V7fddpsWLFhgR18BAAgJv2FY1sKB6WIgJydHqampatSokaZOnaqjR49q4sSJZeenTJmijz76yNJOAgAQSoaFLRyYniYoKSlR/fr1JUnR0dFq0KCB4uPjy87Hx8fr5MmTFcbwer3yegO3cjUMIyRblgIAgECmRwZat26t/fv3l329du1aeTyesq+PHTsWUByUJz09XXFxcQHt7NkCs10BAMAWTnuEseli4LbbbtPx48fLvr7++uvLRgokaePGjerbt2+FMdLS0lRQUBDQoqLsf0AHAABV4bRiwPQ0wbx58yo8P3v2bEVGRlb4GrfbLbfbHXCMKQIAQF3BDoQ1dPLkSU2aNMnqsAAAwCaWFwOnTp3SmjVrrA4LAEDIME1QiY0bN1Z4/qeLCwEACEfsQFiJlJQUuVyuCudTmP8HACB8mJ4m8Hg8Wr9+vfx+f7ktNzfXjn4CABAyhmFY1sKB6WIgKSlJOTk55z1f2agBAAB1HWsGKjFjxgwVFRWd93ynTp2UmZlZo04BAIDQMV0MDBo0qMLzsbGxGjx4cLU7BABAbXPaCLfpYsAuPr+vtrtQY6FYNhkVaf+3rNR31vYcTf7jXttzxMbUszV+cckZW+NLoVmMGxlR8SZhVvAbfttzXAj/dJ86c9r2HAX/WGp7jiYDptiew27hMrxvFcv3GQAAAOGlzowMAABQV7DPAAAADudnzQAAAM7mtJEB1gwAAOBwjAwAABDEadMElo0MdOjQQXv27LEqHAAAtcaw8H/hwPTIwDPPPFPu8UOHDmn16tVKTEyUJP3mN7+pWc8AAEBImC4Gpk2bplatWikqKvCtfr9ff/jDHxQdHS2Xy0UxAAAIW06bJjBdDNx999368MMP9fLLL6tr165lx6Ojo/X222+rW7dulnYQAIBQC5fhfauYXjOwfPlyzZ07V8OGDdOzzz5braRer1eFhYUBzWn7QAMAUFdUawHhjTfeqKysLG3YsEHDhw9XXl6eqfenp6crLi4uoPl99u/JDQBAVfgNw7IWDqp9N0GrVq30zjvv6Oqrr9bll19u6i/7tLQ0FRQUBLSIyEbV7QoAAJbibgITXC6X0tLSNHToUG3btk0ej6dK73O73XK73efEAgAAoWfJPgNJSUmaOnWqmjZtqsOHD2vChAlWhAUAoFYYht+yFg4s34741KlTWrNmjdVhAQAIGb8My1o4MD1NsHHjxgrP79+/v9qdAQCgLnDaHW6mi4GUlBS5XK4K/0Mx/w8AQPgwPU3g8Xi0fv16+f3+cltubq4d/QQAIGScNk1guhhISkpSTk7Oec9XNmoAAEBdZxiGZS0cmJ4mmDFjhoqKis57vlOnTsrMzKxRpwAAQOiYLgYGDRpU4fnY2FgNHjy42h0CAKC2hcvOgVap0aZDCBSKH51S31nbc0RGWH7H6TlCsStXUckZW+Pf6ulra3xJevVYtu05/CH4mboQlhRHhGBhdCiGlBv9xyTbcxQ+N8r2HHYLl50DrWL/v/oAAKBOY2QAAIAg4bLwzyoUAwAABAmXWwKtwjQBAAAOx8gAAABBmCYAAMDhuLXQJMMwtHXrVu3du1cej0fDhg1TdHS0FX0DAKBWMDJQieuuu06vvPKK4uLidOrUKV133XXKzs5WfHy8Tp48qUsvvVTvvfeeLrroIjv6CwAALGZ6AeGmTZvk9XolSQ8++KBOnz6tffv26fjx4zp48KBiY2M1d+5cyzsKAECo8KAiE/72t78pPT1d7du3lyRdfPHFWrRokTZv3mxJ5wAAqA08qKgKXP9/W85vv/1WHTt2DDjXqVMnHT16tML3e73estGFHxmGURYXAACETrVGBsaNG6ebbrpJpaWlOnDgQMC5vLw8NWnSpML3p6enKy4uLqD5faer0xUAACznNwzLWjgwXQyMHTtWLVq0UFxcnEaOHKni4uKA83/+85/Vu3fvCmOkpaWpoKAgoEVENjLbFQAAbGFY+L9wYHqaYPXq1RWenzdvniIjIyt8jdvtltvtDjjGFAEAALXD8u2IT506pXvvvdfqsAAAhAzTBDV06tQprVmzxuqwAACEDHcTVGLjxo0Vnt+/f3+1OwMAAELPdDGQkpIil8tVYbXD/D8AIJyFy8I/q5ieJvB4PFq/fr38fn+5LTc3145+AgAQMk6bJjBdDCQlJSknJ+e85ysbNQAAoK5zWjFgeppgxowZKioqOu/5Tp06KTMzs0adAgAAoWO6GBg0aFCF52NjYzV48OBqdwgAgNoWHn/PW8gIQ2fOnDHmzZtnnDlzhhy1nONC+AzkqDvxyVG3clwInwFV4zKMMJnQ+InCwkLFxcWpoKBAjRs3Jkct5rgQPgM56k58ctStHBfCZ0DVWL7pEAAACC8UAwAAOBzFAAAADheWxYDb7da8efPOefIhOUKf40L4DOSoO/HJUbdyXAifAVUTlgsIAQCAdcJyZAAAAFiHYgAAAIejGAAAwOEoBgAAcLiwLAaWLl2qdu3aqV69eurXr5+ys7Mti/3ee+9pxIgRatmypVwul/7yl79YFluS0tPTdeWVV6pRo0Zq0aKFUlJStGvXLktzLFu2TD179lTjxo3VuHFj9e/fX3/9618tzRFs4cKFcrlcmjZtmmUxH3roIblcroDWpUsXy+JL0pEjR/Tf//3fat68uerXr68ePXro448/tix+u3btzvkMLpdLkydPtiyHz+fTnDlz1L59e9WvX18dO3bUggULLH9a2unTpzVt2jS1bdtW9evX14ABA/TRRx9VO15l15phGJo7d648Ho/q16+v5ORk7dmzx9Ic69ev19ChQ9W8eXO5XC7t2LHDsvilpaWaOXOmevToodjYWLVs2VJjxozR0aNHLf0MDz30kLp06aLY2Fg1bdpUycnJ+vDDDy3N8VP33HOPXC6XlixZYmmOcePGnXOd/OIXvzCVA9UXdsXAunXrlJqaqnnz5ik3N1e9evXSsGHDdPz4cUviFxUVqVevXlq6dKkl8YK9++67mjx5sj744ANt2bJFpaWlGjp0aIVPgjTr4osv1sKFC5WTk6OPP/5Y1157rUaOHKnPPvvMshw/9dFHH+l//ud/1LNnT8tjX3bZZTp27FhZ27Ztm2Wxv/32Ww0cOFDR0dH661//qs8//1xPPfWUmjZtalmOjz76KKD/W7ZskSTdcsstluVYtGiRli1bpmeffVZffPGFFi1apMcff1y///3vLcshSXfddZe2bNmil156SZ988omGDh2q5ORkHTlypFrxKrvWHn/8cT3zzDNavny5PvzwQ8XGxmrYsGE6c+aMZTmKiop01VVXadGiRZZ/huLiYuXm5mrOnDnKzc3V+vXrtWvXLt1www2W5ZCkSy+9VM8++6w++eQTbdu2Te3atdPQoUP1zTffWJbjRxs2bNAHH3ygli1bmvoMVc3xi1/8IuB6eeWVV0znQTXV5oMRqqNv377G5MmTy772+XxGy5YtjfT0dMtzSTI2bNhgedyfOn78uCHJePfdd23N07RpU+P555+3PO7p06eNSy65xNiyZYsxePBgY+rUqZbFnjdvntGrVy/L4gWbOXOmcdVVV9kWvzxTp041OnbsaPj9fstiXn/99caECRMCjt10003G6NGjLctRXFxsREZGGm+88UbA8SuuuMKYPXt2jeMHX2t+v99ITEw0nnjiibJj3333neF2u41XXnnFkhw/deDAAUOSsX379mrFriz+j7Kzsw1JxsGDB23LUVBQYEgy3nnnHUtzfP3110arVq2MTz/91Gjbtq3x9NNPVyv++XKMHTvWGDlyZLVjombCamSgpKREOTk5Sk5OLjsWERGh5ORkZWVl1WLPqq+goECS1KxZM1vi+3w+rV27VkVFRerfv7/l8SdPnqzrr78+4HtipT179qhly5bq0KGDRo8erUOHDlkWe+PGjerTp49uueUWtWjRQpdffrlWrlxpWfxgJSUl+uMf/6gJEybI5XJZFnfAgAHKyMjQ7t27JUn//Oc/tW3bNg0fPtyyHGfPnpXP51O9evUCjtevX9/S0ZofHThwQHl5eQE/V3FxcerXr1/YXuvSv653l8ulJk2a2BK/pKREK1asUFxcnHr16mVZXL/frzvvvFMzZszQZZddZlncYFu3blWLFi3UuXNnTZo0SSdPnrQtFwJF1XYHzDhx4oR8Pp8SEhICjickJOjLL7+spV5Vn9/v17Rp0zRw4EB1797d0tiffPKJ+vfvrzNnzqhhw4basGGDunXrZmmOtWvXKjc3t0bzxhXp16+fXnzxRXXu3FnHjh3T/PnzNWjQIH366adq1KhRjePv379fy5YtU2pqqh544AF99NFH+s1vfqOYmBiNHTvWgk8Q6C9/+Yu+++47jRs3ztK4s2bNUmFhobp06aLIyEj5fD49+uijGj16tGU5GjVqpP79+2vBggXq2rWrEhIS9MorrygrK0udOnWyLM+P8vLyJKnca/3Hc+HmzJkzmjlzpm6//XbLn873xhtv6LbbblNxcbE8Ho+2bNmi+Ph4y+IvWrRIUVFR+s1vfmNZzGC/+MUvdNNNN6l9+/bat2+fHnjgAQ0fPlxZWVmKjIy0LS/+JayKgQvN5MmT9emnn9ryl1Xnzp21Y8cOFRQU6LXXXtPYsWP17rvvWlYQHD58WFOnTtWWLVvO+WvRKj/9y7Znz57q16+f2rZtq1dffVW//OUvaxzf7/erT58+euyxxyRJl19+uT799FMtX77clmLghRde0PDhw6s131qRV199VX/605/08ssv67LLLtOOHTs0bdo0tWzZ0tLP8dJLL2nChAlq1aqVIiMjdcUVV+j2229XTk6OZTkuVKWlpbr11ltlGIaWLVtmefxrrrlGO3bs0IkTJ7Ry5Urdeuut+vDDD9WiRYsax87JydHvfvc75ebmWjqiFey2224r+/89evRQz5491bFjR23dulVDhgyxLS/+JaymCeLj4xUZGan8/PyA4/n5+UpMTKylXlXPlClT9MYbbygzM1MXX3yx5fFjYmLUqVMnJSUlKT09Xb169dLvfvc7y+Ln5OTo+PHjuuKKKxQVFaWoqCi9++67euaZZxQVFSWfz2dZrh81adJEl156qfbu3WtJPI/Hc05x1LVrV0unIn508OBBvfPOO7rrrrssjz1jxgzNmjVLt912m3r06KE777xT06dPV3p6uqV5OnbsqHfffVfff/+9Dh8+rOzsbJWWlqpDhw6W5pFUdj1fCNf6j4XAwYMHtWXLFstHBSQpNjZWnTp10n/8x3/ohRdeUFRUlF544QVLYr///vs6fvy42rRpU3atHzx4UL/97W/Vrl07S3KUp0OHDoqPj7fsekfFwqoYiImJUVJSkjIyMsqO+f1+ZWRk2DIfbgfDMDRlyhRt2LBBf/vb39S+ffuQ5PX7/fJ6vZbFGzJkiD755BPt2LGjrPXp00ejR4/Wjh07bBnW+/7777Vv3z55PB5L4g0cOPCc2zp3796ttm3bWhL/p1avXq0WLVro+uuvtzx2cXGxIiICL+XIyEj5/X7Lc0n/+sXj8Xj07bffavPmzRo5cqTlOdq3b6/ExMSAa72wsFAffvhh2Fzr0r8LgT179uidd95R8+bNQ5LXyuv9zjvv1M6dOwOu9ZYtW2rGjBnavHmzJTnK8/XXX+vkyZOWXe+oWNhNE6Smpmrs2LHq06eP+vbtqyVLlqioqEjjx4+3JP73338fUIkeOHBAO3bsULNmzdSmTZsax588ebJefvll/d///Z8aNWpUNv8ZFxen+vXr1zi+JKWlpWn48OFq06aNTp8+rZdffllbt2619MJt1KjROescYmNj1bx5c8vWP9x3330aMWKE2rZtq6NHj2revHmKjIzU7bffbkn86dOna8CAAXrsscd06623Kjs7WytWrNCKFSssif8jv9+v1atXa+zYsYqKsv6SGzFihB599FG1adNGl112mbZv367FixdrwoQJlubZvHmzDMNQ586dtXfvXs2YMUNdunSp9rVX2bU2bdo0PfLII7rkkkvUvn17zZkzRy1btlRKSoplOU6dOqVDhw6V3fv/Y3GYmJhYpRGIiuJ7PB7dfPPNys3N1RtvvCGfz1d2vTdr1kwxMTE1/gzNmzfXo48+qhtuuEEej0cnTpzQ0qVLdeTIEVO3r1b23ym4iImOjlZiYqI6d+5sSY5mzZpp/vz5+q//+i8lJiZq3759uv/++9WpUycNGzasyjlQA7V8N0O1/P73vzfatGljxMTEGH379jU++OADy2JnZmYaks5pY8eOtSR+ebElGatXr7YkvmEYxoQJE4y2bdsaMTExxkUXXWQMGTLEePvtty2Lfz5W31o4atQow+PxGDExMUarVq2MUaNGGXv37rUsvmEYxuuvv250797dcLvdRpcuXYwVK1ZYGt8wDGPz5s2GJGPXrl2WxzYMwygsLDSmTp1qtGnTxqhXr57RoUMHY/bs2YbX67U0z7p164wOHToYMTExRmJiojF58mTju+++q3a8yq41v99vzJkzx0hISDDcbrcxZMgQ0/8NK8uxevXqcs/PmzevxvF/vF2xvJaZmWnJZ/jhhx+MG2+80WjZsqURExNjeDwe44YbbjCys7Mt/e8UrDq3FlaUo7i42Bg6dKhx0UUXGdHR0Ubbtm2NiRMnGnl5eaZyoPp4hDEAAA4XVmsGAACA9SgGAABwOIoBAAAcjmIAAACHoxgAAMDhKAYAAHA4igEAAByOYgAAAIejGAAAwOEoBgAAcDiKAQAAHI5iAAAAh/t/yYsG73z5mEUAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for repeat in repeats:\n", + " tpm = np.load(f'{save_dir}{repeat}/model/trans_prob.npy')\n", + " #fig,ax = plot_matrices(tpm) \n", + " seaborn.heatmap(tpm)\n", + " plt.show()\n", + " \n", + " with open(f'{save_dir}{repeat}/inf_params/alp.pkl','rb') as file:\n", + " alpha = pickle.load(file)\n", + " \n", + " fo = fractional_occupancies(alpha)\n", + " fig,ax = plot_violin(fo.T,fig_kwargs={'figsize': (10, 4)},sns_kwargs = {'scale':'count','width': 1})\n", + " plt.show()\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "da132bd1", + "metadata": {}, + "source": [ + "Conclusion: when states are \"repeated\", there are run-to-run variability in fractional occupancy. For example, you can see the state 5 and 13 has very different fo in the five runs." + ] + }, + { + "cell_type": "markdown", + "id": "cd2c3790", + "metadata": {}, + "source": [ + "Next, have a look at the temporal correlation of these states. Maybe have a look at repeat 2 and repeat 4 since state 5 is muted in repeat 2 while state 13 is muted in repeat 4." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d0094ce9", + "metadata": {}, + "outputs": [], + "source": [ + "from rotation.utils import calculate_temporal_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4ac693b0", + "metadata": {}, + "outputs": [], + "source": [ + "with open(f'{save_dir}repeat_2/inf_params/alp.pkl','rb') as file:\n", + " alpha_1 = pickle.load(file)\n", + "with open(f'{save_dir}repeat_4/inf_params/alp.pkl','rb') as file:\n", + " alpha_2 = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad174dca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cor = calculate_temporal_correlation(alpha_1,alpha_2)\n", + "seaborn.heatmap(cor)" + ] + }, + { + "cell_type": "markdown", + "id": "09638034", + "metadata": {}, + "source": [ + "Conclusion: there is still decent correlation between state 5 and 13 in the temporal dynamics. It seems that when the model is very good, you can have run-to-run variability in fractional occupancy, but the temporal correlation is itself very high.\n", + "Fractional occupancy variability might be important." + ] + }, + { + "cell_type": "markdown", + "id": "d3efee6c", + "metadata": {}, + "source": [ + "## Question2: Is spatial correlation equivalent to temporal correlation " + ] + }, + { + "cell_type": "markdown", + "id": "5b3abb14", + "metadata": {}, + "source": [ + "Now let's work on the simulation data, where the covariance matrices are free to train. We are asking: is temporal correlation equivalent to spatial correlation? i.e. States are spatially paired should be temporally paired?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2d3e3ca1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import yaml\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c8cdae8", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202403/sigma_0/'\n", + "N_states = list(range(2,17))\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "markdown", + "id": "a30f4758", + "metadata": {}, + "source": [ + "First you need to extract the correlations." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "45165386", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from osl_dynamics.array_ops import cov2stdcorr" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5e7ec287", + "metadata": {}, + "outputs": [], + "source": [ + "for N_state in N_states:\n", + " for repeat in repeats:\n", + " analysis_dir = f'{save_dir}hmm_ICA_25_state_{N_state}/{repeat}/analysis/'\n", + " if not os.path.exists(analysis_dir):\n", + " os.makedirs(analysis_dir)\n", + " covs = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeat}/inf_params/covs.npy')\n", + " np.save(f'{analysis_dir}covs.npy',covs)\n", + " stds,corrs =cov2stdcorr(covs)\n", + " np.save(f'{analysis_dir}stds.npy',stds)\n", + " np.save(f'{analysis_dir}corrs.npy',corrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8e0d0acc", + "metadata": {}, + "outputs": [], + "source": [ + "from osl_dynamics.inference.metrics import twopair_fisher_z_correlations\n", + "from osl_dynamics.inference.modes import hungarian_pair\n", + "from osl_dynamics.utils.plotting import plot_mode_pairing" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a278bb1c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 2/2 [00:00<00:00, 12\n", + "2024-03-04 20:14:45 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_2/analysis/spatial_cor_train_vs_repeat_1.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 2/2 [00:00<00:00, 18\n", + "2024-03-04 20:14:45 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_2/analysis/spatial_cor_train_vs_repeat_2.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 2/2 [00:00<00:00, 13\n", + "2024-03-04 20:14:45 INFO 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"2024-03-04 20:14:58 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_7/analysis/spatial_cor_repeat_1_vs_repeat_4.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 7/7 [00:00<00:00, 89\n", + "2024-03-04 20:14:58 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_7/analysis/spatial_cor_repeat_1_vs_repeat_5.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 7/7 [00:00<00:00, 88\n", + "2024-03-04 20:14:58 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_7/analysis/spatial_cor_repeat_2_vs_repeat_3.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 7/7 [00:00<00:00, 87\n", + "2024-03-04 20:14:58 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_7/analysis/spatial_cor_repeat_2_vs_repeat_4.jpg\n", + "Compute twopair 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+ "2024-03-04 20:15:13 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_2_vs_repeat_3.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 12/12 [00:00<00:00, \n", + "2024-03-04 20:15:13 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_2_vs_repeat_4.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 12/12 [00:00<00:00, \n", + "2024-03-04 20:15:14 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_2_vs_repeat_5.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 12/12 [00:00<00:00, \n", + "2024-03-04 20:15:14 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_3_vs_repeat_4.jpg\n", + "Compute twopair 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"source": [ + "for N_state in N_states:\n", + " analysis_dir = f'{save_dir}hmm_ICA_25_state_{N_state}/analysis/'\n", + " if not os.path.exists(analysis_dir):\n", + " os.makedirs(analysis_dir)\n", + " for i in range(len(repeats)):\n", + " for j in range(i+1,len(repeats)):\n", + " corrs_1 = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[i]}/analysis/corrs.npy')\n", + " corrs_2 = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[j]}/analysis/corrs.npy')\n", + " indices, cor = hungarian_pair(twopair_fisher_z_correlations(corrs_1,corrs_2),distance=False)\n", + " np.save(f'{analysis_dir}spatial_cor_{repeats[i]}_vs_{repeats[j]}.npy',cor)\n", + " indices['mean'] = np.mean(np.diagonal(cor))\n", + " # Open the file in binary write mode\n", + " with open(f'{analysis_dir}spatial_pair_{repeats[i]}_vs_{repeats[j]}.pkl', \"wb\") as f:\n", + " pickle.dump(indices, f)\n", + " plot_mode_pairing(cor,\n", + " indices,\n", + " x_label=repeats[j],\n", + " y_label=repeats[i],\n", + " title=f'Spatial correlation {repeats[i]} vs {repeats[j]}',\n", + " filename=f'{analysis_dir}spatial_cor_{repeats[i]}_vs_{repeats[j]}.jpg')" + ] + }, + { + "cell_type": "markdown", + "id": "83a253e4", + "metadata": {}, + "source": [ + "Spatial mapping has been finished. Now let's work on the temporal mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "da0c50ab", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "from osl_dynamics.inference.metrics import alpha_correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a99f1f8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-03-04 21:15:51 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_2/analysis/temp_cor_train_vs_repeat_1.jpg\n", + "2024-03-04 21:15:51 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_2/analysis/temp_cor_train_vs_repeat_2.jpg\n", + "2024-03-04 21:15:52 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_simulation_202403/sigma_0/hmm_ICA_25_state_2/analysis/temp_cor_train_vs_repeat_3.jpg\n", + "2024-03-04 21:15:52 INFO osl-dynamics 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open(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[j]}/inf_params/alp.pkl','rb') as file:\n", + " alpha_2 = pickle.load(file)\n", + " indices, cor = hungarian_pair(alpha_correlation(alpha_1,alpha_2,return_diagonal=False),distance=False)\n", + " np.save(f'{analysis_dir}temp_cor_{repeats[i]}_vs_{repeats[j]}.npy',cor)\n", + " indices['mean'] = np.mean(np.diagonal(cor))\n", + " # Open the file in binary write mode\n", + " with open(f'{analysis_dir}temp_pair_{repeats[i]}_vs_{repeats[j]}.pkl', \"wb\") as f:\n", + " pickle.dump(indices, f)\n", + " plot_mode_pairing(cor,\n", + " indices,\n", + " x_label=repeats[j],\n", + " y_label=repeats[i],\n", + " title=f'Temporal correlation {repeats[i]} vs {repeats[j]}',\n", + " filename=f'{analysis_dir}temp_cor_{repeats[i]}_vs_{repeats[j]}.jpg')" + ] + }, + { + "cell_type": "markdown", + "id": "644c568a", + "metadata": {}, + "source": [ + "Looking at the case on simulation data when N_states = 16. There are at least two claims we can obtain:\n", + "- Spatial pairing is very similar to temporal pairing, but not necessarily the same.\n", + "- Poor spatial pairing does not damage the mean diagonal value too much, but this is not the case if we calculate temporal pairing." + ] + }, + { + "cell_type": "markdown", + "id": "e80127d8", + "metadata": {}, + "source": [ + "Hypothesis: Temporal pairing correlation is more sensitive to the optimal number of states.\n", + "This holds at least on simulation data. And you do not even need to \"split-half\". Train multiple models on the same dataset is enough." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "190a2f29", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202403/sigma_0/'\n", + "N_states = list(range(2,17))\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c688d9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "summary_dir = f'{save_dir}summary/'\n", + "if not os.path.exists(summary_dir):\n", + " os.makedirs(summary_dir)\n", + "\n", + "for metric in ['spatial','temp']:\n", + " # Initialize a dictionary to store mean values for each number of states\n", + " mean_values_dict = {N_state: [] for N_state in N_states}\n", + "\n", + " # Loop through each value of N_states\n", + " for N_state in N_states:\n", + " # Loop through each pair of repeats\n", + " for i in range(len(repeats)):\n", + " for j in range(i + 1, len(repeats)):\n", + " # Define the path to the pickle file\n", + " file_path = os.path.join(save_dir, f'hmm_ICA_25_state_{N_state}/analysis/{metric}_pair_{repeats[i]}_vs_{repeats[j]}.pkl')\n", + " # Load the pickle file\n", + " with open(file_path, 'rb') as f:\n", + " data = pickle.load(f)\n", + " # Extract the 'mean' value and append it to the dictionary\n", + " mean_value = data['mean']\n", + " mean_values_dict[N_state].append(mean_value)\n", + "\n", + " # Plot boxplots of the mean values for each number of states\n", + " sns.boxplot(data=list(mean_values_dict.values()), showmeans=True)\n", + " plt.xlabel('Number of States')\n", + " plt.ylabel('Mean Value')\n", + " plt.title('Distribution of Mean Value for Different Numbers of States')\n", + " plt.xticks(ticks=range(len(N_states)), labels=N_states)\n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "8875219b", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_simulation_202403/sigma_0/'\n", + "N_states = list(range(2,17))\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "44f9f391", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "\n", + "# Initialize a dictionary to store free energy values for each number of states\n", + "free_energy_dict = {N_state: [] for N_state in N_states}\n", + "\n", + "# Loop through each value of N_states\n", + "for N_state in N_states:\n", + " # Loop through each repeat\n", + " for repeat in repeats:\n", + " # Define the path to the JSON file for the free energy\n", + " file_path = os.path.join(save_dir, f'hmm_ICA_25_state_{N_state}/{repeat}/metrics/metrics.json')\n", + " # Load the JSON file\n", + " with open(file_path, 'r') as f:\n", + " metrics = json.load(f)\n", + " # Extract the 'free_energy' value and append it to the dictionary\n", + " free_energy = metrics['free_energy']\n", + " free_energy_dict[N_state].append(free_energy)\n", + "\n", + "# Plot boxplots of the free energy values for each number of states\n", + "sns.boxplot(data=list(free_energy_dict.values()), showmeans=True)\n", + "plt.xlabel('Number of States')\n", + "plt.ylabel('Free Energy')\n", + "plt.title('Distribution of Free Energy for Different Numbers of States')\n", + "plt.xticks(ticks=range(len(N_states)), labels=N_states)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a67a21fe", + "metadata": {}, + "source": [ + "### Question 3: On real HCP data, what is the relationsihp between \"spatial pairing\" and \"temporal pairing\"" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0a401572", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import yaml\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "48e0945f", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_HCP_yaml_202403/'\n", + "N_states = list(range(2,37))\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b524cb6a", + "metadata": {}, + "outputs": [], + "source": [ + "from osl_dynamics.array_ops import cov2stdcorr\n", + "\n", + "for N_state in N_states:\n", + " for repeat in repeats:\n", + " analysis_dir = f'{save_dir}hmm_ICA_25_state_{N_state}/{repeat}/analysis/'\n", + " if not os.path.exists(analysis_dir):\n", + " os.makedirs(analysis_dir)\n", + " covs = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeat}/inf_params/covs.npy')\n", + " np.save(f'{analysis_dir}covs.npy',covs)\n", + " stds,corrs =cov2stdcorr(covs)\n", + " np.save(f'{analysis_dir}stds.npy',stds)\n", + " np.save(f'{analysis_dir}corrs.npy',corrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "67b2aa62", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 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[00:00<00:00, \n", + "2024-03-04 21:49:08 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_2_vs_repeat_4.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 12/12 [00:00<00:00, \n", + "2024-03-04 21:49:08 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_2_vs_repeat_5.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 12/12 [00:00<00:00, \n", + "2024-03-04 21:49:09 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_3_vs_repeat_4.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 12/12 [00:00<00:00, \n", + "2024-03-04 21:49:09 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_12/analysis/spatial_cor_repeat_3_vs_repeat_5.jpg\n", + "Compute twopair Fisher-z 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"2024-03-04 21:49:13 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_13/analysis/spatial_cor_repeat_4_vs_repeat_5.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 14/14 [00:00<00:00, \n", + "2024-03-04 21:49:13 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_14/analysis/spatial_cor_train_vs_repeat_1.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 14/14 [00:00<00:00, \n", + "2024-03-04 21:49:13 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_14/analysis/spatial_cor_train_vs_repeat_2.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 14/14 [00:00<00:00, \n", + "2024-03-04 21:49:13 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_14/analysis/spatial_cor_train_vs_repeat_3.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 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"2024-03-04 21:49:38 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_20/analysis/spatial_cor_train_vs_repeat_1.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 20/20 [00:00<00:00, \n", + "2024-03-04 21:49:39 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_20/analysis/spatial_cor_train_vs_repeat_2.jpg\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 20/20 [00:00<00:00, \n", + "2024-03-04 21:49:39 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_20/analysis/spatial_cor_train_vs_repeat_3.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 20/20 [00:00<00:00, \n", + "2024-03-04 21:49:39 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_20/analysis/spatial_cor_train_vs_repeat_4.jpg\n", + 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./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/spatial_cor_repeat_3_vs_repeat_5.jpg\n", + "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 36/36 [00:00<00:00, \n", + "2024-03-04 21:51:34 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/spatial_cor_repeat_4_vs_repeat_5.jpg\n" + ] + } + ], + "source": [ + "from osl_dynamics.inference.metrics import twopair_fisher_z_correlations\n", + "from osl_dynamics.inference.modes import hungarian_pair\n", + "from osl_dynamics.utils.plotting import plot_mode_pairing\n", + "\n", + "for N_state in N_states:\n", + " analysis_dir = f'{save_dir}hmm_ICA_25_state_{N_state}/analysis/'\n", + " if not os.path.exists(analysis_dir):\n", + " os.makedirs(analysis_dir)\n", + " for i in range(len(repeats)):\n", + " for j in range(i+1,len(repeats)):\n", + " corrs_1 = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[i]}/analysis/corrs.npy')\n", + " corrs_2 = np.load(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[j]}/analysis/corrs.npy')\n", + " indices, cor = hungarian_pair(twopair_fisher_z_correlations(corrs_1,corrs_2),distance=False)\n", + " np.save(f'{analysis_dir}spatial_cor_{repeats[i]}_vs_{repeats[j]}.npy',cor)\n", + " indices['mean'] = np.mean(np.diagonal(cor))\n", + " # Open the file in binary write mode\n", + " with open(f'{analysis_dir}spatial_pair_{repeats[i]}_vs_{repeats[j]}.pkl', \"wb\") as f:\n", + " pickle.dump(indices, f)\n", + " plot_mode_pairing(cor,\n", + " indices,\n", + " x_label=repeats[j],\n", + " y_label=repeats[i],\n", + " title=f'Spatial correlation {repeats[i]} vs {repeats[j]}',\n", + " filename=f'{analysis_dir}spatial_cor_{repeats[i]}_vs_{repeats[j]}.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "61de782a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-03-04 21:52:06 INFO osl-dynamics [plotting.py:103:save]: Saving 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[plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/temp_cor_repeat_2_vs_repeat_4.jpg\n", + "2024-03-04 22:02:34 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/temp_cor_repeat_2_vs_repeat_5.jpg\n", + "2024-03-04 22:02:38 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/temp_cor_repeat_3_vs_repeat_4.jpg\n", + "2024-03-04 22:02:39 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/temp_cor_repeat_3_vs_repeat_5.jpg\n", + "2024-03-04 22:02:41 INFO osl-dynamics [plotting.py:103:save]: Saving ./results_HCP_yaml_202403/hmm_ICA_25_state_36/analysis/temp_cor_repeat_4_vs_repeat_5.jpg\n" + ] + } + ], + "source": [ + "import pickle\n", + "from osl_dynamics.inference.metrics import alpha_correlation\n", + "\n", + "for N_state in N_states:\n", + " analysis_dir = f'{save_dir}hmm_ICA_25_state_{N_state}/analysis/'\n", + " if not os.path.exists(analysis_dir):\n", + " os.makedirs(analysis_dir)\n", + " for i in range(len(repeats)):\n", + " for j in range(i+1,len(repeats)):\n", + " with open(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[i]}/inf_params/alp.pkl','rb') as file:\n", + " alpha_1 = pickle.load(file)\n", + " with open(f'{save_dir}hmm_ICA_25_state_{N_state}/{repeats[j]}/inf_params/alp.pkl','rb') as file:\n", + " alpha_2 = pickle.load(file)\n", + " indices, cor = hungarian_pair(alpha_correlation(alpha_1,alpha_2,return_diagonal=False),distance=False)\n", + " np.save(f'{analysis_dir}temp_cor_{repeats[i]}_vs_{repeats[j]}.npy',cor)\n", + " indices['mean'] = np.mean(np.diagonal(cor))\n", + " # Open the file in binary write mode\n", + " with open(f'{analysis_dir}temp_pair_{repeats[i]}_vs_{repeats[j]}.pkl', \"wb\") as f:\n", + " pickle.dump(indices, f)\n", + " plot_mode_pairing(cor,\n", + " indices,\n", + " x_label=repeats[j],\n", + " y_label=repeats[i],\n", + " title=f'Temporal correlation {repeats[i]} vs {repeats[j]}',\n", + " filename=f'{analysis_dir}temp_cor_{repeats[i]}_vs_{repeats[j]}.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "62b69a6c", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_HCP_yaml_202403/'\n", + "N_states = list(range(2,37))\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d4070db2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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37w/GmNMxevfujby8PJfX09/3lrcmT54MWZbx1ltvBeR4AHDvvfc6peRITEwEADzyyCNOf+/s28s+F0EQ8PTTT6v39Xo9nn76aVy4cAFZWVkAKv5aALa/+deuXcOWLVt8en6+/v301uLFizF//ny0atUKn3/+OV5++WXcfPPNuPfee9W/TeVx7EGw/5375z//iaKiIvz+++/llvf2ugbi+9ITCoIqQWFhodMHsKxBgwahW7duePLJJ9G4cWMMHjwYa9as8ekFjouL82kQ9PXXX+90n+M4XHfddRWa+uiNv//+G7GxsS7Xw96k+vfffzttb968ucsx6tWrhytXrpR7nuuvv179Ai/vPP4YOnQojhw5gkOHDmHlypUYPHiwZv6nEydOgDGGKVOmoGHDhk7/pk2bBqB0cK+iKJg7dy6uv/56GAwGNGjQAA0bNsTPP/+MvLw8l2OXvT716tUDgHKvDwB14PPKlSsBlA7sHjx4MHQ6nV/18cfx48eRl5eHRo0auVyfwsJC9dr4wv763njjjS6P3XTTTS6vvyAIaNasmX9PQEN5n3lfHT9+HIcPH3a5PjfccAMAuFyjVq1aOd2/ePEirl69ioULF7ocw/7jqewxKvLe8kbr1q0xfPhwLFy4EOfOnQvIMcvWuU6dOgCA+Ph4ze1ln0tsbKzLgHj7Nbb/bazoawEAY8eOxQ033IAHHngAzZo1w6hRo7Bp06Zyn5+vfz+9xfM8xo0bh6ysLFy6dAkbNmzAAw88gO3bt2Pw4MFeHePw4cN4+OGHUadOHdSuXRsNGzbE448/DgBe/a3w9roG4vvSExoTFGBnzpxBXl4errvuOrf7RERE4H//+x927NiBr776Cps2bUJmZiZ69OiBb775Rv1C8sSXcTzecpfQ0Wq1elWnQHB3HsZYlZzfk8TERCQkJOCFF17AyZMnMXToUM397B/Ol19+Gb1799bcx/7+mDlzJqZMmYJRo0bh9ddfR/369cHzPF544QXND3lFrk/Hjh1x0003YdWqVXj11VexatUqMMacZoX5Wh9HHMdp1sNqtTrdVxQFjRo1wooVKzSP480YrYpybPGqKG8+875SFAVt27bFnDlzNB8v+yVf9u+B/bV6/PHHkZSUpHmM2267zel+VXz2Jk2ahIyMDMyePVszmZ+nv0Fa3NU5kM+loq8FADRq1AgHDx7E5s2bsXHjRmzcuBGLFy/GiBEjsHTpUp/rFEgxMTF48MEH8eCDD+Luu+/Grl278Pfff6tjh7RcvXoV3bt3R+3atTFjxgwkJCTAaDTip59+wiuvvOJVgOLtdQ3E96UnFAQFWEZGBgC4/fKz43ke9957L+69917MmTMHM2fOxKRJk7Bjxw707Nkz4Bmmjx8/7nSfMYYTJ044/SGsV6+eZubdv//+G61bt1bv+1K3Fi1aYOvWrSgoKHD6NWNvLvX0QfNFixYt8PPPP0NRFKcvt0CfZ8iQIXjjjTdw8803o3379pr72K+VKIpukyzarV27Fvfccw8++eQTp+1Xr15FgwYNAlJnR8OGDcOUKVPw888/Y+XKlbj++utxxx13BKQ+9erV0+w6KftrNSEhAVu3bkW3bt0CFszbX9+jR4+qzel2R48eDdjrr8Xbz7wvEhIScOjQIdx7771+/S1o2LAhoqOjYbVay30P+qKif5cSEhLw+OOP46OPPlK7qBx5+htUGc6ePeuSHuHYsWMAoHazVfS1sNPr9ejfvz/69+8PRVEwduxYfPTRR5gyZYrbALqq/n7aderUCbt27cK5c+fQokULt893586dyM3NxWeffYa77rpL3X7y5EmXfd0dw5frWt73ZUVQd1gAbd++Ha+//jpatWrlMefK5cuXXbbZv1DNZjMAqB/KQC0HsGzZMqcxC2vXrsW5c+fwwAMPqNsSEhLw/fffw2KxqNv++9//4vTp007H8qVuffr0gdVqxfz58522z507FxzHOZ2/Ivr06YPz588jMzNT3SbLMt5//31ERUWhe/fuATnPk08+iWnTpuHdd991u0+jRo1w991346OPPtJs9nfMw6HT6Vx+nX766ade98v7yv6+nDp1Kg4ePOjyPq1IfRISEvD77787Pb9Dhw65zOIYOHAgrFYrXn/9dZdjyLLs13u+U6dOaNSoEdLT09XPEABs3LgRv/32G/r27evzMb3h7WfeVwMHDkR2djY+/vhjl8eKi4tx7do1j+V1Oh0eeeQRrFu3Dr/++qvL4/7mgqlVq1aFu0UnT54MSZLw9ttvuzyWkJCAvLw8/Pzzz+q2c+fO4fPPP6/QOd2RZRkfffSRet9iseCjjz5Cw4YN0bFjRwAVfy0AuGRi53le/QHq+H4tqzL+fp4/fx5Hjhxx2W6xWLBt2zbwPK8GZe7+1ttbXxz/VlgsFnzwwQcux3X3nvH2unrzfVkR1BLkp40bN+L333+HLMvIycnB9u3bsWXLFrRo0QJffPGFx+RYM2bMwP/+9z/07dsXLVq0wIULF/DBBx+gWbNmuPPOOwHY/hjUrVsX6enpiI6ORq1atZCYmKjZ3+yN+vXr484770RycjJycnKQlpaG6667zmlK75NPPom1a9fi/vvvx8CBA/HHH39g+fLlTgOVfa1b//79cc8992DSpEn466+/0K5dO3zzzTfYsGEDXnjhBZdj++upp57CRx99hJEjRyIrKwstW7bE2rVr8d133yEtLS1g4zVatGiB6dOnl7vfggULcOedd6Jt27YYPXo0WrdujZycHOzduxdnzpzBoUOHAAD9+vXDjBkzkJycjK5du+KXX37BihUrnFreAqlVq1bo2rUrNmzYAAAuX9wVqc+oUaMwZ84c9O7dG0888QQuXLiA9PR03HLLLcjPz1f36969O55++mnMmjULBw8eRK9evSCKIo4fP45PP/0U8+bNw6OPPurT8xJFEbNnz0ZycjK6d++OIUOGqFPkW7Zs6XEJAW9V5DPvq+HDh2PNmjV45plnsGPHDnTr1g1WqxW///471qxZg82bNzslbtTy1ltvYceOHUhMTMTo0aPRpk0bXL58GT/99BO2bt2q+eVSno4dOyIzMxOpqam44447EBUVhf79+/t0DHtrkFY30ODBg/HKK6/g4YcfxnPPPYeioiJ8+OGHuOGGGwI2MN9RbGwsZs+ejb/++gs33HADMjMzcfDgQSxcuFBN0xCI1+LJJ5/E5cuX0aNHDzRr1gx///033n//fbRv397tVHagcv5+njlzBp07d0aPHj1w7733okmTJrhw4QJWrVqFQ4cO4YUXXlBbfdu3bw+dTofZs2cjLy8PBoMBPXr0QNeuXVGvXj0kJSXhueeeA8dxyMjI0OxudPee8fa6evN9WSGVMuesBiubQEqv17MmTZqw++67j82bN89pWqZd2emd27ZtYwMGDGCxsbFMr9ez2NhYNmTIEHbs2DGnchs2bFATbkEjWaIWd1PkV61axSZOnMgaNWrEIiIiWN++fdnff//tUv7dd99Vk6V169aN7d+/X3Pqs7u6aU1xLSgoYC+++CKLjY1loiiy66+/3mOyxLLcTd0vKycnhyUnJ7MGDRowvV7P2rZtqzmN358p8p64m0L9xx9/sBEjRrAmTZowURRZXFwc69evH1u7dq26j8lkYi+99BJr2rQpi4iIYN26dWN79+51+zqWXWLAn+UrFixYwAC4JH/0pT7uzrt8+XLWunVrptfrWfv27dnmzZvdTnteuHAh69ixI4uIiGDR0dGsbdu2bPz48eoUanc8TVnPzMxkHTp0YAaDgdWvX99jskRvBeIzz5hvU+QZs037nz17NrvllluYwWBg9erVYx07dmSvvfYay8vLU/dz97lhzPaZGDduHIuPj2eiKLImTZqwe++91ymxoy/vrcLCQjZ06FBWt25dn5IllnX8+HGm0+k0z/vNN9+wW2+9len1enbjjTey5cuXe0yWqFXnd955x2m71nPUSpbYokULNn/+fJf6VvS1WLt2LevVq5eatLJ58+bs6aef1lyKpyxv/356O0U+Pz+fzZs3j/Xu3Zs1a9aMiaLIoqOjWZcuXdjHH3/sctyPP/6YtW7dWn297NPlv/vuO/aPf/yDRUREsNjYWDZ+/Hi2efNmlyn1nt4z3lxXb78v/cUxVg1GnBJCCCGEVDEaE0QIIYSQsERBECGEEELCEgVBhBBCCAlLFAQRQgghJCxREEQIIYSQsERBECGEEELCEiVL1KAoCs6ePYvo6OiAL19BCCGEkMrBGENBQQFiY2O9Wh+QgiANZ8+edVkUjxBCCCGh4fTp02jWrFm5+1EQpMG+xMLp06dRu3Ztp8ckScI333yjpvr3lr/lgnHOml4ulOoaKuVCqa6hUi6U6krXJvTLhVJdPZXLz89HfHy810slURCkwd4FVrt2bc0gKDIyErVr1/b5RfOnXDDOWdPLhVJdQ6VcKNU1VMqFUl3p2oR+uVCqqzflvB3KQgOjCSGEEBKWKAgihBBCSFiiIIgQQgghYYmCIEIIIYSEJQqCCCGEEBKWKAgihBBCSFiiIIgQQgghYYmCIEIIIYSEJQqCCCGEEBKWKAgihBBCSFgKahD0v//9D/3790dsbCw4jsP69evLLbNz507cfvvtMBgMuO6667BkyRKXfRYsWICWLVvCaDQiMTERP/zwQ+ArTwghhJCQFtQg6Nq1a2jXrh0WLFjg1f4nT55E3759cc899+DgwYN44YUX8OSTT2Lz5s3qPpmZmUhNTcW0adPw008/oV27dujduzcuXLhQWU+DEEIIISEoqAuoPvDAA3jggQe83j89PR2tWrXCu+++CwC4+eabsXv3bsydOxe9e/cGAMyZMwejR49GcnKyWuarr77CokWLMGHCBJ/qV1xcDEEQYDab1W2yLEOSJFy9ehWCYLt8BoNBXazNaDSqtxljMJlMAIDCwkIUFRWhuLgYsiy77EsIIYSQqhVSq8jv3bsXPXv2dNrWu3dvvPDCCwAAi8WCrKwsTJw4UX2c53n07NkTe/fudXtcs9nsFOjk5+cDAIYPHw5BENSgxdHChQvV24IgqMHM6tWrYTQaAQAmkwmPPPKIU7nFixert9etW6fu64kkSU7/e4vKVZ9z1vRywThnTS8XjHOGSrlgnLOmlwvGOSujnK/H4hhjzKcSlYTjOHz++ed46KGH3O5zww03IDk52SnI+frrr9G3b18UFRXhypUriIuLw549e9ClSxd1n/Hjx2PXrl3Yt2+f5nGnT5+O1157zWX7ypUrIYqiU8Cj5amnnoIoii7bJUnyWNZdOUIIIYT4rqioCEOHDkVeXh5q165d7v4h1RJUWSZOnIjU1FT1fn5+PuLj47F48WIIgoD/9P4XDDodAOCAeAofRe/G0wV3ooPUHADAzlzBS1vWA3BuCWKMoXfv3jCZTBg2bBgAYMmSJYiOjgbg3I3miDGmtkwxxlBYWIhdu3ahV69e0Ov1bsuVJUkStmzZgvvuu8+nYKumlwuluoZKuVCqa6iUC6W60rUJ/XKhVFdP5ew9Od4KqSCoSZMmyMnJcdqWk5OD2rVrIyIiAjqdDjqdTnOfJk2auD2uwWCAwWBw2W7vBqunN8AoiGBg+NT4PS6xS/g08nv0MN0ADhxMsqQ2wQmC4PSi6PV6dewQAERHR6tBkDvFxcUu3WhAaVfahg0bEBER4fEYjkRR9KvFqaaXC8Y5a3q5YJyzppcLxjlDpVwwzlnTywXjnIEs5+txQipPUJcuXbBt2zanbVu2bFG7vvR6PTp27Oi0j6Io2LZtm1P3mK/MsgyTLGEPO4Fj/HkAwDH+PPawEzDJEswOY4aqSe8iIYQQQsoR1JagwsJCnDhxQr1/8uRJHDx4EPXr10fz5s0xceJEZGdnY9myZQCAZ555BvPnz8f48eMxatQobN++HWvWrMFXX32lHiM1NRVJSUno1KkTOnfujLS0NFy7dk2dLeaPsRs/BQNDbu9cIAK20FEBZhR/iZjNMeBQ2jVlNpsRGRnp97kA26yxDRs2ALANrh40aBAAYMWKFYiKivJqMDUhhBBCPAtqELR//37cc8896n37uJykpCQsWbIE586dw6lTp9THW7Vqha+++govvvgi5s2bh2bNmuH//u//1OnxADBo0CBcvHgRU6dOxfnz59G+fXts2rQJjRs3rlBdLU0tkBo4jDrnAamBBEtTCwznSrvSGGMoLi52KmufJm+/7dg9Zuc4XZ7jOM3uLqPR6FM3GCGEEELcC2oQdPfdd3vsPtLKBn333XfjwIEDHo+bkpKClJSUilZPteCBR/FK7TW4yjgoXGl9ecYh5i4Rs/MfxbiNa9XtAwYMcHuspKQkzSn3vo7zIYQQQkjFhNTA6GA5KJ7CcSHHZbvCMRwXcnBQLG2touSHhBBCSGigIKgcDAyzzF8DUQC04hsGzDJ/jRg4jw0CgA/63A+DoLN1kSkKfolrjo7ns9WLbpatGPv1psp+CoQQQgjRQEFQeXjAWsuqHQABAFfyeMlgaUcGQQdjyfgfkeMgiiKMogiBZpARQgghQUdBkAerVq1CdHQ0Tl09havmq7BYLJg2dRoAYOq0qeoYnsbRjdEkyX0eIkIIIYRUPxQEeRAREQGO4/DcyOfUbSJsiZhmvTBL3eY4qLnszDBCCCGEVE8hlSyREEIIISRQqCWoHI6JCwFbgsetW7eib9++anpuSl5ICCGEhB4KgspRNnGhIAiIjIxEREREQFeA10qyCJSfaNExySIhhBBCvEdBUDVhNpvV5THc0Uq0SEkWCSGEEP/QmCBCCCGEhCVqCaqG3n/gdhh0tvjUlmiRw5H429H+7EEIUGC2Knh2409BriUhhBAS2igIqoYMOh4GQafeFzm+JNGiDjpG438IIYSQQKDuMEIIIYSEJQqCCCGEEBKWKAgihBBCSFiiIIgQQgghYYmCIEIIIYSEJQqCCCGEEBKWKAgihBBCSFiiPEE1CGPMaa2xwsJCFBUVobi4WF1ug9YaI4QQQmwoCKpBTCYTBgwY4LJ98eLF6m1aa4wQQgixoe4wQgghhIQlagmqQYxGIzZs2ADA1ipkX5V+xYoViIqKUvchhBBCCAVBNQrHcZpdXUajkbrACCGEkDKoO4wQQgghYYmCIEIIIYSEJQqCCCGEEBKWKAgihBBCSFiiIIgQQgghYYmCIEIIIYSEJQqCCCGEEBKWKE9QNcEYU2+bZavTY1aOQZIkmGUrdExxetyxHCGEEEK8R0FQNWE2m9Xbz246oLHHD27LRUZGVlKtCCGEkJqLusMIIYQQEpaoJaiaMBgM6u337+8Ag6BT71s5HgeatkeHcwfV7jB7a5FjOUIIIYR4j4KgaoLjOPW2QdC5BEGiKMIg6KBjnNtyhBBCCPEedYcRQgghJCxREEQIIYSQsERBECGEEELCUtCDoAULFqBly5YwGo1ITEzEDz9oTwUHAEmSMGPGDCQkJMBoNKJdu3bYtGmT0z5WqxVTpkxBq1atEBERgYSEBLz++uuUT4cQQgghToIaBGVmZiI1NRXTpk3DTz/9hHbt2qF37964cOGC5v6TJ0/GRx99hPfffx9HjhzBM888g4cffhgHDpTm1Zk9ezY+/PBDzJ8/H7/99htmz56Nt99+G++//35VPa2AO8BfwLz8eTjAa18XQgghhPguqEHQnDlzMHr0aCQnJ6NNmzZIT09HZGQkFi1apLl/RkYGXn31VfTp0wetW7fGmDFj0KdPH7z77rvqPnv27MGAAQPQt29ftGzZEo8++ih69erlsYWpKvzEn8G8/Hn4iT/jUzkGhsXir7ioXMRi8VcwUIsWIYQQEghBmyJvsViQlZWFiRMnqtt4nkfPnj2xd+9ezTJmsxlGo9FpW0REBHbv3q3e79q1KxYuXIhjx47hhhtuwKFDh7B7927MmTPHbV3MZrNTxub8/HwAtu43SZKc9rXfL7vdTpZliKIIALByHGSOAwPDJ8L3uKhcxCfC9+igPAIOHKwcp+7rXE4HK2eLT7P4HBzXXQUAHNddxY+6S7jV2tCpnFZdZFl2qrO7+mop7zmGerlgnLOmlwvGOWt6uWCcM1TKBeOcNb1cMM5ZGeV8PRbHgjRY5uzZs4iLi8OePXvQpUsXdfv48eOxa9cu7Nu3z6XM0KFDcejQIaxfvx4JCQnYtm0bBgwYAKvVqgYxiqLg1Vdfxdtvvw2dTger1Yo333zTKdgqa/r06Xjttddctq9cudLnJSkYY04BCACckE9ghXmFen+YYRiuE65z2kcQBJecP4wxpBem46z1LBgYOHCI1cXimahnys0PJEkSFi5cCAB46qmn1KCJEEIIqamKioowdOhQ5OXloXbt2uXuH1LJEufNm4fRo0fjpptuAsdxSEhIQHJyslP32Zo1a7BixQqsXLkSt9xyCw4ePIgXXngBsbGxSEpK0jzuxIkTkZqaqt7Pz89HfHw8evXq5XIRJUnCli1bcN9992kGFlevXsWwYcPU+wwMub1zgfqwdT4qwJrsNYjZHAMOpYHMJ598gmeeeQYA8N79t8Mg8Mjic5BtzHY6VrY1G/LZr7H4K9v21atXu7SOAYDJZFKDoB49eiA6OtrdZXVR3nMM9XKhVNdQKRdKdQ2VcqFUV7o2oV8ulOrqqZy9J8dbQQuCGjRoAJ1Oh5ycHKftOTk5aNKkiWaZhg0bYv369TCZTMjNzUVsbCwmTJiA1q1bq/v8+9//xoQJEzB48GAAQNu2bfH3339j1qxZboMgg8GgufyEKIpuXxh3jwmC8yW1NLVAauDQPMcDUgMJlqYWGM4ZnMrZm/F0zAqeMSwTD4NngOLQ6MMzYKXhCCxSBDhwEARBsx6OrVGenocnNb1cMM5Z08sF45w1vVwwzhkq5YJxzppeLhjnDGQ5X48TtIHRer0eHTt2xLZt29RtiqJg27ZtTt1jWoxGI+Li4iDLMtatW4cBAwaojxUVFYHnnZ+WTqeDoiiBfQJuOAZTC/r0Qkx3HnyZpS54xiGmO48FfXpplgOALN0FHNNddQqAAFtAdFzIg6WpJfCVJ4QQQsJIULvDUlNTkZSUhE6dOqFz585IS0vDtWvXkJycDAAYMWIE4uLiMGvWLADAvn37kJ2djfbt2yM7OxvTp0+HoigYP368esz+/fvjzTffRPPmzXHLLbfgwIEDmDNnDkaNGlUlz8lxrM5hQw6O6y657KNwDMd1l3DYkKNZjoFhifgbOAYwjaE/HAMKbiuA/pw+sJUnhBBCwkhQg6BBgwbh4sWLmDp1Ks6fP4/27dtj06ZNaNy4MQDg1KlTTq06JpMJkydPxp9//omoqCj06dMHGRkZqFu3rrrP+++/jylTpmDs2LG4cOECYmNj8fTTT2Pq1KlV+twYGJYZ9nsMZJYZ9oNBcBobBAAyFFzkijXLAbbjWWtZq0GqS0IIISR0BX1gdEpKClJSUjQf27lzp9P97t2748iRIx6PFx0djbS0NKSlpQWohn7igYvcNY+BzCXuGgx8HaBMT50IHd43dUceZ+vyUsDht4Y34+aLv4EHg0W2YtaWo+DK9pURQgghxGtBD4JqKk7hMK9oAIoF22BnK8fhcEwj3JJ7AbqSrASRsh6vKDs1yzdikWjEIkvK8sgTYnEdOw8dU2BWrNAV66rkeRBCCCE1FQVBlaghi4KR2S6xDA5XhFhczxiEkiDIxGRPxQkhhBBSiWhUCSGEEELCEgVBhBBCCAlLFAQRQgghJCxREEQIIYSQsEQDo6shs7V0zjxjDMUKB0mSYJKsEKA4PU4IIYQQ/1AQVA09u/Enja37IQiCywr1hBBCCPEPdYcRQgghJCxRS1A1YTAYsGHDBpftJpMJgwYNAgAsXboUUVFRTo8bjcYqqR8hhBBS01AQVE1wHIeIiAiP+xiNxnL3IYQQQoh3qDuMEEIIIWGJWoJCHGMMJpPJZbvjNpPJBEFwfqmNRiM4jhZgJYQQEr4oCApxJpMJAwYM8LhPUlKSy6yyDRs2UNcaIYSQsEbdYYQQQggJS9QSVIO8e18EDDrbbVuSRREnWg1Bm1OrIECG2Qq8tKU4uJUkhBBCqgkKgmoQgw4wCPZxPhxEjocoijCKPHSMA8CCWT1CCCGkWqHuMEIIIYSEJQqCCCGEEBKWKAgihBBCSFiiMUHEJddQYWEhioqKUFxcrE6t18or5G85QgghpDqgIIi4zTW0ePFi9bZWXiF/yxFCCCHVAXWHEUIIISQsUUsQgdFoVFewd1y1fsWKFeqq9Vqr1ftbjhBCCKkOKAgiblewL2/Ven/LEUIIIdUBdYcRQgghJCxREEQIIYSQsERBECGEEELCEgVBhBBCCAlLNDA6xDFWuiiqWXZeINXKMUiSBLPMoGPM6XHHcoQQQkg4oiAoxJnNZvX2S1tNZR4tBrDQbbnIyMjKqxghhBBSzVF3WA1mbmLGhX4XYG5iLn9nQgghJMxQS1CIMxgM6u13exphEGzrdDEwvFLnMi4LVtTrfg2z8+rAIpe2FjmWI4QQQsIRtQSFOMfFSQ0Cp/47bDThD8HWAvSHYMZho0kNkMqWI4QQQsIRBUE1EAPDcsNl8CVjn3kGLDdcBgMNhiaEEELsqDusBvpJKMZxoXQckMIBxwUzDorFAKgFiBBCCAEoCKpUZtkKwDYdvVhRIEkSTJKkXnT744Hk2AqkOMQ7PANWRV4GQ31wFAgRQgghFARVprFfb3LZJggCZFmutHOWbQWyUzjgD8GC+k0tMJyjQdGEEEIIjQmqQeytQJyboT8cAwpuK6CxQYQQQgiqQUvQggUL8M477+D8+fNo164d3n//fXTu3FlzX0mSMGvWLCxduhTZ2dm48cYbMXv2bNx///1O+2VnZ+OVV17Bxo0bUVRUhOuuuw6LFy9Gp06dKv35GI1GbNiwwWmbyWTCoEGDAABLly5FVFSUZrmKkgFc5GUwN71djAOstawU+gbBvn37sGzZMsTExODOO+8MdnUIIYQgyEFQZmYmUlNTkZ6ejsTERKSlpaF37944evQoGjVq5LL/5MmTsXz5cnz88ce46aabsHnzZjz88MPYs2cPOnToAAC4cuUKunXrhnvuuQcbN25Ew4YNcfz4cdSrV69KnhPHcYiIiHD7uNFo9Ph4RYjgMLewGfI421gjhRNwokkfXHf+a/BMhmRleGe7BE6hMUFVyWQyYcGCBSgoKMCCBQvQqVOngAS9hBBCKiaobQJz5szB6NGjkZycjDZt2iA9PR2RkZFYtGiR5v4ZGRl49dVX0adPH7Ru3RpjxoxBnz598O6776r7zJ49G/Hx8Vi8eDE6d+6MVq1aoVevXkhISKiqp1VhjDEUFxejuLgYJlPpUhgmkwnFxcUe1/1qyARcpxhwnWJAgmJErBCLBMWI6xQDWlsN0BXrquIp1Fj2Fp19+/Z5XWb16tW4fPkyAODy5cvIzMysrOoRQgjxQdBagiwWC7KysjBx4kR1G8/z6NmzJ/bu3atZxmw2u/yCjoiIwO7du9X7X3zxBXr37o3HHnsMu3btQlxcHMaOHYvRo0e7rYvZbHZagys/Px+ArftNkiSnfe33y273xHEgtNYxyzKZTHjkkUdctg8bNgwAsG7dOvU6yLIMURQBAFZOhNUhCaKVE8v8zyCKslpOqx6+1rWi5ez7O/5f2eX8LWsymTB//ny1Raddu3bltuicPXsWmZmZauDKGMPq1atx9913IzY2tlLqGYxywThnTS8XjHOGSrlgnLOmlwvGOSujnK/H4liQlhM/e/Ys4uLisGfPHnTp0kXdPn78eOzatUvzl/bQoUNx6NAhrF+/HgkJCdi2bRsGDBgAq9WqBjH2L6XU1FQ89thj+PHHH/H8888jPT0dSUlJmnWZPn06XnvtNZftK1euDMgio5IkYeFC20KmTz31lBq0eLO/Fm+O4S9f61rRcqFk37592L9/v3q/U6dOSExMdLs/Ywxffvklzpw549R6x3EcmjVrhv79+1PmbkIICaCioiIMHToUeXl5qF27drn7h1QQdPHiRYwePRpffvklOI5DQkICevbsiUWLFqG4uBgAoNfr0alTJ+zZs0ct99xzz+HHH3/02MJUtiUoPj4ely5dcrmIkiRhy5YtuO+++7z+onds2Vm9ejWio6M97s8YU+vDGENhYSF27dqFXr16Qa/Xw2AwqF+eJpMJgwcPBgD8p2eE09IYVk7EkfhBaHM6EzomwSwzvLy1WK2HViuGr3WtaDnAv2takXL+lD179iyeeeYZWK2luZ10Oh3S09PdtuicOnUKY8aMcXvMDz/8EM2bNw9oPYNVLpTqGirlQqmudG1Cv1wo1dVTufz8fDRo0MDrICho3WENGjSATqdDTk6O0/acnBw0adJEs0zDhg2xfv16mEwm5ObmIjY2FhMmTEDr1q3VfZo2bYo2bdo4lbv55puxbt06t3UxGAyaC4qKouj2hfH0WFmOXUXeltPr9ertiIgIREZGonbt2i5lHbu1dEyATmNqmI5JJf+Yuq8gCJr18KeuFSnnqKrLeVuWMYb09HTNx9LT0zFz5kzNFp3WrVujY8eOOHDgABRFUbfrdDp06NABrVu39rolqKZdUypXvc8ZKuWCcc6aXi4Y5wxkOV+PE7SB0Xq9Hh07dsS2bdvUbYqiYNu2bU4tQ1qMRiPi4uIgyzLWrVuHAQMGqI9169YNR48eddr/2LFjaNGiRWCfQDVktgJmmcEsM5gkBfkme5ZqxbY98Amqw8Lp06eRlZXl1AoEAFarFVlZWTh9+rRmOY7jkJKS4hLouNtOCCGkagV1inxqaiqSkpLQqVMndO7cGWlpabh27RqSk5MBACNGjEBcXBxmzZoFwDYmIzs7G+3bt0d2djamT58ORVEwfvx49ZgvvvgiunbtipkzZ2LgwIH44YcfsHDhQo9jbGqKl7YUl9liArBQM0u1fQZaWWVnowmC61vEaDSG1Rd4fHy8xxad+Ph4t2Xj4uIwaNAgrFq1CowxcByHQYMGeTUomhBCSOUKahA0aNAgXLx4EVOnTsX58+fRvn17bNq0CY0bNwZgG1PB86WNVSaTCZMnT8aff/6JqKgo9OnTBxkZGahbt666zx133IHPP/8cEydOxIwZM9CqVSukpaWpM6uIjdlsVhM4upOUlKS5xMeGDRsqLddRdWRvuXnyySc1t5cXEA4ePBibN29Gbm4uYmJiyr3uhBBCqkbQM0anpKQgJSVF87GdO3c63e/evTuOHDlS7jH79euHfv36BaJ61Z5WhmrAuyzVxHsVadExGo0YN24c5s6di7Fjx1KiREIIqSaCHgSRiikvQzWgnaXasStsah8D9CU5FBljMCsiLjQbhrhzK6CD7QGLFZjxtevCrOGkIi06iYmJGDFihMcp9YQQQqoWrSJFoNcBeoGDXuBgEHlEGXmIogiDyKvb9ZRoWm3RiY6OphYdQgipASgIIsQH1KJDCCE1BwVBhBBCCAlLFAQRQgghJCzRwGgSUhhjai6jwsJCFBUVobi4WJ3KH245jAghhPiPgiASUkwmk1OGcABYvHixejvcchgRQgjxH3WHEUIIISQsUUsQCSn25JCOySBXrFihJoOkaeuEEEK8RUFQJbKPX3G3HheNX/GdVnJIrWSQhBBCSHkoCKpEWuNXHNcwo/ErVcdxQDXgOqiaAlJCCAk/FASRsKAVkAKlg6opICWEkPBDQVAlclzctLCwEFu3bkXfvn0hiqL6OCGEEEKCg4KgSuQ4fkUQBERGRiIiIkINgkjVcQxItQZVU0BKCCHhh4IgEha0BlQDNKiaEELCGeUJClOMMfW2RWZO/w6ya0jLS8NBds1pu1ZZQgghJFRRS1CYMpvN6u0ZGy3qbQaG3N65kCIlzC8SEbM5Bhw4l7KRkZFVVldCCCGkMlAQRJxYmlogNZAAAFIDCZamFhjOGZz2MZlMKC4udtnmeNueC8kRTUMnhBBSnVAQFKYMhtLAZuoDeugFDgwMb0Tm4goDGAdwDKh9VyEmF0VDkktbjJKSkjweOykpSV3Q1BFNQyeEEFKd0JigMOXYIqMXOOgFDkf1JvwlWMBKHmIc8JdgwVG9CXqBWnAIIYTULNQSRADYxgKtFa+AZ4DiEO/wDFgrXsEki+sU8tT+IsSSdxBjDBZFRFGT4ahzMQN8yTgiSQbmfClVxVMghBBCfEJBEAEA/MIX46TO4rJd4YCTOgsO60wo23AoCnBoIeKg53hIogiDyIOzNyeBZpIRQgipnvzqDvvjjz8wefJkDBkyBBcuXAAAbNy4EYcPHw5o5UjVsLcCcW7iFY4BnxuugFFAQwghpAbxOQjatWsX2rZti3379uGzzz5DYWEhAODQoUOYNm1awCtIKp8MIJeT1bFAZTEOuMzLNIKMEEJIjeJzd9iECRPwxhtvIDU1FdHR0er2Hj16YP78+QGtHKkaIjjMMMehAFYAgMIJyGnQD40v/Rc8s83yMso85ijWYFaTEEIICSifg6BffvkFK1eudNneqFEjXLp0KSCVIlUvhgmIKXk7KBChE2IRz4zgmW1Qs4UxABQEEUIIqTl87uCoW7cuzp0757L9wIEDiIuLC0ilCCGEEEIqm89B0ODBg/HKK6/g/Pnz4DgOiqLgu+++w8svv4wRI0ZURh1JDcMYQ3Fxsfrv6tWrKCoqctpG65MRQgipbD53h82cORPjxo1DfHw8rFYr2rRpA6vViqFDh2Ly5MmVUUdSw5hMJgwYMMBl++LFi9XblF2aEEJIZfM5CNLr9fj4448xZcoU/PrrrygsLESHDh1w/fXXV0b9CCGEEEIqhd/JEps3b47mzZsHsi4kTBiNRmzYsAGArVVo0KBBAIAVK1YgKipK3YcQQgipTD4HQaNGjfL4+KJFi/yuDAkPHMdpdnUZjUbqAiOEEFJlfA6Crly54nRfkiT8+uuvuHr1Knr06BGwihFCCCGEVCafg6DPP//cZZuiKBgzZgwSEhICUilCCCGEkMoWkIUQeJ5Hamoq5s6dG4jDEUIIIYRUuoCtBvXHH39AluVAHY4QQgghpFL53B2WmprqdJ8xhnPnzuGrr75CUlJSwCpGCCGEEFKZfA6CDhw44HSf53k0bNgQ7777brkzxwghhBBCqgufg6AdO3ZURj0IqVEYYzCZTOr9wsJCdWkQe7ex0WgEx3HBqiIhhIS9gI0JqogFCxagZcuWMBqNSExMxA8//OB2X0mSMGPGDCQkJMBoNKJdu3bYtGmT2/3feustcByHF154oRJqThz9zhVhXv48/M4VBbsqQWdfGsT+b9iwYVi8eDEeffRRdZtjkEQIIaTqedUS1KFDB69/sf70008+VSAzMxOpqalIT09HYmIi0tLS0Lt3bxw9ehSNGjVy2X/y5MlYvnw5Pv74Y9x0003YvHkzHn74YezZswcdOnRw2vfHH3/ERx99hNtuu82nOhHfMTBs4HNxUTFjA2/ATUpjcKBWDkIIIdWXV0HQQw89VGkVmDNnDkaPHo3k5GQAQHp6Or766issWrQIEyZMcNk/IyMDkyZNQp8+fQAAY8aMwdatW/Huu+9i+fLl6n6FhYUYNmwYPv74Y7zxxhuVVn9i8xtnwineDAA4xZvxG2dCGxa+2Z9paRBCCKn+vAqCpk2bViknt1gsyMrKwsSJE9VtPM+jZ8+e2Lt3r2YZs9ns8uURERGB3bt3O20bN24c+vbti549e5YbBJnNZpjNZvV+fn4+AFvXmyRJTvva75fdXh5/y/lb1jFdgdbzkGUZoigCAEyKCMVq284Yg1nRQZIkFMs66MAAABYFEEXF6RiME8E4DgwMX+jywDGAcQDHgC90ebjJGg3GASWngSzLmvXwVE9/n19llfXltRAEwel/ANDpdOp9b1JKVPX7rarfp1Suep0zVMoF45w1vVwwzlkZ5Xw9FscYYz6VCKCzZ88iLi4Oe/bsQZcuXdTt48ePx65du7Bv3z6XMkOHDsWhQ4ewfv16JCQkYNu2bRgwYACsVqsayKxevRpvvvkmfvzxRxiNRtx9991o37490tLSNOsxffp0vPbaay7bV65cicjIyMA82SomSRIWLlwIAHjqqafUgKcyyh2XjmPptaUu25NqJeF68fpqU09/yjLG1GDF8bYgCGoXsePtQNeVEEKI94qKijB06FDk5eWhdu3a5e7v8+wwq9WKuXPnYs2aNTh16hQsFovT45cvX/b1kD6ZN28eRo8ejZtuugkcxyEhIQHJycnqwq2nT5/G888/jy1btnjd3TBx4kSn/Ef5+fmIj49Hr169XC6iJEnYsmUL7rvvPp+/eP0p50tZxpgaCJpMJvWLt2vXroiOjgYAGAwGcBwHk8mEwYMHax7DLiMjw2NrxYv9RYgCsEN3BhxnawWy4xiwo2A12pnjkPal7RirV692eU0c69mjRw+1nuXxppzj9XBXtlu3bmr3lJ39Gtn3feSRRzzWZd26dR7fa/4+R6Dq329V8T6lct4LlbrStQn9cqFUV0/l7D053vI5CHrttdfwf//3f3jppZcwefJkTJo0CX/99RfWr1+PqVOn+nSsBg0aQKfTIScnx2l7Tk4OmjRpolmmYcOGWL9+PUwmE3JzcxEbG4sJEyagdevWAICsrCxcuHABt99+u1rGarXif//7H+bPnw+z2QydTud0TIPBAIPB4HIuURTdvjCeHvPE33LelC0uLtb8wh45cqR6e8OGDYiIiIAgCFi7dq3Lvo7jV/7v//7PJUBwfJxjwO8oHQvkiHHAKc6Mo1wBJKm0W6hs/R2DLF+ujTfl3F0PR0888YRLoGe/RmXP447W8/K1ruUJlXLBOGdNLxeMc4ZKuWCcs6aXC8Y5A1nO1+P4HAStWLECH3/8Mfr27Yvp06djyJAhSEhIwG233Ybvv/8ezz33nNfH0uv16NixI7Zt26YOvlYUBdu2bUNKSorHskajEXFxcZAkCevWrcPAgQMBAPfeey9++eUXp32Tk5Nx00034ZVXXnEJgMIVx3HqF707RqPR4z4MDF/qrqpjgVzOwYCvxTww1A/ZmWI0wJkQQmoun4Og8+fPo23btgCAqKgo5OXlAQD69euHKVOm+FyB1NRUJCUloVOnTujcuTPS0tJw7do1dbbYiBEjEBcXh1mzZgEA9u3bh+zsbLRv3x7Z2dmYPn06FEXB+PHjAQDR0dG49dZbnc5Rq1YtxMTEuGyvaRy/sAHbDLmtW7eib9++anQcyC9sK4DLnKwZAAG2wOgqJ6MWD0DR3qeqPPIvwD4+mTFAUQTUjxmFvKu2blRZBtZ95lrOXbBYXoBICCGk+vM5CGrWrBnOnTuH5s2bIyEhAd988w1uv/12/Pjjj5pdSuUZNGgQLl68iKlTp+L8+fNo3749Nm3ahMaNGwMATp06BZ4vzeloMpkwefJk/Pnnn4iKikKfPn2QkZGBunXr+nzumqbsF7YgCIiMjERERESlDMYVwOEVqSkKOdvUMgYBBfUeRPSVL8ChJCuyzOMjJWhj71WCUBoE2XAQRRGCEJotVIQQQirO5yDo4YcfxrZt25CYmIhnn30Wjz/+OD755BOcOnUKL774ol+VSElJcdv9tXPnTqf73bt3x5EjR3w6ftljkMCpDwH1me1txDgRV4RY1IMRHLNNU7QwBsD3qZqEEEJIZfM6CJo/fz4ef/xxvPXWW+q2QYMGoXnz5ti7dy+uv/569O/fv1IqSQghhBASaF6vHTZp0iTExsZi2LBh2L59u7q9S5cuSE1NpQCIEEIIISHF6yDo/PnzSE9Px9mzZ3HfffehVatWeP3113H69OnKrB8hhBBCSKXwOgiKiIjAiBEjsGPHDhw/fhzDhw/HJ598glatWuH+++/Hp59+6leabkIIIYSQYPA6CHLUunVrzJgxAydPnsTGjRsRExODkSNHIi4uLtD1IySsMMZQXFys/rt69SqKioqctgVxpRtCCKlRfJ4d5ojjOHXdJMYYtQSFAccvYIvs/GXMONt7wCIzcCX7Oe5DX97lM5lMGDBggMv2xYsXq7cdM1oTQgjxn19B0OnTp7F48WIsWbIEp06dwl133YWPP/643CUKSOhzXItr7pdll5SQACx0W9ZkMrksNGoymZxuC4LrW9JoNHpcoJQQQgjxh9dBkMViwWeffYZFixZh+/btaNq0KZKSkjBq1Ch13S5Svezbtw/Lli1DTEwM7rzzTrf7McbUYMRdUBKIQMRsNmsu2mqXlJSkuVZXOLV80DIdhBBSdbwOgpo0aYKioiL069cPX375JXr37u2UyZlULyaTCQsWLEBBQQEWLFiATp06uf3ydNcFM2zYMPW2PRBxzAr+Yn8BeoeMy4wTcbXB46h7aXlpskSZqS1G/mQUDze0TAchhFQdr4OgyZMnY/jw4WjYsGFl1ocEyOrVq3H58mUAwOXLl5GZmYmkpKQKH9exNUgvcGWCINtSFHqBA6exoJhj2WcG6CAKtlYoSRHBGiVBzF0KHrZxQ5IMpG8oWY6jZLCwI2+60QghhBBPvP7mSE1Nrcx6kADKzs5GZmamOhCZMYbMzEz07NlTcwZfVS+8CgCiAIgCB4CDyPGwiCL0Iu8QPJUOojabzWq3kBZ33WiZmZkBrTMhhJCahfqzahjGGObPn+92u9YMLXsXjP1f3bp11YVX7f9oYDIhhJCahvoQapjTp08jKyvLZbvVakVWVhZOnz6N5s2bB6FmgTH8YQ5CSTearIiIbJAMy5XF4GAL0mQZyPicpuITQggpH7UE1TDx8fHo2LGjy6B1nU6HTp06IT4+Pkg1CwyhpBtNL/KINPAlY5B4iAIHUbAFSHaOrV6yXPafLaeRLDN1m1Y5QgghNRe1BNUwHMchJSUFTz75pOb2cOrWcsxptO6zso/KcJfTyGw2IzIy0ufzOaYacES5kAghpHryOQiyWq1YsmQJtm3bhgsXLkBRFKfHHVeYJ8ERFxeHQYMGYdWqVWCMgeM4DBo0CLGxscGuWrVhbmJGXqc81NlfB4bzgZm67y7VgCPKhUQIIdWHz0HQ888/jyVLlqBv37649dZb6ddrNTV48GBs3rwZubm5iImJ8Ti7qqZyzEv0yL+gdpUxMKzWFcDKWyHeU4CBVj2sMqe2FlE+I0IICQ8+B0GrV6/GmjVr0KdPn8qoDwkQo9GIcePGYe7cuRg7dmxYZhl2DNAFoTQIOgkLcjhbMsccXsIZzoJ4GDTL+eufQwCdPehiAGQBN9YeheNFi2DPAmCVgW9XVfhUhBBC/OTzwGi9Xo/rrruuMupCAiwxMREjRoxAYmJisKtSbTAw7EYBuJKxzxwDdqMADIEdDK0TAJ3IQSdyEPQc9JG2QdyCnle362hEHiGEBJXPQdBLL72EefPm0QwaEpL+KmkFsrfGMA7I4ST8zVmCWzFCCCFVzuffort378aOHTuwceNG3HLLLWpGYbvPPnOZhkNIteDYCuS4qgfHgL18ATjo1XxDhBBCaj6fg6C6devi4Ycfroy6EDe8XQ2+qkkyYF/egjEGi6JAkiSYJcVpDbDq4i+HsUCO7K1B9ZtaYDhHg6IJISRc+BwELV68uDLqQdzwZTX4qjbny7IBhS33jiAImtPAg8neCgQGaDb2MKDgtgLoz+mrumqEEEKChDJGV3Naq8ET31kBFMCqHQABAAdYa1npE0EIIWHEr/kpa9euxZo1a3Dq1ClYLM4DSn/66aeAVIz4vhp8VSi74rydyWRScxEtXboUUVFRVV01jwRweBwNUMwUdUt09L9QUPAZABlWGdi6kQenhOaYoLLZqgsLC1FUVITi4mK1VY6yUhNCiDOfg6D33nsPkyZNwsiRI7FhwwYkJyfjjz/+wI8//ohx48ZVRh3DUnmrwc+cOTMoX2j2Fec9MRqNmvsUFxdXVrW8Uhs61Iau5J6IukIsrkIPgIMMQBfE6lV0uQ132aodu6/dZaV2DKAoeCKEhBOfg6APPvgACxcuxJAhQ7BkyRKMHz8erVu3xtSpU9VuG1JxNX01eOKsvIzelbnchlYA5U3w5C9qtSKEVBc+B0GnTp1C165dAQAREREoKCgAAAwfPhz/+Mc/NFsviO/sq8EfOHDAaX02nU6HDh06hPxq8CSwHLspHbsmV6xYoXZNVpcB9RVptSKEkEDyOQhq0qQJLl++jBYtWqB58+b4/vvv0a5dO5w8eZISKAYQrQYfPIwxza47b7qnKuqW4Rx4dbkNBk4W0SUyGfssi6GUvOaKDBzOcP6sueumdNc1WXafDRs2VPvgiRBCAs3nv+I9evTAF198gQ4dOiA5ORkvvvgi1q5di/379+Nf//pXZdQxbNFq8J79iWJsyp+H+8GjtX9j/DWZzWa/uqc8zdw7qxRjY/48tNNxaMK7rytfstyGDQedvmS5DfCwqlPbAvtjQyuA8iZ48lcotVoRQmo2n785Fi5cqHbPjBs3DjExMdizZw8efPBBPP300wGvYLij1eC1MTBs467gomLBNk6PVqxBtc32zBjDAfkqcpkFB5ge94sxYd2SV5FWK0IICSSfgyCe58HzpclUBg8ejMGDBwe0UqRUOKwGfxxFWJ8/Dw9Bhxsgll8AwB8w42zJel9nOQv+YGZch8Bfmwcec14NXrEKiK83CmfzF9lyC8nAxk89H+McMyOX2eqayyw4x8yI5Wre60gIIaHGr9Rw3377LR5//HF06dIF2dnZAICMjAzs3r07oJUjNjV5NXgGho38ZVxULmIjf9mr1dwZGHZw+U4rwe/g8gO+EjxgC4AEkYMgchD1HIwRtu4pUc9D8GIleMYYDskFKO3gAg7JBTVi/Jx97JT939WrV9VZXsXFxTXiORJCajafW4LWrVuH4cOHY9iwYThw4ADMZjMAIC8vDzNnzsTXX38d8EqSmsHxS1GSbbeP8SacEWytJGc4C44oJtygGNXHy5YD7K1ApUt2MA44Cwl/MDNaoHqt/WVrBXKoK4BcJuEcM6NxNaurr8qb5UUzvAgh1Z3PLUFvvPEG0tPT8fHHHzutIN+tWzfKFk08sgfMAJC+QcF762SsyM8D7BkAFGBFfh7eWycjfYOiWa5sK5BdZbYG+atsK5BdTWoNIoSQUOZzS9DRo0dx1113uWyvU6cOrl69Gog6kTBhaWqB1MBhEVYekBpIsHhYzf0k59wKZGdvDTrJmYFq0sJSthXIzt4adJ6zoLrU1R/lzfKqiePXCCE1i88tQU2aNMGJEydctu/evRutW7cOSKVIzWQwlH7hPz2AQ8TdBZotOhF3F+DpAZxLOQaGXboC9zPEGbBLV1AtWoPsrUCe/MyqR139ZZ/lFRER4RTw2Gd5hfMMOEJIaPA5CBo9ejSef/557Nu3DxzH4ezZs1ixYgVefvlljBkzpjLqSGoIxy/Fv/QWZPMSWJnvScYB2byEv/QW13I8kM95Xgk+n6vcleBPs2LMy5+H08zzQmMKgGvM6nGfItCq9YQQEkw+d4dNmDABiqLg3nvvRVFREe666y4YDAa8/PLLePbZZyujjqSGYWDYIuSBY3AJggBba9AWIQ8MMU65fziFQ7LUEBahdCV4ofbDkPM/B2BLXKiXeayrpJXgGWP4XrmKi7Dge+jxCB/j8rhK5tBLaABzyYAnHgLiDQ/itPkLKCV1FWUeWSG6aj0hhNQEPgdBHMdh0qRJ+Pe//40TJ06gsLAQbdq0UTO9+mPBggV45513cP78ebRr1w7vv/8+OnfurLmvJEmYNWsWli5diuzsbNx4442YPXs27r//fnWfWbNm4bPPPsPvv/+OiIgIdO3aFbNnz8aNN97odx1JAPFAHmfVDIAAW2CUByuieJQOmi5RGzqI6ttWhCjEQoIB9iYVCQxa/WWOyZ0ZAxSFQZIkyCWz0DTWJnVxGmZchK2F6iIsOA0zYh3G9DgO4P52NQDoSv7ZHMSXsDVjeZcLiQQGLdhKCHHH77UG9Ho92rRpU+EKZGZmIjU1Fenp6UhMTERaWhp69+6No0ePolGjRi77T548GcuXL8fHH3+Mm266CZs3b8bDDz+MPXv2oEOHDgCAXbt2Ydy4cbjjjjsgyzJeffVV9OrVC0eOHEGtWrUqXGdSMZzCYay5EcyCLQBhnAC5zgAIeRvAsZIvJZnDspJWEq2p9TYMkKSSbczlccdy6z4rWwsZwEIIguBVAMQYww+KbaYXgy2U+UEpwADo4b5/jlQHtGArIcQdr4OgUaNGebXfokWLfKrAnDlzMHr0aCQnJwMA0tPT8dVXX2HRokWYMGGCy/4ZGRmYNGkS+vTpAwAYM2YMtm7dinfffRfLly8HAGzatMmpzJIlS9CoUSNkZWVpzmwjVa8uBIglwQODCIsQCz0M4JxadGxjahxbWJZ/bithYwGw0O05HMtVlK0VyDnfz0VIOIPSGV6OA7//ORjQOTT48BBxXUQyThQvhlJyHKtkbzEi3qAWHUJIoHkdBC1ZsgQtWrRAhw4dApbfxGKxICsrCxMnTlS38TyPnj17Yu/evZplzGazy9TbiIgIj9mq8/LyAAD169cPQK1JqDAYDOoUbkeO07mXLl1abldu2VYgOw7Afq4AAvTgwDl9+epEx4VQAR4cRFGETuYcxjmF7sywYPC3RYcWbCWEuON1EDRmzBisWrUKJ0+eRHJyMh5//PEKBxWXLl2C1WpF48aNnbY3btwYv//+u2aZ3r17Y86cObjrrruQkJCAbdu24bPPPoPVqj0TR1EUvPDCC+jWrRtuvfVWzX3MZrNTq0F+fj4A2/gjSXLO82K/X3Z7efwtF4xz+lPOcUV1retm38eeYJNxOrCSoIFxotP/ttsMomhrFdLpdBAE21t18IMcRJ09iBAh1hsG6coKoKR1RbIyrP7CFlwIgqCWc+S4zfHYdiaTSa0nB+AMTE6tQGodAVziJDRsZkVEToTT8+NhC3zseIhO/9vKM4hlhgfpwEHnUE5Xsr/OaRwRgyjaxzLJLtfam9dCi7/lquqcshf9llrXAyh9zd299t4cmz771adcMM5Z08sF45yVUc7XY3HMh2Yds9mMzz77DIsWLcKePXvQt29fPPHEE+jVq5dfTdBnz55FXFwc9uzZgy5duqjbx48fj127dmHfvn0uZS5evIjRo0fjyy+/BMdxSEhIQM+ePbFo0SIUF7tOWx4zZgw2btyI3bt3o1mzZpr1mD59Ol577TWX7StXrkRkZKTPzyscSZKEhQttXVNPPfWUUzbx6saXujLGkF6YjrPWs5o5fThwiNXF4pmoZ/zuhmGMefUlrEUQBJfz+vtaVOQ1rIpzOl4nSZLUFqDk5GS1nNb1CEQ9CSGhoaioCEOHDkVeXh5q165d7v4+BUGO/v77byxZsgTLli2DLMs4fPiwzzPELBYLIiMjsXbtWjz00EPq9qSkJFy9elWzK8POZDIhNzcXsbGxmDBhAv773//i8OHDTvukpKRgw4YN+N///odWrVq5PZZWS1B8fDwuXbrkchElScKWLVtw3333+fwl4U+5YJzTn3ImkwmPPPIIAGD16tWIjo7W3Gfw4MEAgKcH6CAKpS1BUv3hEC9ngCvJsCzJDB9tsLXuLVmyBCNHjgQAPP5wmZag+kmQLi+FY0vQ8s+ZWg+tbo7y6upYz/seZVjJZ6O47DQ1B3wxj7iv4rB00VK1nrYxQc4tQddFjMSJ4iUOY4IYvl3tOQgyNzEjr1Me6uyvA8N51+zSK1asQN26dX16fu74Wy4Y5wzGc6TPfvUpF0p1DZVyoVRXT+Xy8/PRoEEDr4Mgv2eH8TwPjuPAGHPbFVUevV6Pjh07Ytu2bWoQpCgKtm3bhpSUFI9ljUYj4uLiIEkS1q1bh4EDB6qPMcbw7LPP4vPPP8fOnTs9BkCAbeyI46BWO1EU3b4wnh7zxN9ywTinL+Ucv8TdlXPsquCYAq7MHHmOSWoQxDEGSbK9rwRBKG3iZBxcZ2NJsAdBYAySVNod5q4enurq2C2jyMBDQgOYSoIgDgIaRD6IS0VfgEGGbAX2beQhm2XodLrScoBTjiM7BZIaBClg8NRyy8BQ0K4A1jpWFLQrgP683uWYWs/Rm9dCi7/lgnHOYDzHYJULxjlDpVwwzlnTywXjnIEs5+txfAqCHLvDdu/ejX79+mH+/Pm4//77wfP+pb5NTU1FUlISOnXqhM6dOyMtLQ3Xrl1TZ4uNGDECcXFxmDVrFgBg3759yM7ORvv27ZGdnY3p06dDURSMHz9ePea4ceOwcuVKbNiwAdHR0Th//jwA2/pmNA2WlMexVXDTp0DZfD8ok+9Hp1HOX7c8DvAlLUiXFDPOyyUtXA0kxCZZ0IA3QpEYDtsmQmoG79VR2Zlddo7bTCaTy/gsmu1FCKlMXgdBY8eOxerVqxEfH49Ro0Zh1apVaNCgQYUrMGjQIFy8eBFTp07F+fPn0b59e2zatEkdLH3q1CmnAMtkMmHy5Mn4888/ERUVhT59+iAjI8OpS+DDDz8EANx9991O51q8eLHaXUFIdcSLHHSirYX1D5Pz2mN/KAVopDcCDvPUQiVAcDezy1FSUpJLtyDl7yGEVCavg6D09HQ0b94crVu3xq5du7Br1y7N/T77zCUrXblSUlLcdn/t3LnT6X737t1x5MgRj8cL1BR+Ep4cW1fufwxwbJzgIKJpdDLOFSwGgwRZtrcWBbZV5pLVjDzFua8sT5FwyWpG/RBeeZ4QQqoTr4OgESNGhMyvTkIqwvF9LgiA4DDAmSvJ9yOIHFiZzEGB+nwwxnBcytd87LiUj866irfA+svfbi1HQtK9gKBTjyfKDCOMrbFM/tu2cJxshbx0W+ArTwghZfiULJEQR45fiO6+BGlMh++0WoHs8hQJubwZ9izVjDGX1BDeBCQGg8FlDJO3gYw/3VqZmZmldwQdONF2bA4Ar+cg6kTwvABOMxEBIYRUDr9nhxHibpzHsGHD1Ns0psM3jDEcl7VbgexOWAsQWZKl2mw2qxmQtWgFJIAtKPG3HCGE1BQUBBFSjTAAxYrnlBMmZkUkD3hIXVQldEn/AETHbi0gyXArllqPgHEMkKywLv0+uJUkhBAPKAgifnNckwmwLWi5detW9O3bV83VQGsy+YbnOHSNaAgLs0U4OghoKzyMX+TPYYWtZUaw8jiuuHYxRiRzgGgPSEQMEZOxii2GbO+OlIDixa6dTfzIGwCRLynHY6T+n1ii7AbPlURZkgJlyTHXyoo6cCVBkK1bi4fIi+B1Ajgo1K1FCKn2KAgifuM4zqmrSxAEREZGIiIigpYjqIAIXoD9quogIlaIxSlmgBW2VBFWhUFz8VUR4ETbAq28nocIETx4NcGi29E2Ig9O5EsCGR1ETgTPdAFd5tVxxiaTnLvZGDhIigRmlcHAnB4P1ZmejuPlaLV7QqovCoJIUNi+5xgYY5AUBUySAEkBX/KVK/m3lBapphwHYVuXbXd6TAaw0EO5UFy/T2u8XHmr3RNCqh4FQSQo0jc4jnsxA1gIQRAgy/4twUJCV3nroxFCSGWhIIgQUukcE0nqRvRQp8gzxlBo+QpWZkVhTw6R+vsA2aq2FoXKsiBl2cfLmUwmdRbeihUr1EWmaawcIdUDBUGkypQdSA3A6Uti6dKl6pdEqLOWdPcBAGMAZAWSIEG2KLaZU+o+4cFx/AsnCmoQZJLPwMJyAQAWlgsznwOj2ESzXCgpO14OsL3/qQuMkOqFgiBSZbS+GBy5+5JwTAYoO4wlkhUFkZIEi6yAKwk4NFLbBMW3q8pukbFL7fILRo0Cw3mAs3PXJQODxEtgihUMitPjWgOcGWPIM2ehdC00DnnmLBjEPpVUe0IIcUZBEAkpGZ87fplKKB1LFJqziEKN4wBnpUwOICuAhfi23HJ2Jms2LMolhy0MFuUSTMpZ0NxCQkhVoCCIkAAxGAwu3X1A+V1+jo+HC9dWIDsOefIBxEBUp/aT6qfsGnKUBoCEKgqCSLXn71iiQAw+LTu2R7EqkIwSJIsCcMxpXE953X32OoXyuBDHgcp80j/UZIkAIILHCP5mLFN+g1TSHWZvLSo7wNm1FciOQWK5sDStD8O50BwUHQ7cLZlDaQBIqKEgiFR7/o4lCoSNn5bdYstqE+pje/zlPMBZ5xQEcbBljOYUnUsrjmM5xhjyLFkez1NwWwH05/QBqjXx1r59+7Bs2TLExMTgzjvvDHZ1CKl0FAQRQqqYApld87iHtZYV4G0Bk+PAeDtvVryn7hjfmEwmLFiwAAUFBViwYAE6derktjXVsXWW0gCQUEZBECFlaHW/AeV3wdEffe9wnA5NIh+EwmyBjAAOD/Kx+EI5CxkMTLaCffYDOIWD2Wwud7yUuxXvqTvGN6tXr8bly5cBAJcvX0ZmZiaSkpI093XXOhvq3b0k/FAQREgZ4TC2J9gEPgqALYgUwSFWFwuD1QIeDEyRIRfrPB+ABFR2djYyMzPVVAaMMWRmZqJnz56Ii4sLcu20UdcdCQQKggipga5JxZhXPA/GCB56sWZ8zIUR/QChNNO0KCsYEdEUy6QLUIcgyTLkZf8NXiVDEGMM8+fPd7t95syZardi2VlhQHC6Jn3puiPEk5rx15EQomKM4aLpCkyKBUaTHrFCg5oxNkYozTRtW/GegyiI4DlRTZYZrtmiKjJl/fTp08jKch2obrVakZWVhdOnT6N58+YA3M8Ks6uqrklfuu4I8YSCIEJCmHMGZ9vtItkEk9UCADBZLSgymRApGNXHy5arqcq2WJTdVpMGVFdkynp8fDw6duyIAwcOQFEUdbtOp0OHDh0QHx9fOZX2Uyh23ZHqi4IgQkKYYyZm02KAQUFu7wKgPgAegALkZhcAm/VO09a1MjhXVJF8HvNMW6Ez3gpRaOR+R9mqttgwxqDIDJJRgiLLtnXVZKv7sj4IxQHVwUhCyHEcUlJS8OSTT2pud3eumKR3wQkGMMagk4vxkPEENshtYOVKuixlM3KXvhSwegK+dd0R4g0KgkhYcPxy0WoNCMVf/1osTS2QGkilG3hAaiDB0tRSqckHGWO4bDoEi3IZetMhNKrV0+2+8tJtTvdty22gJPdSGCZfcuBvi05Fp6zHxcVh0KBBWLVqFRhj4DgOgwYNQmxsrNsynGAAJxrAAdDpRYg6ETxvhILKG9TuS9cdId6gIIiEBXdfLsOGDQMQutOpHTMxG0Yy5FoLAMV1v8KeBait08O8hHMp56hYzsW84nnQRTSHINT1uh4m63lYFNsYDYtyGSbreRjhoTWoCokjHnUZUD08oh4ypHxwDgOqpWVrK3SeYA4aDsSU9cGDB2Pz5s3Izc1FTExMtVzKJdS67kj1R0EQIR6U14IEBLZ7QnFapoPBKiuQIiXIFgUKxxz2sXE8r4m3wCI5tAI5sCgSTKIFHAwu5exsrTnHYVHyoTeZ0bBWJ6+eF2MMeaZf4LQavOkXGPQ91H38XVctIFwGVKNkQLWgdhAGYoRUIAYNV3USwrKB28iRI5Geno4nn3xSTVRZnVpJ/e26I8QdCoJIWCibALGwsBBbt25F3759IYqi2y+W8lqQAO1WJH+Dp8MZZb+OJfyiLtPh/quageGyVOD2cQC4LBWgPvRuFyY1y5dhseYDACzWfJjlyzCKMR6PCdhagSTlilNtJOUKzEqOuhq8v7mXtLJF12S+tOhotTwBvrU+uXt/v/XWW+ptb1pJ8+WDmHdtPuoYUhApdPS4b0X503VHiDsUBJGwUPbLRRAEREZGIiIiAqIoeijpH3+DJ7/xgFXxPKDYqljVwdJl2VZ1/8NpW575DxiE+h6P6doKZMchTzqMGBgqtBq88+w351YUBg4Sk8BkGczeeiZVjzFF+uHJgCCWdL/JeDxSxHILA28PemUJlozFng9SjvJanoDAD/52fj3M6rZsyzIUs4soNC1Dgv4WcBynPl62XCCEQtcdCQ0UBBHiQXktSPZ9AnkOO1+6ijiFQ1xEQyg6W4QjQEAfPIyv8Tlk2L4EeSsPSXHfCiRZnVuSJGsBzPJlGFDP7XldW4HsGCR2pcKrwTvOYrNmOCdBvNbEjDc65aHO/jownA/8oO8KzdQSRHCiWNL9poco2uLPQHa/BYKv3W+Or0fuMtvML3NTM4p72MaDFbM/cHbjUy6vudlsRmRkZEDrPW7cOMydOxdjx46lRInEbxQEEeKBvy1IvgRPFVmmw7G7SOB14Eq6PkSIiEUsjDBAAg/A9qUuOYw3slMsVuRZnVuB7PKK/0ADXQf1vlNLAGPIM//isd6VtRo8A0NBuwJY61hR0K4A+vPuu/n8VZHcO1UtcsRLgGB7X9lanywYHHENq6W6kDnb6w9ZQtGyd53KVXRANQNDwW0lg/FLWhntr3mgX4+yEhMTMWLECCQmJlbqeUjNRkEQIZWgqrvffOX4i978za+QemiPJ5JYAczf/ApDyYBq5/xCCqysyON57KvB+8txFptueD91gHOxcgqS9JWtjg0kyCM7IoJvDibJLi1GYUEQwYm2YNPW+iRCFCzgOT04exAcgNM4vh4xI95FIX8EkvRG6Q4lKRmMI59ElNJGbS1yNxuRkGCjIIiQMKb+krdN6tLawe0ve47ToXGtXg6rwfMYwCVgA/sDMhQwWQE++wWcmy44bzh2NXGibZYXYwz5xT/CcTZavvVHROhbBbTtoapnaoUCp64/QY8cKROuA8145FgzESXO0i5HSDVCQRAhYUj9Zc4D1gYeduRKHi/5niv7i17gIwHYxnqI4BHLx8KgXAEPBUyxwloJq8GbrKdgUS44bGGwKBdgsp6CEYFbNiEQuXdqskLlIIqV4xqPKChWjqNQOVjVVSLEZxQEERKG7L/MOYVDQ2MnMME20FeADg/hdqzHT5Bhm23GywI45S+ncsHCGEOe5XtozkazfA+D8K8g1Sy8MDDkyKvg+jrYcciRV6EOWKWPDSKkIigIIiTMCbwRnM42bkSEDrFcLAzsBPiSIIgpitas+qBwbQWyK2kNYqcRrBFXztPHS5NWMgASACaVhgtOj4fiYrY8ILFLcD/SiEFiuQBv1EzJEAj79u3DsmXLEBMTgzvvvLNyTkJqPAqCCCEhgTGGPOl7j/vkyT8gBrqgtD44DhqXlpfOILPAtjaap3KBnD4OANfk3zGvaDUi9IOhF9oE9NiArQWxte4NKDoTGGPgrWbcoz+NndZWUEoWUNVZjchXZgb83IBtjNbcuXNRUFCAuXPnolOnTmE3PosEBgVBJKTYc7dUxRIWpLpRIDPPWbGtrBDg61Ra64M/zE3MyKvEfEZlMcZwwbIBJuUijJYNiNPdXCmfiYJlzgHOl6i6RXCXL1+OggLbe6GgoAArVqzAE088UennJTUPBUEkpGjlbqm0LMykWuE4HZpEDILCbLmRBHB4UFcfX1gvQy7pluGsekDZGpT6OQ4aFx9PBifaMkZflv8PVnYZ13oaESU8WZJNWVJbiwI9fbzI+htMyikAgEk5hSLrb6hVCa1BwZKdnY1PP/3UaduaNWtw//33Iy4ucAPjSXigIIgQEjIEPhpANABABIdYoREMEMDbk0Aq9vzYVc95Or8tY7RJPg4zOwsAMLOzMPN/I0K43m25imKM4bLlCzimD7hs+QKRupsDcnyt7ObeZDYPVFcVYwz/+c9/XMZR2bfPmTOHWoKJTygIIlWuIiuzO/4RrowlLAgJFMYYrkhb4RiQXJG2wqi7zmU/rYVivVkItawi628wK387Hh1m5W8UWX9DJK5zW85b5WU3r+z0AadOncLhw4c1Hzt8+DBOnTqFFi1aVNr5Sc1DQRCpchVZXNTxj3B1y8JMiCOT9QQsSrbDFgaLkg2T9QSMaKluNZvN5S4AqrUQamZmptN911YgO1trUITwol/PI9AcfwT5tB4bIZWgAgntA2fBggVo2bIljEYjEhMT8cMPP7jdV5IkzJgxAwkJCTAajWjXrh02bdpUoWMSQkggObcCObK1BgViWrzzlHwLiiw/l7QClT12SWuQ9Ktm2apm/xE0YMAADBs2DIsXL8ajjz6qbnNsASurefPmuPXWWzUfu/XWW9G8efPKqnaNZG+FtP+7evWqGpQWFxeHZvoGHwW9JSgzMxOpqalIT09HYmIi0tLS0Lt3bxw9ehSNGjVy2X/y5MlYvnw5Pv74Y9x0003YvHkzHn74YezZswcdOnTw65ikalXFyuyhqiJdhSQwtLqnfO2aMrE/yrQCqUe3tQbxf0Arl7Zx+JgyC6FKGBqpYKVFb1sIVZZgyvgQgPOU/KKMd5HbOxeIgdvlT3IvLUUMYsCBq5Rp+VWB4zi89NJLGDVqlNMXNMdxePnll6vl58LbfEaOn32galrJylskOBwmmgQ9CJozZw5Gjx6N5ORkAEB6ejq++uorLFq0CBMmTHDZPyMjA5MmTUKfPn0AAGPGjMHWrVvx7rvvYvny5X4dk1St6r64aDBVpKvQb5ICBtsfYUVmkPQSFMUKxinq4zVJkfwX5hXthKi/G6LQ0uXx8rqntLqmgNLuKQaGq9Yd8JRN+ap1B+prZVMWbAOqAYeFUEUzeNjWbnP7u5wvWazW3fcj57CYbZBfTvuPIH/XY4uLi8Njjz2GNWvWqNsGDhyI2NjYyqu0n0wmExYsWICCggIsWLDAYz6j8gISIDyCkqoW1CDIYrEgKysLEydOVLfxPI+ePXti7969mmXMZrPLmygiIgK7d++u0DEdf1Xl5+cDsHW9SQ6ZXe3bHP/3lr/lgnHOml4uGOf0tpw3OVZkWVaPI8uyGjiKku0bkDEGnayDJErQMR0YV/LVKQGyyFzKYcVJtZxFlrEQvyEiIgLMoS66kn0dy+nAqyuUA7a1wxz/Z2DgHcppPXfH56v1eXM8nwDOKWgQS26LDtsYODWIcNzPvgdjDDmWb0ty6HyLOF0L25R1cIDGc4RDOXtdRVHU/DVuL8d4Biu7DE/ZlK0sD6KhPjiFc34NwZxCHbHktv1/Bgar/frrdGqLVOSQZ1FLKLTlSQKgA3CvrhDbrFEleb8BnjNC4jMA3lZW61rbab0W7vhTThAEp9Y0x+fizWdg0KBB2LhxIwoKChAdHY2BAwd6Xd+q/OyvXLkSubm5AIDc3FysXLkSw4cP19zX189+IOqp0+mwbt06ALYgzP5ja8mSJYiOjtZ8n1T0nJVdztdjcSyInX5nz55FXFwc9uzZgy5duqjbx48fj127dmHfvn0uZYYOHYpDhw5h/fr1SEhIwLZt2zBgwABYrVaYzWa/jjl9+nS89tprLttXrlwZkk3GJHQ5ftk63hYEQf3idbytRZIkLFxoy1H81FNPed265m+5iqjqcx6XjmPptaXq/aRaSbhevN5DCRt3r4U7V5WrKFKKIMsyPvvsMwDAv/71L/WLvhZfC3X4Ov4+jYDW1a6q3zcVfe1PnjyJb7/9Fv/85z/RqlUrn8pWhatXr2LlypVO3XY8z2PIkCGoW7euy/6Or5skSWoLUHJycukPAR9eT18F4/NfGYqKijB06FDk5eWhdu3a5e4f9O4wX82bNw+jR4/GTTfdBI7jkJCQgOTkZCxatMjvY06cOBGpqanq/fz8fMTHx6NXr14uF1GSJGzZsgX33XefT28Sf8sF45w1vVwo1bW8ciaTCYMHD3ba5vhHNyMjQ/MX5qpVq1z+mJpMJvWPYLdu3TTzvQBQz6cbegcg6tRzijIw1NAGK62/Q+YYIFlhXfkjAGD16tWa3QCO5+zRoweio6PdPj9heD9wYumfLBEcHhcaYrl8EZK9pUSSIWf81+kY4vBHwYk6MMZwpngzHKesry7ejGZoCMgKpIy1AGy/gkeOHGkr+/gQtWVJBDBMNGKFZIL9t6Yt6eEql3J2tutiK7915VbN18KxnPHxZ5xaskQwDBYtWC3pIcGWZNG0PN3t+ezntJ8nIiJC85xar0d5r4U7VV3OTpIktGrVqtp8Fh0xxjB16lTN7b/99htmzJjhMZgxmUxqENS7d2+fro2/z68ir0d1+rto78nxVlCDoAYNGkCn0yEnJ8dpe05ODpo0aaJZpmHDhli/fj1MJhNyc3MRGxuLCRMmoHXr1n4f02AwaGZtFUXR7Qvj6TFP/C0XjHPW9HLBOGegywmCgLVr14IxpnbpmkwmJCUlAQDmz5+vBjMGg0H9w6s1wNKxeyIqKkrzj2BxcXFpU/TSPU6PmQEsxG7NpRMEQVDr7zgA1HE/q9XqMgDUsfmflekqspPASoMgMMguzeEMHIBi618wK+edtpuV8yiw/gUj4tXzCILg1KRe9qtKKvlnO0Jp83utWrWwdu1ap30dx7383//9n9vA0n4MXZkuv9JzcrYgCJzbero7rlYQJMuyy3Z3r4UjrfeN436+vL/9LVdWdfksOjp16hR++uknl+2MMfz00084f/68x5lsgbg2vpYLxjkro5yvxwlqEKTX69GxY0ds27YNDz30EABAURRs27YNKSkpHssajUbExcVBkiSsW7cOAwcOrPAxCQk19kHmxcXFmoN5HddTqi6DKqt68DeTZDDGcFX6Flo5dK6av0UjPFa6v58jBPxNJKiVKNEbBoPBJXszUH4GZ8fH3XE3+Lu6vIequ2bNmqF27dqarRK1a9dGs2bNglAroiXo3WGpqalISkpCp06d0LlzZ6SlpeHatWvqzK4RI0YgLi4Os2bNAmCbbpidnY327dsjOzsb06dPh6IoGD9+vNfHJIT4R2vZBKD8L95gpjyQM9bC3NQMS4/LGo8yWFgOiravhAG21mDHSRLVWXlBF1D5GZyJtjNnzrjtlsnPz8eZM2cop1E1EfQgaNCgQbh48SKmTp2K8+fPo3379ti0aRMaN24MwNasyPOlM1BMJhMmT56MP//8E1FRUejTpw8yMjKcBpqVd0xCapqqyr0UiC9ev+sqy2objm06vwIpQoIiSWBc6T5lMTAU3FZgHwbkigEFtxVAf06v2RVVk0WNmApO0AOwXVOzdAhFWAVRNwjRura27bIFhctmBLOaISc+Ph4dO3ZEVlaW03aO49CxY0fEx8cHqWakrKAHQQCQkpLitqtq586dTve7d++OI0eOVOiYhNQ0oZR7yd+6ysucBzxbASwsKe9perHw+L9gZUtQOpKnbIUAawNRzaHjblX3IuufmFe8BaL+Poi61m7PF0jXrMcwr3g9DPqHoNfdGPDjc4IenGgLgsAYLsr/RbFyCRHKBsQbbq+WyQdDAcdxSElJwRNPPAFFKU3MxPM8UlJS6LpWI9UiCCKEkMrC6w1oqhsOK7ONvREAPKiLwhfWQnXFed6qB1O+AqC9qjtjDLmWHTArF2Gw7EBjY6tK/yJjjOGiZWPJOTci1niDx329zTRedrkNu2vKryhWbDmjipWTuGY5gFr8rU77hMMyCoESFxeHwYMHY+XKleq2wYMHV8ukjuGMgiBCSLXl7xiksoN/Bb42BNjSXYgAYoU6MCKvdJaXIrtrJ7Idz/onzMo5AIBZOQeT9U9ECAl+Py9vFFuPwaycKTnnGRRbjyEC2i1Qvgw2dxzzVJhh6+ZiYLZlN+pDbRG7cG4+YjbHOHURhupyG/4IxDIWgwcPxubNm5Gbm4sGDRqUOyCdVD0Kgggh1VZ1GPxrm1m2E475ha5KO2GsxC4xxhguS5ucznlZ2oRY3ZhKOZ+lqQVSA4cwkAekBhIsTS0wnHPtHvR1jbuyAYWnco6CuU5eIJaxMBqNGDduHObOnYuxY8eG7ZqI1RkFQYQQ4oHJ+icsJa1ANgyWktYgIypnhk+x9Rgsyuky5zyNYv64w0IlpXwZbO445ilq+FRAEHFZegNgV1A2fcC1e2ujPsbj2vLXncr6mubA3f52NXlKfmJiIkaMGIHExMRgV4VooCCIEELcYIzhqrwTmvmFpJ1orNNeB6qi53RuBSo95xXrN5oLr/oy2NyxZYUT9SjifoeZ/aVVE5jZXygWj2uWrekcA0t/F3sl1R8FQYQQ4oaJnSzTCmRX0hrEnwz4H1HXViCHc7JsWJrW1+yi8gdjDBeltfC04v0l+XPUKxN4VSQlQ7ek96ETDbZB1nIxbjYcwVFrezDOdiWtkhnfLX02IM8P8H9sj7uuWMq9VLNQEEQIIRoYGPKs33rcJ8/6LeoDAcsvxBjDZVmrFciOU3MaBeR8kCGzXDfnKtmDXQb4SKB0pneFUjLoRAN0oi2I4/UiRF6EoDNCga6Cz0ZbIMb2kJqLgiBCCNHCAzLzvBijzAoAPtopQPCV85R1E2SUHZvjtDestawAH5jp6jwnokXEDFhZAQBAgILeuhxstjaGXDL6iLcaYVLmVPhchFRHFAQRQogDe3DBKRwaYxiYYMuTIwDoIxjwtWxW8wtxsh5Q/utUzleOU9YtKz5Gg8hIKAb3Y014Ew9O4QI2XV3kYyAixnYbCmIFHSIQD6kkCGKKBSZPB6gi5XVruZtJRmN7iCcUBBFCiAOntcNW/Fft6LIC+LLktlbnV6CCEl2RDrqiyukaCjWMMXWB2bK5n8xNzMjrlIf0lHQYzhuQmZmpBjOOAZEvY3u0pvLbz+14u7pN5yf+oyCIEEKCyN0yHY50Oh2sVqvP5crDZOds0IpsLlmPzQzG6Vz2qWpms1kzwSADQ0G7AljrWFHQrgD683qn/fwd41PeVH6gZk/nD0cUBBFCaja3C6/KmguvlhdcaAUk3pRzx5vWA619AtHqoLUwqjfrsQWbY3JHT0kdCSkPBUGEkBpNWrbW6b4Mz1/05QUX7h73Nyjxd2mQcBvH8kjSBxAE29T61ZYXwLGrYFDAgYd4bwz+xc/GZ8vGBex8Lz72AUTBFlgxxmCxFqO43i+ond8RfMl0fkk2Y+6nYwN2TlL1KAgihBAHBoPBJSgpLyABnIMS+9gSb5aUqOqlQfwNuuxlq5LTYPOSm38rPyGHHXfYrCCHHccpdlC7nJ9EwQC9WPp89RAhiyIMeiM4D1+dvuYlCsUlRWoSCoIIITVORb/oPX25eBOQaI0tcbekRFWrDuuxectxkPq6ZWNdF3q1U4BNl2YhBrYFX4O50KuveYnCeUmR6oCCIEJIjRNKX/TEey4LvdqVs+ArqR78zd5dmSgIIoSQAHNsifJlSYmq5utq8FXNcbD5v4YvwFr2isZCr3al2bQDMXPOXxXJSzT7vvkw6GzjnoqVYvzV6jBuONUBQslXtdlqxitbUqrgWVQOf7N3V2bwREEQISQkVPcvbEeOLVG+LClR1XxdDb6qOS/2qkOB5RK8yaYdjPeAVn4hX/c16AwwlAzGFjkBoijCKBqgY+H9VV2ZS5+E95UlhISM6v6FTSqXwIl4PPJDFLOrJVusiOYPo0C5BYAOVtmCrZ+9AU4JThCslc/IkbuxPZmZmZVVpWqnOmbvpiCIEELCVEVWgw+Ec/IBbDbNw23GF9FY6FTu/rX5RqiNRiX3ZNTVXcFV6/UABMiKCbrikgSPDpmmHXkz6yrQ7Jmt6/1UD+JZ15ZAdzPZDukO4MP8eRijexG3y3dUdjWrhC/Zu8s+XlnBEwVBhJCQEOwv7JqoIqvBVxRjDAdNi5GrXMRB02L0qtUxYN1Y7jJNO9JqmVm9erV62yKbnR5jsEKSJFgkM7iS1ePK7lOWY2brvLZ5iDlrm71Wtq5a5VYYFuOichErDIvRQe7kUs5pf4eu4uow2DjQ/A2evEFBECEkJFT1F3YojUEKReesWchVbPl+cpXjOGfNQqwXrUGVyTEgSSuTBNHeolNnfx0Yzns38NqbzNaOLUHmkqDqoJiFPwTbtflDOI4fuL1oL3VUHy9bTqurOBDjZdypjrO8/EVBECGEaKAxSJWHMYafTUvAgVezPv9sWoKmtTq6LSOXCQAUuRiSUYIsmwBOcHrc0RPDPnTK/CzLxdBF/QzO1AkcJ0CSzfhkxRjP9dVYq8xdy8yUBxdALxjAwPCm4UVcYVfBOAUc41H77hhMMs+FJFvw+heu2a1f2ZpSmgtJgS0XkgL8x/IGYjY7tyKFUi6k6oyCIEIIIVXqnDULl5Vj6n0GBZeVYzhnzUJjtNUss26p9vIU5a1zJgoGiA6Zn0W9CCaK4BTXzM+OU+tfeOwD6EuCp9+RhY/4KQBsLToDhozDTegIi2x2aTHSCwboBSN+5n7EX7xDZmtOwV/ccRwVf8VNDs+xbGuJSy4kL3Ig2buKq8tg41BCQRAhhGigMUiVgzGGn82lrUB29tagnvr/BK1ujgGJvmTZDMYYNirLnVqtNvLL0Zbv6vY4DAzr+MXgGQ+FK32OPOOxjl+MV/Guus0x8Hqr5/uYETMRV9lVl3L17orB1NxZmLD1WVv99HrNwd9aHLuuAtFNVRWzvLSWE7Gfz/F22cHt3l4TOwqCCCFEQzAHDddk55UDTq1AdvbWoPPKAXWb1jpugHfLnwTKb9iPUzjqVM9TOIrfsB/XuWm1+oXbj5P8UZftCqfgJHcUh/mf1G2OAcnvEYfVsUBly/0hHMfvEYfVbRaLxePg78pcbqMyByrblbecCKD9HD21CmqhIIgQQkilsg/iZWD4xbIMAAd3WZ9/sSyDAUwd/+LP8ie+tga4wxjDl8oizVarL5VFeAFzXMuUtAJxjAPjXJ8jxzisFzPAHJ6jvdyayOUey62JXO5Szp/n5E/6gFAZ6OwrCoIIIYRUKnXWFQ/kFZ8EItxnfc4rPolGfCNACe7gX8C1FcjO3hp0FD+5PFYkX0OuMUczkAEAxjFcZhcRyRvhEFcBPHCJv+ix3CX+EiJ4g3M5APO7v1u63AaKcfj6E+jwRxsIrHS5jZRdL9lu+5k+wN8WJH+7tRwfX3D/KBh0onq8YkXGr80F3J4NCCUBodkqYdymRT7Xj4IgQgghVYJTODTY1ACKQXG7D2/ig5b12RGDvRWIA9NoteLA4WtumUvLzFtfvABjpB56QwOn/XWCDlbZCkD7OXIKh5l5c2ESbK00CmfFySa/odX5m8EzWxLICDkSrysTXepi0Blg0NnGFgm8CFEUYRCMEBRdBa5AYPjbreXIoBNhFEq7oEVOhCgCRhEQWMXeKxQEEUIIqVSOg3/veuwD6ETbfR5W3MAfwDGlAxTYvrCtkhnfLX3WpZwvHHPoSJJr0kPGS+BKkh46Pu5YzgoZV3BBMwCyHYfhKi6hlkbLjK5IB12RcwBS3iw2AGigNIRBsT1nKyfDLOShtXKdunaYWfGcnNFX8++ZDoNOD6CkhYWZcfi68+hwMh4C08FstSBlx/SAnrO6oSCIEEJIpXIcS6ITDU5BkMiL0CkGcHBttfB3DIpj0sNPVjrnAPKU9NCxnAARr/AfogBXS7ZYURB5GNFFtrXKAMAoR2Kh8goA2xpgZWdE+TqA22y1nd++irwkSTBJZgiwOj3uyQHxIOblz8eLYgruMLvPuwQABp1eXbAVAAROD1HMtbUiMb7cc/lqQa+UMt1aEn5tKeH20xEQYDuf2Sph3DfzA35udygIIoQQEhZ8SXoIAPW4RqhXslYZg4yrwhXU5a5X8wtZ4Dz1PCIiwu0YGEeOg4wdBym/siXFZV9vWpAcn9/iyAxcVC5icWQGOplvr9Ag6kCzdWvp1fsip4coXoNRNFS4W8tfFAQRQkgl2rdvH5YtW4aYmBjceeedHvelpToCw7Eb7YmhH0IsaXk6oWQhQ5kMwJaA8L7hKWhhvVVtLfK3+81RVWUad1puo6SFKEt/AMfFEwCA4+IJ7NXtQ0dLB6cWJHcLtoYrCoIIIaSSmEwmLFiwAAUFBViwYAE6derkMYkcLdXhXnkBomNw6BgkiqItYzRjDNvNGU5JD7ezDIwW5qr7BiO4LJuUE/CuG83xGqTseklzuY032GzEbHddboOUoiCIEEIqyerVq3H58mUAwOXLl5GZmYmkpKQg1yo0lRcglhccHld+RDZzTnqYzY7iD+Y6zb0ifM007i7xoOPxtB4v2+Xmz3IbZR0QfsW8/E/wovAE7pBu86pMqKMgiBDiF19+mYej7OxsZGZmliYKZAyZmZno2bMn4uLiNMvQUh2VgzGGLZJ20sNtylLNBIRSmQVbLVbbgq1miwk8J7jsox6zijKNO3bdvd/9P5jYaKrmchsxd9bDrAsz8Oyul13KObKNJ8osGU+UiU552tmwaxoKggghfqnoL/OajDGG+fNdZ7jYt8+cOVMzQKSlOtwrL0D0FByWbQWyY1BwFsdRv2l9l9aSuZ/6t2BrVXF8/xyO+A3H9X+47KNwCo7r/8DhiN80yznaL/6M48JJAMBx4ST2iz/jNvmmANfaP2ZZcrqfJfyB9/I24jn2ADrKCZr7eIuCIELCnL1FhwbiBs7p06eRlZXlst1qtSIrKwunT59G8+bNg1Cz0OVvgFjaCuQ+6WHBbQXQn9NrlK7+GBiWR6/yuNzG8uhVHpfbYGBYEvmpuuArz3gsifwU7xZPrtS6/6Q7inn565CqewSdPQRc4zaXZoK2j32SoiS8blqNmM0xFZoBR0EQIWFOq0XHm4G4FfllXtPFx8ejY8eOOHDgABSltHtCp9OhQ4cOiI+PD2LtwosVEvKY56SH1lpWgK/Ygq1Be7/zwEXdJc/LbehyYSi7TIeD/eLPOCb8qd5XOAXHhD/xk/7XyqixrV5gWGz4ytb9ZvgKd8g3ehXMOI598nXMk5agB0ELFizAO++8g/Pnz6Ndu3Z4//330blzZ7f7p6Wl4cMPP8SpU6fQoEEDPProo5g1a5b6BrRarZg+fTqWL1+O8+fPIzY2FiNHjsTkyZPp1ywhARQqXTfBmHbOcRxSUlLw5JNPam4P579FVqk0ISDkYkgGCbLVBFYyzsYqBXb2ksDpMdaQjmslSQ8ZrID4KyDdCg46yLIFn298HZzClTtIGfB9pXRfUiT4g1M4zL7wBooFExgYzDDjz/jTuPFMKzXTdKRsxDRlpmb5sq1AdjzjsTzqc7UFyd+FV52m8jt0WWWJR3FMdxoAcEx3Gnu4X9FRulGzW2tB71EwCCIYGF6M+hhXGQeFY+AZh5i7jJhbOBoWWXZqMfJWUIOgzMxMpKamIj09HYmJiUhLS0Pv3r1x9OhRNGrUyGX/lStXYsKECVi0aBG6du2KY8eOYeTIkeA4DnPm2FbznT17Nj788EMsXboUt9xyC/bv34/k5GTUqVMHzz33XFU/RUKqPccWnZo4EDdY087j4uIwaNAgrFq1CowxcByHQYMGITY2NqDnCTX2JTHsvkXlj7OpyzdCXYekh0y4DM5qS3oocSboiitnjS1fUyT4a/p21wBno5fXtGwrkJ3CKTgunlTHS/m78OrSpUvV2+O22MbJqdP5a0Gdzv+6eZnbri2DYFs77EfdcRwXzjrUkeG4cBa/Gv5GW7Qs97lqCXxebB/MmTMHo0ePRnJyMtq0aYP09HRERkZi0SLtaG7Pnj3o1q0bhg4dipYtW6JXr14YMmQIfvjhB6d9BgwYgL59+6Jly5Z49NFH0atXL6d9CCGl7L9+IyIiULduXbU1x/4vnFstKmrw4MGoX78+ACAmJqbcLxFSs2ilSKhO7K1AnJtszRyzjZdy15XoL7VLyx6BOEzn91TXxfot4MvUlWccFuu3+F3HoLUEWSwWZGVlYeLE0hVxeZ5Hz549sXfvXs0yXbt2xfLly/HDDz+gc+fO+PPPP/H1119j+PDhTvssXLgQx44dww033IBDhw5h9+7dakuRFrPZ7JRAKj8/HwAgSRIkyblpzn6/7Pby+FsuGOes6eWCcc6aXi4Y5/S2nE6nw7p16wDYmuYLCwuxa9cu9OrVC3q9Xt2nvOP4U0+dToenn34a77//Pp566imvzlPRc1bHco6vgZ3JZFJb4z755BPNcTaerpenc8qy7NAlawVDaeuE/XbpNqu6ryzLbo/neF5vrs/Zs2ddUiSsXr0ad999d7mtgd6cz99rCkB9vmbeggt8rsfxRNZaVogG0ema/ueeF2DgHRZeVSz4vVU+bjsVAwE8zIoFL+9IU+tp7yKb0+Np6AUBL0XPx1V2BYrDeXnGof5dkZh55Wm8tH2hup3jOFg5YJ9wHEd12S51VDiGo7ps7Bf/8KsbnmNByqF99uxZxMXFYc+ePejSpYu6ffz48di1axf27dunWe69997Dyy+/DMYYZFnGM888gw8//FB9XFEUvPrqq3j77beh0+lgtVrx5ptvOgVbZU2fPh2vvfaay/aVK1ciMjKyAs+SEEKIFkmSsHCh7cvuqaeeqlbjyMryta6MMXz55Zc4c+aM05gYjuPQrFkz9O/f32MLq7/Xxp9yV5WrKFKK3D5ei6+FOnwdr87vjePScSy9ttTt40m1knC9eL3TNsYY0gvTcdZ61u0Mv1hdLJ6JegbFxcUYOnQo8vLyULt27XLrE/SB0b7YuXMnZs6ciQ8++ACJiYk4ceIEnn/+ebz++uuYMmUKAGDNmjVYsWIFVq5ciVtuuQUHDx7ECy+8gNjYWLeZWidOnIjU1FT1fn5+PuLj49GrVy+XiyhJErZs2YL77rvPpw+tv+WCcc6aXi6U6hoq5UKprqFSLpTq6k85k8mkfmH36NED0dHRXp+vvHOaTCYMHjwYAPDE0A8giKWzhxhkwJAFmDuCgwBZMuOTlbacQKtXr9Ycs+NrXU+dOoUPPvjAZTtjDKdPn0bbtm09pkjw99p4U664uFi9Nu/e9Sr0utJWKSun4OeWp3HbX/HQMR4Wqxkv/c823mjJkiVITk4GALx/73gYBNGp3IHmuehwKgY6xsMsS3h229tquZEjR9rK3TcWy2pvB8e7n87/Xd42bP9su9PYoLm9R+BadB4Y736G3zUpDws/WQjZ7NvYsqAFQQ0aNIBOp0NOTo7T9pycHDRp0kSzzJQpUzB8+HB1xkXbtm1x7do1PPXUU5g0aRJ4nse///1vTJgwQX2R27Zti7///huzZs1yGwQZDAbNLJqiKLr9QHt6zBN/ywXjnDW9XDDOWdPLBeOcNb1cMM5ZFeUcu3wCfW2cu7V06qrvdgwAB6Fke+m+giBo1sPXurZu3dpjioTWrVt7bAny99p4U66wsFDd7/ntM7w6LmCbeW2/TjrGQWCuQ4p1jIfAeMiMc7qm9tuMKbjIXfXY/ZbL58FgrQVOKb0+nMyQVjAaefw1MAaYFRl/NBFw00XAPqQ9QjZgmnmtzwPsgxYE6fV6dOzYEdu2bcNDDz0EwNaVtW3bNqSkpGiWKSoqAs87X3idznYJ7E2O7vZxfCMSQggJjqpOzll2+QtZLoaOl8BZTOA4QXPpi4oKtxQJP+l+w7z8FXhRNwyd5Vvc7idCwAfXUnGVKwQAWDmGI41NaJNjhK5kwHOkZMAEZbFTuXGbtCdLeTsDzpOgdoelpqYiKSkJnTp1QufOnZGWloZr166pTW4jRoxAXFwcZs2aBQDo378/5syZgw4dOqjdYVOmTEH//v3VYKh///5488030bx5c9xyyy04cOAA5syZg1GjRgXteRJCCLHxNzmnvz5ZMUZzuyAsqtRp+dU1RYJjr8f8e6bDoCvNlC1zDAda/Y0OJ1tAYBzMVgtSdkx3KeeIgWGJ8XNcVC5iifFz3FHYxu25zVYJtVELtVGrZEC1hItMQrw5AkLJVDGz1b/lL/wV1CBo0KBBuHjxIqZOnYrz58+jffv22LRpExo3bgzA1q/q2KpjT3g4efJkZGdno2HDhmrQY/f+++9jypQpGDt2LC5cuIDY2Fg8/fTTmDp1apU/P0IICSWVndgv3AwePBibN29Gbm5upaZI8KV1zbEVyqDTwyCUBjc6ToEoijAIepfuLrdrjgmHcUz4GwBwTPgb+4XDaCvfoLnvuG9c19MD3OeJyszMdBmjVV727vz8fLdDajTP7fWelSQlJcVt99fOnTud7guCgGnTpmHatGlujxcdHY20tDSkpaUFsJaEEFKzVVViv6pIzll2SRe78r5AA/18jUYjxo0bh7lz52Ls2LEej1+RzOZV3bqm1hkMS4wbwDtkcF5i3ID/mF4OyPHLy86t9biv6R2CHgQRQggJPq3Efu4mk1SE49IUlbXUSmUsf+GvxMREjBgxAomJiR73C1Zmc285L39hS2qYJR7BUeEvdbvCMRwV/sI+/hd1m16vr9brsVEQRAghYS47O9slsV9mZiZ69uyJuLi4INeOlKcqWtccEwqnbHu7dOkLBc5rTyjAbGURYmBbAsNisaBevXrl1j9YwR0FQYQQEsYYY5g/33Wshn37zJkza9xspuqobBeeL8FMVbSuleW4mrsThyUwKrK6e1UJ6tphhBBCguv06dPIysqC1Wp12m61WpGVlYXTp08HqWY1g32wubtVEOwc1/Crjuv4Oc4Oe//efyPmn1FerTnmblZZdUFBECGEhLH4+Hh07NhRM79ap06dEB8fH6Sahb6yg80dBzxXB2arBWbZDLNshkkyocBcAEmSYJZNtu3W0gVNHQMwncDjks5z0kNrLSvAu59VVl1QdxghhISxcEvsV5WqarC5v+w5gJxsdz9l3U6EiAUFk5DHFwAAZDD81vQKbj5XDwI4mGUJM3csdsr6XF1RSxAhhIQ5e2I/x1wy1SGxXyhzN9g8O9t1JfRQ1IjVx/XWFrZ/SnPECrG4XmmO660tcJ21OXTFuvIPUg1QSxAhhJAqS+wXDqrzYHN/cyg5cuwms2V+tkCSJJgkCwTwTo9XdxQEEUII8SmxH/HMPti8LMfB5p5Wka9M/uZQKi4uVm+nbH1bs1x53WjVEXWHEUIIAeB9Yr9QwhhDcXExiouLXTIx27c7JgIMBBpsHjqoJYgQQkiNFYxMzDVxsHl1WYok0KgliBBCCAmwmjbYvGweI/s/xyDH3o1WXXIbeYNaggghhNRY/mZirsiCpnY02Lz6oyCIEEJIjVV2ILC3y0oEohuNBptXfxQEEUIIIZWkJg42r2qBaJVzh4IgQgghpIyKLGhKAqsyB7dTEEQIIYSU4W83Wigpr4XF39YVf88H+N+i4y8KggghhJAwVF4LS6BTB/jbolOZrXIUBBFCCCGk2qrMVjkKggghhJAwVF4LS6DHPFXHcVYUBBFCCCFhqKrHPVXHcVaUMZoQQgghYYmCIEIIIYSEJQqCCCGEEBKWKAgihBBCSFiiIIgQQgghYYlmhxFCCCHEa9Ux87O/KAgihBBCiNcqcy2vqkbdYYQQQggJS9QSRAghhBCvVcfMz/6iIIgQQgghXquOmZ/9Rd1hhBBCCAlLFAQRQgghJCxREEQIIYSQsERBECGEEELCEgVBhBBCCAlLFAQRQgghJCxREEQIIYSQsERBECGEEELCUtCDoAULFqBly5YwGo1ITEzEDz/84HH/tLQ03HjjjYiIiEB8fDxefPFFpwXcACA7OxuPP/44YmJiEBERgbZt22L//v2V+TQIIYQQEmKCmjE6MzMTqampSE9PR2JiItLS0tC7d28cPXoUjRo1ctl/5cqVmDBhAhYtWoSuXbvi2LFjGDlyJDiOw5w5cwAAV65cQbdu3XDPPfdg48aNaNiwIY4fP4569epV9dMjhBBCSDUW1CBozpw5GD16NJKTkwEA6enp+Oqrr7Bo0SJMmDDBZf89e/agW7duGDp0KACgZcuWGDJkCPbt26fuM3v2bMTHx2Px4sXqtlatWlXyMyGEEEJIqAlaEGSxWJCVlYWJEyeq23ieR8+ePbF3717NMl27dsXy5cvxww8/oHPnzvjzzz/x9ddfY/jw4eo+X3zxBXr37o3HHnsMu3btQlxcHMaOHYvRo0e7rYvZbIbZbFbv5+fnAwAkSYIkSU772u+X3V4ef8sF45w1vVwwzlnTywXjnDW9XDDOGSrlgnHOml4uGOesjHK+HotjjDGfSgTI2bNnERcXhz179qBLly7q9vHjx2PXrl1OrTuO3nvvPbz88stgjEGWZTzzzDP48MMP1cftK9empqbisccew48//ojnn38e6enpSEpK0jzm9OnT8dprr7lsX7lyJSIjIyvyNAkhhBBSRYqKijB06FDk5eWhdu3a5e4fUqvI79y5EzNnzsQHH3yAxMREnDhxAs8//zxef/11TJkyBQCgKAo6deqEmTNnAgA6dOiAX3/91WMQNHHiRKSmpqr38/Ly0Lx5c3Tp0gXR0dFO+0qShB07duCee+7xabVcf8sF45w1vVwo1TVUyoVSXUOlXCjVla5N6JcLpbp6KldQUAAA8LZ9J2hBUIMGDaDT6ZCTk+O0PScnB02aNNEsM2XKFAwfPhxPPvkkAKBt27a4du0annrqKUyaNAk8z6Np06Zo06aNU7mbb74Z69atc1sXg8EAg8Gg3rd3h9FYIkIIIST0FBQUoE6dOuXuF7QgSK/Xo2PHjti2bRseeughALZWnG3btiElJUWzTFFREXjeeVa/TqcDUBr1devWDUePHnXa59ixY2jRooXXdYuNjcXp06cRHR0NjuOcHsvPz0d8fDxOnz7tVVNbRcsF45w1vVwo1TVUyoVSXUOlXCjVla5N6JcLpbp6KscYQ0FBAWJjY706VlC7w1JTU5GUlIROnTqhc+fOSEtLw7Vr19TZYiNGjEBcXBxmzZoFAOjfvz/mzJmDDh06qN1hU6ZMQf/+/dVg6MUXX0TXrl0xc+ZMDBw4ED/88AMWLlyIhQsXel0vnufRrFkzj/vUrl3b5zdYRcoF45w1vVwwzlnTywXjnDW9XDDOGSrlgnHOml4uGOcMdDlvWoDsghoEDRo0CBcvXsTUqVNx/vx5tG/fHps2bULjxo0BAKdOnXJq+Zk8eTI4jsPkyZORnZ2Nhg0bon///njzzTfVfe644w58/vnnmDhxImbMmIFWrVohLS0Nw4YNq/LnRwghhJDqK+gDo1NSUtx2f+3cudPpviAImDZtGqZNm+bxmP369UO/fv0CVUVCCCGE1EBBXzYj1BgMBkybNs1pIHVllgvGOWt6uWCcs6aXC8Y5a3q5YJwzVMoF45w1vVwwzhmM51hW0PIEEUIIIYQEE7UEEUIIISQsURBECCGEkLBEQRAhhBBCwhIFQYQQQggJSxQEeWnWrFm44447EB0djUaNGuGhhx5yyUyt5cMPP8Rtt/1/e+ceVkXV9v/vgMCGjSAgR3FvVBQIEUXRQENT2mAGeEh80kjEPAUKmqRUhOITAalYiqllqJlk9sjBLHgQBQ+RipzUOIkayUFNOQgYINzvHz7sHxv2YbCuX+/7sD7Xta+LWbO+c6+Z+c5aN2tm7xkj/VEnFxcX/Pjjj32OHx0dDY7jEBISorTepk2bwHGczMfW1pZXjKqqKrz++uswMjKCtrY2HBwckJubq1JnZWXVKybHcQgMDFSq6+joQHh4OIYNGwZtbW2MGDECW7Zs4fXOl0ePHiEkJARisRja2tpwdXXF5cuXZeqcPXsWXl5esLCwAMdxSE5OlllPRPjggw9gbm4ObW1tuLu7o7y8XKXu+PHjkEgkMDIyAsdxKCgo4BWzvb0dGzZsgIODA4RCISwsLPDGG2+gurpaZcxNmzbB1tYWQqEQBgYGcHd3x8WLF1XqurNy5UpwHIcdO3ao1Pn7+/c6n56enrziFRcXw9vbG/r6+hAKhXB2dkZlZaVKrTwPcRyHVatWKdU1NTUhKCgIlpaW0NbWxnPPPYc9e/aojHf37l34+/vDwsICOjo68PT0RHl5Oa9r/Y8//kBgYCCMjIygq6uLefPm4b333lOp27dvH6ZNmwY9PT1wHIf6+nqV8R4+fIjVq1fDxsYG2traEIlEWLNmDSIiIlTGW7FiBUaMGAFtbW0YGxvDx8cH69at492XERFmzpwJjuPg5+enUjdt2rRe52/lypW8+8+cnBxMnz4dQqEQenp6GDZsGMaPH69Qd/v2bYW+sba2VhqvtrYWfn5+MDMzg1AohJOTExYtWqSynRUVFZgzZw6MjY2hp6cHX19fxMTEKO3n5fnl7t27KscHeX7pQplWkWcaGhpUxpTnmZKSEt5jWXfPJCcnq9Qp8gyfeD394ubmhsePH/dqkzJYEsST7OxsBAYG4ueff0ZGRgba29shkUjQ3NysVGdpaYno6GhcuXIFubm5mD59Onx8fHD9+nXesS9fvoy9e/dizJgxvOrb29ujpqZG+jl//rxKTV1dHSZPngwNDQ38+OOP+OWXX7Bt2zYYGBjwal/3eBkZGQCA+fPnK9XFxMTgs88+w65du1BcXIyYmBjExsZi586dKmO++eabyMjIwFdffYWrV69CIpHA3d0dVVVV0jrNzc1wdHREfHy83G3Exsbi008/xZ49e3Dx4kUIhUJ4eHigrq5Oqa65uRlTpkxBTEyM3HWKtC0tLcjLy0N4eDjy8vJw/PhxlJaWwtvbW2VbR40ahV27duHq1as4f/48rKysIJFIUF1drVTXRVJSEn7++WfpT8mrigcAnp6eMuc1MTFRpa6iogJTpkyBra0tsrKyUFRUhPDwcAgEApXa7rFqamrw5ZdfguM4ODs7K9WtW7cOaWlpOHz4MIqLixESEoKgoCBkZmYq1BERZs+ejZs3byIlJQX5+fkQi8Vwd3fH6dOnVV7ra9euxYkTJ3Ds2DFkZ2ejuroau3fvVqlraWmBp6cn3n33XWmZqr6luroa1dXV2Lp1K65du4YDBw4gLS0Ne/fuVRlv/PjxSEhIQHFxMdLT00FE+Oyzz7Bq1SpefdmOHTukrw66fv06rz5w2bJlMucxNjaWV/+Zk5MDT09PSCQSXLp0CZcvX4a+vj5WrlypUDd06NBevtm8eTPU1dWxfv16pfHeeOMNlJaWIjU1FVevXsXcuXNx5MgRvPLKKwp1zc3NkEgk4DgOp0+fxoULF9DW1ob9+/cjKipKYT8vzy9z585VOT7I80sXyrSKPLN06VKVMeV5RiKRwNzcnNdY1t0zqtqpzDOqdPL8EhQU1OvVWiohxjNx7949AkDZ2dl91hoYGNAXX3zBq+6jR49o5MiRlJGRQVOnTqXg4GCl9SMiIsjR0bHPbdqwYQNNmTKlzzp5BAcH04gRI6izs1NpvVmzZlFAQIBM2dy5c2nRokVKdS0tLaSurk7ff/+9TLmTkxO99957cjUAKCkpSbrc2dlJZmZm9PHHH0vL6uvrSUtLixITExXqunPr1i0CQPn5+bxiyuPSpUsEgH799dc+6RoaGggAnTp1SqXuzp07NGTIELp27RqJxWKKi4tT2c7FixeTj4+P0jbI0y1YsIBef/11pTplbe2Oj48PTZ8+XaXO3t6eIiMjZcp6eqGnrrS0lADQtWvXpGUdHR1kbGxMn3/+ucy2el7r9fX1pKGhQceOHZPWKS4uJgCUk5OjUNedM2fOEACqq6vrtY5P3/Ltt9+SpqYmtbe390lXWFhIAOjGjRsqdfn5+TRkyBCqqamRe9zl6fj0UYq0kyZNovfff7/Pup6MHTu2V78iTycUCunQoUMy9QwNDWXOf09deno6qampUUNDg7ROfX09cRxHGRkZMtvq6uf5+qWnrjvK/KJK24U8z/DRyfOMIp0qz8jT8fVMTx0fv/CBzQQ9Iw0NDQAAQ0ND3pqOjg588803aG5uhouLCy9NYGAgZs2aBXd3d95xysvLYWFhgeHDh2PRokWorKxUqUlNTcWECRMwf/58mJiYYNy4cfj88895x+yira0Nhw8fRkBAQK+Xz/bE1dUVmZmZKCsrAwAUFhbi/PnzmDlzplLdkydP0NHRAYFAIFOura3Na9YLAG7duoXa2lqZ46qvr49JkyYhJyeH1zb+ChoaGsBxHAYNGsRb09bWhn379kFfXx+Ojo5K63Z2dsLPzw+hoaGwt7fvU9uysrJgYmICGxsbrFq1Cg8ePFAZ6+TJkxg1ahQ8PDxgYmKCSZMmKb1Fp4i7d+/i5MmTWLp0qcq6rq6uSE1NRVVVFYgIZ86cQVlZGSQSiUJNa2srAMh4SE1NDVpaWr081PNav3LlCtrb22W8Y2trC5FIJOOdZ+kj+OoaGhqgp6eHAQMGyJQp0zU3NyMhIQHDhg3D0KFDlepaWlqwcOFCxMfHw8zMrE/t/PrrrzF48GCMHj0aYWFhaGlpUam9d+8eLl68CBMTE7i6usLU1BRTp05VeS56cuXKFRQUFPTyjTydq6srjh49iocPH6KzsxPffPMN/vjjD0ybNk2hrrW1FRzHyfxIn0AggJqamrStPft5vn55lvGhL1p5nlGlU+QZeTo+nlEUT5Vneur4+oUXfzqN6od0dHTQrFmzaPLkybzqFxUVkVAoJHV1ddLX16eTJ0/y0iUmJtLo0aPp8ePHRMQvY/7hhx/o22+/pcLCQkpLSyMXFxcSiUTU2NioVKelpUVaWloUFhZGeXl5tHfvXhIIBHTgwAFebe3i6NGjpK6uTlVVVSrrdnR00IYNG4jjOBowYABxHEdRUVG84ri4uNDUqVOpqqqKnjx5Ql999RWpqanRqFGj5NZHj/9KLly4QACourpapt78+fPJ19dXoa47f3Ym6PHjx+Tk5EQLFy7kpTtx4gQJhULiOI4sLCzo0qVLKnVRUVH00ksvSWfl+M4EJSYmUkpKChUVFVFSUhLZ2dmRs7MzPXnyRKGu678/HR0d2r59O+Xn59NHH31EHMdRVlZWn45NTEwMGRgYSL2vTPfHH3/QG2+8QQBowIABpKmpSQcPHlSqa2trI5FIRPPnz6eHDx9Sa2srRUdHEwCSSCTSevKu9a+//po0NTV7tdnZ2ZneeecdhbruKPrPnk/fcv/+fRKJRPTuu+/y0sXHx5NQKCQAZGNjI/MfvSLd8uXLaenSpdLlnsdPkW7v3r2UlpZGRUVFdPjwYRoyZAjNmTNH5T7m5OQQADI0NKQvv/yS8vLyKCQkhDQ1NamsrIz3sVm1ahXZ2dmpjEdEVFdXRxKJROobPT09Sk9PV6q7d+8e6enpUXBwMDU3N1NTUxMFBQURAHr11Vfl9vOq/MJnfFDkF75jS0/PqNIp8owynTLPKNMp84wiHR+/8IUlQc/AypUrSSwW02+//carfmtrK5WXl1Nubi5t3LiRBg8eTNevX1eqqaysJBMTEyosLJSW9WXasIu6ujrS09NTeftNQ0ODXFxcZMpWr15Nzz//fJ/iSSQSeuWVV3jVTUxMJEtLS0pMTKSioiI6dOgQGRoa8kq8bty4QW5ubgSA1NXVydnZmRYtWkS2trZy6/9vS4La2trIy8uLxo0bJzO1rkzX1NRE5eXllJOTQwEBAWRlZUV3795VqMvNzSVTU1OZhJRvEtSTiooKlbffqqqqCAC99tprMlovLy/6xz/+0aeYNjY2FBQU1Ktcnu7jjz+mUaNGUWpqKhUWFtLOnTtJV1dX5vaEPF1ubi45OjpKPeTh4UEzZ84kT09PaR151zqfJEhVH6FoUFOla2hooIkTJ5Knpye1tbXx0tXX11NZWRllZ2eTl5cXOTk5SZNLebqUlBSytramR48eSct6Hj++fWBmZmavWynytF3XY1hYmIzewcGBNm7cyCtmS0sL6evr09atW2XKFemCgoJo4sSJdOrUKSooKKBNmzaRvr4+FRUVKdWlp6fT8OHDieM4UldXp9dff52cnJxo2bJlcvt5VX7hMz4o8gsfrTzPqNIp8owinSrP9GUM7O4ZRTo+fuELS4L6SGBgIFlaWtLNmzefeRszZsyg5cuXK62TlJQk7Zy7PgCkF173/8hVMWHCBJXGEIlEMlk8EdHu3bvJwsKCd5zbt2+TmpoaJScn86pvaWlJu3btkinbsmUL2djY8I7Z1NQkTWR8fX3p5ZdflluvZyfeNaj3TGDc3NxozZo1CnXdedYkqK2tjWbPnk1jxoyh33//nbeuJ9bW1jIzZz11cXFxUr9095CamhqJxeI+xxs8eDDt2bNHoa61tZUGDBhAW7ZskdG988475Orqynsfz549SwCooKCg17qeupaWFtLQ0Oj1fNjSpUvJw8ODV7z6+nq6d+8eERFNnDiR3nrrLSJSfK13ddI9BySRSETbt2/n1UfIG9RU6RobG8nFxYVmzJghM0PWlz6ptbWVdHR06MiRIwp1wcHBCn0zderUPsVramoiAJSWlqa0rTdv3iQA9NVXX8mU+/r60sKFC3nFPHToEGloaEjPpbJ4N27c6PVMGNHTvnnFihW84t2/f196/kxNTSk2NrbXtpYvX67SLz2RNz7wfSaop1aRZ/jE7KK7ZxTpVHmmL/F6ekaeTpVf+sLf/hb5/ysQEVavXo2kpCRkZWVh2LBhz7ytzs5O6TMJipgxYwauXr0qU7ZkyRLY2tpiw4YNUFdX5xWrqakJFRUV8PPzU1pv8uTJvb4KWlZWBrFYzCsOACQkJMDExASzZs3iVb+lpaXXk/zq6uro7OzkHVMoFEIoFKKurg7p6emIjY3lpRs2bBjMzMyQmZmJsWPHAgAaGxtx8eJFrFq1inf8vtLe3g5fX1+Ul5fjzJkzMDIyeuZtqfKRn59fr2fJPDw84OfnhyVLlvQp1p07d/DgwQOYm5srrKOpqQlnZ+c/7aP9+/dj/PjxKp93Ap4ez/b29j/lI319fQBPn6XLzc1FZGQkgoKCFF7r48ePh4aGBjIzMzFv3jwAQGlpKSorK3HhwgXk5OT0qY/g07c0NjbCw8MDWlpaSE1NhUAgeKY+iYjQ2dmJL774AiUlJXJ1GzduxJtvvilT5uDggO3bt+PKlSt9itf18xFmZmZKj6mVlRUsLCx6eae0tBTq6uqora1VGXP//v3w9vaGsbGxymPT9cxJT9+oqanh3LlzqK+vVxlv8ODBAIDTp0/j3r178Pb2llnfdX0q84u853D4jA+K6K6V5xk+up7Q08kSueu7dJs3b5brmbi4OHh5efUpXpdn5PU1XTpFfikrK1P5TGlPWBLEk8DAQBw5cgQpKSkYOHAgamtrATztQLW1tRXqwsLCMHPmTIhEIjx69AhHjhxBVlYW0tPTlcYbOHAgRo8eLVMmFAphZGTUq7w769evh5eXF8RiMaqrqxEREQF1dXW89tprSuOtXbsWrq6uiIqKgq+vLy5duoR9+/Zh3759SnVddHZ2IiEhAYsXL5Z58E4ZXl5e+PDDDyESiWBvb4/8/Hxs374dAQEBKrVdX920sbHBjRs3EBoaCltbW5nBvampCTdu3JAu37p1CwUFBTA0NIRIJEJISAj++c9/YuTIkRg2bBjCw8NhYWEBd3d3md/+6al7+PAhKisrUV1dDQDSC9HMzAy6uroKY5qbm+PVV19FXl4evv/+e3R0dEh9pKmpKfMAe3edkZERPvzwQ3h7e8Pc3By///474uPjUVVVhVmzZilta88kS0NDA2ZmZhgyZIhCnaGhITZv3ox58+bBzMwMFRUVeOedd2BtbY3JkycrjRcaGooFCxbAzc0NL774ItLS0nDixAlkZWWpPB/A04772LFj2LZtG+/zOHXqVISGhkJbWxtisRjZ2dk4dOgQoqKilLb12LFjMDY2hkgkwtWrVxEcHIzZs2cjJSVF6bWur6+PpUuXYt26dTA0NISenh5Wr14NU1NTnDp1SmkfUVtbi9raWun+XL16FTt37kR6ejpSU1Pl6hobGyGRSNDS0oLDhw+jsbERjY2N2LhxI5KTkxXGu3nzJo4ePQqJRAJjY2PcuXMH0dHRICLk5uYqjGdmZib3wda0tDTk5OQojFdRUYEjR47g5ZdfhpGREYqKirB27Vq4ublh7969So8px3EIDQ1FREQEHB0dMXbsWBw8eBDXrl2Djo4OTpw4obTfvXHjBs6ePYsffvgBgOr+2tbWFtbW1lixYgW2bt0KIyMjJCcnIyMjA0KhECdPnlQYLyEhAXZ2djA2NkZOTg6Cg4MxYcIE3L17F1paWr36eUV+cXFxQUpKCtra2hSOD/L8MnDgQIhEInz88ccKxxZFngGAuLg4zJo1S65OkWe0tbWRl5eH4cOHy9Up8oxIJMK+ffsUtlOZZxITE1FfXy9Xp8gvJSUl+O6773q1Qyl9mjfqxwCQ+0lISFCqCwgIILFYTJqammRsbEwzZsygf//738/UBj7PBC1YsIDMzc1JU1OThgwZQgsWLJD71UZ5nDhxgkaPHk1aWlpka2tL+/bt49229PR0AkClpaW8NY2NjRQcHEwikYgEAgENHz6c3nvvPWptbVWpPXr0KA0fPpw0NTXJzMyMAgMDqb6+XqZO1xRyz8/ixYuJ6OnX5MPDw8nU1JS0tLRoxowZVFpaqlKXkJAgd31ERIRSbdftM3mfuLg4hbrHjx/TnDlzyMLCgjQ1Ncnc3Jy8vb3p0qVLKtvak65ngpTpWlpaSCKRkLGxMWloaJBYLKZly5ZRbW0tr3j79+8na2trEggE5OjoKL09yke7d+9e0tbWljmXqnQ1NTXk7+9PFhYWJBAIyMbGhrZt20anT59Wqvvkk0/I0tKSNDQ0SCQS0fvvv0+tra28rvXHjx/TW2+9RQYGBqSjo0Nz5szhpYuIiFBYT5FO0f6r0lVVVdHMmTPJxMSENDQ0yNLSkhYuXPhMfRmfeJWVleTm5kaGhoakpaVF1tbWFBoaKv05Bz4xP/roI7K0tCQdHR1ycXHhrQsLC6OhQ4dSR0eH0vZ215WVldHcuXPJxMSEdHR0aMyYMbx0GzZsIFNTU9LQ0KCRI0fStm3baMmSJUr7eXl+qampUTk+KPJLQkKCUq0yz/j6+irUKfJMSUlJn8cy4OktaGU6ZZ7hE6+nX86dO6ewPYrg/tNYBoPBYDAYjH4F+50gBoPBYDAY/RKWBDEYDAaDweiXsCSIwWAwGAxGv4QlQQwGg8FgMPolLAliMBgMBoPRL2FJEIPBYDAYjH4JS4IYDAaDwWD0S1gSxGAw/nZu374NjuNkfuH576akpATPP/88BAKB9NUqDAbjvwuWBDEYDPj7+4PjOERHR8uUJycng+O4v6lVfy8REREQCoUoLS1FZmam3Dr379/HqlWrIBKJoKWlBTMzM3h4eODChQvSOhzHITk5uc/xrayssGPHjmdsPYPB4ANLghgMBgBAIBAgJiYGdXV1f3dT/jLa2tqeWVtRUYEpU6ZALBYrfNHtvHnzkJ+fj4MHD6KsrAypqamYNm0aHjx48MxxGQzG/z9YEsRgMAAA7u7uMDMzw0cffaSwzqZNm3rdGtqxYwesrKyky/7+/pg9ezaioqJgamqKQYMGITIyEk+ePEFoaCgMDQ1haWmJhISEXtsvKSmBq6srBAIBRo8ejezsbJn1165dw8yZM6GrqwtTU1P4+fnh999/l66fNm0agoKCEBISgsGDB8PDw0PufnR2diIyMhKWlpbQ0tLC2LFjkZaWJl3PcRyuXLmCyMhIcByHTZs29dpGfX09zp07h5iYGLz44osQi8WYOHEiwsLCpG8U7zouc+bMAcdx0uWKigr4+PjA1NQUurq6cHZ2xqlTp2T249dff8XatWvBcZzMbNz58+fxwgsvQFtbG0OHDsWaNWvQ3NwsXb97926MHDkSAoEApqamePXVV+UeAwaDwZIgBoPxH9TV1REVFYWdO3fizp07f2pbp0+fRnV1Nc6ePYvt27cjIiICr7zyCgwMDHDx4kWsXLkSK1as6BUnNDQUb7/9NvLz8+Hi4gIvLy/prEp9fT2mT5+OcePGITc3F2lpabh79y58fX1ltnHw4EFoamriwoUL2LNnj9z2ffLJJ9i2bRu2bt2KoqIieHh4wNvbG+Xl5QCAmpoa2Nvb4+2330ZNTQ3Wr1/faxu6urrQ1dVFcnIyWltb5ca5fPkygKdvHq+pqZEuNzU14eWXX0ZmZiby8/Ph6ekJLy8vVFZWAgCOHz8OS0tLREZGoqamBjU1NQCeJk+enp6YN28eioqKcPToUZw/fx5BQUEAgNzcXKxZswaRkZEoLS1FWloa3NzcVJ8wBqO/0udXrjIYjP86Fi9eTD4+PkRE9Pzzz1NAQAARESUlJVH3biIiIoIcHR1ltHFxcSQWi2W2JRaLpW/0JiKysbGhF154Qbr85MkTEgqFlJiYSEREt27dIgAUHR0trdPe3k6WlpYUExNDRERbtmwhiUQiE/u3334jAFRaWkpERFOnTqVx48ap3F8LCwv68MMPZcqcnZ3prbfeki47OjpSRESE0u189913ZGBgQAKBgFxdXSksLIwKCwtl6uA/b9NWhb29Pe3cuVO6LBaLKS4uTqbO0qVLafny5TJl586dIzU1NXr8+DH961//Ij09PWpsbFQZj8FgELGZIAaDIUNMTAwOHjyI4uLiZ96Gvb091NT+X/diamoKBwcH6bK6ujqMjIxw7949GZ2Li4v07wEDBmDChAnSdhQWFuLMmTPSGRhdXV3Y2toCeDpD0sX48eOVtq2xsRHV1dWYPHmyTPnkyZP7vM/z5s1DdXU1UlNT4enpiaysLDg5OeHAgQNKdU1NTVi/fj3s7OwwaNAg6Orqori4WDoTpIjCwkIcOHBA5hh4eHigs7MTt27dwksvvQSxWIzhw4fDz88PX3/9NVpaWvq0TwxGf4IlQQwGQwY3Nzd4eHggLCys1zo1NTUQkUxZe3t7r3oaGhoyyxzHyS3r7Ozk3a6mpiZ4eXmhoKBA5lNeXi5zy0coFPLe5l+BQCDASy+9hPDwcPz000/w9/dHRESEUs369euRlJSEqKgonDt3DgUFBXBwcFD5IHdTUxNWrFghs/+FhYUoLy/HiBEjMHDgQOTl5SExMRHm5ub44IMP4OjoiPr6+r9wjxmM/x5YEsRgMHoRHR2NEydOICcnR6bc2NgYtbW1MonQX/nbPj///LP07ydPnuDKlSuws7MDADg5OeH69euwsrKCtbW1zKcviY+enh4sLCxkvsYOABcuXMBzzz33p/fhueeek3lQWUNDAx0dHb1i+fv7Y86cOXBwcICZmRlu374tU0dTU7OXzsnJCb/88kuv/be2toampiaApzNo7u7uiI2NRVFREW7fvo3Tp0//6f1iMP4bYUkQg8HohYODAxYtWoRPP/1UpnzatGm4f/8+YmNjUVFRgfj4ePz4449/Wdz4+HgkJSWhpKQEgYGBqKurQ0BAAAAgMDAQDx8+xGuvvYbLly+joqIC6enpWLJkSa9kQRWhoaGIiYnB0aNHUVpaio0bN6KgoADBwcG8t/HgwQNMnz4dhw8fRlFREW7duoVjx44hNjYWPj4+0npWVlbIzMxEbW2t9OcHRo4ciePHj0tnchYuXNhrVszKygpnz55FVVWV9BtwGzZswE8//YSgoCDpLFhKSor0wejvv/8en376KQoKCvDrr7/i0KFD6OzshI2NTZ+OD4PRX2BJEIPBkEtkZGSvgdnOzg67d+9GfHw8HB0dcenSJbnfnHpWoqOjER0dDUdHR5w/fx6pqakYPHgwAEhnbzo6OiCRSODg4ICQkBAMGjRI5vkjPqxZswbr1q3D22+/DQcHB6SlpSE1NRUjR47kvQ1dXV1MmjQJcXFxcHNzw+jRoxEeHo5ly5Zh165d0nrbtm1DRkYGhg4dinHjxgEAtm/fDgMDA7i6usLLywseHh5wcnKS2X5kZCRu376NESNGwNjYGAAwZswYZGdno6ysDC+88ALGjRuHDz74ABYWFgCAQYMG4fjx45g+fTrs7OywZ88eJCYmwt7evk/Hh8HoL3DU8wY/g8FgMBgMRj+AzQQxGAwGg8Hol7AkiMFgMBgMRr+EJUEMBoPBYDD6JSwJYjAYDAaD0S9hSRCDwWAwGIx+CUuCGAwGg8Fg9EtYEsRgMBgMBqNfwpIgBoPBYDAY/RKWBDEYDAaDweiXsCSIwWAwGAxGv4QlQQwGg8FgMPolLAliMBgMBoPRL/kfVpiMj9Hfz2UAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "summary_dir = f'{save_dir}summary/'\n", + "if not os.path.exists(summary_dir):\n", + " os.makedirs(summary_dir)\n", + "\n", + "for metric in ['spatial','temp']:\n", + " # Initialize a dictionary to store mean values for each number of states\n", + " mean_values_dict = {N_state: [] for N_state in N_states}\n", + "\n", + " # Loop through each value of N_states\n", + " for N_state in N_states:\n", + " # Loop through each pair of repeats\n", + " for i in range(len(repeats)):\n", + " for j in range(i + 1, len(repeats)):\n", + " # Define the path to the pickle file\n", + " file_path = os.path.join(save_dir, f'hmm_ICA_25_state_{N_state}/analysis/{metric}_pair_{repeats[i]}_vs_{repeats[j]}.pkl')\n", + " # Load the pickle file\n", + " with open(file_path, 'rb') as f:\n", + " data = pickle.load(f)\n", + " # Extract the 'mean' value and append it to the dictionary\n", + " mean_value = data['mean']\n", + " mean_values_dict[N_state].append(mean_value)\n", + "\n", + " # Plot boxplots of the mean values for each number of states\n", + " sns.boxplot(data=list(mean_values_dict.values()), showmeans=True)\n", + " plt.xlabel('Number of States')\n", + " plt.ylabel('Mean Value')\n", + " plt.title('Distribution of Mean Value for Different Numbers of States')\n", + " plt.xticks(ticks=range(len(N_states)), labels=N_states)\n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9d89dac4", + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './results_HCP_yaml_202403/'\n", + "N_states = list(range(2,37))\n", + "repeats = ['train','repeat_1','repeat_2','repeat_3','repeat_4','repeat_5']" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "fe843125", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "\n", + "# Initialize a dictionary to store free energy values for each number of states\n", + "free_energy_dict = {N_state: [] for N_state in N_states}\n", + "\n", + "# Loop through each value of N_states\n", + "for N_state in N_states:\n", + " # Loop through each repeat\n", + " for repeat in repeats:\n", + " # Define the path to the JSON file for the free energy\n", + " file_path = os.path.join(save_dir, f'hmm_ICA_25_state_{N_state}/{repeat}/metrics/metrics.json')\n", + " # Load the JSON file\n", + " with open(file_path, 'r') as f:\n", + " metrics = json.load(f)\n", + " # Extract the 'free_energy' value and append it to the dictionary\n", + " free_energy = metrics['free_energy']\n", + " free_energy_dict[N_state].append(free_energy)\n", + "\n", + "# Plot boxplots of the free energy values for each number of states\n", + "sns.boxplot(data=list(free_energy_dict.values()), showmeans=True)\n", + "plt.xlabel('Number of States')\n", + "plt.ylabel('Free Energy')\n", + "plt.title('Distribution of Free Energy for Different Numbers of States')\n", + "plt.xticks(ticks=range(len(N_states)), labels=N_states)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecea55aa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main.py b/main.py new file mode 100644 index 00000000..4e3f3e5f --- /dev/null +++ b/main.py @@ -0,0 +1,221 @@ +import glob +import warnings +import sys +import os +import pathlib + +import numpy as np +import scipy.stats as stats +from sklearn.model_selection import KFold +from rotation.preprocessing import PrepareData +from rotation.training import HMM_training, Dynemo_training, MAGE_training,SWC_computation +from rotation.utils import * +from rotation.analysis import HMM_analysis +from osl_dynamics.data import Data +from osl_dynamics.analysis import connectivity + + +def swc_analysis(dataset): + ts = dataset.time_series() + swc = connectivity.sliding_window_connectivity(ts, window_length=100, step_size=50, conn_type="corr") + swc_concat = np.concatenate(swc) + swc_concat = np.abs(swc_concat) + + print(swc_concat.shape) + np.save('results/model_swc/dfc.npy') + ''' + connectivity.save( + swc_concat[:5], + threshold=0.95, # only display the top 5% of connections + ) + ''' + +def model_train(model,dataset,n_channels, n_states,save_dir,learn_means=True,learn_covariances=True,learn_trans_prob=True,learning_rate=1e-3): + if model == 'HMM': + HMM_training(dataset,n_states,n_channels,save_dir, + learn_means=learn_means, + learn_covariances=learn_covariances, + learn_trans_prob=learn_trans_prob, + learning_rate=learning_rate) + elif model == 'Dynemo': + Dynemo_training(dataset, n_states, n_channels,save_dir,learn_means=learn_means) + elif model == 'MAGE': + MAGE_training(dataset,n_states,n_channels,save_dir,learn_means=learn_means) + elif model == 'SWC': + SWC_computation(dataset,window_length=143,step_size=118,save_dir=save_dir) + else: + raise ValueError('The model name is incorrect!') + + +if __name__ == '__main__': + # Index + # 1-30: HMM + # 31-60: Dynemo + # 61-90: MAGE + # 91-96: SWC (training) + # 91-120: SWC (analysis) + + # Mode should be encoded in sys.argv[2] + # sys.argv[2] == 'training' (or no sys.argv[2]): train the model + # sys.argv[2] == 'repeat': reproduce the model training to get reliable KL divergence + # sys.argv[2] == 'split': split the data in half to test reproducibility + models = ['HMM','Dynemo','MAGE','SWC'] + list_channels = [15, 25, 50, 100, 200, 300] + #list_states = [4,8,12,16,20] + # Update swimming 20231015: try train HMM model with more states + #list_states = [25,30,35,40,45] + list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] + + learn_means = False + learn_covariances = True + learn_trans_prob = False + z_score_data = False + learning_rate = 0.01 + + index = int(sys.argv[1]) - 1 + #index = 6 + + # Default mode is training: + mode = 'training' + if len(sys.argv) >= 3: + mode = sys.argv[2] + + # This either represent run repeat_i or the first/second half + sub_index = 1 + if len(sys.argv) >= 4: + sub_index = int(sys.argv[3]) + + # Default strategy for splitting + strategy = '0' + if len(sys.argv) >= 5: + strategy = sys.argv[4] + + + + # For debugging + #index = 0 + #mode = 'repeat' + #strategy = '1' + + model,n_channels, n_states = parse_index(index,models,list_channels,list_states,training=True) + + if n_states is None: + save_dir = f'./results_simulation_202401/sigma_0.1/run_1/{model}_ICA_{n_channels}/' + else: + save_dir = f'./results_simulation_202401/sigma_0.1/run_1/{model}_ICA_{n_channels}_state_{n_states}' + + print(f'Number of channels: {n_channels}') + print(f'Number of states: {n_states}') + print(f'The model: {model}') + + # data_dir = pathlib.Path(f'/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d{n_channels}_ts2/') + #data_dir = pathlib.Path(f'./data/node_timeseries/3T_HCP1200_MSMAll_d{n_channels}_ts2/') + data_dir = pathlib.Path(f'./data/node_timeseries/simulation_202401/sigma_0.1/run_1/') + + spatial_map_dir = f'./data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d{n_channels}.ica/melodic_IC_sum.nii.gz' + spatial_surface_map_dir = f'./data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d{n_channels}.ica/melodic_IC.dscalar.nii' + + if mode == 'training': + prepare_data = PrepareData(data_dir) + subj, dataset = prepare_data.load(z_score_data=z_score_data) + print(f'Number of subjects: {len(subj)}') + model_train(model,dataset,n_channels,n_states,save_dir, + learn_means=learn_means, + learn_covariances=learn_covariances, + learn_trans_prob=learn_trans_prob, + learning_rate=learning_rate) + elif mode == 'repeat': + prepare_data = PrepareData(data_dir) + subj, dataset = prepare_data.load(z_score_data=z_score_data) + print(f'Number of subjects: {len(subj)}') + + save_dir_sub = f'{save_dir}/repeat_{sub_index}' + print(f'save dir sub is: {save_dir_sub}') + if not os.path.exists(save_dir_sub): + #os.rmdir(save_dir_sub) + model_train(model,dataset,n_channels,n_states,save_dir_sub, + learn_means=learn_means, + learn_covariances=learn_covariances, + learn_trans_prob=learn_trans_prob, + learning_rate=learning_rate + ) + + elif mode == 'split': + prepare_data = PrepareData(data_dir) + subj, dataset_1,dataset_2 = prepare_data.load(z_score_data=z_score_data, split_strategy = strategy) + print(f'Number of subjects: {len(subj)}') + if sub_index == 1: + save_dir_sub = f'{save_dir}/split_{strategy}_first_half' + dataset = dataset_1 + else: + save_dir_sub = f'{save_dir}/split_{strategy}_second_half' + dataset = dataset_2 + model_train(model, dataset_1, n_channels, n_states, save_dir_sub, + learn_means=learn_means, + learn_covariances=learn_covariances, + learn_trans_prob=learn_trans_prob, + learning_rate=learning_rate + ) + elif mode == "cross_validation": + prepare_data = PrepareData(data_dir) + subj, dataset = prepare_data.load(z_score_data=z_score_data) + kf = KFold(shuffle=True,random_state=42) + for j, (train_index, test_index) in enumerate(kf.split(range(len(dataset.arrays)))): + save_dir_sub = f'{save_dir}/cross_validation_{j}/' + if not os.path.exists(save_dir_sub): + os.makedirs(save_dir_sub) + + with dataset.set_keep(list(train_index)): + print(f'Cross validation number{j}') + print(f'Please check the length of training dataset: {len(dataset.arrays)}') + model_train(model,dataset,n_channels,n_states,save_dir_sub, + learn_means=learn_means, + learn_covariances=learn_covariances, + learn_trans_prob=learn_trans_prob, + learning_rate=learning_rate + ) + HMM_analysis(dataset, save_dir_sub, spatial_map_dir, spatial_surface_map_dir, n_channels, n_states) + + with dataset.set_keep(list(test_index)): + save_dir_sub_validation = f'{save_dir_sub}/validation/' + print(f'Please check the length of validation dataset: {len(dataset.arrays)}') + if not os.path.exists(save_dir_sub_validation): + os.makedirs(save_dir_sub_validation) + HMM_analysis(dataset, save_dir_sub_validation, spatial_map_dir, spatial_surface_map_dir, n_channels, n_states,model_dir=save_dir_sub) + + else: + raise ValueError('Mode is not available now!') + + + + + ''' + data_dir =pathlib.Path('/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/') + subjs = [] + np_datas = [] + for file in data_dir.glob('*.txt'): + subjs.append(file.stem) + temp = np.loadtxt(file) + temp = stats.zscore(temp,axis=0) + + assert temp.shape == (4800,15) + np_datas.append(temp) + + if len(np_datas)>10: + continue + + print('Number of subjects: ',len(subjs)) + print('Mean of the standardised data: ',np.mean(np_datas[0],axis=0)) + print('Std of the standardised data: ', np.std(np_datas[0], axis=0)) + + + dataset = Data(np_datas) + ''' + # Step 1: Sliding window analysis + #swc_analysis(dataset) + + # Step 2: HMM analysis + #HMM_analysis(dataset) + + # Step 3: Dynemo analysis + #Dynemo_analysis(dataset) diff --git a/main_training_search.py b/main_training_search.py new file mode 100644 index 00000000..fb28a14a --- /dev/null +++ b/main_training_search.py @@ -0,0 +1,80 @@ +import sys +import os +import pathlib + +import numpy as np +import scipy.stats as stats +from sklearn.model_selection import KFold +from rotation.preprocessing import PrepareData +from rotation.training import HMM_training, Dynemo_training, MAGE_training,SWC_computation +from rotation.utils import * +from rotation.analysis import HMM_analysis +from osl_dynamics.data import Data +from osl_dynamics.analysis import connectivity + +if __name__ == '__main__': + model = 'HMM' + n_channels = 50 + n_states = 10 + learn_means = False + data_dir = pathlib.Path(f'./data/node_timeseries/3T_HCP1200_MSMAll_d{n_channels}_ts2/') + + index = int(sys.argv[1]) - 1 + mode = 'training' + if len(sys.argv) >= 3: + mode = sys.argv[2] + + # This either represent run repeat_i or the first/second half + sub_index = 1 + if len(sys.argv) >= 4: + sub_index = int(sys.argv[3]) + + #sequence_lengths = [600,400,200,100,50,25] + #batch_sizes = [1024,512,256,128,64,32] + #learning_rates = [0.0005,0.001,0.005,0.01] + + #sequence_length, batch_size, learning_rate = parse_index(index,sequence_lengths,batch_sizes,learning_rates,training=True) + sequence_lengths = [400,400,400,400,200,200,200,200,100,100,100,100] + batch_sizes = [256,256,256,256,512,512,512,512,1024,1024,1024,1024] + learning_rates = [0.0005,0.001,0.005,0.01,0.0005,0.001,0.005,0.01,0.0005,0.001,0.005,0.01,0.0005,0.001,0.005,0.01] + + sequence_length = sequence_lengths[index] + batch_size = batch_sizes[index] + learning_rate = learning_rates[index] + + + + + save_dir = f'./results_training_config_202402/sl_{sequence_length}_bs_{batch_size}_lr_{learning_rate}' + + print(f'sequence length = {sequence_length}') + print(f'batch size = {batch_size}') + print(f'learning rate = {learning_rate}') + + if mode == 'training': + prepare_data = PrepareData(data_dir) + subj, dataset = prepare_data.load() + print(f'Number of subjects: {len(subj)}') + HMM_training(dataset,n_states,n_channels,save_dir, + sequence_length=sequence_length, + batch_size = batch_size, + learning_rate = learning_rate, + n_epochs = 40, + learn_means=learn_means + ) + elif mode == 'repeat': + prepare_data = PrepareData(data_dir) + subj, dataset = prepare_data.load() + print(f'Number of subjects: {len(subj)}') + + save_dir_sub = f'{save_dir}/repeat_{sub_index}' + print(f'save dir sub is: {save_dir_sub}') + if not os.path.exists(save_dir_sub): + # os.rmdir(save_dir_sub) + HMM_training(dataset,n_states,n_channels,save_dir_sub, + sequence_length=sequence_length, + batch_size=batch_size, + learning_rate=learning_rate, + n_epochs=40, + learn_means=learn_means + ) \ No newline at end of file diff --git a/osl_dynamics/analysis/workbench.py b/osl_dynamics/analysis/workbench.py index 45e0e9c4..4dac944d 100644 --- a/osl_dynamics/analysis/workbench.py +++ b/osl_dynamics/analysis/workbench.py @@ -43,6 +43,7 @@ def render( gui=True, inflation=0, image_name=None, + input_cifti=False ): """Render map in workbench. @@ -59,6 +60,8 @@ def render( Default is :code:`True`. image_name : str, optional Filename of image to save. + input_cifti: bool + Whether the input file is a CIFTI file. """ nii = pathlib.Path(nii) @@ -83,19 +86,33 @@ def render( output_right = stem_right.with_suffix(".func.gii") output_left = stem_left.with_suffix(".func.gii") - volume_to_surface( - nii, - surf=surf_right, - output=output_right, - interptype=interptype, - ) + if input_cifti: + subprocess.run([ + "wb_command", + "-cifti-separate", + str(nii), + "COLUMN", + "-metric", + "CORTEX_LEFT", + str(output_left), + "-metric", + "CORTEX_RIGHT", + str(output_right) + ]) + else: + volume_to_surface( + nii, + surf=surf_right, + output=output_right, + interptype=interptype, + ) - volume_to_surface( - nii, - surf=surf_left, - output=output_left, - interptype=interptype, - ) + volume_to_surface( + nii, + surf=surf_left, + output=output_left, + interptype=interptype, + ) cifti_right = stem_right.with_suffix(".dtseries.nii") cifti_left = stem_left.with_suffix(".dtseries.nii") diff --git a/osl_dynamics/array_ops.py b/osl_dynamics/array_ops.py index f1a248b7..62cdbd86 100644 --- a/osl_dynamics/array_ops.py +++ b/osl_dynamics/array_ops.py @@ -105,6 +105,54 @@ def cov2std(cov): return np.sqrt(np.diagonal(cov, axis1=-2, axis2=-1)) +def stdcorr2cov(stds, corrs, std_diagonal=False): + """ + Convert batches of standard deviations and correlations into covariances + Parameters + ---------- + stds: np.ndarray + Standard deviations. Shape is (..., N) or (..., N, N) if std_diagonal=True. + cors: np.ndarray + Correlation matrices. Shape is (..., N, N). + std_diagonal: bool + Whether the standard deviation is in the form of diagonal matrices + + Returns + ------- + covariances: np.ndarray + covariance matrices. Shape is (..., N, N) + """ + if std_diagonal: + stds = np.diagonal(stds, axis1=-2, axis2=-1) + return corrs * stds[..., None] * stds[..., None, :] + + +def cov2stdcorr(covs): + """ + Converts batches of covariance matrices into batches of standard deviation vectors + and correlation matrices. + + Parameters + ---------- + covs: np.ndarray + Covariance matrices. Shape must be (..., N, N). + + Returns + ------- + stds: np.ndarray + Standard deviations. Shape is (..., N). + corrs: np.ndarray + Correlation matrices. Shape is (..., N, N). + """ + # Validation + if covs.ndim < 2: + raise ValueError("input covariances must have more than 1 dimension.") + + # Extract batches of standard deviations + stds = np.sqrt(np.diagonal(covs, axis1=-2, axis2=-1)) + normalisation = np.expand_dims(stds, -1) @ np.expand_dims(stds, -2) + return stds, covs / normalisation + def sliding_window_view( x, window_shape, diff --git a/osl_dynamics/config_api/__init__.py b/osl_dynamics/config_api/__init__.py index ad370628..1e0cba2d 100644 --- a/osl_dynamics/config_api/__init__.py +++ b/osl_dynamics/config_api/__init__.py @@ -61,4 +61,4 @@ command line interface. """ -from osl_dynamics.config_api import pipeline, wrappers +from osl_dynamics.config_api import pipeline, wrappers, batch diff --git a/osl_dynamics/config_api/batch.py b/osl_dynamics/config_api/batch.py new file mode 100644 index 00000000..f6b0ca8f --- /dev/null +++ b/osl_dynamics/config_api/batch.py @@ -0,0 +1,324 @@ +""" +Functions for batch training + +In some use cases, e.g. compare and evaluate different models or hyperparameters, +we need to train many models and submit them to cluster using batch +This module contains useful functions to initialise proper batch training +See ./config_train_prototype.yaml for an example batch file. +""" +import os +import random +import time +import json +from itertools import product + +import yaml +import numpy as np +import pandas as pd +from .pipeline import run_pipeline_from_file +from ..data.base import Data +from ..utils.misc import override_dict_defaults +class IndexParser: + + """ + Parse the training config file with index for batch training. + Typically, a root config YAML file looks like the following, where + batch_variable contains list of variables for different configurations + non_batch_variable contains all other hyperparameters for training + Given a training index, we need to find the specific batch_variable + and combine that with other non_batch variables + header: + # We assign a time stamp for each batch training + time: 2024-02-02T16:05:00.000Z + # Add custom notes, which will also be saved + note: "test whether your yaml file works" +# where to read the data +load_data: + inputs: './data/node_timeseries/simulation_202402/sigma_0.1/' + prepare: + select: + timepoints: + - 0 + - 1200 + standardize: {} +# Where to save model training results +save_dir: './results_yaml_test/' +# where to load the spatial map and spatial surface map +spatial_map: './data/spatial_maps/' +non_batch_variable: + n_channels: 25 + sequence_length: 600 + learn_means: false + learn_covariances: true + learn_trans_prob: true + learning_rate: 0.01 + n_epochs: 30 + split_strategy: random + init_kwargs: + n_init: 10 + n_epochs: 2 +# The following variables have lists for batch training +batch_variable: + model: + - 'hmm' + - 'dynemo' + - 'mdyenmo' + - 'swc' + n_states: [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] + # Mode can be: train, repeat, split, cross_validation + mode: + - train + - repeat_1 + - repeat_2 + - repeat_3 + - repeat_4 + - repeat_5 + - split_1 + - split_2 + - split_3 + - split_4 + - split_5 + - cv_1 + - cv_2 + - cv_3 + - cv_4 + - cv_5 + """ + def __init__(self,config:dict): + + # Sleep for random seconds, otherwise the batch job might contradicts + time.sleep(random.uniform(0.,2.)) + + self.save_dir = config['save_dir'] + self.batch_variable = config['batch_variable'] + self.non_batch_variable = config['non_batch_variable'] + self.other_keys = {key: value for key, value in config.items() + if key not in ['batch_variable','non_batch_variable']} + + # Check whether the save_dir exists, make directory if not + if not os.path.exists(config['save_dir']): + os.makedirs(config['save_dir']) + # Check whether the root configuration file exists, save if not + if not os.path.exists(f'{self.save_dir}config_root.yaml'): + with open(f'{self.save_dir}config_root.yaml', 'w') as file: + yaml.dump(config, file, default_flow_style=False) + + # Check if the config list file exists, create if not + if not os.path.exists(f'{self.save_dir}config_list.csv'): + self._make_list() + def parse(self,index:int=0): + """ + Given the index, parse the correct configuration file + Parameters + ---------- + index: the index passed in from batch. + + Returns + ------- + config: dict + the configuration file given the index + """ + # Read in the list + config_list = pd.read_csv(f'{self.save_dir}config_list.csv', index_col=0) + + # sv represents batch_variable given specific row + bv = config_list.iloc[index].to_dict() + + # concatenate three parts of the dictionary + new_config = {} + new_config.update(self.other_keys) + new_config.update(bv) + new_config.update(self.non_batch_variable) + + new_config['save_dir'] = f'{new_config["save_dir"]}{new_config["model"]}' \ + f'_ICA_{new_config["n_channels"]}_state_{new_config["n_states"]}/{new_config["mode"]}/' + + return new_config + + + def _make_list(self): + """ + Make the list of batch variables with respect to index, + and save them to f'{self.header["save_dir"]}config_list.xlsx' + Returns + ------- + """ + from itertools import product + combinations = list(product(*self.batch_variable.values())) + # Create a DataFrame + df = pd.DataFrame(combinations, columns=self.batch_variable.keys()) + df.to_csv(f'{self.save_dir}config_list.csv', index=True) + +class BatchTrain: + """ + Convert a batch training configuration file to another config + for training pipeline + """ + mode_key_default = 'mode' + + train_keys_default = ['n_channels', + 'n_states', + 'learn_means', + 'learn_covariances', + 'learn_trans_prob', + 'initial_means', + 'initial_covariances', + 'initial_trans_prob', + 'sequence_length', + 'batch_size', + 'learning_rate', + 'n_epochs', + ] + def __init__(self,config:dict,train_keys=None): + self.train_keys = self.train_keys_default if train_keys is None else train_keys + + # Validate the configuration file + if 'load_data' not in config: + raise ValueError('No data directory specified!') + # The default mode of 'mode' is train + if 'mode' not in config: + config['mode'] = 'train' + if 'init_kwargs' not in config: + config['init_kwargs'] = {} + # Check whether save directory is specified + if 'save_dir' not in config: + raise ValueError('Saving directory not specified!') + + if not os.path.exists(config['save_dir']): + os.makedirs(config['save_dir']) + if not os.path.isfile(f'{config["save_dir"]}batch_config.yaml'): + with open(f'{config["save_dir"]}batch_config.yaml','w') as file: + yaml.safe_dump(config,file, default_flow_style=False) + self.config = config + + def model_train(self,cv_ratio=0.8): + ''' + Batch model train method + cv_ration: float,optional + the proportion of sessions to use as the training data + Returns + ------- + ''' + prepare_config = {} + prepare_config['load_data'] = self.config['load_data'] + + prepare_config[f'train_{self.config["model"]}'] = { + 'config_kwargs': + {key: self.config[key] for key in self.train_keys if key in self.config}, + 'init_kwargs': + self.config['init_kwargs'] + } + + if "split" in self.config["mode"]: + # We need to know how many sessions in advance + indice_1, indice_2 = self.select_indice() + + # Save the selected and remaining indices to JSON files + with open(f'{self.config["save_dir"]}indices_1.json', 'w') as json_file: + json.dump(indice_1, json_file) + with open(f'{self.config["save_dir"]}indices_2.json', 'w') as json_file: + json.dump(indice_2, json_file) + + for i in range(0,2): + temp_save_dir = f'{self.config["save_dir"]}half_{i+1}/' + if not os.path.exists(temp_save_dir): + os.makedirs(temp_save_dir) + prepare_config['keep_list'] = f'{self.config["save_dir"]}indices_{i+1}.json' + with open(f'{temp_save_dir}prepared_config.yaml', 'w') as file: + yaml.safe_dump(prepare_config, file, default_flow_style=False) + run_pipeline_from_file(f'{temp_save_dir}prepared_config.yaml', + temp_save_dir) + + + elif "cv" in self.config["mode"]: + indice_1, indice_2 = self.select_indice(ratio=cv_ratio) + + # Save the selected and remaining indices to JSON files + with open(f'{self.config["save_dir"]}indices_train.json', 'w') as json_file: + json.dump(indice_1, json_file) + with open(f'{self.config["save_dir"]}indices_validate.json', 'w') as json_file: + json.dump(indice_2, json_file) + + prepare_config['keep_list'] = f'{self.config["save_dir"]}indices_train.json' + with open(f'{self.config["save_dir"]}prepared_config.yaml', 'w') as file: + yaml.safe_dump(prepare_config, file, default_flow_style=False) + run_pipeline_from_file(f'{self.config["save_dir"]}prepared_config.yaml', + self.config['save_dir']) + + + else: + with open(f'{self.config["save_dir"]}prepared_config.yaml', 'w') as file: + yaml.safe_dump(prepare_config, file, default_flow_style=False) + run_pipeline_from_file(f'{self.config["save_dir"]}prepared_config.yaml', + self.config["save_dir"]) + + + def select_indice(self,ratio=0.5): + if "n_sessions" not in self.config: + data = Data(self.config["load_data"]["inputs"]) + n_sessions = len(data.arrays) + else: + n_sessions = self.config["n_sessions"] + + all_indices = list(range(n_sessions)) + # Calculate the number of indices to select (half of the total) + n_selected_sessions = int(n_sessions * ratio) + + # Randomly select indices without replacement + selected_indices = random.sample(all_indices, n_selected_sessions) + + # Calculate the remaining indices + remaining_indices = list(set(all_indices) - set(selected_indices)) + return selected_indices,remaining_indices + +def batch_check(config:dict): + ''' + Check whether the batch training is successful, raise value Error + and save the list if some batch training is not successful. + Parameters + ---------- + config: str + configuration file of batch training + ''' + # check the bad directories + bad_dirs = [] + + # Check whether the fail training list exists, delete if so. + bad_dirs_save_path = f'{config["save_dir"]}failure_list.yaml' + + # Check if the file exists, delete if so + if os.path.exists(bad_dirs_save_path): + os.remove(bad_dirs_save_path) + + for values in product(*config['batch_variable'].values()): + combination = dict(zip(config['batch_variable'].keys(), values)) + vars = override_dict_defaults(config['non_batch_variable'],combination) + check_dir = f'{config["save_dir"]}{vars["model"]}_ICA' \ + f'_{vars["n_channels"]}_state_{vars["n_states"]}/{vars["mode"]}/' + + # Check whether batch_config exists + if not os.path.isfile(f'{check_dir}batch_config.yaml'): + bad_dirs.append(check_dir) + # Check whether prepared_config exists + try: + if "split" in vars['mode']: + check_dir = f'{check_dir}half_2/' + assert os.path.isfile(f'{check_dir}prepared_config.yaml') + # Check whether model training is successful + assert os.path.exists(f'{check_dir}model') + # Check if the covs.npy file exists + assert os.path.isfile(f'{check_dir}inf_params/covs.npy') + # Check if the means.npy file exists + assert os.path.isfile(f'{check_dir}inf_params/means.npy') + # Check whether the alp.pkl exists + assert os.path.isfile(f'{check_dir}inf_params/alp.pkl') + except AssertionError: + bad_dirs.append(check_dir) + + if len(bad_dirs)>0: + # Serialize and save the list to a file + with open(bad_dirs_save_path, 'w') as file: + yaml.safe_dump(bad_dirs, file,default_flow_style=False) + raise ValueError(f'Some training cases failed, check {bad_dirs_save_path} for the list') + else: + print('All model training successful!') diff --git a/osl_dynamics/config_api/pipeline.py b/osl_dynamics/config_api/pipeline.py index 562016a3..51d23dc6 100644 --- a/osl_dynamics/config_api/pipeline.py +++ b/osl_dynamics/config_api/pipeline.py @@ -6,6 +6,7 @@ import argparse import logging +import json import pprint from pathlib import Path @@ -120,15 +121,22 @@ def run_pipeline(config, output_dir, data=None, extra_funcs=None): _logger.info(f"load_data: {load_data_kwargs}") data = wrappers.load_data(**load_data_kwargs) + # Set keep list, use the original if not found + keep_list = config.pop('keep_list',data.keep) + if isinstance(keep_list,str): + with open(keep_list,'r') as file: + keep_list = json.load(file) + # Loop through each item in the config - for name, kwargs in config.items(): - func = find_function(name, extra_funcs) - if func is not None: - try: - _logger.info(f"{name}: {kwargs}") - func(data=data, output_dir=output_dir, **kwargs) - except Exception as e: - _logger.exception(e) + with data.set_keep(keep_list): + for name, kwargs in config.items(): + func = find_function(name, extra_funcs) + if func is not None: + try: + _logger.info(f"{name}: {kwargs}") + func(data=data, output_dir=output_dir, **kwargs) + except Exception as e: + _logger.exception(e) # Delete the temporary directory created by the Data class if data is not None: diff --git a/osl_dynamics/config_api/wrappers.py b/osl_dynamics/config_api/wrappers.py index 905ee406..01cd132b 100644 --- a/osl_dynamics/config_api/wrappers.py +++ b/osl_dynamics/config_api/wrappers.py @@ -15,6 +15,7 @@ """ import os +import json import logging from pathlib import Path @@ -66,6 +67,7 @@ def train_hmm( init_kwargs=None, fit_kwargs=None, save_inf_params=True, + calculate_free_energy=True ): """Train a `Hidden Markov Model `_. @@ -84,6 +86,8 @@ def train_hmm( - :code:`/model`, which contains the trained model. - :code:`/inf_params`, which contains the inferred parameters. This directory is only created if :code:`save_inf_params=True`. + - :code:`/metrics`, which contains the free energy on the training data. + This directory is only created if :code:`calculate_free_energy=True`. Parameters ---------- @@ -171,6 +175,25 @@ def train_hmm( save(f"{inf_params_dir}/means.npy", means) save(f"{inf_params_dir}/covs.npy", covs) + if calculate_free_energy: + # Make output directory + metric_dir = output_dir + "/metrics/" + os.makedirs(metric_dir,exist_ok=True) + + # Get the free energy + free_energy = model.free_energy(data,return_components=True) + free_energy, log_likelihood, entropy, prior = model.free_energy(data, return_components=True) + evidence = model.evidence(data) + metrics = {'free_energy': float(free_energy), + 'log_likelihood': float(log_likelihood), + 'entropy': float(entropy), + 'prior': float(prior), + 'evidence': float(evidence), + } + with open(f'{metric_dir}metrics.json', "w") as json_file: + # Use json.dump to write the data to the file + json.dump(metrics, json_file) + def train_dynemo( data, diff --git a/osl_dynamics/data/base.py b/osl_dynamics/data/base.py index 9494a403..3100de45 100644 --- a/osl_dynamics/data/base.py +++ b/osl_dynamics/data/base.py @@ -31,8 +31,8 @@ class Data: Parameters ---------- inputs : list of str or str or np.ndarray - - A path to a directory containing :code:`.npy` files. Each - :code:`.npy` file should be a subject or session. + - A path to a directory containing :code:`.npy` or :code:`.txt` files. Each + :code:`.npy` :code:`txt` file should be a subject or session. - A list of paths to :code:`.npy`, :code:`.mat` or :code:`.fif` files. Each file should be a subject or session. If a :code:`.fif` file is passed is must end with :code:`'raw.fif'` or :code:`'epo.fif'`. @@ -320,8 +320,8 @@ def validate_data(self): if not np.equal(n_channels, n_channels[0]).all(): raise ValueError("All inputs should have the same number of channels.") - def select(self, channels=None, use_raw=False): - """Select channels. + def select(self, channels=None, timepoints:list=None, use_raw=False): + """Select channels and timepoints This is an in-place operation. @@ -329,6 +329,8 @@ def select(self, channels=None, use_raw=False): ---------- channels : int or list of int, optional Channel indices to keep. If None, all channels are retained. + timepoints: list, optional + [start,end], timepoints[start:end] are kept. use_raw : bool, optional Should we select channel from the original 'raw' data that we loaded? @@ -361,7 +363,16 @@ def select(self, channels=None, use_raw=False): # Select channels new_arrays = [] for i in tqdm(range(self.n_sessions), desc="Selecting channels"): - array = arrays[i][:, channels] + array = arrays[i] + if timepoints is None: + array = array[:, channels] + else: + assert len(timepoints) == 2 + time_start,time_end = timepoints[0], timepoints[1] + assert time_start >=0 + assert time_start <= time_end + assert time_end <=len(array) + array = array[time_start:time_end,channels] if self.load_memmaps: array = misc.array_to_memmap(self.prepared_data_filenames[i], array) new_arrays.append(array) diff --git a/osl_dynamics/data/rw.py b/osl_dynamics/data/rw.py index ef0c486f..8dcee071 100644 --- a/osl_dynamics/data/rw.py +++ b/osl_dynamics/data/rw.py @@ -29,7 +29,7 @@ def validate_inputs(inputs): """ if isinstance(inputs, str): if path.isdir(inputs): - validated_inputs = list_dir(inputs, keep_ext=_allowed_ext) + validated_inputs = sorted(list_dir(inputs, keep_ext=_allowed_ext)) else: validated_inputs = [inputs] diff --git a/osl_dynamics/inference/metrics.py b/osl_dynamics/inference/metrics.py index d4f9203b..a36df896 100644 --- a/osl_dynamics/inference/metrics.py +++ b/osl_dynamics/inference/metrics.py @@ -2,28 +2,36 @@ """ +import warnings import numpy as np from scipy.linalg import eigvalsh from sklearn.metrics import confusion_matrix as sklearn_confusion_matrix from tqdm.auto import trange -def alpha_correlation(alpha_1, alpha_2): +def alpha_correlation(alpha_1, alpha_2, return_diagonal=True): """Calculates the correlation between mixing coefficient time series. Parameters ---------- - alpha_1 : np.ndarray + alpha_1 : np.ndarray or list of numpy.ndarray First alpha time series. Shape must be (n_samples, n_modes). - alpha_2 : np.ndarray + alpha_1 will be concatenated if it's a list. + alpha_2 : np.ndarray or list of numpy.ndarray Second alpha time series. Shape must be (n_samples, n_modes). - + alpha_2 will be concatenated if it's a list. + return_diagonal: bool, optional + (optional, default=True) Whether to return the diagonal elements of the correlation. Returns ------- corr : np.ndarray Correlation of each mode in the corresponding alphas. - Shape is (n_modes,). + Shape is (n_modes,) if return_diagonal = True, (n_modes,n_modes) otherwise. """ + if isinstance(alpha_1, list): + alpha_1 = np.concatenate(alpha_1, axis=0) + if isinstance(alpha_2, list): + alpha_2 = np.concatenate(alpha_2, axis=0) if alpha_1.shape[1] != alpha_2.shape[1]: raise ValueError( "alpha_1 and alpha_2 shapes are incomptible. " @@ -31,7 +39,9 @@ def alpha_correlation(alpha_1, alpha_2): ) n_modes = alpha_1.shape[1] corr = np.corrcoef(alpha_1, alpha_2, rowvar=False) - corr = np.diagonal(corr[:n_modes, n_modes:]) + corr = corr[:n_modes, n_modes:] + if return_diagonal: + corr = np.diagonal(corr) return corr @@ -366,3 +376,204 @@ def pairwise_l2_distance(arrays, batch_dims=0): axis=tuple(range(pairwise_axis + 2, arrays.ndim + 1)), ) ) + + +def fisher_z_transform(r): + """ + Fisher z-transform each element of the input + :math:`z = \frac{1}{2}\ln(\frac{1+r}{1-r})` + Parameters + ---------- + r: np.ndarray + Elements in (-1,1) + + Returns + ------- + z: np.ndarray + Fisher z-transformation of the input + """ + return 0.5 * np.log((1 + r) / (1 - r)) + + +def fisher_z_correlation(M1, M2): + """ + Calculate the Fisher z-transformed vector correlation. + M1 and M2 are symmetric semi-positive definite matrices. Shape is (N, N). + Step 1: Obtain the lower triangular element of M1 and M2, + unwrap to vectors v1, v2 with length N * (N - 1) / 2 + Step 2: Fisher z-transform vectors v1, v2 to z1, z2 + Step 3: return the Pearson's correlation between z1, z2 + Parameters + ---------- + M1: np.ndarray + The first matrix. The shape is (N, N). + M2: np.ndarray + The second matrix. The shape is (N,N). + + Returns + ------- + dist: (float) distance between M1 and M2 + """ + N = M1.shape[0] + assert N == M2.shape[0] + + # Obtain the upper triangular elements + upper_indices = np.triu_indices(N, k=1) + v1 = M1[upper_indices] + v2 = M2[upper_indices] + + # Fisher-z-transformation + z1 = fisher_z_transform(v1) + z2 = fisher_z_transform(v2) + + # return the correlation + return np.cov(z1, z2, ddof=0)[0, 1] / (np.std(z1, ddof=0) * np.std(z2, ddof=0)) + + +def pairwise_fisher_z_correlations(matrices): + """ + Compute the pairwise Fisher z-transformed correlations of matrices. + Parameters + ---------- + matrices: numpy.ndarray + set of symmetric semi-positive definite correlation matrices. Shape is (M, N, N). + + Returns + ------- + correlations: numpy.ndarray + Fisher z-transformed correlation. Shape is (M, M) + """ + + N = len(matrices) + correlation_metrics = np.eye(N) + for i in trange(N, desc='Compute Fisher-z transformated correlation'): + for j in range(i + 1, N): + correlation_metrics[i][j] = fisher_z_correlation( + matrices[i], matrices[j] + ) + correlation_metrics[j][i] = correlation_metrics[i][j] + + return correlation_metrics + + +def twopair_vector_correlation(vectors_1, vectors_2): + """ + Compute the pairwise correlation between ith vectors_1 and jth vectors_j + Parameters + ---------- + vectors_1: np.ndarray + The first set of vectors with shape (N_vectors_1,vector_length) + vectors_2: np.ndarray + The second set of vectors with shape (N_vectors_2,vector_length) + + Returns + ------- + correlation: np.ndarray + The vector correlations with shape (N_vectors_1, N_vectors_2) + """ + M1, N1 = vectors_1.shape + M2, N2 = vectors_2.shape + assert N1 == N2 + correlations = np.empty((M1, M2)) + + for i in range(M1): + for j in range(M2): + correlation = np.corrcoef(vectors_1[i, :], vectors_2[j, :])[0, 1] + correlations[i, j] = correlation + return correlations + + +def twopair_fisher_z_correlations(matrices_1, matrices_2): + """ + Compute the pairwise Fisher z-transformed correlations of matrices. + Parameters + ---------- + matrices_1: np.ndarray + set of symmetric semi-positive definite correlation matrices. Shape is (M1, N, N). + matrices_2: np.ndarray + set of symmetric semi-positive definite correlation matrices. Shape is (M2, N, N). + + Returns + ------- + correlations: np.ndarray + Fisher z-transformed correlation. Shape is (M1, M2) + """ + M1 = len(matrices_1) + M2 = len(matrices_2) + fisher_z_transformed_correlation = np.zeros([M1, M2]) + for i in trange(M1, desc='Compute twopair Fisher-z transformated correlation'): + for j in range(M2): + fisher_z_transformed_correlation[i][j] = fisher_z_correlation( + matrices_1[i], matrices_2[j] + ) + + return fisher_z_transformed_correlation + + +def regularisation(matrices, eps=1e-6): + """ + Regularise a bunch of matrices by adding eps to the diagonal + Parameters + ---------- + matrices: np.ndarray + The set of matrices with shape (M, N, N) or (N, N) + eps: float + The regularisation on the diagonal + + Returns + ------- + regularised_matrices: np.ndarray + The shape is (M, N, N) or (N, N). + """ + if matrices.ndim == 2: + N = len(matrices) + elif matrices.ndim == 3: + _, N, _ = matrices.shape + else: + return ValueError('Matrices dimension is incorrect!') + + return matrices + np.eye(N) * eps + +def twopair_riemannian_distance(matrices_1,matrices_2,eps_value=[0,1e-9,1e-8,1e-7,1e-6,1e-5],threshold:float=1e-3): + """ + Compute the Riemannian distance between two sets of matrices + Parameters + ---------- + matrices_1: np.ndarray + The first set of positive semi-definite matrices. + The shape is (N_modes_1, N_channels, N_channels). + matrices_1: np.ndarray + The second set of positive semi-definite matrices. + The shape is (N_modes_2, N_channels, N_channels). + eps: list,optional + The regularisation factor to try. + threshold: float + Threshold to apply when there are negative eigenvalues. Must be positive. + Returns + riemannian_distances: np.ndarray + Riemannian distance. The shape is (N_modes_1,N_modes_2) + ------- + """ + M1,N1,P1 = matrices_1.shape + M2,N2,P2 = matrices_2.shape + assert N1 == P1 + assert N1 == N2 + assert P1 == P2 + + matrices_1.astype(np.float64) + matrices_2.astype(np.float64) + riemannian_distances = np.zeros([M1,M2]) + for eps in eps_value: + try: + for i in trange(M1, desc="Computing Riemannian distances"): + for j in range(M2): + riemannian_distances[i][j] = riemannian_distance( + regularisation(matrices_1[i],eps), + regularisation(matrices_2[j],eps), + threshold=threshold + ) + return riemannian_distances + except np.linalg.LinAlgError: + warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}') + + raise ValueError('Riemannian distance failed for all eps values!') diff --git a/osl_dynamics/inference/modes.py b/osl_dynamics/inference/modes.py index a15c5237..6998a9e7 100644 --- a/osl_dynamics/inference/modes.py +++ b/osl_dynamics/inference/modes.py @@ -365,6 +365,39 @@ def match_modes(*mode_time_courses, return_order=False): else: return matched_mode_time_courses +def hungarian_pair(matrix,distance=False): + """ + Use the Hungarian algorithm to pair different modes using the matrix as the metric. + + Parameters + ---------- + matrix : np.ndarray + Distance/correlation matrix as the metric for pairing. The shape is (M, M). + distance : bool, optional + Whether the matrix measures distance (True) or correlation (False). Default is False. + + Returns + ------- + tuple + A tuple containing the following elements: + + indices : dict + A dictionary containing the row and column indices after reordering based on the Hungarian algorithm. + It has the following structure: + {'row': [row_index_1, row_index_2, ...], 'col': [col_index_1, col_index_2, ...]} + + matrix_reordered : np.ndarray + The input matrix reordered based on the row and column indices obtained from the Hungarian algorithm. + """ + if distance: + row_indices, col_indices = linear_sum_assignment(matrix) + else: + row_indices, col_indices = linear_sum_assignment(np.max(matrix) - matrix) + + indices = {'row':row_indices.tolist(),'col':col_indices.tolist()} + matrix_reordered = matrix[row_indices,:] + matrix_reordered = matrix_reordered[:,col_indices] + return indices, matrix_reordered def reduce_state_time_course(state_time_course): """Remove states that don't activate from a state time course. diff --git a/osl_dynamics/models/hmm.py b/osl_dynamics/models/hmm.py index 131b7821..5462da63 100644 --- a/osl_dynamics/models/hmm.py +++ b/osl_dynamics/models/hmm.py @@ -20,6 +20,7 @@ import sys import warnings from dataclasses import dataclass +from typing import Union from pathlib import Path import numba @@ -72,19 +73,21 @@ class Config(BaseModelConfig): Should we make the mean vectors for each state trainable? learn_covariances : bool Should we make the covariance matrix for each staet trainable? - initial_means : np.ndarray - Initialisation for state means. - initial_covariances : np.ndarray + initial_means : np.ndarray or str + Initialisation for state means. String indicated the *.npy directory. + initial_covariances : np.ndarray or str Initialisation for state covariances. If :code:`diagonal_covariances=True` and full matrices are passed, the diagonal is extracted. + String is the *.npy directory. diagonal_covariances : bool Should we learn diagonal covariances? covariances_epsilon : float Error added to state covariances for numerical stability. - initial_trans_prob : np.ndarray + initial_trans_prob : np.ndarray or str Initialisation for the transition probability matrix. + string works for the :code: 'random'. learn_trans_prob : bool Should we make the transition probability matrix trainable? state_probs_t0: np.ndarray @@ -126,14 +129,14 @@ class Config(BaseModelConfig): # Observation model parameters learn_means: bool = None learn_covariances: bool = None - initial_means: np.ndarray = None - initial_covariances: np.ndarray = None + initial_means: Union[np.ndarray,str] = None + initial_covariances: Union[np.ndarray,str] = None diagonal_covariances: bool = False covariances_epsilon: float = None means_regularizer: tf.keras.regularizers.Regularizer = None covariances_regularizer: tf.keras.regularizers.Regularizer = None - initial_trans_prob: np.ndarray = None + initial_trans_prob: Union[np.ndarray,str] = None learn_trans_prob: bool = True state_probs_t0: np.ndarray = None @@ -143,6 +146,10 @@ class Config(BaseModelConfig): observation_update_decay: float = 0.1 def __post_init__(self): + if isinstance(self.initial_means,str): + self.initial_means = np.load(self.initial_means) + if isinstance(self.initial_covariances,str): + self.initial_covariances = np.load(self.initial_covariances) self.validate_observation_model_parameters() self.validate_trans_prob_parameters() self.validate_dimension_parameters() @@ -160,14 +167,15 @@ def validate_observation_model_parameters(self): def validate_trans_prob_parameters(self): if self.initial_trans_prob is not None: - if ( - not isinstance(self.initial_trans_prob, np.ndarray) - or self.initial_trans_prob.ndim != 2 - ): - raise ValueError("initial_trans_prob must be a 2D numpy array.") + if self.initial_trans_prob != 'random': + if ( + not isinstance(self.initial_trans_prob, np.ndarray) + or self.initial_trans_prob.ndim != 2 + ): + raise ValueError("initial_trans_prob must be a 2D numpy array or equals 'random'.") - if not all(np.isclose(np.sum(self.initial_trans_prob, axis=1), 1)): - raise ValueError("rows of initial_trans_prob must sum to one.") + if not all(np.isclose(np.sum(self.initial_trans_prob, axis=1), 1)): + raise ValueError("rows of initial_trans_prob must sum to one.") class Model(ModelBase): @@ -440,7 +448,12 @@ def random_state_time_course_initialization( self.reset() training_data_subset = training_dataset.shuffle(buffer_size).take(n_batches) - self.set_random_state_time_course_initialization(training_data_subset) + try: + self.set_random_state_time_course_initialization(training_data_subset) + except Exception as e: + _logger.warning(f"Initialization {n} failed: {e}, skip to the next initialization.") + continue # Skip to the next iteration if initialization fails + history = self.fit(training_data_subset, epochs=n_epochs, **kwargs) if history is None: continue @@ -908,8 +921,9 @@ def set_trans_prob(self, trans_prob): Parameters ---------- - trans_prob : np.ndarray + trans_prob : np.ndarray or str State transition probabilities. Shape must be (n_states, n_states). + trans_prob can be set as random, where it is sampled randomly. """ if trans_prob is None: trans_prob = ( @@ -918,6 +932,12 @@ def set_trans_prob(self, trans_prob): / (self.config.n_states - 1) ) np.fill_diagonal(trans_prob, 0.9) + elif trans_prob == 'random': + trans_prob = np.random.rand(self.config.n_states, self.config.n_states) + + # Divide each row by its sum to ensure rows sum to 1 + trans_prob /= trans_prob.sum(axis=1, keepdims=True) + self.trans_prob = trans_prob def set_state_probs_t0(self, state_probs_t0): @@ -1018,8 +1038,10 @@ def set_regularizers(self, training_dataset): self.config.diagonal_covariances, ) - def free_energy(self, dataset): + def free_energy(self, dataset,means:np.ndarray=None,covariances:np.ndarray=None,return_components:bool=False): """Get the variational free energy. + This method is modified by swimming 13th Dec 2023 + I added two arguments,means and covariances to substitute the means, and covariances in the model This calculates: @@ -1032,7 +1054,13 @@ def free_energy(self, dataset): ---------- dataset : tf.data.Dataset or osl_dynamics.data.Data Dataset to evaluate the free energy for. - + means: np.ndarray + N_states * N_channels to substitute the mean activation. Do not change if none. + covariances: np.ndarray + N_states * N_channels * N_channels to substitute the covariance matries. Do not change if none + return_components: bool + whether the components of free energy, i.e. posterior expected log-likelihood, + posterior entropy and posterior expected prior probability should be returned. Returns ------- free_energy : float @@ -1045,6 +1073,8 @@ def free_energy(self, dataset): # Calculate variational free energy for each batch free_energy = [] + if return_components: + log_likelihoods, entropies, priors = [],[],[] for data in dataset: x = data["data"] batch_size = x.shape[0] @@ -1059,8 +1089,17 @@ def free_energy(self, dataset): # = - int q(s) log p(x | s) ds [log_likelihood] # + int q(s) log q(s) ds [entropy] # - int q(s) log p(s) ds [prior] - + if means is not None: + means_temp = self.get_means() + self.set_means(means) + if covariances is not None: + covariances_temp = self.get_covariances() + self.set_covariances(covariances) log_likelihood = self.get_posterior_expected_log_likelihood(x, gamma) + if means is not None: + self.set_means(means_temp) + if covariances is not None: + self.set_covariances(covariances_temp) entropy = self.get_posterior_entropy(gamma, xi) prior = self.get_posterior_expected_prior(gamma, xi) @@ -1068,8 +1107,16 @@ def free_energy(self, dataset): seq_fe = (-log_likelihood + entropy - prior) / batch_size free_energy.append(seq_fe) + if return_components: + log_likelihoods.append(log_likelihood / batch_size) + entropies.append(entropy / batch_size) + priors.append(prior / batch_size) + # Return average over batches - return np.mean(free_energy) + if return_components: + return np.mean(free_energy),np.mean(log_likelihoods),np.mean(entropies),np.mean(priors) + else: + return np.mean(free_energy) def evidence(self, dataset): """Calculate the model evidence, :math:`p(x)`, of HMM on a dataset. diff --git a/osl_dynamics/utils/misc.py b/osl_dynamics/utils/misc.py index 37f77465..d7df9b26 100644 --- a/osl_dynamics/utils/misc.py +++ b/osl_dynamics/utils/misc.py @@ -14,6 +14,9 @@ import yaml from yaml.constructor import ConstructorError +import nibabel as nib +from nibabel.nifti1 import Nifti1Image + _logger = logging.getLogger("osl-dynamics") @@ -31,7 +34,9 @@ def nextpow2(x): The smallest power of two that is greater than or equal to the absolute value of x. """ - res = np.ceil(np.log2(x)) + if x == 0: + return 0 + res = np.ceil(np.log2(np.abs(x))) return res.astype("int") @@ -422,3 +427,69 @@ def set_logging_level(logger, level): yield finally: logger.setLevel(current_level) + +def IC2brain(spatial_map,IC_metric): + """ + Project the IC_metric map to a brain map according to spatial maps of IC components. + For example, IC_metric can be the mean activation of states, or the rank-one decomposition + of FC matrix corresponding to different states. + Parameters + ---------- + spatial_map: Nifti1Image + The spatial map obtained from groupICA + IC_metric: np.ndarray + The shape is (K, N). + K is the number of independent components, and N is the number of states + + Returns + ------- + brain_map: Nifti1Image + represent the whole brain map of different states. + """ + spatial_map_data = spatial_map.get_fdata() + spatial_map_shape = spatial_map_data.shape + K, N = IC_metric.shape + # Assert the matrix multiplication is plausible + assert spatial_map_shape[-1] == K + + # Implement the multiplication + brain_map_data = np.matmul(spatial_map_data,IC_metric) + #Store brain map to a Nifti1Image type + brain_map = Nifti1Image(brain_map_data, affine=spatial_map.affine, header=spatial_map.header) + + return brain_map + +def IC2surface(spatial_map: nib.cifti2.cifti2.Cifti2Image,IC_metric:np.ndarray)->nib.cifti2.cifti2.Cifti2Image: + """ + Project the IC_metric map to a brain map according to spatial maps of IC components. + For example, IC_metric can be the mean activation of states, or the rank-one decomposition + of FC matrix corresponding to different states. + Different from IC2brain, this function works on CIFTI spatial maps. + + Parameters + ---------- + spatial_map: Cifti2Image + The spatial map obtained from groupICA + IC_metric: np.ndarray(K, N) + The shapeis (K, N) + K is the number of independent components, and N is the number of states + Returns + ------- + new_cifti: Cifti2Image + The whole brain map of different states. + """ + spatial_map_data = spatial_map.get_fdata() + header = spatial_map.header + K, N = IC_metric.shape + new_data = np.matmul(IC_metric.T, spatial_map_data) + + # Define the new axes: the state i(i=1,...,N) + new_axes = nib.cifti2.cifti2_axes.ScalarAxis([f'State {i + 1}' for i in range(N)]) + + # Define the new header using new_axes + axes[1] from old header + new_header = nib.cifti2.cifti2.Cifti2Header.from_axes((new_axes, header.get_axis(1))) + + # Combine new data and new header to obtain new CIFTI object + new_cifti = nib.cifti2.cifti2.Cifti2Image(new_data, new_header) + + return new_cifti \ No newline at end of file diff --git a/osl_dynamics/utils/plotting.py b/osl_dynamics/utils/plotting.py index 257c53f3..6247c151 100644 --- a/osl_dynamics/utils/plotting.py +++ b/osl_dynamics/utils/plotting.py @@ -20,7 +20,6 @@ from osl_dynamics.utils.topoplots import Topology from osl_dynamics.utils.parcellation import Parcellation - _logger = logging.getLogger("osl-dynamics") @@ -141,8 +140,8 @@ def rough_square_axes(n_plots): empty : int Number of axes left blank from the rectangle. """ - long = np.floor(n_plots**0.5).astype(int) - short = np.ceil(n_plots**0.5).astype(int) + long = np.floor(n_plots ** 0.5).astype(int) + short = np.ceil(n_plots ** 0.5).astype(int) if short * long < n_plots: short += 1 empty = short * long - n_plots @@ -179,20 +178,20 @@ def get_colors(n, colormap="magma"): def plot_line( - x, - y, - labels=None, - legend_loc=1, - errors=None, - x_range=None, - y_range=None, - x_label=None, - y_label=None, - title=None, - plot_kwargs=None, - fig_kwargs=None, - ax=None, - filename=None, + x, + y, + labels=None, + legend_loc=1, + errors=None, + x_range=None, + y_range=None, + x_label=None, + y_label=None, + title=None, + plot_kwargs=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Basic line plot. @@ -296,7 +295,7 @@ def plot_line( # Plot lines for x_data, y_data, label, e_min, e_max in zip( - x, y, labels, errors_min, errors_max + x, y, labels, errors_min, errors_max ): ax.plot(x_data, y_data, label=label, **plot_kwargs) if e_min is not None: @@ -323,22 +322,22 @@ def plot_line( def plot_scatter( - x, - y, - labels=None, - legend_loc=1, - errors=None, - x_range=None, - y_range=None, - x_label=None, - y_label=None, - title=None, - markers=None, - annotate=None, - plot_kwargs=None, - fig_kwargs=None, - ax=None, - filename=None, + x, + y, + labels=None, + legend_loc=1, + errors=None, + x_range=None, + y_range=None, + x_label=None, + y_label=None, + title=None, + markers=None, + annotate=None, + plot_kwargs=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Basic scatter plot. @@ -488,19 +487,19 @@ def plot_scatter( def plot_hist( - data, - bins, - labels=None, - legend_loc=1, - x_range=None, - y_range=None, - x_label=None, - y_label=None, - title=None, - plot_kwargs=None, - fig_kwargs=None, - ax=None, - filename=None, + data, + bins, + labels=None, + legend_loc=1, + x_range=None, + y_range=None, + x_label=None, + y_label=None, + title=None, + plot_kwargs=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Basic histogram plot. @@ -615,17 +614,17 @@ def plot_hist( def plot_bar_chart( - counts, - x=None, - x_range=None, - y_range=None, - x_label=None, - y_label=None, - title=None, - plot_kwargs=None, - fig_kwargs=None, - ax=None, - filename=None, + counts, + x=None, + x_range=None, + y_range=None, + x_label=None, + y_label=None, + title=None, + plot_kwargs=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Bar chart plot. @@ -721,20 +720,20 @@ def plot_bar_chart( def plot_gmm( - data, - amplitudes, - means, - stddevs, - bins=50, - legend_loc=1, - x_range=None, - y_range=None, - x_label=None, - y_label=None, - title=None, - fig_kwargs=None, - ax=None, - filename=None, + data, + amplitudes, + means, + stddevs, + bins=50, + legend_loc=1, + x_range=None, + y_range=None, + x_label=None, + y_label=None, + title=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Plot a two component Gaussian mixture model. @@ -840,22 +839,23 @@ def plot_gmm( def plot_violin( - data, - x=None, - x_label=None, - y_label=None, - title=None, - fig_kwargs=None, - sns_kwargs=None, - ax=None, - filename=None, + data, + x=None, + x_label=None, + y_label=None, + title=None, + fig_kwargs=None, + sns_kwargs=None, + ax=None, + filename=None, ): """Violin plot. Parameters ---------- data : list of np.ndarray - Data to plot. + Data to plot. When working on fractional occupancy, + the input should be (n_states,n_sessions). x : list or np.ndarray, optional x-values for data. x_label : str, optional @@ -931,13 +931,13 @@ def plot_violin( def plot_time_series( - time_series, - n_samples=None, - y_tick_values=None, - plot_kwargs=None, - fig_kwargs=None, - ax=None, - filename=None, + time_series, + n_samples=None, + y_tick_values=None, + plot_kwargs=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Plot a time series with channel separation. @@ -1027,12 +1027,12 @@ def plot_time_series( def plot_separate_time_series( - *time_series, - n_samples=None, - sampling_frequency=None, - fig_kwargs=None, - plot_kwargs=None, - filename=None, + *time_series, + n_samples=None, + sampling_frequency=None, + fig_kwargs=None, + plot_kwargs=None, + filename=None, ): """Plot time series as separate subplots. @@ -1107,19 +1107,19 @@ def plot_separate_time_series( def plot_epoched_time_series( - data, - time_index, - sampling_frequency=None, - pre=125, - post=1000, - baseline_correct=False, - legend=True, - legend_loc=1, - title=None, - plot_kwargs=None, - fig_kwargs=None, - ax=None, - filename=None, + data, + time_index, + sampling_frequency=None, + pre=125, + post=1000, + baseline_correct=False, + legend=True, + legend_loc=1, + title=None, + plot_kwargs=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Plot continuous data, epoched and meaned over epochs. @@ -1222,14 +1222,14 @@ def plot_epoched_time_series( def plot_matrices( - matrix, - group_color_scale=True, - titles=None, - main_title=None, - cmap="viridis", - nan_color="white", - log_norm=False, - filename=None, + matrix, + group_color_scale=True, + titles=None, + main_title=None, + cmap="viridis", + nan_color="white", + log_norm=False, + filename=None, ): """Plot a collection of matrices. @@ -1330,12 +1330,12 @@ def plot_matrices( def plot_connections( - weights, - labels=None, - ax=None, - cmap="hot", - text_color=None, - filename=None, + weights, + labels=None, + ax=None, + cmap="hot", + text_color=None, + filename=None, ): """Create a chord diagram representing the values of a matrix. @@ -1503,18 +1503,18 @@ def plot_connections( def topoplot( - layout, - data, - channel_names=None, - plot_boxes=False, - show_deleted_sensors=False, - show_names=False, - title=None, - colorbar=True, - axis=None, - cmap="plasma", - n_contours=10, - filename=None, + layout, + data, + channel_names=None, + plot_boxes=False, + show_deleted_sensors=False, + show_names=False, + title=None, + colorbar=True, + axis=None, + cmap="plasma", + n_contours=10, + filename=None, ): """Make a contour plot in sensor space. @@ -1582,13 +1582,13 @@ def topoplot( def plot_brain_surface( - values, - mask_file, - parcellation_file, - filename=None, - subtract_mean=False, - mean_weights=None, - **plot_kwargs, + values, + mask_file, + parcellation_file, + filename=None, + subtract_mean=False, + mean_weights=None, + **plot_kwargs, ): """Plot a 2D heat map on the surface of the brain. @@ -1647,16 +1647,16 @@ def plot_brain_surface( def plot_alpha( - *alpha, - n_samples=None, - cmap="Set3", - sampling_frequency=None, - y_labels=None, - title=None, - plot_kwargs=None, - fig_kwargs=None, - filename=None, - axes=None, + *alpha, + n_samples=None, + cmap="Set3", + sampling_frequency=None, + y_labels=None, + title=None, + plot_kwargs=None, + fig_kwargs=None, + filename=None, + axes=None, ): """Plot alpha. @@ -1786,17 +1786,17 @@ def plot_alpha( def plot_state_lifetimes( - state_time_course, - bins="auto", - density=False, - match_scale_x=False, - match_scale_y=False, - x_range=None, - x_label=None, - y_label=None, - plot_kwargs=None, - fig_kwargs=None, - filename=None, + state_time_course, + bins="auto", + density=False, + match_scale_x=False, + match_scale_y=False, + x_range=None, + x_label=None, + y_label=None, + plot_kwargs=None, + fig_kwargs=None, + filename=None, ): """Create a histogram of state lifetimes. @@ -1923,13 +1923,13 @@ def plot_state_lifetimes( def plot_psd_topo( - f, - psd, - only_show=None, - parcellation_file=None, - topomap_pos=None, - fig_kwargs=None, - filename=None, + f, + psd, + only_show=None, + parcellation_file=None, + topomap_pos=None, + fig_kwargs=None, + filename=None, ): """PLot PSDs for parcels and a topomap. @@ -2015,13 +2015,13 @@ def plot_psd_topo( def plot_summary_stats_group_diff( - name, - summary_stats, - pvalues, - assignments, - fig_kwargs=None, - ax=None, - filename=None, + name, + summary_stats, + pvalues, + assignments, + fig_kwargs=None, + ax=None, + filename=None, ): """Plot summary statistics for two groups as violin plots. @@ -2116,19 +2116,19 @@ def plot_summary_stats_group_diff( def plot_evoked_response( - t, - epochs, - pvalues, - significance_level=0.05, - offset_between_bars=0.01, - labels=None, - legend_loc=1, - x_label=None, - y_label=None, - title=None, - fig_kwargs=None, - ax=None, - filename=None, + t, + epochs, + pvalues, + significance_level=0.05, + offset_between_bars=0.01, + labels=None, + legend_loc=1, + x_label=None, + y_label=None, + title=None, + fig_kwargs=None, + ax=None, + filename=None, ): """Plot an evoked responses with significant time points highlighted. @@ -2211,10 +2211,10 @@ def plot_evoked_response( fig, ax = create_figure(**fig_kwargs) for i, e, l, s in zip( - range(epochs.shape[1]), - epochs.T, - labels, - significant.T, + range(epochs.shape[1]), + epochs.T, + labels, + significant.T, ): # Plot evoked response p = ax.plot(t, e, label=l) @@ -2253,18 +2253,18 @@ def plot_evoked_response( def plot_wavelet( - data, - sampling_frequency, - w=5, - standardize=True, - time_range=None, - frequency_range=None, - title=None, - add_colorbar=True, - fig_kwargs=None, - plot_kwargs=None, - ax=None, - filename=None, + data, + sampling_frequency, + w=5, + standardize=True, + time_range=None, + frequency_range=None, + title=None, + add_colorbar=True, + fig_kwargs=None, + plot_kwargs=None, + ax=None, + filename=None, ): """Plot a wavelet transform. @@ -2354,3 +2354,103 @@ def plot_wavelet( save(fig, filename) elif create_fig: return fig, ax + + +def plot_mode_pairing( + metrics, + indices=None, + x_label=None, + y_label=None, + title=None, + fig_kwargs=None, + sns_kwargs=None, + ax=None, + filename=None, + +): + """ + Plot the states/modes pairing using the metrics + Parameters + ---------- + metrics: np.ndarray + measure the similarity/distance between states/modes. + indices: dict, optional + Indices as xticks and yticks. + The format should be {'row':[state_1, state_2,...state_N_1],'col':[state_1, state_2,...state_N_2]} + x_label : str, optional + Label for x-axis. + y_label : str, optional + Label for y-axis. + title : str, optional + Figure title. + fig_kwargs : dict, optional + Arguments to pass to :code:`plt.subplots()`. + sns_kwargs : dict, optional + Arguments to pass to :code:`sns.violinplot()`. + ax : matplotlib.axes.axes, optional + Axis object to plot on. + filename : str, optional + Output filename. + + Returns + ------- + fig : plt.figure + Matplotlib figure object. Only returned if :code:`ax=None` and + :code:`filename=None`. + ax : plt.axes + Matplotlib axis object(s). Only returned if :code:`ax=None` and + :code:`filename=None`. + """ + + # Validation + if ax is not None: + if filename is not None: + raise ValueError( + "Please use plotting.save() to save the figure instead of the " + + "filename argument." + ) + if isinstance(ax, np.ndarray): + raise ValueError("Only pass one axis.") + + if fig_kwargs is None: + fig_kwargs = {} + default_fig_kwargs = {"figsize": (8, 6), + # "xtick.labelsize": 13, + # "ytick.labelsize": 13, + } + fig_kwargs = override_dict_defaults(default_fig_kwargs, fig_kwargs) + + if sns_kwargs is None: + sns_kwargs = {} + default_sns_kwargs = { # "font_scale": 1.2, + "cmap": "coolwarm", + "square": True, + "linewidths": 0.5, + # "cbar_kws": {"shrink": 0.75}, + "fmt": ".2f" + } + sns_kwargs = override_dict_defaults(default_sns_kwargs, sns_kwargs) + # Set up the figure and axis + + # Create figure + create_fig = ax is None + if create_fig: + fig, ax = create_figure(**fig_kwargs) + + # Create a heatmap of the correlation matrix + ax = sns.heatmap(data=metrics, ax=ax, **sns_kwargs) + # Set xticks and yticks + if indices is not None: + ax.set_xticks(np.arange(len(indices["col"])) + 0.5, indices["col"], fontsize=15) + ax.set_yticks(np.arange(len(indices["row"])) + 0.5, indices["row"], fontsize=15) + + # Set title and axis labels + ax.set_title(title, fontsize=20) + ax.set_xlabel(x_label, fontsize=15) + ax.set_ylabel(y_label, fontsize=15) + + # Save the figure if a filename has been pass + if filename is not None: + save(fig, filename, tight_layout=True) + elif create_fig: + return fig, ax diff --git a/post_analysis.py b/post_analysis.py new file mode 100644 index 00000000..9c667a15 --- /dev/null +++ b/post_analysis.py @@ -0,0 +1,107 @@ +import sys +import pathlib + +import scipy.stats as stats +import numpy as np + +from osl_dynamics.data import Data +from rotation.utils import * +from rotation.preprocessing import PrepareData +from rotation.analysis import HMM_analysis, Dynemo_analysis, \ + MAGE_analysis, SWC_analysis, comparison_analysis, HMM_cv_reproduce + +def HMM_post(dataset): + from osl_dynamics.models import load + + # Load the trained model + model = load("results/model") + alpha = model.get_alpha(dataset) + print('The shape of alpha sample is: ', alpha[0].shape) + data = model.get_training_time_series(dataset, prepared=False) + + for a, x in zip(alpha, dataset.time_series()): + print(a.shape, x.shape) + print('#################################') + +if __name__ == '__main__': + # Index + # 1-30: HMM + # 31-60: Dynemo + # 61-90: MAGE + # 91-96: SWC (training) + # 91-120: SWC (analysis) + ''' + data_dir = pathlib.Path('/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/') + subjs = [] + np_datas = [] + for file in data_dir.glob('*.txt'): + subjs.append(file.stem) + temp = np.loadtxt(file) + temp = stats.zscore(temp, axis=0) + + assert temp.shape == (4800, 15) + np_datas.append(temp) + + dataset = Data(np_datas) + HMM_post(dataset) + ''' + models = ['HMM','Dynemo','MAGE','SWC'] + list_channels = [15, 25, 50, 100, 200, 300] + list_states = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] + learn_mean = False + + #list_states = [25,30,35,40,45] + index = int(sys.argv[1]) - 1 + #index = 15 + + # index = 120 represent comparison analysis. + if index == 120: + save_dir = './results_simulation_202311_toy_12/comparison/' + result_dir = './results_simulation_202311_toy_12/' + comparison_analysis(models,list_channels,list_states,result_dir,save_dir) + + model,n_channels, n_states = parse_index(index,models,list_channels,list_states,training=False) + + save_dir = f'./results_simulation_202311_toy_12/{model}_ICA_{n_channels}_state_{n_states}/' + spatial_map_dir = f'./data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d{n_channels}.ica/melodic_IC_sum.nii.gz' + spatial_surface_map_dir = f'./data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d{n_channels}.ica/melodic_IC.dscalar.nii' + + print(f'Number of channels: {n_channels}') + print(f'Number of states: {n_states}') + print(f'The model: {model}') + + if model == 'SWC': + old_dir = f'./results_simulation_202311_toy_12/{model}_ICA_{n_channels}/' + SWC_analysis(save_dir,old_dir,n_channels,n_states) + else: + # Work on Jalapeno + #data_dir = pathlib.Path(f'/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d{n_channels}_ts2/') + + # Work on BMRC + #data_dir = pathlib.Path(f'./data/node_timeseries/3T_HCP1200_MSMAll_d{n_channels}_ts2/') + data_dir = pathlib.Path(f'./data/node_timeseries/simulation_toy_12/') + prepare_data = PrepareData(data_dir) + # Note: z_score_data should be False if we work in simulation data. + subj, dataset = prepare_data.load(z_score_data=False) + print(f'Number of subjects: {len(subj)}') + + if model == 'HMM': + HMM_analysis(dataset, save_dir, spatial_map_dir, spatial_surface_map_dir, n_channels,n_states,learn_mean=learn_mean, reproducible=True) + elif model == 'Dynemo': + Dynemo_analysis(dataset, save_dir, spatial_map_dir, spatial_surface_map_dir, n_channels, n_states,learn_mean=learn_mean) + elif model == 'MAGE': + MAGE_analysis(dataset, save_dir, spatial_map_dir, spatial_surface_map_dir, n_channels, n_states,learn_mean=learn_mean) + else: + raise ValueError('The model name is incorrect!') + + # This step is to conduct FO analysis on the first split-half strategy.from + subj, dataset_1, dataset_2 = prepare_data.load(z_score_data=False,split_strategy='1') + if model == 'HMM': + HMM_analysis(dataset_1,f'{save_dir}/split_1_first_half/', spatial_map_dir, spatial_surface_map_dir, n_channels, + n_states,learn_mean=learn_mean,reproducible=False) + HMM_analysis(dataset_2, f'{save_dir}/split_1_second_half/', spatial_map_dir, spatial_surface_map_dir, + n_channels, n_states, learn_mean=learn_mean,reproducible=False) + HMM_cv_reproduce(dataset_1,dataset_2, f'{save_dir}/split_1_first_half/', f'{save_dir}/split_1_second_half/', + n_channels, n_states,learn_mean=learn_mean) + + diff --git a/qsub.sh b/qsub.sh new file mode 100644 index 00000000..a0acbf67 --- /dev/null +++ b/qsub.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +# Specify a job name +#$ -N swimming + +# --- Parameters for the Queue Master --- +# target queue (Please specify in the command line!) +##$ -q short.q + +# Run the job in the current working directory +#$ -cwd -j y + +# Log locations which are relative to the current +# working directory of the submission +#$ -o ./log/ +#$ -e ./log/ + +# Parallel environemnt settings +# For more information on these please see the wiki +# Allowed settings: +# shmem +# mpi +# node_mpi +# ramdisk +##$ -pe shmem 1 + +# Print some useful data about the job to help with debugging +echo "------------------------------------------------" +echo "SGE Job ID: $JOB_ID" +echo "SGE Job ID: $SGE_JOB_ID" +echo "SGE task ID: $SGE_TASK_ID" +echo "Run on host: "`hostname` +echo "Operating system: "`uname -s` +echo "Username: "`whoami` +echo "Started at: "`date` +echo "------------------------------------------------" + +# >>> conda initialize >>> +# !! Contents within this block are managed by 'conda init' !! +__conda_setup="$('/opt/fmrib/conda/python3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" +if [ $? -eq 0 ]; then + eval "$__conda_setup" +else + if [ -f "/opt/fmrib/conda/python3/etc/profile.d/conda.sh" ]; then + . "/opt/fmrib/conda/python3/etc/profile.d/conda.sh" + else + export PATH="/opt/fmrib/conda/python3/bin:$PATH" + fi +fi +unset __conda_setup +# <<< conda initialize <<< +# Finally, we can run our real computing job + +conda activate osld # Environment name +python main.py $SGE_TASK_ID +# End of job script diff --git a/rotation/analysis.py b/rotation/analysis.py new file mode 100644 index 00000000..e2d2d849 --- /dev/null +++ b/rotation/analysis.py @@ -0,0 +1,1525 @@ +import os +import random +import json +import warnings +import pickle + +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.cluster import KMeans +import networkx as nx +import nibabel as nib + +from osl_dynamics.array_ops import cov2corr +import osl_dynamics.data +from osl_dynamics.inference.metrics import pairwise_frobenius_distance,\ + pairwise_matrix_correlations, pairwise_riemannian_distances, pairwise_congruence_coefficient +from osl_dynamics.utils import plotting +from rotation.utils import plot_FO, stdcor2cov,cov2stdcor, first_eigenvector,\ + IC2brain,IC2surface, regularisation, pairwise_fisher_z_correlations,\ + twopair_vector_correlation, twopair_riemannian_distance, twopair_fisher_z_transformed_correlation,\ + hungarian_pair,heatmap_reorder_matrix + +def construct_graph(tpm:np.ndarray): + """ + Construct the graph based on input matrix + Parameters + ---------- + tpm: transition probability matrix + + Returns + ------- + G: (networkx.classes.digraph.DiGraph) + """ + + # Work with directed weighted graph + G = nx.DiGraph() + + for i in range(len(tpm)): + G.add_node(i) # Add nodes to the graph + + for j in range(len(tpm)): + weight = tpm[i][j] + if (weight > 0) & (i != j): + G.add_edge(i, j, weight=weight) + + return G +def HMM_analysis(dataset:osl_dynamics.data.Data, save_dir:str, + spatial_map_dir:str, spatial_surface_map_dir:str, n_channels:int, n_states:int, + learn_mean:bool=False,model_dir=None,reproducible=False): + """ + Post-training analysis of HMM model + Parameters + ---------- + dataset: osl_dynamics.data.Data + dataset to work on + save_dir: str + directory to save results + spatial_map_dir: str + directory of groupICA spatial maps + spatial_surface_map_dir: str + directory of groupICA spatial surface maps + n_channels: int + number of channels + n_states: int + number of states + model_dir: str + where the model is saved. If none, by default should be the same as save_dir + reproducible: bool + whether to conduct reproducible analysis + Returns + ------- + """ + from osl_dynamics.models import load + from osl_dynamics.utils import plotting + from osl_dynamics.inference import modes + + if model_dir is None: + model_dir = save_dir + + model = load(model_dir) + + if not os.path.isfile(f'{save_dir}alpha.pkl'): + alpha = model.get_alpha(dataset) + pickle.dump(alpha, open(f'{save_dir}alpha.pkl', "wb")) + + # Calculate the metrics + if not os.path.isfile(f'{save_dir}metrics_repeat.json'): + calculate_metrics(model, dataset, save_dir) + + # Summary statistics analysis + plot_dir = f'{save_dir}plot/' + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + # Plot the state probability time course for the first subject + with open(f'{save_dir}alpha.pkl', 'rb') as file: + alpha = pickle.load(file) + + plotting.plot_alpha(alpha[0], n_samples=1200) + plt.savefig(f'{plot_dir}state_prob_example.jpg') + plt.savefig(f'{plot_dir}state_prob_example.pdf') + plt.close() + + # Hard classify the state probabilities + stc = modes.argmax_time_courses(alpha) + # Plot the state time course for the first subject (8 seconds) + plotting.plot_alpha(stc[0], n_samples=1200) + plt.savefig(f'{plot_dir}state_hard_example.jpg') + plt.savefig(f'{plot_dir}state_hard_example.pdf') + plt.close() + + # Calculate fractional occupancies + fo = modes.fractional_occupancies(stc) + np.save(f'{save_dir}fo.npy', fo) + # Plot the distribution of fractional occupancy (FO) across subjects + plotting.plot_violin(fo.T, x_label="State", y_label="FO") + plt.savefig(f'{plot_dir}fo_violin.jpg') + plt.savefig(f'{plot_dir}fo_violin.pdf') + plt.close() + + # Calculate mean lifetimes (in seconds) + mlt = modes.mean_lifetimes(stc, sampling_frequency=1 / 0.7) + np.save(f'{save_dir}mlt.npy', mlt) + # Plot distribution across subjects + plotting.plot_violin(mlt.T, x_label="State", y_label="Mean Lifetime (s)") + plt.savefig(f'{plot_dir}mlt_violin.jpg') + plt.savefig(f'{plot_dir}mlt_violin.pdf') + plt.close() + + # Calculate mean intervals (in seconds) + mintv = modes.mean_intervals(stc, sampling_frequency=1 / 0.7) + np.save(f'{save_dir}mintv.npy', mintv) + # Plot distribution across subjects + plotting.plot_violin(mintv.T, x_label="State", y_label="Mean Interval (s)") + plt.savefig(f'{plot_dir}mintv_violin.jpg') + plt.savefig(f'{plot_dir}mintv_violin.pdf') + plt.close() + + # Plot the convergence of loss function + #if not os.path.isfile(f'{plot_dir}loss_history.pdf'): + # plot_loss_history(save_dir,plot_dir) + + # Analyze the transition probability matrix + # using Louvain community detection algorithm + if not os.path.isfile(f'{save_dir}tpm_partition.pkl'): + tpm = np.load(f'{model_dir}trans_prob.npy') + # Added by swimming 2023-08-09: try to reproduce Diego's results + # Only work on HMM_ICA_50_state_12 + for i in range(len(tpm)): + tpm[i,i] = 0 + tpm[i,:] = tpm[i,:] / np.sum(tpm[i,:]) + G = construct_graph(tpm) + partition = nx.community.louvain_communities(G) + print('The final partition is: ', partition) + + with open(f'{save_dir}tpm_partition.pkl', 'wb') as file: + pickle.dump(partition, file) + + # Obtain the statistics (mean,std,cov,cor) of states + if not os.path.isfile(f'{save_dir}state_covariances.npy'): + extract_state_statistics(save_dir,model_name='HMM',model_dir=model_dir) + + # Plot the statistics (mean,std,cor) of states + if not os.path.isfile(f'{plot_dir}plot_state_correlations.pdf'): + plot_state_statistics(save_dir,plot_dir, + model_name='HMM', + n_channels=n_channels, + n_states=n_states + ) + + # Analyze the distance between different states/modes + dist_dir = f'{save_dir}distance/' + if not os.path.exists(dist_dir): + os.makedirs(dist_dir) + if not os.path.isfile(f'{dist_dir}riemannian_distance_cor.npy'): + compute_distance(save_dir,dist_dir) + + # Plot the distance between different states/modes + if not os.path.isfile(f'{plot_dir}/correct_distance_plot_cor.pdf'): + plot_distance(dist_dir,plot_dir, + model='HMM', + n_channels=n_channels, + n_states=n_states) + + # Plot the influence of regularisation on Riemannian distance + if not os.path.isfile(f'{plot_dir}/Riemannian_distance_regularisation_plot.pdf'): + compute_plot_distance_regularisation(save_dir,dist_dir, plot_dir, + model='HMM', + n_channels=n_channels, + n_states=n_states) + + + # Fractional occupancy analysis + FO_dir = f'{save_dir}FO_analysis/' + if not os.path.exists(FO_dir): + os.makedirs(FO_dir) + fo_matrix = np.load(f'{save_dir}fo.npy') + n_subj, n_state = fo_matrix.shape + fo_corr = np.corrcoef(fo_matrix.T) + np.save(f'{save_dir}fo_corr.npy',fo_corr) + + # Plot the FO distribution of each state + #plot_FO(fo_matrix,FO_dir) + + from scipy.spatial.distance import squareform + # Convert correlation matrix to 1D condensed distance + # Do not check symmetry because there might be round-off + # errors in fo_corr. + fo_dist = squareform(1 - fo_corr,checks=False) + + import scipy.cluster.hierarchy as sch + Z = sch.linkage(fo_dist, method='ward',optimal_ordering=True) + np.save(f'{save_dir}cluster_Z.npy',Z) + + # Plot the dendrogram + plt.figure(figsize=(10, 5)) + sch.dendrogram(Z) + plt.xlabel('Data Points') + plt.ylabel('Distance') + plt.title('Dendrogram') + plt.savefig(f'{FO_dir}dendrogram_FO.jpg') + plt.savefig(f'{FO_dir}dendrogram_FO.pdf') + plt.close() + + + # Compute the mean activation map + if not os.path.isfile(f'{save_dir}mean_activation_surface_map.dscalar.nii'): + mean_mapping(save_dir,spatial_map_dir,spatial_surface_map_dir) + + # Compute rank-one decomposition of FC map + if not os.path.isfile(f'{save_dir}FC_sum_of_degree_surface_map.dscalar.nii'): + FC_mapping(save_dir,spatial_map_dir,spatial_surface_map_dir) + + #if not os.path.isfile(f'{plot_dir}mean_FC_relation.pdf'): + # mean_FC_relation(save_dir,plot_dir,'HMM',n_channels,n_states) + + reproduce_analysis_dir = f'{save_dir}reproduce_analysis/' + if not os.path.exists(reproduce_analysis_dir): + os.makedirs(reproduce_analysis_dir) + ''' + if reproducible: + if not os.path.isfile(f'{reproduce_analysis_dir}FCs_distance_plot_split_5.pdf'): + reproduce_analysis(save_dir,reproduce_analysis_dir,'HMM', n_channels,n_states, learn_mean=learn_mean,split_strategy='1', dataset=dataset) + reproduce_analysis(save_dir, reproduce_analysis_dir, 'HMM', n_channels,n_states,learn_mean=learn_mean,split_strategy='2', dataset=dataset) + reproduce_analysis(save_dir, reproduce_analysis_dir, 'HMM', n_channels,n_states,learn_mean=learn_mean,split_strategy='3', dataset=dataset) + reproduce_analysis(save_dir, reproduce_analysis_dir, 'HMM', n_channels, n_states,learn_mean=learn_mean,split_strategy='4', dataset=dataset) + reproduce_analysis(save_dir, reproduce_analysis_dir, 'HMM', n_channels, n_states, learn_mean=learn_mean,split_strategy='5', dataset=dataset) + ''' +def HMM_cv_reproduce(dataset_1,dataset_2, save_dir_1:str, save_dir_2:str,n_channels:int, n_states:int,learn_mean=False): + """ + Conducting cross validation + Parameters + ---------- + dataset_1: osl_dynamics.data.Data + Dataset where the first half of the model was trained on + dataset_2: osl_dynamics.data.Data + Dataset where the second half of the model was trained on + save_dir_1: str + directory where the first model saved + save_dir_2: str + directory were the second model saved + n_channels: int + number of channels + n_states: int + number of states + learn_mean: bool + whether the mean activation is learned. + Returns + ------- + """ + from osl_dynamics.models import load + + model_1 = load(save_dir_1) + model_2 = load(save_dir_2) + + # Get mean and covariances of model 1 + try: + if learn_mean: + means_1 = np.load(f'{save_dir_1}state_means.npy') + else: + means_1 = np.zeros((n_states, n_channels)) + covariances_1 = np.load(f'{save_dir_1}state_covariances.npy') + except Exception as e: + if learn_mean: + means_1 = model_1.get_means() + else: + means_1 = np.zeros((n_states, n_channels)) + covariances_1 = model_1.get_covariances() + # Get mean and covariances of model 2 + try: + if learn_mean: + means_2 = np.load(f'{save_dir_2}state_means.npy') + else: + means_2 = np.zeros((n_states,n_channels)) + covariances_2 = np.load(f'{save_dir_2}state_covariances.npy') + except Exception as e: + if learn_mean: + means_2 = model_2.get_means() + else: + means_2 = np.zeros((n_states, n_channels)) + covariances_2 = model_2.get_covariances() + + # Calculate the original free energy (model_1 on dataset_1, model_2 on dataset_2) + free_energy_cv = {} + free_energy_cv['first_first'] = model_1.free_energy(dataset_1) + free_energy_cv['second_second'] = model_1.free_energy(dataset_2) + + # Pair these states + riem_distance = twopair_riemannian_distance(covariances_1, covariances_2) + index_1, riem_distance_reordered = hungarian_pair(riem_distance, distance=True) + covariance_2_reordered = covariances_2[index_1['col']] + free_energy_cv['first_second'] = model_1.free_energy(dataset_1,covariances=covariances_2_reordered) + + + +def Dynemo_analysis(dataset:osl_dynamics.data.Data, save_dir:str, + spatial_map_dir:str,spatial_surface_map_dir:str, n_channels:int,n_states:int,learn_mean:bool=False): + """ + Post-training analysis of Dynemo model + Parameters + ---------- + dataset: (osl_dynamics.data.Data) dataset to work on + save_dir: (str) directory to save results + spatial_map_dir: (str) directory of spatial maps + spatial_surface_map_dir: (str) directory of groupICA spatial surface maps + n_channels: (int) number of channels + n_states: (int) number of states + + Returns + ------- + """ + from osl_dynamics.models import load + model = load(save_dir) + + # Specify plot directory + plot_dir = f'{save_dir}plot/' + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + + # Plot the convergence of loss function + if not os.path.isfile(f'{plot_dir}loss_history.pdf'): + plot_loss_history(save_dir, plot_dir) + + + if not os.path.isfile(f'{save_dir}alpha.pkl'): + alpha = model.get_alpha(dataset) + pickle.dump(alpha, open(f'{save_dir}alpha.pkl', "wb")) + + # Calculate the metrics + if not os.path.isfile(f'{save_dir}metrics_repeat.json'): + calculate_metrics(model, dataset, save_dir) + + + # Obtain the statistics (mean,std, cov,cor) of states + if not os.path.isfile(f'{save_dir}state_covariances.npy'): + extract_state_statistics(save_dir, model_name='Dynemo',model_dir=model_dir) + + # Plot the statistics (mean,std,cor) of states + if not os.path.isfile(f'{plot_dir}plot_state_correlations.pdf'): + plot_state_statistics(save_dir, plot_dir, + model_name='Dynemo', + n_channels=n_channels, + n_states=n_states + ) + # Fractional occupancy analysis (similar to HMM) + FO_dir = f'{save_dir}FO_analysis/' + if not os.path.exists(FO_dir): + os.makedirs(FO_dir) + if not os.path.isfile(f'{FO_dir}fo_violin.pdf'): + FO_dynemo(save_dir,FO_dir,n_channels,n_states) + # Analyze the distance between different states/modes + dist_dir = f'{save_dir}distance/' + if not os.path.exists(dist_dir): + os.makedirs(dist_dir) + if not os.path.isfile(f'{dist_dir}riemannian_distance_cor.npy'): + compute_distance(save_dir, dist_dir) + + # Plot the distance between different states/modes + if not os.path.isfile(f'{plot_dir}/correct_distance_plot_cor.pdf'): + plot_distance(dist_dir, plot_dir, + model='Dynemo', + n_channels=n_channels, + n_states=n_states) + + # Plot the influence of regularisation on Riemannian distance + if not os.path.isfile(f'{plot_dir}/Riemannian_distance_regularisation_plot.pdf'): + compute_plot_distance_regularisation(save_dir, dist_dir, plot_dir, + model='Dynemo', + n_channels=n_channels, + n_states=n_states) + + # Compute the mean activation map + if not os.path.isfile(f'{save_dir}mean_activation_surface_map.dscalar.nii'): + mean_mapping(save_dir,spatial_map_dir,spatial_surface_map_dir) + + if not os.path.isfile(f'{save_dir}FC_sum_of_degree_surface_map.dscalar.nii'): + FC_mapping(save_dir,spatial_map_dir,spatial_surface_map_dir) + + if not os.path.isfile(f'{plot_dir}mean_FC_relation.pdf'): + mean_FC_relation(save_dir,plot_dir,'Dynemo',n_channels,n_states) + + reproduce_analysis_dir = f'{save_dir}reproduce_analysis/' + if not os.path.exists(reproduce_analysis_dir): + os.makedirs(reproduce_analysis_dir) + if not os.path.isfile(f'{reproduce_analysis_dir}FCs_distance_plot_split_4.pdf'): + reproduce_analysis(save_dir,reproduce_analysis_dir,'Dynemo',n_channels,n_states,split_strategy='1') + reproduce_analysis(save_dir, reproduce_analysis_dir, 'Dynemo',n_channels,n_states, split_strategy='2') + reproduce_analysis(save_dir, reproduce_analysis_dir, 'Dynemo',n_channels,n_states, split_strategy='3') + reproduce_analysis(save_dir, reproduce_analysis_dir, 'Dynemo', n_channels, n_states, split_strategy='4') + +def MAGE_analysis(dataset:osl_dynamics.data.Data, save_dir:str, + spatial_map_dir:str, spatial_surface_map_dir:str, n_channels:int, n_states:int,learn_mean:bool=False): + """ + Post-training analysis of MAGE + Parameters + ---------- + dataset: (osl_dynamics.data.Data) dataset to work on + save_dir: (str) directory to save results + spatial_map_dir: (str) directory of groupICA spatial maps + spatail_surface_map_dir: (str) directory of groupICA spatial surface maps + n_channels: (int) number of channels + n_states: (int) number of states + + Returns + ------- + + """ + from osl_dynamics.models import load + model = load(save_dir) + + # Specify plot directory + plot_dir = f'{save_dir}plot/' + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + + # Plot the convergence of loss function + if not os.path.isfile(f'{plot_dir}loss_history.pdf'): + plot_loss_history(save_dir, plot_dir) + + if not os.path.isfile(f'{save_dir}alpha.pkl'): + alpha = model.get_alpha(dataset) + pickle.dump(alpha, open(f'{save_dir}alpha.pkl', "wb")) + + # Calculate the metrics + if not os.path.isfile(f'{save_dir}metrics_repeat.json'): + calculate_metrics(model, dataset, save_dir) + + + # Obtain the statistics (mean,std, cov,cor) of states + if not os.path.isfile(f'{save_dir}state_covariances.npy'): + extract_state_statistics(save_dir, model_name='MAGE',model_dir=model_dir) + + # Plot the statistics (mean,std,cor) of states + if not os.path.isfile(f'{plot_dir}plot_state_correlations.pdf'): + plot_state_statistics(save_dir, plot_dir, + model_name='MAGE', + n_channels=n_channels, + n_states=n_states + ) + + # Analyze the distance between different states/modes + dist_dir = f'{save_dir}distance/' + if not os.path.exists(dist_dir): + os.makedirs(dist_dir) + if not os.path.isfile(f'{dist_dir}riemannian_distance_cor.npy'): + compute_distance(save_dir, dist_dir) + + # Plot the distance between different states/modes + if not os.path.isfile(f'{plot_dir}/correct_distance_plot_cor.pdf'): + plot_distance(dist_dir, plot_dir, + model='MAGE', + n_channels=n_channels, + n_states=n_states) + + # Plot the influence of regularisation on Riemannian distance + if not os.path.isfile(f'{plot_dir}/Riemannian_distance_regularisation_plot.pdf'): + compute_plot_distance_regularisation(save_dir, dist_dir, plot_dir, + model='MAGE', + n_channels=n_channels, + n_states=n_states) + + + # Compute the mean activation map + if not os.path.isfile(f'{save_dir}mean_activation_surface_map.dscalar.nii'): + mean_mapping(save_dir,spatial_map_dir,spatial_surface_map_dir) + + if not os.path.isfile(f'{save_dir}FC_sum_of_degree_surface_map.dscalar.nii'): + FC_mapping(save_dir,spatial_map_dir,spatial_surface_map_dir) + + if not os.path.isfile(f'{plot_dir}mean_FC_relation.pdf'): + mean_FC_relation(save_dir,plot_dir,'MAGE',n_channels,n_states) + + reproduce_analysis_dir = f'{save_dir}reproduce_analysis/' + if not os.path.exists(reproduce_analysis_dir): + os.makedirs(reproduce_analysis_dir) + if not os.path.isfile(f'{reproduce_analysis_dir}FCs_distance_plot_split_4.pdf'): + reproduce_analysis(save_dir, reproduce_analysis_dir, 'MAGE',n_channels,n_states, split_strategy='1') + reproduce_analysis(save_dir, reproduce_analysis_dir, 'MAGE',n_channels,n_states, split_strategy='2') + reproduce_analysis(save_dir, reproduce_analysis_dir, 'MAGE',n_channels,n_states, split_strategy='3') + reproduce_analysis(save_dir, reproduce_analysis_dir, 'MAGE', n_channels, n_states, split_strategy='4') +def SWC_analysis(save_dir,old_dir,n_channels,n_states): + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + plot_dir = f'{save_dir}plot/' + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + + measures = ['cor','cov'] + file_name = {'cor':'state_correlations', + 'cov':'state_covariances'} + + if not os.path.isfile(f'{save_dir}split_1_second_half/state_stds.npy'): + for measure in measures: + K_means_clustering(old_dir,save_dir,file_name,n_states,n_channels,measure) + + # Split half and implement K_means again + random_seed = 42 + random.seed(random_seed) + swc = np.load(f'{old_dir}cor_swc.npy') + random_index = random.sample(range(len(swc)), int(len(swc) / 2)) + + if not os.path.exists(f'{save_dir}split_1_first_half/'): + os.makedirs(f'{save_dir}split_1_first_half/') + K_means_clustering(old_dir,f'{save_dir}split_1_first_half/',file_name,n_states,n_channels,measure,split_index=random_index) + + if not os.path.exists(f'{save_dir}split_1_second_half/'): + os.makedirs(f'{save_dir}split_1_second_half/') + random_index_C = list(set(np.arange(len(swc))) - set(random_index)) + K_means_clustering(old_dir,f'{save_dir}split_1_second_half/',file_name,n_states,n_channels,measure,split_index=random_index_C) + + # Reproducibility analysis + correlations_1 = np.load(f'{save_dir}split_1_first_half/state_correlations.npy') + correlations_2 = np.load(f'{save_dir}split_1_second_half/state_correlations.npy') + FCs_fisher_z_transformed_correlation = twopair_fisher_z_transformed_correlation(correlations_1, correlations_2) + row_column_indices_FCs_Fisher, FCs_correlation_reorder_fisher = hungarian_pair( + FCs_fisher_z_transformed_correlation, distance=False) + + reproduce_analysis_dir = f'{save_dir}reproduce_analysis/' + if not os.path.exists(reproduce_analysis_dir): + os.makedirs(reproduce_analysis_dir) + split_strategy = '1' + np.save(f'{reproduce_analysis_dir}FCs_fisher_correlation_split_{split_strategy}.npy', + FCs_fisher_z_transformed_correlation) + with open(f'{reproduce_analysis_dir}FCs_row_column_indices_Fisher_split_{split_strategy}.json', 'w') as json_file: + json.dump(row_column_indices_FCs_Fisher, json_file) + np.save(f'{reproduce_analysis_dir}FCs_fisher_correlation_reorder_split_{split_strategy}.npy', + FCs_correlation_reorder_fisher) + + heatmap_reorder_matrix(FCs_correlation_reorder_fisher, reproduce_analysis_dir, + 'FCs_fisher_z_correlation', row_column_indices_FCs_Fisher, + 'SWC', n_channels, n_states, split_strategy) + + # Analyze the distance between different states/modes + dist_dir = f'{save_dir}distance/' + if not os.path.exists(dist_dir): + os.makedirs(dist_dir) + if not os.path.isfile(f'{dist_dir}fisher_z_correlation_cor.npy'): + compute_distance(save_dir, dist_dir) + + # Plot the distance between different states/modes + if not os.path.isfile(f'{plot_dir}/correct_distance_plot_cor.pdf'): + plot_distance(dist_dir, plot_dir, + model='SWC', + n_channels=n_channels, + n_states=n_states + ) + +def K_means_clustering(old_dir:str,save_dir:str,file_name:dict,n_states:int,n_channels:int,measure:str,split_index=None): + """ + Use K_means algorithm to cluster the correlations/covariances from SWC + Parameters + ---------- + old_dir: (str) the directory to where the original correlations/covariances are saved + save_dir: (str) the directory to save the centroids + file_name: (dict) the file name dictionary + n_states: (int) number of states + n_channels: (int) number of channels + measure: (str) 'cor' or 'cov' + split_index: (list) the index to split the data, if is None, then do not split + Returns + ------- + + """ + + # Fix the random seed + + swc = np.load(f'{old_dir}/{measure}_swc.npy') + + if split_index is not None: + swc = swc[split_index,:,:,:] + + + + swc = np.concatenate(swc) + + # Initiate the K-means class + kmeans = KMeans(n_clusters=n_states, verbose=0) + + # Get indices that correspond to an upper triangle of a matrix + # (not including the diagonal) + i, j = np.triu_indices(n_channels, k=1) + + # Now let's convert the sliding window connectivity matrices to a series of vectors + swc_vectors = swc[:, i, j] + + # Check the shape of swc vectors + print(f'SWC vectors shape: {swc_vectors.shape}') + + # Fitting + kmeans.fit(swc_vectors) # should take a few seconds + + centroids = kmeans.cluster_centers_ + print(f'centroids shape: {centroids.shape}') + + # Convert from a vector to a connectivity matrix + kmean_networks = np.empty([n_states, n_channels, n_channels]) + kmean_networks[:, i, j] = centroids + kmean_networks[:, j, i] = centroids + np.save(f'{save_dir}{file_name[measure]}.npy', kmean_networks) + + if measure == 'cov': + stds, _ = cov2stdcor(kmean_networks) + np.save(f'{save_dir}state_stds.npy', stds) +def extract_state_statistics(save_dir:str,model_name:str,model_dir:str=None): + if model_dir is None: + model_dir = save_dir + from osl_dynamics.models import load + model = load(model_dir) + if model_name == 'MAGE': + means, stds, correlations = model.get_means_stds_fcs() + # Compact stds (M*N*N) to (M*N) + if stds.ndim == 3: + stds = np.array([np.diag(matrix) for matrix in stds]) + covariances = stdcor2cov(stds, correlations) + else: + means, covariances = model.get_means_covariances() + stds, correlations = cov2stdcor(covariances) + np.save(f'{save_dir}state_means.npy', means) + np.save(f'{save_dir}state_stds.npy', stds) + np.save(f'{save_dir}state_correlations.npy', correlations) + np.save(f'{save_dir}state_covariances.npy', covariances) + +def calculate_metrics(model,dataset,save_dir): + """ + Calculate the metrics: free energy and model evidence of the data + Parameters + ---------- + model: the after-training model + dataset: data to calculate metrics on + save_dir: where to save the metrics + + Returns + ------- + """ + from osl_dynamics.models import load + + if not os.path.isfile(f'{save_dir}metrics.json'): + free_energy,log_likelihood, entropy,prior = model.free_energy(dataset,return_components=True) + evidence = model.evidence(dataset) + metrics = {'free_energy':float(free_energy), + 'log_likelihood':float(log_likelihood), + 'entropy':float(entropy), + 'prior':float(prior), + 'evidence':float(evidence), + } + with open(f'{save_dir}metrics.json', "w") as json_file: + # Use json.dump to write the data to the file + json.dump(metrics, json_file) + if not os.path.isfile(f'{save_dir}metrics_repeat.json'): + free_energy_list = [] + evidence_list = [] + log_likelihood_list, entropy_list, prior_list = [], [], [] + # We repeat the model for five times + for i in range(1,6): + if os.path.exists(f'{save_dir}repeat_{i}'): + repeat_model = load(f'{save_dir}repeat_{i}/') + free_energy, log_likelihood, entropy, prior = repeat_model.free_energy(dataset,return_components=True) + evidence = repeat_model.evidence(dataset) + free_energy_list.append(float(free_energy)) + log_likelihood_list.append(float(log_likelihood)) + entropy_list.append(float(entropy)) + prior_list.append(float(prior)) + evidence_list.append(float(evidence)) + with open(f'{save_dir}metrics_repeat.json',"w") as json_file: + json.dump({'free_energy':free_energy_list, + 'log_likelihood':log_likelihood_list, + 'entropy':entropy_list, + 'prior':prior_list, + 'evidence':evidence_list},json_file) + + + + +def plot_state_statistics(save_dir:str, plot_dir:str,model_name:str,n_channels:int,n_states:int): + """ + plot the mean, std, correlation of states + Parameters + ---------- + save_dir: (str) directory to read in mean,std,correlations + plot_dir: (str) directory to save the plot + model_name: (str) model name + n_channels: (int) number of channels + n_states: (int) number of states + + Returns + ------- + + """ + + # When plotting, change the model name from MAGE to mDynemo + if model_name == 'MAGE': + model_name = 'mDynemo' + means = np.load(f'{save_dir}state_means.npy') + stds = np.load(f'{save_dir}state_stds.npy') + corrs = np.load(f'{save_dir}state_correlations.npy') + + # means box plot + fig, ax = plt.subplots(figsize=(10,6)) + boxplot = ax.boxplot(means.T,vert=True) + ax.set_xticklabels([f'{i+1}'for i in range(n_states)],fontsize=14) + ax.tick_params(axis='y', labelsize=14) + ax.set_xlabel('State',fontsize=17) + ax.set_ylabel(r'$\mu$',fontsize=17) + plt.tight_layout() + plt.suptitle(f'Mean, {model_name}_ICA_{n_channels}_state_{n_states}',fontsize=22) + plt.savefig(f'{plot_dir}plot_state_means.jpg') + plt.savefig(f'{plot_dir}plot_state_means.pdf') + plt.close() + + # stds box plot + fig, ax = plt.subplots(figsize=(10, 6)) + boxplot = ax.boxplot(stds.T, vert=True) + ax.set_xticklabels([f'{i + 1}' for i in range(n_states)],fontsize=14) + ax.tick_params(axis='y', labelsize=14) + ax.set_xlabel('State', fontsize=17) + ax.set_ylabel(r'$\sigma$',fontsize=17) + plt.tight_layout() + plt.suptitle(f'Standard deviation, {model_name}_ICA_{n_channels}_state_{n_states}', fontsize=22) + plt.savefig(f'{plot_dir}plot_state_stds.jpg') + plt.savefig(f'{plot_dir}plot_state_stds.pdf') + plt.close() + + + # Plot correlation matrix + # Calculate the number of rows and columns for the subplot grid + num_cols = 1 + num_rows = n_states // num_cols + + # Create a figure and a grid of subplots + fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 3 * num_rows)) + + # Loop through each correlation matrix and plot it in the corresponding subplot + for i in range(n_states): + row = i // num_cols + col = i % num_cols + if num_rows == 1: + ax = axes[col] + elif num_cols == 1: + ax = axes[row] + else: + ax = axes[row, col] + corr_matrix = corrs[i, :, :] + # Set the diagonal to zero + corr_matrix = corr_matrix - np.eye(len(corr_matrix)) + + # Plot the correlation matrix as an image (heatmap) + cax = ax.imshow(corr_matrix, cmap='coolwarm', vmin=-1, vmax=1) + + # Set subplot title + ax.set_title(f'FC {i + 1}',fontsize=13) + # Add a colorbar to show the correlation values + cbar = fig.colorbar(cax, ax=axes, fraction=0.05, pad=0.04) + cbar.set_label('Correlation',fontsize=15) + # Adjust spacing between subplots + #plt.tight_layout() + plt.savefig(f'{plot_dir}plot_state_correlations.jpg') + plt.savefig(f'{plot_dir}plot_state_correlations.pdf') + plt.close() + + +def compute_distance(save_dir:str,dist_dir:str): + """ + Compute distance of corrletion and covariance matrices + Parameters + ---------- + save_dir: (str) directory to read these matrices + dist_dir: (str) directory to save distances + + Returns + ------- + + """ + #means = np.load(f'{save_dir}state_means.npy') + #stds = np.load(f'{save_dir}state_stds.npy') + correlations = np.load(f'{save_dir}state_correlations.npy') + covariances = np.load(f'{save_dir}state_covariances.npy') + + # Regularisation for Riemannian distance + # Please work on correlation matrix only + eps_values = [0,1e-9,1e-8,1e-7,1e-6,1e-5] + + # Compute four distance/correlation metrics + np.save(f'{dist_dir}frobenius_distance_cov.npy', pairwise_frobenius_distance(covariances)) + np.save(f'{dist_dir}matrix_correlation_cov.npy', pairwise_matrix_correlations(covariances)) + np.save(f'{dist_dir}congruence_coefficient_cov.npy', pairwise_congruence_coefficient(covariances)) + np.save(f'{dist_dir}fisher_z_correlation_cov.npy', pairwise_fisher_z_correlations(covariances)) + try: + np.save(f'{dist_dir}riemannian_distance_cov.npy', pairwise_riemannian_distances(covariances)) + except np.linalg.LinAlgError: + warnings.warn("Riemannian distance is not computed properly for covariances!") + + np.save(f'{dist_dir}frobenius_distance_cor.npy', pairwise_frobenius_distance(correlations)) + np.save(f'{dist_dir}matrix_correlation_cor.npy', pairwise_matrix_correlations(correlations)) + np.save(f'{dist_dir}congruence_coefficient_cor.npy', pairwise_congruence_coefficient(correlations)) + np.save(f'{dist_dir}fisher_z_correlation_cor.npy', pairwise_fisher_z_correlations(correlations)) + + for eps in eps_values: + try: + np.save(f'{dist_dir}riemannian_distance_cor.npy', pairwise_riemannian_distances(regularisation(correlations,eps))) + print(f'Riemannian distance calculation succeeds when eps = {eps}') + break + except np.linalg.LinAlgError: + print(f'Riemannian distance is not computed properly when eps = {eps}') + +def plot_distance(dist_dir:str,plot_dir:str,model:str,n_channels:int,n_states:int): + """ + Plot the distance distribution within each N_states, N_channels + Parameters + ---------- + dist_dir: (str) directory containing distance matrices + dist_plot_dir: (str) directory to save the plot + model: (str) the model name + n_channels: (int) number of channels + n_states: (int) number of states + + Returns + ------- + """ + # When plotting, change the model name from MAGE to mDynemo + if model == 'MAGE': + model = 'mDynemo' + measures = ['cor','cov'] + # Remark by swimming 8th Aug 2023 + # Correct implementations are Riemannian distance and Fisher z-transformed correlation + # So our plot should be only 2 * 2 now. + # For those Riemannian distance is not available (numerical issues), + # only plot the histogram of Fisher z-transformed + for measure in measures: + fisher = np.load(f'{dist_dir}fisher_z_correlation_{measure}.npy') + N_states = len(fisher) + fisher_d = fisher[np.triu_indices(N_states, k=1)] + try: + riemannian = np.load(f'{dist_dir}riemannian_distance_{measure}.npy') + riemannian_d = riemannian[np.triu_indices(N_states, k=1)] + except FileNotFoundError: + fig = plt.figure() + plt.hist(fisher_d, bins=20, color='blue', alpha=0.7) + plt.title(f'{measure} distance, {model}_ICA_{n_channels}_states_{n_states}',fontsize=20) + else: + fig, axes = plt.subplots(2, 2, figsize=(8, 8)) + axes[0, 0].hist(riemannian_d, bins=20, color='blue', alpha=0.7) + axes[0, 0].set_xlabel('Riemannian',fontsize=15) + axes[0, 0].set_ylabel('Frequency',fontsize=15) + axes[0, 0].set_xticks(axes[0, 0].get_xticks()) + axes[0, 0].set_xticklabels(axes[0, 0].get_xticklabels(),fontsize=13) + axes[0, 0].set_yticks(axes[0, 0].get_yticks()) + axes[0, 0].set_yticklabels(axes[0, 0].get_yticklabels(),fontsize=13) + + axes[1, 1].hist(fisher_d,bins=20,color='blue',alpha=0.7) + axes[1, 1].set_xlabel('Fisher-z',fontsize=15) + axes[1, 1].set_ylabel('Frequency',fontsize=15) + axes[1, 1].set_xticks(axes[1, 1].get_xticks()) + axes[1, 1].set_xticklabels(axes[1, 1].get_xticklabels(),fontsize=13) + axes[1, 1].set_yticks(axes[1, 1].get_yticks()) + axes[1, 1].set_yticklabels(axes[1, 1].get_yticklabels(),fontsize=13) + + axes[0, 1].scatter(fisher_d, riemannian_d, color='blue', alpha=0.5) + axes[0, 1].set_xlabel('Fisher-z',fontsize=15) + axes[0, 1].set_ylabel('Riemannian',fontsize=15) + + axes[0, 1].set_xticks(axes[0, 1].get_xticks()) + axes[0, 1].set_xticklabels(axes[0, 1].get_xticklabels(),fontsize=13) + axes[0, 1].set_yticks(axes[0, 1].get_yticks()) + axes[0, 1].set_yticklabels(axes[0, 1].get_yticklabels(),fontsize=13) + + plt.suptitle(f'{measure} distance, {model}_ICA_{n_channels}_states_{n_states}',fontsize=20) + + plt.savefig(f'{plot_dir}correct_distance_plot_{measure}.jpg') + plt.savefig(f'{plot_dir}correct_distance_plot_{measure}.pdf') + plt.close() + + + + + ''' + for measure in measures: + frobenius_distance = np.load(f'{dist_dir}frobenius_distance_{measure}.npy') + correlation_distance = np.load(f'{dist_dir}matrix_correlation_{measure}.npy') + riemannian_distance = np.load(f'{dist_dir}riemannian_distance_{measure}.npy') + congruence_distance = np.load(f'{dist_dir}congruence_coefficient_{measure}.npy') + + N_states = len(frobenius_distance) + f_d = frobenius_distance[np.triu_indices(N_states, k=1)] + corr_d = correlation_distance[np.triu_indices(N_states, k=1)] + r_d = riemannian_distance[np.triu_indices(N_states, k=1)] + cong_d = congruence_distance[np.triu_indices(N_states, k=1)] + + distances = np.array([f_d, corr_d, r_d, cong_d]) + measures = ['Frobenius', 'Correlation', 'Riemannian', 'Congruence'] + n_measures = len(distances) + # Start plotting + fig, axes = plt.subplots(n_measures, n_measures, figsize=(12, 12)) + + # Loop through each pair of measures and plot histograms on the diagonal and scatter plots on the off-diagonal + for i in range(n_measures): + for j in range(n_measures): + if i == j: + # Plot histogram for diagonal entries + axes[i, j].hist(distances[i, :], bins=20, color='blue', alpha=0.7) + else: + data_1 = distances[j, :] + data_2 = distances[i, :] + # Plot scatter plot for off-diagonal entries (i,j) + axes[i, j].scatter(data_1, data_2, color='blue', alpha=0.5) + axes[i, j].set_xlabel(measures[j]) + axes[i, j].set_ylabel(measures[i]) + + # Add labels to the diagonal plots + for i in range(n_measures): + axes[i, i].set_xlabel(measures[i]) + axes[i, i].set_ylabel('Frequency') + + # Title + plt.suptitle(f'{measure} distance, {model}_ICA_{n_channels}_states_{n_states}',fontsize=20) + + plt.savefig(f'{plot_dir}distance_plot_{measure}.jpg') + plt.savefig(f'{plot_dir}distance_plot_{measure}.pdf') + plt.close() + ''' + +def compute_plot_distance_regularisation(save_dir:str,dist_dir:str, plot_dir:str,model:str,n_channels:int,n_states:int): + """ + This function serves to compute the effect of regularisation on the Riemannian distance. + The Riemannian distance is numerically unstable because some correlation matrices + are "almost" positive semi-definite,i.e., scipy.linalg returns small negative eigenvalues. + One of the solutions is to add a small \epsilon to the diagonal (see rotation.utils.regularisation). + We need to understand: (1) the appropriate value of \epsilon, (2) the effect of regularisation + + So we add \epsilon=1e-7,...,1e-4 to the correlation matrix, and scatter plot + the "regularised" Riemannian distance against each other + Parameters + ---------- + save_dir: (str) directory to read correlation matrices + dist_dir: (str) directory to store the distances + plot_dir: (str) directory to save the plot + model: (str) The model to use + n_channels: (int) number of channels + n_states: (int) number of states + + Returns + ------- + + """ + + # When plotting, change the model name from MAGE to mDynemo + if model == 'MAGE': + model = 'mDynemo' + + eps_values = [0,1e-8,1e-7,1e-6,1e-5] + distances = {} + correlations = np.load(f'{save_dir}state_correlations.npy') + + for eps in eps_values: + reg_correlations = regularisation(correlations,eps) + try: + distances[eps] = pairwise_riemannian_distances(reg_correlations) + except np.linalg.LinAlgError: + print(f'Riemannian distance when eps = {eps} failed!') + + np.savez(f'{dist_dir}Riemannian_distance_regularisation.npz',distances) + + # Flatten + N_states = len(correlations) + for eps in distances.keys(): + distances[eps] = (distances[eps])[np.triu_indices(N_states, k=1)] + + n_measures = len(eps_values) + # Start plotting + fig, axes = plt.subplots(n_measures, n_measures, figsize=(15, 15)) + + # Loop through each pair of measures and plot histograms on the diagonal and scatter plots on the off-diagonal + for i,eps_i in enumerate(eps_values): + if eps_i in distances.keys(): + for j,eps_j in enumerate(eps_values): + if i == j: + # Plot histogram for diagonal entries + axes[i, j].hist(distances[eps_i], bins=20, color='blue', alpha=0.7) + axes[i, j].set_xlabel(str(eps_i),fontsize=15) + axes[i, i].set_ylabel('Frequency',fontsize=15) + else: + if eps_j in distances.keys(): + data_1 = distances[eps_j] + data_2 = distances[eps_i] + # Plot scatter plot for off-diagonal entries (i,j) + axes[i, j].scatter(data_1, data_2, color='blue', alpha=0.5) + axes[i, j].set_xlabel(str(eps_j),fontsize=15) + axes[i, j].set_ylabel(str(eps_i),fontsize=15) + + + # Title + plt.suptitle(f'Riemannian distance regularisation, {model}_ICA_{n_channels}_states_{n_states}', fontsize=20) + plt.tight_layout() + plt.savefig(f'{plot_dir}Riemannian_distance_regularisation_plot.jpg') + plt.savefig(f'{plot_dir}Riemannian_distance_regularisation_plot.pdf') + plt.close() + + + +def mean_mapping(save_dir:str,spatial_map_dir:str,spatial_surface_map_dir:str): + """ + Obtain mean activation spatial maps of each state/mode in the specified directory + Parameters + ---------- + save_dir: (str) directory to work in + spatial_map_dir: (str) spatial map of independent components + spatial_surface_map_dir: (str) spatial surface map of independent components + Returns + ------- + """ + state_means = np.load(f'{save_dir}state_means.npy') + spatial_map = nib.load(spatial_map_dir) + spatial_surface_map = nib.load(spatial_surface_map_dir) + + mean_activation_map = IC2brain(spatial_map, state_means.T) + mean_activation_surface_map = IC2surface(spatial_surface_map,state_means.T) + + nib.save(mean_activation_map, f'{save_dir}mean_activation_map.nii.gz') + mean_activation_surface_map.to_filename(f'{save_dir}mean_activation_surface_map.dscalar.nii') + + + +def FC_mapping(save_dir:str,spatial_map_dir:str,spatial_surface_map_dir:str): + """ + obtain the FC spatial maps of each state/mode in the specified directory + pipeline: correlation/covariance matrix --> rank-one approximation --> brain map + Parameters + ---------- + save_dir: (str) directory to work in + spatial_map_dir: (str) spatial map of independent components + spatial_surface_map_dir: (str) spatial surface map of independent components + Returns + ------- + """ + # Obtain the correlation matrices first (or compute from covariance matrices) + try: + correlations = np.load(f'{save_dir}state_correlations.npy') + except FileNotFoundError: + covariances = np.load(f'{save_dir}state_covariances.npy') + correlations = cov2corr(covariances) + # Remember to save back! + np.save(f'{save_dir}state_correlations.npy', correlations) + + # Rank-one approximation + r1_approxs = [] + sum_of_degrees = [] + for i in range(len(correlations)): + correlation = correlations[i,:,:] + r1_approxs.append(first_eigenvector(correlation)) + np.fill_diagonal(correlation,0) + sum_of_degrees.append(np.sum(correlation,axis=1)) + r1_approxs = np.array(r1_approxs) + sum_of_degrees = np.array(sum_of_degrees) + np.save(f'{save_dir}r1_approx_FC.npy', r1_approxs) + np.save(f'{save_dir}sum_of_degree.npy',sum_of_degrees) + + # Component map to spatial map + spatial_map = nib.load(spatial_map_dir) + spatial_surface_map = nib.load(spatial_surface_map_dir) + + FC_degree_map = IC2brain(spatial_map, r1_approxs.T) + FC_degree_surface_map = IC2surface(spatial_surface_map,r1_approxs.T) + + nib.save(FC_degree_map, f'{save_dir}FC_map.nii.gz') + FC_degree_surface_map.to_filename(f'{save_dir}FC_surface_map.dscalar.nii') + + sum_of_degree_map = IC2brain(spatial_map, sum_of_degrees.T) + sum_of_degree_surface_map = IC2surface(spatial_surface_map,sum_of_degrees.T) + + nib.save(sum_of_degree_map, f'{save_dir}FC_sum_of_degree_map.nii.gz') + sum_of_degree_surface_map.to_filename(f'{save_dir}FC_sum_of_degree_surface_map.dscalar.nii') +def FO_dynemo(save_dir:str,FO_dir:str,n_channels:int,n_states:int): + """ + Fractional Occupancy Analysis in Dynemo, similar to HMM. + The aim is to summarize the mixing coefficient time course + Parameters + ---------- + save_dir: (str) directory to work in + FO_dir: (str) directory to save the results + n_channels: number of channels + n_states: number of states + + Returns + ------- + """ + # Step 1: Plot unnormalised alpha + alpha = pickle.load(open(f"{save_dir}alpha.pkl","rb")) + plotting.plot_alpha(alpha[0],n_samples=200) + plt.title('Unnormalised alpha',fontsize=20) + plt.savefig(f'{FO_dir}example_unnormalised_alpha.jpg') + plt.savefig(f'{FO_dir}example_unnormalised_alpha.pdf') + plt.close() + + # Step 2: normalise the alpha + def normalize_alpha(a:np.ndarray, D:np.ndarray) -> np.ndarray: + """ + Calculate the weighting for each mode + measured by the trace of each mode covariance + Parameters + ---------- + a: (np.ndarray) N_time_points * N_states + D: (np.ndarray) covariance matrices N_states * N_channels * N_channels + + Returns + ------- + wa: (np.ndarray) weighted alpha, having the shape as a + """ + # Calculate the weighting for each mode + # We use the trace of each mode covariance + w = np.trace(D, axis1=1, axis2=2) + + # Weight the alphas + wa = w[np.newaxis, ...] * a + + # Renormalize so the alphas sum to one + wa /= np.sum(wa, axis=1, keepdims=True) + return wa + + # Load the inferred mode covariances + covs = np.load(f'{save_dir}state_covariances.npy') + + # Renormalize the mixing coefficients + norm_alpha = [normalize_alpha(a, covs) for a in alpha] + + # Plot the renormalized mixing coefficient time course for the first subject (8 seconds) + plotting.plot_alpha(norm_alpha[0], n_samples=200) + plt.title('Normalised alpha',fontsize=20) + plt.savefig(f'{FO_dir}example_normalised_alpha.jpg') + plt.savefig(f'{FO_dir}example_normalised_alpha.pdf') + plt.close() + + # Step 3: Summary statistics + # Analogous to FO in HMM, we can compute the time average mixing coefficient + mean_norm_alpha = np.array([np.mean(a, axis=0) for a in norm_alpha]) + np.save(f'{save_dir}fo.npy',mean_norm_alpha) + + plot_FO(mean_norm_alpha, FO_dir) + + std_norm_alpha = np.array([np.std(a, axis=0) for a in norm_alpha]) + np.save(f'{save_dir}normalised_alpha_std.npy',std_norm_alpha) + + # Plot the distribution of fractional occupancy (FO) across subjects + plotting.plot_violin(mean_norm_alpha.T, x_label="State", y_label="FO") + plt.savefig(f'{FO_dir}fo_violin.jpg') + plt.savefig(f'{FO_dir}fo_violin.pdf') + plt.close() + +def FO_MAGE(save_dir:str,FO_dir:str,n_channels:int,n_states:int): + """ + Fractional Occupancy Analysis in MAGE, similar to HMM. + The aim is to summarize the mixing coefficient time course + Parameters + ---------- + save_dir: (str) directory to work in + FO_dir: (str) directory to save the results + n_channels: number of channels + n_states: number of states + + Returns + ------- + + """ + # Step 1: Plot alpha + alpha = pickle.load(open(f"{save_dir}alpha.pkl", "rb")) + alpha_1 = alpha[0] + alpha_2 = alpha[1] + + plotting.plot_alpha(alpha_1, n_samples=200) + plt.title('alpha 1', fontsize=20) + plt.savefig(f'{FO_dir}example_alpha_1.jpg') + plt.savefig(f'{FO_dir}example_alpha_1.pdf') + plt.close() + + plotting.plot_alpha(alpha_2, n_samples=200) + plt.title('alpha 2', fontsize=20) + plt.savefig(f'{FO_dir}example_alpha_2.jpg') + plt.savefig(f'{FO_dir}example_alpha_2.pdf') + plt.close() + + # Step 2: Summary Statistics + mean_alpha_1 = np.array([np.mean(a, axis=0) for a in alpha_1]) + np.save(f'{save_dir}fo_alpha_1.npy', mean_alpha_1) + mean_alpha_2 = np.array([np.mean(a, axis=0) for a in alpha_2]) + np.save(f'{save_dir}fo_alpha_2.npy', mean_alpha_2) + + plot_FO(mean_alpha_1, FO_dir,file_name='fo_alpha_1_hist') + plot_FO(mean_alpha_2,FO_dir,file_name='fo_alpha_2_hist') + + std_alpha_1 = np.array([np.std(a, axis=0) for a in alpha_1]) + np.save(f'{save_dir}alpha_1_std.npy', std_alpha_1) + + std_alpha_2 = np.array([np.std(a, axis=0) for a in alpha_2]) + np.save(f'{save_dir}alpha_2_std.npy', std_alpha_2) + + # Plot the distribution of fractional occupancy (FO) across subjects + plotting.plot_violin(mean_alpha_1.T, x_label="State", y_label="FO") + plt.savefig(f'{FO_dir}fo_alpha_1_violin.jpg') + plt.savefig(f'{FO_dir}fo_alpha_1_violin.pdf') + plt.close() + + plotting.plot_violin(mean_alpha_2.T, x_label="State", y_label="FO") + plt.savefig(f'{FO_dir}fo_alpha_2_violin.jpg') + plt.savefig(f'{FO_dir}fo_alpha_2_violin.pdf') + plt.close() + + + +def comparison_analysis(models:list,list_channels:list,list_states:list,result_dir:str, save_dir:str,learn_mean:bool=False): + """ + Compare results obtained from different models, channel numbers, state numbers. + Parameters + ---------- + models: list + model list. + list_channels: list + N_channels list + list_states: list + N_states list + result_dir: str + where to find the results of different models + save_dir: str + where to save comparison results + learn_mean: bool + where mean activation is learned + + Returns + ------- + """ + # We do not have results for N_channels = 200,300 + list_channels = list_channels[:-2] + # Create directory + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + # Compare the reproducibility across all models + for model in models: + mean_correlation = {} + FC_correlation = {} + if model != 'SWC': + FC_distance = {} + for N_channel in list_channels: + for N_state in list_states: + FC_correlation[f'ICA_{N_channel}_state_{N_state}'] = [] + for i in range(5): + data = np.load(f'{result_dir}{model}_ICA_{N_channel}_state_{N_state}/reproduce_analysis/FCs_fisher_correlation_reorder_split_{i+1}.npy') + FC_correlation[f'ICA_{N_channel}_state_{N_state}'].append(np.mean(np.diagonal(data))) + + if model!= 'SWC': + if learn_mean: + mean_correlation[f'ICA_{N_channel}_state_{N_state}'] = [] + for i in range(5): + data = np.load(f'{result_dir}{model}_ICA_{N_channel}_state_{N_state}/reproduce_analysis/means_correlation_reorder_split_{i+1}.npy') + mean_correlation[f'ICA_{N_channel}_state_{N_state}'].append(np.mean(np.diagonal(data))) + FC_distance[f'ICA_{N_channel}_state_{N_state}'] = [] + for i in range(5): + data = np.load( + f'{result_dir}{model}_ICA_{N_channel}_state_{N_state}/reproduce_analysis/FCs_distance_reorder_split_{i+1}.npy') + FC_distance[f'ICA_{N_channel}_state_{N_state}'].append(np.mean(np.diagonal(data))) + group_comparison_plot(FC_correlation, model, list_channels, list_states, 'Fisher_z_transformed_correlation', save_dir) + if model != 'SWC': + group_comparison_plot(FC_distance, model, list_channels, list_states, 'Riemannian_distance', + save_dir) + if learn_mean: + group_comparison_plot(mean_correlation, model, list_channels, list_states, 'mean_correlation', + save_dir) + + # Compare the free energy and model evidence across all methods + for model in models: + free_energy = {} + evidence = {} + for N_channel in list_channels: + for N_state in list_states: + with open(f'{result_dir}{model}_ICA_{N_channel}_state_{N_state}/metrics_repeat.json', "r") as json_file: + # Use json.load to load the data from the file + metrics = json.load(json_file) + free_energy[f'ICA_{N_channel}_state_{N_state}'] = metrics['free_energy'] + #evidence[f'ICA_{N_channel}_state_{N_state}'] = metrics['evidence'] + + group_comparison_plot(free_energy, model, list_channels, list_states, 'free_energy', save_dir) + #group_comparison_plot(evidence,model,list_channels,list_states,'model_evidence',save_dir) + + # Compare the cross-validation free energy across all methods + for model in models: + free_energy_cv = {} + for N_channel in list_channels: + for N_state in list_states: + for i in range(5): + temp = [] + with open(f'{result_dir}{model}_ICA_{N_channel}_state_{N_state}/cross_validation_{i}/validation/metrics.json', + "r") as json_file: + # Use json.load to load the data from the file + temp.append(json.load(json_file)['free_energy']) + free_energy_cv[f'ICA_{N_channel}_state_{N_state}'] = temp + + group_comparison_plot(free_energy_cv, model, list_channels, list_states, 'free_energy_cv', save_dir) + # group_comparison_plot(evidence,model,list_channels,list_states,'model_evidence',save_dir) + + +def group_comparison_plot(metric:dict,model_name:str,list_channels:list,list_states:list,metric_name:str,save_dir:str): + """ + Plot the group comparison results. + The metric should be a dictionary with keys: ICA_{N_channels}_state_{N_states} + the values should represent some metrics. + Parameters + ---------- + metric: (dict) the metrics to plot + model_name: (str) the model name + list_channels: (list) the list of N_channels + list_states: (list) the list of N_states + metric_name: (str) the name of the metric plotted + save_dir: (str) where to save the plots + Returns + ------- + """ + + # Create a bar plot + + # When plotting, change the model name from MAGE to mDynemo + if model_name == 'MAGE': + model_name = 'mDynemo' + plt.figure(figsize=(10, 6)) + + bar_width = 0.15 + colors = plt.cm.viridis(np.linspace(0, 1, len(list_states))) + + lower_bound, upper_bound = 1000000., 0. + for i, N_states in enumerate(list_states): + channel_keys = [f'ICA_{N_channels}_state_{N_states}' for N_channels in list_channels] + values = [metric[key] for key in channel_keys] + means = [np.mean(vals) for vals in values] + lower_bound = min(lower_bound,np.min(np.array(means)) * 0.9) + upper_bound = max(upper_bound,np.max(np.array(means)) * 1.01) + stds = [np.std(vals) for vals in values] + plt.bar(np.arange(len(channel_keys)) + i * bar_width, means, bar_width, label=f'N_states = {N_states}', + yerr=stds,color=colors[i]) + + plt.xlabel('N_channels', fontsize=15) + plt.ylabel(metric_name, fontsize=15) + plt.ylim(lower_bound, upper_bound) + plt.title(f'Comparison of {metric_name} by N_channels and N_states', fontsize=20) + plt.xticks(np.arange(len(channel_keys)) + bar_width * (len(list_states) - 1) / 2, + [str(N_channels) for N_channels in list_channels], fontsize=15) + plt.yticks(fontsize=15) + #plt.autoscale(axis='y',tight=True) + #plt.legend(prop={'size': 15}) + plt.tight_layout() + plt.savefig(f'{save_dir}{model_name}_{metric_name}_comparison.jpg') + plt.savefig(f'{save_dir}{model_name}_{metric_name}_comparison.pdf') + plt.close() + +def mean_FC_relation(save_dir:str,plot_dir:str,model_name:str,n_channels:int,n_states:int): + """ + Explore the relationship between mean activation and FC map. + Question: Within each state, does the activated component have strong correlation with other componens. + Rationale: for HMM, Dynemo, because it's single dynamics, we should expect + mean activation and FC to be independent, but this is not the case for Dynemo and MAGE. + Method: compute the sum of FC across rows/columns, but we need to explore whether + the sign plays a role (both in activation and FC) in that case. So there should be + four histograms! + Parameters + ---------- + save_dir: (str) where state mean FC are saved and where to save the raw data for plotting + plot_dir: (str) where to plot the results + model_name: (str) model name + n_channels: (int) number of channels + n_states: (int) number of states + Returns + ------- + """ + means = np.load(f'{save_dir}state_means.npy') + means_abs = np.abs(means) + correlations = np.load(f'{save_dir}state_correlations.npy') + FC = np.sum(correlations,axis=1) + FC_abs = np.sum(np.abs(correlations),axis=1) + + result = {'means_FC':np.array([np.corrcoef(means[i, :], FC[i, :])[0, 1] for i in range(n_states)]), + 'means_abs_FC':np.array([np.corrcoef(means_abs[i, :], FC[i, :])[0, 1] for i in range(n_states)]), + 'means_FC_abs':np.array([np.corrcoef(means[i, :], FC_abs[i, :])[0, 1] for i in range(n_states)]), + 'means_abs_FC_abs':np.array([np.corrcoef(means_abs[i, :], FC_abs[i, :])[0, 1] for i in range(n_states)]) + } + np.savez(f'{save_dir}mean_FC_relation.npz',result) + + # Create a 2x2 subplot + fig, axes = plt.subplots(2, 2, figsize=(10, 8)) + fig.suptitle(f'{model_name}_ICA_{n_channels}_state_{n_states}', fontsize=20) + + # Loop through dictionary keys and plot histograms + for i, (key, value) in enumerate(result.items()): + row = i // 2 + col = i % 2 + ax = axes[row, col] + ax.hist(value, color='blue', alpha=0.7) + ax.set_title(key) + + plt.tight_layout() + plt.savefig(f'{plot_dir}mean_FC_relation.jpg') + plt.savefig(f'{plot_dir}mean_FC_relation.pdf') + plt.close() + +def reproduce_analysis(save_dir:str, reproduce_analysis_dir:str,model_name:str,n_channels:int,n_states:int,learn_mean:bool=False,split_strategy:str='1',dataset:osl_dynamics.data.Data=None): + """ + Analysis the reproducibility of each model + Parameters + ---------- + save_dir: str + the root directory of the model results + reproduce_analysis_dir: str + directory to save reproducibility analysis results + model_name: str + the model name + n_channels: int + number of channels + n_states: int + number of states + learn_mean: bool + whether to learn the mean activation in the model. + split_strategy: str + split strategy '1','2','3','4' + dataset: osl_dynamics.data.Data + the dataset to use. We need to calculate the temporal dynamics if dataset is used. + Returns + ------- + """ + extract_state_statistics(f'{save_dir}split_{split_strategy}/first_half/',model_name) + extract_state_statistics(f'{save_dir}split_{split_strategy}/second_half/', model_name) + if learn_mean: + means_1 = np.load(f'{save_dir}split_{split_strategy}/first_half/state_means.npy') + means_2 = np.load(f'{save_dir}split_{split_strategy}/second_half/state_means.npy') + means_correlation = twopair_vector_correlation(means_1, means_2) + row_column_indices, means_correlation_reorder = hungarian_pair(means_correlation, distance=False) + np.save(f'{reproduce_analysis_dir}means_correlation_split_{split_strategy}.npy', means_correlation) + with open(f'{reproduce_analysis_dir}means_row_column_indices_split_{split_strategy}.json', 'w') as json_file: + json.dump(row_column_indices, json_file) + np.save(f'{reproduce_analysis_dir}means_correlation_reorder_split_{split_strategy}.npy', + means_correlation_reorder) + + heatmap_reorder_matrix(means_correlation_reorder, reproduce_analysis_dir, + 'means_correlation', row_column_indices, + model_name, n_channels, n_states, split_strategy) + + print("We are working on the FCs in reproducibility analysis!") + print(f'split strategy is {split_strategy}') + print('#################') + # Work on FCs + correlations_1 = np.load(f'{save_dir}split_{split_strategy}/first_half/state_correlations.npy') + correlations_2 = np.load(f'{save_dir}split_{split_strategy}/second_half/state_correlations.npy') + + FCs_distance = twopair_riemannian_distance(correlations_1,correlations_2) + FCs_fisher_z_transformed_correlation = twopair_fisher_z_transformed_correlation(correlations_1,correlations_2) + row_column_indices_FCs,FCs_distance_reorder = hungarian_pair(FCs_distance,distance=True) + row_column_indices_FCs_Fisher,FCs_correlation_reorder_fisher = hungarian_pair(FCs_fisher_z_transformed_correlation,distance=False) + + np.save(f'{reproduce_analysis_dir}FCs_distance_split_{split_strategy}.npy', FCs_distance) + with open(f'{reproduce_analysis_dir}FCs_row_column_indices_split_{split_strategy}.json', 'w') as json_file: + json.dump(row_column_indices_FCs, json_file) + np.save(f'{reproduce_analysis_dir}FCs_distance_reorder_split_{split_strategy}.npy', FCs_distance_reorder) + + np.save(f'{reproduce_analysis_dir}FCs_fisher_correlation_split_{split_strategy}.npy', FCs_fisher_z_transformed_correlation) + with open(f'{reproduce_analysis_dir}FCs_row_column_indices_Fisher_split_{split_strategy}.json', 'w') as json_file: + json.dump(row_column_indices_FCs_Fisher, json_file) + np.save(f'{reproduce_analysis_dir}FCs_fisher_correlation_reorder_split_{split_strategy}.npy', FCs_correlation_reorder_fisher) + + heatmap_reorder_matrix(FCs_distance_reorder,reproduce_analysis_dir, + 'FCs_distance',row_column_indices_FCs, + model_name,n_channels,n_states,split_strategy) + + heatmap_reorder_matrix(FCs_correlation_reorder_fisher, reproduce_analysis_dir, + 'FCs_fisher_z_correlation', row_column_indices_FCs_Fisher, + model_name, n_channels, n_states, split_strategy) + + FCs_distance_first_half = twopair_riemannian_distance(correlations_1, correlations_1) + FCs_fisher_z_transformed_correlation_first_half = twopair_fisher_z_transformed_correlation(correlations_1, correlations_1) + + np.save(f'{reproduce_analysis_dir}FCs_distance_split_{split_strategy}_first_half.npy', FCs_distance_first_half) + np.save(f'{reproduce_analysis_dir}FCs_fisher_correlation_split_{split_strategy}_first_half.npy', FCs_distance_first_half) + + +def plot_loss_history(save_dir:str,plot_dir:str): + """ + Plot the history of loss function. + The figures should include original training, repeat_1,2,3,4 + Parameters + ---------- + save_dir: the directory where the trained model is saved. + plot_dir: where to plot the results + + Returns + ------- + + """ + loss_history = np.load(f'{save_dir}/loss_history.npy') + epochs = np.arange(1, len(loss_history) + 1) + plt.plot(epochs, loss_history) + for i in range(1,5): + loss_history = np.load(f'{save_dir}/repeat_{i}/loss_history.npy') + plt.plot(epochs, loss_history) + + # Plotting the loss history + + plt.xlabel('Epochs',fontsize=15) + plt.ylabel('Loss',fontsize=15) + plt.title('Training Loss Over Epochs',fontsize=20) + plt.savefig(f'{plot_dir}loss_history.jpg') + plt.savefig(f'{plot_dir}loss_history.pdf') + plt.close() \ No newline at end of file diff --git a/rotation/preprocessing.py b/rotation/preprocessing.py new file mode 100644 index 00000000..a9f83366 --- /dev/null +++ b/rotation/preprocessing.py @@ -0,0 +1,131 @@ +import random + +import pathlib +import numpy as np +import scipy.stats as stats + +from osl_dynamics.data import Data + +class PrepareData(): + def __init__(self,data_dir:pathlib.Path,n_timepoint:int=1200): + self.data_dir = data_dir + self.n_timepoint = n_timepoint + + def load(self,split_session:bool = True,split_strategy:str ='0', z_score_data=True): + ''' + Load data from specified directories + Parameters + ---------- + split_session: bool + whether to split the session + split_strategy: str + how to split the whole dataset + '0': no splitting + '1': randomly split into half + '2': For each subject, session 12 - 34 + '3': For each subject, session 13 - 24 + '4': For each subject, session 14 - 23 + z_score_data: bool + whether to z_score the data in pre-processing + Returns + ------- + tuple: A tuple containing the following + - subjs (list): A list of read-in subjects + - dataset (osl_dynamics.data.Data): the wrapped dataset + - dataset_2 (osl_dynamics.data.Data): if split strategy ! '0', the second half needs to be returned. + ''' + subjs = [] + data_list = [] + for file in sorted(self.data_dir.glob('*.txt')): + subjs.append(file.stem) + loaded_data = np.loadtxt(file) + n_session = int(len(loaded_data) / self.n_timepoint) + if split_session: + splitted_data = np.split(loaded_data,n_session) + for i in range(len(splitted_data)): + if z_score_data: + data_list.append(z_score(splitted_data[i])) + else: + data_list.append(splitted_data[i]) + else: + if z_score_data: + data_list.append(z_score(loaded_data,n_session)) + else: + data_list.append(loaded_data) + + print('Read from directory: ',self.data_dir) + print('Number of subjects: ',len(subjs)) + + if split_strategy == '0': + return subjs, Data(data_list,load_memmaps=False) + elif split_strategy == '1': + # Set the random seed for reproducibility + random_seed = 42 + random.seed(random_seed) + random_index = random.sample(range(len(data_list)),int(len(data_list) / 2)) + + first_half = [data_list[i] for i in random_index] + second_half = [array for i, array in enumerate(data_list) if i not in random_index] + return subjs, Data(first_half,load_memmaps=False), Data(second_half,load_memmaps=False) + elif split_strategy == '2': + N = len(data_list) // n_session + # Divide the list into groups: 4i, 4i+1 and 4i+2, 4i+3 + first_half = [data_list[n_session * i] for i in range(N)] + first_half.extend(data_list[n_session * i + 1] for i in range(N)) + + second_half = [data_list[n_session * i + 2] for i in range(N)] + second_half.extend(data_list[n_session * i + 3] for i in range(N)) + return subjs, Data(first_half, load_memmaps=False), Data(second_half, load_memmaps=False) + elif split_strategy == '3': + N = len(data_list) // n_session + # Divide the list into groups: 4i, 4i+2 and 4i+1, 4i+3 + first_half = [data_list[n_session * i] for i in range(N)] + first_half.extend(data_list[n_session * i + 2] for i in range(N)) + + second_half = [data_list[n_session * i + 1] for i in range(N)] + second_half.extend(data_list[n_session * i + 3] for i in range(N)) + return subjs, Data(first_half, load_memmaps=False), Data(second_half, load_memmaps=False) + elif split_strategy == '4': + N = len(data_list) // n_session + # Divide the list into groups: 4i, 4i+3 and 4i+1, 4i+2 + first_half = [data_list[n_session * i] for i in range(N)] + first_half.extend(data_list[n_session * i + 3] for i in range(N)) + + second_half = [data_list[n_session * i + 1] for i in range(N)] + second_half.extend(data_list[n_session * i + 2] for i in range(N)) + return subjs, Data(first_half, load_memmaps=False), Data(second_half, load_memmaps=False) + else: + raise ValueError('Incorrect split strategy!') + + + +def z_score(data:np.ndarray,n_session:int = 1): + """ + z_score the input data. + If n_session = 1, then z_score directly + If n_session > 1, then divide the data into different sessions, + z-score separately, and then concatenate back. + + Parameters: + data (np.ndarray): (n_timepoints,n_channel) + n_session (int): the number of sessions + + Returns + np.ndarray: z-scored data + """ + + if n_session == 1: + return stats.zscore(data,axis=0) + else: + if len(data) % n_session > 0: + raise ValueError('Number of time points is not divisible by n_session!') + n_timepoint, n_channel = data.shape + + # Split to n sessions + data = np.reshape(data,(n_session,-1,n_channel)) + + # z-score + data = stats.zscore(data,axis=1) + + # Concatenate and reshape + return np.reshape(data,(n_timepoint,n_channel)) diff --git a/rotation/simulation.py b/rotation/simulation.py new file mode 100644 index 00000000..ec2ed852 --- /dev/null +++ b/rotation/simulation.py @@ -0,0 +1,68 @@ +import numpy as np +from osl_dynamics import data, simulation +from osl_dynamics.inference import tf_ops +from osl_dynamics.models.hmm import Config, Model +from osl_dynamics.utils import plotting +from rotation.utils import cov2stdcor, stdcor2cov +from sklearn.model_selection import KFold +from matplotlib import pyplot as plt + +def perturb_covariances(covariances:np.ndarray,perturbation_factor: float=0.002,random_seed:int=None): + """ + Perturb the state covariances, the default setting is to only perturb the correlations + (perserving the standard deviations) + Parameters + ---------- + covariances: np.ndarray + The shape is n_states, n_channels, n_channels. The covariances matrix to perturb + perturbation_factor: float + Control the scaling of perturbation. + random_seed: int + Random seed to generate the perturbation. + + Returns + ------- + perturbed_covariances: np.ndarray + Shape is the same as covariances. Perturbed covariances. + """ + + if random_seed is not None: + np.random.seed(random_seed) + + perturbed_covariances = np.zeros_like(covariances) + + for i in range(len(covariances)): + # Generate a random perturbation matrix + cholesky_matrix = np.linalg.cholesky(covariances[i,:,:]) + perturbation_matrix = np.tril(np.random.normal(0, perturbation_factor, cholesky_matrix.shape)) + + # Add the perturbation to the original correlation matrix + perturbed_cholesky_matrix = cholesky_matrix + perturbation_matrix + + # Store the perturbed matrix in the result array + perturbed_covariances[i,:,:] = np.dot(perturbed_cholesky_matrix, perturbed_cholesky_matrix.T) + + return perturbed_covariances + + +def HMM_single_subject_simulation(save_dir:str, n_scans:int, n_states:int, n_samples:int,n_channels:int, + trans_prob:np.ndarray,means:np.ndarray,covariances:np.ndarray,subj_name:str='10001'): + tf_ops.gpu_growth() + time_series = [] + state_time_course = [] + for i in range(n_scans): + sim = simulation.HMM_MVN( + n_samples=n_samples, + n_states=n_states, + n_channels=n_channels, + trans_prob=trans_prob, + means=means, + covariances=covariances, + ) + time_series.append(sim.time_series) + state_time_course.append(sim.state_time_course) + time_series = np.concatenate(time_series,axis=0) + state_time_course = np.concatenate(state_time_course) + np.savetxt(f'{save_dir}{subj_name}.txt',time_series) + np.save(f'{save_dir}{subj_name}_state_time_course.npy',state_time_course) + np.save(f'{save_dir}{subj_name}_state_covariances.npy', covariances) \ No newline at end of file diff --git a/rotation/training.py b/rotation/training.py new file mode 100644 index 00000000..4ad3e7bd --- /dev/null +++ b/rotation/training.py @@ -0,0 +1,178 @@ +import pickle +import os +import numpy as np +import time +import json + +from osl_dynamics.analysis import connectivity +from rotation.utils import group_high_pass_filter + +def HMM_training(dataset,n_states,n_channels,save_dir,compute_state=False, + sequence_length=600,learn_means=False,learn_covariances=True,learn_trans_prob=True, + batch_size=64,learning_rate=1e-3,n_epochs=30): + from osl_dynamics.models.hmm import Config, Model + # Create a config object + config = Config( + n_states=n_states, + n_channels=n_channels, + sequence_length=sequence_length, + learn_means=learn_means, + learn_covariances=learn_covariances, + learn_trans_prob=learn_trans_prob, + batch_size=batch_size, + learning_rate=learning_rate, + n_epochs=n_epochs, + ) + + # Record the start time before training + start_time = time.time() + + # Initiate a Model class and print a summary + model = Model(config) + model.summary() + + # Initialization + init_history = model.random_state_time_course_initialization(dataset, n_epochs=2, n_init=10) + + # Full training + history = model.fit(dataset) + model.save(save_dir) + + #loss_history = history["loss"] + np.save(f'{save_dir}/loss_history.npy',np.array(init_history['loss'] + history['loss'])) + + end_time = time.time() + + # Calculate the training duration + training_duration = end_time - start_time + + # Print or save the training duration + print(f"Training time: {training_duration} seconds") + # Save the training duration to a JSON file + data = {"duration": training_duration} + + with open(f'{save_dir}/time.json', "w") as json_file: + json.dump(data, json_file) + + + # Compute state + if compute_state: + alpha = model.get_alpha(dataset) + pickle.dump(alpha, open(f'{save_dir}/alpha.pkl', "wb")) + + + +def Dynemo_training(dataset, n_modes, n_channels, save_dir,learn_means=False,compute_state=False): + from osl_dynamics.models.dynemo import Config, Model + # Create a config object + config = Config( + n_modes=n_modes, + n_channels=n_channels, + sequence_length=100, + inference_n_units=64, + inference_normalization="layer", + model_n_units=64, + model_normalization="layer", + learn_alpha_temperature=True, + initial_alpha_temperature=1.0, + learn_means=learn_means, + learn_covariances=True, + do_kl_annealing=True, + kl_annealing_curve="tanh", + kl_annealing_sharpness=5, + n_kl_annealing_epochs=10, + batch_size=64, + learning_rate=0.01, + n_epochs=30, # for the purposes of this tutorial we'll just train for a short period + ) + + # Initiate a Model class and print a summary + model = Model(config) + model.summary() + + # Initialization + init_history = model.random_subset_initialization(dataset, n_epochs=2, n_init=10,take=1.0) + + # Full training + history = model.fit(dataset) + model.save(save_dir) + + loss_history = history["loss"] + np.save(f'{save_dir}/loss_history.npy', np.array(loss_history)) + + # Compute state + if compute_state: + alpha = model.get_alpha(dataset) + pickle.dump(alpha, open(f'{save_dir}/alpha.pkl', "wb")) + + +def MAGE_training(dataset, n_modes, n_channels, save_dir, learn_means=False,compute_state=False): + from osl_dynamics.models.mdynemo import Config, Model + # Create a config object + config = Config( + n_modes=n_modes, + n_channels=n_channels, + sequence_length=100, + inference_n_units=64, + inference_normalization="layer", + model_n_units=64, + model_normalization="layer", + learn_alpha_temperature=True, + initial_alpha_temperature=1.0, + learn_means=learn_means, + learn_stds=True, + learn_fcs=True, + do_kl_annealing=True, + kl_annealing_curve="tanh", + kl_annealing_sharpness=5, + n_kl_annealing_epochs=10, + batch_size=64, + learning_rate=0.01, + n_epochs=30, # for the purposes of this tutorial we'll just train for a short period + ) + + # Initiate a Model class and print a summary + model = Model(config) + model.summary() + + # Initialization + init_history = model.random_subset_initialization(dataset, n_epochs=2, n_init=10, take=1.0) + + # Full training + history = model.fit(dataset) + model.save(save_dir) + + loss_history = history["loss"] + np.save(f'{save_dir}/loss_history.npy', np.array(loss_history)) + + # Compute state + if compute_state: + alpha = model.get_alpha(dataset) + pickle.dump(alpha, open(f'{save_dir}/alpha.pkl', "wb")) + +def SWC_computation(dataset,window_length,step_size,save_dir): + ''' + + Parameters + ---------- + dataset: (osl_dynamics.data.Data): Dataset for training + window_length: (int) sliding window length + step_size: (int) step size of sliding window + save_dir: save directions + + Returns + ------- + + ''' + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + ts = dataset.time_series() + filtered_ts = group_high_pass_filter(ts) + # Calculate the sliding window connectivity + swc = connectivity.sliding_window_connectivity(filtered_ts, window_length=window_length, step_size=step_size, conn_type="corr") + np.save(f'{save_dir}/cor_swc.npy',swc,allow_pickle=True) + + swc_cov = connectivity.sliding_window_connectivity(filtered_ts, window_length=window_length, step_size=step_size, + conn_type="cov") + np.save(f'{save_dir}/cov_swc.npy', swc_cov, allow_pickle=True) diff --git a/rotation/utils.py b/rotation/utils.py new file mode 100644 index 00000000..7fe83687 --- /dev/null +++ b/rotation/utils.py @@ -0,0 +1,525 @@ +from tqdm import trange +import warnings +import numpy as np +from scipy.sparse.linalg import eigsh +from scipy.optimize import linear_sum_assignment +import matplotlib.pyplot as plt +import seaborn as sns +import nibabel as nib +from nibabel.nifti1 import Nifti1Image + +from osl_dynamics.inference.metrics import riemannian_distance + +def parse_index(index:int,models:list,list_channels:list,list_states:list,training:bool=False): + ''' + This function is used in the array job. Given an index, + return the model, n_channels, n_states accordingly + + Parameters: + index: int + the input index in the array job + models: list + the model list + list_channels: list + the n_channel list + list_states: list + the n_state list + training: bool + Whether we are in the training mode. + + Returns: + model: string + The model to use + n_channels: int + The number of channels to use + n_states: int + The number of states to use + ''' + + N_n_channels = len(list_channels) + N_n_states = len(list_states) + + model = models[index // (N_n_channels * N_n_states)] + index = index % (N_n_channels * N_n_states) + + # For SWC, we do not need to specify n_states + if (model == 'SWC') & training: + n_channels = list_channels[index] + return model, n_channels, None + + n_channels = list_channels[index // N_n_states] + n_states = list_states[index % N_n_states] + + return model, n_channels, n_states + +def plot_FO(fo_matrix:np.ndarray,plot_dir:str,file_name:str=None): + """ + Plot the histogram of fractional occupancy (FO) + and save the plot to plot_dir + Parameters + ---------- + fo_matrix: np.ndarray + the fractional occupancy matrix + plot_dir: str + the save direction + file_name: str + the file name + Returns + ------- + """ + n_subj, n_state = fo_matrix.shape + + # Calculate the layout of subplots + n_cols = n_state // 4 + + # Create the subplots + fig, axes = plt.subplots(n_cols, 4, figsize=(15, 5 * n_cols), sharex=True) + + # Flatten the axes if x=1 to avoid issues with indexing + #if n_cols == 1: + axes = np.array(axes).flatten() + + # Iterate over states and plot histograms in each subplot + for state_idx in range(n_state): + row = state_idx // 4 + col = state_idx % 4 + + ax = axes[row * 4 + col] + + # Plot histogram for the current state + ax.hist(fo_matrix[:, state_idx], bins=20, edgecolor='black') + ax.set_title(f'State {state_idx + 1}') + + # Adjust the layout + plt.tight_layout() + + if file_name is None: + file_name = 'fo_hist' + # Show the plot + plt.savefig(f'{plot_dir}/{file_name}.jpg') + plt.savefig(f'{plot_dir}/{file_name}.pdf') + plt.close() + +def stdcor2cov(stds: np.ndarray, corrs:np.ndarray): + """ + Convert from M standard deviations vectors (N) or diagonal matrices (N*N) and M correlation matrices (N*N) + to M covariance matrices + Parameters + ---------- + stds: np.ndarray + standard deviation vectors with shape (M, N) or (M,N,N) (diagonal) + cors: np.ndarray + correlation matrices with shape (M, N, N) + + Returns + ------- + covariances: np.ndarray + covariance matrices with shape (M, N, N) + """ + + if stds.ndim == 2: + return (np.expand_dims(stds,-1) @ np.expand_dims(stds,-2)) * corrs + elif stds.ndim == 3: + return stds @ corrs @ stds + else: + raise ValueError('Check the dimension of your standard deviation!') + +def cov2stdcor(covs:np.ndarray): + """ + convert from covariance matrices (M*N*N) to + stds (M*N) and correlation matrices (M * N * N) + Parameters + ---------- + covs: np.ndarray + shape M*N*N + + Returns + ------- + stds: np.ndarray + shape M*N + corrs: np.ndarray + shape M*N*N + """ + # Validation + if covs.ndim < 2: + raise ValueError("input covariances must have more than 1 dimension.") + + # Extract batches of standard deviations + stds = np.sqrt(np.diagonal(covs, axis1=-2, axis2=-1)) + normalisation = np.expand_dims(stds, -1) @ np.expand_dims(stds, -2) + return stds, covs / normalisation + +def first_eigenvector(matrix: np.ndarray): + """ + Compute the first eigenvector (corresponding to the largest eigenvector) + of a symmetric matrix + Parameters + ---------- + matrix: numpy.ndarray + N * N. Symmetric matrix. + + Returns + ------- + eigenvector: numpy.ndarray + N: the first eigenvector. + """ + _, eigenvector = eigsh(matrix,k=1,which='LM') + eigenvector = np.squeeze(eigenvector) + # Ensure that the returned eigenvector has norm 1 + return eigenvector / (np.linalg.norm(eigenvector) + 1e-10) + +def IC2brain(spatial_map:Nifti1Image,IC_metric:np.ndarray): + """ + Project the IC_metric map to a brain map according to spatial maps of IC components. + For example, IC_metric can be the mean activation of states, or the rank-one decomposition + of FC matrix corresponding to different states. + Parameters + ---------- + spatial_map: Nifti1Image, represent the spatial map obtained from groupICA + IC_metric: np.ndarray(K, N), + where K is the number of independent components, and N is the number of states + + Returns + ------- + brain_map: Nifti1Image, represent the whole brain map of different states. + """ + spatial_map_data = spatial_map.get_fdata() + spatial_map_shape = spatial_map_data.shape + K, N = IC_metric.shape + # Assert the matrix multiplication is plausible + assert spatial_map_shape[-1] == K + + # Implement the multiplication + brain_map_data = np.matmul(spatial_map_data,IC_metric) + #Store brain map to a Nifti1Image type + brain_map = Nifti1Image(brain_map_data, affine=spatial_map.affine, header=spatial_map.header) + + return brain_map + +def IC2surface(spatial_map: nib.cifti2.cifti2.Cifti2Image,IC_metric:np.ndarray)->nib.cifti2.cifti2.Cifti2Image: + """ + Project the IC_metric map to a brain map according to spatial maps of IC components. + For example, IC_metric can be the mean activation of states, or the rank-one decomposition + of FC matrix corresponding to different states. + Different from IC2brain, this function works on CIFTI spatial maps. + + Parameters + ---------- + spatial_map: Cifti2Image, represent the spatial map obtained from groupICA + IC_metric: np.ndarray(K, N), + where K is the number of independent components, and N is the number of states + Returns + ------- + new_cifti: Cifti2Image, represent the whole brain map of different states. + """ + spatial_map_data = spatial_map.get_fdata() + header = spatial_map.header + K, N = IC_metric.shape + new_data = np.matmul(IC_metric.T, spatial_map_data) + + # Define the new axes: the state i(i=1,...,N) + new_axes = nib.cifti2.cifti2_axes.ScalarAxis([f'State {i + 1}' for i in range(N)]) + + # Define the new header using new_axes + axes[1] from old header + new_header = nib.cifti2.cifti2.Cifti2Header.from_axes((new_axes, header.get_axis(1))) + + # Combine new data and new header to obtain new CIFTI object + new_cifti = nib.cifti2.cifti2.Cifti2Image(new_data, new_header) + + return new_cifti + + +def fisher_z_transform(v:np.ndarray)->np.ndarray: + """ + Fisher z-transform each element of the input + z = \frac{1}{2}\ln(\frac{1+r}{1-r}) + Parameters + ---------- + v: (numpy.ndarray) Elements in [-1,1] + + Returns + ------- + Fisher z-transformed matrix + """ + return 0.5 * np.log((1 + v)/(1 - v)) +def fisher_z_correlation(M1:np.ndarray,M2:np.ndarray)->float: + """ + Compute the Fisher z-transformed vector correlation. + Fisher z-transformation: z = \frac{1}{2}\ln(\frac{1+r}{1-r}) + Suppose M1 and M2 are in shape N*N, semi-positive definite + Step 1: Obtain the lower triangular element of M1 and M2, + unwrap to vectors v1, v2 with length N * (N - 1) / 2 + Step 2: Fisher z-transform vectors v1, v2 to z1, z2 + Step 3: return the Pearson's correlation between z1, z2 + Parameters + ---------- + M1: (numpy.ndarray) correlation/matrix 1 + M2: (numpy.ndarray) matrix 2 + + Returns + ------- + dist: (float) distance between M1 and M2 + """ + N = M1.shape[0] + assert N == M2.shape[0] + + # Obtain the upper triangular elements + upper_indices = np.triu_indices(N, k=1) + v1 = M1[upper_indices] + v2 = M2[upper_indices] + + # Fisher-z-transformation + z1 = fisher_z_transform(v1) + z2 = fisher_z_transform(v2) + + # return the correlation + return np.cov(z1,z2,ddof=0)[0,1]/ (np.std(z1,ddof=0) * np.std(z2,ddof=0)) + +def pairwise_fisher_z_correlations(matrices:np.ndarray)->np.ndarray: + """ + Compute the pairwise Fisher z-transformed correlations of matrices + See function fisher_z_correlation for details + Parameters + ---------- + matrices: numpy.ndarray with shape M*N*N + + Returns + ------- + correlations: numpy.ndarray with shape M * M + """ + + N = len(matrices) + correlation_metrics = np.eye(N) + for i in trange(N,desc='Compute Fisher-z transformated correlation'): + for j in range(i + 1, N): + correlation_metrics[i][j] = fisher_z_correlation( + matrices[i], matrices[j] + ) + correlation_metrics[j][i] = correlation_metrics[i][j] + + return correlation_metrics + +def high_pass_filter(data:np.ndarray,T:float,cutoff_frequency:float,order:int)->np.ndarray: + """ + + Parameters + ---------- + data: (np.ndarray) N_channels*N_timepoints + T: (float) temporal resolution of the signal + cutoff_frequency: (float) cutoff frequency of the filter + order: (int) order of the filter + Returns + ------- + filtered_data: np.ndarray, having the same format as data + """ + from scipy.signal import butter,filtfilt + N = data.shape[1] + fs = 1 / T # Sampling frequency + nyquist = 0.5 * fs #Nyquist frequency + b_butter,a_butter = butter(order,cutoff_frequency,btype="highpass",fs=fs) + filtered_data = filtfilt(b_butter,a_butter,data) + + return filtered_data + +def group_high_pass_filter(ts:list,T:float=0.7,cutoff_frequency:float=0.25,order:int=16) -> list: + """ + Apply high pass filter to the group time series + Parameters + ---------- + ts: (list) group time series, each element should be a np.ndarray (N_timepoint, N_channels) + T: (float) temporal resolution of the signal + cutoff_frequency: (float) cutoff frequency of the filter + order: (int) order of the high-pass filter + + Returns + ------- + returned_ts: (list) filtered signal with the same format as ts + """ + returned_ts = [] + for i in trange(len(ts),desc='high pass filtering signals'): + returned_ts.append(high_pass_filter(ts[i].T, + T=T, + cutoff_frequency=cutoff_frequency, + order=order).T + ) + + return returned_ts +def regularisation(matrices:np.ndarray,eps:float=1e-6)->np.ndarray: + """ + Regularise a bunch of matrices by adding eps to the diagonal + Parameters + ---------- + matrices: np.ndarray with shape (M,N,N) or (N,N) + eps: float + + Returns + ------- + regularised_matrices: np.ndarray with shape (M,N,N) or (N,N) + """ + if matrices.ndim == 2: + N = len(matrices) + elif matrices.ndim == 3: + _,N,_ = matrices.shape + else: + return ValueError('Matrices dimension is incorrect!') + + return matrices + np.eye(N) * eps + +def twopair_vector_correlation(vectors_1:np.ndarray,vectors_2:np.ndarray)->np.ndarray: + """ + Compute the pairwise correlation \rho_{ij} + between ith vectors_1 and jth vectors_j + Parameters + ---------- + matrices_1: (M1*N), M1 is the number of vectors, + N is the vector length + marices_2: (M2*N) M2 is the number of vectors, + N is the vector length + + Returns + ------- + correlation_map: M1*M2 + """ + M1, N1 = vectors_1.shape + M2, N2 = vectors_2.shape + assert N1 == N2 + correlations = np.empty((M1, M2)) + + for i in range(M1): + for j in range(M2): + correlation = np.corrcoef(vectors_1[i, :], vectors_2[j, :])[0, 1] + correlations[i, j] = correlation + return correlations + +def twopair_riemannian_distance(matrices_1:np.ndarray,matrices_2:np.ndarray,eps_value:list=[0,1e-9,1e-8,1e-7,1e-6,1e-5],threshold:float=1e-3)->np.ndarray: + """ + Compute the Riemannian distance between two sets of matrices + Parameters + ---------- + matrices_1: (np.ndarray) M1*N*N, M1 is the number of matrices,N*N is the matrix size + matrices_2: (np.ndarray) M2*N*N, M2 is the number of matrices,N*N is the matrix size + eps: (list) regularisation factor + threshold: (float) Threshold to apply when there are negative eigenvalues. Must be positive. + Returns + riemannian_distances: (M1*M2) two pair riemannian distance + ------- + """ + M1,N1,P1 = matrices_1.shape + M2,N2,P2 = matrices_2.shape + assert N1 == P1 + assert N1 == N2 + assert P1 == P2 + + matrices_1.astype(np.float64) + matrices_2.astype(np.float64) + riemannian_distances = np.zeros([M1,M2]) + for eps in eps_value: + try: + for i in trange(M1, desc="Computing Riemannian distances"): + for j in range(M2): + riemannian_distances[i][j] = riemannian_distance( + regularisation(matrices_1[i],eps), + regularisation(matrices_2[j],eps), + threshold=threshold + ) + return riemannian_distances + except np.linalg.LinAlgError: + warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}') + + raise ValueError('Riemannian distance failed for all eps values!') + +def twopair_fisher_z_transformed_correlation(matrices_1:np.ndarray,matrices_2:np.ndarray)->np.ndarray: + """ + Compute the Fisher_z_transformed correlation between two sets of matrices + Parameters + ---------- + matrices_1: (np.ndarray) M1*N*N, M1 is the number of matrices,N*N is the matrix size + matrices_2: (np.ndarray) M2*N*N, M2 is the number of matrices,N*N is the matrix size + + Returns + fisher_z_transformed_correlation: (M1*M2) two pair fisher_z_correlation + ------- + """ + M1 = len(matrices_1) + M2 = len(matrices_2) + fisher_z_transformed_correlation = np.zeros([M1,M2]) + for i in trange(M1, desc='Compute twopair Fisher-z transformated correlation'): + for j in range(M2): + fisher_z_transformed_correlation[i][j] = fisher_z_correlation( + matrices_1[i], matrices_2[j] + ) + + return fisher_z_transformed_correlation + + +def hungarian_pair(matrix:np.ndarray,distance:bool=False): + """ + Use the hungarian algorithm to pair different "states" according to the matrix + Parameters + ---------- + matrix: (M*M) distance/correlation matrix as the measure for pairing + distance: (str) True: matrix measures distance. + False: matrix measures correlation + + Returns + ------- + + """ + if distance: + row_indices, col_indices = linear_sum_assignment(matrix) + else: + row_indices, col_indices = linear_sum_assignment(np.max(matrix) - matrix) + + indices = {'row':row_indices.tolist(),'col':col_indices.tolist()} + matrix_reordered = matrix[row_indices,:] + matrix_reordered = matrix_reordered[:,col_indices] + return indices, matrix_reordered +def heatmap_reorder_matrix(matrix:np.ndarray,plot_dir:str,plot_statistics:str,indices:dict,model_name:str,n_channels:int,n_states:int,split_strategy:str=''): + """ + Plot the reordered matrix while putting the row and column indices onto the x and y ticks + + Parameters + ---------- + matrix: np.ndarray + matrix to plot + plot_dir: str + the directory to save the plot + plot_statistics: str + 'means_correlation' or 'FCs_distance' + indices: dict + indices with keys: 'row' and 'col' + model_name: str + model name + n_channels: int + number of channels + n_states: int + number of states + split_strategy: str + Specific to split half plot, split_strategy = '1','2','3','4' + + Returns + ------- + """ + + # When plotting, change the model name from MAGE to mDynemo + if model_name == 'MAGE': + model_name = 'mDynemo' + + # Set up the figure and axis + plt.figure(figsize=(8, 6)) + sns.set(font_scale=1.2) # Adjust font size for labels + + # Create a heatmap of the correlation matrix + sns.heatmap(matrix, cmap="coolwarm", square=True, + linewidths=.5, cbar_kws={"shrink": 0.75}, fmt=".2f") + plt.xticks(np.arange(len(indices['col'])) + 0.5, indices['col'], fontsize=13) + plt.yticks(np.arange(len(indices['row'])) + 0.5, indices['row'], fontsize=13) + + plt.title(f'{model_name}_ICA_{n_channels}_state_{n_states}_split_{split_strategy}, {plot_statistics}', fontsize=20) + if split_strategy is not None: + plt.savefig(f'{plot_dir}{plot_statistics}_plot_split_{split_strategy}.jpg') + plt.savefig(f'{plot_dir}{plot_statistics}_plot_split_{split_strategy}.pdf') + else: + plt.savefig(f'{plot_dir}{plot_statistics}_plot.jpg') + plt.savefig(f'{plot_dir}{plot_statistics}_plot.pdf') + plt.close() diff --git a/sbatch.sh b/sbatch.sh new file mode 100644 index 00000000..762bd7ad --- /dev/null +++ b/sbatch.sh @@ -0,0 +1,51 @@ +#!/bin/bash + +# Specify a job name +#SBATCH -J swimming + +# Account name and target partition +#SBATCH -A win.prj +#SBATCH -p short + +# Check whether the directory log is available. Create otherwise. +#if [ ! -d "log" ]; then +# mkdir log +#fi + +# Generate timestamp +#timestamp=$(date +"%Y%m%d_%H%M%S") + +# Log locations which are relative to the current +# working directory of the submission +#SBATCH --output=./log/slurm_%A_%a.out + + +# Parallel environment settings +# For more information on these please see the documentation +# Allowed parameters: +# -c, --cpus-per-task +# -N, --nodes +# -n, --ntasks +#SBATCH -c 1 + +# Some useful data about the job to help with debugging +echo "------------------------------------------------" +echo "Slurm Job ID: $SLURM_JOB_ID" +echo "Slurm Array Task ID: $SLURM_ARRAY_TASK_ID" +echo "Run on host: "`hostname` +echo "Operating system: "`uname -s` +echo "Username: "`whoami` +echo "Started at: "`date` +echo "------------------------------------------------" + +# Load the environment +module purge +module load Anaconda3/2022.05 +eval "$(conda shell.bash hook)" +conda activate osld # Environment name + +# Begin writing your script here + +python main.py $SLURM_ARRAY_TASK_ID + +# End of job script diff --git a/sbatch_gpu.sh b/sbatch_gpu.sh new file mode 100644 index 00000000..c4f77dd2 --- /dev/null +++ b/sbatch_gpu.sh @@ -0,0 +1,53 @@ +#!/bin/bash + +# Specify a job name +#SBATCH -J swimming + +# Account name and target partition +#SBATCH -A win.prj +#SBATCH -p gpu_short +#SBATCH --gres gpu:1 + +# Check whether the directory log is available. Create otherwise. +#if [ ! -d "log" ]; then +# mkdir log +#fi + +# Generate timestamp +#timestamp=$(date +"%Y%m%d_%H%M%S") + +# Log locations which are relative to the current +# working directory of the submission +#SBATCH --output=./log/slurm_%A_%a.out + + +# Parallel environment settings +# For more information on these please see the documentation +# Allowed parameters: +# -c, --cpus-per-task +# -N, --nodes +# -n, --ntasks +#SBATCH -c 1 + +# Some useful data about the job to help with debugging +echo "------------------------------------------------" +echo "Slurm Job ID: $SLURM_JOB_ID" +echo "Slurm Array Task ID: $SLURM_ARRAY_TASK_ID" +echo "Run on host: "`hostname` +echo "Operating system: "`uname -s` +echo "Username: "`whoami` +echo "Started at: "`date` +echo "------------------------------------------------" + +# Load the environment +module purge +module load Anaconda3/2022.05 +module load cuDNN +eval "$(conda shell.bash hook)" +conda activate osld # Environment name + +# Begin writing your script here + +python main.py $SLURM_ARRAY_TASK_ID + +# End of job script diff --git a/simulation_main.py b/simulation_main.py new file mode 100644 index 00000000..3e78736f --- /dev/null +++ b/simulation_main.py @@ -0,0 +1,85 @@ +import os +import numpy as np +from rotation.simulation import HMM_single_subject_simulation, perturb_covariances + +if __name__ == '__main__': + save_dir ='./data/node_timeseries/simulation_202401/sigma_0.025/run_0/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + perturbation_factor = 0.025 + # Define the parameters + n_scans = 100 + n_states = 8 + n_samples = 1200 + n_channels = 25 + + # Read from early TPM + #tpm = np.load('./results_HCP_202311_no_mean/HMM_ICA_50_state_4/trans_prob.npy') + #tpm = np.array([[0.8,0.2],[0.2,0.8]]) + #tpm = np.array([[0.96,0.01,0.01,0.01,0.01], + # [0.01,0.96,0.01,0.01,0.01], + # [0.01,0.01,0.96,0.01,0.01], + # [0.01,0.01,0.01,0.96,0.01], + # [0.01,0.01,0.01,0.01,0.96]]) + #tpm = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/trans_prob.npy') + + # tpm for simulation_toy_6 + #diagonal_value = 0.99 + #off_diagonal_value = (1 - diagonal_value) / (n_states - 1) + #tpm = diagonal_value * np.eye(n_states) + off_diagonal_value * (1 - np.eye(n_states)) + + # tpm for simulation_toy_9 (two subjects) + tpm = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/trans_prob.npy') + + + #means = np.load('./results_202310/HMM_ICA_50_state_4/state_means.npy') + means = np.zeros((n_states,n_channels)) + #covariances = np.load('./results_HCP_202311_no_mean/HMM_ICA_50_state_4/state_covariances.npy') + #covariances = np.array([[[0.25,0.2],[0.2,0.25]],[[1.,0.8],[0.8,1.]]]) + #covariances = np.array([ + # [[1.,-0.1],[-0.1,1.]], + # [[1.,-0.05],[-0.05,1.]], + # [[1.,0.],[0.,1.]], + # [[1.,0.05],[0.05,1.]], + # [[1.,0.1],[0.1,1.]] + #]) + covariances = np.load('./results_HCP_202311_no_mean/HMM_ICA_25_state_8/state_covariances.npy') + + # Update 20240119: generate ten subjects for simulation + for i in range(10001,10011): + HMM_single_subject_simulation(save_dir=save_dir, + n_scans=n_scans, + n_states=n_states, + n_samples=n_samples, + n_channels=n_channels, + trans_prob=tpm, + means=means, + covariances=perturb_covariances(covariances,perturbation_factor=perturbation_factor), + subj_name = str(i)) + ''' + ### Update 6th Dec 2023 + ### This is a simulation from Chet, it's only for comparison purposes. + ### Please comment out all previous codes when doing the following simulation + save_dir = './data/node_timeseries/simulation_toy_8/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + from osl_dynamics import data, simulation + sim = simulation.HMM_MVN( + n_samples=25600, + n_states=5, + n_channels=11, + trans_prob="sequence", + stay_prob=0.9, + means="zero", + covariances="random", + random_seed=123, + ) + + from osl_dynamics.data import Data + # Create Data object for training + data = Data(sim.time_series) + np.savetxt(f'{save_dir}10001.txt', sim.time_series) + np.save(f'{save_dir}10001_state_time_course.npy', sim.state_time_course) + np.save(f'{save_dir}10001_state_covariances.npy',sim.obs_mod.covariances) + ''' \ No newline at end of file diff --git a/tests/test_analysis/test_modes.py b/tests/test_analysis/test_modes.py new file mode 100644 index 00000000..8f6df744 --- /dev/null +++ b/tests/test_analysis/test_modes.py @@ -0,0 +1,21 @@ +import numpy as np +import numpy.testing as npt + +def test_fractional_occupancies(): + from osl_dynamics.analysis.modes import fractional_occupancies + + # Test case 1: One session with a 2D numpy array + state_time_course_1 = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0]]) + expected_result_1 = np.array([2 / 3, 2 / 3, 1 / 3]) + npt.assert_equal(fractional_occupancies(state_time_course_1), expected_result_1) + + # Test case 2: Multiple sessions with a 3D numpy array + state_time_course_2 = np.array([[[1, 0, 1], [0, 1, 0], [1, 1, 0]], + [[1, 1, 1], [0, 0, 0], [1, 0, 1]]]) + expected_result_2 = np.array([[2 / 3, 2 / 3, 1 / 3], [2 / 3, 1 / 3, 2 / 3]]) + npt.assert_equal(fractional_occupancies(state_time_course_2), expected_result_2) + + # Test case 3: Multiple sessions with a list of numpy arrays + state_time_course_3 = [np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0]]), + np.array([[1, 1, 1], [0, 0, 0], [1, 0, 1]])] + npt.assert_equal(fractional_occupancies(state_time_course_3), expected_result_2) \ No newline at end of file diff --git a/tests/test_array_ops/test_array_ops.py b/tests/test_array_ops/test_array_ops.py new file mode 100644 index 00000000..68b8046a --- /dev/null +++ b/tests/test_array_ops/test_array_ops.py @@ -0,0 +1,191 @@ +import numpy as np +import numpy.testing as npt + +def test_get_one_hot(): + from osl_dynamics.array_ops import get_one_hot + + # Case 1: Categorical input + input_1 = np.array([0,2,0,1]) + output_1 = np.array([ + [1,0,0], + [0,0,1], + [1,0,0], + [0,1,0] + ]) + npt.assert_equal(get_one_hot(input_1),output_1) + + # Case 2: Categorical input, but input n_states + input_2 = np.array([0, 2, 0, 1]) + output_2 = np.array([ + [1, 0, 0, 0], + [0, 0, 1, 0], + [1, 0, 0, 0], + [0, 1, 0, 0] + ]) + npt.assert_equal(get_one_hot(input_2,n_states=4), output_2) + + # Case 3: (n_samples, n_states) to be binarized + input_3 = np.array([ + [0.99,0.03,0.03], + [0.02,0.02,0.9], + [0.80,0.4,0.5], + [-1.,-0.5,-1.] + ]) + output_3 = np.array([ + [1,0,0], + [0,0,1], + [1,0,0], + [0,1,0] + ]) + npt.assert_equal(get_one_hot(input_3),output_3) + + # Case 4: (n_samples, n_states) to be binarized, input n_states + input_4 = np.array([ + [0.99, 0.03, 0.03], + [0.02, 0.02, 0.9], + [0.80, 0.4, 0.5], + [-1., -0.5, -1.] + ]) + output_4 = np.array([ + [1, 0, 0, 0], + [0, 0, 1, 0], + [1, 0, 0, 0], + [0, 1, 0, 0] + ]) + npt.assert_equal(get_one_hot(input_4,n_states=4), output_4) + +def test_cov2std(): + from osl_dynamics.array_ops import cov2std + + # Case 1: One covariance matrix + cov = np.array([[16.0, 4.0], [4.0, 4.0]]) + std = cov2std(cov) + npt.assert_equal(std, np.array([4.0, 2.0])) + + # Case 2: Two covariance matrices + covs = np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]]) + stds = cov2std(covs) + npt.assert_equal(stds, np.array([[4.0, 2.0], [10.0, 20.0]])) + +def test_cov2corr(): + from osl_dynamics.array_ops import cov2corr + + # Case 1: One covariance matrix + cov = np.array([[100.0, -40.0], [-40.0, 400.0]]) + corr = cov2corr(cov) + npt.assert_equal(corr, np.array([[1.0, -0.2], [-0.2, 1.0]])) + + # Case 2: Two covariance matrices + covs = np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]]) + corrs = cov2corr(covs) + npt.assert_equal(corrs, np.array([[[1.0, 0.5], [0.5, 1.0]], [[1.0, -0.2], [-0.2, 1.0]]])) + +def test_stdcorr2cov(): + from osl_dynamics.array_ops import stdcorr2cov + # Case 1: One covariance matrix, std is a vector + std = np.array([4.0, 2.0]) + corr = np.array([[1.0, 0.5], [0.5, 1.0]]) + cov = stdcorr2cov(std,corr) + npt.assert_equal(cov,np.array([[16.0, 4.0], [4.0, 4.0]])) + + # Case 2: Two covariance matrices, std is two vectors + stds = np.array([[4.0, 2.0], [10.0, 20.0]]) + corrs = np.array([[[1.0, 0.5], [0.5, 1.0]], [[1.0, -0.2], [-0.2, 1.0]]]) + covs = stdcorr2cov(stds,corrs) + npt.assert_equal(covs,np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]])) + + # Case 3: One covariance matrix, std is a diagonal matrix + std = np.array([[4.0,0.0],[0.0, 2.0]]) + corr = np.array([[1.0, 0.5], [0.5, 1.0]]) + cov = stdcorr2cov(std, corr,std_diagonal=True) + npt.assert_equal(cov, np.array([[16.0, 4.0], [4.0, 4.0]])) + + # Case 4: Two covariance matrices, std is two diagonal matrices + stds = np.array([[[4.0,0.0],[0.0,2.0]], [[10.0,0.0],[0.0,20.0]]]) + corrs = np.array([[[1.0, 0.5], [0.5, 1.0]], [[1.0, -0.2], [-0.2, 1.0]]]) + covs = stdcorr2cov(stds, corrs,std_diagonal=True) + npt.assert_equal(covs, np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]])) + +def test_cov2stdcorr(): + from osl_dynamics.array_ops import cov2stdcorr + # Case 1: One covariance matrix + cov = np.array([[16.0, 4.0], [4.0, 4.0]]) + std,corr = cov2stdcorr(cov) + npt.assert_equal(std, np.array([4.0, 2.0])) + npt.assert_equal(corr,np.array([[1.0, 0.5], [0.5, 1.0]])) + + + # Case 2: Two covariance matrices + covs = np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]]) + stds,corrs = cov2stdcorr(covs) + npt.assert_equal(stds, np.array([[4.0, 2.0], [10.0, 20.0]])) + npt.assert_equal(corrs, np.array([[[1.0, 0.5], [0.5, 1.0]], [[1.0, -0.2], [-0.2, 1.0]]])) + +def test_check_symmetry(): + from osl_dynamics.array_ops import check_symmetry + # Case 1: One matrix, symmetric + matrix = np.array([[1.0,0.0],[0.0,1.0]]) + npt.assert_equal(check_symmetry(matrix),np.array([True])) + + # Case 2: One matrix, non-symmetric + matrix = np.array([[1.0, 0.1], [0.0, 1.0]]) + npt.assert_equal(check_symmetry(matrix), np.array([False])) + + # Case 3: Two matrices + matrix = np.array([[[1.0, 0.1], [0.0, 1.0]],[[1.0,0.0],[0.0,1.0]]]) + npt.assert_equal(check_symmetry(matrix), np.array([False,True])) + + # Case 4: One matrix, symmetric wrt eps=1e-6 + matrix = np.array([[1.0, 0.99e-6], [0.0, 1.0]]) + npt.assert_equal(check_symmetry(matrix,precision=1e-6), np.array([True])) + npt.assert_equal(check_symmetry(matrix, precision=1e-7), np.array([False])) + +def test_ezclump(): + from osl_dynamics.array_ops import ezclump + # Test case 1: No clumps (all False) + arr1 = np.array([False, False, False, False, False]) + expected_result1 = [] + npt.assert_equal(ezclump(arr1), expected_result1) + + # Test case 2: Single clump of True values + arr2 = np.array([False, True, True, True, False]) + expected_result2 = [slice(1, 4)] + npt.assert_equal(ezclump(arr2), expected_result2) + + # Test case 3: Multiple clumps of True values + arr3 = np.array([False, True, False, True, True, False, True, False]) + expected_result3 = [slice(1, 2), slice(3, 5), slice(6, 7)] + npt.assert_equal(ezclump(arr3), expected_result3) + + # Test case 4: Clumps at both ends + arr4 = np.array([True, False, False, True, True, False, False, True]) + expected_result4 = [slice(0, 1), slice(3, 5), slice(7, 8)] + npt.assert_equal(ezclump(arr4), expected_result4) + + # Test case 5: All True values + arr5 = np.array([True, True, True, True, True]) + expected_result5 = [slice(0, 5)] + npt.assert_equal(ezclump(arr5), expected_result5) + +def test_slice_length(): + from osl_dynamics.array_ops import slice_length + + # Test case 1: Slice with positive values + s1 = slice(3, 7) + expected_length1 = 4 + npt.assert_equal(slice_length(s1), expected_length1) + + # Test case 2: Slice with negative values + s2 = slice(-5, -2) + expected_length2 = 3 + npt.assert_equal(slice_length(s2), expected_length2) + + # Test case 3: Slice with zero-based start + s3 = slice(0, 5) + expected_length3 = 5 + npt.assert_equal(slice_length(s3), expected_length3) + + # Test case 4: Empty slice + s4 = slice(10, 10) + expected_length4 = 0 + npt.assert_equal(slice_length(s4), expected_length4) diff --git a/tests/test_config_api/test_pipeline.py b/tests/test_config_api/test_pipeline.py new file mode 100644 index 00000000..87cdc1f9 --- /dev/null +++ b/tests/test_config_api/test_pipeline.py @@ -0,0 +1,95 @@ +import numpy as np +import numpy.testing as npt +from osl_dynamics.config_api.pipeline import run_pipeline_from_file + +def test_run_pipeline_from_file(): + import os + import json + import yaml + from shutil import rmtree + save_dir = './test_pipeline_temp/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + def generate_obs(cov,n_timepoints=300): + return np.random.multivariate_normal(np.zeros(len(cov)),cov,n_timepoints) + # Define the covariance matrices of state 1,2 in both splits + cor = [0.25,0.8] + split_1_cov_1 = np.array([[1.0,cor[0]],[cor[0],1.0]]) * 2 + split_1_cov_2 = np.array([[1.0,-cor[0]],[-cor[0],1.0]]) * 2 + split_2_cov_1 = np.array([[1.0,cor[1]],[cor[1],1.0]]) * 0.5 + split_2_cov_2 = np.array([[1.0,-cor[1]],[-cor[1],1.0]]) * 0.5 + split_0_cov = np.array([[100.,-25.0],[-25.0,100.0]]) + split_0_state = generate_obs(split_0_cov, n_timepoints=3600) + + # save these files + data_dir = f'{save_dir}data/' + if not os.path.exists(data_dir): + os.makedirs(data_dir) + for i in range(0, 500, 2): + state_1 = generate_obs(split_1_cov_1) + state_2 = generate_obs(split_1_cov_2) + obs = np.tile(np.concatenate([state_1,state_2], axis=0),(2,1)) + + filename = f"{data_dir}{10001 + i}.txt" + np.savetxt(filename, np.concatenate([obs,split_0_state],axis=0)) + + for i in range(0, 500, 2): + state_1 = generate_obs(split_2_cov_1) + state_2 = generate_obs(split_2_cov_2) + obs = np.tile(np.concatenate([state_1, state_2], axis=0), (2, 1)) + filename = f"{data_dir}{10002 + i}.txt" + np.savetxt(filename, np.concatenate([obs,split_0_state],axis=0)) + + with open(f'{save_dir}list_1.json','w') as file: + json.dump(list(range(0, 1000, 2)),file) + + with open(f'{save_dir}list_2.json','w') as file: + json.dump(list(range(1, 1001, 2)),file) + + for i in range(1,3): + config = f""" + load_data: + inputs: {data_dir} + prepare: + select: + timepoints: + - 0 + - 1200 + standardize: {{}} + keep_list: {save_dir}list_{i}.json + train_hmm: + config_kwargs: + n_states: 2 + learn_means: False + learn_covariances: True + learn_trans_prob: True + learning_rate: 0.005 + n_epochs: 100 + sequence_length: 100 + init_kwargs: + n_init: 10 + n_epochs: 2 + + """ + output_dir = f'{save_dir}split_{i}/' + if not os.path.exists(output_dir): + os.makedirs(output_dir) + with open(f'{output_dir}train_config.yaml', "w") as file: + yaml.safe_dump(yaml.safe_load(config), file,default_flow_style=False) + + run_pipeline_from_file(f'{output_dir}train_config.yaml',output_dir) + + covs = np.load(f'{output_dir}/inf_params/covs.npy') + npt.assert_almost_equal(np.sort(covs[:, 0, 1]), np.array([-cor[i-1], cor[i-1]]),decimal=2) + + # Remove the directory after testing + rmtree(save_dir) + + + + + + + + diff --git a/tests/test_config_api/test_wrappers.py b/tests/test_config_api/test_wrappers.py new file mode 100644 index 00000000..fe1e41f1 --- /dev/null +++ b/tests/test_config_api/test_wrappers.py @@ -0,0 +1,23 @@ +import numpy as np +import numpy.testing as npt +from osl_dynamics.config_api.wrappers import load_data + +def test_load_data(): + import os + import json + save_dir = './test_temp/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + vector = np.array([-1.5 ** 0.5, 0, 1.5 ** 0.5]) + input_1 = np.array([vector, vector + 10.0]).T + input_2 = np.array([vector * 0.5 + 1., vector * 100]).T + np.savetxt(f'{save_dir}10001.txt', input_1) + np.savetxt(f'{save_dir}10002.txt', input_2) + prepare = {'standardize':{}} + + data = load_data(inputs=save_dir,prepare=prepare) + npt.assert_almost_equal(data.arrays[0],np.array([vector,vector]).T) + npt.assert_almost_equal(data.arrays[1],np.array([vector,vector]).T) + # Remove the directory after testing + from shutil import rmtree + rmtree(save_dir) diff --git a/tests/test_data/test_base.py b/tests/test_data/test_base.py new file mode 100644 index 00000000..5789e445 --- /dev/null +++ b/tests/test_data/test_base.py @@ -0,0 +1,151 @@ +import numpy as np +import numpy.testing as npt +from osl_dynamics.data import Data + +def test_data_nparray(): + # Note: the input should be (n_timepoints,n_channels) + input = np.array([[1.,0.5],[2.,0.4],[1.5,0.3]]) + + data = Data(input) + + # Test self.__iter__ + for obj in data: + npt.assert_almost_equal(obj,input) + # Test self.__item__ + npt.assert_almost_equal(data.arrays[0],input) + # Test @property self.raw_data + npt.assert_almost_equal(data.raw_data[0],input) + # Test @property self.n_channels + npt.assert_equal(data.n_channels,2) + # Test @property self.n_sammples + npt.assert_equal(data.n_samples,3) + # Test set.sampling_frequency + data.set_sampling_frequency(1.0) + npt.assert_equal(data.sampling_frequency,1.0) + # Test set buffer_size + data.set_buffer_size(100) + npt.assert_equal(data.buffer_size,100) + # Test self.select + data.select(channels=[1],timepoints=[1,3]) + npt.assert_almost_equal(data.arrays[0],input[1:3,1:]) + +def test_data_list_nparray(): + input_1 = np.array([[1.,0.5],[2.,0.4],[1.5,0.3]]) + input_2 = input_1 / 2 + data = Data([input_1,input_2]) + # Test self.__getitem__ + npt.assert_almost_equal(data.arrays[0],input_1) + npt.assert_almost_equal(data.arrays[1],input_2) + # Test @property self.n_arrays + npt.assert_equal(data.n_arrays,2) + + # Test data.set_keep + with data.set_keep([0]): + npt.assert_equal(data.keep,[0]) + for element in data.dataset(sequence_length=3,batch_size=1): + npt.assert_almost_equal(element['data'].numpy(),np.array([input_1])) + + with data.set_keep([1]): + npt.assert_equal(data.keep,[1]) + for element in data.dataset(sequence_length=3,batch_size=1): + npt.assert_almost_equal(element['data'].numpy(),np.array([input_2])) + + # Test self.select + data.select(channels=[1]) + npt.assert_almost_equal(data.arrays[0], input_1[:, 1:]) + npt.assert_almost_equal(data.arrays[1], input_2[:, 1:]) + + + +def test_data_files(): + import os + import numpy as np + save_dir = './test_temp/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + input_1 = np.array([[1., 0.5], [2., 0.4], [1.5, 0.3]]) + input_2 = input_1 / 2 + np.save(f'{save_dir}10001.npy',input_1) + np.savetxt(f'{save_dir}10002.txt',input_2) + data = Data(save_dir) + npt.assert_almost_equal(data.arrays,[input_1,input_2]) + + # Remove the directory after testing + from shutil import rmtree + rmtree(save_dir) + +def test_standardize(): + import numpy as np + vector = np.array([-1.5 ** 0.5,0,1.5 ** 0.5]) + input_1 = np.array([vector,vector + 10.0]).T + input_2 = np.array([vector * 0.5 + 1.,vector * 100]).T + data = Data([input_1,input_2]) + data.prepare({'standardize':{}}) + + npt.assert_almost_equal(data.arrays[0],np.array([vector,vector]).T) + npt.assert_almost_equal(data.arrays[1],np.array([vector, vector]).T) + npt.assert_almost_equal(data.raw_data[0],input_1,decimal=6) + npt.assert_almost_equal(data.raw_data[1], input_2,decimal=6) + + # Test self.time_series() + npt.assert_almost_equal(data.time_series(prepared=True)[0],np.array([vector,vector]).T) + npt.assert_almost_equal(data.time_series(prepared=True)[1], np.array([vector, vector]).T) + npt.assert_almost_equal(data.time_series(prepared=False),np.array([input_1,input_2]),decimal=6) + +def test_prepare(): + """ + This function mimics the real case of HCP data, where + we builds up two subjects with 4800 time points and 2 channels. + These subjects are then saved in temporary text files. + In the prepare step, we keep the first 1200 time points and 2 channels. + standardize the data and check the output. + Finally, we test the corresponding set_keep function. + """ + import numpy as np + import os + + save_dir = './test_prepare_temp/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + # Construct the input arrays + vector_1 = np.array([-1.5 ** 0.5, 0, 1.5 ** 0.5]) + vector_2 = np.array([1.5 ** 0.5, 0, -1.5 ** 0.5]) + input_1_short = np.array([vector_1 + 1.0, vector_1 * 5.0]).T + input_2_short = np.array([vector_2 * 0.1 + 1.0, vector_2 * 2.0 - 3.0]).T + # Copy input_i_short into a (1200,2) ndarray input_i_long + input_1_long = np.tile(input_1_short,(400,1)) + input_2_long = np.tile(input_2_short,(400,1)) + # Concatenate input_i_long with gaussian noise to get (4800,2) ndarray input_i + gaussian_array = np.random.normal(100.,5.0,size=(3600, 2)) + input_1 = np.concatenate([input_1_long, gaussian_array], axis=0) + input_2 = np.concatenate([input_2_long,gaussian_array],axis=0) + + # save the input_1 and input_2 + np.savetxt(f'{save_dir}10001.txt',input_1) + np.savetxt(f'{save_dir}10002.txt',input_2) + + data = Data(save_dir) + prepare_dict = {'select':{'timepoints':[0,1200]}, + 'standardize':{} + } + data.prepare(prepare_dict) + + answer_1 = np.tile(np.array([vector_1, vector_1]).T,(400,1)) + answer_2 = np.tile(np.array([vector_2, vector_2]).T, (400, 1)) + npt.assert_almost_equal(data.arrays[0],answer_1,decimal=6) + npt.assert_almost_equal(data.arrays[1], answer_2,decimal=6) + + with data.set_keep([0]): + npt.assert_equal(data.keep, [0]) + for element in data.dataset(sequence_length=1200, batch_size=1): + npt.assert_almost_equal(element['data'].numpy(), np.array([answer_1]),decimal=6) + + with data.set_keep([1]): + npt.assert_equal(data.keep, [1]) + for element in data.dataset(sequence_length=1200, batch_size=1): + npt.assert_almost_equal(element['data'].numpy(), np.array([answer_2]),decimal=6) + + + # Remove the directory after testing + from shutil import rmtree + rmtree(save_dir) diff --git a/tests/test_inference/test_metrics.py b/tests/test_inference/test_metrics.py new file mode 100644 index 00000000..b34f1a79 --- /dev/null +++ b/tests/test_inference/test_metrics.py @@ -0,0 +1,123 @@ +import numpy as np +import numpy.testing as npt + + +def test_alpha_correlation(): + from osl_dynamics.inference.metrics import alpha_correlation + + alpha_1 = [np.array([[1, 0, -1], [1, -1, 0]]).T] + alpha_2 = [np.array([[1, 0, -1], [-1, 1, 0]]).T] + + npt.assert_almost_equal(alpha_correlation(alpha_1, alpha_2, return_diagonal=False), + np.array([[1., -0.5], [0.5, -1]])) + npt.assert_almost_equal(alpha_correlation(alpha_1, alpha_2, return_diagonal=True), + np.array([1., -1.])) + + +def test_confusion_matrix(): + from osl_dynamics.inference.metrics import confusion_matrix + + # Define two example state time courses + state_time_course_1 = np.array([0, 1, 1, 0, 2]) # Example states + state_time_course_2 = np.array([0, 1, 2, 2, 2]) # Example states + + # Expected confusion matrix + expected_cm = np.array([[1, 0, 1], + [0, 1, 1], + [0, 0, 1]]) + + # Calculate the confusion matrix + cm = confusion_matrix(state_time_course_1, state_time_course_2) + + # Check if the calculated confusion matrix matches the expected one + npt.assert_equal(cm, expected_cm) + + +def test_dice_coefficient(): + from osl_dynamics.inference.metrics import dice_coefficient + # Test case 1: One-dimensional input + sequence_1 = np.array([0, 1, 1, 0, 2]) + sequence_2 = np.array([0, 1, 2, 2, 2]) + expected_dice = 0.6 + npt.assert_equal(dice_coefficient(sequence_1, sequence_2), expected_dice) + + # Test case 2: two-dimensional input + sequence_1 = np.array([ + [0.1, 0.2, 0.3], + [1.0, 0.5, 0.0], + [0.0, 1.0, 0.0], + [0.4, 0.5, 0.6] + ]) + sequence_2 = np.array([ + [0.1, 0.3, 0.4], + [0.5, 1.0, 0.0], + [0.0, 1.0, 0.0], + [0.6, 0.5, 0.4] + ]) + expected_dice = 0.5 + npt.assert_equal(dice_coefficient(sequence_1, sequence_2), expected_dice) + + +def test_pairwise_fisher_z_correlations(): + from osl_dynamics.inference.metrics import pairwise_fisher_z_correlations + x1, x2, x3 = -0.2, 0.5, 0.3 + y1, y2, y3 = 0.7, -0.1, 0.4 + + def fisher_z_inverse(x): + return (np.exp(2 * x) - 1) / (np.exp(2 * x) + 1) + + answer = np.corrcoef(np.array([[x1, x2, x3], [y1, y2, y3]])) + + matrices = np.array([[[0.0, x1, x2], [x1, 0.0, x3], [x2, x3, 0.0]], + [[0.0, y1, y2], [y1, 0.0, y3], [y2, y3, 0.0]]]) + matrices = fisher_z_inverse(matrices) + np.eye(3) + + npt.assert_almost_equal(pairwise_fisher_z_correlations(matrices), answer, decimal=6) + + +def test_twopair_fisher_z_correlations(): + from osl_dynamics.inference.metrics import twopair_fisher_z_correlations + x1, x2, x3 = -0.2, 0.5, 0.3 + y1, y2, y3 = 0.7, -0.1, 0.4 + + def fisher_z_inverse(x): + return (np.exp(2 * x) - 1) / (np.exp(2 * x) + 1) + + answer = np.corrcoef(np.array([[x1, x2, x3], [y1, y2, y3]])) + + matrices = np.array([[[0.0, x1, x2], [x1, 0.0, x3], [x2, x3, 0.0]], + [[0.0, y1, y2], [y1, 0.0, y3], [y2, y3, 0.0]]]) + matrices = fisher_z_inverse(matrices) + np.eye(3) + + npt.assert_almost_equal(twopair_fisher_z_correlations(matrices, matrices), answer, decimal=6) + + +def test_regularisation(): + from osl_dynamics.inference.metrics import regularisation + + matrix = np.eye(2) + regularised_matrix = regularisation(matrix, 1e-6) + npt.assert_equal(regularised_matrix, np.eye(2) * (1 + 1e-6)) + + matrices = np.array([[[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 1.0]]]) + regularised_matrix = regularisation(matrices, 1e-6) + npt.assert_equal(regularised_matrix, np.array([[[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 1.0]]]) * (1 + 1e-6)) + + +def test_twopair_vector_correlation(): + from osl_dynamics.inference.metrics import twopair_vector_correlation + vectors_1 = np.array([[1.0, 0.0, -1.0], [1.0, 0.0, -1.0]]) + vectors_2 = np.array([[1.0, 0.0, -1.0], [-1.0, 0.0, 1.0]]) + true_correlation = np.array([[1.0, -1.0, ], [1.0, -1.0]]) + npt.assert_equal(true_correlation, twopair_vector_correlation(vectors_1, vectors_2)) + +def test_twopair_riemannian_distance(): + from osl_dynamics.inference.metrics import twopair_riemannian_distance + from math import sqrt + corr_1 = np.array([[1.0,0.0],[0.0,1.0]]) + corr_2 = np.array([[2.0,0.0],[0.0,1.0]]) + corr_3 = np.array([[1.0,0.0],[0.0,2.0]]) + matrices_1 = np.stack([corr_1,corr_2]) + matrices_2 = np.stack([corr_1,corr_3]) + answer = np.array([[0.0,np.log(2)],[np.log(2),sqrt(2) * np.log(2)]]) + npt.assert_almost_equal(twopair_riemannian_distance(matrices_1,matrices_2),answer,decimal=6) diff --git a/tests/test_inference/test_modes.py b/tests/test_inference/test_modes.py new file mode 100644 index 00000000..20690baf --- /dev/null +++ b/tests/test_inference/test_modes.py @@ -0,0 +1,76 @@ +import numpy as np +import numpy.testing as npt + + +def test_argmax_time_courses(): + from osl_dynamics.inference.modes import argmax_time_courses + + # Test case 1: When alpha is a list, concatenate is false + alpha_list = [np.array([[0.1, 0.9], [0.8, 0.2]]), np.array([[0.2, 0.8], [0.6, 0.4]])] + expected_result = np.array([[[0, 1], [1, 0]], [[0, 1], [1, 0]]]) + result = argmax_time_courses(alpha_list, concatenate=False) + npt.assert_array_equal(result, expected_result) + + # Test case 2: When alpha is a list, concatenate is true + expected_result = np.array([[0, 1], [1, 0], [0, 1], [1, 0]]) + result = argmax_time_courses(alpha_list, concatenate=True) + npt.assert_array_equal(result, expected_result) + + # Test case 3: When alpha is a 2D numpy array + alpha_2d = np.array([[0.1, 0.9], [0.8, 0.2]]) + expected_result = np.array([[0, 1], [1, 0]]) + result = argmax_time_courses(alpha_2d) + npt.assert_array_equal(result, expected_result) + + # Test case 4: When alpha is a 3D numpy array + alpha_3d = np.array([[[0.1, 0.9], [0.8, 0.2]], [[0.2, 0.8], [0.6, 0.4]]]) + expected_result = np.array([[[0, 1], [1, 0]], [[0, 1], [1, 0]]]) + result = argmax_time_courses(alpha_3d) + npt.assert_array_equal(result, expected_result) + + +def test_hungarian_pair(): + from osl_dynamics.inference.modes import hungarian_pair + + # When distance is true + matrix = np.array([[8, 4, 7], [5, 2, 3], [9, 4, 8]]) + indices, matrix_reordered = hungarian_pair(matrix, distance=True) + matrix_reordered_true = np.array([[8, 7, 4], [5, 3, 2], [9, 8, 4]]) + npt.assert_equal(np.array(indices['row']), [0, 1, 2]) + npt.assert_equal(np.array(indices['col']), [0, 2, 1]) + npt.assert_equal(matrix_reordered, matrix_reordered_true) + + # When distance is False + indices, matrix_reordered = hungarian_pair(-matrix, distance=False) + matrix_reordered_true = - np.array([[8, 7, 4], [5, 3, 2], [9, 8, 4]]) + npt.assert_equal(np.array(indices['row']), [0, 1, 2]) + npt.assert_equal(np.array(indices['col']), [0, 2, 1]) + npt.assert_equal(matrix_reordered, matrix_reordered_true) + + +def test_reduce_state_time_course(): + from osl_dynamics.inference.modes import reduce_state_time_course + + state_time_course = np.array([ + [1, 0, 1], + [0, 1, 0], + [0, 0, 0], + [1, 1, 1] + ]).T + expected_result = np.array([ + [1, 0, 1], + [0, 1, 0], + [1, 1, 1] + ]).T + reduced_state_time_course = reduce_state_time_course(state_time_course) + npt.assert_equal(reduced_state_time_course, expected_result) + + +def test_correlate_modes(): + from osl_dynamics.inference.modes import correlate_modes + + alpha_1 = np.array([[1, 0, -1], [1, -1, 0]]).T + alpha_2 = np.array([[1, 0, -1], [-1, 1, 0]]).T + + npt.assert_almost_equal(correlate_modes(alpha_1, alpha_2), + np.array([[1., -0.5], [0.5, -1]])) diff --git a/tests/test_utils/test_misc.py b/tests/test_utils/test_misc.py new file mode 100644 index 00000000..a2dd5f18 --- /dev/null +++ b/tests/test_utils/test_misc.py @@ -0,0 +1,168 @@ +import numpy as np +import numpy.testing as npt + +def test_nextpow2(): + from osl_dynamics.utils.misc import nextpow2 + + import numpy.testing as npt + + # Test case 1: Positive integer + npt.assert_equal(nextpow2(10), 4) + + # Test case 2: Negative integer + npt.assert_equal(nextpow2(-5), 3) + + # Test case 3: Zero + npt.assert_equal(nextpow2(0), 0) + + # Test case 4: Large positive integer + npt.assert_equal(nextpow2(1000), 10) + + # Test case 5: Large negative integer + npt.assert_equal(nextpow2(-500), 9) + + +def test_leading_zeros(): + from osl_dynamics.utils.misc import leading_zeros + + import numpy.testing as npt + + # Test case 1: Single-digit number with largest number less than 10 + npt.assert_equal(leading_zeros(3, 9), '3') + + # Test case 2: Single-digit number with largest number equal to it + npt.assert_equal(leading_zeros(7, 7), '7') + + # Test case 3: Single-digit number with largest number greater than 10 + npt.assert_equal(leading_zeros(5, 15), '05') + + # Test case 4: Double-digit number with largest number less than 100 + npt.assert_equal(leading_zeros(50, 99), '50') + + # Test case 5: Double-digit number with largest number equal to it + npt.assert_equal(leading_zeros(100, 100), '100') + +def test_override_dict_defaults(): + from osl_dynamics.utils.misc import override_dict_defaults + + # Test case 1: Override some default values + default_dict = {'a': 1, 'b': 2, 'c': 3} + override_dict = {'b': 20, 'c': 30} + expected_result = {'a': 1, 'b': 20, 'c': 30} + npt.assert_equal(override_dict_defaults(default_dict, override_dict), expected_result) + + # Test case 2: No override dictionary provided + default_dict = {'a': 1, 'b': 2, 'c': 3} + expected_result = {'a': 1, 'b': 2, 'c': 3} + npt.assert_equal(override_dict_defaults(default_dict), expected_result) + + # Test case 3: Empty default dictionary and override some values + default_dict = {} + override_dict = {'a': 10, 'b': 20, 'c': 30} + expected_result = {'a': 10, 'b': 20, 'c': 30} + npt.assert_equal(override_dict_defaults(default_dict, override_dict), expected_result) + +def test_listify(): + from osl_dynamics.utils.misc import listify + + # Test case 1: None input + result = listify(None) + expected_result = [] + npt.assert_equal(result, expected_result) + + # Test case 2: List input + input_list = [1, 2, 3] + result = listify(input_list) + expected_result = [1, 2, 3] + npt.assert_equal(result, expected_result) + + # Test case 3: Tuple input + input_tuple = (4, 5, 6) + result = listify(input_tuple) + expected_result = [4, 5, 6] + npt.assert_equal(result, expected_result) + + # Test case 4: Other object input + input_obj = "hello" + result = listify(input_obj) + expected_result = ["hello"] + npt.assert_equal(result, expected_result) + + +def test_replace_argument(): + from osl_dynamics.utils.misc import replace_argument + # Define a dummy function for testing purposes + def dummy_function(a, b, c=None): + return (a, b, c) + + # Test case 1: Replace an existing argument in args list + args = [1, 2, 3] + kwargs = {'c': 4} + name = 'b' + item = 5 + append = False + expected_args = [1, 5, 3] + expected_kwargs = {'c': 4} + modified_args, modified_kwargs = replace_argument(dummy_function, name, item, args, kwargs, append) + npt.assert_equal(modified_args, expected_args) + npt.assert_equal(modified_kwargs, expected_kwargs) + + # Test case 2: Replace an existing argument in kwargs + args = [1, 2] + kwargs = {'c': 3} + name = 'c' + item = 4 + append = False + expected_args = [1, 2] + expected_kwargs = {'c': 4} + modified_args, modified_kwargs = replace_argument(dummy_function, name, item, args, kwargs, append) + npt.assert_equal(modified_args, expected_args) + npt.assert_equal(modified_kwargs, expected_kwargs) + + # Test case 3: Append to an existing argument list + args = [1, [2, 3]] + kwargs = {'c': [4, 5]} + name = 'b' + item = 6 + append = True + expected_args = [1, [2, 3, 6]] + expected_kwargs = {'c': [4, 5]} + modified_args, modified_kwargs = replace_argument(dummy_function, name, item, args, kwargs, append) + npt.assert_equal(modified_args, expected_args) + npt.assert_equal(modified_kwargs, expected_kwargs) + + +def test_IC2brain(): + from osl_dynamics.utils.misc import IC2brain + import nibabel as nib + spatial_maps_data = np.array(np.reshape(np.arange(16),(2,2,2,2)),dtype=np.float64) + mean_activation = np.array([[1.0,0.0,],[0.0,-1.0]]) + + # Construct from spatial_maps data to spatial maps Nifti1Image + spatial_map = nib.Nifti1Image(spatial_maps_data,affine = np.eye(4)) + brain_map = IC2brain(spatial_map,mean_activation) + brain_map_data = brain_map.get_fdata() + + brain_map_true = np.array(np.reshape(np.array([i * (-1) ** i for i in range(16)]),(2,2,2,2)),dtype=np.float64) + npt.assert_equal(brain_map_data,brain_map_true) + +def test_IC2surface(): + from osl_dynamics.utils.misc import IC2surface + import nibabel as nib + + spatial_map_data = np.array([[0,2,4,6,8,10,12,14],[1,3,5,7,9,11,13,15]], dtype=np.float64) + mean_activation = np.array([[1.0, 0.0, ], [0.0, -1.0]]) + axis_1 = nib.cifti2.cifti2_axes.ScalarAxis([f'Component {i + 1}' for i in range(2)]) + #axis_2 = nib.cifti2.cifti2_axes.BrainModelAxis(['CORTEX_LEFT'] * 2,vertex=np.arange(2)+1,affine=np.eye(4),volume_shape=(1,1,1)) + axis_2 = nib.cifti2.cifti2_axes.BrainModelAxis.from_mask(np.ones((2, 2, 2)),affine=np.eye(4),name='thalamus_left') + header = nib.cifti2.cifti2.Cifti2Header.from_axes((axis_1, axis_2)) + spatial_map = nib.cifti2.cifti2.Cifti2Image(spatial_map_data, header) + + surface_map = IC2surface(spatial_map, mean_activation) + surface_map_data = surface_map.get_fdata() + + surface_map_true = np.array([[0,2,4,6,8,10,12,14],[-1,-3,-5,-7,-9,-11,-13,-15]], dtype=np.float64) + npt.assert_equal(surface_map_data, surface_map_true) + + + diff --git a/tests/test_utils/test_plotting.py b/tests/test_utils/test_plotting.py new file mode 100644 index 00000000..5151031e --- /dev/null +++ b/tests/test_utils/test_plotting.py @@ -0,0 +1,472 @@ +import os + +import numpy as np +import numpy.testing as npt + +def test_rough_square_axes(): + from osl_dynamics.utils.plotting import rough_square_axes + + + # Test case 1: Even number of plots + n_plots1 = 16 + short1, long1, empty1 = rough_square_axes(n_plots1) + npt.assert_equal(short1, 4) + npt.assert_equal(long1, 4) + npt.assert_equal(empty1, 0) + + # Test case 2: Odd number of plots + n_plots2 = 25 + short2, long2, empty2 = rough_square_axes(n_plots2) + npt.assert_equal(short2, 5) + npt.assert_equal(long2, 5) + npt.assert_equal(empty2, 0) + + # Test case 3: Prime number of plots + n_plots3 = 17 + short3, long3, empty3 = rough_square_axes(n_plots3) + npt.assert_equal(short3, 5) + npt.assert_equal(long3, 4) + npt.assert_equal(empty3, 3) + + # Test case 4: Single plot + n_plots4 = 1 + short4, long4, empty4 = rough_square_axes(n_plots4) + npt.assert_equal(short4, 1) + npt.assert_equal(long4, 1) + npt.assert_equal(empty4, 0) + + +def test_plot_line(): + from osl_dynamics.utils.plotting import plot_line + + # Create a directory to store test plots + test_plot_dir = 'test_plot' + line_plot_dir = os.path.join(test_plot_dir, 'line_plot') + if os.path.exists(line_plot_dir): + for file in os.listdir(line_plot_dir): + os.remove(os.path.join(line_plot_dir, file)) + else: + os.makedirs(line_plot_dir) + + # Test cases + # Test case 1: Basic line plot with single line + x1 = np.linspace(0, 10, 100) + y1 = np.sin(x1) + plot_line([x1], [y1], filename=os.path.join(line_plot_dir, 'test_case1.png')) + + # Test case 2: Basic line plot with multiple lines and legend + x2 = np.linspace(0, 10, 100) + y2 = np.sin(x2) + y3 = np.cos(x2) + labels2 = ['Sin', 'Cos'] + plot_line([x2, x2], [y2, y3], labels=labels2, filename=os.path.join(line_plot_dir, 'test_case2.png')) + + # Test case 3: Line plot with legend, title, and axis labels + x3 = np.linspace(0, 10, 100) + y3 = np.sin(x3) + plot_line([x3], [y3], labels='Sin', title='Sine Wave', x_label='X-axis', y_label='Y-axis', + filename=os.path.join(line_plot_dir, 'test_case3.png')) + + # Test case 4: Line plot with specified ranges for x and y axes + x4 = np.linspace(0, 10, 100) + y4 = np.sin(x4) + plot_line([x4], [y4], x_range=[0, 5], y_range=[-1, 1], filename=os.path.join(line_plot_dir, 'test_case4.png')) + + # Test case 5: Line plot with error bars + x5 = np.linspace(0, 10, 100) + y5 = np.sin(x5) + errors_min = y5 - 0.2 + errors_max = y5 + 0.2 + plot_line([x5], [y5], errors=[[errors_min], [errors_max]], filename=os.path.join(line_plot_dir, 'test_case5.png')) + + +def test_plot_scatter(): + from osl_dynamics.utils.plotting import plot_scatter + + # Create scatter_plot folder if it doesn't exist + if not os.path.exists('test_plot/scatter_plot'): + os.makedirs('test_plot/scatter_plot') + else: + # Delete previous files in scatter_plot folder + filelist = [f for f in os.listdir('test_plot/scatter_plot')] + for f in filelist: + os.remove(os.path.join('test_plot/scatter_plot', f)) + + # Test case 1: Basic scatter plot with single data set + x1 = np.random.rand(50) + y1 = np.random.rand(50) + plot_scatter([x1], [y1], filename='test_plot/scatter_plot/test_plot_scatter_1.png') + + # Test case 2: Scatter plot with multiple data sets and legend + x2 = [np.random.rand(50) for _ in range(3)] + y2 = [np.random.rand(50) for _ in range(3)] + labels2 = ['Data 1', 'Data 2', 'Data 3'] + plot_scatter(x2, y2, labels=labels2, filename='test_plot/scatter_plot/test_plot_scatter_2.png') + + # Test case 3: Scatter plot with error bars + x3 = np.random.rand(50) + y3 = np.random.rand(50) + errors3 = np.random.rand(50) * 0.2 # Example error bars + plot_scatter([x3], [y3], errors=[errors3], filename='test_plot/scatter_plot/test_plot_scatter_3.png') + +def test_plot_gmm(): + from osl_dynamics.utils.plotting import plot_gmm + + # Create plot_gmm folder if it doesn't exist + if not os.path.exists('test_plot/plot_gmm'): + os.makedirs('test_plot/plot_gmm') + else: + # Delete previous files in plot_gmm folder + filelist = [f for f in os.listdir('test_plot/plot_gmm')] + for f in filelist: + os.remove(os.path.join('test_plot/plot_gmm', f)) + + # Test case 1: Basic GMM plot + data1 = np.random.normal(loc=0, scale=1, size=1000) + amplitudes1 = np.array([0.5, 0.5]) + means1 = np.array([-1, 1]) + stddevs1 = np.array([1, 1]) + plot_gmm( + data=data1, + amplitudes=amplitudes1, + means=means1, + stddevs=stddevs1, + bins=50, + legend_loc=1, + x_range=None, + y_range=None, + x_label="X", + y_label="Density", + title="Gaussian Mixture Model", + filename='test_plot/plot_gmm/test_plot_gmm_1.png' + ) + + # Test case 2: GMM plot with customized style + data2 = np.random.normal(loc=0, scale=1, size=1000) + amplitudes2 = np.array([0.3, 0.7]) + means2 = np.array([-1, 1]) + stddevs2 = np.array([1, 1]) + fig_kwargs2 = {'figsize': (10, 6)} + plot_gmm( + data=data2, + amplitudes=amplitudes2, + means=means2, + stddevs=stddevs2, + bins=50, + legend_loc=1, + x_range=None, + y_range=None, + x_label="X", + y_label="Density", + title="Customized GMM Plot", + fig_kwargs=fig_kwargs2, + filename='test_plot/plot_gmm/test_plot_gmm_2.png' + ) + +def test_plot_hist(): + from osl_dynamics.utils.plotting import plot_hist + + # Create hist_plot folder if it doesn't exist + if not os.path.exists('test_plot/hist_plot'): + os.makedirs('test_plot/hist_plot') + else: + # Delete previous files in hist_plot folder + filelist = [f for f in os.listdir('test_plot/hist_plot')] + for f in filelist: + os.remove(os.path.join('test_plot/hist_plot', f)) + + plot_kwargs = {'histtype':'bar'} + + # Test case 1: Basic histogram plot with single data set + data1 = np.random.normal(loc=0, scale=1, size=1000) + bins1 = 20 + plot_hist(data=[data1], bins=[bins1], plot_kwargs=plot_kwargs, filename='test_plot/hist_plot/test_plot_hist_1.png') + + # Test case 2: Histogram plot with multiple data sets and legend + data2 = [np.random.normal(loc=i, scale=1, size=100) for i in range(3)] + bins2 = [20, 15, 10] + labels2 = ['Data 1', 'Data 2', 'Data 3'] + plot_hist(data=data2, bins=bins2, labels=labels2, plot_kwargs=plot_kwargs, filename='test_plot/hist_plot/test_plot_hist_2.png') + + # Test case 3: Histogram plot with customized plot style + data3 = [np.random.normal(loc=i, scale=1, size=1000) for i in range(2)] + bins3 = [20, 15] + plot_kwargs3 = {'histtype': 'bar', 'alpha': 0.5,} + plot_hist(data=data3, bins=bins3, plot_kwargs=plot_kwargs3, filename='test_plot/hist_plot/test_plot_hist_3.png') + + +def test_plot_violin(): + from osl_dynamics.utils.plotting import plot_violin + # Create plot_violin folder if it doesn't exist + if not os.path.exists('test_plot/plot_violin'): + os.makedirs('test_plot/plot_violin') + else: + # Delete previous files in plot_violin folder + filelist = [f for f in os.listdir('test_plot/plot_violin')] + for f in filelist: + os.remove(os.path.join('test_plot/plot_violin', f)) + + # Generate sample 2D data + np.random.seed(0) + n_sessions = 100 + n_states = 2 + data = np.zeros((n_sessions, n_states)) + + # Populate data[:,0] with a normal distribution with mean 0.1 and std 0.1 + data[:, 0] = np.random.normal(loc=0.1, scale=0.1, size=n_sessions) + + # Populate data[:,1] with a normal distribution with mean 0.0 and std 0.1 + data[:, 1] = np.random.normal(loc=0.0, scale=0.1, size=n_sessions) + + + # Test the function + plot_violin(data.T, x_label="State", y_label="Value", title="Violin Plot",filename='test_plot/plot_violin/toy_example.png') + +def test_plot_time_series(): + from osl_dynamics.utils.plotting import plot_time_series + + # Create directory if it doesn't exist + save_dir = 'test_plot/plot_time_series/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + else: + # Delete previous files in the directory + filelist = [f for f in os.listdir(save_dir)] + for f in filelist: + os.remove(os.path.join(save_dir, f)) + + # Generate synthetic time series data + n_samples = 1000 + n_channels = 3 + time_series = np.random.randn(n_samples, n_channels) + + # Test case 1: Basic plot with default parameters + plot_time_series(time_series, filename=os.path.join(save_dir, 'plot_basic.png')) + + # Test case 2: Plot with specified number of samples and y_tick_values + n_samples_plot = 500 + y_tick_values = ['Channel A', 'Channel B', 'Channel C'] + plot_time_series(time_series, n_samples=n_samples_plot, y_tick_values=y_tick_values, + filename=os.path.join(save_dir, 'plot_samples_ticks.png')) + + # Test case 3: Plot with customized plot appearance + plot_kwargs = {'lw': 1.2, 'color': 'red'} + plot_time_series(time_series, plot_kwargs=plot_kwargs, filename=os.path.join(save_dir, 'plot_custom.png')) + +def test_plot_separate_time_series(): + from osl_dynamics.utils.plotting import plot_separate_time_series + + # Create directory if it doesn't exist + save_dir = 'test_plot/plot_separate_time_series/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + else: + # Delete previous files in the directory + filelist = [f for f in os.listdir(save_dir)] + for f in filelist: + os.remove(os.path.join(save_dir, f)) + + # Generate synthetic time series data + n_samples = 1000 + n_lines = 3 + time_series1 = np.random.randn(n_samples, n_lines) + time_series2 = np.random.randn(n_samples, n_lines) + + # Test case 1: Basic plot with default parameters + plot_separate_time_series(time_series1, filename=os.path.join(save_dir, 'plot_basic.png')) + + # Test case 2: Plot with specified number of samples and sampling frequency + sampling_frequency = 100 # Hz + plot_separate_time_series(time_series1, n_samples=500, sampling_frequency=sampling_frequency, + filename=os.path.join(save_dir, 'plot_samples_frequency.png')) + + # Test case 3: Plot with customized appearance + plot_kwargs = {'lw': 1.2, 'color': 'red'} + plot_separate_time_series(time_series2, plot_kwargs=plot_kwargs, + filename=os.path.join(save_dir, 'plot_custom.png')) + +def test_plot_epoched_time_series(): + from osl_dynamics.utils.plotting import plot_epoched_time_series + + # Create directory if it doesn't exist + save_dir = 'test_plot/plot_epoched_time_series/' + if not os.path.exists(save_dir): + os.makedirs(save_dir) + else: + # Delete previous files in the directory + filelist = [f for f in os.listdir(save_dir)] + for f in filelist: + os.remove(os.path.join(save_dir, f)) + + # Generate synthetic continuous data + n_samples = 10000 + n_channels = 4 + continuous_data = np.random.normal(loc=1.0, scale=1.0, size=(n_samples, n_channels)) + + # Generate synthetic time index for epochs + epoch_length = 200 + time_index = np.arange(epoch_length, n_samples, epoch_length) + + # Test case 1: Basic plot with default parameters + plot_epoched_time_series(continuous_data, time_index, filename=os.path.join(save_dir, 'plot_basic.png')) + + # Test case 2: Plot with baseline correction + plot_epoched_time_series(continuous_data, time_index, baseline_correct=True, + filename=os.path.join(save_dir, 'plot_baseline_corrected.png')) + + # Test case 3: Plot with legend and custom appearance + plot_kwargs = {'lw': 1.2, 'color': 'red'} + plot_epoched_time_series(continuous_data, time_index, legend=True, plot_kwargs=plot_kwargs, + filename=os.path.join(save_dir, 'plot_legend_custom.png')) + +def test_plot_matrices(): + from osl_dynamics.utils.plotting import plot_matrices + + # Create directory and delete existing files if it already exists + plot_dir = 'test_plot/plot_matrices' + if not os.path.exists(plot_dir): + os.makedirs(plot_dir, exist_ok=True) + else: + for file in os.listdir(plot_dir): + os.remove(os.path.join(plot_dir, file)) + + # Test case 1: Single matrix plot + matrix1 = np.random.rand(15, 15) + plot_matrices([matrix1], main_title="Single Matrix Plot", titles=["Matrix 1"], + filename=os.path.join(plot_dir, 'test_single_matrix.png')) + + # Test case 2: Multiple matrices plot + matrix2 = np.random.rand(15, 15) + matrix3 = np.random.rand(15, 15) + matrices = [matrix1, matrix2, matrix3] + plot_matrices(matrices, main_title="Collection of Matrices", + titles=["Matrix 1", "Matrix 2", "Matrix 3"], + filename=os.path.join(plot_dir, 'test_multiple_matrices.png')) + + # Test case 3: Matrices with log normalization + matrix4 = np.random.rand(10, 10) + matrix5 = np.random.rand(10, 10) + matrix6 = np.random.rand(10, 10) + matrices_log = [matrix4, matrix5, matrix6] + plot_matrices(matrices_log, main_title="Matrices with Log Normalization", + titles=["Matrix 4", "Matrix 5", "Matrix 6"], + log_norm=True, + filename=os.path.join(plot_dir, 'test_matrices_log_normalization.png')) + +def test_plot_connections(): + from osl_dynamics.utils.plotting import plot_connections + + def generate_weights_matrix(shape): + # Generate a random matrix with the specified shape + return np.random.rand(*shape) + + plot_dir = 'test_plot/plot_connections' + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + else: + for file in os.listdir(plot_dir): + os.remove(os.path.join(plot_dir, file)) + + # Test case 1: Square weights matrix + shape_1 = (3, 3) + weights_1 = np.array([[1.0,2.0,0.0], + [2.0,1.0,-0.5], + [0.0,-0.5,1.0]]) + + plot_connections(weights_1, labels=[f"Node {i}" for i in range(1, shape_1[0] + 1)], + filename=os.path.join(plot_dir, f'test_connections_{shape_1[0]}x{shape_1[1]}.png')) + + # Test case 2: Rectangular weights matrix + shape_2 = (8,8) + weights_2 = generate_weights_matrix(shape_2) + plot_connections(weights_2, labels=[f"Node {i}" for i in range(1, shape_2[0] + 1)], + filename=os.path.join(plot_dir, f'test_connections_{shape_2[0]}x{shape_2[1]}.png')) + + # Test case 3: Large square weights matrix + shape_3 = (10, 10) + weights_3 = generate_weights_matrix(shape_3) + plot_connections(weights_3, labels=[f"Node {i}" for i in range(1, shape_3[0] + 1)], + filename=os.path.join(plot_dir, f'test_connections_{shape_3[0]}x{shape_3[1]}.png')) + +def test_plot_alpha(): + from osl_dynamics.utils.plotting import plot_alpha + + def generate_alpha(n_samples=100, n_modes=3, n_alphas=2): + """Generate example alpha matrices.""" + alphas = [] + for _ in range(n_alphas): + alpha = np.random.randn(n_samples, n_modes) + alphas.append(alpha) + return alphas + + plot_dir = 'test_plot/plot_alpha' + if os.path.exists(plot_dir): + for file in os.listdir(plot_dir): + os.remove(os.path.join(plot_dir, file)) + else: + os.makedirs(plot_dir) + + alphas = generate_alpha() + + # Ensure the plot directory exists or create it + os.makedirs(plot_dir, exist_ok=True) + + # Test case 1: Basic plot + plot_alpha(*alphas, filename=os.path.join(plot_dir, 'basic_plot.png')) + + # Test case 2: Plot with customized parameters + plot_alpha( + *alphas, + n_samples=150, + #cmap="Blues", + sampling_frequency=1000, + y_labels=["Alpha 1", "Alpha 2"], + title="Example Alpha Plot", + filename=os.path.join(plot_dir, 'custom_alpha_plot.png') + ) + +def test_plot_state_lifetimes(): + from osl_dynamics.utils.plotting import plot_state_lifetimes + + # Create directory for test plots if it doesn't exist + plot_dir = "test_plot/plot_state_lifetimes" + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + else: + # Delete existing files in the directory + for file_name in os.listdir(plot_dir): + file_path = os.path.join(plot_dir, file_name) + os.remove(file_path) + + # Test case 1: Basic input + state_time_course = np.random.randint(0, 2, size=(100, 5)) # Generate random state time course + filename = os.path.join(plot_dir, "test_basic.png") # Define output filename + plot_state_lifetimes(state_time_course, filename=filename) # Plot and save + + # Test case 2: Customized parameters + filename = os.path.join(plot_dir, "test_customized.png") # Define output filename + x_label = "State Lifetime" # Customize x-axis label + y_label = "Frequency" # Customize y-axis label + plot_kwargs = {"alpha": 0.5} # Customize plot appearance + fig_kwargs = {"figsize": (8, 6)} # Customize figure size + plot_state_lifetimes(state_time_course, x_label=x_label, y_label=y_label, + plot_kwargs=plot_kwargs, fig_kwargs=fig_kwargs, + filename=filename) # Plot and save + +def test_plot_mode_pairing(): + from osl_dynamics.utils.plotting import plot_mode_pairing + + # Create directory for test plots if it doesn't exist + plot_dir = "test_plot/plot_mode_pairing" + if not os.path.exists(plot_dir): + os.makedirs(plot_dir) + else: + # Delete existing files in the directory + for file_name in os.listdir(plot_dir): + file_path = os.path.join(plot_dir, file_name) + os.remove(file_path) + + metrics = np.random.randn(8,8) + indices = {'row':[3,2,4,5,1,7,0,8],'col':list(range(0,8))} + plot_mode_pairing(metrics,indices,x_label='split_2',y_label='split_1',title='Pairing test', + filename=f'{plot_dir}/toy_example.png') diff --git a/tests/tests_hmm.py b/tests/tests_hmm.py new file mode 100644 index 00000000..0004a48e --- /dev/null +++ b/tests/tests_hmm.py @@ -0,0 +1,3 @@ +import numpy as np +import numpy.testing as npt + diff --git a/tests/tests_preprocessing.py b/tests/tests_preprocessing.py new file mode 100644 index 00000000..9471c197 --- /dev/null +++ b/tests/tests_preprocessing.py @@ -0,0 +1,49 @@ +import numpy as np +import numpy.testing as npt + +def test_prepare_data(): + import shutil, os, pathlib + from rotation.preprocessing import PrepareData + temp_dir = './test_temp/' + # Create the directory if not exists + if not os.path.exists(temp_dir): + print(f'Create the temporary directory {temp_dir}') + os.makedirs(temp_dir) + + # Create a subject + subj_name = '10001' + data = np.array([[-1,1,-1,1],[1,-1,1,-1]]).T + np.savetxt(f'{temp_dir}{subj_name}.txt', data) + + # Use the PrepareData class + prepare_data = PrepareData(pathlib.Path(temp_dir),2) + subj,result = prepare_data.load() + npt.assert_equal(subj[0],'10001') + npt.assert_equal(result[0],np.array([[-1,1],[1,-1]])) + npt.assert_equal(result[1],np.array([[-1,1],[1,-1]])) + + # Delete the directory + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + print(f'Delete temporary directory {temp_dir}') + +def test_z_score(): + + # Example 1: one session + x = np.array([[0.5,1.5,2.5],[-2.0,-1.0,0.0]]).T + y = z_score(x) + npt.assert_equal(y[:,0], y[:,1]) + + # Example 2: two sessions + np.random.seed(42) + x1 = np.random.normal(loc=100.0,scale=100.0,size=(100,2)) + x2 = np.random.normal(loc=0.0,scale=2.0,size=(100,2)) + x = np.concatenate([x1,x2]) + y = z_score(x,n_session=2) + + # Check the data are z-scored + npt.assert_almost_equal(np.mean(y[:100,:]),0.0,decimal=3) + npt.assert_almost_equal(np.std(y[:100,:]),1.0,decimal=3) + npt.assert_almost_equal(np.mean(y[100:,:]),0.0,decimal=3) + npt.assert_almost_equal(np.std(y[100:,:]),1.0,decimal=3) + \ No newline at end of file diff --git a/tests/tests_utils.py b/tests/tests_utils.py new file mode 100644 index 00000000..9da7d388 --- /dev/null +++ b/tests/tests_utils.py @@ -0,0 +1,186 @@ +from math import sqrt +import numpy as np +import numpy.testing as npt + + +def test_stdcor2cov(): + from rotation.utils import stdcor2cov + stds = np.array([[4.0,2.0],[10.0,20.0]]) + corrs = np.array([[[1.0,0.5],[0.5,1.0]],[[1.0,-0.2],[-0.2,1.0]]]) + covs = stdcor2cov(stds, corrs) + npt.assert_equal(covs,np.array([[[16.0,4.0],[4.0,4.0]],[[100.0,-40.0],[-40.0,400.0]]])) + + stds = np.array([[[4.0,0.0],[0.0,2.0]],[[10.0,0.0],[0.0,20.0]]]) + covs = stdcor2cov(stds,corrs) + npt.assert_equal(covs, np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]])) + +def test_cov2stdcor(): + from rotation.utils import cov2stdcor + covs = np.array([[[16.0, 4.0], [4.0, 4.0]], [[100.0, -40.0], [-40.0, 400.0]]]) + stds, corrs = cov2stdcor(covs) + npt.assert_equal(stds, np.array([[4.0,2.0],[10.0,20.0]])) + npt.assert_equal(corrs, np.array([[[1.0,0.5],[0.5,1.0]],[[1.0,-0.2],[-0.2,1.0]]])) +def test_first_eigenvector(): + from rotation.utils import first_eigenvector + matrix = np.array([[2.0,0.0],[0.0,1.0]]) + first_eigen = first_eigenvector(matrix) + npt.assert_almost_equal(np.abs(first_eigen),np.array([1.0,0.0]),decimal=6) + +def test_IC2brain(): + from rotation.utils import IC2brain + import nibabel as nib + spatial_maps_data = np.array(np.reshape(np.arange(16),(2,2,2,2)),dtype=np.float64) + mean_activation = np.array([[1.0,0.0,],[0.0,-1.0]]) + + # Construct from spatial_maps data to spatial maps Nifti1Image + spatial_map = nib.Nifti1Image(spatial_maps_data,affine = np.eye(4)) + brain_map = IC2brain(spatial_map,mean_activation) + brain_map_data = brain_map.get_fdata() + + brain_map_true = np.array(np.reshape(np.array([i * (-1) ** i for i in range(16)]),(2,2,2,2)),dtype=np.float64) + npt.assert_equal(brain_map_data,brain_map_true) + +def test_IC2surface(): + from rotation.utils import IC2surface + import nibabel as nib + + spatial_map_data = np.array([[0,2,4,6,8,10,12,14],[1,3,5,7,9,11,13,15]], dtype=np.float64) + mean_activation = np.array([[1.0, 0.0, ], [0.0, -1.0]]) + axis_1 = nib.cifti2.cifti2_axes.ScalarAxis([f'Component {i + 1}' for i in range(2)]) + #axis_2 = nib.cifti2.cifti2_axes.BrainModelAxis(['CORTEX_LEFT'] * 2,vertex=np.arange(2)+1,affine=np.eye(4),volume_shape=(1,1,1)) + axis_2 = nib.cifti2.cifti2_axes.BrainModelAxis.from_mask(np.ones((2, 2, 2)),affine=np.eye(4),name='thalamus_left') + header = nib.cifti2.cifti2.Cifti2Header.from_axes((axis_1, axis_2)) + spatial_map = nib.cifti2.cifti2.Cifti2Image(spatial_map_data, header) + + surface_map = IC2surface(spatial_map, mean_activation) + surface_map_data = surface_map.get_fdata() + + surface_map_true = np.array([[0,2,4,6,8,10,12,14],[-1,-3,-5,-7,-9,-11,-13,-15]], dtype=np.float64) + npt.assert_equal(surface_map_data, surface_map_true) + + + +def test_pairwise_fisher_z_correlations(): + from rotation.utils import pairwise_fisher_z_correlations + x1, x2, x3 = -0.2,0.5,0.3 + y1, y2, y3 = 0.7,-0.1,0.4 + + def fisher_z_inverse(x): + return (np.exp(2 * x) - 1) / (np.exp(2 * x) + 1) + + answer = np.corrcoef(np.array([[x1,x2,x3],[y1,y2,y3]])) + + + matrices = np.array([[[0.0,x1,x2],[x1,0.0,x3],[x2,x3,0.0]], + [[0.0,y1,y2],[y1,0.0,y3],[y2,y3,0.0]]]) + matrices = fisher_z_inverse(matrices) + np.eye(3) + + npt.assert_almost_equal(pairwise_fisher_z_correlations(matrices),answer,decimal=6) + +def test_group_high_pass_filter(): + + from rotation.utils import group_high_pass_filter + N = 1200 # Number of time points + T = 0.7 # Time resolution (seconds) + fs = 1 / T # Sampling frequency + + + f1 = 0.2 + f2 = 0.3 + t = np.array([i * T for i in range(N)]) + signal_1 = np.sin(2 * np.pi * f1 * t) + signal_2 = np.cos(2 * np.pi * f2 * t) + signals = [np.array([signal_1,signal_2]).T] + + filtered_signals = group_high_pass_filter(signals)[0].T + filtered_signal_1 = filtered_signals[0] + filtered_signal_2 = filtered_signals[1] + + frequencies = np.fft.fftfreq(N,1/fs) + spectrum_1 = np.fft.fft(filtered_signal_1,norm='forward') + spectrum_2 = np.fft.fft(filtered_signal_2,norm='forward') + ''' + import matplotlib.pyplot as plt + plt.plot(t,signal_1) + plt.title('Original Signal 1 (t)') + plt.show() + plt.plot(frequencies,np.abs(np.fft.fft(signal_1,norm='forward'))) + plt.title('Original Signal 1 (f)') + plt.show() + plt.plot(t,signal_2) + plt.title('Original Signal 2 (t)') + plt.show() + plt.plot(frequencies,np.abs(np.fft.fft(signal_2,norm='forward'))) + plt.title('Original Signal 2 (f)') + plt.show() + + plt.plot(frequencies,np.abs(spectrum_1)) + plt.title('Filtered Signal 1 (f)') + plt.show() + plt.plot(frequencies,np.abs(spectrum_2)) + plt.title('Filtered Signal 2 (f)') + plt.show() + ''' + npt.assert_almost_equal(np.max(np.abs(spectrum_1)),0,decimal=3) + npt.assert_almost_equal(np.max(np.abs(spectrum_2)),0.5,decimal=2) + +def test_regularisation(): + from rotation.utils import regularisation + + matrix = np.eye(2) + regularised_matrix = regularisation(matrix, 1e-6) + npt.assert_equal(regularised_matrix,np.eye(2) * (1 + 1e-6)) + + matrices = np.array([[[1.0,0.0],[0.0,1.0]],[[1.0,0.0],[0.0,1.0]]]) + regularised_matrix = regularisation(matrices,1e-6) + npt.assert_equal(regularised_matrix,np.array([[[1.0,0.0],[0.0,1.0]],[[1.0,0.0],[0.0,1.0]]])*(1 + 1e-6)) + +def test_twopair_vector_correlation(): + from rotation.utils import twopair_vector_correlation + vectors_1 = np.array([[1.0,0.0,-1.0],[1.0,0.0,-1.0]]) + vectors_2 = np.array([[1.0,0.0,-1.0],[-1.0,0.0,1.0]]) + true_correlation = np.array([[1.0,-1.0,],[1.0,-1.0]]) + npt.assert_equal(true_correlation,twopair_vector_correlation(vectors_1,vectors_2)) + +def test_twopair_riemannian_distance(): + from rotation.utils import twopair_riemannian_distance + corr_1 = np.array([[1.0,0.0],[0.0,1.0]]) + corr_2 = np.array([[2.0,0.0],[0.0,1.0]]) + corr_3 = np.array([[1.0,0.0],[0.0,2.0]]) + matrices_1 = np.stack([corr_1,corr_2]) + matrices_2 = np.stack([corr_1,corr_3]) + answer = np.array([[0.0,np.log(2)],[np.log(2),sqrt(2) * np.log(2)]]) + npt.assert_almost_equal(twopair_riemannian_distance(matrices_1,matrices_2),answer,decimal=6) + +def test_twopair_fisher_z_transformed_correlation(): + from rotation.utils import twopair_fisher_z_transformed_correlation + x1, x2, x3 = -0.2,0.5,0.3 + y1, y2, y3 = 0.7,-0.1,0.4 + + def fisher_z_inverse(x): + return (np.exp(2 * x) - 1) / (np.exp(2 * x) + 1) + + answer = np.corrcoef(np.array([[x1,x2,x3],[y1,y2,y3]])) + + + matrices = np.array([[[0.0,x1,x2],[x1,0.0,x3],[x2,x3,0.0]], + [[0.0,y1,y2],[y1,0.0,y3],[y2,y3,0.0]]]) + matrices = fisher_z_inverse(matrices) + np.eye(3) + + npt.assert_almost_equal(twopair_fisher_z_transformed_correlation(matrices,matrices),answer,decimal=6) +def test_hungarian_pair(): + from rotation.utils import hungarian_pair + # When distance is true + matrix = np.array([[8,4,7],[5,2,3],[9,4,8]]) + indices,matrix_reordered = hungarian_pair(matrix,distance=True) + matrix_reordered_true = np.array([[8,7,4],[5,3,2],[9,8,4]]) + npt.assert_equal(np.array(indices['row']),[0,1,2]) + npt.assert_equal(np.array(indices['col']),[0,2,1]) + npt.assert_equal(matrix_reordered,matrix_reordered_true) + + # When distance is False + indices, matrix_reordered = hungarian_pair(-matrix, distance=False) + matrix_reordered_true = - np.array([[8, 7, 4], [5, 3, 2], [9, 8, 4]]) + npt.assert_equal(np.array(indices['row']), [0, 1, 2]) + npt.assert_equal(np.array(indices['col']), [0, 2, 1]) + npt.assert_equal(matrix_reordered, matrix_reordered_true) \ No newline at end of file diff --git a/yaml_lib/config_HCP.yaml b/yaml_lib/config_HCP.yaml new file mode 100644 index 00000000..fb00fa14 --- /dev/null +++ b/yaml_lib/config_HCP.yaml @@ -0,0 +1,53 @@ +header: + # We assign a time stamp for each batch training + time: 2024-03-03T22:00:00.000Z + # Add custom notes, which will also be saved + note: "Training configuration for HCP data" +# where to read the data +load_data: + inputs: './data/node_timeseries/3T_HCP1200_MSMAll_d25_ts2/' + prepare: + select: + timepoints: + - 0 + - 1200 + standardize: {} + kwargs: + load_memmaps: False +# Where to save model training results +save_dir: './results_HCP_yaml_202403/' +# where to load the spatial map and spatial surface map +spatial_map: './data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d25.ica' +non_batch_variable: + n_channels: 25 + sequence_length: 400 + batch_size: 256 + learn_means: false + learn_covariances: true + learn_trans_prob: true + learning_rate: 0.001 + n_epochs: 30 + split_strategy: random + init_kwargs: + n_init: 10 + n_epochs: 2 +# The following variables have lists for batch training +batch_variable: + model: + - 'hmm' + n_states: [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36] + # Mode can be: train, repeat, split, cross_validation + mode: + - train + - repeat_1 + - repeat_2 + - repeat_3 + - repeat_4 + - repeat_5 + - split_1 + - split_2 + - split_3 + - split_4 + - split_5 + + diff --git a/yaml_lib/config_simulation.yaml b/yaml_lib/config_simulation.yaml new file mode 100644 index 00000000..147d31d2 --- /dev/null +++ b/yaml_lib/config_simulation.yaml @@ -0,0 +1,55 @@ +header: + # We assign a time stamp for each batch training + time: 2024-02-05T17:00:00.000Z + # Add custom notes, which will also be saved + note: "Configuration for simulation data,sigm a =0" +# where to read the data +load_data: + inputs: './data/node_timeseries/simulation_202402/sigma_0/' + prepare: + select: + timepoints: + - 0 + - 1200 +# Where to save model training results +save_dir: './results_simulation_yaml/sigma_0/' +# where to load the spatial map and spatial surface map +spatial_map: './data/spatial_maps/groupICA_3T_HCP1200_MSMAll_d25.ica' +non_batch_variable: + n_channels: 25 + sequence_length: 400 + batch_size: 64 + learn_means: false + learn_covariances: true + learn_trans_prob: true + learning_rate: 0.01 + n_epochs: 30 + split_strategy: random + init_kwargs: + n_init: 10 + n_epochs: 2 +# The following variables have lists for batch training +batch_variable: + model: + - 'hmm' + n_states: [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] + # Mode can be: train, repeat, split, cross_validation + mode: + - train + - repeat_1 + - repeat_2 + - repeat_3 + - repeat_4 + - repeat_5 + - split_1 + - split_2 + - split_3 + - split_4 + - split_5 + - cv_1 + - cv_2 + - cv_3 + - cv_4 + - cv_5 + + diff --git a/yaml_lib/config_train_prototype.yaml b/yaml_lib/config_train_prototype.yaml new file mode 100644 index 00000000..7a1bc3c7 --- /dev/null +++ b/yaml_lib/config_train_prototype.yaml @@ -0,0 +1,58 @@ +header: + # We assign a time stamp for each batch training + time: 2024-02-02T16:05:00.000Z + # Add custom notes, which will also be saved + note: "test whether your yaml file works" +# where to read the data +load_data: + inputs: './data/node_timeseries/simulation_202402/sigma_0.1/' + prepare: + select: + timepoints: + - 0 + - 1200 + standardize: {} +# Where to save model training results +save_dir: './results_yaml_test/' +# where to load the spatial map and spatial surface map +spatial_map: './data/spatial_maps/' +non_batch_variable: + n_channels: 25 + sequence_length: 600 + learn_means: false + learn_covariances: true + learn_trans_prob: true + learning_rate: 0.01 + n_epochs: 30 + split_strategy: random + init_kwargs: + n_init: 10 + n_epochs: 2 +# The following variables have lists for batch training +batch_variable: + model: + - 'hmm' + - 'dynemo' + - 'mdyenmo' + - 'swc' + n_states: [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] + # Mode can be: train, repeat, split, cross_validation + mode: + - train + - repeat_1 + - repeat_2 + - repeat_3 + - repeat_4 + - repeat_5 + - split_1 + - split_2 + - split_3 + - split_4 + - split_5 + - cv_1 + - cv_2 + - cv_3 + - cv_4 + - cv_5 + + diff --git a/yaml_lib/config_train_test.yaml b/yaml_lib/config_train_test.yaml new file mode 100644 index 00000000..8aabd06a --- /dev/null +++ b/yaml_lib/config_train_test.yaml @@ -0,0 +1,42 @@ +header: + # We assign a time stamp for each batch training + time: 2024-02-02T16:05:00.000Z + # Add custom notes, which will also be saved + note: "test whether your yaml file works" +# where to read the data +load_data: + inputs: './data/node_timeseries/simulation_202402/sigma_0.1/' + prepare: + select: + timepoints: + - 0 + - 1200 + standardize: {} +# Where to save model training results +save_dir: './results_yaml_test/' +# where to load the spatial map and spatial surface map +spatial_map: './data/spatial_maps/' +non_batch_variable: + n_channels: 25 + sequence_length: 600 + batch_size: 64 + learn_means: false + learn_covariances: true + learn_trans_prob: true + learning_rate: 0.01 + n_epochs: 30 + split_strategy: random + init_kwargs: + n_init: 10 + n_epochs: 2 +# The following variables have lists for batch training +batch_variable: + model: + - 'hmm' + n_states: [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] + # Mode can be: train, repeat, split, cross_validation + mode: + - train + - repeat_1 + +