\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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\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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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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CHCVCJiGgHGnR84lICZS+UooImOhzILINPdhIrLCQCUYidRY6SvPWZeJLR0tcFwXf83P/IRZVjA8OTQp0OZya5XtuLrBTaaIXbovYQ5G5mtWBmEGBYZc0SjZNMQVx5i7KQ2lSIV8GAZiIyicQ8tei6IFSp7Oqh1JxRzqEvi1g7wRLR3QLmSCXtcSiL3LXgG0+M3IF944oHElMSqvNyvv47MOrAAAhlCMtAEiCEuM4c8RF5DwOU3RFjgHPGvXAh7khXfr8vsjRFbkjLfrsw3KIvshctKWccl8g5pV7fcLLxkLCQGTmBqAZ+gG5W5j3HIkUx9XAfZ9t5PUloSWuxx3/8S/8+3gr3cE4WuGFnimuv5AYtfmYp0h17LRUVEz3pQTvi+8DAA5sapLYKIzSxLcqcxFmetOAL7KN04AhEr+1hgjHJy1CLpvEdRDNN+yaBRRSFUFp5i74mFdQmuGs6jo7G0IvyKE0w6xK8OmT6/VnVeazrvZtcTwskNnPp7airiXOZ7rHAEw/IWCiLpoWRBFgzCt0ReHqU5Qy+unwWdl1ZOXrufpBgcCLclcydB0ClRbo8AJKM6QqQl+Y/emKwkVml8MpLgdT/K5nP4MW74hziasdT/YY4A994k+iUCO8d/DICRsHPMNUdjHgGU5l30U9vjSByGQ/mOPl7AaejQ9xt9rFgZghYRKldV2YqI4t6tfF7YSXLsU8sY4Kfm3H9fitnTsdXmBlI5heYKMVS1YUgeU6ROw1SPetOp7ec1LWKdvKFumJ1HIVOJeJRVavfhaVQC8pUCjhiAqoSYszjUyGSESJw2KAS9HcuZgC22UfBL++Rdt45m1jbjVZleYYBBkWVeSIKuYVOqLESoZYyQjDMMNKRS7KXMjYaulCk4ra78wXzbb48tFGXF9D/OBL341PT2/g+b5phyHSOrCRAumQaLWPTvpMh5jaes2urbWQg+fN8ASlDtxr5ypBpgNMZA8S3NWsxsK8jiKtO4URr/pRnO+TBZgoitJHWvLvihwJq4z/uxbIVYhURjaisVGMZ5lsiCmyRX2OkClHWoEloXmV4DPH19xrlrkht6+7bKLKm50zR3grGz1GNiqiAv3leOaOTaqMsn9adbGsYnCmnNIdAC5ZiYNyixARUhm56GwpY0jNUFhiDpgCZwrjsE5blWZYyhgcGgeRaVOqlHCf1Re506gpzdAXOfbDOf7Ue39q88RoQWhTxccN/9ln/9eYVQn6IsdOuNxYZbtm+/Co/kROotOqu1Fbonaam6Ep4pM5XqkDnMiec4Kg5f71Zmpqkyl0gLOq5wr0QD1Ugi5q0ibVrTiVMwacyo7pFSTrZF41bJkpRX2UD1Fq7i586nMUTLva1mvTfRwt+pDSLhYIhRvjCXbjFDc7hqT9diEAeGu12yAuwBC7iXwS22oUQkA7ktwPTWrdF1nDgbXUAqUSmFVme4ggVzJ0xEWESxFXZf32e0HuVhsrJRr7KqCQ6xAcCn2R41p0hj/xwk9vniAtgJa4Hh/8yOd+G45LYy2zHzalASNhTu6xSB1hzVXHtdgcl7WsYCFjxLxy0cLN8NRMq+EZUh07yQPVbEjrtT4ajNLHpYpd/eq47DtRqA8iCIqkaHsJuQrrhmh7Uftp2EImmFadRlpIaSSln4sqwrxKcG9hiJrZgvnzQ1O3utU5aZC8L/YMmcRMdiCt7xZQR1Ok4VonLpJFmMEcukFcAFBaf7FHVuhLdT3OtCMvzjQENFYqRMAk+kEBqY2+S4JjWcWOvIi4chmgF5ioayzSlry2o61xPQ74oZe+C4IlSHiJATOrYCRgFFCmiGvrIW6klg5ciuhrnWJemfQDGiORYqli7AWlIy3f1ZTM80gD5l+YgImifNIi0EVLoFU3GmQBmAiC3BwAo52i96ZVTc4UchWafePWwC9IIaDr9JNJF23dXw4dYXWDEmm1Od6MZB4jV1yv7WbOqq4jtFTG6IocfZFhIRPnXgGYKCtVkbW5sROKmEJmj7fpk6yQqwA3kjNMqw4GQb3iqWyqS9vS4SV6QY5J2UE/KOrVU+81EhwxK5EjwKyiBYw2Pvhy0UZcvwr44Ze/E6UKIGyKIWAEllRD8kdwkX9Uac3pAFiRqPmZxJUU7exbTyrBNB6VI3cRUZ3Mt1wG4MhrqWLkdnWStFlEXqXiCLnZNrK9IdIivRLVhwY8c/ovv7hPnu++tql2JBVukEWpBY7LAZRmri8wrcy+Xu9OABjNlS9BAOCJbeXGlCDfH6vUoiES9Q0Q98I5QiYx4JlbqaVjQD5jtJ/9IEOuQkyrDkImsZIRZlWCjiic4LcX1EaJVEsMeR0N5iqw0ZnCUTFAzE1tcCdI0Q8y/KUX/0e0aKBNFb9W+MGXvtumDM1+OLog1kd6+UNSqQCvvO+vLzJ30Uc2ojmt+i4KWcikIQMATL2MLubEs3spPHKkXj5gk6wItO2U4lLNzLdKpoUEeq7/M0U1CSudvqrUAosqwX2rsTpc1X2Q43iFUbhyYtF18vLTULl2jg94huNqsEFcVH8j4Skd/0yHyFXootuTsoeVDDEOV+jywumyaKFhUnbREYXrYNgJlu47fVQO3Wc6otUcgin0RYazsoeZ7ToA4KxyYl7hv/jgP0ILhzZV/NXG33312/CoHKHLTdQh7QXsk44/Kt5P3ahITLUk33ud/NN90prLpDFdZyq74Ey5BmyqkT0THxly2TI4gkNjKFZuJY0iA6ApJfBJCIBrvvZFqwKq4esuWG15Q4/RfpK+KpMhFmXs0kKqafmpHZGgBNtIryjtJg+vUgvsB3Nj+2OPmXsfFbjn0UprwkqUTGAUpO6GAZgUL4wkzsqeU/RLzTEOU9dzqcCteWGFUgd4Jj7CXCWGmJVAyGXju9wJl67ob/o9Q9NVsGWIbYvtaCOurwL+9ud/i7vDk9VxHWk1i+OFNxmaUNeI6ouNpuH4EYzfM9gXef06XnopjyEUKlLviuXGdGlKFenvqYocqZoBrMoV6qnVhZqJaxJiXg2MuVSXtvVqeNaYwUjR3YNihIeZIdjjrBa37idLDIIc49BERrSC2OWFczj1jxdJMcw+cwzsNCFKVUstnM8Y3UD8NDZhpfXiN1ITstGhNLAjvJoV4AiMVlipTjkQmR3c0XXRpJ9m06ASAPjc8qojtg4v3We0UZdDG3H9auFvvvLbEbKaXIi0SAgJnE9alEL1vKES5FRAUcAoSCF1AKU5jsu+9zkBdkLjgX5W9RCz0t3VryVnLsUZiZVxMoVCl+fIdYhr0Zkb6wWY6IsuwISXjiBHdvIOkZUfadH2KzDT1Kxq14iRSJGqGANbDI9YhVRFmNrtmxRU7Dbv9eLo0bnHt8tzcKbdcykdzlVtw7NSEVYqci4QgC2Ae4QnwRqSVCITAQ3BFK7EMzy0so2QKddUDRjC90mL4K9yjkWK06rvamPcRoSpitHlOeYqQU/kboAt6eD4OwcSLSzaiOsriI+/8uHGqtr6+K/13wkUWQnv73PZQaYDnJQDBLawS3d8UpbHvB50SqRFeJiPGtEGAHygdw8Rq7Br24L8Aj2H9i5e8zpKOSkt86f0+KRFCwv0GgCufgUYp1KKgI6qgSPhE2t7fFqY/0ngeSWZQmmOm4nRpdXWN+VGZEedBH60tXL1LKOVSnjZWAX1SYqOT71QUrusSl3b/Fy2JEhRZKZC05uoA6dlI1CDumn7qe2mSSYy4BlCJnEqe3iYj3Ba9BCLChGvsBcukasAH/36H0OLtjj/VcfHX/mwi6r8iICK1VSD8e/KQG1A5xezJ7KLu9mu+51A9R5aWo8skV2JaGWx9rmq24JirGSIg8iQ1Qudh9gVCwimndaLVjHpddQsDNREVDuE1tsjwV2tzbe+mcqOWyGl2taN6NQVvsmptNQCr8yuNI7jt+y+Ue8vL5CrELvBYms9MFXxVqmISQsZQm6K4TSE1k3+sS6s7n1sNEp1KKCumdF3lKrY2UhT2kng3lg3HxIcCyv3IBEsRWojkSJkEi+n13Ba9BBwiY4ocT2eGCKFbgdutKniVw9/5/PfjkyHDQ8sP32qVwsD+5hdgbOPU1pIz6OLet3LnDPtHBkCJp0Smy7GUgtno8yhsJKRIzjCpOxiEnUxkV08HzfTMX9cl4/1WYjrPX9+pOY7o9JjgFkJnciubbiO68k6kmE3NjWzmFfOyx0wtTigKTEATDFbMIWJ7DrC8feb3itXgROiLmTiyCtiFcDqDoBH5QgJL43kRMZuVBpFdbkldeoO8KNaoI6eqcgfMunkIQLKWdosVYz9YI5T2XMDQQDgejxBrgLTrhRkmFYdjILVRt2zRRPnd562+KIg0jJWxLWvlFnqjzG1FxdFXJSG+JOjC8+JYS472AmWbtWqIwqnLFeaIVcB9sIlbiZnGIgMz8RHZjCDFlipyEzpseRGnxFwiUIFuJPuNbb9C/lllDrYcIoYiBW6PMdBMHMXj2AKgikoMESsclEWWTBPZQdT2XEOpqUSblXUT1f92lepTVGaRJxKc3R54f4RQXJocFt3EmspdlfkG573XWHEpH2R42o0dRqqAc+QsBIxM2ltlxeYqwRdkSNTIXKrszNpYx3dxbw0olS77XW/qKlPpspM+c5VaJw3dOjMDEMmHSnW7VYVQl65mmLMS1cCCHndRnQ1nOD7P/37v4Sz8N2JNlX8ZeLvfP7b3Rh6Kr7TxeYPCfUnIwM1aVGkRCRAzc7H5QAJL527KMkcnu0cu/egtO1U9lxEAZjVRT/aA4xfelrFGIQZchlgP17genzmHr8cTE1TthW0UsrzqBphIrtmjNe6wNPTmhERpTJu9FB2RdEoXPtaKlqwoGO065nyDcSqQeZ+Cpbp0O3f1K7akRyB3ChGwcoUwW30FDLppvk41b1d9Sy1wKnsuVmIZlvIsVQb00bl72vcELea5/mRtlm1JHFwj+fgUFA2LeW2pYhSXH+V8bX0MnIVYBhmeDo+Amcah+UQIZP4gV/z3+NdijZV/Eri4698GBI1aQG1KR8tza+vOK2DWk0KHSBiFXo8d7KEu1ltF9zxdEz7wdzV0XId4iCYY2JrUCYqMf9KRYsAGuNwhXG4wnFu7vDHed+R5n64wHE1rImw6tvVv5V734ns4nIwhQJ3KRrtJ2FRJa5xmEikVMLtf65Cp5syLUp1muunouuiVYroBFPg0Bvqd2pJ4lauEXtFeF83RhGb9AiKPi9hFThXrm5FpByitssBYNux8oY7hg8BDQVDXmdVr1HDJLKKrUzF9IFWLnqbyg5iYVT0V6IJUhVjN1jgajhp3CRa1Ggjri8TH3/lw1YhzsxJ783Wo7/TSbueGq6f7BRNUAHcJ6x5lWBk61g0DguAs3Gm9yp0gONy4FImwLTsrKve51boGXlapOc7h3hUegMoPPkAkQSp8BNblKbIsNTCEYgpOpubIzVn+yREoIiHZiwSKMoxPZp1jyWtzhFyFTrZAP0OmHSL0kGzDTUZUt1uIrvu/Ui9T+krvZe/+rs+8WfdApv2jephJlUMsKgSN7Go9qdPXZTpk54vqaDOhdgKVAfcRGMKHAkr3612z23E9ZXAx1/5sCuy00qRL3GIvbSh592dY7sqRatTEsaj/fX8knPWnNpCOvk3jcKm80KpBS6FMyxVjIhVLjUqdID9cA5l230o2lKaYRhkLt3seWZ+VDd7UIwaBPfG6gCxqN/7qfikYRRIpEUEApiLj0NBQrhFBPM3vWG/E4pa25aIEmORugiSvO8d6XsDYTMVOnIH6hU/itycxY79n8iqbrdhjsycZz4S91pTgBeA31XgVX8pJY155dqvurxwynsOQ8YDtnLbdlb2oJhNa6tuQ8grNUfCS1dTg6oXMiS4mY4dGgJOWD0pqEWNNuL6EvFDL30XQqubon9UAwmZdHduKsKukxaABmk9KEZYVjEeZQMkonK1m4BL6/dkR2bZVbUP9O65KIhW3Oh3f+BpuuYoCpg6F9mvAM1VT3Ij9Z8/DFboBxl2xbJBXJQm+jW8hWcSSO9D5OM/RmSZ8BJ9keGKHeohmG7UkZy41bP1oc9cR8wN+RESVjRWPetBH4ZAScxLVs40TUisRcSUgpLPmdK8IbtQmmEUrBxh0s2LIj/A+NYT2RGabV3MET3JYegz6BhdDqYQTENqhr1gge985qWNY/CEo9Vx/UpApEXDF9ZJixp5hdXz0CALwFx4AsoVgR/mI5yWXSzKGJWNjsaxiRy4Sy21a7GJeIWuKDG00cWN6LQuDNsL8/X88kaEw5luLAxMq44jrv1w3ijoA3BunetTcgiplyICdbuQP56sKwq3DwrMrS4qcLetV6MpbkVHuBJMXXRzv2qOAANQj1BbIy5/RVYw5aJDwTT8GYbmb/VxEqyukRGBUWoJ1JGe7+e/3pepNHORn99WRNqu9dT1QTFu9JD66bPTmjUaxet5jPvB3DhxWP+uAc9QaIE/+vwnNo7VE4w2Vfzl4ode+i6clD0833lkIi3I2p1A1GE/gC+btIZRVpNJtECpBYZB5pTxZC1DpNUVORQYlI3kSPVOKVOu+oY81pT6QB3x0MVMU2zIomUd6zWqdfcFIib/uWabDXksqsSo61EvFFAN7UpQk2KqNz/bfG6w9nvtge8srTVHCVoUATKEjhQ40+62a0iudK+7nEyRqtgdJ/q+jquha+AW0DZNq9NOvxfxtOrXNU1dOc2WXzPbDZb10BBraWQPntlmiAZB+g3o9H/EJBSMZdC25vh3K9qI6x3w3f/6z+Dp3gme7zyqV6m8OhatogG1cnyuEhyXA5AbJ4fCrDLuDYUKXGRDQx5uJGdYWK2XT1p9kbuTl2xkKC2i9hEF5qILuohoSZ6eR43ZvnvpumKc0sX3ds1UIX8yM/UC+m1M9eujRmq2GyzdhZ+qyEV1lPZ9S/91ALW19FLFjUEgAgqFFnaF0px6RPpAU3pA+7a+cksrkcCmFIWis4hJDMTKeoh1XEpZaIG5Mnq0Ac8aPaO+Xz89N9chJrLrIi1jSFi5zgn6fGrf6vICSnMMxKpRYljIxBCuMu8pbSq6Eyydr1qpzcyBa+EZfs9zv4h3CdpU8cvFH/rEn8SVZIb3dR40WnJ81FGAdpqqL5W0xmHqoiC/gZdSNwnueUaZKIZW33z/K1pq9yMiGl7h+8SvbzNhIWNnqULv3+WFMzQEYEW29alA9syUegmoxhxB+numQqxkaBYbgpVbEX0xvucK/RPVxfp8x1yHmFtBq78AAtTktW5rQ2kipW/me1Hu/QhuJW9Li07tjmG2jUwXKV31j1/MSrfdpOfLdOBsiwDYxQfpjlemQxfF0fH1yUtq7obWxrzC1WjSWOQh6cznVtfwN7/+Rze2/wlEmyp+OSDSei45dBoiOuEptF+quC7maoGzsoeFjF3qJ5jGQsaotIBgGj1RIGcBCiWcVYtPWFQn2QmXbmXKLNtX7i5OF/v6dGkSMfoXM+mKTI3JfP/+9GgFDmmnzQCGFH2TQYosqabnT8Ch/43aXKOCwD5bbKRegJksLZjC5XCKa+EZBiyD1BwTVa8S+vIAf/VwXez5Tro431zQj6CAejXUqPNzFDpwJEDHRYFBKe60WoC/0lk1am2OvHiJHs9dCprZVJMeP6oGzlJ6LFJAGVJ8UIywEy5xIzx1A33HIrXRW+CO7WExxNVoAnCzX4XtdPCnaL9b0UZca/izv/iHsJQxvqH/lrsY/IJzyKTpk3PDHRK30uSTFg1lWFlLE3J36FhfqStxXeehor/fjO3D1M70RjHXRFxB4/n0ekoD3ZI7DDEQaRE6vEBX5BuOrJS+rTtd0H6baUAMgml3IVE/oJ+KEuk8FRnl/9PhMVIdYWaJgBq8fcIi33s6ntT8HDLZSGPXj5HwCvcAcGKnftcN2JuGiL5rBB0voCY7oC7iKzBX2PcL8RQRZzowUgaYiGkUpC5aeliMcTfdwShcYRymOCr66IsCtzrHeD5+BKkZ5qqDieziXr6DlTQj1a7EUxwE84Zh42nVR64C/NWv+2+3HosnCG3E9aXAJy26aEixTb8vVWzqPHa1yFkCbyEtej1Qyw18JbyA3qocl2vp3GnVd44CgF8w5w13VXVO8ZZqYtJux0pFCJjcqAEBsA3jNTm6uo49hfxoyl/qj23xGoDtQWRIgrJRAxuKzBXj/feZqwScKTvPsClAJRhBZuGK35GnbYpZ2SBjPzIl3ZY7FpyiLLNDzuzQ7h+l4WS9Q88nMtwVS2NVo+uWIoqy7qxMP2hoSwvTqouuyJHKGH2RYS9e4MR2MNye7yIJKryx2MPdwS5++/izdrsURKzwhdVlKM2di+1ImAlBYGY1cyGb+/VuQ7tM4eFKPHWkJWA6+lMVNaIPZWs7ginMZAfCs7AJmUTAJIZB5iKrmFfu3zDMUGlh0kcvmPUv7mnVdSrqVMaNqOW06tfL8V5f5DaEvCla9Gtb6/UdAX0u6XGmXTR4VvUgmMJKRSgVd/vip3CJZyvti1vfLnfdzxPZg7Irjqeyj1yFOC4HDdLiTLsGa1+2MBYpxiJFbBum/X3h9l2pIZuaqLdBaeMoS9+DgHLb4Q/UoAjPX+3jTGE/nDvbHcBElwfRAh1RGgtpXf+9K3JHiHvxAidFD92gRFYFUGB4bX6An5h8EIlNPUcixV5o5kFWSjgBa70NDF1R4Hs/9e9t3bd3A9pU0eKHXvouV5wG6lWkdRsX0utkKtwqvgRqgSdpeJTmbuBpsJbuEMGclT30rdwBaE6jIceIbRHSuikhbSOBCIkuHNom6u0D6kI36Y+IeMgMj6IcQ6ZRQ5tElsp+a5D/eWTT07ULAJetFOJU1o6rZIdDx83XmwFG1LueIlIh34+0/AZqwERyRCznNb2ve2zN7QofEV7t4cXXXle54+Kn0SfW8jkWFQIbudJK8dKeLx1XS+R4lA2xFy/x+mwPWjP86ad/CnPZgQLD28UuzsouQq6wEywxEqmxN7KLMg+LMV5f7OMffevfwhOKc1PFNuIC8IMvfTd2g0XDmoYabn0S8EkrV4FNzxiUrfVQBOLcAJgGZ/Xw0YA1VyZDXmFRJW5gxELGOCn6DdKSmjXIQoJt1MBId+Rb55wXRVH0R6mlgHZme2T9AqBBYICJHGi/AbgLiJ5Ldj6+1mi9T3GdtFIVuVSI3pPU7FQfWq9r0UVr3B2alQ4BhcimwGq9TsjLTXmE15QdMompJQzaX9rG9boitX0lzDSNZyrEYTnEWdm1MyQD5DLASoaYVYkjLCLblQoRcBOZX05miHiF54YnYEzjb93+LRiIFTg0roYT7IQpSmUatynaNs3hJTgUnu7VHmbvJrzrieuHX/5O9EWGUtceVkrztbl9dVTjX5h0t6+jGhtpSYq0zO+VoqbgoPFeRFjkY0WkkMtgIw3qB1lj0AMAJ3nYbN5upolUlCeVeyNttNFGTViVXeon5bpNWT0PeYqIqHZGy/pOfW5X3h6VI8yqBAsZ416+g1PZd6R1WAzd/vv7PgrSxkSchJXW4501tsFtC5RLH/1hH4CJ1Hxy8vfZrxfS5xDoGI3ECiOxMr2h9jjVK8l1jZNSedqHpYxwWtaLDRGvMCm7EExjJc0U60oJrGSEYZBhJ0hxOZrh3zl4A88MT/BffuG3u9deDScIuUKpOKZVp1HfpBvQn/z579k4Lk863tXE9cMvf2eDCMgQ0Dg/GKU1uVqaRtvYkYwCdw3NATcpirJGfoEtTgM1aQHUmGvTSVvQpZaYkBu9UkcUrj4GAE8nx25Cz5cDak6mlNY3yCN7Yx/rEQlJBgATGfrksr662RW5iTrXUuuQSddCdCmaubrS1E6aXifRxDY8k2rdrWh6NSN6DW2v8OpoIausEaIxI/RXBrmdaTjyeht7PG+ITPsiM83SImvowYB6MYAiVdKN+dFgPyiQqwAPU5P6zqrERcEdUaBUAh1RoiNKdzwnZRe5Ds0/FeCZzgl2Oil+4uQD7ry7FM6sA4VxAvGdOWJeYTda4pt//C/j3YR39aqicSmox8QnvATXuhEV0cmTsBIlF0hQAkjMxY8YsXUgDZlEroOG3zy971LG6ACYqQ72rPc7GeARYlaiH2Xusf2wrrcBcKprwHWMNMjDjxZrm2jWKJBTekt9kH2ROWU2iS0pIqILlaKi9fqT2y5R65h80vJ/3glSlCpAImoPKtcKRCuRDRsZL0Wlad72+T2+WWwvvKjJrIqa118LzxrtV/X7y4YSntJDJyhek1XQcyhy41o5MSlgvpvXigMAwNvLsTluZYyrnQw0JQgwI85UI5U2j83KBKX9+0LE+PrxPZyWPfzc/Bn82v5b6HLj5novH2Nmv4+uyF2NVWmG33r91Y3j8iTjXRtxffTlj7gev5FInUKZxnZ1ee6WuwEq1tcRy0LGteUua5rOUa0GMAVZKsb2gtylVNTSAWyu8l2Pz6y9TbN154vBJy0inJWK3Aoe0HRAEFA4rXoY8BUUOCayC+61zNzJ98GtkLbWc9WuBqNgZVuM6v2NWOVqMArMkcIoSF2k5DeD+ykmRUqE9aiQnh+z0hbhBQZ81SCYhJXYEwvs2UlGtO8CaiMiXKq48Y+25SCY4yCYb1hF183R9XtQBEfDSAAg4PS9mf97QY5xuLIOHfV7CmaEr9RZAQDzMsGk7GI3XKLSwrVcZSrEXrhEqQUmZddKLAyJr2SElQzx+3/mf79xvJ5UvCuJ66MvfwRAXesgguK2iZZAbS3+kARqyaE2GfrHmXY1B/pHF14vyNELcgxE1rhQ+yLDpXCGUbByNil+PYYutnU7Zv89AENY22QR+Zp7Jl3EqddUTQaBPsx07DpCyVXgtkFp8x6VV5inC9ov5h9XA+MOoYRrXfKPH2AWK0Ju/vkRJhGhH93RseBeNFxqgUfVCKkykhFyLQUMKa3bPtNii28hTf98+HVM8tqnfSX4TejbyKsbFI6w3Gt4HXlxptxijflZoVABUhnhpOjhTroHqRk+PbuJTIduxXsYZJBgLtoSdvGn1ALXOhO8W/CuI64feum7EDJpV9FWGFtvK+PLVF8op7Lv7nZ0olOaU6cU29UiFKUAJrI5LXo4LXqukA3UKnPzfOV68pSuo4Pme27KHnIVNu7+mQ4aFyJFcoHVlwWWnAFjQeOr1aWbAVjb18yqxKUm5vPqSKlUTQKkXrpCBw1/MJ/MjcygcNsEmEiCIl+qUW0j7JFIG/UpP/Ujv/y5ThBCbrgoZCq0K4B2XmTVxYNi3GhfOqt6DQLr8mJj5Jjf6mO210/VzXd+qVOTF5F75aJc7gTIMffajez2VkogoGHBSmBRJgi4xJ3VPo6rgfODI4PIhYzR4fVAlVwF+Pf/zf8W7wa864jrVnyM/WCGK4FpYPUbfI2LAnN1nlyFjrzmMnFFbYqw1iMhoElm5G5q/l4Tjx9VcbciVftN+QRCMgP/uc3VrbouI+z0Gmc3w6WzxCHQthN2xQJz1WkQwVwmmMsEIVcuSiCUul6UAIDTqoezyrS5kDQEMC1B5xF7zCtXXCetFrBdY1Wb/dXvRdu6VDHuFzu4XRzgtq0x+X5ac9VpkMtAZBiL1A3nKJUwK3XeAsq6bTNNYip04P6+nrbTOTK0jeb7yRLXO0b6UWmBUnGn5QLqc2Rd97d+c6o0x6I0N4+3sx1Tk/U6FJaVSeFjXtkMQGEQZPhN//zPbznqTxbeVcX5v/vqtyFmyptlGGAiu87bu9QCS9v86+uL/LqIb/KndD10lUAnr3FFUAi9WtV6StYYtsGaj5mxWBoc0qsv+YV3s03m4m6e8LR9BGr0Na9jjSbt13IzkJWimbdy07aS6xCpDN2qKGcKlTaTalYywkE0h0Qtis1ViBzm4iQiA4Br0Znbx2NdR2J+xBmyqlHb8rfejzyJsGhFzflolQPciE7xsBzjSjgxj3k+XyZFrxp+9i8kD/C51TX3+0ImuOR57meWJGgb6BygeqPSHImNnkpZf3dXkxkWleksINInOQNA5M5Rau6soDuidC4anElwbZvVZYhCCRSFMYE8K7r4Tbt1Ef6s6mJWJRgGpgRRcom8CnDQefKbsN81EdePfO63IWb18vnDaoylitHlufNVoubpw2K4YegGNCUDlI6sk9ZCxu5vk6KLk6KHo3wApTke5UM8yofuxCfUI85kwxaFQC1APvw617rFC5EAhzbphRfhACZNTNfMA4+rQSN1JC2a+90OWAUMidGE7LOy20jNMhWiwwt0eIFbcT1STWqOG9EpRkGKa9GZ8xbbFcsNVfw2C6H52v4DwP185P6tI2EFElbY96v7DTkU9gNDUPvhAvvhwtUCKdIkmQS9HmgSKKX2/o2osjIZwMgiADTqceuRq9uvMsG07CCVEQp7zCldjHgdHSvNUEiBT0yeNa1GOsROkGIlQxwXfeQ6RMwr9IIcXVHgt/7U9239vCcF75qIayxSRExirhKUTNjBFREkkkbaRc21ADY0STS5RTDlisycaZcS+qnRpKjfxx8fH/MKU1v4HolVg8C6vGhcIH6EYH6PGnUWAe1aVoA65UhVjJFYbURnnBnfLDeVSAnMde1z7heuAUOIxfldF8bIT5im7ZWKGlEUDUJd339fzkBK+nUF/Hnwt+12tufS9WGQNcZ4+Q3cmQqRIsJIpM7mhrZhbo9bzJo1K5onCRgCnNvBGj1uhsf65wUJbQE00tm6Sb82m/QV9JUSWKL+fivFEXGJQgZIRGnmDnCJRJSoNDfRlxQoVIC5ncy9kMYHrFBmOMkwzBDzCqmM0A1KfMe//F785G/++Jd0bC8a3hUR10df/gg4lLO/vV/WhVkiglyFWFSJKwr77SZ0QhNp+SDS2o2W7jUPsyESUbpUghCvNT5T+856WwlgVrP89h4Sk/rYRlr0d///daQychOYT8oeTkpTp/JTvNKmM/Ukae0uzPWIkUO5WYp+JHgQzHAQzDAWS4zFsnHsfAlIaD3HQla5Bmm/treO21k9lZtScwC4Ekwax4j8tAAzAs5vmQmZdAM73HHxiHHiRZ+0GgygkT6SfMP3598L5+jwokFiO8ESMSsbLV+FOp+sY1FZoWod8XVFgUhIFFLg07MbOCwG6IsMoR2uMq9izMp6BF0kJA4X/fM+4sLjiSeuj778EVwNzzCVJqU5lT0IaOd6IGBm4vmuokQwvlRgnQRWMnQF191oaf8WOf/2SonGybmSISZlLUAl5Cps+Fitj5qnHkHAH/6gN9JDwnqU6I+1958TWzU8beusrGcj5io4t7AecOl6L4G6zYm2n8CZwkT2MJG9jeK/cQDliJh0vYV+YV42otCm4PSs7GEnSKE0w6VojquRIQ1/apBgutHEDcCNvJ/KbmNbrgQTvNB56H5fbyvyB1gQKGJLVYRTS/ZDsUJnLeWlojkAt5o6L81kccCQV6W5+ac4CrWpWwPqWZhdUSARFbIqwEluelo5NDIZIpMhTooe3lzuYxSu0A9yXOov8E0//gNb3/Oi44knrpiX1kc8wKk0U4RJE+M7J+QqwE64xH64cHdYv6CtwNxydKnr1o1xmLqC9zBYYRisMAjqi62RJorK1asWMmmkpQSatjyR3YbNC72X/35GDMmwqfOqsD6Lb5ucwvTL1RHKsoobkof11TN+Dlmuk7tvqfOoGuJ+OcZRNcCdfH/r6wl+egnUav6El47AdsIlQi5xJa4L6R/s3nXkeFQNnNnfqezhrWLPkZYPn7wArJFX3IisCCNrp+NjIOo01QlpRe7qW74/v388ibAIJFol8gq4RD8ocBAvIJhGLIwLbiQqjOMVKs1xN93BMMwwCleIbJtZoYRrHQu4Qi96MutdT3SN64df/k7sCtMG4k+YJtvekpmazkBk6Apm23aM1kqBmYEUmjkLGN9PHTAXfigkuqKWIFChdydKsfQK4LGoL2a/aJt50Z4v1BTQTgw7lc2LLOGlSyGpDubbNyuYIRK0IrlOWs200jy2kiE6ojRaIi4bPZYK7FzSWk9//Z5GH0TC94sdXIvO3Da7fSJCsB+7xxaY2YI8Ha/LwRTH1RAjUc80JHIDai0ZYBT808pMr05V5Gx3fFwJppirxJGYP7bttOpjEK3cZ5RauFXUXIYYiMx9bwfBHHdc+405f/zjMq06WK3NWCS7aPo/EdWGi0jAJCotEPEKhScCJjxK+wi4xLPdY6xkCKUZKs0xr2IENJAlLHBv+uSZDj7RxDUSK6N8VvVSNtm1SFrS9ppnyU+clNJ0otBFQqQ1rTruwqYUgAhrViYbRMGh3Srdgpn3oNpMrgI34XkhEwixOZijEf2BuRU2clGgor2/GrlNY0bk0VDdg2FuayMdYdp0qrWUhUgrWJNZdERd/1v/PPLhp9T5IGoSR5cXbuK0T+RAPYl6aGUMlCrfsVotoJYoEOmUWrgUbio7plnba5miBYCQVY3VwgHPwIMz64E/aCwwnMq+6+WkxRm/6d5sh8HlcOoMF/2Zk+vfZSJKGxXVImY3T9OSmA9DYiEiXiGXAQKmUGnu0se7izEA4NnuMY6KPqZlB4vSfM+RPa8uD+b4ph//AXzyI38VTwqeWOL6O5//diRMuuk7A567gRccGhJmQASN+QKM/iiVkXNAJc8pcoWofck1hI2g6CSlu+QwNCf+oorOjVIAMxKMiq99kTfqWqUW6PK8saroXywE3x0VMPWyujBfNf6+rXYzDDNMyjqaW8rIKbv9C4hDN0grZNKQnCUrwVRD01Zq4Wo77jW8cnbXfr2NSIQu2vXFD8JcdaxnmlnFoyjq1Bbg190g6D0bCwK8bERoQC1YHfMUd+zfTquei3aPqoFb/ST3EN+5YqnijS6Hbd/Vyls0MGkdb5wfdHw5dP0drDmMVJoDmhZHGC53F7i3HOKN6R5G4cqOt4uQiAqLKgKkIa9hlG/IWy46nsga1w+//J3YDRYudXJ6J0taboKNZ09iakqbpOWfhLQSR1g3BvSjjr7tVesFzQvYPK+p4UpV5KIov0fPkFfUaLgmrFS0kZJllqDMv9gOVVjrV9Ts3NU6oBbO+oh55RYDiLQAU2/yi9a+po30bLGoXJoc8gqXw6lT/vvk6hPlw3Lc/FeN3WN+D+VxNXArnP7KrLEPatai/H0yE5qChoQCMJIZ2k9fN9bjOSJWbXWm8OHbBdG25DpstE1tQ8QrcJg6Vi/I675Q+z2tp+M+duIVCinwbx7dwqcn19ERBfphhn5QoLIEx6Fx0Fnit/yL/+Qdt+Mi4YkkrlvRCaZWTBp7HuhdXkDYSdOmsbomLapt+KryXAU4KXsbPXtySxq2LTUDYF1Rufs3LTuYV3U9xAfpqOoeuniDRMhfi7YhlWbwamN2oqrTFf/vfvRRaYFJ2XFpYsClWUa3F0nAmr8DQFeUTkjp1+NCJjdsrOn55ICxK5bYFbWiO2El5rIDCd5waLhb1FKHU9lzERUJhMlXa90MccAzp0p3XQVenyQdX1rJBOopQL7BIWFSdsGhcSs8biwYLFXs9GD+/q9PGAJgG9rDrSu0Ea/cOZHJcONx/z3MPlaNffNbzjqh+czjtIeDaIGYV4jszWJWxMhkgIhXGMcZ/ui/+RPnftZFwpMVPwL4+CsfxkBEmMhuo74wEqklpQihkG60Fnmin5VdJ4UgH6uVd9L5fWWcaZSKo/SlB/aCpihsUnZcc+02+HfR9R49SkWMr7l12vSsZQhqy32n3LKkvq7/Upo1UsS0CrGf1NHE+h1+/cLzSeuwMCt4NA2bZkZ2PQ3bzfjERr/KvVemQwz4qrFtR5VpszqVPTwsxu7vO8GyIRnxU+Jm36dGiHpUGAC3iEHRku/D5feEEuG8sTJ1tGGwwuvZJTd129SvmpcLh27MZiTQsFeqTwH1zS7k0qVt22pa5jn25qmoO6OZ5tPNrFAh0spOPbIar9vpHsbhyjVsFxBIqxCRkIi4xHHew5OAJy7iuhxOMZFdDHgtGiTSynTkGqkF0y4dOCyHDekDgIZMgECktSE/4GQY49dTKvREjp7IG+nfujCVnDHp37ojAU362QbXgmM9tNb98f2LIuZlo2eRpBV04s/KDmZlp2HNAjQFnuRNT/D7OQmTsotp2YHUzA6c9V0wmgRIEoa75R5ey684keg6aQFwjrS7ohb6+jemdRPDRnM1z5pqemtx09yWZKts4tPZU1t8uYKtXQEkml1fJKGZBCRK7awJkwHTl0g3vfV6lDhHU+cfz0IKRELi5+/fxCce3UImA3SDEv3QnF+nWQeLMkLEJb77X/+Zre93kfBERVwff+XDSNWBKcaLzKmfUxW7JW3ARCpzaYqqh+UQAZMoVAcBMy6mgG1n8SIPOvH8k2rhVsyaF7uP1drdkgqtnagpVqTVSX/167TqY7WlpkbF7VRFhjA9D3jzXvlGbaue5GOEpyeFufMWSiDiEv21WtxOmLqLs8uLjcZtIq3jYrs6e8dGZa69SAvXSF5q4VpqfIX63JM/GKPCOrIbi9QV35vE0Gy/8ecFrIt01zVYQNNXazdY4C52cVp2cXu5i5u9M+yES/zC6mm8mNzDTCZOwuATo58mnspmRNMRJea2zNATufU2Iyfb5g1wJaOGWt5HoZozEQBgkicYxxkmeYJVaReJhMLxqYmCBddIAtsIrgQyqdFlJY5XFz/qeqIiLhqvdSM6dXdYcjY9qoYbqdab+QECJjcuvvXaExXY/YiKSMu4WPL6n73DruuZ/CgtFrWHe64CR1pESCGrMJXd5kqjam4T+VwBtcleV+QbcwSpyZowKbsNMl2WEc7ypk5sJ0wdOewGS0da3E60+dziKtZxfznErEgwKxL0ROG2fT0SpL9NZNetDpKcg7oGiIinVRe3ohN8IHkbXdv47BfI1xc56PdtXQV1czzbuk3mMb6VOO6s9nFS9ZGquFHvIqEvh6q7G2xXg5m5aL7//WjhXHABU08chHUkWiiBrm3M7ojSzTCg7dwW/RMmeYJ5nqCSHHkVoKzq/brcm0Nw5aLCUpm0MRTywlvfPFER1+1s3zlp+nezuUzciiJgCqxUjF9f8TGpYF3n+WKktQ3+RUF3aFcrE836Ec3yA2xks6Z4T2VTld2wOrYuD+vREFCnWNsm4xCOvDvvcdbDOF45EgXgfKu6vNhYUT21EdtnD69ir1cX3a92Zt7rrb++ip2oF4B1RV25/ScQWdL/fmobsQopmvUoMJPiNfomPbL3f85U6PRyfm2JVo59QpoV5vt46ewqBDRuJqd4UO44659me1IzavJlLJeiWaM7grotZlWCZRWjH+RG4uBxKAmACUsrJq30ZoxBZQt/NGoSm207fDDG0ds7GF6ZY5DkEFy5UgeHBjvn3L0oeGIirh9++TsBADeiU6QqcndASi9CJjEWS1f4jq33ecwrV49ZVjHmZYJryQR70QI3k1PsBsZ2hSKqWWUulEWZYG7/pTJ0BLNudeJDgSGXZuYeuaIShK1TUcF4vem5I8pGegKYC3BdL5WwqjH5mfoVCcNghYBLR1p5GWBVhhjHhkiouO+POKOLe/3O/8rpZQDAybKHQga42TtzvYy+W+n6iidQi2aJiGmu44Bnjd5KUtcXOkCX541aFhn4cduQDtSR1+YA16a2itI9YyVkRpy9nl0CYNpvTjNDOJ8+qz27qIa1vtgBNMksXuu97PLCCXCVZuiLHMMga9QPfVA5gWxqekFutHTW4vk46+I46+LRfFDfEAMJqTztXSTBMo7ZpIt5ZlNs2zYUB0aA/Q3/w3+29fMvAp4Y4jopB3guOXQn1TZFtxk9Vk8dBuoiPC1JUwhPEUumQsysdmhRWZlCFdXDIbw0LLVtF3RnIzmBPzQCaEaDp2W3scJHn0lWy/6+OP0TrzYKxhy6QTak8KbX0kxG2u9RlCEWlduW09w6J3AaDsK2emCtZIh/e/8m/u39m1hkEQZJjoG3Ivl0coKnE7MSZ9ppTF1oJjtYyAS59X33VwX9mhWNoSer5dOq7/bbTFsqGk3Ztd11TUT+fofMTvSxx2siuxsLF4QPdu82Ik4A6AQl5jIxZpOabSye+FH3uj7Ob9uiz6OBvIRR2BTEku6Nnke1Qor811cFH80HiAKJUEhcGczxwf0HuDKaIeyYz2CTELNJF6siRKkE8ipw2q5OWF7YsWZPBHF99OWPYChW1nXA9Bj6jpWA6TfzSeuwGJrhnCqwKzpqK2kRfNIiRO8gDCQozdydkgYiuMe8i+ek7GFadRrDJPyWESJYY7uzvfmZIopMb2qH1us+2drK1TDMUSmBMytroJXMudWIZSrE52aX8bnZZfea1TLG/cMxlkWEZRHhSjRzz6VoCEDDBNFPuWnkWsIMWXV54SIh2hdf50W/E8jWh4jKF+quT+OZyG6tbbMriqmKNlTv7nhxhU5Q4n3DR41ywrp7B2AWe+RavW1df0debgSSjVyykdg4TN3f6Nisg8grEhJHnmVNVpnvcjeuX39z9wzhtRRMA8gEAqHcflHUZWRBF5MCLuZWb8FusMBcJVvD+APreNnjuR3HZcSKJOqLuHR+Wt/Yv4Mezxsn9L3VCNOyg7Oi51IhbusOxpJEoFLC2eUQ/GjPdwOolLDGcDV5+EvkuQo20kyjXreWvqo5HJVgTO6akcR6QftqNHVWMABwYzDFjUH9O2ca06qzISj9zPQ6jtMejtMeOnGJPK+P83t3j/De3SP80vxaY4HAH9xB6Tjtiz8dyCcC+plS5qns4O1y10U2Ak3pidGGqa0+/rQNRGDkx+X3ExLBkqg0U8awDwBWVb2PlCbSGDP/mPtGlOtar/OIQTDlLI6e7R6747JO7CGXuBTN0Q8K9IPCyVguD+bQmmHcXWG3k6IbGqI7zPq42Z+499C7BfhS4MGdPZRWMhEwhW5Quqjrg//k/7R1Gx9nPBHFeTpxFjLBSKTurhxZL3PBNEJWuZOqro2QrsZ86ZTi+HgrMwLV8+YaJmtTW+h9Yy9tm3t3bCKv9T5G8vfy6x7CFl/pvSnFUpq59qQvdkxIIEkXNvmjv294aPYv3QGAraOtFjLGrErw5qJWs09mdbF5Z8dGppaAPzC87yK70n6m8Trz9WSV93PTBhu255GiE9MfqrEbLEx0xJutT+RISvAjNZMisq3dCYCJusxwX3M+dHmxUWh/YXSE6/GkoUXz3yezQtj1HkmgdokAan8zuhnQwoS/vcMga7SirSNmJXId4lb/FHcWuxuPA8CVZIYryQyCaYzDFa505/iZLzwLcA2+FDhddDGMM3RD02c6ijMcr3oXslB/4SOuj778EewESyc69FMUX15Q6mBrNLaNtOh5vxzSApp1D7+gXaylZ9HaCiN5lRNyGTQ+W4Ej103rYH/uH7CZIknNndULsCki3Y2bgxWotrKSUSNFirhEJCQu7cxxaWeOX/fUXffYB4b38YHhfQDAel9lzEv0Re5mRwKmjkWe7aUWThBaeMLOzH5f9I+IPlMhBNOImHRpZdJwU62H5xJ5zWXiUl+/5/I8XO9O8cLoCC90HzX+TtOypWYbERvBXyggzKqO6ywAmqlzuMVIkVBaEl/Hrf4pAODK0GQSuQzw3OB4q2ziG5+9i/4zU8iBKcy/dbqDRRkhYMq0A1UBBkmO5z72sXOPx+OIJyLiAszJQKuFpohrTgSam0gnGmfa2N3EuvF3V1PRNQEGTGJeJS5KIi9wAnXp58y2cDDViLR8AlsnLYXaAHCdsNZFq6T3Wn/eNicF4dW7Si2cBGB9ivPN5NS5d/a7hWsQ9kmYvLreN3yENxbGBPB9O+ZiTqsYv/fWp9xzfVHsokrQt5FjyKSTVUiwDWJdBw3fTVgF8GZtjiIbYb/bHs+dgNXfv5FI3fBXty9eo7jSHCCbGqZctHUq+1sboskZJNVRY7U0VbHzefMXHOrHm9/jsorRC3Isqgi5DFxNyx/A4uvfaFu2RdY3ehN7LMxjpBlzcxtFhV2kuC+NF1c8zlEUAaKoMj5dAPYTIzKWikNdeucG8scNF564qOHWWS+rALAXEZFWwkpIbsfQy7qROualvYOb38nmZv0uCqDRpkOrhj4aUQYr3aiuflDgrKhnDGZV4HySADTcUoGatMgGJZeBK8qSaj8I7CqjEgi53HBF5UxBeo4WSnMXZSk7+JVIq9QcoTexhmprsTD+T7Rf37TzFrBTjxXzoxy/rrOwF1uzHWfND9+t+JrvLLfFbPpeuDX/A8x3ZHy3zPSd2EY8ITeiT183FbFqIwUX0PBjmZBV1hSy9j0zKV+Fe/mOe94znWN07dxNQ3TNfXUFfhm7mh6Rl9+1MKsSaw8ebHXeEPbY11Ojko2UlUwefdBIMzIZpI6I06LXuPkETOH5vWN89sFVVIsQ6AOLMnJtaIIpLKsIYafCcx/7GF7/vovhlnqhiev/9rl/F5wZhTX5QO0EKRJWelNayF+KVNUKU9l19RWftIBm6M+Ztra4603H5n/yDveta6itJGYlTsrm0jX1BVJfmU9apIxfqdCprImonHsmqweB9oLcpFwURdg7/6bbad1yY+QQlDIpnFijvxU23S3ovXIVYBhkbpyZn2bWI9wYjkvTZuJ3DFBq6Ncd/UI2Te8226OdlZAfkVFNL7Grh1SvdMfNEgl9j679BqVrxg6F2RffR2sdn01vup8vxzObXtaupdR+lOlwY65jrkIX6ZNHGklJaBXZ/y6pPDEpu06QHKz1XtKx9EmIFoDWU9HIGgNQiu+/LuIVoAI8f3CE00HXLTzMihjDKMc4znDvbIziNAG7QFHXhSauTIcIvYGpwp74QD2WHajTjcL2oxGm1ibFPBa4Yj4AdyH6FzTd5YBaJOiTWj8oXGQlmHJ3yU5ngtfmtXunH3EBm+08x1aYSipnN67dbmtf5Btq+W01m1IFyMHcPlEkMJfJBqnS59GFVqEeBOtjsXZBk088WQZVuucsrtMiapCpGRFXu8+a49ScGu0WE3TgtnfgkaW/IuhjLjsYiJWZwO2ZKW4bfeavsta22StMqw4KFZxLbv7QWwBOerPNHnt9VZhU7nQ+lfZzt7XPC6ad/Aaoh7DQd07nXEeU7ntw5yXV9zxyM88378ehXUqaViESUaETFyjKDsLdCi/+5Y/j5b/2vVv3/3HChSauhJWOKMyyuvl54KUzISRSHSFhBQrdFHqu37kI55EW4TzSIvi1p+O833ivk1UP1638wO+Lo+nGyy06MUoDOqLcqjvyP2+bxqvUAqVcGx0WLexwWhryWgtqydcKMHUuSl87onA6s1Cb90llhOvxmfs8Ggj72vLAbQ9Fu7t2wjK3RXOgro1RTylQ14b8etJ6kXquEqQqQu4X7nWAAa+9ulI78Bcw0WGmww2C//nF0+5nIm2aDm383CqUWuC4HLjzSoJtOHZQ1GVSczstyUZZlNLT7xQRcaaRyhBde0NcIXTHmuYVTDVzNSl6n0IF7tzw34uEpQoMSyuS9m24d5MUh8s+lkWEXlQACsg8CihOE5RXt+vaHjdcWOL6kc/9NvR46ZTQANDhBd7fMatbRFp00dBIdrpb+1ObgVo2MJUd15S8F5oLzY9uHuZDd5fzC6KUIm4jLcKJbbM5WvVMUVgUbrQZ3YFH4crdXQ3pcCxkhMvx3G0Ltc2ctzJGd1TavpA3zfQEU5DaWDeXHkHR6iSNgq/V7daLDNzdHHbCzTHvD4oxHuXGNUJpjpn1PidBry9FcHMK2fpoMma7AJrHk4rtuadVo7FyEsYRwy/k+7bXVI8ikqQBrnez3Y3US2qGlayHXSxkbDR0Tl+22fvpVke3RIL70cI18ftDSHIVOKL0vxuC37NIDh4+fPJaxzafL/Lr6sd5w7OtUhyX+gvgKWDxuR2oqxcjXbywxEWRB9UbyJEBMKnQlQDnktZc2YEWzrYlR6GbU1Qo4tgJl+4z/MIvpQJjr2WjvsBNvYlI6bjoO9IC6ovx7eWoUR8bBhlmVeKIUoG59/dXzehziKAoGqHPBdDoc4SCcx/wHUS7IreeWTke5UNw1HdozhTAmitaHJuDPBJWuilEry6Mqp4ICwB+/e5tlFrgajhx+z1gfi+l6RUlV1IJ0UjxaPGEFhkIlEa62ha0i65M54DCtOq6Bntguxqd4DvBbkOmQhfxnAdatAmZRFesMGfbLZtptZa25rTouXPFJzc/WqpsJ3Ula6/6ZWVkDXNsNu/T9viIhHR1VvOYrTtWAtPTHngI4CzCM//gh/HmH/lL5+7n44ALS1whk07jQ4TVFQUmsgsB5RxQI1Y5L64Zko2x9usDR4EmaRG+VNKibQNMO8fLaxYwUjGsSkOkt4ZnjcdiXuEgWjgPrg4vnGgxVZFxS1DCXRxAfZcmRTUVhaV1OR2HK6Q6QqhMe4zUAgmrIGyE+VRsNEF0DClV8S+Eel6idJ79RBypjt3dfS829cMrSa3Efyoy+jiqORql+eZp1xxrH7h9Boy3vD8U1x96orwanpMVKN64oQlm9uGsMoNTPju/jtem++hH5rsPuMJ+vPR8zRQO8wEuxXNTv9JrK8gwEWnDBUQzKHCEaK7+VVrgajzFg3xkFhZsySFXQcP14RdPb+BW35wPi6o+R9cjLeCdx8UR8VVK2IlN3M1sJPhROwBUUiDpFyjOzPFOeucT/OOCC0tcPnx1uYDCQTBDqTdHQ70T6CQceUV9Sh99t4WzqgvAOEu4Ln6vWfm8z7w+mOKt6RiAIa9nx4YwOqJs1IgAAN7Nsy/q2g+Ri9LMGR76EYCCWX0rlUCXF5ig4xq4J2UHIVM4iOZYoEnKpRbYCZfIVYiuMIMmSNbgNx27lVhPK9a3w3MHfIXn4kcucgJM1DbkK2Q62qouJ9CiCoey6Wi9Kje1Q0wAeBIJGr5RIrEpLd2AtmqpFJzdEZEWACyK2JEXYFKyTCYuOiVQHanUAjGakY1vTukjtiLb89xrP39mnCiiYDNScvUqplFp3tAOroMez2WwNepaRzcokVahI00i6yIPEN1cIDvpIBAKT/83fx23/4O/+EXf72uFC0tcIZPIdGgbdRm63oy/0hsKan6vTy6/nuLXo9Z70BJWuru5fxEBNYEA2yMTanUBgEGQ4Sg3xf5xx5DiMMpdQ/f91bhBXEpzDNaGQVB0SUV6H67wu/b3o2KAmFeYEnEVHasp6+JW79TZI8esdMeOpuOETGIkVo1tqDVWdc/gun97LXUIzOqttgsoqDVaEsJGS8o9dyqj2mKGKScqJS+rXIcoFce6ds4/3hIcXVb3SIZMYq6bmrJ7+bh+rZA46CzrEWBrxX9yLqU6kh/h0nYqMKyvQMa82jBznFUJeiLHUsaupki4fzLCtb0pQi5xPx3iWnfmjvO2eZYkZM5kUGsD7d+udafu+ZUXtVVrx64blFjaqH9ZRDg5HoAHCtmjLhBoLOYJxnubNczHCReSuP7iZ34f3t+xNrag4RVGJT5XZjl8IFYurcl02BhrBZi04rxlbyoMUwoyEBnul2NwpnA9PnO2xf5dNpWR0y35uJmcOeIaRs0T+qnkFEia6nCSBxhiDtxjRLi+LfNx2TcXnvTI2a0SKiyr2BWAaTw7ADzIhrhki/2pjlw04u87fR6HMrbVUCi0aXE20ZFGCHPzoEG6gFlxG/AMc5ngWnhmxJXgrol5rjro8Rxv5pcgmHIaL7dETzcJS1onZc+Rsp+OrzeY000qVTFGIsWb+YE7jiGT+NTU6LQKJXCpu9hInyIuUSiBQgp0g1peEHlFelqs2XCH0Awcynmj+Sr6hJeNq2xWJXh1cgmCK0hPBnOSmpqkL5WhleptgmcfqypEJyhxnPWxnzRtxAslPAJsbjdFcnv7c5wcD8AqBq0YWP/x7128kMRF1sz+Cs+6/fGXAr/JdVeYLzzhpatx0cXmr0D6jpaA0WD5TdAmrelAgTstzjBcOQKhkyiXgTeGLMLVaOLeU2m+MaWm4TElahsXwPi/K2WMEAFgN1oi5pVr2PYv8C4v6tYYz9Odxrj5x8+XXhgBp8Jcdtw+HFeGkP3uA+NaGuBGdOIiMjp+E9k1Joxer16pA+cVNpcda6YYGe+otYEWlLL5SnI6JlJz5AgdUZJ0IZW1c2uhmjeqy8kMyypGJkNkMsTCRiGLMkY/zB1pbbOCBszC0PpszXpbz7dbfvuuaVpn0ny3b+V7GO8aUpwXMQZR7gaZdIN6OOz6hHGlGaZFcwHgOOujH+ZIRLl17BmloZ2wRLWmHyTEifnMD/3Tv4yf+x1/7dz9+FriQhKXf/fxTx5adqdCMF1MFG2Rml5a6bsEx3RtjBkh02HDYgRokta20WBfCqiw2hFlYzXJ1wk1nDqZaqQpNBIegJ1cFOLp5BgPirGLCOj47HoyBAnmjAbX9WvUQ5iqyE1IIiS8bNSnIk+1vhsskKmwobcai9Qp2wXTOPEm57hUck0G4M+yJNKiRYn1vk2jrCc5RNBQ56/D1371wwwdUaIrSgRMbvQkEmkFbsyccr2ogmmnuaMWHSJfkk7QTZDSROoKIOX+Ly2Mk+r/cvsZ95liJiCHdpzdaQ83rp4iYAqrMsROUkfvRFiUEtK2AsB0laCyavhxd2U/uzn9BwASUbmU0Z9UNU5WmGQdiKMIwYKhHGqkZx1El+bnEtvjgAtJXAuZNCxLlpWZ2OO3o1B7x0T2Grohs/xe34n81RlKj/zRVdQKcimqvdSPij72InOhzKqkUXsK1/RTgLmAm6uS3A2FzVWA3WiJHAEWMm6kbb7HFJEJra51eW7+IcdUdnEjOnXPpe2XYLUzKJqE1UjvqImcWo1U6DVNxwhZVaeq9u9ENr4DB4FqjBMZu2iHtklAQ/Dahtq4KZjBqfRcirTcYoS3irYumKXPp9TR1IbqSPp+bjRbFKGdFyH577kN502T9ge0bsN5izXBWX3pdQeZ3TabunXSxn5u+3kdZSVwNOvjYLjAoozQD4sGwa3bKXFoRFwikwHGyQon+yWCRYRwzgAEKHYC9JPHV9P1+FLqO4BOSl+rRCSRsDrVeydQuuR623wjOmtU91ax7/726dlNnJQ9vJ2Nzes1w0nRTBvP3V4rYQDMUncmQ2MTY+sqD7Oh81lPZeR+pokxtBDhE2pqx8iXOmh4tNMxoH8hqzASKUYixc3w1E2Tjm2rjnQC1NpB1BS7malXiZU7NgOeQUAhYWaoR8gqJKxwLTQRk7Z5uYIEd6ucPmkBtfUNCUqpZYbaUaQ3kZpWa8n+mr7byq6c0r74oGjosBi4ARVAc0bkMMgwLTuoNG9c4ADQD80Qi4N4gX5QNJxJAeB+NsasTJwSnqLvkMnGKiKR1lv5rous5aMO+FJADiTkQGJ8rb4h0kRqc4x04+fCO4eoQH+amvMvEJvkuL5P69FTtUa4IpLIDyQ0A+TAdE/kVYCn/+u/sfHejwMuZMQFAA/zEUKuGnMO66K6uZAmdsadH20B5gKcqK67CBpj0+3ffNJ6kI3ecVtyFWBiSSxaGz01LZoTo+lCLGQPw8g8bxSucFL2sBcuzeQeO0jCXZD2HPZrcsaKhTtySnjpUmTSqg14nW748xq7vMBCJm71cl3mANRN502XB26HeqwPLC0bMggftEKo1ghy28KIc6hQtAq8PboAzNAPoElaEhyvr8z3Nik7zlAPAIZiha7IXTsXDSqhOlBiZQmV4hs+8P4g4EfeyiQAN0rM3/51/LM77wMApG8Ntj4ehRJff2A6PlTcdM0lONKyx6aQAaanfWBlP9N+dfeWEa5cnpj3Deop26GQqBTf6JMlXD84w1tntS13VRqivHJ1svX5X2tcSOKiovSiMp3wwtoNXw0n7/i6HjN36ML6Kylw2+/YHLTpX4RH3szFB6uRO4FmZccVUAG4Eec+chk4O5xJ0WkscQOmCLyfLFxKtJAxFjLGTpg2CMP3kao1XSGejQ/dcxK2vaju27D4F1bMywZhU+RZ6KBBeOsWK2C1y0ahBSIr1fBTRZoYTrUxn2yBepgsRcmknSu1wLIyHle0qkcOHHQcKfoJuXSe9RIMYNsnaxOo9rQfzvHq8orZVxmAQ6MblI4kLyemhngzOcVJOcDQbht9R76y3W+sppVEWtw5Ls1588/vvlAfOg1oBsTXTfT1a6+9fe72AmgIVP1iez/KsShsZNeRwEogPhTILzXPv/XVyPV0trI1PB/FnoRYCqBvjvfDh+N33MavFS4kcX1q+hS6Qd7o14p55aQQmQ7NpBhe2t43jkvCnJC0UkgX9HpXPwAcVQMnegTMytR5tQUCFUEB4CTvbS2Q+kis/mtRJohEhUGQuYujVAIljD6LFhyAJiHdio7dz77bgT8ZmggEaA7+iFjVKLLXfZYaXdQWxn4KRKDHpDcMkFJDAYVT2Xef50/goeNMCxwLGWOH1wQ9lR23KkqktSiTRl2H6kzbRtgTurxwda39aIFRYD5jIROcnDN1mz7jxcED8x6W5PbCOUoVOFJ7ZIlxw/OLaVRauCh4G/LPjcABBM/Xiys0VINIeVuJI1hzuHXnDWIXbfXeNP8HtwWWt4CHb+zhxvOHjfcppdhIRdfP6avPHeNo0od62LHbwzDY2Vz0eBxwIYnLx3lDWb9UUFolNd+wz6ULwMdetHSFdYPtS85A7btF4TmRrBvBrhkiYczvllWMfdsyQ4S1vuoGANfCWqw69Fb/lrYfk/aBVlJJvEqgyIouvoFYbURoREr+QApjjV0hZCYiJQITTLubAUVYvqusf2M4LIaYVR28sdhDJCTivjkeZ7AzHlWA0pLdrEyQVqGrzezYxZDYuldQ2nxYGjK5tzLfVaECDMNV3ZRuj2FfZDhBTVy5CnCQzF30dDmua03ryFToSBWoFwvCNV0UyTXIMuh/+J++GQAQpAywi5j5KkTcKfH1V++711Hv4sqTO2xLk7tBgYXtAy28idXFCIimQOnx8vGsj51+Cgk4F9xtCLhCXgUNMlYDCeTb097HBReWuCZFF1079YQwrbrYD+cuAqGLa09QvcZcKHSRZ2s6oXVtGN3hD+J5bea3hmGUucnHiSgx8WpafurYFYVTQG+7s/oXjhnfHiJFZGymPY0UbXOP524/1u/y1GQM3RyI64Mi0tB1F1BKWTtA+ITW47kjKMDIHiayC6lZI9Lyhb7botk37OANWkGjqG5WJk5VPrMNz4siRhKUTstEx49uDMdlH4Jp3E13wFk9+o1IayAynFkSSVXktocmNfmgVG83WDaEv9M1NwgyeVxvr4m9tPsXT29s7HeQAfl7Mjx77QjPDOoV4IhXjjz9NPQ89MPckdfBU6eYfOqg8Xh0IlDsre2bNOJsupESGqYCYJCK42C8wIPlLqAY5CxCJR5PMeqFJK5FGbmTGTA+UbkKEArZsBcxg0Ur6wvePNEGYoWsotFZ9bzFdTLbhkGQN1o3ko5Xf3AuDJ7MQtR1tJApxJbEKCXyrZNjXjlb5adi06Bc6AA9nluLX2YjHnKEqKfJuJVSUL1KuP0mgmgOlqiPCZE8OTP4NTO6ESQM7njS35cqxlx1GgsHwGa0ddcOHkkr47JQae7qh37UXCmj07o7G5vt4RLzIkY3MITY75rBrHczY7P8yUMjV4nDCrcGZ7ieTBrGg8B2Kxo61pUSeE/30O47Rbhme8iUkKx6SErxTj2Bn5w8BQB46/OXgZ5CsORY3TDH/D3Xjs59nX8+VS4i35zPCABneQedyLzn4SXzf3Gplliwynw/Z4sudvpfeqoXCmlGmA1zFPMIKBlEKvD1f/bj+PSPPF7mgheSuEgk2PMagKndYixSnMo+bobmol8XW1KUslSxS4Uyb3JOwko8sH18ZDMDADcS077i3BMALLw62LPdY9uAbVApgWnZQeK5VPaC3N1R+yJHX+Rb+wEB48i5kMZ+Z8Az568umLYe7J1G4ZykCD4iJs1CBNMQkI6otq0ANiYHrUVaBNKShQDuVYY4yNsKaNpeh0w6ryuSCBzmA+dKMI7IA5+7Nic6NieZOY40Up5SHaoF0U3h81PTqByHZr+uJ5ON/fIbs/02HCJ+mjG5bmK4TsQ+6LyhmwH9/tLsGl55eBlXdmZIDu1sxpuWtJ59uPE+FL0JXkGR+4WoUFWbROuT2PXeFPeW5rjvXJnj7OEALBOQHQ2xMscsW5r3mDCNcW9loi5uoi66frY5RwBAv5PjLAugbcngnD7xrykuJHEBddpwq3OCw2L7MrOPibIXgzdJBahrXOcV3/tB4dKAkEnQehvZK9MwTxq/dTvbc3qh3Whp5yVGOLATi8HrFNSPBIwtilmC7vLCRVHXIq8BGxxK1xGivx8StRd73dDMkdjnEIkRafmNz0sVN8iK3pciVsAOHLE3gW3DRNb/RlELaapmVYJElLjcXSARJdIqQqRISU+rdBpvLcYIuURWWG81u3q2G6+QydAdg0+fXDd/75qI4sXRQ3fzIlcGpZkbqgpsptQhr6NHqQO3rwBc1DurEigwF22Rgp6ErpU3GemVh0ZOcO9TVxECKMYaYh5ADio8Pzxyr9sGGnqRy6bdjU9Y6+T1yon5vJ0rc0xujxvvxyYhkhvNvsVcBo68KGWsFHeSIq2ZqymKUEKqCN17DF9kXeprggtLXAAwtHqpnTBFlxe4HE7BocygUB052cNSxdizE3yAuu6T8LLhz2UEnQKjYOWWs/22n9srU59ZlLGzPnkjNbqh37zzeQDA8x1zQjwqa1GpiOqVpIbMgdw9VeimyPgLBH2ROVJZX7HyScuXdCSsNGPiXbpYiyT9SEuBO4NFf//NNm6mQpRCU6QFAG8Xu/X2222inkpfq1U/rh1pJVtWBindPls1hb2ksdqxIs67dogtNSm/OKqjmdAtgGxebX5fo/Pu8iJyMh2cVp1Gkd0HFeBrbaD5/xMnT5v3u9ODijQAhv5zEwDAb7z2ZmMbaNAtwd8GOgaN4rxPuN7PO506DZwMzPmqi/NLHVJxR+JUP8yq0JgUSHPTZExDP45MtYYLTVy5DIDAXOBfTC1/UvVdr+Kp7Z/LdPiOk1+A2sfJf47fgAsA3zy87X6mi56aphNWbtTNetZxteFSauFPqPZ9pnz4xDL2evSoflV6UgUa2UXb4A8WaU6Brhz5SXCMuXlfP0o7kfX2Uoq4ble8bnBIj9/qmIL0QiY4spbWFFUEzAxxTUSJgCsc9BZ4ODN1pUiYKT/dIEeuApwszWuv9MzN4L2DemirAkNuzSXJlwyoI63S821bB6Wz1CO5bb5iaPsPnfeb5z779tkYQiiUkfUqe3+K9YSPvOH73ndaaoHcrlj68wb8NI70WCSrcTbMtq3nZNnDcGeJ2Zn5fsTKjn27PYC+lGMhFHZ6qTsWROpZFTZWJ6n0QNGuHpaoujFEU4/7WOBCElciqo079rYiJgD8Yvo0uiJHKmNcjSbOZphaXzIdep5SkTsR+2ueSiGTOM7MiUHh9FP9M3zz8LZL+Yg4pBW2Aubk7rG8UTOhiUKXwykmdlSasO6lRDr74dy9l6stsapRyE5YUU814vUI9wief5N9PGElUh3XI8XWMhaKvkJWIWISqY6dvxWRF60cnnqN0z6kl8qUlkAoAlNgZmivolYTY50d8QodUWJZxY4Q6PhGtnZ1qWtuHjGvsEAdjdzoTOpjQX2mJMXY4qQAePY/yveZb45O8wex0gJKh+qqgalLkpiWM4l/+IVvAgCspsmGiOG33nzVHtfza2Y+Ai6RWgKrFG84WtA+zIsYbEvKyU9stBbWj7HDGBhkOFt2HXn58PtASyWglFldFIGCjhS0APr3H7+VxQtKXP5wBe4GLvgTXR6Ww3PrVkCd+hwWw62DHwh+v9mLo4f4zJnp8n9uYIr/PmlRnSdhFVIdN0ahcSgMtoxZJ/nA3LbgAE0SJrsdApFswktHLH4KSGkHuYlSbct/vfRWIgGTNtYpceDej+qC/nZO7Eg3ZcdymdfbARd8U0jpm/2dlV30Re6IoScKl+5TXXCeWBGq7bXbt2Z/RGp9+3zjSMoxKTvO6ob+zu0CBqH0SCpeS4PXScuvQdF7CqbdvMv1sWOfnV13P/d2Vti7uUTHrnjf6q0523qIeeWiu16Q4/5q7B5bt98BNs0A3XNlAM6t39ZTSxSH5nuKTgS4BIod5aKqVRGiE5UoKrMCvyzMMdYbw411wyvsccSFJC6CU2PLLhI+/SLPNlNouiLHYTHESoYYh0Z8eVb2nJ/Xm6u6uE4nl3+nvNRZuEGu15KJnTzMUQqSDFTOMZUzhbOqh31rReNb0gAmkrkcmu0mkhvwFebK04LZNhKgJiPzOQWUNe3za1/+6UYk5ruKAs3J0+fBJ7t1E0bzHs1+QtpG5zFGxXFRuDmKfZG7upHSHB17nDu8sA3XZrsOVyaio167cbRy49sAs/oYr4l53f6vXYT+zct/7DyFO5EVOcf6r6G+x5gZ6+5/dfgeAMDeYImz5faG+2UV121KTJ6bqhIyGbjm+3X4LTxaMxd19aLCkVB0aQW80oyIy7d7CG80b85ZtXkOlJVAWdUEnqcBRveBIGsjrq8I3lqM8bQn4jsPn0+vYhym+Mzshmuc7YjYnZw+/HrTrEpwENWRzmE+QMCUO5nmVYybnbPG6l6uQkyrjlvS34sWjfecyi6msutIrK4FAWOxRGKvLwmOLs9xScwhwRyJUUREUda2PkP/YvTrXJk2NR9/UAWlnL4GTDAFpdEY+bZsLF6Qy+ra6tyavGLh1YdoFTdTYYO0yC2BegEf2C6Fu4sxAKAXmTFa44j6GGvSIvjbQQJS/+IWUBCsKfXYBvLYypWxQybS8lEpgbvpjj2P1ry88voY7cRme3fj5YZNs7+dgClHLGSMZRVjFK4wLTtOtOw7l9LQC8CkcyTeneWJW7gQW2QNPvfJ1wbABzPcPxlhd5i6iEpKM96NSHBbcT4fPn7F+gtJXADwcDVEFoV4e7WDy8kMN6JTHFVDZ/JGJwfZ0BB80rq3GrvxYJxpdL0U9KcfPYcb/SkiKzasNMfNTh36Uw0s4SVOq54bnkFRw8N86Ia4klzj2c6RSyd3xXJjwEayxWXhQBhFvdTcpbe1vXK9grZhDuhdNNvm/QHNGYvkZ3WepfV5pOWDTAABe8dWAUop3LFSmjeOP/2d9G93Fma1sKjM4Ie9JHXHKJMhKs/ahdsiHY2d92UK50FAbURg9Xgz8/9p0XMRz2lutos0Zz5OrBwm5NKt7pE1t98x0RElFjJGX+R1fyBbm1tgv8dukGNRUhdG5Yrx6yUP3xHFj5zisMJyGQPPrYCHCZIjhiDlUDFQdYCTsz54oHA666Lf86Z5KwatOaQ03y3jzWP48/+P79t6PL+WuJDE9VP/7v8FH/mf/yxmRX3H+WKYlh2MwhVyFTQuvmUVOxuaL8wPnMskALy9GGFsnShv9U4bK02+4R9gLsKJ6tr0MmosW9PSN1mqAE2bl1uh0YJR76GvJ1p6K5J+b6IP8msi+KuYZKJoiClYGwNWjzZz05+r2KXg+ZaeQ99TCwC49fQyqn67T974q66oFxDoYu0F+VbSIu/0TlA6skirCN2gaERafmM9/a80R8Bl8yJnTZJd7yxwx8F7bzqORFo+EZpzo4MH6WDjMb+TAzDuIQfxfG1Wp/l5paINd10fvp0N/RwJ6QZcCG5cUtcRhRWWsNHflQw4MpFj9wEwexbgb3eAp5eosgAp1xBCIRCb28EAlKU5Dp3jx3Oy9YUkLsCsrIy8O+Fr2WVcjSYby/EPV0METCERFU7yvivu+jjJ+67gn4jKkZd/ctyyWd/NxERdVJM6rfruM0kUK5Q5oTteMX4YrNyFs+s1UF8Lz3Aq++DM6M96rHkB9KzwU7KmBYlPbgUEElusXqq4oQHyScwnLT8K29YS806e6YTScyjdRlpE7nOZ4JGdAK7AcC8dY14luLsYYxRlOM26YEy7VprdeFWnfNpEWwFTGIRZ3Xbj+fxLsMbv66lrpYV7XWWjzFyHgG72GD7MjASD+k3JicEfNOIPuyVEQqLSHJXk6IfFRoM0OeSa/a+/A9+WeqVCCKYxilZOFpGIEqd5d2sUSZKIRq/usoMglCjmMTpvhNACoPvr8A1g8n4NPOgisrY6RR5CBlbbJjmEUPA3PTwOgXcg2K8lLixxScWxLCNkMjBujwNzgr00u4ZuUCCtIvTD84e9Vpo37uL+YIFFEW+cLH5ofzmcNlKxUZAit9qh47LfICx/UrWAMsVqMMhzbHVO7EpejxUNcoogAbbZwgRgay0FMBIHWlX0C/I+afk/byvaTyt716b2qvVozB7DkNfDPMiSh/aPIlUFhkcrE61QLevUtvfcGpgbApFzoQyx0A2F3CH8Fcb1gaxuP2x9zxd5+quB60M4gFpcPCsScOitrTA+1if9mH2tB1wkdrzZwHNd9bc3924W/vdXKlFPHFIB+mGBtApRSIFYVMhl8GWt+K0PnpI9CSa5meZDddVKAEw7iYhPvDJ+/OpbwAUmLq2Z0Z2AuZPMTX2u6mktgDVMg1/bMBoivxu/sCeMnyr2omKjvjASqbvYnQuDRZcXeCo+RaoijETqVPl9kRm3Byed8FtL+m6c2r1qB3tigR4z5LbUYeO5AKyVs3IXZQGOASuQqhAT1XFR1Vx2tqaFQDPCql0hakeEedWzo8KirZ7pRP5+lOU/zyct8t+ii/PRcoCsDKA1w6XBHJ2gxM3eBCsZIuKVq2WZ19tVs4B87aVt+eH1iuk5E3jM9plBF/Q/AJwUXSzKxEXeaRW7SHmxJZqiDolCCcxs+5FUNjpZAxXQfbmOv4pI27IOpTk4dKP+V3g3St/PbS9JcZj2kQQlkqB0Ns4PjkZQE/O9jj4nEM/s6iMH0gOG5VPN7WUMkLmAXNnG7IWAuLyCkhxcKMg0xPABMHv68SSux1us8Q74xG//4cbvr8/3G7+f5R2c5R3TA6hZo4jp17goDaCVmkRUEFy5OkJeBcirAJ8+uu6WwwGjHM+VmSm4kEljUIc/ccb/+06wxE6wdJ7rI5FuEMOJ7GOmksZq3raLkzzhUxUi/SJp3XkR1jYnjLncVIxvA5FWzCvnFe/POjwp+riz2sesSly0ej+tG7IvDazTaG/iItTC1h850+gGBRJROtKqlGlwp/0G4Jq4aUoR/aPvXGlWGx9+kRkB95dD59M+jDL0w7wx0dq/gZEuijz7V1XYeJxunL45pO/dZdJgily3xw5+Z8Y4ztD17H1IkAvALR4BAB/X25sPmSuzdo/siuGoQDzOwJiG9Py2xKkdhfeoA30cozrqgE8CxFONl//K4+UKQbiwERcAzFaJO2EGSY6fmj2P5atjM/JJMjz1/KOG02NWBegHOUIu0eM5VjJEYZtazSAGhkIKDGyKuchjLNIY/a75/V/dfw/UVfNewyDbuvrmj4Kn8WA9nhs5gOyblTtbV/FXFetpMapeNdzSM2hGzlspg+YY8AJzFWGiOk79bnyyaqseqhX5kZY/1IEunlGwwqRsRkhE1n5xXjDpbnm+gJNkD2S6t7AeZid5H0crO/A0qBAFFZ7qn6Fv08+JlR8UdjpzxKuGX5Ykz3ewhuvttrTZh5/u0zYd2lT10arWOvlpH80B8GUJb83NamckKid9WBYxkpD0ddp7fV0aaCwebIkR/O0vvQxgfaq2+Wx57riww0m96DN+RaD7yHYv9BnSywyL95jtpE9zgtWHEeJTILukEU04irFCsOAohwq8AqrO4xltARecuKKwQlaESKw30XK5PVrgTG9dfeyIEovSuBb4PYKAOWFXeehSgn68OVadLITJ+xxoapp6a32GV4KJi6QiazHtg3oQUx3hwCrmje1xM+IqLRHTiiNneqNhehsoQvGRqsitst3O9iCg7b4V7nNJ0Z+wql5hXFPNA6Ye9na6g7QKXfoecblR0L7eq00TJ2UHqYwQMAXOtNPblZobNwjqZyR/evu4b77nW0MDzeiGTAmBmrTm3vZkZYgeyRiExGnexaVk4W52RFoAcLzoIwlLVxsyn0XmkAzjuI6u96PmqjPQ9O+nm57vywY060uBjczN+3MEwrhGUJ9ioQTeODSZhj6JMXrV3qwuc4RLO77tkcbC6GSh5yHyLEByP0AMIFgCnRONaGE+M5pwzJ9VEBlDfMLwi3/r8Yy2gAucKgLAJz/yV93FNc+81GomEMw57v/CVYyjFcbRCn2vSE4F5b7IcavXFLL2wwL9sDkN++ysh7sPd3H34a6LSAA0xoJRNENjtPxGaBrnBRjCIq0WNVF3ed54fsikGUu/5esJmXb/hp7ZH72eFPKCqUaE5a8SpmsGSxK84SX2TqCRYqmMcC8f46zs4qTsOXFpWoWY5CaCqhTHrIxRSIFeWKAXFo60qI2G/PzN+C2FpYywlJFbLKk0b8wEnJdJY1Hli9W4CCQqnRYmSp+uEkxXCfKqblwG0Pju76f18I3jec+liJXiqBRvFMkTL2XbiVIs7TEOed3tkKvA2lOLxmRyugFSXatSop509EXMAwjBlbqMEaQaTAHZLsPpB4BgGjhlCCutCeQ9YHhXIUxtGmk3v/cWR3zC0Hv4+KnlfVzoiAsAVlmI+UkPWjIkDwLsfF6DKQ3NgONvYHjtbB/v2TE6qX6Qez5H9Ul9OZlDaobToucumN14hYdiiPmkC2ajruHI1K4mZRcv9GpXAt+dwl9e911ExyKFgEbmrGMkTmSzNWPd8x4w6QRFNOb1xmNrQlYxUDiS/XrBYFsNS9e+WH5kubIRzUqFjszpuJCT6yhI3WdTlDCtOu7C88Wfb1u7mXkWu5riMM4cKVA0wZnCsopMFMEUUmlqROtj46kozZlGxczP/uiwgMvaWdaLsnzCojQ4ESXuLWsiigPpSGtZxIg6KfphgaNVD7f6BX7p5Ip77jLzUuwiQhxWjcETVHsqpMDljmdh5G1TuqVb4zxEooI/Scgn6kLWqeSjxQDl0jaBvx4hHwO9+xp8cz0FvVdDyBjo2Bka8dz63O/aVfY+IHJAFEC40Pi5//rxE536uNARFwC89gf+U6BkCGYClKVpbk7c/U+ZC/Tl48voBXnDgRSwU6hZ6chmN1riWmfSuMsPxim0ZNCSYXrac+9DiK3SnDOFLs/dkFYFo6laqhiFt2IH1Olkj+fYEwsM+aqRVkrbl2eGq3oGf3ZAbKkZknPmGNJ7+8Z+iy0F921tTwSqa9GEISLluUycPOKs6mIlI9vo3MXd5Q4erfq4Ox27SKRhyaxNlLIoowY5pVXojjeJPimiAZp1qsg2U2/zjP9iWFQxLtuidsAUujayGiQ5oqDCqgpdHe5zp5fq163q6FQrhrIUW0kLaEZdHV46hfs20qKFhHmZYF4mbgwbUBfvfY98AI0V7wdzQ8LjgwU6r9fEGq40VACoAAhSIJwxJEfN8oAKgdlNjtlNc4y1AOKJRjLR2Hm1wL/9e483aQFPQMQFwMyWWxhCmryHYfyaxuxpW3Oyd21SzgP1ChUA5N7KUa4CnOR9t8K4mDUv+LBTObvg/i4RTQfPJfUoqHV9T0MmYKc/ExkNkGNua14mqmq+1kgU6gujYFRwry+Eieq4utn9sq7HUHREo7kAM4eSsO5yABh3Typik9fZiVX7k4eWTzrdIG+kMsfznlNiU01x2wBS48GvwZlEpWNnJxxZd05ToJd1A7coXdTp+737hn5+8dtfqSPhKBX0A+/7GdgR81JxcLudb71hvt/d61OcnRoi0xVH1DfniS+DSILS6bamRYIpgEmR4Jn+qdsGv9viOOs5oiMSGkerhvThPEeTSVEvRN093UFpZQzRmwkQAfEZ0HvoRZ1R832CJQANFEPAz67LHtA51hAlEKQK4XRT+/g44okgrtt/9C/ihb/2cURTs/TLSw1eMKgImM86GAwNYfnNs7MycZYqqQxdNEN4lPYRd2vSyI66QGf7xegPPE2czXMtktyz3l+lDpy3FtWv9njq0seJMkNmIybtwIrt8wN9bye/DkaN1L6r69kWF8/zSAswaXPMylpwCuBBZroPjrMeFBiGYd17CABvL0c4nA8geNMOZRiTrzxDxKUTBFMUlcmwEaVwppGIyl2gDT3UGmGtw3fO8NELCqdETz3i7oQl5kWMe48M2Yv7Mapx/b0SafkQQmE1T3AKoJ/kruZVRtt9tkrrT6Y0d15uZr99YrXdB1a+sf53Do1JUZPf3VOzvWGnAnu5WWqQoZWJFIAobO3KikppAHfVg7NiDuemzgUAyUmF8CzHP/u5/3zrvjxueCKICwCqjgavGKA1VMgwfEth9hTH5X1TDCarlGGU1931ZeIsbAh78QL30jFGcYaHGGJ1asiOAShPE9yWZhXn1fuX8b5rxjL4fVfMINH1ojzQrFslrHTRVuhFVw2zuLV2HX/yjJknqFDCkNe9aoxUxQhZhVOvzkXFX38FkaKtbaRF7TDkr0/EQGkhYKKFflhgVsZuenehhPsZMBe2qgQGSW6iGHsjCLhypEVklNm6FrWtdIPSEVrXTUqqJyO5bfX6EY1EoiYrSskENA7zWiLAmXLiUj/NI9JaB08FkArovRz8oZ1j6HFitozQt9FaWkRIiwh7/WWDXH01/KePrrmf5/MO+n1zI+3HBU4BXOvNGqmvH4H5pHW87KHM6sf0nkT3vvk+mQLyHYbktL75zm4xkGu4jAEdGNKKZiZC0xwIFwoiV8jHAX7qJ/7C1uPxOOKJIa43vvf78C1/5GMIU41szFAMOObvL9GRdhnZiwQqzfGhnTdxx7Z5CGjsRYuNoRucK/T2TbS0unv+QI6fnj6Pbx29BqBJVFTvmtix9kttVswGPEcEaWUM9VdAFzo5jvq20sJFcE3jO/ocIq3UG3tPvvnzKnH1FqrdVUo4EgmCzQhyIWMsZIy7S3NxT4sEk7wDwRVGUWaW5b3iNgBcHczQtelTxI3uiPRHJCRdV46vR69+lLVOWO44bendK73v99Rb+V2U9WJE4blLvPboAEEkURUC3ZcMOVRjIH5Yfx+VJTumGIKHEaorBZhfcC+8GZyWtO5PR3j7bIyDoamnPZqa86YTl5jP6xtBP26uXPsdHO797U0hlwGmK7ONw1GK2bQLPfVstxdAetnu71NA5wGDX9bMR4bYaO5KOAeCXCNcKqQHAtGC42d+9D9ZP6SPNZjW77js+XiviW7Bt/yRjyFcKmRjjtMPAtW4wqWrE/TjHPtJimudCXIV4FbHOJjuiiVOJU1Srlfm7qRmEMSnH17DTm+Few+8u7OnOn7uPQ/cz3/g2ifdzzRxmtxFqfguYKb4+Hfkieo2HAuIwIY8c1FTYmtjTkyrAxzKAVIVOwlEqQVSVRsfpjJCqiJHXIUKXBTjExctNvSDAh0r7zgpezgruliUsSmo26EKgiv0bGF7XsQYWA1U16v30E2CoqyINy2nT/LNNIwEn4ko3T7GooK0USZhvSjfFaUjrVILVw6olMChLbb3w8L1RB5OBihP66u6d7v+LtcVIVVHIz7z+/bqx0QBpDftttg5hr0bc9fvdzBcONICgKoIQJfaeJQ6XeDlTq332kZaALCqwobnV14EWNn6q7afHR2GULH5ALlfAAu7ej21Lg9HAJNwC1hMASLXj6VljYdzFbBPTMRFUKLO9ZkEWG4irsyuXt1fjXGtM8HDfIRv7N8BAOzCRFXH2pxoGULc6hp915WnTar5jx7sQMS2k94SlxjWd82n+rVX19NRPfizcL2Axtu+RLP3zPlN6QgDblKIHiucA6kvGjUrjebkpPmQXZ47WYWxrTErUoeFdTooOy71yGTQqCGtg1JGv7i9KGuPLTKre7QYQCrmittUcO4GpS2C13WbbVbI7thIEmEqt33OssaS1vpx8pXzQO1NlasAS89tgUgLqBu51xE/sros+7DIAN8owyetIK2JS/jBUuVtoyWt9LSDO6cdMDsFmkfEFoa0ANOV8dz4xL02k4H7Xirb9uSPDuvHOU7m9T5F/QL5JIawddfyhrLFK2/1eiogewrBjCPbBboPgfQqIGMNuVfh9h/7/q3H5SLgiYu4ABN1ZWOGcgDkuxpVXyHYy/DB6yY6+sNXPgEAbpLNG8UlZDp0DcEAMLN2xXTxxLzCKzOj7ZkVMR7N6rvp8/vH7ucP7dx2P39d5y0ATQU9RUeUUgooFFY+kbAC14KZO4FrwhIb6dHcTpCm91uq2Ey4sUS5TlzbGogpyqGG44No4aI1qhEdZz1HMFJzrIrQ/X5taEh9P166aI5IMuJmoAlFWrkMoMCQVpGLJgK33F9HVBE3FjERl+jaSFCs7btPXALaEe1SRk78mlahS2FDIXHv/m79+ns1O/mqEhkBVPLkHjlZKRvynSZpFSPPCrqjoTrNaJAJXZMWgKcu12JnilQDXpcxlGaNdh+lmSOuzFosrYoQeWnT/lxAyXMUTZqBTwIEM45iRwGBRrCTocoC3P4P/uL21zx+ODfiuvA6rm34xD/4PiQTDRkB8SlDsOAIAoVXHl1GwBT+348+hLeLPff83WBhHB3sGXpS9lAqjlIZD6y+MFN63j986BwCCFdHdfvK5aQWH5IGCqjrUX5daqli09enYldgpkJ8bT1cr3Rui5IoQiP4pBXzyvUA+ljvd6PUq+9dka7eYvs4aZtWdmZfIBT2e0sETDmhKoFDm8EZa1OYFlXcIK1KcaRV2EiJ1i/cRZlgUSYNx891+NFhWkWIhDSme973dLasjwObN9+LvhIvWNsgre6hQvdQ4erPlhi9oTB6QyGcAb279gbTaRIrCxVYqMBjCcaNn2Gvn+NkaSKmgdfPeF7/of99Z54vXF4GYHb+IRMajOuGYynj2oYbGnq3QPl0huBghWh/hdf+wH96kUjrHfHEpYqET/yD78M3//GP4fBbK4wvzzGfd7C/s3CrNLvBAverncbcwlRFeDura1kd0bwolWbO6/7pwSluz3ehNMOsiPH+sVHS38vHuBTNcVgOcVgO8a09M56KmpF7PMfM+shLa9FSauBp64Ia0SBXxl1qSM3WSx26dJDebyxSzGSyQWy5Cpyo8dRGIdE5ok0ircYINY8sZrk5ZpklLuoNJSlDoQKktgYWcekcCwTTKJXAYk1cmcugoXej2o6vsfL3x1+tOw9+GurfXBae6n2dtAgygru3K1Ebp3aOgWhm5QKn9bFL92uykddyaGlrXEMrr5kl4KGdEWAb9MNA4qC3cPvmL0pUqmkS6afYIZcouZ3SU4boJgXmC/N9CKGctkHpeh94qMC4Brcup5/7PRdD4vDl4IlMFX188x//GI5+U4nR/gKxXT17744RjD7dPcF7kkfo8hy/tLrhVhUr6/MFAD2xKch7eVq3g3CmXX/bU906Ffhg923383NRLVDNdNCYsg2Y2tSuWGDIM4wtkRbYbE2ipmpq5aFBGjOZYCq7mKsE06qLXAU4zvsubSPiIlBqRtHStc7EORhQofv2cs8RwCxPME8ThPb4XbEpIhGX84FfIy4n5LXEdbRqFuUFU43md99lgTPtoq/r3WnjdX6qOPeaqBdV7LZ5nieOtPIsgjytCYwGpgLWF5HkFfY+JXJDWoAhruRUgldWQnMrQnrZWjC/aIhKS4auJa2bOxPX+Exi1W5SONIC6prgOmH58LVepRIu2s1tK1WWh5CVgAgkqrKO5BnXCEMJIRR+6bt/cOt7XyCcmyo+8cQFAN/8xz6G+e9a4Juvv4WnuyeYlF0EXG4MfRVM4bAYYCVDt5I1DDPnD56rwCmh316OAZgVH1plu9aduulAp2UPlyNzgYdc4oWkHhNPAzD8mYdDnuGA5ira580b5n+hK8hnOkQIoywvdIBH1cjJIV5amjl/D1bG+8rZUNuULBYVAq6cnfUoWqEnctdXVyqOh9kImQwwK2IczgdIotIR104vdfsbMOXIJRGVI66AS0dCnGnXrJyWEQRXzlGBXgc0XSbod8LQW50E4GYErDzTQVPHM8fn7ZnZ98WiAzmzkZgtoovMDoTwgk+e28UcbXr5uocUZa35yN+yQtbLDKsXMxzszXFzOHGfe5qaeuNk0W0o7J8/ONo6Nm2dtPw+WjfAVtUzEZ2FkJWh5EWAMrcEp02k9fof/AE8QXh3Exfh6b/3Ufya597GN+7cBYDGZOt0y+h1aiCmi3ooVs5lFUDDKeJaMnE/v5nWpoZ+mvS7D37B/fxr4vvu5wFdkPZrSphAqiUKrR15UZvPibLTZdaI67Tq414+djUfIq5HSxNFUkF9p5MiErJBXLvh0kVbDzPzujuzHcyzGMuzDrrjFS4P5yiVcBouAM7HDKgJiIiLCGVRRvVYN5tyJkHpVigpWvVrPWkVNiKxYZg3oixfElFZi2P6LCItAJjer5uqxUK4Cc+8YOCFJSvLL0zVDcgAsPu5+qZ29MEYha0g6BfNjWncW+HmcOKe89Z0bPYnzqG9ovp+x9Q6ibgqxbf6bQF1dE3HgojKjRJTHFkVILWRZLGMAOgnpm61Be8eOcQ74fYf+348/Xf+JoZfn+F6Z4qp7OAgmOOoGqBro6+uyPHQzvgbhpkr0g+DDF2Royty3IpMHvFmfgmvpZfwdX1DhMfVwLicDpf4hdktADVhRELiF5ZP4xt7twEASxWhZ3OTbT5ZXSYgUWHMJDLNoFiFiergdnEAwNTjroUT7AmzILAbLHAvHyPmFd5emavsMLUC1CxGHFaoJEdaRkhECqUZDuxigt9X99pkD8cPRuCxhC45gm4z6gDqC4vb5X6qndGKYCX5Rr1qWWyuaibvULsid4lIyIZ6PBGVa9Uy7hD1QsBrkz3XKzlPE3/QUoO0CH5baeewLtAHGVD2bO/rs55S/cU63aNyA21HPzY3QJpJ+OzoxLUY+YS0vmq47X8Ch0mXpeIQXLk+znwS485/eHGlDF8JvKuICwBu/0d/DgDw9H/zN/AHvv7ncVb2cD02Gqwez3FUDXAlniJkEtOqiw6HI7Vp1cUoqL3kn4kP8e/0Pg8AGFgS6jGJzxaX8fT+MX528R4cZoY87p6NkVUBXp1dwgfH9/FcdIhMB9jjGRLrTDfgIWJmaxm6xAghckgACimA8doq4v1yjDvFnpu2c1rUNaSAK0yWHYy620eaje3gUhJwzqoEr05qVwS9CAGrW7s8NAQ3iPKNCTb0WcDmWK1KcZeq0sBRrVljeCmRII3eCm3RmhYHqIi9TnLNFpk6pZ6n51tPE2npUINlXjE80q4p2e8AI9JaPKNw+QVzs3pmVGuv/G14//iRq7f5x0hpdi5hrf/su/W6z2AKEMDbd/cvtO7qK413Vaq4Dc/96F/Fr3vqLt4/MBqvLm+uJDbnBVa4EZ46XZYEx3CNTAg/NX+xQST/7JX3u5+fumZO/v/r8z/q/vZsYA51n9eRSarMtjxS1iRQRfiF1S28le+5STV7oSGVV5em5+Pt5RiV4jhe9FFJjiyNwIWGCM1Fdmlknn9rcIZRuEJXlM6L/dXJJbx918hEWMmRHBh5yDddv4tMhm7aDFATChWa/f5B17qkhKv/+I8TiIyItNx+r/3e8+QDkZDYjVP7+qZ/1+uTOkWfr2JkS/M+2hav+Vw4Iz1hiUtF1uX1NsPSthTKRLu/d26a4zXs5HhmdOKiu2FQe7BRtDwtO27fyXnVJ61trUpEbARKMYsqwM/9jr+28fx3Gdoa1xfD8z/2V/AN1+/hWmeCjighoJzzQsgkEl6iLzLXyrOO1/LL7uf7nqTin77+/sbzgs8YMvudv+8T7m9/eOdnXZ3rKZHYz7SKcF0i1xWOlCGKf7F8Hm/le3hUDDEOV5iVCUrNcZr3sBsv3aLBw9lwK3FdGc9wubPAKFq5utFKhnh1cgnH8x70KwMwCVR9jfDpBW7unuFSZ+GIi1I4qkEFTCESdeOznyISFBhOM5NK+Rcv/VxaMszsAoIfdZxHWr51M2AGuJ6tDAHPrYdWtowcaQH1UAjAEBZtCgVD4ZRDJvX2xc/NsNMzN6Yb/akTxFJjfsiVEyiTHdBKhpiViSMsd1zsgggJTgslGgLT6SrB9M4Yt//0xeoZ/CqjJa4vFS/8lY+DvTjH1dEMH9x54DREQ2FOYBoEexDUYtPCW/07ruqC8BsrU4+igv/P3HkG+g1bXF8A5QC49k338W0HrwMAfvf4FzDmBS5z2+bBzetmynz2/5hew6eWTwEAHtgiekeUKDXH4WqAgCtX11pksSMuAAiTCp24wKiTNYjrkR2C+tLDKyjf7iGYczAJ5FcqJPsr3Nw9c5FRujbNph8Wro2I0kVDZBUiXrlx8n7qNMkTN1CCorFcBo1evPWZhjTiHgB247RpBul1BLx1uuNSUgBIp54MJOdgebMoznRj4DjgTckZjlP04wI3+rUUYydaOucMWrChyCpXARaVcX71IyxuZwYEtkblJmWvujg5HuD2H31iC+tfCbTE9cvF03/7b+Lqe47xnpGpcVzvTFBpgUthrZj3x5GRoFUwbR1MzYn++fwq/qdH7wMAvPnpGwCA8efqLv4bv+dNAMBv3HsDHx68hDEv8JRIELIA3/Kp34dCCoh/vIuTby3wB77h5/Hmch+caRRK4CzroBcWmNoi9tGsj6oIUC1CcNvLRsT1wq7ZDyrMP8qGuDcf4WzZQfl2D+HE+qM/VeDK1YnZv2SFiEtHXBQZ9SOyqjGf0Q9zF0VRNLcoE0dcw3DlSOdzp5fQsUJWn7SAmrjmq9hpxwAjAyDXBcBEanRjmWQdFDa6ylY2PS0E4JFVg7gYoCNLkIn5n3GF4dh8l7vdFJc6SxfR0WBXv1MglWaiEa0c+7bKNFSW0sDTrIu0CPHp/9V/gRZfMtpVxV8ubv+pP9f4/T0f/Tiqqzm6gwzvPaibqUl86vt70Wrdc8khShXg2w+M9U30jRIvv3YdJx8C9n/WkMDtH38Gi/eUGEYZXppfww/f+O/wQK5wICIznuu/N712e/9LhP/v8W/A133L6ygq4eQE8zJGXgaIw2YRW6cBEEsoWdeYaLKMXx+q3uiDwTQSV7vmPU4XXTyzb+pxJk0MsSpDxDZNXBSxI6/zQBOoy7XJ4QCcqNKvf/mk5YO0S0CdWhImWR1ZEWlpyYyJFp363s868O7HlrR4KDEcrrDTMYRFGASZG2YR8wqVFs4jHzB1PCKsSnG3/ZO8g2URYTbr4I0/9Jff8Ri1+PLREteXide+/3s3/vZtv/u/xCu/1syAyq6Zi/rXf+B1lxbcXe3g3nLk9DhHsz7CfoH90RIPv22E7qvmIj34mQBf+Jn3YfYswz/+vW8AAL6j94oZwfZdx9j9naZ9qPtdH8Jn8Bxe/NCbznImsUXyw8kAZR44qYKP3W7a+H1Rxk5/BAC8rIeIRgNDiFQ41/rcm18DfirpN0eHTOET95/GU7vNGuEyjxCF9YivZW4+j2b/zWdN1f8xqxc8KilcakgGe9o6NOhSAIE2wlPPBVAHGpC0uqggLGERbvTq1HAQZoh5hZhXGIgMmTLGjosqwrTsYFHGmBcxQiGRlhGyKsDZWQ9qEeL2n/zzX9LxavHLQ0tcXwH89P/vSztJf8cHfwB3f+c+lDUqOOJdoK9QDAAugdlzDOPPa4xf1fiHf+U78fA3aBx+2xDLWYLOv6xrZ8k/+Tlcw4fwinoG7/+WNxEIiawK3UUPAHIaGSKKFYJkU7y58Fplpqc9UAxT7VaIdjPXjwjUAsjcih9TRNjp1yRIdS5Cb63xGgB+6cy0Sb1lrYfTE1NIJ6NGKvY7VXjePDVFKBFF9WdUXtTVcAX1ivFYcfCyqZSXDNBJnX4Ohysnz7jWN+n/IMxQKeGiZyKthYzxVrqLQgrMi9h4nEmB05M+2EmMN/7sY+1t9UShrXE9xvh1f+JjqBJmRkb93kM8vLOL5/93P+cef/Nv/AY8/+vv4P5siEoKaG1SpSCUKE464BmHChWC/QydpMR+f4Frdq5hIQM8WvXNatZpD+E96/b5wgKj7gp5FaAXFxBcQSqO2SqBtBYqkU1He3HhUsUbvYnbLj/S+p9vPwcAOBgtcLowZJXf7UN26wL85RvNKCz30sJl2kwZq2xtYo4vMVDM/AMA24/IS960rvE+l/VL7O4tHGE9XJgug197cA+70RL3VmNwprEbLfFWuotZESMtI5wuumBMI7szbMnqq4u2xnUR8fN/d/OieDb9GJ77vp8FADzzF34Wi+/6ELp/6gSHZ3UEVZwmAI1qSxTKRYROUqJUotEPCADTowEgFMqrBQa7qfN6IhBpAUBVGXsgwn5nuXWKz53Fppf70bQP+ZZN82yNiYXN1UPBFdKiJqb5rANVNetZDvRSgZqsAEdYABCfCGdb45fXWN+OpD+KkQ1yvHG6h25kosSjwxFWu4d4kI3w2eOrLhpLswhFHoLd6eC1v7BZLmjxq4s24rrA+A7++wEA2Xd9CHe/W0N0S8g0BEoGVAxMMuhEQQuN4f4S79k7dhO9316OsCxiTBYdFAuTYhJxjborRIHRZqVFiEqKRurW7RQIA+kilUhIDO3ot5+9+4x7HjmC5pMEwaxmDh1oqE5NWsMrZoWzFxeOuKi2pSoBXXptOqEGlt57xZ7ldSrAbDN1OLd1LGFIy/a7Q+TA8np9Wu99Fjj+tRp6aJ0upmY/d54/xcnxANAMyRsRPveDLVl9DdBGXE8iflL9GABDYINnfyPmHwTANDRn9TdeMrBEotrilDlZ1IXvS1emWBUhxr0VkrCEVNy234QIhERuTxURKORl0JApvDkxRTvyUScUNgqkbaHVPJ+0wnH9mmUeOc+vIJamdpXzel8qBmyWzwDYyTwW8akVfVrlhOwYwgKAy/92hTOr7+LSbE9yyIHDCPmBck4R08/s43YbWT22aInrCcBPqh8z0df/8Tdi/oIEQgUI5tJFwHhCkZr77mLsrGWKZYSwV2KRRegn57ACAFkIgBniAsyEZ9Vr3hBJj5UdWdcMz5lTXaoJil4VxPUGrq9a+gV339fdvJkXgWUc3K4aBkvPI96S1vjN+jPiU7N/O5+3Eo1RBFYqACHO3m+U88ES+KW/3hLW446WuJ4Q/KT6MXzg+z8OaEAshZv4Ao8Q/HrUnUd7UOV2exVftiEVd1OTqXAguEI/KXCU9nHQrQWhp/dGEFPhVijlSLp6FoEJ7RxDq1wg7pYovLoaCVjjjqlDrXIBWGkHs69DwRpFeUdY2qSE5BE/uGdtqT/5Nr05AEDtDZEfdJEeCKSXA1Rd4LU/35LVRUJLXE8QXvro9+LZH/kYWAmES46qr8wFLplxTRhtf11oVwn91LFvLYcFN5OJAAAMKFYhOj3PqyrtY/LaTiP9AwB5uRm9UUTFfJdTrzhPxnu0cllVnuJdrkVcfr9jzh1hES79nKmZiTet51mvA+QF1DXThJ0+NcDJ+wWqPvDqX24J6yKiLc4/gXjxBz4OFRhtWHpNAn2JqFegExeYnvUQxBXkow5ExlDuSAjruaVKgahnCKffzbHK7ZDZsw5YoME8V0+KmvjEG0w65SjG5jl6bFfubLqovfSOB02Si+Jan7Vu61Isa22atikhK5ghLIvOoXnN6A1rTvjmEvwLZsISrGWQfu4G0hs9LK4J/OLfasnqgqDtVXy34UP/m4+h6DNkB0D2TAEeSgwGK0zPeuAnEbTQjriAWppAxJVPEjBrJkjgsQTOIoil5y1lI63eHY5iXH9+vmffd2QITCvmFOvc1ra4UA15BeCZ7hUm4VSlcIQFpiEWXlpprWniCTD+gu0p/IJVvr9lbIpYtwN1dQ8Pvm2Ez3y8JawLhnZV8d2G5FQiOQV4FSA5jjB7TqN8qQO8zwpG/7nCg2/1LF9ObN/gYYhyz0RAOheuwJ7cCxHOQixvNYlm/5N2cIS1wlp3JfZHxbvP6nmqfMkb/uzSpoguUivrehazj/GMQQcaOtCITxg0M4Ql+zHYqc0Z4wjyqSt49U/HuP09f+FLOWQtLhDaiOsJxrd/50cRZBInvyZB2TekIhOg8wgYvF1h9lSA3GpFgxWwuqKdypwisZ1PB1hdAjwzDOz9UonlNXPPs3NtEc0Vllc8u5lnvWGppFa30VnYt8M2rKTCX1EsS4q0vP5Cr95FBBufMDAN9O+Zz9n9qTtAJwFmc2A0xI9//q9/mUerxWOINuJ6NyJcVtCCYe+lDA+/xWiqBnc0qsScD9FcI5oDqwNbOLekNXiTYZXWp0bnEKAZIjuvmSf17leAYJDh5rklYyBI679LGvptUz6/5xAAmjdP61YaqrphugJQckAyyJ5C746w26DRe2AXAZIYuhPhn776t7/Eo9PiIqONuJ5wfOT9fwnFlSHy3RBMaqz2zUXfOZbo3DdqdyYlFs8MwDQwvSWc7/rqMpAcAzuvFginJsVcXe2C5zaCEjU5zW/YsfCW4PIdwA4mQrlra1pDQzKdrh0SwpUTxlLUVRRW6GrTxyKNACsuDaYC0Qzov23T1zOJYCUxfSbGJ//vbf3qCUQbcb1boboRoi88QP7rjXPq/s+dgk0W0IMe2HwJeWW38fzRHYnlZUMUt/7JBABQ7Ndj2EjEqRlQDeyA2nGdIgYZUHZNWYoGgZfWsE9ZUWkwaPr0S89zvUFYAPgkQPLIPB5PgHiqwUuN5LTEv/jJ1j303Yo24noX4CM3/g+A1iifu4JgsgKb1KJReWUXTErwR2dQB6bgxdMcxbURgpkdUDvPoDuGSKpxrfWaP1U3dpd9Bm/MJErrwlN1Nap9kxqS7KLfM+9LFjZScVSFZ01zaBT4yUOO7qFGkAG81IA2vYc/+w9bX/Z3CdqI612NQQ+YLRB+/m2Ac0DZCGixBJ9MwYamCMWPzoCeYZ/o/tQUugFg0AdbmfCpvDHYfH8AMqwL9QDcLS9YMlQjc/7JqSG/uScoVYWAsP5YchIheShcKjh6PYOKTLR19kKET/1XbTrYwqCNuN4l+Mj1/xiobFE8jqHn1IFs9VaWvNRkCiZs6setNOFgz71Pdak2NASAyQsmAiv6loxs1pePzf8qAIpLzWI875fAYR2tRRPz2v5djeHtAtHRAvnVAVb7AT7x/2qjq3cxWgFqC+C3978HfGcMBAJYZdB5AbVMwTgDGAcLA2gbjbHQ1K90UYB36xyweu9NFwWt9mtVez5qCriIuIA6bQTMymUxNqfVlZ+1RfZj6+n++iOovSHm7xniZ360JawWbarYAgAfDaGXKdiwv/VxXVaAMASkFiYiY5HXciMlgtN6kIQKRsh31k4h71SjlsLhm/WkaADYfcWmgv/yNftEBr23gzt/5Cm8/FfadLDFF0cbcb3L8OHoD4MP+9BZbqIqmypqKQ1x+dAKWso6dQQgnn8W2sog8qs2lGJAtuu14lRAMbCeWCtzCuUjjks/bUajscMT6KIA6yRQ1w/wE5/8P39V9rXFhUebKrao8TuGf6yZEkoJTQTmkZeWEtCqSVzjcf34tYPG+8pBXbeCPa+CYxO56cNjsyjAOSAl/uns731F96nFE4mWuFo08R3iD4IJAZ7EQBSCMQ5dFNCFbYq2ZMY4g1b1acCEgNgx/jj6yj5Y4U3e2etDvHzb/ELnlZTmfZUG7yT4p9P/56/K/rV4ItASV4tNfIf4gwAA0euCxUY7pYvCkQ5FZWpVD7n1oy/eMRGWrrworah9uIjwflL+6Fdj81s8+WiJq8X5+HD0h8GisEFKoJ8tKVHUBAC6KptvwDigm64R5IffosWvAC1xtfji+HD47wGAi76gNaC0WWm0BKbKaoOkgJaoWnxV0BJXiy8f38F/v4mmfGjVklSLXy20xNWiRYsLh3OJa/uYlxYtWrR4jNESV4sWLS4cWuJq0aLFhUNLXC1atLhwaImrRYsWFw4tcbVo0eLCoSWuFi1aXDi0xNWiRYsLh5a4WrRoceHQEleLFi0uHFriatGixYVDS1wtWrS4cGiJq0WLFhcOLXG1aNHiwqElrhYtWlw4tMTVokWLC4eWuFq0aHHh0BJXixYtLhxa4mrRosWFQ0tcLVr8/9utYxMAYBgIYgS8/8wZIaQxHEjtN64OkyNcQI5wATnCBeQIF5AjXECOcAE5wgXkCBeQI1xAjnABOcIF5AgXkCNcQI5wATnCBeQIF5AjXECOcAE5wgXkCBeQI1xAjnABOcIF5AgXkCNcQI5wATnCBeQIF5AjXECOcAE5wgXkCBeQI1xAjnABOcIF5AgXkCNcQI5wATnCBeQIF5AjXECOcAE589jPyhUAH3xcQI5wATnCBeQIF5AjXECOcAE5F27zK4ijHT14AAAAAElFTkSuQmCC\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",
+ "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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atWvXhomJCcaOHSvdP6U5f/PmzdG8eXO0bt0aHTt2RHR0tE7L7HUtX5uC1/frr78iLCwMffr0waeffop69erB1NQU06dPl/5gocqLwQ1VOvmTMWvUqFHsf2Z169aFhYWF1NNT0IULF2Tb+b0phZ8am//XecEybWxsoFKpDPYfaf715E+uLU5ISAjmzJmDrVu3Yvv27ahbt67eQ2wApB6zW7duydK1tdnFixdhaWkpreDRtR0aNWqEo0eP4tmzZ7IhoYL0fYJurVq1tD7p9+rVq8UukzaE/FU7Fy5c0JjgeuHCBa2renTRo0cPLF++HAcPHpRWPBV04MABpKam4oMPPtCrfFdXV6jValy5ckXWQ6dtBV9pbNy4ER07dsSKFStk6Q8ePJAtGCitou7hglxdXXH27FkIIWT3WmmueePGjWjYsCE2b94sK5MPFKwaOOeGKh0/Pz80atQIs2fPxqNHjzT23717F8Dzv76CgoKwZcsWXLt2Tdp/7tw5xMbGyo6pUaMG7OzsNJY7L1q0SLZtamqK/v37Y9OmTUhOTi7y3CXx2muvwd3dHXPnztX4RV2456BFixZo0aIFfvrpJ2zatAnvvPOOTsM9cXFxWnsh8ofmCg+txMfHy+YNpKWl4ffff0eXLl2kZ5/o2g79+/dHRkYGFixYoJEvv075q59K+kqCRo0a4ciRI8jNzZXStm3bhrS0tBKVow9/f3/Uq1cPS5YskfUSbt++HefOnSvR6qOCPv30U1hYWOCDDz7AvXv3ZPvu37+PkSNHwtLSEp9++qle5ecHw4XvbUM/qdrU1FTjntuwYUOxjywozoEDB2RDP/mKuocLCgoKwo0bN2Tzy54+ffrCuTrFye/dKXiNR48eRXx8vN5lUvlhzw1VOiYmJvjpp5/QrVs3eHt7Izw8HE5OTrhx4wbi4uJQo0YNbN26FcDz56bs2LEDbdu2xahRo5CXl4f58+fD29tbo+v/vffew4wZM/Dee+/B398f+/fvx8WLFzXOP2PGDMTFxSEgIAAjRoxA06ZNcf/+fZw8eRK7du3C/fv3S3w9ixcvRs+ePeHj44Pw8HA4Ojri/PnzOHPmjEYgFhISggkTJgCAzkNSH330ER4/foy+ffvCy8sLubm5OHz4MNatWwc3NzeEh4fL8jdr1gxBQUGypeAAZE+B1bUdQkJCsGbNGowbNw7Hjh1D27ZtkZ2djV27dmHUqFHo3bs3LCws0LRpU6xbtw5NmjRB7dq10axZsxfOQ3rvvfewceNGdO3aFYMGDUJKSgp+/fXXYpdaG0r16tUxc+ZMhIeHo3379ggODpaWgru5ueGTTz7Rq1wPDw/8/PPPGDJkCJo3b67xhOKMjAysXbtW72v08/ND//79MXfuXNy7d09aCp5/r+vbi1ZYjx49MHXqVISHh6NNmzZISkpCdHS03j1qM2fOxIkTJ9CvXz9pyPPkyZNYs2YNateurfEIhoI++OADLFiwAMHBwRgzZow0dy1/Irg+19yjRw9s3rwZffv2Rffu3XHlyhUsWbIETZs21fpHF1UyFbBCi14SL1oem798d8OGDVr3JyQkiH79+ok6deoIc3Nz4erqKgYNGiR2794ty7dv3z7h5+cnzMzMRMOGDcWSJUs0lscK8XwpeUREhLC1tRU2NjZi0KBB4s6dO1qXcaanp4vIyEjh7OwsqlevLhwcHESnTp3EsmXLXlj/opa+Hjx4UHTu3FnY2NgIKysr0aJFCzF//nyN675165YwNTUVTZo00dou2mzfvl0MHz5ceHl5CWtra2FmZiYaN24sPvroI5Geni7LC0BERkaKX3/9VXh4eAhzc3Ph6+srWy5bknYQ4nnbfvHFF8Ld3V3KN2DAAJGSkiLlOXz4sPR9KtjmoaGhwsrKqshrmzNnjnBychLm5ubijTfeEMePHy9yKXjh70VR92D+/XH37t3imlUIIcS6deuEr6+vMDc3F7Vr1xZDhgwR169f1+k8xTl9+rQIDg4Wjo6OUpsFBwdLjxDQtb7a7vXs7GwRGRkpateuLaytrUWfPn3EhQsXBAAxY8YMjXoXXgrevXt3jfMUbvOnT5+K8ePHC0dHR2FhYSHeeOMNER8fr5FP16Xghw4dEpGRkaJZs2bC1tZWVK9eXbi4uIiwsDDZfaStLkIIcfnyZdG9e3dhYWEh6tatK8aPHy82bdokAIgjR47IjvX29tY4f2hoqOzxAGq1Wnz77bfC1dVV+hnZtm2bRj4huBS8MlIIUc4z6ojKweTJkzFlypRynzBqCBkZGXB0dMSkSZPw1VdfGbx8hUKByMhIrcNIZLwSExPh6+uLX3/9VXoCtLGbO3cuPvnkE1y/fr3Yh2CS8eGcG6JKZvXq1VCpVBg2bFhFV4WqqCdPnmikzZ07FyYmJmjXrl0F1KjsFb7mp0+fYunSpfDw8GBg8xLinBuiSmLPnj04e/Yspk2bhj59+sDNza2iq0RV1KxZs3DixAl07NgR1apVw/bt27F9+3a8//77Gs/8MRb9+vWDi4sLfHx8kJmZiV9//RXnz59HdHR0RVeNKgCDG6JKYurUqTh8+DDeeOMNg69soZdLmzZtsHPnTnz99dd49OgRXFxcMHnyZHzxxRcVXbUyExQUhJ9++gnR0dFQqVRo2rQpYmJiMHjw4IquGlUAzrkhIiIio8I5N0RERGRUGNwQERGRUXnp5tyo1WrcvHkTNjY2BnuYFREREZUtIQQePnyI+vXra30XXUEvXXBz8+ZNo10tQEREZOzS0tLQoEGDYvO8dMGNjY0NgOeNU/iNu0RERFQ5ZWVlwdnZWfo9XpyXLrjJH4qqUaMGgxsiIqIqRpcpJZxQTERVnhACz1RqWVrhbSJ6eTC4IaIqL2TlMbSctgvZOXkAgBNX78Pji+1YGHepgmtGRBWBwQ0RVXkH/snAg8fPsP/iXQDAF78lAwC+i71QkdUiogrC4IaIiIiMCoMbIiIiMioMboiIiMioMLghIiIio8LghoiIiIwKgxsiIiIyKgxuiIiIyKgwuCEiIiKjwuCGiIiIjAqDGyIiIjIqDG6IiIjIqDC4ISIiIqPC4IaIiIiMCoMbIiIiMioMboiIiMiolDq4efr0aamOX7hwIdzc3KBUKhEQEIBjx47pdFxMTAwUCgX69OlTqvMTERGRcdEruFGr1fj666/h5OQEa2trXL58GQDw1VdfYcWKFTqXs27dOowbNw5RUVE4efIkXn31VQQFBeHOnTvFHpeamooJEyagbdu2+lSfiIyIEKKiq0BElYxewc0333yD1atXY9asWTAzM5PSmzVrhp9++knncr7//nuMGDEC4eHhaNq0KZYsWQJLS0usXLmyyGNUKhWGDBmCKVOmoGHDhvpUn4iMiJqxDREVoldws2bNGixbtgxDhgyBqamplP7qq6/i/PnzOpWRm5uLEydOIDAw8H+VMTFBYGAg4uPjizxu6tSpqFevHiIiInQ6T05ODrKysmQfIjIe7LkhosL0Cm5u3LiBxo0ba6Sr1Wo8e/ZMpzIyMjKgUqlgb28vS7e3t8ft27e1HnPw4EGsWLECy5cv17mu06dPh62trfRxdnbW+VgiqvwY2hBRYXoFN02bNsWBAwc00jdu3AhfX99SV0qbhw8fYtiwYVi+fDns7Ox0Pm7ixInIzMyUPmlpaWVSPyKqGOy4IaLCqulz0KRJkxAaGoobN25ArVZj8+bNuHDhAtasWYNt27bpVIadnR1MTU2Rnp4uS09PT4eDg4NG/pSUFKSmpqJnz55Smlqtfn4R1arhwoULaNSokcZx5ubmMDc3L8nlEVEVIth3Q0SF6NVz07t3b2zduhW7du2ClZUVJk2ahHPnzmHr1q3o3LmzTmWYmZnBz88Pu3fvltLUajV2796N1q1ba+T38vJCUlISEhMTpU+vXr3QsWNHJCYmcriJ6CVVsOdGoai4ehBR5aFXzw0AtG3bFjt37izVyceNG4fQ0FD4+/ujVatWmDt3LrKzsxEeHg4ACAkJgZOTE6ZPnw6lUolmzZrJjq9ZsyYAaKQT0cujYHDDISoiAkoR3BjC4MGDcffuXUyaNAm3b9+Gj48PduzYIU0yvnbtGkxM+BBlIioah6WIqDCdg5tatWpBoWOf7/3793WuwOjRozF69Git+/bu3VvssatXr9b5PERknNhbQ0SF6RzczJ07V/r63r17+OabbxAUFCTNj4mPj0dsbCy++uorg1eSiKgojG2IqDCdg5vQ0FDp6/79+2Pq1KmyHpePP/4YCxYswK5du/DJJ58YtpZEREXgQ/yIqDC9JrTExsaia9euGuldu3bFrl27Sl0pIiJdFQxtuFqKiAA9g5s6derg999/10j//fffUadOnVJXiohIV0Jd4Gt24hAR9FwtNWXKFLz33nvYu3cvAgICAABHjx7Fjh07SvRqBCKi0uJqKSIqTK/gJiwsDK+88gp+/PFHbN68GQDwyiuv4ODBg1KwQ0RUHthbQ0SF6f2cm4CAAERHRxuyLkREJcbYhogK0yu4uXbtWrH7XVxc9KoMEVFJcbUUERWmV3Dj5uZW7AP9VCqV3hUiIioJhjZEVJhewU1CQoJs+9mzZ0hISMD333+PadOmGaRiRES6UBfouWGgQ0SAnsHNq6++qpHm7++P+vXr47vvvkO/fv1KXTEiIp0UiGjUHKIiIuj5nJuieHp64u+//zZkkURExSoYzjC2ISJAz56brKws2bYQArdu3cLkyZPh4eFhkIoREemiYEDD2IaIAD2Dm5o1a2pMKBZCwNnZGTExMQapGBGRLgo+xI8rp4gI0DO4iYuLk22bmJigbt26aNy4MapV0/vROUREJcZ4hogK0ysSUSgUaNOmjUYgk5eXh/3796Ndu3YGqRwR0YvIVksx0CEi6DmhuGPHjrh//75GemZmJjp27FjqShER6UpwtRQRFaJXcCOE0PoQv3v37sHKyqrUlSIi0gdjGyICSjgslf/8GoVCgbCwMJibm0v7VCoVTp8+jTZt2hi2hkRExeBqKSIqrETBja2tLYDnPTc2NjawsLCQ9pmZmeH111/HiBEjDFtDIqJiaFstVdzrYYjI+JUouFm1ahWA5++WmjBhAoegiKjCaeu54ZJwopebXnNuoqKiGNgQkV5UaoGZO85jz/l0g5SnLqdxqeX7L2P98bSyOwERGYzOPTevvfYadu/ejVq1asHX17fYbt+TJ08apHJEZHy2nb6JxXtTsBhA6ozupS6vYDxTVqulrt17jGl/ngMADPJ3LpNzEJHh6Bzc9O7dW5pA3KdPn7KqDxEZuZsPnhq0vPLouMl6+qyMSiaisqBzcBMVFaX1ayKiilW+D/FTqwVMTDhhmagyK9W7EnJzc3Hnzh2o1WpZuouLS6kqRUSkK3nPTdmvllILARMwuCGqzPQKbi5evIiIiAgcPnxYlp7/cD+VSmWQyhERvUjBzpr8QKcsV0upuRCLqNLTK7gJDw9HtWrVsG3bNjg6OvKZEkRUYWTvlirn8xFR5aTXUvDExEQsXboU3bp1g4+PD1599VXZpyQWLlwINzc3KJVKBAQE4NixY0XmXb58Odq2bYtatWqhVq1aCAwMLDY/ERk/2bBUOQQeDG6IKj+9gpumTZsiIyOj1Cdft24dxo0bh6ioKJw8eRKvvvoqgoKCcOfOHa359+7di+DgYMTFxSE+Ph7Ozs7o0qULbty4Ueq6EFHVJA9uyv58HJYiqvz0Cm5mzpyJ//znP9i7dy/u3buHrKws2UdX33//PUaMGIHw8HA0bdoUS5YsgaWlJVauXKk1f3R0NEaNGgUfHx94eXnhp59+glqtxu7du/W5DCIyAtpev1CWVIxuiCo9vebcBAYGAgA6deokSy/JhOLc3FycOHECEydOlNJMTEwQGBiI+Ph4nerx+PFjPHv2DLVr1y4yT05ODnJycqTtkgRfRFT5aXvOTVnOA+SrHYgqP72Cm7i4uFKfOCMjAyqVCvb29rJ0e3t7nD9/XqcyPvvsM9SvX18KtrSZPn06pkyZUqq6ElHVwNVSRAToGdy0b9/e0PUosRkzZiAmJgZ79+6FUqksMt/EiRMxbtw4aTsrKwvOznx8OpGxKI/VUgVjJQ5LEVV+egU3p0+f1pquUCigVCrh4uIivaqhKHZ2djA1NUV6uvzleenp6XBwcCj22NmzZ2PGjBnYtWsXWrRoUWxec3PzF9aFiKqu8lgtJQugOCxFVOnpFdz4+PgUO6ZdvXp1DB48GEuXLi2yV8XMzAx+fn7YvXu39K6q/MnBo0ePLrLsWbNmYdq0aYiNjYW/v78+1SciI6LtIX6GVjC4YccNUeWn12qp3377DR4eHli2bBkSExORmJiIZcuWwdPTE//973+xYsUK7NmzB19++WWx5YwbNw7Lly/Hzz//jHPnzuHDDz9EdnY2wsPDAQAhISGyCcczZ87EV199hZUrV8LNzQ23b9/G7du38ejRI30ug4iMgJANS5V9z42KPTdElZ5ePTfTpk3DvHnzEBQUJKU1b94cDRo0wFdffYVjx47BysoK48ePx+zZs4ssZ/Dgwbh79y4mTZqE27dvw8fHBzt27JAmGV+7dg0mJv+LvxYvXozc3FwMGDBAVk5UVBQmT56sz6UQURWnrefG0KulCvbWqNl1Q1Tp6RXcJCUlwdXVVSPd1dUVSUlJAJ4PXd26deuFZY0ePbrIYai9e/fKtlNTU0tcVyIybtqWghtawYCGHTdElZ9ew1JeXl6YMWMGcnNzpbRnz55hxowZ8PLyAgDcuHFDY5k3EVFBhukF0Qw8DD3pV8VhKaIqRa+em4ULF6JXr15o0KCBtFopKSkJKpUK27ZtAwBcvnwZo0aNMlxNicjoqIWACUo3hCQbMiqjwEOUwzmIyHD0Cm7atGmDK1euIDo6GhcvXgQADBw4EO+++y5sbGwAAMOGDTNcLYnIKBmi46Z83ifFpeBEVYlewQ0A2NjYYOTIkYasCxG9BAquaDJEL4jQEngYekJxwQf3qdQGLZqIyoDewQ0AnD17FteuXZPNvQGAXr16lapSRPRyMEhwU/DrMupU4bAUUdWiV3Bz+fJl9O3bF0lJSVAoFBp/Leny4kwiejkpCsyxMfSwlLbi8l/oWxryh/gxuCGq7PRaLTVmzBi4u7vjzp07sLS0xJkzZ7B//374+/trLN8mIiqo4LCUId7TJF6wWsoQsUjBeqo5LEVU6enVcxMfH489e/bAzs4OJiYmMDExwZtvvonp06fj448/RkJCgqHrSURGyBCTc180ZGSIfpbyWJFFRIajV8+NSqWSVkXZ2dnh5s2bAJ4/xO/ChQuGqx0RGZ3yHpYy9KRlBjdElZ9ePTfNmjXDqVOn4O7ujoCAAMyaNQtmZmZYtmwZGjZsaOg6EpERkb2nycDDUtCyWsogw1IMboiqFL2Cmy+//BLZ2dkAgClTpqBnz55o27Yt6tSpg5iYGINWkIiMi7al26Urr8DX2vYbYGBKPixV6uKIqIzpFdwUfGGmh4cHzp8/j/v376NWrVoGf74EERkXQwcKL1oKboiOFtmwFKMbokqvRMHN8OHDdcq3cuVKvSpDRMZP9kA8Qz/E7/+HOgZ/t5SB60xEZatEwc3q1avh6uoKX19fPoKciPRi6F4QoTnlpsj9+lK/4BxEVLmUKLj58MMPsXbtWly5cgXh4eEYOnQoateuXVZ1IyIjZOhAQf46B23nM8ScG04oJqpKSrQUfOHChbh16xb+85//YOvWrXB2dsagQYMQGxvLnhwi0knBYR3DDEsV+BpaVkuV+gzyHiZDrPAiorJV4ufcmJubIzg4GDt37sTZs2fh7e2NUaNGwc3NDY8ePSqLOhKRETF0L4isCK3DUoZdLcW/44gqP70e4icdbGIivVuK75MiIl3I58gY+MWZL9ivLw5LEVUtJQ5ucnJysHbtWnTu3BlNmjRBUlISFixYgGvXrsHa2ros6khERkS28sgA72nS9twcQ79bytAPHiSislWiCcWjRo1CTEwMnJ2dMXz4cKxduxZ2dnZlVTciMkKG7gV50ZCRQYalCr44k7ENUaVXouBmyZIlcHFxQcOGDbFv3z7s27dPa77NmzcbpHJEZHxe9KJLPUosUF7x59OX2sBDaURUtkoU3ISEhPAJxERUKgWHddQGGZYq8HVZrZYy8AovIipbJX6IHxFRaRh8tVTBr8tstRSHpYiqklKtliIiKim1gYelXhTQGKbnRnvZRFQ5MbghonIln5xriJ6b4t8ybohYRMWH+BFVKQxuiKhcGXqIR9arouUcBnmWDoeliKoUBjdEVK5kw1IGeXGmZk+Q0BLwlIahh9KIqGwxuCGiclWWK4/yi3vRJOOSkq/wYnBDVNkxuCGiciUfMip9edp6aeQTijksRfSyqfDgZuHChXBzc4NSqURAQACOHTtWbP4NGzbAy8sLSqUSzZs3x59//llONSUiQzD4ailoBkuG7rnhsBRR1VKhwc26deswbtw4REVF4eTJk3j11VcRFBSEO3fuaM1/+PBhBAcHIyIiAgkJCejTpw/69OmD5OTkcq45EelLbeCVR/JYQzO6MUQootIyr4eIKi+FqMCHNgQEBKBly5ZYsGABAECtVsPZ2RkfffQRPv/8c438gwcPRnZ2NrZt2yalvf766/Dx8cGSJUt0OmdWVhZsbW2RmZmJGjVqGOZC/r+jl+9h38W7Bi2TyNgs2psifd2mUR34ONcsVXkH/slA0o1MadvXpSYSrj2Qtgf6NUBdG/NSnSPm7zTcz84FADSqa4Ugb4dSlUdkzFxqW2KAXwNUMzVs/0lJfn+X6AnFhpSbm4sTJ05g4sSJUpqJiQkCAwMRHx+v9Zj4+HiMGzdOlhYUFIQtW7YUeZ6cnBzk5ORI21lZWaWreBES0x5g8LIjZVI2kbE6nHIPh1PuGbTMgoENAGw4cd2g5afczZYFaESk6caDJxjfxbPCzl9hw1IZGRlQqVSwt7eXpdvb2+P27dtaj7l9+3aJ8gPA9OnTYWtrK32cnZ1LX3ktXGpbwszAUSoREVFV1LphnQo9f4X13JSXiRMnynp7srKyyiTAqW1lhovTuhm8XCIiIiqZCgtu7OzsYGpqivT0dFl6eno6HBy0j2c7ODiUKD8AmJubw9y8dOPtREREVHVU2DiKmZkZ/Pz8sHv3bilNrVZj9+7daN26tdZjWrduLcsPADt37iwyPxEREb18KnRYaty4cQgNDYW/vz9atWqFuXPnIjs7G+Hh4QCAkJAQODk5Yfr06QCAMWPGoH379pgzZw66d++OmJgYHD9+HMuWLdP5nPmLw8pqYjEREREZXv7vbZ0WeYsKNn/+fOHi4iLMzMxEq1atxJEjR6R97du3F6GhobL869evF02aNBFmZmbC29tb/PHHHyU6X1pamsDzR1/www8//PDDDz9V7JOWlvbC3/UV+pybiqBWq3Hz5k3Y2NhAoVBUdHUqXP4E67S0NIM/94f+h+1cPtjO5YdtXT7Yzv8jhMDDhw9Rv359mJgUP6vG6FdLFWZiYoIGDRpUdDUqnRo1arz0Pzjlge1cPtjO5YdtXT7Yzs/Z2trqlI8PZiEiIiKjwuCGiIiIjAqDm5ecubk5oqKi+CygMsZ2Lh9s5/LDti4fbGf9vHQTiomIiMi4seeGiIiIjAqDGyIiIjIqDG6IiIjIqDC4ISIiIqPC4OYlk5OTAx8fHygUCiQmJsr2nT59Gm3btoVSqYSzszNmzZqlcfyGDRvg5eUFpVKJ5s2b488//yynmlcNqampiIiIgLu7OywsLNCoUSNERUUhNzdXlo9tXTYWLlwINzc3KJVKBAQE4NixYxVdpSpl+vTpaNmyJWxsbFCvXj306dMHFy5ckOV5+vQpIiMjUadOHVhbW6N///5IT0+X5bl27Rq6d+8OS0tL1KtXD59++iny8vLK81KqjBkzZkChUGDs2LFSGtvYAEr0Yiaq8j7++GPRrVs3AUAkJCRI6ZmZmcLe3l4MGTJEJCcni7Vr1woLCwuxdOlSKc+hQ4eEqampmDVrljh79qz48ssvRfXq1UVSUlIFXEnltH37dhEWFiZiY2NFSkqK+P3330W9evXE+PHjpTxs67IRExMjzMzMxMqVK8WZM2fEiBEjRM2aNUV6enpFV63KCAoKEqtWrRLJyckiMTFRvP3228LFxUU8evRIyjNy5Ejh7Owsdu/eLY4fPy5ef/110aZNG2l/Xl6eaNasmQgMDBQJCQnizz//FHZ2dmLixIkVcUmV2rFjx4Sbm5to0aKFGDNmjJTONi49BjcvkT///FN4eXmJM2fOaAQ3ixYtErVq1RI5OTlS2meffSY8PT2l7UGDBonu3bvLygwICBAffPBBmde9Kps1a5Zwd3eXttnWZaNVq1YiMjJS2lapVKJ+/fpi+vTpFVirqu3OnTsCgNi3b58QQogHDx6I6tWriw0bNkh5zp07JwCI+Ph4IcTz/2dMTEzE7du3pTyLFy8WNWrUkN3zL7uHDx8KDw8PsXPnTtG+fXspuGEbGwaHpV4S6enpGDFiBH755RdYWlpq7I+Pj0e7du1gZmYmpQUFBeHChQv4999/pTyBgYGy44KCghAfH1+2la/iMjMzUbt2bWmbbW14ubm5OHHihKzNTExMEBgYyDYrhczMTACQ7t8TJ07g2bNnsnb28vKCi4uL1M7x8fFo3rw57O3tpTxBQUHIysrCmTNnyrH2lVtkZCS6d++u8XPONjYMBjcvASEEwsLCMHLkSPj7+2vNc/v2bdkPCgBp+/bt28Xmyd9Pmi5duoT58+fjgw8+kNLY1oaXkZEBlUrFNjMgtVqNsWPH4o033kCzZs0APL8vzczMULNmTVnegu2sy/39souJicHJkycxffp0jX1sY8NgcFOFff7551AoFMV+zp8/j/nz5+Phw4eYOHFiRVe5ytK1rQu6ceMGunbtioEDB2LEiBEVVHMi/URGRiI5ORkxMTEVXRWjkpaWhjFjxiA6OhpKpbKiq2O0qlV0BUh/48ePR1hYWLF5GjZsiD179iA+Pl7j3ST+/v4YMmQIfv75Zzg4OGjMxs/fdnBwkP7Vlid/vzHTta3z3bx5Ex07dkSbNm2wbNkyWT62teHZ2dnB1NSUbWYgo0ePxrZt27B//340aNBASndwcEBubi4ePHgg61ko2M4ODg4aq9QK398vsxMnTuDOnTt47bXXpDSVSoX9+/djwYIFiI2NZRsbQkVP+qGyd/XqVZGUlCR9YmNjBQCxceNGkZaWJoT43yTX3Nxc6biJEydqTHLt0aOHrOzWrVtzkmsh169fFx4eHuKdd94ReXl5GvvZ1mWjVatWYvTo0dK2SqUSTk5OnFBcAmq1WkRGRor69euLixcvauzPn+y6ceNGKe38+fNaJ7sWXKW2dOlSUaNGDfH06dOyv4hKLisrS/b/cVJSkvD39xdDhw4VSUlJbGMDYXDzErpy5YrGaqkHDx4Ie3t7MWzYMJGcnCxiYmKEpaWlxvLkatWqidmzZ4tz586JqKgoLk8u5Pr166Jx48aiU6dO4vr16+LWrVvSJx/bumzExMQIc3NzsXr1anH27Fnx/vvvi5o1a8pWlFDxPvzwQ2Frayv27t0ru3cfP34s5Rk5cqRwcXERe/bsEcePHxetW7cWrVu3lvbnL1Pu0qWLSExMFDt27BB169blMuViFFwtJQTb2BAY3LyEtAU3Qghx6tQp8eabbwpzc3Ph5OQkZsyYoXHs+vXrRZMmTYSZmZnw9vYWf/zxRznVumpYtWqVAKD1UxDbumzMnz9fuLi4CDMzM9GqVStx5MiRiq5SlVLUvbtq1Sopz5MnT8SoUaNErVq1hKWlpejbt68seBdCiNTUVNGtWzdhYWEh7OzsxPjx48WzZ8/K+WqqjsLBDdu49BRCCFHuY2FEREREZYSrpYiIiMioMLghIiIio8LghoiIiIwKgxsiIiIyKgxuiIiIyKgwuCEiIiKjwuCGiIiIjAqDGyIiPeTm5qJx48Y4fPiwQcvdsWMHfHx8oFarDVou0cuEwQ0RISwsTOubzi9dulTRVau0lixZAnd3d7Rp00ZKUygU2LJli0besLAw9OnTR6dyu3btiurVqyM6OtpANSV6+TC4ISIAz3+p3rp1S/Zxd3fXyJebm1sBtatchBBYsGABIiIiyqT8sLAw/Pjjj2VSNtHLgMENEQEAzM3N4eDgIPuYmpqiQ4cOGD16NMaOHQs7OzsEBQUBAJKTk9GtWzdYW1vD3t4ew4YNQ0ZGhlRednY2QkJCYG1tDUdHR8yZMwcdOnTA2LFjpTzaejpq1qyJ1atXS9tpaWkYNGgQatasidq1a6N3795ITU2V9uf3isyePRuOjo6oU6cOIiMj8ezZMylPTk4OPvvsMzg7O8Pc3ByNGzfGihUrIIRA48aNMXv2bFkdEhMTi+25OnHiBFJSUtC9e/cStjKQmpqqtZesQ4cOUp6ePXvi+PHjSElJKXH5RMTghoh08PPPP8PMzAyHDh3CkiVL8ODBA7z11lvw9fXF8ePHsWPHDqSnp2PQoEHSMZ9++in27duH33//HX/99Rf27t2LkydPlui8z549Q1BQEGxsbHDgwAEcOnQI1tbW6Nq1q6wHKS4uDikpKYiLi8PPP/+M1atXywKkkJAQrF27Fj/++CPOnTuHpUuXwtraGgqFAsOHD8eqVatk5121ahXatWuHxo0ba63XgQMH0KRJE9jY2JToegDA2dlZ1juWkJCAOnXqoF27dlIeFxcX2Nvb48CBAyUun4gAvhWciERoaKgwNTUVVlZW0mfAgAFCiOdvLPb19ZXl//rrr0WXLl1kaWlpaQKAuHDhgnj48KEwMzMT69evl/bfu3dPWFhYyN5+DED89ttvsnJsbW2lt1D/8ssvwtPTU6jVaml/Tk6OsLCwELGxsVLdXV1dRV5enpRn4MCBYvDgwUIIIS5cuCAAiJ07d2q99hs3bghTU1Nx9OhRIYQQubm5ws7OTqxevbrI9hozZox46623NNIBCKVSKWtHKysrUa1aNdG7d2+N/E+ePBEBAQGiR48eQqVSyfb5+vqKyZMnF1kHIipatYoNrYiosujYsSMWL14sbVtZWUlf+/n5yfKeOnUKcXFxsLa21ignJSUFT548QW5uLgICAqT02rVrw9PTs0R1OnXqFC5duqTRQ/L06VPZkI23tzdMTU2lbUdHRyQlJQF4PsRkamqK9u3baz1H/fr10b17d6xcuRKtWrXC1q1bkZOTg4EDBxZZrydPnkCpVGrd98MPPyAwMFCW9tlnn0GlUmnkHT58OB4+fIidO3fCxETekW5hYYHHjx8XWQciKhqDGyIC8DyYKWoYpmCgAwCPHj1Cz549MXPmTI28jo6OOq+yUigUEELI0grOlXn06BH8/Py0rhyqW7eu9HX16tU1ys1fSm1hYfHCerz33nsYNmwYfvjhB6xatQqDBw+GpaVlkfnt7Oyk4KkwBwcHjXa0sbHBgwcPZGnffPMNYmNjcezYMa3DW/fv35ddIxHpjsENEZXYa6+9hk2bNsHNzQ3Vqmn+N9KoUSNUr14dR48ehYuLCwDg33//xcWLF2U9KHXr1sWtW7ek7X/++UfWW/Haa69h3bp1qFevHmrUqKFXXZs3bw61Wo19+/Zp9Kjke/vtt2FlZYXFixdjx44d2L9/f7Fl+vr6YvHixRBCQKFQlLhOmzZtwtSpU7F9+3Y0atRIY39+z5Svr2+JyyYiTigmIj1ERkbi/v37CA4Oxt9//42UlBTExsYiPDwcKpUK1tbWiIiIwKeffoo9e/YgOTkZYWFhGkMvb731FhYsWICEhAQcP34cI0eOlPXCDBkyBHZ2dujduzcOHDiAK1euYO/evfj4449x/fp1nerq5uaG0NBQDB8+HFu2bJHKWL9+vZTH1NQUYWFhmDhxIjw8PNC6detiy+zYsSMePXqEM2fOlKDVnktOTkZISAg+++wzeHt74/bt27h9+zbu378v5Tly5AjMzc1fWA8i0o7BDRGVWP369XHo0CGoVCp06dIFzZs3x9ixY1GzZk0pgPnuu+/Qtm1b9OzZE4GBgXjzzTc15u7MmTMHzs7OaNu2Ld59911MmDBBNhxkaWmJ/fv3w8XFBf369cMrr7yCiIgIPH36tEQ9OYsXL8aAAQMwatQoeHl5YcSIEcjOzpbliYiIQG5uLsLDw19YXp06ddC3b1+9HrR3/PhxPH78GN988w0cHR2lT79+/aQ8a9euxZAhQ4odGiOioilE4QFvIqIy0qFDB/j4+GDu3LkVXRUNBw4cQKdOnZCWlgZ7e/sX5j99+jQ6d+6MlJQUrROr9ZWRkQFPT08cP35c60MUiejF2HNDRC+1nJwcXL9+HZMnT8bAgQN1CmwAoEWLFpg5cyauXLli0PqkpqZi0aJFDGyISoETionopbZ27VpERETAx8cHa9asKdGxYWFhBq+Pv78//P39DV4u0cuEw1JERERkVDgsRUREREaFwQ0REREZFQY3REREZFQY3BAREZFRYXBDRERERoXBDRERERkVBjdERERkVBjcEBERkVFhcENERERG5f8BdymFSfeO8U0AAAAASUVORK5CYII=",
+ "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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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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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C8vPz8fXXX2P16tU4ceKE7DY5Qm+gtRg0aBB+/PFH3L59G40bN0bv3r3RvXt3PPXUU8wvVDmUPgtLjBaWro3eMnO6LioqwsKFC/HTTz/h8OHDKCwsNCvLFrquqMjpVeiBNKdXeyA3M1jo3bUk36P3denSJdy8eRMAMGnSJEyaNMmiMgk9S5YQFRWFp556Cn///Tf++9//YvPmzXjhhRfQqVMntG3b1qxX5lFatGgBZ2fz3XLz5s3h6uqKwsJCHDt2zCSd4zisXLkSixcvxr59+3D//n2zsh59Ly5evMh/uD711FMWlVeI0TO2efNmi5e+UarniggZUZWQgIAA/reaSiycomv82n8UlsuaxZ07d/jfrK8aVrqlsBo6vb7UcStlHKWlpaFz584Wf+HLNW5q6dKlC7755hu8++67uH//PtasWYM1a9YAKF0Y8l//+hdGjRplsbfIiC2ehRZdZ2VloVu3bkhOTrboWrbQdUVFTq9GnQLSerUXlpbZknyP3ldmZqaqMt27d0/xOT///DP69u2LpKQknDx5EidPnsSMGTPg4uKCtm3b4pVXXsGQIUNkPy7NeWeNODs7w9/fH+np6cjKyhKlPXjwAP/+9795bxKLR98LoVElN1xuDjW6fhzeTTKiKiFG1zcAHD58WNG5JSUlOHr0KIDSDjMsLEwyn5OTk/oCOiCvvvoqLl68CJ1Oh6FDh6J///5o2LAhv0aKTqeDwWDg9SIc2rMmo0ePRt++fbFq1SokJCTgn3/+QU5ODq5du4bvv/8eCxcuxAcffICPP/7YJtcvD95++23egOrTpw9ee+01NG3aFEFBQXB3d+e/gmvWrIkrV648XmvSEIoQGlVTpkyxOMTAy8tL8bWqV6+OPXv2IDExEevWrcPOnTtx8uRJFBUV4e+//8bff/+Nzz77DJs2bTLrVdOyePHMmTN5A6pjx44YPXo0WrZsiZCQEHh4ePCGZocOHfD3339b/b0w6vqZZ57BnDlzrCq7IkNGVCWkcePG8Pf3R1ZWFnbt2oWcnBz4+vpadO7WrVv5rzQ1Ll9LEXqybt68KevKN7rr7cXp06exe/duAJA1UB79crQVQUFBGDt2LMaOHQuDwYCUlBSsX78e33zzDb8YYOvWrWVjL4Q8+iyqV69uNq+tn0Vubi7vXRswYABWrFhhNq/Qg0ZUToSechcXFzRu3Njm1+zSpQu6dOkCoHTizdatW7Fw4UJs27YNqamp6Nevn9mPV6mFOIUUFxfz7YhweJbjOPzwww8AStvlbdu2ibx4Qsy1Q8IRihs3bsiWQ4pq1arh+vXrKCwsLBc9VxQosLwSotPpMGjQIACl7tRFixZZfO68efP438bVdW3BE088wf9mDdtYY6VhLZw4cYL/3a9fP7P57FFOvV6Pli1bYsaMGaJVnX/55ReLZVSkZ3Hu3DkUFRUBkNf16dOnVcX7EfI42jZPtWrV4j8Q//nnn3K/frVq1dCvXz8kJibiueeeAwCkpKTg3LlzkvlTUlJkV5U/cuQIH/8nNFSysrL40Iy+ffuaNaDy8vJw5swZybSoqCg+tmzXrl3yNyaBMRD94MGDsjGKjxtkRFVS3n77bX4m2rRp03D+/HnmOatXr+a3YWjcuDH+9a9/2ax8xu0fAMh6GzIyMrB582ablcMShI1efn6+2XwLFiwoj+KYpWXLlrxXSUmwdadOnfhG+aeffjKb78iRIzafCecoun5cMcbzFBQU2LkkluHk5ISePXsCALZs2cLcTsiWGL1TgPn3Lysri59NKMWSJUv433FxcfxvS9+LH374wayRptfr0atXLwDAzp07FYd6GI3EnJwcLF26VNG5jgwZUZWUyMhIft2ivLw8dOnSRbYD/OWXXzB48GAApcsX/PTTTzb9KnV3d+e9ZQcOHMBXX31lksdgMOA///kPvwegvTBOxwfMT2/+7rvvrDKDUI41a9bIBmoePHiQH+KKioqyWG6NGjX4xnXt2rXYsGGDSZ779+9jxIgRygqsgjp16vD1bvny5ZJxHRs3bsQ333xj87JURowBx9ZcQsPWTJo0CU5OTjAYDHjxxRdl98AsKSnBypUrmftkPkpKSors2m8cx/FLEuh0OkRGRprNO27cOMlhvZ07d/JrsUVHR6N169Z8WmBgIO9F+vnnnyWN3AMHDmDy5Mmy9zFhwgTo9XpwHIf+/fvL6uHRtMGDB/NrR02YMIHpzdq9ezd27twpm8cRoJioSsybb76J1NRUfPXVV7h8+TJatWqFl19+Gc899xwiIiJQVFSE06dPY9WqVfxQkJubG1asWIHmzZvbvHxTp07Fr7/+ivT0dIwdOxbJyckYMGAAAgMDcf78eXz11VfYs2cP2rRpg/379wOwz3BDixYt0LhxYxw/fhzff/897ty5g1dffRWhoaG4evUqVqxYgbVr1+LJJ5+06ZDC+++/j5EjR6J3797o0KED6tWrBy8vL9y+fRu7d+/mh2KdnJzw+uuvK5L9xRdfIDExEffu3UPfvn0xatQoPP/88/Dx8cHx48cxZ84cnDx5Eq1bt8aBAwdscXsASodHevbsiT///BPx8fHo1q0bRo0ahYiICGRmZuK3337DsmXLUKtWLWRnZ9s8Rmvt2rWiYUNjbNyjv4HShW2NG3g7Ku3atcP27dtx4MABfPLJJ3jmmWf4IGwPDw/ZeDl70aRJE3z22Wd45513cPLkSTRu3BgjRoxA586dERwcjAcPHiAtLQ1JSUlYu3Ytbty4gWPHjskuNPkoKSkpGDp0KFq3bo1nn32WD+guKirCxYsXsXTpUiQkJAAo9diYm/3WrFkznDx5EtHR0Zg0aRLatGmDgoICbNq0Cf/9739RXFwMZ2dnzJ8/X3SeXq/HgAEDMH/+fBw9ehTt27fHuHHjULduXeTk5GDTpk349ttv4e3tjbCwMJw9e1by+s2bN8e0adMwefJknD17Fk2aNMHo0aPx9NNPo1q1asjOzkZKSgrWrVsHJycnfuNxoLRv+OWXX9CpUyfk5eWhc+fO6N+/P/r06YOoqCgYDAbcuHEDycnJWL9+PY4dO4Z58+ahY8eOFuu5QmKnldKJCsR3333Hb0gs99ewYUNu165dsrLktlh5FEs2IE5JSeECAwPNlmnIkCHc4sWL+f+np6crKpMlZTAit+XF4cOHuapVq5otZ5MmTbjr169bvBWGmm1fhFtHmPtzc3OTvE9LtkHZsmWL5IbSwnuaPHkyB4Bzd3eXlCF3/0Lkntnly5e5mjVrmi1HzZo1uRMnTsg+L2tt+2KJzo1/lrwTUijZ9kUOa9zz1atXzbYVajcglqvrlt7b4MGD+S1TzLFw4ULO09OT+ZxcXV2Z26fI3Y/cX7t27bhbt26ZnC+sq4sWLTK7AbGrqyv3888/S5YhOztbdvNpf39/bufOnRa10TNnzjRbBlZ9TkpK4sLDwy3Sx/LlyxXpuSJCw3kERo4cidTUVMybNw89evRAeHg43N3d4e3tjdq1a6N///74+eefcezYMZvOyJPC+GU2fvx41K1bF25ubggICMDTTz+NVatWYenSpaJV0y2dZWhtmjdvjpSUFIwcORIRERFwcXGBv78/2rRpg88++wz79+9XtfaKErZv346vvvoKL7zwApo0aYLAwEA4OzvDx8cHLVq0wIQJE3Dy5EnVEwK6du2K48eP4z//+Q8iIiLg6uqK4OBg9OrVC/Hx8Zg6dSr/LGz5HMLDw3Ho0CG8++67qFevHtzc3ODr64tmzZrho48+QkpKCho1amSz61dmqlevjv3792PYsGGoU6eO1bZcKg+GDx+OCxcuYNq0aXjyyScREBAAZ2dneHl5oV69enjhhRewYMECXLt2TfHitC+//DI2bdqEd955B+3bt0dUVBQ8PT3h6uqKGjVq4LnnnsPKlSvx999/m11bz8jrr7+Ov//+Gy+99BLCwsLg6uqK6tWrY9CgQTh8+LDZldt9fX3xzz//YMaMGWjSpAnfhjds2BATJkzAkSNHZLekEfLBBx/g5MmTGDt2LBo3bgwfHx84OzsjMDAQHTt2xMcff2w2PrJt27Y4d+4cFixYgF69evH34O7ujvDwcHTr1g0zZ87E6dOn+ZANR0bHcbSICuHYvP7661i8eDFq1KiBK1eu2Ls4lZq4uDgkJiaiffv2kvuYEQRhSmRkJC5duoTBgwc/FvvJVSbIE0U4NPfv3+cDttu2bWvn0lRurl+/zgeT0rMgCKIyQEYUUaFJTU01u7JuSUkJRo0axU8XNs4eJGyD3DIY9+/fx5AhQ/g1nB4HNz1BEAQLmp1HVGhmzJiB/fv3o3///oiJiUFQUBDu37+Po0ePYtGiRTh06BCA0mEk4zR8wja8/vrryM/Px0svvYTo6Gj4+/vj7t27OHjwIL799lveyBo2bBiaNGli59ISBEHYHjKiiArPqVOn8NFHH5lNf/LJJ7F69WqHW03ZETl48KDsquTPP/+8aFV7giCIxxkyoogKzaRJk1CvXj1s3boVaWlpuHnzJoqKilCtWjW0atUK/fr1Q//+/c1uc0BYjy+++ALr16/Htm3bcPXqVdy8eRMcxyEoKAht27bF4MGD+dWhCYIgKgM0O48gCIIgCEIF5ImyIQaDAdevX0eVKlVoqIkgCIIgHASO43D37l2EhYXJjnSQEWVDrl+/zu8lRBAEQRCEY3HlyhXZLYDIiLIhVapUAVD6EHx8fOxcGoIgCIIgLCE3Nxfh4eF8P24OMqJsiHEIz8fHh4wogiAIgnAwWKE4NKWJIAiCIAhCBWREEQRBEARBqICMKIIgCIIgCBWQEUUQBEEQBKECMqIIgiAIgiBUQEYUQRAEQRCECsiIIgiCIAiCUAEZUQRBEARBECogI4ogCIIgCEIFj4URtWvXLjz77LMICwuDTqfDhg0bmOfs2LEDLVu2hJubG+rUqYNly5aZ5Jk/fz4iIyPh7u6OmJgY7N+/3/qFJwiCIAjCIXksjKj8/Hw0a9YM8+fPtyj/xYsX0atXLzz99NNISUnB2LFj8frrr2Pz5s18njVr1mDcuHH46KOPcOjQITRr1gzdu3dHZmamrW6DIAiCIAgHQsdxHGfvQlgTnU6H9evXo0+fPmbzvP/++/jzzz9x/Phx/lj//v2RnZ2N+Ph4AEBMTAxat26Nb775BgBgMBgQHh6ON998ExMnTrSoLLm5ufD19UVOTg7tnUcQBEEQDoKl/fdj4YlSSlJSEuLi4kTHunfvjqSkJABAYWEhkpOTRXn0ej3i4uL4PBWJ+4UlAACO4/CgqMQqsopKDCgsNqiWIyzLg6ISaLHVi0oMKCoxiMqnFmFZtMqyhd6LSe9MHEHvBoN6vQvL8rjrvcTAoaBYvayKrHdjWaypd2vJsobejbK06l1YFq33Zw8qpRGVnp6O4OBg0bHg4GDk5ubi/v37uHXrFkpKSiTzpKenm5VbUFCA3Nxc0Z+t2XoyAw2nxOO7HakYuSIZDSbH4+qde6pkfbn1LBpOicfOszfx1Kfb0XZ2IopL1HUsw5YfRIPJ8Th+LQcNJsdj8NIDquSUGDi0+2Qb2n+6DbvP3ULDKfH4fMsZVbKuZ99Hg8nxGP5jMhbuSkXDKfGIP27+ecqx/UwmGk6JxzfbzmHMz4fRYHI8Lt9Wp/f528+j4ZR4bD+diU6f7UDrmVtVd+j/+SlZpPcBP+xTJcdg4PDUp9sROzsRe86X6v3T+NOqZGXmPkCDyfF4bdkBLNl9EQ2nxOPPozdUyfr73E00nBKP/yacxTtrUtBgcjxSb+apkrVgZ2kdSDiZga7/3YWWMxJUdyyjVx0S6b3/wr2q5HAch45zdyBm1lbsvXAbDafEY+afJ1XJupVXgAaT4zFoyX78mJSGhlPi8XvKNVWyklJLyzJ382lM+PUoGkyOx7mMu6pk/fD3BTScEo+/jt1Ajy93ocX0BNVG2durS+vAsas5eOKjzXhhwR5VcgCgyxc70erjBOy/mIWGU+Ix9X8nVMm5k1+IhlPi8coPe7Fy3yU0nBKPdYeuqpJ1IK20LLM3ncIH64+h4ZR4nLqhrl9Z9k/pu7fxyHX8a95uNJ26BfcKi1XJGv/rETScEo8jV7LRZOpm9Pn2H1VyAKD7l7vQcnoCki+V3uv/bTimWpY9qJRGlK2YPXs2fH19+b/w8HCbX/O9344CAD6NP43NJzIAAKv3X1El68ut5wAA4385gvTcB8jKL8SNnAeqZG07XRo79tqyUuNp19mbquTczivAzbsFyMgtwLhfUgAA87adVyXrl4Oletl6KgOzNpUaBBN+PaJK1gfrSl/0z7ac5Q2Cn/amqZI1d3OpUfj+b0dx9c595NwvwuUsdQbZlpOldWDIQ6N1T+ptVXLuPihGeu4D3MorxFurUwAA3+1IVSVr/eHSjnv7mZuY/kepQTB2zWFVsj76vbRj+yrxHDakXAcALP3noipZn/xVWgfeXXsEF2/lI6+gGOcy1Blkm46VGuPG+r4/LUuVnMISA65l38ede0V46+dSHS36W939/XGkVD9/n7uFKQ/19vbDZ6mUaRtLz5+/PRW/PTQIFu66oErWx3+eAgCM++UIzmXm4V5hCU5cz1El638P73HY8gMoMXA4fDlblRyO43Dp9j3kPijGGysPAQCW7UlTJWvLyXRwHLD3QhY+XF8aMjLuF3XtzKxNpbr6ftcF/PywXZ+/XV37N3Vj6bv35s+HcepGLgqKDTh0KVuVrHWHSt/p4T8eRFEJh6NX1T0/ADifmYf8whKMWlGq9xV7L6uWZQ8qpREVEhKCjIwM0bGMjAz4+PjAw8MDAQEBcHJykswTEhJiVu6kSZOQk5PD/125os6YsYR/zt/Coct3JIdrOHDYciIdp9PZXywcx2Hjkeu4dDufP6bXlaUXlhiw/vBVZOSyjal7hcX4Lfkq7uQXCmSVCcu+V4jfkq8iv4D99ZOZ+wDrD19FgcAjI5R1+fY9/O/IdYvcyGcz7mLzidKG7VE4jsPhy3fw9znLjLw9qbeQfCnLjKxSz+DJ65bp/Y+j13HxVpneBbeHohIDNhy+hhs595my7heW4Lfkq8gS6b0sPedeEX5Lvoo8S/R+9wHWHboq8sgIZV3JuoffU65ZpPdzGXcRf/wGpHJyHHDkSjZ2Wmhc771wGwfSsszK2nY6w6KOmOM4/Hn0hlnvlVHv17PZen9QVKr3W3kF/DFhHc25X6r3uw+KmLJu3i3AukNX8aBIur5fvVOq9xIL9H4+Mw+bjknrHQCOX8vB9jOWTZDZfzEL+y5IG+IcSj2yx69Z1oH+dewGzmdK6724hMPvKdcs8qI/KCrBukNXcfOutN7vPijVe64Fer+dV6r3+0XS9f1a9n1sOHzNIo/8hZt5+PPoDcm2ASjV+7bTGdKJj3AwLQtJqbel2xkAO8/exNGr2RbJij+ebtZrWMKV6v2KBR9tBcUlWH/4KjLvlvUFQr3nFZS2/zn32HrPyi/tC4TDd0JZN3JK9V6kciSkPHG2dwHsQWxsLDZt2iQ6lpCQgNjYWACAq6sroqOjkZiYyAeoGwwGJCYmYsyYMWblurm5wc3NzWblNnLzbgE/TFPV08UkPeVKNuZvL/UapH3SS1bW/45cN/k6FXbm3+1Ixdrkq/D3csWhyV1lZU3ecAK/HbqK5uF+krKGLT+I5EulBsuX/VvIyuo1bzdu3i1A/9Zl3jyhrA5ztwMojWP4d8sasrK6/XcXAOCpugEmaRyA578tHQLYO6kLQnzdzcq5k1+IVxaV6j2oiulzPnE9Fz/sLvUasPS++UQ6xqwSe2OEDeYPf1/Eb4euwtvNGcendZeVNf2Pk/h5/2U0Ci0LfhTq6j8rDmLvhSwkns7AtwOiZWU9P38PrmXfx8ttpPX+1JxSvT8oKkG/1jVlZXV9qPdO9QNN0jgAveeXDgH8/d7TCPf3NCvn7oMifnisup+HSfrZjLtYua/065Wl922nMzF61SHRMcHtYfmeNGxIuQ5XZz3OfvyMrKzZm05hedIl1A3yLpMlEPbmz4ex6+xNxB0Pxg+DW8nK6rtgD9Ju38PAtmU6Fcpq/2mp3nPvF+HV2EhZWXFf7AQAPC2hdwD417zdAIDtEzohKsDLrJz7hSV46fvSGNCaEs/n4q18DH3o8WTp/e9zNzFq5SN6F9zfqv2X8ftDzyJL1udbzmDR3xcRUa2sTEJZ76xJwdZTmehwJBA/vtZGVlb/hXtxLjMPB9LuSMp6eu4OFJYYkJVfiNfaR8nK6vx5qd7jGgZLphv1nvBOB9QNrmJWTlGJAS8uKNV77UDT53P1zn0MXlK63A5LV/su3MbIFcmiYzpdWVvzy8ErvCedJeurrefw7Y5UhAraSKGu3lt7BJuOpSO2VjX8PKKtrKxXF+/Dieu5OCDw2Apldf1iF/IKSr3hIzvWlpVlbx4LT1ReXh5SUlKQkpICoHQJg5SUFFy+XNqwTpo0CYMGDeLzjxw5EhcuXMB7772H06dP49tvv8Uvv/yCd955h88zbtw4LFq0CMuXL8epU6cwatQo5OfnY+jQoeV6b1IIvUJSHz2nb1geq7D/oumwg07QrWx/OCwn9HKYwxhvkXIlmz8m/LpIvlTaUBmHYeQwfmUahwUflWVEqvzmOCWhF6HhwvL63M4v+/KVcgicM/OVLcVBQaMthdFDY4n3yDhsc1IQKyHU1d4LpToyDjnJce2hBybhpLzejTItQcozJ/SgXmN4fXLul33ZSn2ZXriZb3LMHIcuy+v973O3AMCimLQ/j5V2PsLnLtSVcQh76ym29yHtYTxd/PGyvFJ6/+e85cOzx67Je0TTbsvrTVj37kkE/F5SEAN4RNAmSKHkvv56GMcovL5QV1tPldZdS0IIjM9u07GyGD2hrMKH9c1STzUApocolVFfhd73uw9M3/9rd9heUiPHGF7CvQqG+zefKNW7MMRDqCtj+5JkxnMp5MTDNkEYG/moVwsAdljoMbUnj4Un6uDBg3j66af5/48bNw4AMHjwYCxbtgw3btzgDSoAiIqKwp9//ol33nkHX331FWrUqIEffvgB3buXffH369cPN2/exJQpU5Ceno7mzZsjPj7eJNjc0bHm+ha2WCtDoh8RX1PBRaVkcYJSs0QJr6V1ZRDJYSkrymKojYlQV1KylNy/tN6FsuTPF6az6gMLc8Mj1pJlazgFpWXqiqV3QQZb6F2tSFvrXbK+KzmfeWPy0oTvlla9S6FjlkAae6yF5AgLMD0WRlSnTp1kG3Wp1cg7deqEw4flg1vHjBkjO3xnLwyCe5VsnBS8eKzOUIksg4QsvUZfp47R1Epd07wsU5QYRkLvk6TholFX1pSl09j6Cs+WkqVkRrPUM1Sid9YjVqYra8qyfn1nyVKmd3lYdVBkvErJt5PepeqLNfVui/quRJZBpHdTWfZqZ6RE2bq+O4IR9VgM51U2hC+ZdIBv2ZvBcZzkMNWDohJk5Rea+TIXHiyTVWLgJAPMs+8V4l5hsZmvTem39H5hiSgA3Uhm7gNFyypwKB1qlJoifSPnPvOr7lGPSEbuA5PgXYOBQ3rOA5Fe2B0QZ3J9oFTvt/MKzAam82UVHDen95x7RcgvKDZjREmX635hieTQ7KN6F3miJGQZOA538gsl13WxRO9COMjrXXh/0lVDfAEpvRcUl+BWXoGkJ0eclV3fc+4XIa+g2IwHUPpmje/bo2TeffDIEKVAb2aMT+P79iiK9c5Jv298fVfoAVSqd6GxIhRvvP6j5D4o1btUkyen99uCwH8jpnoXlsv0GMdx/Pv2KNbSO99WswxOiXMe1XthsQE37xZI60pUwLLfcnq/+6BI0ng1p3fjc3+Um3cLREPl4p5Gor6jVO9SYQ1S920PyIhyQIQVh+UR+XLrOcTO3mYyPb3t7ES0nJEgWdHN1cshS/cjZlaiKA4p534Rmk9PQIvpCYruIfrjBLSYkSCayZF8KQttZiVi0JKyPQqF9yL1wmTlF6LljAS0nrlVdHzRrguInb0NXyScLZMl9cILRP5z/jZiZiVi2HLxmlajViaj7exEJJ4qG5+XMl6F0r/dkYrY2dtMpiM/NWc7oj/eKprhIlkYAcN/PIiYWYn45/wt/lheQTGaTd+CJlM3K/pai5m1FS1nJIg6lpQr2WgzKxEvLypb24glM+d+EVrMSEDLGeLnvvSfi4idvQ2fxpet5cXqf/ddzELMrEQ+WNbIW6sPo+3sRGw5KYzlkjcYF+ws1ft/Hy7XYeTpuTvQ6uOtuJ5tqndzDfGoFcmImZUomkF4v7AEzaZtQeOPNitaYPDJT7ah5YwE0XM/fi0HbWYmou+CsgV8WXq/+6D0fWs6dYvo+Iq9lxA7exu/fADA9ogkX76DNrMS8coja4mN//UI2s5OxB9Hy2IXWcNxi3eXPnfjch1Gun6xC60+3qpoyY43fy597ltPlsWHPSgqQdOppXovUVDhO84tfd+ExsGZ9LtoMzMRzwvWNmJ1xvkP37cnPtosOr7mwGXEzt4mWlOKVd+PXi1934xB+0be/+0oYmdvwwbBWl4sg2zZnjTEzt7GL4VgpMdXu9B65lak3ZKPvxIF5P+SgrazExF/vCxOqbDYgKZTt6DJ1C0oKrFc710+34lWH28VzbY8n5mH1jO34tmHAfaWcL+oBM2mlz534TNad+gqYmdvw4cbjsucXT6QEeWAiL0n8hX7q8TSzuTRhRKzHxovrMBs4UtmDLr9MSmNP2YMGi4wE4irN9MIGANVhYGPPyVdAiBe20gquFbIwYezOx4NwJz5sFERrillzqNiZPHu0nVvdpwRB5Ea19/6fmeZIcoyXo2dyWdbzoryGAPmWes3CWUZg+uF6yEZp4obOOmymNNb7kM9HRKsp/Pzw5ltBxjB7kKMz+3+Ix7AaQ/Xolkg0BVraNFYn3YLjEQA+ONh0Om3gg8AVv9pXPvp60SxEXX9YSe6kxGoKiyqcc2tH/4uWw9JGIwtZUOZq++3H3qhhO/b2uTS9ZaEEzGE15eSdebhVPXiRy7+8cMFORfvLqsjrA545d5LJmUCytb1EuqQVd9nPFz/69tHPtaMxpNwgogUQlnGgH1hHRIaQVIeJHN6z8gtfd+EgeHrDpfq/biZwHupd+eCwBgRtrnG9eaWP2y7AHZ9X/NwvbpDj6xp9cvB0nKJP/xMEYo3vm+PriVmnGwRf8LyxYSNsyO/EXz4CSfTSC3VYU7vVx8Gvwvb0o0PJ8CcMbPcgpSsq4IgemGdn/PwI23VPvuvKUVGlAMifImlXduW4yRRc6UH88oQDruw1q1hNSjFBoPgN2OITOKYVPnNlkXimNCIYskSpisZupSUJaEX8XCeaXqxSO8Cl7iC4Typ85l6l0iWKr9aWLKE6ZJDl0qupaK+F5dI13epus+q78JzhHWfL4toCM1UllAXwucuWRbZkiit75YP5UjKkqzv8kOXxSJdCdo8jXovYXhUpEQJy1+soP2TglXfRd53qXSBrlivodS1pAfzyhDWd+FvvUR9UaR3CV2Jh0Hl67uoXBq2mbE2ZEQ5IKKYKI1jwkqMECNCt26RREcghPWSm3thLUWREcUIGGV5vYTX0qp3NYHfQv0In4G0R0ReflEJozNnlEVYftawFutWWboQXUtj28nSixRC/Qi9IEpi0crO11bfhbooMlMfpPJKocSI0qp3VfVdZOgLPxqk5MvLKjJjkPEyBb+l6ojOTGcu5RXTWt/1OsvbGVZ9Vqd3aV2p0rtAP2r6ClF9N1Mf7A0ZUQ6IaGaTRDo7sFHwdcH6UpGQJfRisL7qWC+5uRfWUoTy2V4xhixGpyK8ltZ4RifGmydVVmHDUSJq3NR4orTpXVh+VuPINk4tv5aaexWVReprmiHLnOdBcnYe88vccg+gFCK9MyZgMOu7gs5c6yxeVfXdjMEpFRPF1HuJ5R2wpCdKUP7CEun30NKysHQhrKIsw4X1DSl1LdakEbH3SPDRoOJelXgApWSJ6nux5f1OeUJGlAMiXuJAuZtd+NUqVXHFwxuWu9nVYO6FlUJyiq3oy1xjZ66kU2EEOEshfFaSwxtCWRLnC3Ul9oiY5mXZFcWMxo05FGTmy1wKNUMO5tJtMozKkGVuGEHNUI6S86XebSV6V2IkScEcVhJ2xrKStNd3a+pdSm+s8utFepc3hLXWd9GMOca7zar70sN5OsnfRorM3J+SgH6p89X0FcJThOezPtzKEzKiHBCt60QJv8QkY0QYdb2YMRQkhPWlxIqJYn0BC8uv1aDTOrzBahqLGPEFLIrMxOZIwYxFY3yZKxnOY3pEGLJYutCJjFepdHn5wi9oyWfMqu9mOmAlgeVS50vpTZHeNXYkrPouGlZixFwxh6hU1HfzMYAS8pntjOXDeVIIr8mapaa1vouTrT9UzlpEtcRsfZfyRMlfn9VXiIdRTc8Xtv9FDA+gvXgsFtusdAjqj5LYnHMZd5FyJRvtBXvIqYmJKjFw2HvhNrLyCy3oQOXlFxYb8NexG/D1dNEcE5V7vwh/HbuBpjX8pMvCHM4r+5159wG2ncrE0w2CJK+lZH2S85l5OHT5jmgPObV6338xC5l3H1jgNZOXVWTgEH/8BnzcXcwYr/LnC3V190Extp7KROPqPuZPkEGoi1t5Bdh6MgOd6pfp3dnJ8hgRIak385Ccdgdxjcp2GVCzOGCJwYCDaVm4kfMAnq5OsnnZXmADNp9Ih5ers6qOQFjv8h4UY+eZm2gYKq139hBbWYbbeQVIOJmBDvXK6qiS4Wu9DjDO07x4Kx8HLmah+xNlm7Wrqe/FBgOSL93Btez7CPBy5Y+rGUYtLjEg4WQG3Jz10npn3J/wmvmFxVh36Crqyex/J4dQF1n5hdhyIh1PmdE7q6jCvJdu52Pvhdvo0TiUPyZ8dyyl2FC6KfvlrHuivSqVrM9lpMRgwNaTGXB20rE//CRkCU+5V1i6AXKdwCoVKrCcjCgHRFh9lNSlFxckIed+kWiTU+m2Tf5L5VZeAb8h7L9bVpe9JqshT0q9jXUPp1RLbbYpLpX8zX63IxU/7b2EKm7S1VrJkMO7vx7FzrM3EVurWlk6yxNl5mb7L0zCrbxCkd6Zs5Uk0rPyC/m1Zfq1CjdJF5WFcbf7L2bxU44bhMh3BlIGo/DQor8v4MekS/BwkTYwlAyjTvztKLaeykTryKqS6exA27Jn88qivcjILcArVxl6F/yWKmrO/SJ+Q9hXYuQ3XWbV90OX7/BrOTWt4SubV+pOhUMqy/akYdmeNLiY6SiZ9V1Qnz9cfxzxJ9LRoqZf2fkCAaxV8UufcWmegT/sw7Xs+0gR7CHHnI0qkZ73oBgvfFe6Ofig2AjZsrA4cjWHX26kpeAeJZEQL3zff0q6hGV70sw+ayX1fcrvx/HH0RtoUr2sLugZehceEuYdtGQ/Lt2+J1oyg6V3Ke4VFPObsg9pFyk4T/nQ5fHrufj64TIzbaL85TNLIDS8Vu+/giWCZV4qCjSc54Awl/I3c9y4keuxqzn8MebwgcSx24KVl43rHpmD1aBkCs6/c890HRKmm12Q4/j10vu6a2bTXiVDDsYFFoWbaYoDPi1vyG/llepL2Lip2Tgs+16Z3qUX6xSIZ8jPFKzEfduCzaUfRTi8c+rhxsePrhlVVhh5WcKyGjePFa1ZJdK7vHihYWBcIyhZIIs5dChxLFtQLzNzNdZ3wfm3JN4dJVsPnUkvXW/H3PASexZY2W/jgqaHBWsXiTyvspLEz9C4obRw/SkVk8REa78J9aZmNqqwnbkpscAwC2Gbm3qzdI02s49KQX03bvAuXC9PNHwtadAJwgJEnqjSNblE6+ypqO/5gl0IhOtzqdN72fnS9V32dNG9sjbMthdkRDkg7H2oGENojNgadkxU2RCeJbvdyyGaAsuQJdmgCE5hBjgzysIMcFbQqUi1XUXF8rFsLI8Ia5q2EPb6XPKxOebKZUToEdEaI6IkNkc6BrAsnRUoKzmRguEBVKIrFsJnWMjQm3R9tzweUavehalKOnMjQl1Jr88l7/EuVnCvLEQxgCy9SxwzN6lDCs31XdArS30wi/bWk2xnhPXdNF3czrDaCW1xSEJdFzLbGYl7teK7ZyvIiHJAtK5RJJrZpaIzVtaZy5dFyYwLzY2bghgRKdiduXReqfKxjFfpKd+WG6+shlxRp8IY3mBPLrBcr9LpgrKoCLQtVFDfpWAtkMm6vhBWQL8QqXtVZLyyYgA1zkZlGlEKPopYZdV6r6yAfnG55O/VmrMimXFGkpeSN/oLGbOvlaC1fS9itDOiGaASlypRoHd7QUaUI8L0REn/NlLMmB4vPCTVoIg7YCt2KipeEgNneeOoZJE7NeksvQuNRDWGsPBZaV0Tq0hBpyJZFsaUcSUw9cqY8s3qzFlrO7E8gEKs2Zmrqu+KPhos96xKx/hBNl3kEZGQXyTSu2xRmLDqO3NxWY3rc4n0rvFmWMuysGKihLqQ+u5jLxUj7wE0dy0plHwks1bol0J4efJEEVaDGRPF9IgwOhXGBsfiiq2tcSsUTYGV986whhRsPazEShe2EZLDQkK9q1jOQQirIWevWM7qVIR1QN4jwnLTa/UAir9WlXcqrNXdldizrI8GJXpnLXHA8gDafFhJ5HmVr6/MjzUVywoI0eplZnlEODO/jYg8gCwvsIL6LvXuiT2ApghPkapvrGUJhCjRmxTM+l4srO9Semf0NTScR9gCpvUu7Mwl0os1ekTMyZKCvbeStheD5Z1heYeEsNcrYpVFW2cuuhZrqjyjIWfNKGfvaSV/vhJPFOtelKwUz/JESQ4/W3F9GWvGoqlbfNDy87UO57E8USLjVeIZKurMLVgaQvZ8VjvDiM1REtDPjkXTVt8h0rvE0KJBWN9NTy9kjDQIUbImoBTMsAFRO6HC462gvtsLMqIcEFZVYn0hKlm0kYXml4wZ5CmfzoqJUhIRoKBtk0TUmTMCnNWs/iuEGWjL9ABqM16Fxdc6pMtayoZtvArySqSz9rtTglZvZzHDoGMVz9xCiNJl0fZRIDxfOk5ImNeUQqvWd216F3m8VdR94f1rXaGfhfB01keD1J2z6rvobI2eKNZTFce8qvloEMiimCjCWjC3ixD8lvwyL7b8S4WF1qEcZZ25VEMub0QpWTCQvbs6YzhP5GY3TReWT3J4RL54IrQar0qGR1gBn1KNo9gDqG2YlOUxYQ8rld/wBru+W/6UJWdwKhnO09qZM+KblczOk7T5hUaYxlg0FsxtkhjthOhjTaP3neX10jMULzwk9eoI7485cYi1GLJWvYuG85QH9Itk0XAeYS1YHQFzWImxqaQSbO2JYsEaVrLmjugsTxVzqryC4TwWLONTyXYMamB5AJVsXssybpV4oqSGSsQbxjKuxaixTK+bvHjNU/UVxQCyjH4FcXUsg07qUkLxzA2MZVO1G4xqvE9CRLFoxdruRUk7JL3EgbzxKoTlAdSqN9a7W8TYrkcJFWm/PCFkRDkgbCOq7Lf0sJJgOE+rR0Tj9HYlnQrbIyIhi/E1LYTtpbDcE8WKVVCz9YQQ9pCCthgTUUA/I521oStziEpjByseRrU8rxHWekVCtE6k0Gy8imJEtHUqWjtYViwa61pivVuvvkqez/LeiybTyOdltnmMXpVpyDM9gNJ5pZAqqsj4lT9d87C/kvrOHBqU/EhWWCAbQEaUA6Jk93fW+i6sbQWUTEGVgvWSa/06MbDKquBeWJ0Ky8jiWJaHElkMAWpXrS+Trw2hrlhDk0qMfjUoUDuzvjP1prHCaj9f+reaa7FsMLZ8y/PaeokDJlorvFCU5vpquWdWMhbNwEnmlUJrDCA7dISVLo+S4knl1boOljUgI8oBUdIpsb5EpKd8K/h60Oi9UfKKS36VCRsUiXRWZ29Olqp0oXdGhSzhEWYHJp/MjofR2HiVsPTOWJtJCEuvTF0K5LMafa1T7bX2xYqupcRoV4GSsABWOmuWrfSSHrKnKMrL7KwVaF7ztVgfa4w6KmyzWIHlrA8/9jpPrPgt2WQmzDps9j9Sea1oCVsRMqIcENYXpJKXTGtMFLMzZ52v9auM4YYQd+byZWF11koMIzXeF2XGq3w6K7aH3elYnltrp6PVUyXyrCowdNVg6w6WtUabkmtpNZKYBimjs2ddS3iE7bFW4BZTnqzsA0bjtbR+VCgJHGcvpSKP1nu15geKVoPOVpAR5YAoMYy0NqS2tv61dxTCvLbtNJR1KtrK4kgw64jGL3NFnYrGDkyzp0nj+6To+sx71ZpecTrzirREkNY2y5pDcIpCDFSgdZ0pazZzFagKiCAjygFhNVhaYxWsWVmtOWatvYOTT9dqRAmNWyUGl5GK2khobSg1e0QUdBSqhjcUKL48hxRsPZSi1fARLdiq4lqKPK/WND41otVTxayjCvSipE2ShOUBLNf6rtzrVRHaTDKiHBBrvoRaYb6j5Rj3p7lx0+yJkv6tRhYbbedr/UJVdC1GOmuYVMlzUWO8KqEiORC1ejxY7YSS2X/MZ6g1dlKrQantdGXX0vqxpmBGm9Y2iz2cZ3FRKi1kRDkgWt3wSrB1fICSomqNldbamTO3HVBwL5IL/glF2dhNzu6Ay3Iw41UYsjR7+BQZr9q8K7aeZaaovmuME2Jdih2bwxCgoCxS19IpWEvM3rE9SmSxPafy8pXUMa31Wat8zQjkswzpiuF3MoWMKAfE1sGzyoY35LHmmL2Sjl9NWawZE6UGJWezDUZrdhraCmPrYVQl12KtE8WWr81wUfSMNdrsWo0kJfWZHddmekxREH056p2FVr1rHoJTkFfJ7haS6baOIRRIsKahW56QEeWAKFknyt6Up1dM6/l2N6Ks+DVckQI6bT2Mqqws2s5nfxRoky+SpXnIVj7dmnpnDl9rjAEsV73b2M1rzRnTrLzavWLl19eokVQR+joyohwQmy9eaMXMVl1I0cYzTTQvcaBZ79bzylnXeLWth03zMKpGFBmvtiuGyQVsbfBpjUXTWhYlE2C0el6VYGtHPut9UlLfWXm1eudtXd/Fm1zb+GI2gowoB6Q8p89rdZNrdScreY21Nl6sBQNZ6eWJ1iFda8520jp0aGsPnxKYy2Rorc8ajQVRXpYspt4ZC2SWoxdZ6zIXWq+vyAjTXJ/lz7em3jUvY2HrDxgrtu/24rExoubPn4/IyEi4u7sjJiYG+/fvN5u3U6dO0Ol0Jn+9evXi8wwZMsQkvUePHuVxK0xY2xnZemq0krzsRfIsvpQFZdF2qfIcVtKKrQP+rVkWrTEiWjePZSG8utatUJiLPlrVY6Lt3atI9Z0dolBOBbEAW7cj1jRc1MSqCalIeq+oa+s527sA1mDNmjUYN24cFixYgJiYGHz55Zfo3r07zpw5g6CgIJP869atQ2FhIf//27dvo1mzZujbt68oX48ePbB06VL+/25ubra7CQXYujIJpdt7dofI5a9NlBVWaNZYAAbC4tl6bSUWrO10FMmy8Ze7VkTrTGn0immv75VH7+JraRx2smI7Zeth1Iqkd3t7gZUslixVlIpgVj0WnqgvvvgCw4cPx9ChQ9GoUSMsWLAAnp6eWLJkiWR+f39/hISE8H8JCQnw9PQ0MaLc3NxE+apWrVoet1Oh0Do7Q+vXLGf2PypkafRUlafxqnUYVOvIozUNaVZptcZtWBOtMSLWrO82D4LXeL410T6LTGMBFHTmLLRO/HGk+m7NUQ+t3nV74fBGVGFhIZKTkxEXF8cf0+v1iIuLQ1JSkkUyFi9ejP79+8PLy0t0fMeOHQgKCkL9+vUxatQo3L5926plV4utK5MS74/m2R0M+QYrfplrbbxs3rhZ0ROlOWBU9GWu9RlqTS8/41Wrx8O659u2LHav7wquZeuyiqfaM/JqLiurLOWHvbcFU+IBrKA2lOMP5926dQslJSUIDg4WHQ8ODsbp06eZ5+/fvx/Hjx/H4sWLRcd79OiBf//734iKikJqaio++OADPPPMM0hKSoKTk5OkrIKCAhQUFPD/z83NVXFHFQut7l6tDVJ5Tm/X2tlbE60xIuzYHcb1FbRYWqdB23qITAk2r++M6ytb4FI+XXuAs+Vl0YrN67vG6wvRPkxacawBR4pFq0h6E+LwRpRWFi9ejCZNmqBNmzai4/379+d/N2nSBE2bNkXt2rWxY8cOdOnSRVLW7NmzMW3aNJuWF7B9xRYuHKtknz4ptHaQwutr3UGG+RJqNDw0I7hBrY2b1gZHeH32SsKWy5JCiVfMFugEl7C73kXn21bvdq/vAjQbUVb0iLBX6Nf24Wfv+i7E3sarEJbepYpaEewqhx/OCwgIgJOTEzIyMkTHMzIyEBISIntufn4+Vq9ejWHDhjGvU6tWLQQEBOD8+fNm80yaNAk5OTn835UrVyy7CYXYut4oCXBmTgnXfL4wrzY0T/ct1+E8VlZGQ651eEPBcB4L9tYU9tW7UDq7A7RtWZUM57HQHiRfnsN58ulaDRclaDUcKnp9F2Lv+i66lo0NZVvh8EaUq6sroqOjkZiYyB8zGAxITExEbGys7Lm//vorCgoKMHDgQOZ1rl69itu3byM0NNRsHjc3N/j4+Ij+bIHt+/KyCzBX19XcoLDOL8ugvTOv2J2KSO9a14HS2CApiUWz/bCSjfUuEG/7+m75l7/WZ8i+F8fpzG29R5xIlsZ0zffCkG9NbF3frUlF8DpJ4fBGFACMGzcOixYtwvLly3Hq1CmMGjUK+fn5GDp0KABg0KBBmDRpksl5ixcvRp8+fVCtWjXR8by8PLz77rvYu3cv0tLSkJiYiN69e6NOnTro3r17udyTPRF7ouTz2npqspIgdxas8+3dYCjrzDUOKSh5rhrvW3tnru36SrB5fVdQFq3Gq/aAfkYBrIjm+l6eHhGNZalIxmtF8s476t55j0VMVL9+/XDz5k1MmTIF6enpaN68OeLj4/lg88uXL0OvF9uLZ86cwe7du7FlyxYTeU5OTjh69CiWL1+O7OxshIWFoVu3bpgxY0aFWCuqPN2aWmOiKtKQgq1X37Umto5FY17fiqrSei/lia3re3lC9d02KInjlKIizUJzpPpOgeU2ZsyYMRgzZoxk2o4dO0yO1a9f36zl6+Hhgc2bN1uzeFalPOuSNWNrJNNtfH1bybI19ta7NSG924byfHdsjfbFYa1UEAvQ6omqSDxO9d1ePBbDeZUNW1cmoXytL4nW6e9KYkRYVPS2zZp6t+Y0a83DqBVc70JsX9+VlMW2Q+UVCVvrXRnahmRJ7+pg67ViKpaMKMKmVKQtDioTj1MH60iU5/AGPcMyynWTatI7jzXre0WKjVQCGVGOSDlOtdeKNSt+RZ3iai2s+aVlVb1Tr2Ex1q3vhKVYc49qqu6WY836buN9xm0GGVEOiO2H88pvxoUyWVYTVSGx6oBEObrZiTKovtsHqu/2wbr13TE1T0YUYVOs6WbXuHB2pcKqereapMcfqu/2oaJ+gDzulGdMVEWFjCgHxOajeRV0OK+ijolbC2vq3bqxClYT9dhTnktDEGVYs75XpGUqKjrWHc5zTL2TEeWA2Do2qKKuzURfiJZjXUOY9G4p1q3vVhP12FOe248QZVizvjtqO0NGlAPiQHHl1LjZCXKz2wfSu32gjwb7QO07GVGEBFYdVqJOxS5YdcE/UrzFWHVxWAcd3rAHVh3Oc9Te3A5QfScjyiFxpKpm3ZgoR7pz+0J6tw+kd/tg1RAEUrvFUH0nI8ohcaS6RlO+7QPFKtgH6671RXq3lIoax/m4Q/WdjCiHxJEWnXRUF62jQ1/m9oFmRdoH8ojYB6rvZEQRNsZRXwxHh/RuH6gztw/00WAfqL6TEeWYOGZdIwhCAQ7ap9gF0pXj46jPkIwoB8RB6xpBEARBPFaQEeWA0KKTBEEQBGF/yIgiCIIgCIJQARlRDgg5ogiCIAjC/pAR5YCQDUUQBEEQ9oeMKAeEPFEEQRAEYX/IiCIIgiAIglABGVEOiCOtWE4QBEEQjytkRDkgNJxHEARBEPaHjCiCIAiCIAgVkBFFEARBEAShAjKiHBBasZwgCIIg7A8ZUQ4ImVAEQRAEYX/IiHJAyBFFEARBEPaHjCiCIAiCIAgVkBHlgNA6UQRBEARhf8iIckBoOI8gCIIg7M9jY0TNnz8fkZGRcHd3R0xMDPbv328277Jly6DT6UR/7u7uojwcx2HKlCkIDQ2Fh4cH4uLicO7cOVvfhkWQDUUQBEEQ9uexMKLWrFmDcePG4aOPPsKhQ4fQrFkzdO/eHZmZmWbP8fHxwY0bN/i/S5cuidLnzJmDr7/+GgsWLMC+ffvg5eWF7t2748GDB7a+HYIgCIIgHIDHwoj64osvMHz4cAwdOhSNGjXCggUL4OnpiSVLlpg9R6fTISQkhP8LDg7m0ziOw5dffon/+7//Q+/evdG0aVP8+OOPuH79OjZs2FAOdyQPDecRBEEQhP1xeCOqsLAQycnJiIuL44/p9XrExcUhKSnJ7Hl5eXmIiIhAeHg4evfujRMnTvBpFy9eRHp6ukimr68vYmJiZGWWH2RFEQRBEIS9cXgj6tatWygpKRF5kgAgODgY6enpkufUr18fS5Yswe+//44VK1bAYDCgXbt2uHr1KgDw5ymRCQAFBQXIzc0V/REEQRAE8Xji8EaUGmJjYzFo0CA0b94cHTt2xLp16xAYGIjvv/9ek9zZs2fD19eX/wsPD7dSicXQcB5BEARB2B+HN6ICAgLg5OSEjIwM0fGMjAyEhIRYJMPFxQUtWrTA+fPnAYA/T6nMSZMmIScnh/+7cuWKkluxGDKiCIIgCML+OLwR5erqiujoaCQmJvLHDAYDEhMTERsba5GMkpISHDt2DKGhoQCAqKgohISEiGTm5uZi3759sjLd3Nzg4+Mj+rMFtNgmQRAEQdgfZ3sXwBqMGzcOgwcPRqtWrdCmTRt8+eWXyM/Px9ChQwEAgwYNQvXq1TF79mwAwPTp09G2bVvUqVMH2dnZmDt3Li5duoTXX38dQOnMvbFjx+Ljjz9G3bp1ERUVhcmTJyMsLAx9+vSx120SBEEQBFGBeCyMqH79+uHmzZuYMmUK0tPT0bx5c8THx/OB4ZcvX4ZeX+Z0u3PnDoYPH4709HRUrVoV0dHR2LNnDxo1asTnee+995Cfn48RI0YgOzsb7du3R3x8vMminPaAhvMIgiAIwv7oOI66ZFuRm5sLX19f5OTkWHVob8KvR7A2+arV5BEEQRCEI5L2SS+byLW0/3b4mKjKCJm9BEEQBGF/yIgiCIIgCIJQARlRDgjNziMIgiAI+0NGlCNCNhRBEARB2B0yohwQsqEIgiAIwv6QEUUQBEEQBKECMqIcEFqVgiAIgiDsDxlRDgiZUARBEARhf8iIckDIEUUQBEEQ9oeMKIIgCIIgCBWQEeWAkCOKIAiCIOwPGVEOCAWWEwRBEIT9+0MyohwQMqEIgiAIwv4xwmREEQRBEAThkNjbqUBGlCNi71pDEARBEBUAGs4jFEMbEBMEQRCE/X0KZEQ5IPYeAyYIgiAIgowogiAIgiAcFHs7FciIckDsXWkIgiAIoiJg7/AWMqIcEHtXGoIgCIIgyIgiCIIgCMJBsffIDBlRDoi9Kw1BEARBEGREOSRkQxEEQRCE/SEjygEhTxRBEARB2L8/JCOKIAiCIAiHxN4TrciIckjIFUUQBEEQ9oaMKAfE3u5LgiAIgqgI2Ls/JCPKASEbiiAIgiDs3x86azn59u3bWLFiBfbv349bt26hS5cueO+99wAAJ06cQGpqKuLi4uDp6WmVwhIEQRAEQVQUVBtRv/76K15//XXk5eWB4zjodDpUr16dT7927Rqef/55LF++HAMHDrRKYYlSOHv7LwmCIAiiAmDv/lDVcF5SUhJeeeUVODs74/PPP8f+/ftNbqRLly7w9fXFunXrrFJQogwyoQiCIAjC/v2hKk/UrFmzoNfrkZCQgJYtW0rmcXJyQsuWLXH8+HFNBSRMIUcUQRAEQdgfVZ6oPXv2IDY21qwBZSQkJAQ3btxQVTClzJ8/H5GRkXB3d0dMTAz2799vNu+iRYvw1FNPoWrVqqhatSri4uJM8g8ZMgQ6nU7016NHD1vfBkEQBEEQFmJvp4IqI+revXsIDAxk5rtz544a8YpZs2YNxo0bh48++giHDh1Cs2bN0L17d2RmZkrm37FjB15++WVs374dSUlJCA8PR7du3XDt2jVRvh49euDGjRv8388//1wet8OEHFEEQRAEAbt3iKqMqOrVq+PEiROyeTiOw/HjxxEVFaWqYEr44osvMHz4cAwdOhSNGjXCggUL4OnpiSVLlkjmX7lyJd544w00b94cDRo0wA8//ACDwYDExERRPjc3N4SEhPB/VatWtfm9WIK9A+kIgiAIglBpRPXo0QNnzpzB6tWrzeb54YcfcOXKFfTq1Ut14SyhsLAQycnJiIuL44/p9XrExcUhKSnJIhn37t1DUVER/P39Rcd37NiBoKAg1K9fH6NGjcLt27dl5RQUFCA3N1f0RxAEQRCEbXDIbV8mTpwIX19fDBo0CO+//z727t0LAMjPz8fhw4cxZcoUvPnmmwgMDMQ777xj1QI/yq1bt1BSUoLg4GDR8eDgYKSnp1sk4/3330dYWJjIEOvRowd+/PFHJCYm4tNPP8XOnTvxzDPPoKSkxKyc2bNnw9fXl/8LDw9Xd1MEQRAEQTCx98CMqtl5NWrUwJ9//okXXngBc+fOxWeffQadToe1a9di7dq14DgOQUFB+P333xEUFGTtMluVTz75BKtXr8aOHTvg7u7OH+/fvz//u0mTJmjatClq166NHTt2oEuXLpKyJk2ahHHjxvH/z83NtYkhZe9KQxAEQRCEhsU2Y2NjcebMGSxevBgJCQlIS0uDwWBAjRo10LVrV/znP/+Br6+vNcsqSUBAAJycnJCRkSE6npGRgZCQENlzP/vsM3zyySfYunUrmjZtKpu3Vq1aCAgIwPnz580aUW5ubnBzc1N2Ayqwt/uSIAiCICoC9u4NNW37UqVKFYwdOxZjx461UnGU4+rqiujoaCQmJqJPnz4AwAeJjxkzxux5c+bMwcyZM7F582a0atWKeZ2rV6/i9u3bCA0NtVbRVUOeKIIgCIKw/0Srx2ID4nHjxmHRokVYvnw5Tp06hVGjRiE/Px9Dhw4FAAwaNAiTJk3i83/66aeYPHkylixZgsjISKSnpyM9PR15eXkAgLy8PLz77rvYu3cv0tLSkJiYiN69e6NOnTro3r27Xe6RIAiCIIiKhUWeqMuXL2u6SM2aNTWdz6Jfv364efMmpkyZgvT0dDRv3hzx8fF8sPnly5eh15fZi9999x0KCwvx4osviuR89NFHmDp1KpycnHD06FEsX74c2dnZCAsLQ7du3TBjxoxyGa5jQZ4ogiAIgrD/cJ6Os8AXptfrodPp1F1Ap0NxcbGqcx2d3Nxc+Pr6IicnBz4+PlaT239hEvZeyLKaPIIgCIJwRA58GIfAKtZ3bljaf1vkierQoYOJEVVQUMAvbVC1alVEREQAKPX6ZGVlQafTISYmpkJ4bh43yBNFEARBEPafaGWREbVjxw7R/+/evYvOnTujcePGmDt3rkmc0JYtW/Dee++hqKgImzdvtlphCYIgCIIgKgqqAssnT56M1NRUbNu2TTLQulu3bti6dStSU1Px4Ycfai4kIYYcUQRBEAQBu3eIqoyodevWoXPnzggICDCbJyAgAJ07d8b69etVF44wA1lRBEEQBGH37lCVEXXz5k2LgsWLi4tx69YtNZcgZLD3GDBBEARBECqNqMjISCQmJuLKlStm81y5cgWJiYmIjIxUWzaCIAiCIAiz2HuilSojatiwYcjPz0fHjh3x448/4sGDB3xaQUEBfvrpJ3Ts2BH37t3DsGHDrFZYohR7VxqCIAiCqAjYe2RG1bYv48aNQ3JyMtasWYOhQ4di6NChCAwMBFA61AeULsXet29f0Ya8hHUgG4ogCIIg7I8qT5Rer8fPP/+Mn3/+Ge3bt4eLiwsyMzORmZkJFxcXtG/fHqtWrcKaNWtEK4UTBEEQBEFYC3uPzGjagLhfv37o168fiouLcfv2bQBAtWrV4OysSSzBwN4bLhIEQRBERcDevaFVrB1nZ2d+nzrC9ti70hAEQRAEoXI4j7Av5IgiCIIgCPuPzKjyRNWqVcvivDqdDqmpqWouQxAEQRAEYRZ7OxVUGVFpaWlWLgahBHJEEQRBEIT9UTWcZzAYJP9KSkqQlpaGhQsXIjQ0FO+++y4MBoO1y0zY2/QmCIIgCMI6geVGdDodatasiddffx2tWrVCbGws6tSpg+HDh1vzMpUeMqEIgiAIwv4+BZsFljdv3hxt2rTBvHnzbHUJgiAIgiAIu2HT2XkBAQE4f/68LS9RKbG35U0QBEEQFQF7b/tiMyMqKysL//zzD/z8/Gx1iUqLvSsNQRAEQVQE7O1UUBUTtWvXLrNpeXl5OHv2LL777jvcvHkTI0eOVF04Qhp7VxqCIAiCIFQaUZ06dYJOp5PNw3EcOnbsiE8++URVwQiCIAiCIOSwt09BlRE1aNAgs0aUq6srQkND0bFjRzz99NOaCkdIQ54ogiAIgnDQFcuXLVtm5WIQSiAbiiAIgiDsj6rA8suXLyMrK4uZ786dO7h8+bKaSxAy2NvyJgiCIIiKgL17Q1VGVFRUFN59911mvvfee0/RPnsEQRAEQRCWYm+fgiojiuM4i70h5DUhCIIgCOJxxKaLbd66dQseHh62vESlhOxSgiAIggDsPaBncWD5o2tDpaenm10vqri4GGfOnMHmzZvxxBNPaCshYQIttkkQBEEQ9ncqWGxEPbo21ObNm7F582az+TmOg06nw/jx47WVkCAIgiAIQgJ7uxQsNqKEa0MtX74ctWvXxpNPPimZ19XVFWFhYXj22WfRsmVL65SU4LG35U0QBEEQhAIjSrg21PLly9G+fXssWbLEFmUiGJANRRAEQRD2dyqoCiw3GAwVzoCaP38+IiMj4e7ujpiYGOzfv182/6+//ooGDRrA3d0dTZo0waZNm0TpHMdhypQpCA0NhYeHB+Li4nDu3Dlb3oLF0IxHgiAIgrB/jLBNZ+eVF2vWrMG4cePw0Ucf4dChQ2jWrBm6d++OzMxMyfx79uzByy+/jGHDhuHw4cPo06cP+vTpg+PHj/N55syZg6+//hoLFizAvn374OXlhe7du+PBgwfldVsEQRAEQVRgdJwFbo3p06dDp9Nh9OjR8Pf3x/Tp0y2/gE6HyZMnayoki5iYGLRu3RrffPMNgFJPWXh4ON58801MnDjRJH+/fv2Qn5+PP/74gz/Wtm1bNG/eHAsWLADHcQgLC8P48eMxYcIEAEBOTg6Cg4OxbNky9O/f36Jy5ebmwtfXFzk5OfDx8bHCnZbS+fMduHAz32ryCIIgCMIR+evtp9Aw1Hr9qxFL+2+LYqKmTp0KnU6Hfv36wd/fn/+/JcNKtjaiCgsLkZycjEmTJvHH9Ho94uLikJSUJHlOUlISxo0bJzrWvXt3bNiwAQBw8eJFpKenIy4ujk/39fVFTEwMkpKSzBpRBQUFKCgo4P+fm5ur9rbkodE8giAIgrB7TJRFRtTSpUsBAKGhoaL/VwRu3bqFkpISBAcHi44HBwfj9OnTkuekp6dL5k9PT+fTjcfM5ZFi9uzZmDZtmuJ7UIqr82MxCksQBEEQDo1FRtTgwYNl/0+UMmnSJJGHKzc3F+Hh4Va/TvzYDvhvwll8lVgxAt0JgiAIwh5QYLlGAgIC4OTkhIyMDNHxjIwMhISESJ4TEhIim9/4rxKZAODm5gYfHx/RH0EQBEEQtsHew3kOb0S5uroiOjoaiYmJ/DGDwYDExETExsZKnhMbGyvKDwAJCQl8/qioKISEhIjy5ObmYt++fWZlljeCxeMJgiAIgrADFg3nvfbaa6ovoNPpsHjxYtXnW8K4ceMwePBgtGrVCm3atMGXX36J/Px8DB06FEDpauvVq1fH7NmzAQBvv/02OnbsiM8//xy9evXC6tWrcfDgQSxcuJAv89ixY/Hxxx+jbt26iIqKwuTJkxEWFoY+ffrY9F4sRQeyogiCIAjCnlhkRAlXK1dKeRhR/fr1w82bNzFlyhSkp6ejefPmiI+P5wPDL1++DL2+zOnWrl07rFq1Cv/3f/+HDz74AHXr1sWGDRvQuHFjPs97772H/Px8jBgxAtnZ2Wjfvj3i4+Ph7u5u03shCIIgCMIy7D2cZ9E6UTt37tR0kY4dO2o631Gx1TpRAPB14jl8kXDWqjIJgiAIwpHYOKY9mtTwtbpcq64TVVmNoIoMDeYRBEEQlR2anUcQBEEQBKECew/nWeSJkmPPnj34+++/cf36dQBAWFgY2rdvjyeffFJz4Qjz0Ow8giAIgrAvqo2oY8eOYciQIUhJSQEAfgsY3cPevVmzZli2bBmaNm2qvZSECTqyogiCIIhKjr13QVNlRJ05cwYdO3ZEdnY2atSogRdffBGRkZEAgEuXLuG3335DSkoKOnXqhD179qBBgwbWLDNBEARBEIRFe/jaElVG1AcffIDs7GxMnDgR06dPh7OzWMycOXMwZcoUzJ49Gx9++CF+++03qxSWIAiCIAiioqAqsHz79u144oknMGvWLBMDCgCcnJwwc+ZMPPHEE9i+fbvmQhKm0GgeQRAEUdmx93CeKiOqqKjIolinpk2boqioSM0lCIIgCIIgZLH37DxVRlSzZs2QmprKzJeamopmzZqpuQTBgLZ9IQiCIAj7osqI+vDDD3HgwAEsWbLEbJ6lS5fiwIED+OCDD1QXjjAPDecRBEEQhAMGlnt5eWHUqFEYPnw4li1bhn79+iEiIgJA6ey8X375Bbt378aoUaPg7e2NXbt2ic7v0KGD9pITBEEQBFGpsfdwnkV75z2KXq+HTqczWRvKiLnjRkpKSpRe0iGx5d553+9Mxey/TltVJkEQBEE4EmtHxqJVpL/V5Vp177xHGTRoEC32aGdI/QRBEERlx96z81QZUcuWLbNyMQiCIAiCIJRh7+E82oDYQaHZeQRBEARhX8iIclBoOI8gCIKo7Djkti8AUFBQgNWrV2Pnzp24ceMGCgoKJPPpdDokJiaqLiBBEARBEIQUDhkTdfnyZcTFxSE1NZVpBVIAOkEQBEEQtsDeMVGqjKi33noL58+fR+fOnfH222+jVq1a8Pb2tnbZCBnIOCUIgiAI+6LKiEpMTETdunURHx8vuQExQRAEQRCEreHsPKCnKrDc1dUVLVu2JAPKjpAfiiAIgqj0OOISB+3atcP58+etXRZCATSaRxAEQRD2RZURNW3aNJw6dQqLFi2ydnkIgiAIgiAswiFn57Vs2RJbtmzBwIEDsWLFCnTr1g3Vq1eHXi9tkw0aNEhTIQlTyBFFEARBVHYccnYeACQkJCAzMxNpaWnYvXu3ZB6O46DT6ciIsgE0O48gCIIg7IsqI2ru3LmYNm0a3Nzc8Pzzz9MSBwRBEARBlDv2np2nyoj67rvv4OPjg3379qF+/frWLhNhAeSIIgiCICo79h7OUxVYnp6ejo4dO5IBZUfIhiIIgiAI+6LKiKpduzYMBoO1y0IQBEEQBGEx9p6dp8qIGjlyJLZv3460tDQrF4ewGBrPIwiCICo5rP17bY0qI2r06NEYMWIEnnrqKSxbtgzXrl2zdrkIBmRCEQRBEIR9UWVEOTk54auvvsK1a9cwbNgw1KxZE05OTpJ/tt4aJisrCwMGDICPjw/8/PwwbNgw5OXlyeZ/8803Ub9+fXh4eKBmzZp46623kJOTI8qn0+lM/lavXm3TeyEIgiAIwnLsPZynysIJDw+3aJ0i4zpRtmTAgAG4ceMGEhISUFRUhKFDh2LEiBFYtWqVZP7r16/j+vXr+Oyzz9CoUSNcunQJI0eOxPXr17F27VpR3qVLl6JHjx78//38/Gx5K4qg0TyCIAii0uOIi20qiYWyZQD6qVOnEB8fjwMHDqBVq1YAgHnz5qFnz5747LPPEBYWZnJO48aN8dtvv/H/r127NmbOnImBAweiuLhY5Dnz8/NDSEiIzcqvBR0N6BEEQRCEXVE1nGcJhw8fxrhx41CjRg1bXQJJSUnw8/PjDSgAiIuLg16vx759+yyWk5OTAx8fH5Ohx9GjRyMgIABt2rTBkiVLmAFsBQUFyM3NFf0RBEEQBGEbHHKxTXNcuXIFK1euxIoVK3Dq1CmbD+elp6cjKChIdMzZ2Rn+/v5IT0+3SMatW7cwY8YMjBgxQnR8+vTp6Ny5Mzw9PbFlyxa88cYbyMvLw1tvvWVW1uzZszFt2jTlN6ICGs4jCIIgKjv2XmxTsxF19+5d/Prrr1ixYgV27doFjuPAcRyqV6+Ofv364eWXX1Ysc+LEifj0009l85w6dUptkXlyc3PRq1cvNGrUCFOnThWlTZ48mf/dokUL5OfnY+7cubJG1KRJkzBu3DiR/PDwcM3llIJsKIIgCIKwL6qMqJKSEsTHx+Onn37Cxo0b8eDBA36oS6fTYceOHXjqqadUe6HGjx+PIUOGyOapVasWQkJCkJmZKTpeXFyMrKwsZizT3bt30aNHD1SpUgXr16+Hi4uLbP6YmBjMmDEDBQUFcHNzk8zj5uZmNo0gCIIgCOviUJ6oAwcO4KeffsKaNWtw69YtcBwHFxcXPPfccxg4cCDmzJmDgwcPokOHDpoKFRgYiMDAQGa+2NhYZGdnIzk5GdHR0QCAbdu2wWAwICYmxux5ubm56N69O9zc3PC///0P7u7uzGulpKSgatWqFcZIouE8giAIorLjEEscfPzxx1i5ciXOnj3Le5zatWuHgQMH4qWXXoK/vz8A4Msvv7RZQaVo2LAhevTogeHDh2PBggUoKirCmDFj0L9/f35m3rVr19ClSxf8+OOPaNOmDXJzc9GtWzfcu3cPK1asEAWABwYGwsnJCRs3bkRGRgbatm0Ld3d3JCQkYNasWZgwYUK53p8cNDuPIAiCIOyLRUbUlClToNPpEBISgjfeeAMDBgxAZGSkjYtmGStXrsSYMWPQpUsX6PV6vPDCC/j666/59KKiIpw5cwb37t0DABw6dIifuVenTh2RrIsXLyIyMhIuLi6YP38+3nnnHXAchzp16uCLL77A8OHDy+/GWJANRRAEQVRy7L3ti8XDeRzHIT09HZs3b0ZQUBD69u1bIRaf9Pf3N7uwJgBERkaKlNypUyem0nv06CFaZJMgCIIgiIqHvYfzLFonat++fRg9ejSqVauG3bt3Y+TIkQgNDcULL7yAdevWoaioyNblJB6BHFEEQRAEYV8sMqJat26NefPm4fr16/j999/x4osvQqfTYf369ejbty9CQ0Pxn//8BxkZGbYuL/EQW2+nQxAEQRAVHXvPzlO0YrmzszOeffZZrFmzBunp6Vi0aBGeeuop3LlzB4sWLUJqaiqA0nWeUlJSbFFegiAIgiCIh9jXilK97YuPjw+GDRuGHTt2IC0tDTNnzkSDBg3AcRzmzp2L6OhoNGzYEDNmzLBmeYmHkB+KIAiCqOw4lCfKHOHh4Zg0aRJOnDiBgwcP4q233kJQUBDOnDljshI4YR1oNI8gCIIg7IvVNyBu2bIl/vvf/+LatWv4888/0b9/f2tfgiAIgiAIwu6z86y6AbEQvV6PZ555Bs8884ytLlGpIU8UQRAEUdl5LIbziPKHViwnCIIgCPtCRhRBEARBEA4J56iz8wj7QsN5BEEQRGWHhvMIgiAIgiAcEDKiCIIgCIJwSOw9O4+MKAeFtn0hCIIgKjucncfzyIhyUMiEIgiCIAj7QkaUg0KOKIIgCIKwL2REOSi0ThRBEARR2aHZeYQqyBNFEARBEPaFjCgHhWwogiAIorJDi20SBEEQBEGogIbzCFXQcB5BEARB2BcyohwWsqIIgiCIyg15oghVkCeKIAiCqOzQiuWEKsiGIgiCIAj7QkaUg0LbvhAEQRCVHdr2hVAFmVAEQRBEZYeG8whVkCOKIAiCIOwLGVEOChlRBEEQRKWHZucRaqC98wiCIIjKDq1YTqiDbCiCIAiCsCtkRDkoZEMRBEEQlR1abJNQBS1xQBAEQVR2aHaeRrKysjBgwAD4+PjAz88Pw4YNQ15enuw5nTp1gk6nE/2NHDlSlOfy5cvo1asXPD09ERQUhHfffRfFxcW2vBVFkAlFEARBEPbF2d4F0MqAAQNw48YNJCQkoKioCEOHDsWIESOwatUq2fOGDx+O6dOn8//39PTkf5eUlKBXr14ICQnBnj17cOPGDQwaNAguLi6YNWuWze5FCeSIIgiCICo7BjuP5zm0EXXq1CnEx8fjwIEDaNWqFQBg3rx56NmzJz777DOEhYWZPdfT0xMhISGSaVu2bMHJkyexdetWBAcHo3nz5pgxYwbef/99TJ06Fa6urja5HyXQ7DyCIAiiskMxURpISkqCn58fb0ABQFxcHPR6Pfbt2yd77sqVKxEQEIDGjRtj0qRJuHfvnkhukyZNEBwczB/r3r07cnNzceLECbMyCwoKkJubK/qzFeSJIgiCICo79o6JcmhPVHp6OoKCgkTHnJ2d4e/vj/T0dLPnvfLKK4iIiEBYWBiOHj2K999/H2fOnMG6det4uUIDCgD/fzm5s2fPxrRp09TejiLIhiIIgiAqPTScZ8rEiRPx6aefyuY5deqUavkjRozgfzdp0gShoaHo0qULUlNTUbt2bdVyJ02ahHHjxvH/z83NRXh4uGp5spAVRRAEQVRyyBMlwfjx4zFkyBDZPLVq1UJISAgyMzNFx4uLi5GVlWU23kmKmJgYAMD58+dRu3ZthISEYP/+/aI8GRkZACAr183NDW5ubhZfVwsUE0UQBEFUduwdE1UhjajAwEAEBgYy88XGxiI7OxvJycmIjo4GAGzbtg0Gg4E3jCwhJSUFABAaGsrLnTlzJjIzM/nhwoSEBPj4+KBRo0YK78Y2UEwUQRAEUdnh7GxFOXRgecOGDdGjRw8MHz4c+/fvxz///IMxY8agf//+/My8a9euoUGDBrxnKTU1FTNmzEBycjLS0tLwv//9D4MGDUKHDh3QtGlTAEC3bt3QqFEjvPrqqzhy5Ag2b96M//u//8Po0aPLzdPEgmwogiAIorJj7+E8hzaigNJZdg0aNECXLl3Qs2dPtG/fHgsXLuTTi4qKcObMGX72naurK7Zu3Ypu3bqhQYMGGD9+PF544QVs3LiRP8fJyQl//PEHnJycEBsbi4EDB2LQoEGidaXsDa1YThAEQVR2DDScpw1/f3/ZhTUjIyNF7r7w8HDs3LmTKTciIgKbNm2yShltAdlQBEEQRGWHhvMIVZANRRAEQRD2hYwoB4U8UQRBEERlx96z88iIcljIiiIIgiAqN5ydQ8vJiHJQyBNFEARBVHbIE0WogmwogiAIorJDSxwQqqAlDgiCIIjKjoFm5xFqIBOKIAiCqOzQcB6hCnJEEQRBEIR9ISPKQaENiAmCIIjKDi22SaiCPFEEQRBEZYeG8wiCIAiCIFRAs/MIVZAniiAIgqjskCeKUAXFRBEEQRCVHVrigFAFeaIIgiCIyg4N5xGqICOKIAiCqPSQJ4pQAw3nEQRBEJUd8kQRqiBPFPG4Ys26Te8JUdGh+q4NCiwnVFEJ35VyR09Ktgt6K/YE1pT1uGPN+k7vjuVQfdcGZ2dfFBlRDkolfFfKHSfqCeyCk1U7FauJeuyxZn2nd8dyqL5rgzxRhEoq4dtSzuiocbML1h3eIMVbijV1RXq3HKrv2jCQEUWooRK+K+WOdYc36IFZinWHN6wm6rHHmrqypnflcYfquzZoOI9Qhdp3hdo2y7FmR0B6txxrDgXRLFbLoWEl+2DVYdTK2NCQJ4ooT8gjYjnW1FVldLOrxZqqos7ccijA2T5Yt75XPr3TEgeEKtR2yo97p2LVITgrCnvc9W5NrPllXhk7FbVYtb5ThbcYq9b3Sqh3jhbbJNSgejiPhjcsxprtEendcqxq+JDaLYaWOLAPFBOlDZqdR6iCPrClse6MOoqJsgeVsSOoCNDwtX2gCSzaoOE8QhWqPRuP+TtmzduzZkdg768lR8KqU+2tJunxx7r1nSq8pVj1w68SfoEYaDiPUEMl/OAodyphe1QhIL3bB9K7fbBu2EDlw972OhlRjwFkUNkGlmtcid7pGVmONfVOWE55DueRwVZGeQ7B0btjfciIclCEL4OSTkfrO1TRX0LRvWosK2vWjJLGj/RuOcz6rEAWqzNnlbWi612Ireu7EliSHidD2db1XQksUY+T3o3V1d5Dx2REOSjCzoFV7627iJ7jvGVa75vZwbKuL+iUtMY9VPRF9ISl01pWfTm2SuX57tgaW9d3a8pipVcmvVN9V4exfbV39J3DG1FZWVkYMGAAfHx84Ofnh2HDhiEvL89s/rS0NOh0Osm/X3/9lc8nlb569eryuCWLEFZ15teHqDPXdl3Wxyr7S0jb9VkIA+5ZQZbMhlzj+eW5ZlVF8qiwysrSS0VaKV6r3stz2MrW9d2asCbGUH1XB0uvSvoKNeeXq94fXszeMVHO9r28dgYMGIAbN24gISEBRUVFGDp0KEaMGIFVq1ZJ5g8PD8eNGzdExxYuXIi5c+fimWeeER1funQpevTowf/fz8/P6uVXi3j4RAc5e9y6nZL8teRTH55fTrWedd96nQ4lMmVhu74ZumCkK4F1L0y9W60kbCzRu9yMGov0LnO+NauYZr0/RvXdmmj1RFWm+m5NtPYVFUrvvCfKvlaUQxtRp06dQnx8PA4cOIBWrVoBAObNm4eePXvis88+Q1hYmMk5Tk5OCAkJER1bv349XnrpJXh7e4uO+/n5meStKIg8LuX4hWlJrIN8gwGUWK00EggKyI5pki+LVi+DMF3rE7AkPovVUNtyKrDQWHC2KJZM3giSvRajLE46HYotvFeWkaNd7zau7wJsXd+Voc2jYUkdsmd9F2Lr+m5NtPYV5alXFkaDz97FcejhvKSkJPj5+fEGFADExcVBr9dj3759FslITk5GSkoKhg0bZpI2evRoBAQEoE2bNliyZAkzgK2goAC5ubmiP1shDha3vMHSunI266vJMu+N7RBKZw89ymdgf5Up0IXG29YaMGrrr12R3pmKl0/WavQL75Ulif1RYPm1JOWXYw9p8/qu5KPBxsOo9q7vomvZub6LLsUsirahx4pU342XMtBwnnrS09MRFBQkOubs7Ax/f3+kp6dbJGPx4sVo2LAh2rVrJzo+ffp0dO7cGZ6entiyZQveeOMN5OXl4a233jIra/bs2Zg2bZryG1GBsKoq6WC11nGtY+LlGSPCjGlinK+1IVbSmbNgGgN2jlUQylfioZNO11ZY06Fu9VSkuDgWFaG+G70UWm9ba+xlRfLu2Lq+C9HaTlijvpe/55Vm55kwceJEs8Hfxr/Tp09rvs79+/exatUqSS/U5MmT8eSTT6JFixZ4//338d5772Hu3Lmy8iZNmoScnBz+78qVK5rLaBZRp2X514X2xk3bS2brPeSE19e6RIE1g/BZr7mjB3wKnbTa9a61Ibee8aq1UynPPRPtXt8F12fWd4ZeNOu9AhmvNvcCK/hoYJXFsep7qfli7+G8CumJGj9+PIYMGSKbp1atWggJCUFmZqboeHFxMbKysiyKZVq7di3u3buHQYMGMfPGxMRgxowZKCgogJubm2QeNzc3s2nWRlhZtX5dKLqu5mElqxWFidayaO10lOhdB/mOR+vwRbkOb2guq7x8HUNbivSucQisUtV3yNdRJRNYKvrwtBLsXVZlMYDy6fa+FyU4PXQBrT5wBZOeaQhfTxe7lKNCGlGBgYEIDAxk5ouNjUV2djaSk5MRHR0NANi2bRsMBgNiYmKY5y9evBjPPfecRddKSUlB1apVy81IYqEs3KYc3cWM9Io0A4jZQTL8tGzXueUeEeaMM9b5jPTy/Bq2+bCStmRxXlbQL+t8Rnp51ne2F0FjfWfVUQVtEivd3kOTStBaVq0ouVVb692aN8uobqJnfDu/gIwoNTRs2BA9evTA8OHDsWDBAhQVFWHMmDHo378/PzPv2rVr6NKlC3788Ue0adOGP/f8+fPYtWsXNm3aZCJ348aNyMjIQNu2beHu7o6EhATMmjULEyZMKLd7Y2Gv7wFmcCrLtV2B9ntgNijWjFXQHOejUe82VrvQO6TVCKpQdUSjt9HW9yLsaLSu9G7NoRjtQ+Fa63vF8bwyjX6tbYOChQW0e1YZz8WKeleyJIc9PWQObUQBwMqVKzFmzBh06dIFer0eL7zwAr7++ms+vaioCGfOnMG9e/dE5y1ZsgQ1atRAt27dTGS6uLhg/vz5eOedd8BxHOrUqYMvvvgCw4cPt/n9WIqSF8+agbasPsHZSf5zljUdWCuiYU6NLgvtjZtQlqaisKeEs/TOSNeMqI7JZ7X5EJiCsrBgldX+9b2s+7R3HJGovjNnDGvzirH0XpEC+q25jZFtBJSh9aNA6VC6nKfJSadDiYVLQ5TnQrGP4vBGlL+/v9mFNQEgMjJScmmCWbNmYdasWZLn9OjRQ7TIZkVE3EFb/pJqrWpavxAt8ZhombKqU9CBanZtW9VTJZ+udX0XezYyj2KN2BzL0207LGRvvQuH2Jh61ThUY8070ap3rc9FCU56HUpkGiWtXmBrOlG0Gspa70WJ2lmxXEo+xuzpva6Qs/MINkqMBeteV5s7tyK54R1pp3mtbvSKtCeW9qFNKxXEAlnMTqccO3OtMX4VpwawYRtJ8udbc3KBVsOhPNdOYsE2XuXTrfnRoNWrJUy2Z1tNRpSDoiR+oTw7He2eKCt2OlrPt2Jnb8URKkm06t2qQzlW/FpVQ3l6CK1Zn7UOg2r17mhFNJHCivVJCmvOImMvMqpxaNLW9d2KsrR635UYUZZsl2Npuj0/EsmIclQU1BmRx9TGjZuzk3wOVoyINRtfrWszWXN4g+0l0NZ4MfXOSFd2L5YdM3++bT0mwuF77fVJm16t2alIpSur79rcbtacBcaOR5RPt+ZwHtMjwrgWaycLreueKXu3LM8reT5r/S5rGq9WHAal4TxCMdb0cpiTqyZda2Ci9tWqFXz5a7qSMgG27lS0DucpWdVeujO3nt7Zxq01vZXaOjhregCZ74ZEa63kddHazyjzfmszlLXPRrWi8SpxLWvqnVkHLb8UE60GnVWH8zReSyyLjChCIeLhE8tfDK3eFa2xOezNOgVlUfGSWdM7xDxfQQ72Cs6MdBs3bkpc41J1QMlO6pq/lpkGp8Ag1ChLa31XEkRv67g2zf2MAs+tZu8MoyjW7IDVDHULb0/r6uwslH0Y2tZ4Zc2atK4niuEBFMoiI4pQijXjPhS5TTV+ITox3kJlnbnpMSuOXNp5RyYxNp8VqRf+1taQPk7YelhJmGzrqfIVabVpFsx4mIrkEWE0FBVpggoLrTNnrWm8Khmis2ebVImaw8cLRR4X1ji3oLJKGWdCY4bVDrNjROTPF7447P3iNKbLF8WqVpTUtUQeQhs3Xsx0jZ4oJetzlef+icy8jHStW/8oCY619VR4rV5oRdeycRyQ1rXGhO+DknfDiOiQAs+oLbCm+PJcWsKa68XZcwkXMqIcFEVDdKLOWqpBkB/+ULIyLDu+wPJF8pgNISPQViusISrrbpRrmlnr0Ka5a7HSNa/jZOP2TIkxoGToTwqtXgpFeldhCFs1TomlK+YFLC6KFWbfyZ+vbHq8to8x1seWVoORXd8t/8i19WLL4lELVrq2d0dtXmtDRpSDouTFYe1NyeqsxZ4qeVmsxosdE1WW7qzA4FID615Yi34q8nhI6ZXh4RPq0uZfiIJkNbNmtOpCCayOQIkDkekhZHRh7BXNLdelkndDClsPP1uzn7LmB4iadOFzVVXfBb+1zgJmYV29y6ezPhyZ9Z3RZonCNbRODhAUlYwoQjEs75ISxJ05q/FhyNI8bVXBFySrdWPA7JQY1qeyF1eiQRHGIdlY7yy0BoQquTpb7/Lna20vlXwUMDvzctQ7q74bGIorzzrCNoI0FUXzvSipQ9Kz88qOsdoJ1scg09NUjvGvBoN8utZFTK05RCcKLKfhPEILLpordtlBKVnCxlnJF54USrxiUp2CiyCoSmtn7sII0GJ9YWrdF03kdZPwWHAKvrSYsa9Mg1D6ukZYX5hKcGF4Z1h6Zz03IaxhUilZrDoqhHErTMNGJ6rvrGvJ13dWuVleMRba67tQFuPdY7YT1mvzWHrXWt9dWfWVIV9R+85IZ+mdVV9ZuhDqkhU6oqRNkoIT9UvyeW0JGVEOirB+sjbjZMVPCb9wpWSJqrpGdzCrf9KLXjLTdJERpbFxY3UqrIac1ZkLpbOG86QaN9FMQ42NhCK9S6QLdSXlEVHytcyqr6yGnLlgq/A3owPValho9aoJdSnVqQgNTq1f2y4aDRfN9V1vud6ZnTnLS8H4ABGfzrqW6TElw3kuzlrbKQUfDSyDTGN9Z3qHhAvdSqQr0RvzGQvl0nAeoRThF7aSF0Ny9p1eKEuiSijwiMht1Ako9IhIvGbCe2V96bDQ6onS2iAJGwlXCVmsjkQIW+/y57O+EJUYr1o7Y63nC5EqKbO+K4D1jITJLINOSpKwfFLnK+k7tHbmWnUlvFemd4YpSz7dwPBSsD7WXMuxvrMQns9qcyQ/khmeVyUoWe2BFRMlpTfRkh8a9V5ekBHloOgUvBisplM0nMfozFkvUQmjYrPShZ25lF0gvFetLlyWR4MZ66CxMxcNb2gcVmIZUWw3vTCvaTpL78qGUbUO5wkNaYmysILsRfVdm0HHqs8GBV/mBgnFi/XO0pu22Bx2fbf8w016GNV6HkApXYnSRXqX+tiSzmuE7Xm1pJSlsDyAzPMFZZF6hkom+7DaLK2GCeujQVgWVriG1vpeXpAR5aAIq5f2gNGy31KNm7Cys14yVuPGXslYPi8zJkpJ46axQREaYWoClMUzEbV9dZUwMiiJ+5Bu3OQ9gMIKye7MWZ+zlgdIq/myFhaf9e6wjE92fS/7Lbm+FnP4Wn44T2ggsIKCWd4fVh0R6poZLC1Z38t+s5Y6YbYzjHRWCIJe1JmbprM6c6HeWfWdvVinkvouVRZ5dAraGWZ9ZxhJrPhZVgygNT2v5QUZUQ6KzszXtBqDShxYblolhJWdZfuzPCKsdLERZZoubERsHeDMalDEbnblX4h6BcYAuzOXv5ayYVRTtNYxc7KkUNKZKwm6NaJE71rru7CDlaqvwsurGc5TgtY4JGdWZ86a/MAYvlZSFqHepaqA2HiVKIuC4WvNHm/mvcqf78poZ1iIh/O0eYGFepfqK5QMX0tdzZrte3lBRpSDIqxeYnev8oqn5Mue1RkrafxYSLvZBY2bitor0pXGzlw0vKFG70LjlRGvwtKaktgc6XWeLO9U1Bnq0rKkUGK8KhlSNSLUO6sz11rfRXVIaniD4QEU1jHtEym01ndtnbmidoYhS6grqftieUTYE1gEnleNS/hq9f44i9osje2MFdt3VuiHVEmdGB5A4bNUEhNqT8iIclBEs/P0LI+IsEGQl8XszFludCumS75kevlOhdXECHWlNSaKvdwC48vczDNUUxYlMVEsj4GUKCVrK0kukaDACFMyVZ81vMGafKA1RoQ97CTvMdGxjCi9vCdKyTCyGkNfiCtraJHleVUQE6WkM5eSJPKISJZFWpYRVuyOuWtJoXXoUtxmsZZLMD2kV2JEyUsXlVWqDog+1hhL0EjXd3njVuTHqiA2FhlRDorOTIPEapyYU481BkGyYnNKGJ0K68VwdbY88FAKcQdsvYBPYbmkkAy0ZQyPCFHSWUvB1jsn+busfILOXEI+q3FzUeCFYHqilHQqEggvr3XtI1Z9VzKzleURUeDELTtfqCuNhosST5S04aLEIyKbrOijgemJkjjfleURYYQdmItjkqpubM+r5e2EpN4VLC3BsqJY9Z1j6J0dA1imd/a1ZJPLDTKiHgOiArwAlL64QVXcFJ8vrOpP1Q0AANQO9JLMq2TM/N8tqwMA+jQP448JA3GVLEdQL9gbANChbqBsWVjDUoFV3PiOM7Kap+w1WS9pRDWvh9cEgn3cZfOy4gNia1WzqEzmEOr9pVY1AAC9mobyx0R6Z3jIhP1To1AfAECXBkGS1+JlMp6lv7crb2iqvUcjkQFl54f6yetdCqEHMybKHwBQ3c9DMq+S+v5KTE0AQI8nQvhjLI+J6FqCizWr4QsA6NaoTJak3kXnm6b7ebrA3cWod+l3Wur6UkT4l+ndnL74cjHWZmpRsyoAIITx3phDqIp+rcMBAHENy+qo6F4YHjzhM2pZ0w8A0LOJ4N1heESk8HF3hperEwCglqAtVfPhV1Pw3GpUNX13mLNRBYpvWsMPABDg7SqZV0l9fzG6VO8d65W1yaLAc4nzzXkA2zx8D59tJt1mVWSc7V0AQjvREVUxICYC3u7OeGd1CiO3adUWWvyvP1ULTzcIQu0AbzSbvsUkLzsGpCx99r+b4OU2NdG0hi82pFw3uZaS5uTXke2QejMPdYO88c3282bLIupUJOT4uLtg13tPI/dBEc5n5mF50iWz12TNumleww9D2kXC09UJk9Ydk83LutfB7SLxdIMgRAV4ofn0BImyyCPU68d9mqBvq3A0qe6LP4/eMEmXanPNyV/9n7Y4l5GHhqFVeL1LfSGy+gZvNxf88m4ssu8V4Xr2ffyw+6L8CTI8EeaLhHdqwd3FCdP/OClbFtYSCAPaRqBzw2BE+HuixQxTvSup71OffQLPt6iOJtV90WBy/MN06euyZK14PQZnM+6iSXU/+frO8Kh4uTnjf2PaIyu/EHfuFeL7XRcenmdqNLHqe4PQKtj6dEe4OukxZ/Np2bwsT1S/1uHo0iAI4f6eaCmhdyWd+eR/NcKzzcJEemd5RIQIn9GPw2JwJj0XzcOr8novlpi1warvHi5OiB/bATfvFuBBUQnmb08VlEWZcVAn0BuJ4zvCWa/DN9vOm5ZFVC554/XFljUQ1zAI1f08EP3xVpO8Sur7pJ4N8EyTEDQO80XDKfEm6axRD+Gllg5pjVM3ctGiZlVeV0wvbwWBjKjHACe9DvVDqvC/lSJsI1yd9Gj58CtRCiVudjdnJ7SO9DebLtWgmBtS8PVwQcuaVfGgqERSlqVlddbrEObngTB4IO1Wvuz5zG049DrUCzavd1ZDK2xwXJ31/Ne5FMwYEeEzdNab6F2YLtWpmBPv4+6C6IiqIl2rmQnorNch1NcDob4eyMh9IHu+JXvA1X2od63Dca5OejQP9zObrqS+S+qdZbwKh1EFx6u4uyA6wl+Urqa+O+l1CPF1R4ivO/ZeuM0f1+t0Jp0Uu77rUSeo1CMspXfWkxC+I65OejST1TtjeFo4S8zJVO/Cs5lhA4Lf3m7OiI4w/+5YWlYnJx2CfdwR7OOOlCvZZWXRAygR57UksLx2oDf/WymimdzOOt4bJYWS+s7Su2Q8ouhaZbm93JzRSqavkC5rxTCyaDjvMcBZQdyDVEMufjEYcROMryjmIngGRqcie7Z4zFzqWizDRRQ/ZsXFB9XE5hgeaZBk82qMESlh6p1tuFh6LalUc7ForOBUKZRshcKq78w4ISvWd0njVZiX4WmSrO+yVxcbO6xJGeyNdIWxNcrru1A+e3kReVlKPCZKPIBSqKrvZiawSOtdVrz4fBWTYcxNYJEyhLXWd9Z+duL6LitK8lpmbF+7QkbUY4C5htJShI2ImplTrAXUhIgDnJWXVdSZswIPJV4zYSPipDGYW8lMP6mGXKgr5kxBVuOmKLBcm/eGOYxqxgNoRPgM1QTaOjFmo7IQT9O27aKPrIB+JddSVd/NLOmh5gNG/NHAuBnJ5yqUxZolpq2+C68l3ZkLPKsKDDIjosU2GfVdtPq5ZDtg+QeMmo2bhcdYHyBa67tY7/LylbRZUudXFMiIegxwEr0YymfNKPlqk6rEwpdFycrZ0sMbsqeblSWQKivLSaPBaU4WyyCTokSj8SqEPQ1b8IxVyGddSxybIzG8YUbvLO+MFEq+zKWC6K1pRCmr78o7LXOyyoTKy3Iy5xFhuQkkUKJ3KUpEepd/D9nDSvLpYo+MvHzWI2C+W0rqu6T3Rx5rfiSLV51XoXclHkCJdKGu2HqXT68oBhUZUY8BLgyPiKIFARmw3MWKhvOk5Ctw0jJdyxLHWPtQieQrGN5QswaPkpmK1vxClFK8kvZI1VR7MwtkSnrFWJ25xsVlheVXMzwiklXR67sZj4jUsJJVPYCSHhFBZ84IQWDVd9ZzESazqpjWd0sKc0P9ktWNWd8t955LPUOxB1B+SFZrfVdi2LCfoXLj1R6QEfUY4GSmobQUJauIS+Vk7bsmupYVh5XUzN4QL0xadn3pfaAYshR4AKXQOvtE2CAriYnSqvdilhtAAnNf5qz9t6QwN1QihVRDa05X0jEi8iip70qW9GDJshSzcXsq6ruSWDQpvYjqoMa16bSuR6cEqfquZLNn0Yw0ibzKPtaUx06a26pFKi5Na33XuqivkGIHWeKAjCgH5un6gagV6IXoiKr8sXe714ePuzPe6lKXP8ZqezvWC0S4vweeaRwiOr54cCt4uTrh2wEtZWUJO+UPezWCj7szJnSrJ8rzXo/6qOLujMm9GvHHWH3KgoEt4eXqhEWDWomO/6tpKKr7eaCzYO0iFmPj6sLH3Rnv9ajPH2sW7oc6Qd7oUC+QOcwppGujYERW80Tbh2s7AcA7XevC18MFb3SqzR9jtd2xtaohsponujYKFh1f/lobeLk64euXW/DHpL7KhI3U+z0awNfDBW8LnjsAfNCzAaq4O2N67yf4Y6xOZdGg0ue+YGBL0fE+zcMQ5usuWkOHxYRu9VDF3Rn/16shf6xBSBXUD66CJ+tUM6N3aVk9m4Qg3N8DHeuVPffRT9eBn6cL/tOhliCn/JBudERV1Ar0wtP1A0XHlw0t1ft/+zWTPV/IuK714OvhgjFP1xEdn/yvRqji5ozZzzfhj7H0vnRIa3i5OuGbV1qIjr8YXQMhPu7o3SzMzJmmTHymAaq4OWPqc2XPPSrAC41CfRAT5S/pSTJX359tFobqfh6ievqfDrVQ1dMFrz0ZxR8Tr75uKueJMF/UDfLm16IzYqxvc19syh9jeSHGPHzuIzvWFh2f3vsJVHFzxud9y54ha1004/v2Vf/mouP9W4cjsIob+rYKlz1fyP/1aogqbs6Y+Xxj/lh1Pw80qe6L6Iiq8HI1nRBvrr7/u0V1hPq6i9Z7G/JkJKp5uWJwbERZRlFMqqmwukHeaBBSBbG1qomM128HRMPbzRmf/LusjrLq+8iOtVHV0wXDn4oSHf+4T2N4uznj6/7ybZaQJQ/ru/BZAcDAtjUR4O2GV9rUlC9MBUHHVZR5go8hubm58PX1RU5ODnx8fKwun+M4cJzpOHuJgYOTXofIiX8CAIJ93JCRW2Dy20j/1uGY+XwT6HWmXw9ysox4uzkjr6AYAPD76CfRuLqv5JdqiYHDqRu5+Ne83QBKFwLMvlckyhNUxQ2Zd0vlp33Si7++lKyp/zuBn/ZeEh0PrOKGmw/PD/B2w608eVkGAwedDqj74V8mXz7V/TxwLfu+ybUvzu5pkd5Dfd1xI6d0On+Nqh64ekcs6/kW1fFZ32YW6V0oy4izXseXefWItmgd6W9WVxdv5SHui10AgGperridXyjKY4mujLJmbzplss6TUKYlz9Co96ZTt+Duw7pjJNzfA1eypPVu4Ey9II/qSlgHpJ5hjydC8O2AltCprO9Clr/WBu3rBJjV1Y2c+2j/6XYApQtLpj+yvIOS+v7l1rOY98g6QT7uzsh9UKo/JfW99cytuJUnrgNy9d0SvQvrgNQzfLp+IBYPbm2R3qXqu5AFA6PRtVGwWV1l3yvk10GSeoZK6vt3O87jsy1nRcfdnPUoKC71UCmp70/N2W7SDpir7+ZkPaorYR2QeoZta/lj1ettrVLfv+rfHP9qGmZWV/cKi9Fkaun6glLPUKirC7N6goO0V7PEwGHpPxfx8Z+nzJblUb1bG0v7b1onyoHR6XTMBc2AR+IDJL7KdDrz7nk5WWUyLZclWghRMpf89c3JsgQpWUZDSIksNXo3pzdNen9El/Ky5IfQWNcXHmfunWeBLN4AlRpWMjNaqNPpIDV6pzTIWaeTDvCVksX6xGTpXekQnqwsRZIYelcgTY3epZ6hTqeznt6Z7Yy8N5J1fXOypFBS3yWHr2VGx6VkKa7vsKbedbK6Ym3sLDxkrkxGWVqHv8sLGs6rBIhiiiXqpZLKKvmOCU5nxdsI0yVnK1lcEjN7M6mUJj1zSpuTlhU3oUTvrGnWcg0S8OhQklRey+9Vek8sgSQFatMan8VCeiKExvquQJZQ71L1QUkNY9cXJc9QwYWthJJrsmJvlOldCiVtg3x6ha/vCnp5dn1npQuMKKn6rkhX8ukVZQjN4Y2omTNnol27dvD09ISfn59F53AchylTpiA0NBQeHh6Ii4vDuXPnRHmysrIwYMAA+Pj4wM/PD8OGDUNeXp4N7sD2iDeXNU1X0rhJxuYIfyt4ySRnWSt4y6QNwrLfSuISVcz4ZsK6viK9M9KVNG5S0pToSrozl5dvDlt05uZWATeipB9j1UclepfSsZL6zl6s02JRqtYr0ormjzUBLL2z9tDUqitO5n9KZVkT1sQf5vkajVd2+2s9XVWUSCSHN6IKCwvRt29fjBo1yuJz5syZg6+//hoLFizAvn374OXlhe7du+PBg7Lx2wEDBuDEiRNISEjAH3/8gV27dmHEiBG2uAWbYQw479+6LEBP+HUQ7FO6WfEzjS0PFH50mX9A3GBJbZApJEy0YazpSyIMkmfR/eFGrwHe0psu93+4Mancth5GjAGqok17BS/pCy1riPLJ0bZWqY4GxJTpXSirRtXSzVt7NrVc77GCIHYegfqiArxlzw/yKdORVFCxJToy0rVRaWC3r4eLZLpxY9InwthxgK8/VRoQ3v2JsqBloa5eblMqa1j7KKYsY8DyoNhISVnGjbqfsyBA2/Whjp6qGyibr06wvN6FOvJ8uCGtkCfCfJllMdLpYSC8lBygNBAZKNusW47X2kcCgGhyhlBXr7YtDVwWBTCbwSjjNcEzEsoyluf5h+WTw7hpb6d68npvxKhbQh1J1dP6D7cNsgRjvTK3yvpzzUrvq1aA/AbPADDooT6FwfVCXRnrubHey2Hc6Hr4U2WTKoSyjO/fi9E1mLKMOurSIFg2X5Pq8vXVVdC2SLXLtRjtlJDY2hJtnoB/NS19j43tqb14bALLly1bhrFjxyI7O1s2H8dxCAsLw/jx4zFhwgQAQE5ODoKDg7Fs2TL0798fp06dQqNGjXDgwAG0alU6Myw+Ph49e/bE1atXERZm2SwZWweWs3hQVIKzGXdR1dMVT80pDW4VBuMdm9oNl7PuWdSQZ+UX4nZeAX5Puc5vzGnEz9MF8W93QEFxCSIYO8UDwOXb9+DirMOz8/7hgzuN/KdjLfSNroGqnq6oZsY4EnLyei5q+Hug6cNgRuH9bZ/QCXkPilEnyBseZjoeIwYDh6PXctAo1Af1/u8vE1nnZj6DE9dz0cRM0LyQB0UlOJN+F2F+Hmg9szS4VRhUfHxad6TdyscTYT7Mr/M7+YW4mVeAhJMZmLv5jCjNzVmPXe89jfuFJYi0oAG/knUPTnod+i5IMgk+HRwbgVdjI+Hn6WLWKBVy6kYuwvw80Gxaqd6FwdzxY59CUTGH2kFe8JSYjSTEqPeGoVVQ//9KNzEV6sqo98ZhPsxVro16jwr0kqwPJ6Z1x0UL9Z5zrwjpuQ/w97mbksGt+z/sgrwHxagVyO4UrmTdg16vw+Al+3E+U+zN7t86HMM71IKPuwsCq7D1fjo9F6E+Hvzm4FXcnPnA/PVvtIOLkx5RAV7wcmPr/di1HDQQ6F2oq/Mzn8FxC/VeUFyC0zfuoqHg3RE+Q6V6v55zH8mX7uD/Nhw3ST/wYRxyHxTxe8nJcS37PgwGDiNXJOPE9VxRWu/mYXi7S114uzsjqIq7GQllnEm/i2AfN35zcFcnPQofLn2wekRbeLs5IzLAC94W6r1+SBV+s2Shrox6fyLMh7kWl1HvTar7otYHmwCIn+HJ6d2RmpmPxtUt0Pv90o3Bj1/Lwbtrj5qkH/y/OGTfK+L3TpTjevZ9FJdwGPdLCg5euiNK6/FECN5/pgE8XZ0Q7MPW+9mMuwj0dpPcHHzp0NYI9HazSO9qoMByM1y8eBHp6emIi4vjj/n6+iImJgZJSUno378/kpKS4OfnxxtQABAXFwe9Xo99+/bh+eefl5RdUFCAgoIyoyA3N1cyX3nh7uKEpjX8cF3QYbq5lL2YVdxdLP4S9vdyhb+XK1ydTV9sTxcnhPiyXwgjNauVequkvqhdnfSoE2T5V+KjX6TC+9PrgCY1LLs/vV5n4o0RynJhbFQrxN3FCc3C/ZAlmAEnlOXt5ozGjC86I1W9XFHVyxU7zmSapFnaEBkJ95fRu7PeogbSSMNQsd7dBffnpNOhQQ3LPhpsofd8wWw/oSwvBXr39XSBr6eLaNNeI56uTgiq4g5Lq6lR7x4u0nq3xCAw0iDkEb27OvFGlJNeZ/H96fU6kw2AhbpyVqB3N+dSvYs2H9eo92NXc0zLrCs11i0xNoHSmWqAtN7dnPUWGcBGjBu8G3F3KTOi7K13c7I8XZ0tbv98PVzg6+GCM+l3JdMDvN0s+rgCgLCHeneX0ruLnvcIW0K9RzyGQuPVWYHebYnDD+cpJT09HQAQHCx2WwYHB/Np6enpCAoSr0Hk7OwMf39/Po8Us2fPhq+vL/8XHm75+iK2JNTXHZ0bBKHHEyFYNKgVIqt5mqyJYimD20WifnAVvBNXD98NaImIap74/tVW7BMl+Pbh+fNfaYnxXeuhXrA3Xm9fi32iBPNeblFaloGt0KtJKDrWC0RNf/mhRXO81bkOGoRUwW+j2qFRqI9FQ3hSVPV0QddGwYhrGIwfHur9i5easU+U4JWYCDQIqYK3OtfBwlejEVHNE0uGtFYl6+uXWyCymie+frkF3utRH3WCvDGqUx32iRIYn+F3A6LxXLMwPFU3QJExJmTcwzrw68hYNK7uY7IWjaV4uTnjmcYheLp+IJYNbYOoAC/MEaxBpIQXo2ugYagPRnWqjcWDWyGimid+GhajStbnLzVDZDVP/LdfM3zQswFqB3rhzc512SdK8P3DOvDDoFb4d8vqiK1VDY0VDA0Kebd7fdQN8sZvI9uhWQ1fDGkXqUqOk16HZx/WgeUP9S5cg0gJzzUPQ+PqPhjRoRaWDW2NiGqeWD0iVpWsT15oishqnpj7YlNM/lcj1Ar0wvhu9dknSvDDoNI6sOy1NnipVQ20ifJHy5qWhyAImfhMA9QJ8saaEbFoHu7HD/Wp4fkW1dGudjWsHNYWtQK8MKNPY/ZJEvRoHIKmNXzx2pNR+GlYG9T098Sq4erq+8d9GiMqwAufvtAEU58t1fv7PRqokrV0aGvU9PfEyuExeLlNTURHVJUOcbADFXI4b+LEifj0009l85w6dQoNGpQ9EEuH8/bs2YMnn3wS169fR2hoWUzKSy+9BJ1OhzVr1mDWrFlYvnw5zpwRD58EBQVh2rRpZuOvpDxR4eHhdhvOIwiCIAhCOQ49nDd+/HgMGTJENk+tWuo8FiEhpcF4GRkZIiMqIyMDzZs35/NkZoqHT4qLi5GVlcWfL4Wbmxvc3CxzeRIEQRAE4dhUSCMqMDAQgYHyszPUEhUVhZCQECQmJvJGU25uLvbt28d7mGJjY5GdnY3k5GRER0cDALZt2waDwYCYGHWuTYIgCIIgHi8cPibq8uXLSElJweXLl1FSUoKUlBSkpKSI1nRq0KAB1q9fD6B0Ov7YsWPx8ccf43//+x+OHTuGQYMGISwsDH369AEANGzYED169MDw4cOxf/9+/PPPPxgzZgz69+9v8cw8giAIgiAebyqkJ0oJU6ZMwfLly/n/t2jRAgCwfft2dOrUCQBw5swZ5OSUzfZ47733kJ+fjxEjRiA7Oxvt27dHfHw83N3LZjqtXLkSY8aMQZcuXaDX6/HCCy/g66+/Lp+bIgiCIAiiwlMhA8sfF+y9ThRBEARBEMqxtP92+OE8giAIgiAIe0BGFEEQBEEQhArIiCIIgiAIglABGVEEQRAEQRAqICOKIAiCIAhCBWREEQRBEARBqICMKIIgCIIgCBWQEUUQBEEQBKECMqIIgiAIgiBU4PDbvlRkjIvB5+bm2rkkBEEQBEFYirHfZm3qQkaUDbl79y4AIDw83M4lIQiCIAhCKXfv3oWvr6/ZdNo7z4YYDAZcv34dVapUgU6ns5rc3NxchIeH48qVK7QnnwNAz8txoGflWNDzchwc7VlxHIe7d+8iLCwMer35yCfyRNkQvV6PGjVq2Ey+j4+PQ1RGohR6Xo4DPSvHgp6X4+BIz0rOA2WEAssJgiAIgiBUQEYUQRAEQRCECsiIckDc3Nzw0Ucfwc3Nzd5FISyAnpfjQM/KsaDn5Tg8rs+KAssJgiAIgiBUQJ4ogiAIgiAIFZARRRAEQRAEoQIyogiCIAiCIFRARhRBEARBEIQKyIhyQObPn4/IyEi4u7sjJiYG+/fvt3eRKh2zZ89G69atUaVKFQQFBaFPnz44c+aMKM+DBw8wevRoVKtWDd7e3njhhReQkZEhynP58mX06tULnp6eCAoKwrvvvovi4uLyvJVKxyeffAKdToexY8fyx+hZVRyuXbuGgQMHolq1avDw8ECTJk1w8OBBPp3jOEyZMgWhoaHw8PBAXFwczp07J5KRlZWFAQMGwMfHB35+fhg2bBjy8vLK+1Yee0pKSjB58mRERUXBw8MDtWvXxowZM0T7zT32z4sjHIrVq1dzrq6u3JIlS7gTJ05ww4cP5/z8/LiMjAx7F61S0b17d27p0qXc8ePHuZSUFK5nz55czZo1uby8PD7PyJEjufDwcC4xMZE7ePAg17ZtW65du3Z8enFxMde4cWMuLi6OO3z4MLdp0yYuICCAmzRpkj1uqVKwf/9+LjIykmvatCn39ttv88fpWVUMsrKyuIiICG7IkCHcvn37uAsXLnCbN2/mzp8/z+f55JNPOF9fX27Dhg3ckSNHuOeee46Liori7t+/z+fp0aMH16xZM27v3r3c33//zdWpU4d7+eWX7XFLjzUzZ87kqlWrxv3xxx/cxYsXuV9//ZXz9vbmvvrqKz7P4/68yIhyMNq0acONHj2a/39JSQkXFhbGzZ49246lIjIzMzkA3M6dOzmO47js7GzOxcWF+/XXX/k8p06d4gBwSUlJHMdx3KZNmzi9Xs+lp6fzeb777jvOx8eHKygoKN8bqATcvXuXq1u3LpeQkMB17NiRN6LoWVUc3n//fa59+/Zm0w0GAxcSEsLNnTuXP5adnc25ublxP//8M8dxHHfy5EkOAHfgwAE+z19//cXpdDru2rVrtit8JaRXr17ca6+9Jjr273//mxswYADHcZXjedFwngNRWFiI5ORkxMXF8cf0ej3i4uKQlJRkx5IROTk5AAB/f38AQHJyMoqKikTPqkGDBqhZsyb/rJKSktCkSRMEBwfzebp3747c3FycOHGiHEtfORg9ejR69eoleiYAPauKxP/+9z+0atUKffv2RVBQEFq0aIFFixbx6RcvXkR6erroWfn6+iImJkb0rPz8/NCqVSs+T1xcHPR6Pfbt21d+N1MJaNeuHRITE3H27FkAwJEjR7B7924888wzACrH86INiB2IW7duoaSkRNSQA0BwcDBOnz5tp1IRBoMBY8eOxZNPPonGjRsDANLT0+Hq6go/Pz9R3uDgYKSnp/N5pJ6lMY2wHqtXr8ahQ4dw4MABkzR6VhWHCxcu4LvvvsO4cePwwQcf4MCBA3jrrbfg6uqKwYMH87qWehbCZxUUFCRKd3Z2hr+/Pz0rKzNx4kTk5uaiQYMGcHJyQklJCWbOnIkBAwYAQKV4XmREEYRGRo8ejePHj2P37t32LgohwZUrV/D2228jISEB7u7u9i4OIYPBYECrVq0wa9YsAECLFi1w/PhxLFiwAIMHD7Zz6YhH+eWXX7By5UqsWrUKTzzxBFJSUjB27FiEhYVVmudFw3kOREBAAJycnExmDWVkZCAkJMROparcjBkzBn/88Qe2b9+OGjVq8MdDQkJQWFiI7OxsUX7hswoJCZF8lsY0wjokJycjMzMTLVu2hLOzM5ydnbFz5058/fXXcHZ2RnBwMD2rCkJoaCgaNWokOtawYUNcvnwZQJmu5drAkJAQZGZmitKLi4uRlZVFz8rKvPvuu5g4cSL69++PJk2a4NVXX8U777yD2bNnA6gcz4uMKAfC1dUV0dHRSExM5I8ZDAYkJiYiNjbWjiWrfHAchzFjxmD9+vXYtm0boqKiROnR0dFwcXERPaszZ87g8uXL/LOKjY3FsWPHRA1IQkICfHx8TDoSQj1dunTBsWPHkJKSwv+1atUKAwYM4H/Ts6oYPPnkkyZLhZw9exYREREAgKioKISEhIieVW5uLvbt2yd6VtnZ2UhOTubzbNu2DQaDATExMeVwF5WHe/fuQa8XmxFOTk4wGAwAKsnzsndkO6GM1atXc25ubtyyZcu4kydPciNGjOD8/PxEs4YI2zNq1CjO19eX27FjB3fjxg3+7969e3yekSNHcjVr1uS2bdvGHTx4kIuNjeViY2P5dOO0+W7dunEpKSlcfHw8FxgYSNPmywHh7DyOo2dVUdi/fz/n7OzMzZw5kzt37hy3cuVKztPTk1uxYgWf55NPPuH8/Py433//nTt69CjXu3dvySnzLVq04Pbt28ft3r2bq1u3rsNMmXckBg8ezFWvXp1f4mDdunVcQEAA99577/F5HvfnRUaUAzJv3jyuZs2anKurK9emTRtu79699i5SpQOA5N/SpUv5PPfv3+feeOMNrmrVqpynpyf3/PPPczdu3BDJSUtL45555hnOw8ODCwgI4MaPH88VFRWV891UPh41ouhZVRw2btzINW7cmHNzc+MaNGjALVy4UJRuMBi4yZMnc8HBwZybmxvXpUsX7syZM6I8t2/f5l5++WXO29ub8/Hx4YYOHcrdvXu3PG+jUpCbm8u9/fbbXM2aNTl3d3euVq1a3Icffiha9uNxf146jhMsLUoQBEEQBEFYBMVEEQRBEARBqICMKIIgCIIgCBWQEUUQBEEQBKECMqIIgiAIgiBUQEYUQRAEQRCECsiIIgiCIAiCUAEZUQRBEARBECogI4ogCIcmMjISOp3O3sVQTefOnVGjRg0UFBSoOn/Dhg3Q6XT45ZdfrFwygiBYkBFFEESFJS0tDTqdDp06dbJ3UWzCn3/+ie3bt+ODDz6Am5ubKhm9e/dGs2bN8MEHH6CoqMjKJSQIQg4yogiCcGgSExNx6tQpexdDFR988AECAwPx+uuvq5ah0+kwceJEpKam4ocffrBi6QiCYEFGFEEQDk3t2rXRoEEDexdDMf/88w+OHj2Kfv36wdXVVZOs3r17o0qVKliwYIGVSkcQhCWQEUUQRIVk6tSpiIqKAgDs3LkTOp2O/xsyZAifTyomSjgMmJ+fj3HjxiE8PBweHh5o2bIlNm7cyOf99ddfERMTAy8vLwQHB+Ott97C/fv3Jct07949zJ49Gy1atIC3tze8vb3Rtm1bLF++XPH9Gb1GL7/8smT6nj170KdPH0RERMDNzQ0hISFo06YNJk6ciLy8PFFeDw8P9OnTB0ePHsW+ffsUl4UgCHXQBsQEQVRINmzYgBUrVuC3335DcHAwevTowae1b9+eHwKLjIzEpUuXIGzK0tLSEBUVhdjYWBgMBly8eBEdOnTArVu3sGvXLuh0OsTHx+PYsWN477330LFjR/j4+GDXrl24ffs2XnnlFaxcuVJUnszMTHTt2hVHjx5FSEgIWrZsCY7jsGfPHuTk5GDMmDGYN2+exfcXFBSEvLw85ObmwtnZWZS2ceNG9OnTBxzHoU2bNoiKikJ2djbOnTuH1NRUXLx4EZGRkaJzlixZgmHDhmHy5MmYPn26xeUgCEIDHEEQRAXl4sWLHACuY8eOZvNERERwjzZlxvMAcJ07d+by8vL4tKVLl3IAuDp16nBVq1blDhw4wKddu3aNCwoK4gBwqampIpk9e/bkAHBvv/029+DBA/54eno616pVKw4A99dff1l0X6dOneIAcO3atZNM79ChAweAW7t2rUna/v37udzcXJPjx44d4wBwHTp0sKgMBEFoh4bzCIJ4bNHr9fjuu+/g5eXFHxs0aBACAgJw/vx5jB49Gq1ateLTwsLCMGDAAADArl27+OMpKSnYtGkTWrdujS+++EI0ky44OBgLFy4EAHz33XcWlevo0aMAgPr160um37x5EwAQFxdnkta6dWtUqVLF5LgxLiwlJcWiMhAEoR0yogiCeGyJjIxEvXr1RMf0ej0iIiIAAN26dTM5p1atWgCAGzdu8Me2bNkCAOjTpw/0etNm0xgjtX//fovKlZmZCQCoWrWqZHp0dDQA4NVXX8WBAwdgMBiYMp2dnVGlShXk5uaisLDQonIQBKENMqIIgnhsqV69uuRxb29vs+nGNOHil2lpaQCADz/8UBTgLvzLy8vDrVu3LCpXTk4OAEh6lABg1qxZaNasGTZu3Ig2bdogICAAzz33HH744Qc8ePDArFwfHx8AQHZ2tkXlIAhCG87sLARBEI6JlNdISboRoyeoffv2qF27tuZy+fr6AgDu3r0rmR4eHo6DBw9i27Zt+OOPP7Bz505s3LgRGzduxJw5c5CUlIRq1aqZnGc0zvz8/DSXkSAINmREEQRBMKhRowaA0uG88ePHa5YXFBQEAMjKyjKbx9nZGd26deOHHC9duoTXXnsN27Ztw6effoo5c+aI8hcVFSEvLw8+Pj6a150iCMIyaDiPIIgKi9EYKC4utms5unbtCgBYv369VeQ1a9YMAHDmzBmLz4mIiMD7778PADh+/LhJ+unTpwEAzZs3115AgiAsgowogiAqLAEBAXBxcUFqaipKSkrsVo6YmBh07doV//zzD0aPHo3c3FyTPEeOHEF8fLxF8urXr4+goCCkpKRIGoj//e9/kZ6ebnJ806ZNAEqH+x7FGNTesWNHi8pAEIR2yIgiCKLC4urqih49eiA9PR3NmjXDoEGD8Prrr2Pp0qXlXpYVK1agRYsW+PbbbxEREYGnn34aAwYMwL/+9S/UrFkTzZs3t9iIAoCePXvi/v37kiuMT5s2DdWrV0fLli3Rr18/vPTSS6hfvz6++uor+Pv7Y8KECSbn7NixAwDQq1cv1fdIEIQyyIgiCKJC88MPP+DVV1/F7du3sWrVKixevBg7d+4s93IEBQVhz549+Prrr9GoUSMcPnwYa9euxdGjR1GrVi3MnTtX0rgxx/DhwwEAq1atMkmbN28e+vfvj3v37uGvv/5CfHw8nJ2dMW7cOBw9ehR169YV5b9//z42bNiApk2bIiYmRtuNEgRhMbTtC0EQhJ1o0aIFrl69iqtXr4oW8FTKzz//jFdeeQXffvstRo0aZcUSEgQhBxlRBEEQdmLTpk3o1asX5s2bhzFjxqiSwXEcWrRogby8PJw8eZJm5hFEOUJGFEEQhB3p3Lkzzp49i9TUVFXeqA0bNuD555/HmjVr8NJLL9mghARBmIOMKIIgCIIgCBVQYDlBEARBEIQKyIgiCIIgCIJQARlRBEEQBEEQKiAjiiAIgiAIQgVkRBEEQRAEQaiAjCiCIAiCIAgVkBFFEARBEAShAjKiCIIgCIIgVEBGFEEQBEEQhArIiCIIgiAIglDB/wNhxVgnoHa/JgAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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o2QtQUlmfm7bjXtpnYazPqLzs7Ozg4uKi8SX6okuXLsHV1RW2trZq5S/+rlQUXY7XV199henTp2PMmDGYPXs27O3tYWZmhkmTJqnOn/Lsv3Xr1mjdujU6dOiArl27Ijo6Wqdp9rpuX5uS7fvhhx8QFhaGAQMG4LPPPoOjoyOkUinmzZun+oeFXl1MbqjSFQ/GtLW1LfOPWb169WBhYaHq6Snpxo0baq+Le1NevGts8X/nJbdpY2MDpVJptD+kxe0pHlxbltGjR2Px4sXYvXs39u7di3r16hl8iQ2AqsfsxXtqaDtmv//+OywtLVUzeHQ9Do0aNcKpU6fw9OlTtUtCJRk6Q6ROnTpa7/R7+/btMqdJG0PxrJ0bN25oDHC9ceOG1lk9uujTpw/WrFmDY8eOqWY8lXT06FGkpKTg/fffN2j7Hh4eKCoqQnJysloPnbYZfOWxfft2dO3aFevWrVMrf/jwodqEgfIq7RwuycPDA1evXoUQQu1cK0+bt2/fjoYNG2Lnzp1q2zSVGwrWdBxzQ5XOz88PjRo1wqJFi/Do0SON9+/fvw/g+X9fwcHB2LVrF+7cuaN6/9q1a4iNjVVbx9bWFg4ODhrTnb/77ju111KpFIMHD8aOHTtw+fLlUvetj9deew1eXl5YsmSJxhf1iz0Hbdq0QZs2bbB27Vrs2LEDb7/9tk6Xe+Li4rT2QhRfmnvx0kpCQoLauIHU1FT89NNPePPNN1X3PtH1OAwePBiZmZlYtmyZRr3imIpnP+n7SIJGjRrh5MmTKCwsVJXt2bMHqampem3HEP7+/nB0dMTKlSvVegn37t2La9eu6TX7qKTPPvsMFhYWeP/99/HgwQO197KysvDBBx/A0tISn332mUHbL06GXzy3jX2naqlUqnHObdu2rcxbFpTl6NGjapd+ipV2DpcUHByMu3fvqo0ve/LkyUvH6pSluHenZBtPnTqFhIQEg7dJ1Qd7bqjSmZmZYe3atejVqxdatmyJ8PBwuLq64u7du4iLi4OtrS12794N4Pl9U/bt24fAwECMGzcOz549w7fffouWLVtqdP2/++67mD9/Pt599134+/vjyJEj+P333zX2P3/+fMTFxSEgIABjx45FixYtkJWVhXPnzuHAgQPIysrSuz0rVqxA37594ePjg/DwcLi4uOD69eu4cuWKRiI2evRoTJkyBQB0viT10UcfIT8/HwMHDkSzZs1QWFiIEydOYMuWLfD09ER4eLha/VatWiE4OFhtKjgAtbvA6nocRo8ejU2bNmHy5Mk4ffo0AgMDkZeXhwMHDmDcuHHo378/LCws0KJFC2zZsgVNmzaFvb09WrVq9dJxSO+++y62b9+Onj17YtiwYUhKSsIPP/xQ5lRrY6lVqxYWLFiA8PBwdO7cGSEhIaqp4J6envjkk08M2m6TJk3w/fffY+TIkWjdurXGHYozMzOxefNmg9vo5+eHwYMHY8mSJXjw4IFqKnjxuW6s+6z06dMHs2bNQnh4ODp27IjExERER0cb3KO2YMECnD17FoMGDVJd8jx37hw2bdoEe3t7jVswlPT+++9j2bJlCAkJwcSJE1Vj14oHghvS5j59+mDnzp0YOHAgevfujeTkZKxcuRItWrTQ+k+XLnbv3q26D9DTp09x6dIlzJkzBwDQr18/o17qpZeoghlaZCJeNj22ePrutm3btL5//vx5MWjQIFG3bl0hl8uFh4eHGDZsmDh48KBavcOHDws/Pz8hk8lEw4YNxcqVKzWmxwrxfCp5RESEsLOzEzY2NmLYsGHi3r17WqdxZmRkiPHjxws3NzdRq1Yt4ezsLLp37y5Wr1790vhLm/p67Ngx0aNHD2FjYyOsrKxEmzZtxLfffqvR7rS0NCGVSkXTpk21Hhdt9u7dK8aMGSOaNWsmrK2thUwmE40bNxYfffSRyMjIUKsLQIwfP1788MMPokmTJkIulwtfX1+16bL6HAchnh/bL7/8Unh5eanqDRkyRCQlJanqnDhxQvU5lTzmoaGhwsrKqtS2LV68WLi6ugq5XC7+8Y9/iDNnzpQ6FfzFz6K0c7D4/Lh//35Zh1UIIcSWLVuEr6+vkMvlwt7eXowcOVL8+eefOu2nLJcuXRIhISHCxcVFdcxCQkJUtxDQNV5t53peXp4YP368sLe3F9bW1mLAgAHixo0bAoCYP3++RtwvTgXv3bu3xn5ePOZPnjwRn376qXBxcREWFhbiH//4h0hISNCop+tU8OPHj4vx48eLVq1aCTs7O1GrVi3h7u4uwsLC1M4jbbEIIcStW7dE7969hYWFhahXr5749NNPxY4dOwQAcfLkSbV1W7ZsqbH/0NBQtdsDFBUVia+++kp4eHiofkf27NmjUU8I3aeCF9/2QNvysuNDxiURopJH3BEZQVRUFGbOnFnpA0aNITMzEy4uLpgxYwamT59u9O1LJBKMHz9e62UkMl0XLlyAr68vfvjhB9UdoE3dkiVL8Mknn+DPP/8s8yaYVPNwzA1RJdu4cSOUSiVGjRpV1aHQK+rx48caZUuWLIGZmRk6depUBRFVvBfb/OTJE6xatQpNmjRhYkMaOOaGqJIcOnQIV69exdy5czFgwAB4enpWdUj0ilq4cCHOnj2Lrl27wtzcHHv37sXevXvx3nvvadzzx1QMGjQI7u7u8PHxQXZ2Nn744Qdcv34d0dHRVR0aVUNMbogqyaxZs3DixAn84x//MPrMFqpZOnbsiP3792P27Nl49OgR3N3dERUVhS+//LKqQ6swwcHBWLt2LaKjo6FUKtGiRQvExMRg+PDhVR0aVUMcc0NEREQmhWNuiIiIyKQwuSEiIiKTUuPG3BQVFeGvv/6CjY2N0W52RURERBVLCIHc3FzUr19f67PqSqpxyc1ff/1lsrMJiIiITF1qaioaNGhQZp0al9zY2NgAeH5wXnwiLxEREVVPOTk5cHNzU32Pl6XGJTfFl6JsbW2Z3BAREb1idBlSwgHFRCZGCIGnyqKqDoOqAD93oueY3BCZmA9+OAvfWfvxML+wqkOhSrQ3MQ1NvtyLHWf/rOpQiKockxsiExN7JQOPCp7hl8S0qg6FKtGH0ecAAJ9uu1jFkRBVPSY3REREZFKY3BAREZFJYXJDREREJoXJDREREZkUJjdERERkUpjcEBERkUlhckNEREQmhckNERERmRQmN0RERGRSmNwQERGRSWFyQ0RERCaFyQ0RERGZFCY3REREZFKY3BAREZFJYXJDREREJqXcyc2TJ0/Ktf7y5cvh6ekJhUKBgIAAnD59Wqf1YmJiIJFIMGDAgHLtn4iIiEyLQclNUVERZs+eDVdXV1hbW+PWrVsAgOnTp2PdunU6b2fLli2YPHkyIiMjce7cObRt2xbBwcG4d+9emeulpKRgypQpCAwMNCR8IiIiMmEGJTdz5szBxo0bsXDhQshkMlV5q1atsHbtWp238/XXX2Ps2LEIDw9HixYtsHLlSlhaWmL9+vWlrqNUKjFy5EjMnDkTDRs2NCR8IiIiMmEGJTebNm3C6tWrMXLkSEilUlV527Ztcf36dZ22UVhYiLNnzyIoKOj/gjEzQ1BQEBISEkpdb9asWXB0dERERIRO+ykoKEBOTo7aQkRERKbLoOTm7t27aNy4sUZ5UVERnj59qtM2MjMzoVQq4eTkpFbu5OSE9PR0rescO3YM69atw5o1a3SOdd68ebCzs1Mtbm5uOq9LRERErx6DkpsWLVrg6NGjGuXbt2+Hr69vuYPSJjc3F6NGjcKaNWvg4OCg83pTp05Fdna2aklNTa2Q+IiIiKh6MDdkpRkzZiA0NBR3795FUVERdu7ciRs3bmDTpk3Ys2ePTttwcHCAVCpFRkaGWnlGRgacnZ016iclJSElJQV9+/ZVlRUVFT1vhLk5bty4gUaNGmmsJ5fLIZfL9WkeERERvcIM6rnp378/du/ejQMHDsDKygozZszAtWvXsHv3bvTo0UOnbchkMvj5+eHgwYOqsqKiIhw8eBAdOnTQqN+sWTMkJibiwoULqqVfv37o2rUrLly4wMtNREREBMDAnhsACAwMxP79+8u188mTJyM0NBT+/v5o3749lixZgry8PISHhwMARo8eDVdXV8ybNw8KhQKtWrVSW7927doAoFFORERENZfByY0xDB8+HPfv38eMGTOQnp4OHx8f7Nu3TzXI+M6dOzAz402UiYiISHc6Jzd16tSBRCLRqW5WVpbOAUyYMAETJkzQ+l58fHyZ627cuFHn/RAREVHNoHNys2TJEtXPDx48wJw5cxAcHKwaH5OQkIDY2FhMnz7d6EESERER6Urn5CY0NFT18+DBgzFr1iy1HpePP/4Yy5Ytw4EDB/DJJ58YN0oiIiIiHRk0oCU2NhY9e/bUKO/ZsycOHDhQ7qCIiIiIDGVQclO3bl389NNPGuU//fQT6tatW+6giIiIiAxl0GypmTNn4t1330V8fDwCAgIAAKdOncK+ffv0ejQCERmXEKKqQyAiqnIGJTdhYWFo3rw5/v3vf2Pnzp0AgObNm+PYsWOqZIeIKh9zGyKictznJiAgANHR0caMhYjKibkNEZGByc2dO3fKfN/d3d2gYIiofHhZiojIwOTG09OzzBv6KZVKgwMiIsMxtSEiMjC5OX/+vNrrp0+f4vz58/j6668xd+5cowRGRPpjxw0RkYHJTdu2bTXK/P39Ub9+ffzrX//CoEGDyh0YEelPsO+GiMiw+9yUxtvbG7/99psxN0lEemDPDRGRgT03OTk5aq+FEEhLS0NUVBSaNGlilMCIiIiIDGFQclO7dm2NAcVCCLi5uSEmJsYogRGR/thzQ0RkYHITFxen9trMzAz16tVD48aNYW5u8K1ziKicOOaGiMjA5EYikaBjx44aicyzZ89w5MgRdOrUySjBEZF+2HNDRGTggOKuXbsiKytLozw7Oxtdu3Ytd1BEZBjmNkREBiY3QgitN/F78OABrKysyh0UERmGdygmItLzslTx/WskEgnCwsIgl8tV7ymVSly6dAkdO3Y0boREpDOmNkREeiY3dnZ2AJ7/d2hjYwMLCwvVezKZDK+//jrGjh1r3AiJSGfsuCEi0jO52bBhA4Dnz5aaMmUKL0ERVTdMboiIDJstFRkZaew4iMgIOBWciEiP5Oa1117DwYMHUadOHfj6+pb5VPBz584ZJTgi0g8vSxER6ZHc9O/fXzWAeMCAARUVDxGVA3MbIiI9kpuSl6J4WYqoeuJUcCIiA8fcFCssLMS9e/dQVFSkVu7u7l6uoIjIMExtiIgMTG5+//13RERE4MSJE2rlxTf3UyqVRgmOiPRTsuOGnThEVFMZlNyEh4fD3Nwce/bsgYuLS5mDi4mo8pScLcXchohqKoMev3DhwgWsWrUKvXr1go+PD9q2bau26GP58uXw9PSEQqFAQEAATp8+XWrdNWvWIDAwEHXq1EGdOnUQFBRUZn2iGqdkRsOuGyKqoQxKblq0aIHMzMxy73zLli2YPHkyIiMjce7cObRt2xbBwcG4d++e1vrx8fEICQlBXFwcEhIS4ObmhjfffBN3794tdyxEpkCU8jMRUU1iUHKzYMEC/POf/0R8fDwePHiAnJwctUVXX3/9NcaOHYvw8HC0aNECK1euhKWlJdavX6+1fnR0NMaNGwcfHx80a9YMa9euRVFREQ4ePGhIM4hMGjtuiKimMmjMTVBQEACge/fuauX6DCguLCzE2bNnMXXqVFWZmZkZgoKCkJCQoFMc+fn5ePr0Kezt7UutU1BQgIKCAtVrfZIvoleN+oBiZjdEVDMZlNzExcWVe8eZmZlQKpVwcnJSK3dycsL169d12sbnn3+O+vXrq5ItbebNm4eZM2eWK1aiVwUHFBMRGZjcdO7c2dhx6G3+/PmIiYlBfHw8FApFqfWmTp2KyZMnq17n5OTAzc2tMkIkqnScCk5EZGByc+nSJa3lEokECoUC7u7uqkc1lMbBwQFSqRQZGRlq5RkZGXB2di5z3UWLFmH+/Pk4cOAA2rRpU2ZduVz+0liITAUHFBMRGZjc+Pj4lHlvm1q1amH48OFYtWpVqb0qMpkMfn5+OHjwoOpZVcWDgydMmFDqthcuXIi5c+ciNjYW/v7+hoRPZLJKjrPhmBsiqqkMmi31448/okmTJli9ejUuXLiACxcuYPXq1fD29sZ///tfrFu3DocOHcK0adPK3M7kyZOxZs0afP/997h27Ro+/PBD5OXlITw8HAAwevRotQHHCxYswPTp07F+/Xp4enoiPT0d6enpePTokSHNIDI5zGeIiAzsuZk7dy6WLl2K4OBgVVnr1q3RoEEDTJ8+HadPn4aVlRU+/fRTLFq0qNTtDB8+HPfv38eMGTOQnp4OHx8f7Nu3TzXI+M6dOzAz+7/8a8WKFSgsLMSQIUPUthMZGYmoqChDmkJkspjoEFFNZVByk5iYCA8PD41yDw8PJCYmAnh+6SotLe2l25owYUKpl6Hi4+PVXqekpOgdK1FNojagmKNuiKiGMuiyVLNmzTB//nwUFhaqyp4+fYr58+ejWbNmAIC7d+9qTPMmooqlNhWcuQ0R1VAG9dwsX74c/fr1Q4MGDVSzlRITE6FUKrFnzx4AwK1btzBu3DjjRUpEL6Xec0NEVDMZlNx07NgRycnJiI6Oxu+//w4AGDp0KEaMGAEbGxsAwKhRo4wXJRHphM/NJCIyMLkBABsbG3zwwQfGjIWIykltKjj7boiohjI4uQGAq1ev4s6dO2pjbwCgX79+5QqKiAzDnhsiIgOTm1u3bmHgwIFITEyERCJR/bdYfGM/XR6cSUTGx4SGiMjA2VITJ06El5cX7t27B0tLS1y5cgVHjhyBv7+/xvRtIqpMzG6IiAzquUlISMChQ4fg4OAAMzMzmJmZ4Y033sC8efPw8ccf4/z588aOk4h0oP7gTCY6RFQzGdRzo1QqVbOiHBwc8NdffwF4fhO/GzduGC86ItILx9wQERnYc9OqVStcvHgRXl5eCAgIwMKFCyGTybB69Wo0bNjQ2DESkY54nxsiIgOTm2nTpiEvLw8AMHPmTPTt2xeBgYGoW7cuYmJijBogEemOdygmIjIwuSn5wMwmTZrg+vXryMrKQp06dVQzpoio8vHZUkREeiY3Y8aM0ane+vXrDQqGiMpHfUBx1cVBRFSV9EpuNm7cCA8PD/j6+nImBlE1pHZZqgrjICKqSnolNx9++CE2b96M5ORkhIeH45133oG9vX1FxUZEehKcLkVEpN9U8OXLlyMtLQ3//Oc/sXv3bri5uWHYsGGIjY1lTw5RNcPfSCKqqfS+z41cLkdISAj279+Pq1evomXLlhg3bhw8PT3x6NGjioiRiHTEMTdERAbexE+1spmZ6tlSfJ4UUdVTH3PD7IaIaia9k5uCggJs3rwZPXr0QNOmTZGYmIhly5bhzp07sLa2rogYiUhH7LkhItJzQPG4ceMQExMDNzc3jBkzBps3b4aDg0NFxUZEehKl/ExEVJPoldysXLkS7u7uaNiwIQ4fPozDhw9rrbdz506jBEdE+ik5sJ89N0RUU+mV3IwePZp3ICaqxtR7bpjdEFHNpPdN/Iio+hK8LkVEVL7ZUkRU3fAOxURETG6ITIj6bCmmN0RUMzG5ITIhfPoCERGTGyKTotZzU3VhEBFVKSY3RCaEU8GJiJjcEJkUTgUnIqoGyc3y5cvh6ekJhUKBgIAAnD59usz627ZtQ7NmzaBQKNC6dWv8+uuvlRQpUfXHxy8QEVVxcrNlyxZMnjwZkZGROHfuHNq2bYvg4GDcu3dPa/0TJ04gJCQEEREROH/+PAYMGIABAwbg8uXLlRw5UfXE3hoiIkAiqnC+aEBAANq1a4dly5YBAIqKiuDm5oaPPvoIX3zxhUb94cOHIy8vD3v27FGVvf766/Dx8cHKlSt12mdOTg7s7OyQnZ0NW1tb4zTk/zt16wEO/37fqNsk0sfVtBzE33h+DlrJpAjt6Fm1AVWya2k5yMp/io6N6qKm3Uv9u/gk1c/jujSqwkiopnO3t8QQvwYwlxq3/0Sf72+97lBsTIWFhTh79iymTp2qKjMzM0NQUBASEhK0rpOQkIDJkyerlQUHB2PXrl2l7qegoAAFBQWq1zk5OeULvBQXUh9i+OqTFbJtIkPkFSrVvvBqkoupD6s6hCpVUz93qj7uPnyMT9/0rrL9V9llqczMTCiVSjg5OamVOzk5IT09Xes66enpetUHgHnz5sHOzk61uLm5lT94LdztLSEzcpZKRET0KurQsG6V7r/Kem4qy9SpU9V6e3JyciokwbG3kuH3ub2Mvl0iIiLST5UlNw4ODpBKpcjIyFArz8jIgLOzs9Z1nJ2d9aoPAHK5HHK5vPwBExER0Suhyq6jyGQy+Pn54eDBg6qyoqIiHDx4EB06dNC6TocOHdTqA8D+/ftLrU9EREQ1T5Velpo8eTJCQ0Ph7++P9u3bY8mSJcjLy0N4eDgAYPTo0XB1dcW8efMAABMnTkTnzp2xePFi9O7dGzExMThz5gxWr16t8z6LJ4dV1MBiIiIiMr7i722dJnmLKvbtt98Kd3d3IZPJRPv27cXJkydV73Xu3FmEhoaq1d+6dato2rSpkMlkomXLluKXX37Ra3+pqakCz2/kyoULFy5cuHB5xZbU1NSXftdX6X1uqkJRURH++usv2NjYQCIx7TthFA+eTk1NNfo9fao7tp1tr2ltB2p2+9l202+7EAK5ubmoX78+zMzKHlVj8rOlXmRmZoYGDRpUdRiVytbW1qRP+LKw7Wx7TVST28+2m3bb7ezsdKrHG7MQERGRSWFyQ0RERCaFyY0Jk8vliIyMrJH3+WHb2faaqCa3n22vmW0vTY0bUExERESmjT03REREZFKY3BAREZFJYXJDREREJoXJDREREZkUJjcmJCsrCyNHjoStrS1q166NiIgIPHr06KXrJSQkoFu3brCysoKtrS06deqEx48fV0LExmVo+4Hnd77s1asXJBIJdu3aVbGBVgB9256VlYWPPvoI3t7esLCwgLu7Oz7++GNkZ2dXYtSGWb58OTw9PaFQKBAQEIDTp0+XWX/btm1o1qwZFAoFWrdujV9//bWSIjU+fdq+Zs0aBAYGok6dOqhTpw6CgoJeeqyqO30/+2IxMTGQSCQYMGBAxQZYgfRt+8OHDzF+/Hi4uLhALpejadOmr/S5rze9HsxE1VrPnj1F27ZtxcmTJ8XRo0dF48aNRUhISJnrnDhxQtja2op58+aJy5cvi+vXr4stW7aIJ0+eVFLUxmNI+4t9/fXXolevXgKA+PHHHys20Aqgb9sTExPFoEGDxM8//yxu3rwpDh48KJo0aSIGDx5ciVHrLyYmRshkMrF+/Xpx5coVMXbsWFG7dm2RkZGhtf7x48eFVCoVCxcuFFevXhXTpk0TtWrVEomJiZUcefnp2/YRI0aI5cuXi/Pnz4tr166JsLAwYWdnJ/78889Kjtw49G1/seTkZOHq6ioCAwNF//79KydYI9O37QUFBcLf31+89dZb4tixYyI5OVnEx8eLCxcuVHLkVYfJjYm4evWqACB+++03VdnevXuFRCIRd+/eLXW9gIAAMW3atMoIsUIZ2n4hhDh//rxwdXUVaWlpr2RyU562l7R161Yhk8nE06dPKyJMo2jfvr0YP3686rVSqRT169cX8+bN01p/2LBhonfv3mplAQEB4v3336/QOCuCvm1/0bNnz4SNjY34/vvvKyrECmVI+589eyY6duwo1q5dK0JDQ1/Z5Ebftq9YsUI0bNhQFBYWVlaI1Q4vS5mIhIQE1K5dG/7+/qqyoKAgmJmZ4dSpU1rXuXfvHk6dOgVHR0d07NgRTk5O6Ny5M44dO1ZZYRuNIe0HgPz8fIwYMQLLly+Hs7NzZYRqdIa2/UXZ2dmwtbWFuXn1fORcYWEhzp49i6CgIFWZmZkZgoKCkJCQoHWdhIQEtfoAEBwcXGr96sqQtr8oPz8fT58+hb29fUWFWWEMbf+sWbPg6OiIiIiIygizQhjS9p9//hkdOnTA+PHj4eTkhFatWuGrr76CUqmsrLCrHJMbE5Geng5HR0e1MnNzc9jb2yM9PV3rOrdu3QIAREVFYezYsdi3bx9ee+01dO/eHX/88UeFx2xMhrQfAD755BN07NgR/fv3r+gQK4yhbS8pMzMTs2fPxnvvvVcRIRpFZmYmlEolnJyc1MqdnJxKbWd6erpe9asrQ9r+os8//xz169fXSPZeBYa0/9ixY1i3bh3WrFlTGSFWGEPafuvWLWzfvh1KpRK//vorpk+fjsWLF2POnDmVEXK1wOSmmvviiy8gkUjKXK5fv27QtouKigAA77//PsLDw+Hr64tvvvkG3t7eWL9+vTGbYbCKbP/PP/+MQ4cOYcmSJcYN2kgqsu0l5eTkoHfv3mjRogWioqLKHzhVO/Pnz0dMTAx+/PFHKBSKqg6nwuXm5mLUqFFYs2YNHBwcqjqcSldUVARHR0esXr0afn5+GD58OL788kusXLmyqkOrNNWz/5lUPv30U4SFhZVZp2HDhnB2dsa9e/fUyp89e4asrKxSL7e4uLgAAFq0aKFW3rx5c9y5c8fwoI2oItt/6NAhJCUloXbt2mrlgwcPRmBgIOLj48sReflVZNuL5ebmomfPnrCxscGPP/6IWrVqlTfsCuPg4ACpVIqMjAy18oyMjFLb6ezsrFf96sqQthdbtGgR5s+fjwMHDqBNmzYVGWaF0bf9SUlJSElJQd++fVVlxf/MmZub48aNG2jUqFHFBm0khnz2Li4uqFWrFqRSqaqsefPmSE9PR2FhIWQyWYXGXC1U9aAfMo7iQaVnzpxRlcXGxpY5qLSoqEjUr19fY0Cxj4+PmDp1aoXGa2yGtD8tLU0kJiaqLQDE0qVLxa1btyor9HIzpO1CCJGdnS1ef/110blzZ5GXl1cZoZZb+/btxYQJE1SvlUqlcHV1LXNAcZ8+fdTKOnTo8MoOKNan7UIIsWDBAmFraysSEhIqI8QKpU/7Hz9+rPG73b9/f9GtWzeRmJgoCgoKKjP0ctP3s586darw8PAQSqVSVbZkyRLh4uJS4bFWF0xuTEjPnj2Fr6+vOHXqlDh27Jho0qSJ2nTgP//8U3h7e4tTp06pyr755htha2srtm3bJv744w8xbdo0oVAoxM2bN6uiCeViSPtfhFdwtpQQ+rc9OztbBAQEiNatW4ubN2+KtLQ01fLs2bOqasZLxcTECLlcLjZu3CiuXr0q3nvvPVG7dm2Rnp4uhBBi1KhR4osvvlDVP378uDA3NxeLFi0S165dE5GRka/0VHB92j5//nwhk8nE9u3b1T7f3NzcqmpCuejb/he9yrOl9G37nTt3hI2NjZgwYYK4ceOG2LNnj3B0dBRz5sypqiZUOiY3JuTBgwciJCREWFtbC1tbWxEeHq72hyw5OVkAEHFxcWrrzZs3TzRo0EBYWlqKDh06iKNHj1Zy5MZhaPtLelWTG33bHhcXJwBoXZKTk6umETr69ttvhbu7u5DJZKJ9+/bi5MmTqvc6d+4sQkND1epv3bpVNG3aVMhkMtGyZUvxyy+/VHLExqNP2z08PLR+vpGRkZUfuJHo+9mX9ConN0Lo3/YTJ06IgIAAIZfLRcOGDcXcuXOr9T8uxiYRQojKvRBGREREVHE4W4qIiIhMCpMbIiIiMilMboiIiMikMLkhIiIik8LkhoiIiEwKkxsiIiIyKUxuiIiIyKQwuSEiMkBhYSEaN26MEydOGHW7+/btg4+Pj+pZSESkPyY3RISwsDCtTx2/efNmVYdWba1cuRJeXl7o2LGjqkwikWDXrl0adcPCwjBgwACdttuzZ0/UqlUL0dHRRoqUqOZhckNEAJ5/qaalpaktXl5eGvUKCwurILrqRQiBZcuWISIiokK2HxYWhn//+98Vsm2imoDJDREBAORyOZydndUWqVSKLl26YMKECZg0aRIcHBwQHBwMALh8+TJ69eoFa2trODk5YdSoUcjMzFRtLy8vD6NHj4a1tTVcXFywePFidOnSBZMmTVLV0dbTUbt2bWzcuFH1OjU1FcOGDUPt2rVhb2+P/v37IyUlRfV+ca/IokWL4OLigrp162L8+PF4+vSpqk5BQQE+//xzuLm5QS6Xo3Hjxli3bh2EEGjcuDEWLVqkFsOFCxfK7Lk6e/YskpKS0Lt3bz2PMpCSkqK1l6xLly6qOn379sWZM2eQlJSk9/aJiMkNEeng+++/h0wmw/Hjx7Fy5Uo8fPgQ3bp1g6+vL86cOYN9+/YhIyMDw4YNU63z2Wef4fDhw/jpp5/wv//9D/Hx8Th37pxe+3369CmCg4NhY2ODo0eP4vjx47C2tkbPnj3VepDi4uKQlJSEuLg4fP/999i4caNagjR69Ghs3rwZ//73v3Ht2jWsWrUK1tbWkEgkGDNmDDZs2KC23w0bNqBTp05o3Lix1riOHj2Kpk2bwsbGRq/2AICbm5ta79j58+dRt25ddOrUSVXH3d0dTk5OOHr0qN7bJyKATwUnIhEaGiqkUqmwsrJSLUOGDBFCPH/isK+vr1r92bNnizfffFOtLDU1VQAQN27cELm5uUImk4mtW7eq3n/w4IGwsLAQEydOVJVBy1PY7ezsxIYNG4QQQvznP/8R3t7eoqioSPV+QUGBsLCwELGxsarYPTw81J54PHToUDF8+HAhhBA3btwQAMT+/fu1tv3u3btCKpWKU6dOCSGEKCwsFA4ODmLjxo2lHq+JEyeKbt26aZQDEAqFQu04WllZCXNzc61PpH78+LEICAgQffr0EUqlUu09X19fERUVVWoMRFQ686pNrYiouujatStWrFihem1lZaX62c/PT63uxYsXERcXB2tra43tJCUl4fHjxygsLERAQICq3N7eHt7e3nrFdPHiRdy8eVOjh+TJkydql2xatmwJqVSqeu3i4oLExEQAzy8xSaVSdO7cWes+6tevj969e2P9+vVo3749du/ejYKCAgwdOrTUuB4/fgyFQqH1vW+++QZBQUFqZZ9//jmUSqVG3TFjxiA3Nxf79++HmZl6R7qFhQXy8/NLjYGISsfkhogAPE9mSrsMUzLRAYBHjx6hb9++WLBggUZdFxcXnWdZSSQSCCHUykqOlXn06BH8/Py0zhyqV6+e6udatWppbLd4KrWFhcVL43j33XcxatQofPPNN9iwYQOGDx8OS0vLUus7ODiokqcXOTs7axxHGxsbPHz4UK1szpw5iI2NxenTp7Ve3srKylJrIxHpjskNEenttddew44dO+Dp6Qlzc80/I40aNUKtWrVw6tQpuLu7AwD+/vtv/P7772o9KPXq1UNaWprq9R9//KHWW/Haa69hy5YtcHR0hK2trUGxtm7dGkVFRTh8+LBGj0qxt956C1ZWVlixYgX27duHI0eOlLlNX19frFixAkIISCQSvWPasWMHZs2ahb1796JRo0Ya7xf3TPn6+uq9bSLigGIiMsD48eORlZWFkJAQ/Pbbb0hKSkJsbCzCw8OhVCphbW2NiIgIfPbZZzh06BAuX76MsLAwjUsv3bp1w7Jly3D+/HmcOXMGH3zwgVovzMiRI+Hg4ID+/fvj6NGjSE5ORnx8PD7++GP8+eefOsXq6emJ0NBQjBkzBrt27VJtY+vWrao6UqkUYWFhmDp1Kpo0aYIOHTqUuc2uXbvi0aNHuHLlih5H7bnLly9j9OjR+Pzzz9GyZUukp6cjPT0dWVlZqjonT56EXC5/aRxEpB2TGyLSW/369XH8+HEolUq8+eabaN26NSZNmoTatWurEph//etfCAwMRN++fREUFIQ33nhDY+zO4sWL4ebmhsDAQIwYMQJTpkxRuxxkaWmJI0eOwN3dHYMGDULz5s0RERGBJ0+e6NWTs2LFCgwZMgTjxo1Ds2bNMHbsWOTl5anViYiIQGFhIcLDw1+6vbp162LgwIEG3WjvzJkzyM/Px5w5c+Di4qJaBg0apKqzefNmjBw5ssxLY0RUOol48YI3EVEF6dKlC3x8fLBkyZKqDkXD0aNH0b17d6SmpsLJyeml9S9duoQePXogKSlJ68BqQ2VmZsLb2xtnzpzRehNFIno59twQUY1WUFCAP//8E1FRURg6dKhOiQ0AtGnTBgsWLEBycrJR40lJScF3333HxIaoHDigmIhqtM2bNyMiIgI+Pj7YtGmTXuuGhYUZPR5/f3/4+/sbfbtENQkvSxEREZFJ4WUpIiIiMilMboiIiMikMLkhIiIik8LkhoiIiEwKkxsiIiIyKUxuiIiIyKQwuSEiIiKTwuSGiIiITAqTGyIiIjIp/w9OY0oUi8nXqQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Magnitude')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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9eV7vvXv3DP9ftmxZxXnVpucoWrSo4nQXl+zbTVZWltm0tLQ0dO/eHa+//jp27tyJhw8fKq5Lbbo9RUZG4rnnnkN6ejqKFy+ODRs2oE6dOvm2faXjmnNMAenjqhVL02zJfKZDOzjiniHnl19+wbPPPgsg++Vq8uTJ6N69O0qXLo2mTZti8uTJSEpKUlyH2j0CeHyfMA0shBAYNGgQnnnmGaxfv141CDa9LhISEgwvUjml0Xlhj/tlQcaRtElTuW/qgwYNwogRIyxaLqfoW4qrq6tF25swYQJefvlli7ZXrFgxyd/z+kZmjZ49eyI8PBzLli3Dpk2bsGfPHty+fRt37tzBwoULsXDhQvTv3x9z5841evgUJBMnTsQ///wDAAgLC8OwYcPQuHFjBAQEwMvLy7Bfbdq0wZ49e/LtMx2nT59Gly5dkJKSAk9PT6xatQohISH5sm2S5oh7hhxvb2+sWbMGERER+PPPP7Fz505ERUUhKysLkZGRiIyMxPfff49Vq1YZqvJM2XKPmDt3LubMmQMAaNiwIUaOHImQkBBUqFABRYsWNdzr+vXrhz/++MPu10XOsW7QoAEWLlxo8XI5VX4FHQMk0lTuKgIhhMM/g1K6dGnD/7u7u1u1vdwlVDdv3lScV226pXx8fDBkyBAMGTIEAHDmzBmsXr0a06ZNw40bN7BgwQI0atTI4odF7n24ffu2YlVHTpWkowghMHv2bABA69atsX37dtlALz+L7mNiYtCxY0fcvXsXbm5uWLZsGTp06JBv2ydp+X3PALKrynOqy1NSUrBz507Mnz8fK1aswK1bt9CzZ0/ExMRIVrtacg/ImSf3vgHArFmzAGR/G3D//v2y1bpy10WpUqXg4uICvV6PuLg41XSYyrlfpqamFspPVBXM100qECx5c/Lw8MDTTz8NANi3b5+jk4SqVavCx8fHpu1Vq1bNcKM6fPiw4rxq061Vp04djB07FgcPHjSUblk68jAAwzEHskfpVmLJiOK2SEhIMLQPefnll2WDo9TUVERHRzs0LTmuXbuG8PBwxMXFwcXFBQsWLECPHj3yZdv5LT9KQe0pv+8ZpkqUKIFnn30Wf//9N9577z0A2aPA7927V3L+S5cu4e7du7Lru337tmEkbtMg5PTp0wCA5557TjY4EkLg6NGjktNyvwRaU/Kau2NFXttwPQkYIJHDFClSBABUP7mR07Pi7Nmz2LRpk0PT5Orqim7dugEANm/ejDNnzuR5Hbk/W7J582bZNzO9Xo8FCxZYnVZLBAYGGkp/8tJwOTg42BAoKhWd37x50+F5krs9yf3792Xnmz17dr70jrl16xbCw8MND60ZM2agd+/eDt+uViy9Tp1Jft4zlOQuUZS7/oQQ+P3332XXMX/+fEPgEh4ebjQt53xXui5Wr16tWDqU04bq0qVLWL16tex8UnKOsxACP/30U56WfRIwQCKHyWkUqPa9qhEjRqB48eIAsru85rw1yVm/fr3iN4PUjBs3Dq6urtDr9XjppZdw7do12XmzsrKwaNEis3mGDh0KIPuh8tZbb0k2kJ00aZJsrxRLrVq1SrHHzNWrV3H27FkA2b1lLFWkSBH069cPQHYpl9TNT6/X46233kJaWlreEp1HZcuWNfRYWrJkieSD+vDhw/jkk08cmg4ASExMROfOnQ0lVT/++CMGDx7s8O1qKec6vXjxYr617bJVftwzLl68aPhOnZzNmzcb/l/p+vvyyy8lSz/PnDmDiRMnAsjOB9NSyho1agDIHppEqhotJiYGw4YNU0zj8OHDDaXMb731llEPVlOm97lOnToZqhYnT56sWkp98uRJwzAqTwK2QSKHadGiBXbs2IHDhw/jm2++QdeuXQ0XqpeXl6Ehn7+/PxYsWICXXnoJcXFxCA4OxhtvvIGuXbuiYsWKyMjIwLVr1xAREYG//voLFy9exNq1axW78yupV68evv/+e4waNQr//vsv6tatiyFDhqB9+/bw9/dHWloaYmNjceDAAfz111+Ii4vDyZMnDWPwANlvZc8++yzWrl2LtWvXomXLlhg1ahRq1KiBW7duYf78+Vi2bBmCg4NtqqKaOnUq+vTpg+7du6N9+/aoU6cOfHx8cO/ePURGRmLatGmGnitvv/12ntb92WefYfny5YiPj8fIkSNx5MgR9OnTB2XLlsWFCxfw008/Yf/+/WjWrJlhDCZHVMe4uLigT58++PXXX3HixAm0atUKo0ePRo0aNZCUlIQNGzbgt99+Q/HixVG+fHmcO3fO7mkAsoPd7t27G7qQ9+nTB+Hh4YoPlGLFiuUpMHVGLVq0wLx583Dr1i2MHj0affv2NZQuuru7o3Llyhqn0Fx+3DOuXLmCdu3a4amnnsILL7yA4OBgwz3r6tWrWLZsmSFgaNiwoWzj/erVq+P27dto3rw5PvzwQ0Pp886dO/HNN98YesFNmzbNrCF5v3798MEHH+DGjRsIDQ3Fhx9+iLp16yItLQ3bt2/H1KlTkZ6ejsaNG8tWswUEBGD69Ono168fbt26hWbNmmHw4MHo2rUrAgICkJqailOnTmHNmjWIjo42e6FdvHgxmjVrhoSEBPTq1QsLFy5Er169UKNGDbi6uuLWrVs4duwY1q5di4MHD+L99983lFoVePk4KCVpJL+/xZbj2rVrolSpUpIjdkuN2rtmzRrZ+XP/ubi4iO3btxsta80oyjNnzpT9sGruPw8PD8nPYCQnJ4uWLVvKLteoUSOjD5taM7qzJR/+dXFxEV9++aXZspYck6ioKFG2bFnZdb/xxhtizpw5hn9Lfe7E0hHTlc6XxMREw2jAUn+lSpUSu3btUhz12daRtE0/V2PJnyWjT8tRuibVrj2pNFs7enhKSoqoWrWq5P5Z+7HaHTt2yG7P0n379NNPDfPJseWeocbSLxDUrl3b7NuSQhiPUL5u3TrZe42Li4vsSNuPHj0SnTp1kt22l5eX+PPPP1XvxUIIMX/+fOHl5aW4L3LLR0dHG30VQOnv888/z9NxdmasYiOHqVChAiIiIjBw4EBUr17d0NZBzrPPPotLly7h+++/N5TmuLu7w8vLC1WqVMEzzzyDKVOmIDY2Fu3atbM5fYMHD8bFixfx+eefo2XLlihTpgzc3NxQrFgx1KxZEz179sSMGTNw/fp1VK9e3Wz5EiVKYOfOnZg2bRqaNm2K4sWLo0SJEmjYsCEmTZqE/fv3m/VKyaslS5Zg5syZ6N27Nxo2bIiAgAC4ubmhePHiePrppzF06FAcO3YMH3/8sVXrb9CgAf7991+8//77qFGjBjw9PVGmTBm0a9cOixcvxrx585CcnGyYP6dkwd58fHywb98+fPnll6hXrx6KFCmC4sWLo06dOvi///s/HD9+3DBSL9lX8eLFsX//fowYMQJ16tRRHc/KmTjyntG6dWvs3LkT48aNQ7t27VC9enWUKFEC7u7u8Pf3R6dOnTBjxgxERUWpliJ2794dkZGRGDBgACpXrgwPDw/4+fmhZ8+e2Lt3L95//33J5dzd3bF+/Xr8/PPPCA4ORtGiReHl5YXq1avj7bffxtGjRy0eqqR///6IiYnBRx99hCZNmsDX1xeurq4oWbIkmjdvjvHjx2Pjxo2Sy9asWRNRUVFYvHgxevbsiUqVKsHLywseHh4oV64c2rZti48//hhHjhzBhAkTLEpPQaATooBUOhORJgYNGoQ5c+agYsWKuHr1qtbJISoQ2rZti127diEsLMzwaSIqWFiCRESyHj58aOj50rx5c41TQ0SUfxggERViMTExsj2XsrKyMHToUEP3ZVs/vEtEVJCwFxtRIfbll18iIiICr776KkJCQuDn54eHDx/ixIkTmDVrlqFnTHh4OLp3765xaomI8g8DJKJC7syZM/j0009lp7ds2RJLly4tcCMuExHZggESUSE2btw41KxZE1u3bkVsbCxu376NjIwMlC5dGsHBwejVqxdeffXVAvsRXCIia7EXGxEREZEJliBZSa/X48aNGyhRogSrHoiIiAoIIQRSUlJQvnx5xdJxBkhWunHjBgIDA7VOBhEREVnh6tWrRp+QMsUAyUolSpQAkH2Avb29NU4NERERWSI5ORmBgYGG57gcBkhWyqlW8/b2ZoBERERUwKg1j2HXFCIiIiITDJCIiIiITDBAIiIiIjLBAImIiIjIBAMkIiIiIhMMkIiIiIhMMEAiIiIiMsEAiYiIiMgEAyQiIiIiEwyQiIiIiEwwQCIiIiIywQCJiIiIyAQ/VktERESaeJSph4sOyBICHq4uqh+QzU8MkIiIiCjfPcrUI/irLRACSMvMQmi1Mvj9zWZaJ8uAARIRERHluysJ95Gclmn49+5ztzVMjTm2QSIiIiINOE91mhQGSEREREQmGCARERFRvnOi9tiSGCARERERmWCARERERPnOyQuQGCARERERmWKARERERGTiiQiQdu/ejWeffRbly5eHTqfDqlWrVJfZuXMnGjduDE9PT1SvXh3z5893eDqJiIgomzONmi3liQiQ7t+/jwYNGuDXX3+1aP5Lly6he/fuaNeuHaKiojBy5EgMGjQImzZtcnBKiYiIqCB4IkbS7tq1K7p27Wrx/DNmzECVKlXwww8/AADq1KmDvXv34scff0Tnzp0dlUwiIqIC6VZyGhYciMVrzSqhYsmidlmnc5cfPSElSHl14MABhIeHG/3WuXNnHDhwQHaZ9PR0JCcnG/05khAChy7eRdKDDMlps3ZflB2WPeH+IyTcf+SQdKVnZuHCrVTJaTcSH+LnbedxNzXdbJoQAtHxKcjM0jskXdcTH+KezD5vPh2PPyOvSk67n56JS3fuOyRNer3A2fhkZOmF2bSHj7Lw644LiI5PkVw29s59pKZnSk6zVdLDDFxNeCA57dT1JPy28wIeZZrnU2aWHmfjkyGE+f7YQ8ztVDx8lCU57Y+Dl7FL5ny/k5qOm8lpDknTo0w9zt1Mkdznm8lp+HnbedxKMd+2EALnbqZIHkd7iEt6KHmdAcCO6FtYfOiK5LSHj7IQc1v6+rWVEPLne3pm9vl++kaS5LJX7j5Acpr5vc4eUtIycPmu9DV+Ji4Zv+64gLQM8/Mu67/r11Hn+8XbqXjwSPoaXx55FTuib0lOu5OaLnv92oNcmt5eeAS/7ohB71mHHLZtZ1MoA6T4+Hj4+/sb/ebv74/k5GQ8fPhQcplJkybBx8fH8BcYGOjQNK45fgO9Zh5El592m03bd+EuJm44g35zI8ymPcrUo/GXW9D4yy3IkAhGbqWk4UyccnB3JzUd/96QnufVmQcRPmUXNp2ON5vWa+YBTNlyDiOWRplN++PgZXSeuhvvLjkmud4T1xKR9FD5BnnqehISH5gHQQn3H6HlN9vR6MstkssN+eMIxvx1QvKmEjZ5J9p9vxOnrpvfuB88ysTRK/egl7jh53j4KAtHLkvPM2XLOXSZugefrz1tNm3qtnOYvCkanaea5290fArafr8ToZO2SW7z0p37uHZP+QZ5+e592Ztog883o/V3OyTX8cy0vfhuYzTm779kNu3/lh9Hl6l7MGvPRbNpWXqBI5cTJB80lsyzP+YOOvywC89M22M27cS1RHyy6hT6S5zver1A8FdbEfL1Nskb+12FcznHvfuPJPMfAAbMj0CnH3djVdR1s2lvzDuMKVvOYfDvR8ym/XXkGjr9uBuDf4+UXK/cuZzb6RtJki86qemZCJ20HU2+2iqd5nmHMX7lScnrvPPU3ejwwy4cjk0wm5aWIX8uG8+TIBkE/bYzBl2m7sGHf58wmzZr90VM3hSN7j/vNZt2+e59tJm8A42/kL5+r9x9gCt3lc/3qwkPECvzotNs4jaETd4pGRh2/WkPJm+Kxm87LphN+3jVKXSZugc/bTtvNk2vFzhy+Z7i+Z4zj1TQf+zKPbT/YRc6/LDLbFrM7VR88NcJDJh3WHK9wV9tRevvdkieP7dT0nHk8j3ZNAHZ16FccP3D5mg8NWGT5MvI0SuJAIArdgzOnLwJUuEMkKwxbtw4JCUlGf6uXpUukbCXDSfjAABxSeZvp9cT5U/Q3BfNfYkSiGYTt6HrT3twUeEtMvirrej28x7J0o1j/10kfx423/+rCdnB5d4Ld8ym/W9X9kP1n1PmgdXuc7fx3C/70OGHnbJpOnjxLp6Zthetvt1hNk2uFMaU1MPmzn83ii3/3jSb1nvWIbz4234sjpB+GweyH6A9p+/HvP2xZtN++e+m+/uBy2bTjl9NlF3nzv/eHFPSzPMvJS0D7b7fiVbf7pB9s334KAthk3ei9Xc7JIPkHEo30jNx5sd0VdQNAMCvO2LMpv207Tx6Tj+AEUulA2AAmLErBj2nH8Bbf5gHFKuPZa875rb5Q+5GonzpUIb+8f7FS1wrTf47l5WCpGZfb8Uz0/YiSiJP9l24C0A6D3MCEKm8nLcvFgAkHzSRsQl4ZtpehE7aLpumE9cS0f3nvQj+yjxosLT04FaK+UMw5+G2/kSc2bSBCw6j5/T9mL3XPADO8c6io+g5/YBkQJETSPx15JrZtFPX5Y//wYvZxzhTIuhKy8hCm8k70GbyDqRnSgcjmVl6tP5uB9p+v1MySH74XxCzT+K+lOOkRIC85L/rfupW8wBp5p6L6Dl9v2wADAC/H4hFz+n70W+ueYnLxv/ug1L399sS+SblokRA2HTiVvScvh+H/jumUnr97wCafLVVMoCetj07Xz9fY/5Sp+RqwgN0+nGX5HOhICuUAVJAQABu3jR+IN68eRPe3t7w8vKSXMbT0xPe3t5GfwXZ4N8jMWXLOaPftp+9iZG5HnAREm+ZjpBTGnUn9RHeXXIMEZceb1cIgc/WnDa8TTmq2klKzgPz41WnMPrPKKO3xfM3UzB88VEcvJid1sWHzB+gjpC7Kmn8ypNYblJ1+MeBWKMg5aHCG649zfmvVGnT6ZsYvvioUZVGSloGRi2LwuRN0QCkgwZHG7TgML7556zRb3vO38aIpceQkZX9YN57Pn/SlVN18jAjCyOWHsN+kwf3xPX/4s352ee7QmGO3eUEg19vOItRy6KMXrAu3bmP4YuPYvvZ7LQvOBCbL2nK/ZLwf8tPYJHJdbbs8BWM+etxidXdVMc0LTD1x38B857zdzB88VGjZgcPH2Vh9LIofLb2XwDA4VjlEh1H6DXzIEYuPWb0EnX+Zgp+3nYekf+9GP0tEcha67M1p3HuZirGSJQeFmSFMkAKDQ3Ftm3G1RdbtmxBaGioRilyDJ1CE7iY2/fN2k+8OT/SUEqQvXz+yF3Muvb4Dbzyv8dtwSIuJWD+/ljFB709imnV1rHi6HXMz1VK1GvmQazL9Raef91VH29nScRVfPCX8Q3pk9WnsTlXaZgjUiW1q7n3f92JOKM365+2nsfKY+ZVVGrrzHu65FdyIykNM3bFGFWzvD4nAqtzn+/5lIe5r8vVUTfQe/bjEoYzccmYtecS7ig86HMn01HtY1Yeu47fdj4uJRowL8LofJc6sxx9rq09fgMfrTxlNP3Dv09ihcq55WjrTsTh9TmP83DWnovqabLH+a4yfVXUDaNS4o4/7jZ6Kbbn6Z77/mzaVEIIgZXHrkm2P1N6RjmDJyJASk1NRVRUFKKiogBkd+OPiorClSvZRaTjxo1Dv379DPO//fbbuHjxIsaMGYOzZ8/it99+w59//olRo0ZpkXwjWXqBCatPYdNp8yqfvFp+5Kqh+FqOpY1IbyQ+xIxd5lUr1pi+M0a2DtyUVDVTblv/vYm1x28ozmOJPefvSFYN5HYnV9G3UiP4B48y8T87HatZuy/i3E3LqhDVnLqehNkS7YfyKvFBBmbtvihb5QEAF3NVlcWpNKBefOgKDl2yvbTy7yPXZDsu5JBrBG7qZnIapu+03/ku1ZBbilS1eG47o29h5dHHD19r46ODF+9iqULVMQDcTH58vscqtAFKy8jCzN0xSLdDg/TZey6qthmz1Nn4ZLuc7wAwc3eMYnuj3FVlah0Glkdexf4LyvdlS6w5fkO2IXcOS/Pk4u1UvP/ncZvTBGS3cfw1VxXs3gt3MGrZccn2Z87uiejmHxkZiXbt2hn+PXr0aABA//79MX/+fMTFxRmCJQCoUqUK1q9fj1GjRuGnn35CxYoVMXv2bKfo4r/uxA3J9g4WyxWQf7X+DAAg9pvuNqYKeGn6ftyQqC+3xrcbz2Jn9C0se8u2Eju9XmCQQhuAvDhy+R6OXL6HhoE+qO5XwqZ1/bD5HObsNW/kbKncb3YTN5zBxA1n7JKHz0yz3w1q4oYzSM/MwvD2NWxaz+HYBIxfedIuafplxwVgh33O99fnHMK5m9b39sqdh99uPIv1J29g3butbU7XGzINd/PqbHwKxq44idrlvNEw0Nemdf22MwY/SzRktlTuUgR73rO6TDVv8G+trzecxd37jzCuax2b1nMmLtms1Nda8/bFYt6+WMVjZWn5TJ/ZhyTbQ1nKtDRq8qZoDGtXHQBwVqI9o9xyzuaJCJDatm2rWNQsNUp227ZtceyYfINSrVjaQM9elKoVck8yDY5sPbFzlxgoFbMqbccRlQs3k9NlAySltOSeJNVDyF7yetyV89e2TIy6+rjI3HRNuVettJXLKr2T7M3SXTYNjmy9j+dupKx4HuVxQ7ZeA9fvPZQNkJSSkjudx644ro1NXo+7pdeoNY7mqq6yNg/jkqR7STuMhTttFhyZVOOa9gB8649IfNz9KQSWUh8PydmDICVPRBXbk0TtofXT1vOYtedx6cSQ3yONeqTktU7XHufu1YQHGLb4qNFv7y05ZlQNZe1FYsvFteLoNXyw/HGx8Tf/nMXM3crVJsoPBcsSozTXo0w9/m/5cUPjbgAYtvioUTdzLerlj1xOwPBcebjy2HWMW3FCsjt3DuWHkS7XfApBmkq6ftp63qib9ehlUYYenkDeq5jscWzjk9KMjhUADF98FLdyVa1Yf97mbcHcL4brTtwwqiaZsjnaqKpDcmt2CNaU8jczS48P/zqBjbmGBRm2+KhxUKXBA/TEtUSje9aO6Nv4YPlxxV6flp47ii98Kuv4364Y/LA52vDvsX+fwBo7NCGwxcpj1xE+xXg4kk2nb2LUsihtEpSPnogSpCeJ0uUTn5SGH7ca9zzb/O9NbP73JrrXt65I2tKHnJLhS46ZdXVec/wG3Fx0mNKrocPSoNZAdbRJnfqBi3dx4OJdDGlTTWmD1kwy3geFHVp2+IpZW6f1J+Kw/kScRdUKeX6jtnC+ntPNB0ldEnEVbWqURdd65dTXbVaEZFkalPI+JS3D7Hxfcew6Vhy7bnUVjD0Cgg/+Oo495417nq07EYe0jCzM7t9UPQ1Wpk9K7itg+GLjEvHj15Jw/FoSBraqgiLurnlPi+KD3rJ1rDsRh2UmPS3zdL7n8YBYOv9zv+wz+235kWsIDiqJXk0ryaw893aU0qCUQPlJQghMMulpufTwVSw9fBXPNSivsFKlzdlegjxfYggTALh2L59LwzTAEqQCJL+6bOcQFhbgyw3Qdj3RsgvIQR1x8pWl+6DUO8mi7Th4flNqg3fabUMSlEqvHMHSPJQbKM/SB0Z+n+6OuL4sXeU9lcEwVbeTzzeHexJfLpDikGPqpPdBexbwvfVHpOSYc86KAZKTkQrqD168i4ePslRP1JvJaXbr8aTmdko6jlrQ9kAIgYhLCZKfTLGV1P3k6JV7FrXjSk7LkOzhZ48qGKk17I+5Y9FnVs7GJ+OqyijZ9hJ75z4u3FI/XzKy9GZj9QCOG15hf8wd1d5cQHaJqtKAm/Z070EGjlxWb1smhEBkbALu3XfMZzNMHb+aaNFnVlLTM3EgRuJ8d1QeXrijWF2V4/zNFNnPgNjb5bsPLBpUNjNLj/0xEue7HdIgd2+w5DMrcUkPjcaIyzcyJ4k1586m0zcNQ4D8eyNZ8jg7E1axORmpc+7VmQcRVrMsvujxtOKyIV9Lf5pCeXvW1Zcfv5aEF3/bj5XvtJC9UHS67JGz31l0VHqGXPMpJEKW1BvXi7/tB6DeC+aFX/dJjtqsyIbqmd6zDmFUeE3F+e6mpqv2vMnrPUnpDXzKlnOYsuUcznzRRX57OuDrDWcMI0NbmjALaxwl9Z51CI0r+WLeG80U52su8ykWJdZWjVy6cx89px/A4kEhinmw69xt1Z5mVp7ukl6akV01qtiTSQf0nnkQJ64lyc4jt5zsNJVle88+hLfaVEU5nyKy86SkZaDjj+af2jFOg/3KL2bvvYTZey/h+IRO8ttD9ojgOSNKy6dLYVoe09V71iHU9C+Of0a0UZxPbuR1IYTscbI2nRdv38frcw7hl96NZeczrmbNez51+9l+vQwdhSVIBcSuc7edclCtQ5cSFC4gHTZLfLPNGcgFR44cdHJJxBXFPLzqgDp9S0rtld5eddDJBkf2KW2TXsfRK4maNN5Vs/v8HYWHkQ47o/N/lHBLyAdHjikxBbJH21YKcPJr1GtT8QolbjodsPDgZdlptpI7HrYMKeEoe87fwW87Lii8AOsghMCGk3GKn67Kv0F07Y8lSE7G0m73zkIH+TTrdI69OCxtI5UXjipGB3KOh2O3bQ3F7dqhUbPi6h28fnvT6RTyN19TYh+OfOjroHPK/FXrFKK0PzZvW2Gate2thHDcsVSr+tsRfUuxhmB11HVNPi1kLyxBKiTupKZL16srXFiX796X/KCh6fKKAYFKuo5fTVT8GKlknf0Fy+rsrZWWkYW956XaIMjvTcL9DBy6eFfxJqeDbT2Yrtx9gH9V8sPUiatJ6o3lVYI2pXTp9QL7Y+6YjXiuNg7S3vN3VEdxt+Wen/jgkXSbG6USvISHRsMtyKZJoUpZLQ9PXU9SbMwt9XDeH3PHpjZ8as/d9EyZ811hX5IeZuBAjMr5rnL9qwUc1+49kPyIrJLT15NUP+ardg2qBTEHL95FokJ+SOXh3vN3FEfiBmxrvJ+SliE5/prSvlxLfIgT1xJV1qycS8evyufP9cSHGLE0SnV0e2fGEiQn46g3rjbf7cADCz+zkON/uy/if7svImpCR/k0Kbwl6qB8t4m5nYoev5p3t1XTe/Yh1PIvgdXDW+Z5WTU6nQ7/t/y4yXen1N1JTUevmQcxq1+wYhWMLcMqtJm8I09pAoC+/30jSnm0XaVSy+ypUjdvnS672/3/5RprKi/p6tu8EpoGlcrzspboPHW30acyLLEk4gqWRFzBofEdZOdRfyGQP5bXEx9aNZp571mHULGkF/Z+2D7Py6rRAZiw6rRZV3w16Zl6vDbrIKb2aqhcomZDiXirb/N+vg/54wgA9TZZSmxtUyml75xDeLFRBbzQuEKel1UjAPScvj/P1XQ5wy3s+qCt7DxKtQBqLwQJGlWh2hNLkJyM8tuN9RGSXHBkyRqVHjQ6hVdqtQeG2tt69jqkl4+2obee2luvXHBkyeFX+zaSM1ahqlX7KVU55B600XTa4/VLL7/w4BXF42HLG7XcOWvJMZYbtgJQr4JRWv95C85ZucVtGXNGqSpap9MpBEfqB2uTQhvDnODa+Si/ECjdz7ba0EVd7QO21nbzF0LIBkeWPDPOKvTsUyr1Vru/O2MVeV4xQCJVLoqlWspXgS3LOoojxxtxUShhcFG52rS6objYkIdK02yl1bgwLgo7pba/ysfqSTzfFUqQ1Y6VI08epe3akIe23rMUB77N9xGysimdl4r5q/JCoNX5bk8MkJyNUglDPibDaLs2tFFRvoCsThIAx9z4bT3GqlWOKstqQb1NhuwOWdS9WLkdivNRTJNCqYhapwRbHxiOGDTR0hTJJl2pBFlpuTxs297UPn2jdP1acs/K74bpSmeFJduz9vpXbWPmjBd3HrENkpPJ7xNu7IqTcPmvu6b8dtUe7NYuJz/9zfmHoRdCsXrP2jcuax8zc/dewslrSfB0k3+vULuBOiIPh/weCQHAzQFv5KrtNWR+f/AoCwPmRSAjS2CvxCCTFnFAADxh9SkU83BTeaionLfy8aJVywHZ5zuQ3bhcjtVVMNYthiURV3Al4T483Vxlt60W9Dki6H/7jyPIEsIhpZfWtk8CsvMwPTML+yU6B1jC+io2+WkjlhxDxZJFEVRG/qOySiXbCvGvagBs7QvB1YQH8PcuAg+F+2x+YYBUgDjiZrP9rHKbGUCtCFb5gaG2rC3pcgSlazo9U6/6sM9udyG9EheVOnu1Kjg5mx04dL9yiZhy/u6wYEyg/H7LzP2RYDlqVYryJWrWd2t3xvMdAPZdUH7YK13/Li6OqYLZaOPYaopVaJBPl06nnGZL8jC/C1VuJKXhRlIaIiR6uOWw9ntt2Z1OrK+OltP6ux1oGOiLVcPs3wknr7QP0ciIMxZLqtXJKxbBOrA+35ZGjfJsbGOgk1+Frb3YHEWtiF6+jYnO6qDOsO4C1iZDLWBUDIBtPd+tXU6lU4ItXJSqHOGcVTCW9NpUmm7jxu3O1uvE2lJitWUHzFceUV5JVD59QkgNAyQn44wPDNt6KtiyrGM48igqP1yVaXY8VCJNaz9Ho0YtYNCqkbYtbUicsc2NIw+jQ4MJB1HPQ23uWZp1SlDZX2tfCGzpeeksGCA5GWe8p1jbSNtRddQ5HHE/sccLouwqdAWvwTJgff6qrtfG5R1FOSBUWE4lf23tteWYRtq2n/COOj+0oFbibWu7J0eUEtt6Wlhdqq1SQ/AkYBukAsTaC+GLtf/atN0Jq0/JT1Ts1aN8O/hszWlbkoXvNp61ajml4zhn7yUrU5NttsLyag8MpWkfrzppfaIAfLJKPg/VGizLJWvrmZt5Hnw0t0y9wKJDl61Kl5Kv1tl2vn+6Rv5YKfVUU8vfT1fbdr5P3HDGquWUzvffD8Ral5j/rDh6He6uClXsSiXiCulSOl8toXS9WPvCZ48Roeftk78/WFtDMHXreWuTAwB4fU6E7DS1PHzC4yOWIBUkVve+UrgoLaHU2NZF5YGhVEqk+gkMFb8fkH+4KlG6Ea3P4wjaeeGi0F4LUC7aX3jwik3b/kPmA5xqlPLQluAoh1IjYGtLTJSCVEscvZIoO00pYMwuYZDPw7x+JsaU3EeDVSkcRksa0qvJyJLegE6tSE2BtedrDquvF5U8tJVShwprX4Bn7IqxMjXqbOmE8yRggORkCtrpptY+6Qm/fvJEtdF6/iXFiNKNWanReWGk3iaDcqgdD63aVCqe7+A9KzeljiVqnU6eBAyQChBHtEGwB/mLxDmvHs0a/zrr8VD6FIWGqXbOs12pV5/CRA1p1rlDoXQZ0O46VKJl9jnh4VBsiG3LoXr9v29EOjsGSE7GCe+vitSH7S9gO+RABfVQOGtvJC2oD4xIOZz1hUAN71m5qLSZtPbesOe8lYPH5jMGSE7G2kaNmVl6B6TGMopv1BpRKm3T8s1V6Yai1yhdqlVsGtEr5qF2mSj/Rq3dwVI6HlqdV4BywKiUv46ktF0t89AZz3elcb8yswRu2NiO1NnphLPW2zi55ORk+Pj4ICkpCd7e3nZb75+Hr2LM3yckp5XzKYK4pDTJaZ5uLkjP1C5IcjYli7rj3oOMPE8j51HMwxX3ZRqC+3i5I+kh8zCH0vEo4emGlPTMfE4R5ZV3ETckp0nnU2G8Z8V+091h67b0+c0SJGej8AIjFxwBYHBkQulmUthuNAWVXHAEgMGRCaXjweCoYJALjgDes7TCAMnJsPabiIhIewyQiIiIiEwwQHIy7DFERESkPQZIRERERCYYIDkZlh8RERFpjwGSk2ENGxERkfYYIBERERGZYIDkZFiCREREpD0GSE6moH6/iIiI6EnCAImIiIjIBAMkJ8MqNiIiIu0xQCIiIiIywQCJiIiIyAQDJCfDT40QERFpjwESERERkQkGSE6G5UdERETaY4DkZFjDRkREpD0GSEREREQmGCA5GY6kTUREpD0GSE5GQGidBCIiokKPARIRERGRCQZITkawAImIiEhzDJCIiIiITDBAcjIsQCIiItIeAyQnI1jHRkREpDkGSEREREQmGCARERERmWCARERERGSCAZKTYRMkIiIi7TFAcjIcSZuIiEh7DJCIiIiITDBAcjKsYiMiItIeAyQnwwCJiIhIewyQiIiIiEwwQHIyLEAiIiLSHgMkIiIiIhMMkJwMv8VGRESkPQZITobhERERkfYYIBERERGZYIDkbFiEREREpDkGSE6GnxohIiLSHgMkIiIiIhMMkJwMO7ERERFpjwESERERkYknJkD69ddfERQUhCJFiiAkJAQRERGy886fPx86nc7or0iRIvmYWnksQCIiItLeExEgLVu2DKNHj8ann36Ko0ePokGDBujcuTNu3bolu4y3tzfi4uIMf5cvX87HFMtjFRsREZH2nogAacqUKRg8eDAGDBiAp556CjNmzEDRokUxd+5c2WV0Oh0CAgIMf/7+/vmYYiIiIpJz+e59rZNQ8AOkR48e4ciRIwgPDzf85uLigvDwcBw4cEB2udTUVFSuXBmBgYHo0aMHTp8+rbid9PR0JCcnG/05Arv5ExFRYRc2eSeiriZqmoYCHyDduXMHWVlZZiVA/v7+iI+Pl1ymVq1amDt3LlavXo2FCxdCr9ejRYsWuHbtmux2Jk2aBB8fH8NfYGCgXfcjB6vYiIiIgA0n4zTdfoEPkKwRGhqKfv36oWHDhggLC8OKFStQtmxZ/O9//5NdZty4cUhKSjL8Xb16NR9TTEREVLhk6bUtMXDTdOt2UKZMGbi6uuLmzZtGv9+8eRMBAQEWrcPd3R2NGjXChQsXZOfx9PSEp6enTWm1BAuQiIiIAL3GVSoFvgTJw8MDTZo0wbZt2wy/6fV6bNu2DaGhoRatIysrCydPnkS5cuUclUwiIiLKA62bnBT4EiQAGD16NPr374/g4GA0a9YMU6dOxf379zFgwAAAQL9+/VChQgVMmjQJAPDFF1+gefPmqF69OhITEzF58mRcvnwZgwYN0nI3sml9RhARETkBVrHZQa9evXD79m1MmDAB8fHxaNiwITZu3GhouH3lyhW4uDwuLLt37x4GDx6M+Ph4lCxZEk2aNMH+/fvx1FNPabULBgyPiIiIgCyNCwx0QrDIwhrJycnw8fFBUlISvL297bbe3w/EYsJq5SEHiIiInnSvNQvEpBfr2329lj6/C3wbpCcNw1UiIiJAr9d2+wyQnAwL9IiIiLSvYmOARERERE5Hr3EjbQZIToblR0RERBwHiYiIiMiMxgVIDJCcDZsgERERsQ0SmWB8RERExDZIRERERGbYBomMsJs/ERER2yARERERmWEVGxEREZEJNtImI6xhIyIiYhUbERERkRmt2+QyQHIygh39iYiIkMU2SJQbq9iIiIgYIBERERGZ0brAgAGSk2EBEhEREQeKJCIiIjLDbv5kROsiRSIiImeg9UCRbrYsfPfuXSxcuBARERG4c+cOOnTogDFjxgAATp8+jZiYGISHh6No0aJ2SWxhwF5sRERE2o+DZHWAtHz5cgwaNAipqakQQkCn06FChQqG6devX8cLL7yABQsWoG/fvnZJLBERERUOBbIN0oEDB9C7d2+4ubnhhx9+QEREhNmATh06dICPjw9WrFhhl4QWFqxiIyIi0r6bv1UlSF9//TVcXFywZcsWNG7cWHIeV1dXNG7cGKdOnbIpgURERFT4FMgSpP379yM0NFQ2OMoREBCAuLg4qxJGREREpBWrAqQHDx6gbNmyqvPdu3fPmtUXalp/e4aIiIisDJAqVKiA06dPK84jhMCpU6dQpUoVqxJGREREpBWrAqQuXbogOjoaS5culZ1n9uzZuHr1Krp372514gojFiARERFpz6pG2mPHjsXixYvRr18/HDt2DC+88AIA4P79+zh27BhWrlyJ7777DmXLlsWoUaPsmuAnHeMjIiIi7VlVglSxYkWsX78eZcqUweTJk9GyZUvodDr89ddfCA4OxldffQVfX1+sWbMGfn5+9k4zERERkUNZPVBkaGgooqOjMWfOHGzZsgWxsbHQ6/WoWLEiOnbsiLfeegs+Pj72TGuhwCo2IiIi7Z+HNn1qpESJEhg5ciRGjhxpp+QQPzVCRESkPX6sloiIiJyOTqft9i0qQbpy5YpNG6lUqZJNyxcmWhcpEhERkYUBUlBQEHRWhnI6nQ6ZmZlWLUtERESFk9YFBhYFSG3atDELkNLT03Hw4EEAQMmSJVG5cmUA2aVNCQkJ0Ol0CAkJgaenp52T/GRjARIREZH2LAqQdu7cafTvlJQUtG/fHnXr1sXkyZPRuXNno+mbN2/GmDFjkJGRgU2bNtktsYWC1iEzERERWddI+5NPPkFMTAy2b99uFhwBQKdOnbB161bExMTgo48+sjmRREREVLho3UjbqgBpxYoVaN++PcqUKSM7T5kyZdC+fXusXLnS6sQVRiw/IiIi0r5CxaoA6fbt2xY1vM7MzMSdO3es2UShpfUJQURERFYGSEFBQdi2bRuuXr0qO8/Vq1exbds2BAUFWZs2IiIiIk1YFSANHDgQ9+/fR1hYGH7//XekpaUZpqWnp+OPP/5AWFgYHjx4gIEDB9otsYUBR9ImIiLSnlWfGhk9ejSOHDmCZcuWYcCAARgwYADKli0LILv6DQCEEHj55ZcxevRo+6WWiIiIKB9YVYLk4uKCJUuWYMmSJWjVqhXc3d1x69Yt3Lp1C+7u7mjVqhUWL16MZcuWwcWFXzPJC7ZBIiIi0p5NH6vt1asXevXqhczMTNy9excAULp0abi52bTaQo3xERERkfbsEsm4ubnB39/fHqsiIiIi0hzrv5wMq9iIiIi0r1GxqgSpatWqFs+r0+kQExNjzWaIiIiINGFVgBQbG2vnZFAOdvMnIiICNP7SiHVVbHq9XvIvKysLsbGxmDlzJsqVK4cPPvgAer3e3ml+sjE+IiIi0pxdu5vpdDpUqlQJgwYNQnBwMEJDQ1G9enUMHjzYnpshIiKiJ5zW5QUOa6TdsGFDNGvWDNOmTXPUJp5IWp8QRERE5OBebGXKlMGFCxccuYknjmA3NiIiIs05LEBKSEjAvn374Ovr66hNEBERETmEVW2Qdu/eLTstNTUV586dw/Tp03H79m28/fbbVieuMGIBEhERkfasCpDatm0LnU65A54QAmFhYfjmm2+sShgRERGRVqwKkPr16ycbIHl4eKBcuXIICwtDu3btbEpcYcQCJCIiIu1ZFSDNnz/fzsmgHKxiIyIi0r7TklWNtK9cuYKEhATV+e7du4crV65YswkiIiIizVgVIFWpUgUffPCB6nxjxozJ03fbiJ8aISIiAqDa1tnRrAqQhBAWF31pXURW0PBwERERac+hA0XeuXMHXl5ejtwEERERPYG0LmCxuJG26dhH8fHxsuMhZWZmIjo6Gps2bcLTTz9tWwqJiIiI8pnFAZLp2EebNm3Cpk2bZOcXQkCn0+H999+3LYVERERE+cziACn32EcLFixAtWrV0LJlS8l5PTw8UL58eTz77LNo3LixfVJaSGhdpEhERER5CJByj320YMECtGrVCnPnznVEmgo1hkdERETas2qgSL1eb+90EBERETkNh/Zio7xjDRsREZH2LCpB+uKLL6DT6TBs2DCUKlUKX3zxhcUb0Ol0+OSTT6xOYGHDgSKJiIi0pxMWtAp2cXGBTqfDmTNnULNmTcO/LWlQrNPpkJWVZZfEOpPk5GT4+PggKSkJ3t7edlvvx6tOYuFBfp6FiIgKt2pli2Hb+23tvl5Ln98WlSDNmzcPAFCuXDmjfzuTX3/9FZMnT0Z8fDwaNGiAadOmoVmzZrLzL1++HJ988gliY2NRo0YNfPvtt+jWrVs+plgaq9iIiIi0Z1GA1L9/f8V/a23ZsmUYPXo0ZsyYgZCQEEydOhWdO3dGdHQ0/Pz8zObfv38/XnvtNUyaNAnPPPMMFi9ejOeffx5Hjx5F3bp1NdgDIiIiciYWVbE5u5CQEDRt2hS//PILgOxedoGBgXj33XcxduxYs/l79eqF+/fvY926dYbfmjdvjoYNG2LGjBkWbdNRVWzjV57E4kOsYiMiIlozvCXqV/S16zotfX4X+F5sjx49wpEjRxAeHm74zcXFBeHh4Thw4IDkMgcOHDCaHwA6d+4sOz8ApKenIzk52ejPEQp+uEpERGQfp6475llrCYuq2N58802rN6DT6TBnzhyrl1dz584dZGVlwd/f3+h3f39/nD17VnKZ+Ph4yfnj4+NltzNp0iR8/vnntieYiIiILJLrC2f5zqIAKfco2nnl6AApv4wbNw6jR482/Ds5ORmBgYF2384LjSpgSQSr2IiIiDSMjywLkHbs2OHodFitTJkycHV1xc2bN41+v3nzJgICAiSXCQgIyNP8AODp6QlPT0/bE6yiWZVSCK/jj61nbqrPTERE9ARz0bAIyaIAKSwszNHpsJqHhweaNGmCbdu24fnnnweQ3Uh727ZtGD58uOQyoaGh2LZtG0aOHGn4bcuWLQgNDc2HFKvTskiRiIjIaTh7FZuzGz16NPr374/g4GA0a9YMU6dOxf379zFgwAAAQL9+/VChQgVMmjQJADBixAiEhYXhhx9+QPfu3bF06VJERkZi5syZWu4GERER5eL0JUhK9u/fjz179uDGjRsAgPLly6NVq1Zo2bKlzYmzVK9evXD79m1MmDAB8fHxaNiwITZu3GhoiH3lyhW4uDzusNeiRQssXrwYH3/8McaPH48aNWpg1apVTjMGEguQiIiItH0eWj0O0smTJ/HGG28gKioKAAyfHdH9F+01aNAA8+fPR/369e2TUifjqHGQAGDI75HY/C/bIBERUeH2Y68GeKFRRbuu066fGjEVHR2NsLAwJCYmomLFinjppZcQFBQEALh8+TL+/vtvREVFoW3btti/fz9q165t1U4UVmyDREREBOg0LEOyKkAaP348EhMTMXbsWHzxxRdwczNezXfffYcJEyZg0qRJ+Oijj/D333/bJbFERERUeGhZYGDVSNo7duzA008/ja+//tosOAIAV1dXTJw4EU8//bRTDxHgrLSMmImIiJyFTsMIyaoAKSMjw6K2RfXr10dGRoY1myAiIqJCTsviAqsCpAYNGiAmJkZ1vpiYGDRo0MCaTRRqbINERESkbTd/qwKkjz76CIcPH8bcuXNl55k3bx4OHz6M8ePHW504IiIiKryc/ltspooVK4ahQ4di8ODBmD9/Pnr16oXKlSsDyO7F9ueff2Lv3r0YOnQoihcvjt27dxst36ZNG9tT/gRjCRIRERHgouHz0KpxkFxcXKDT6czGPsoh93uOrKysvG7S6ThyHKR3Fh3BhpPxdl0nERFRQTOjbxN0qSv/nVRrOHQcpH79+mnasvxJx15sREREBbCKbf78+XZOBhEREZGxAtdImxyMBUhEREQFr5s/ERERkaO5aBilWFXFBgDp6elYunQpdu3ahbi4OKSnp0vOp9PpsG3bNqsTWBixAImIiKgAfovtypUrCA8PR0xMDNQ6wbExNxEREVmjwDXSfu+993DhwgW0b98eI0aMQNWqVVG8eHF7p63QYlBJRESk7fPQqgBp27ZtqFGjBjZu3Cj5sVoiIiIiWxW4RtoeHh5o3LgxgyMHYfkRERFRAezm36JFC1y4cMHeaSEiIiIy0LLFiVUB0ueff44zZ85g1qxZ9k4Pgd9iIyIiAgpgI+3GjRtj8+bN6Nu3LxYuXIhOnTqhQoUKcJEZsKBfv342JZKIiIgKnwLXzR8AtmzZglu3biE2NhZ79+6VnEcIAZ1OxwApj1iAREREBLgUtBKkyZMn4/PPP4enpydeeOEFdvMnIiIiuytw3fynT58Ob29vHDp0CLVq1bJ3mgo9joNERERUABtpx8fHIywsjMEREREROYyWVWxWBUjVqlWDXq+3d1roPyw/IiIiArR8IloVIL399tvYsWMHYmNj7ZwcIiIiomwFrgRp2LBhGDJkCFq3bo358+fj+vXr9k5X4cYiJCIiooLXSNvV1RVAdjf+gQMHKs6r0+mQmZlpzWaIiIioECtw3fwDAwMtiupyxkGivNFyYCwiIiJnUeAGisxL2yM25iYiIiJrFLhu/pY4duwYRo8ejYoVKzpqE08sFroREREVwG+xybl69SoWLVqEhQsX4syZM6xiIyIiIqsVuCq23FJSUrB8+XIsXLgQu3fvhhACQghUqFABvXr1wmuvvWaPdBYqDCmJiIgAF4fVc6mzKkDKysrCxo0b8ccff2Dt2rVIS0uDEAJAdq+1nTt3onXr1iw9IiIiIqtpWYKUp9js8OHDeO+991C+fHk899xz+PPPP5GZmYnnnnsOy5cvR9OmTQEAbdq0YXBkAx46IiKiAtDN/6uvvsKiRYtw7tw5Q0lRixYt0LdvX7zyyisoVaoUAGDq1KkOSygREREVLk7fSHvChAnQ6XQICAjAO++8gz59+iAoKMjBSSu8OA4SERERUCC+xSaEQHx8PDZt2oQtW7YgMTHRgckiIiKiws7pv8V26NAhDBs2DKVLl8bevXvx9ttvo1y5cujZsydWrFiBjIwMR6ezUGEbJCIiIm2/xWZRgNS0aVNMmzYNN27cwOrVq/HSSy9Bp9Nh5cqVePnll1GuXDm89dZbuHnzpqPTS0RERIWE05cg5XBzc8Ozzz6LZcuWIT4+HrNmzULr1q1x7949zJo1CzExMQCAsWPHIioqyhHpLRRYgkRERFSAuvnn5u3tjYEDB2Lnzp2IjY3FxIkTUbt2bQghMHnyZDRp0gR16tTBl19+ac/0EhERUSFR4L/FFhgYiHHjxuH06dOIjIzEe++9Bz8/P0RHR+Ozzz6zxyYKGRYhERERFfgAKbfGjRvjxx9/xPXr17F+/Xq8+uqr9t4EERERFQJaNtK268dqc3NxcUHXrl3RtWtXR23iicU2SERERAWokTYRERFRfimQjbTJcViARERExBIkIiIiInMMkCg3tkEiIiICXJx9JG0iIiKi/KZleQEDJCekZaM0IiIiZ+H032IjIiIiym9spE1G2AaJiIiI3fyJiIiIzOg0jFIYIDkhFiARERGxkTYRERGRU2GA5IS0bLVPRETkLNiLjYiIiMiJMEAiIiIiMsEAiYiIiJwSG2mTETZBIiIiAoq4u2q2bQZIRERE5HROftYJrhoOpc0AyQnxW2xERFTYlSjirun2GSARERERmWCA5ITYBomIiEhbDJCIiIiITDBAckIsQCIiItIWAyQiIiIiEwyQnBDbIBEREWmrwAdICQkJ6NOnD7y9veHr64uBAwciNTVVcZm2bdtCp9MZ/b399tv5lGIiIiJydm5aJ8BWffr0QVxcHLZs2YKMjAwMGDAAQ4YMweLFixWXGzx4ML744gvDv4sWLeropFpMy68XExERUQEPkM6cOYONGzfi8OHDCA4OBgBMmzYN3bp1w/fff4/y5cvLLlu0aFEEBATkV1KJiIioACnQVWwHDhyAr6+vITgCgPDwcLi4uODQoUOKyy5atAhlypRB3bp1MW7cODx48EBx/vT0dCQnJxv9OQrLj4iIiLRVoEuQ4uPj4efnZ/Sbm5sbSpUqhfj4eNnlevfujcqVK6N8+fI4ceIEPvzwQ0RHR2PFihWyy0yaNAmff/653dJOREREzsspA6SxY8fi22+/VZznzJkzVq9/yJAhhv+vV68eypUrhw4dOiAmJgbVqlWTXGbcuHEYPXq04d/JyckIDAy0Og2KWIRERESkKacMkN5//3288cYbivNUrVoVAQEBuHXrltHvmZmZSEhIyFP7opCQEADAhQsXZAMkT09PeHp6WrxOIiIiKricMkAqW7YsypYtqzpfaGgoEhMTceTIETRp0gQAsH37duj1ekPQY4moqCgAQLly5axKr73pWIRERESkqQLdSLtOnTro0qULBg8ejIiICOzbtw/Dhw/Hq6++aujBdv36ddSuXRsREREAgJiYGHz55Zc4cuQIYmNjsWbNGvTr1w9t2rRB/fr1tdwdIiIichIFOkACsnuj1a5dGx06dEC3bt3QqlUrzJw50zA9IyMD0dHRhl5qHh4e2Lp1Kzp16oTatWvj/fffR8+ePbF27VqtdsEMh0EiIiLSllNWseVFqVKlFAeFDAoKghDC8O/AwEDs2rUrP5JGREREBVSBL0F6ErEAiYiISFsMkIiIiIhMMEByQmyDREREpC0GSEREREQmGCARERERmWCARERERGSCARIRERGRCQZIRERERCYYIBERERGZYIBEREREZIIBEhEREZEJBkhEREREJhggEREREZlggERERERkggESERERkQkGSEREREQmGCARERERmWCA5ISE0DoFREREhRsDJCIiIiITDJCIiIiITDBAIiIiIjLBAImIiIjIBAMkIiIiIhMMkIiIiIhMMEAiIiIiMsEAiYiIiMgEAyQiIiIiEwyQiIiIiEwwQCIiIiIywQCJiIiIyAQDJCfkotNpnQQiIqJCjQGSE3JzZYBERESkJQZITsjNhQESEREVXv7enlonAW5aJ4DMubkybiUiosKp41P+GNO5ltbJYIDkjFiCREREhdWsfsFaJwEAq9ickjtLkIiIiDTFJ7ETcmUJEhERkaYYIDkhd/ZiIyKiJ0izoFJaJyHPGCA5IVcXZgsRET05vny+rtZJyDM+iZ0QS5CIiOhJUhDHP2aA5ITcWIJERESkKT6JnRAbaRMR0ZOkID7VGCA5IVaxERHRk4RVbGQX+VGCpLSJYh6uDt++lEqlispOK+9TJB9TYuzp8t6y0xpV8s2/hBQAjRWOh5bHSumcLlXMIx9T8phfCflPKfgWdc/HlBhrUNFHdppS/hZGSsejXgX54+hoSoMNt6/tl48pKdgYIDkhRw8U2aiSL97vJD+M+9/vtHDo9uWsVNjuVy/UhZe7NoHb7P7yo7qufKdlPqbksS96PI2a/sUlp7WuUQZD21bL5xRlW6FwPH5/s1k+puSxqmWKYXz3OrLTV2mUhyuHyW/3k+5PoZxGLwXzB8jnk1L+OtLojjVlu4nXr+iDcV1r53OKsikdj7lvNM3HlDxWqpgHfnq1kez0b16sl4+pya3gFSExQHJCjv7UiBC2TXcUnUIZrBCAgDYJ0znhhS2Ecj5plYdKlPLXkQSUj4dWRf9Km1VLsyM5Y1WI0vWfPc35aHUchRDK90onzF9nxQDJCTn6Y7VCCAiFu6/eCZ+uQgB650uWZvRC/haYHTzxYOVQOlbOSgjhlNehVvRCyAaMAjxWuelVXp604oyBtxoGSE7I4SVIcNILSGGaMPwn/znjha1YeqRZWZsyrQ6jcNITXu280irFTlliqjTNWQMCjbYrVF4InDF/nRUDJCdUsqhjG42qFUk7Y9G+arGxAznj7ST7ma9Q5eCETwzNqhxUzhztqtgUqpSh4UPfKU945RJTZ6RtlbL8QdGu6k+b7dqCAZITqlS6KD5WaFRqCb8SnrI9dwTki6sB5RP5s2efsildnz/3tFXLKT0wageUsD5B/3mxcQWb12Hq6xdsawzZL7Sy7DSlt0S1N+pOT/nblC5HfDJgcOsqNi3v7qpDWZleYbaUMNh6vlu9vEKRacWSXlanJ4fSuWUtW8/3Hg3Ly05TfCFQmAYArwRXtCldjmjU/FqzSjavo1rZYtITbAhEutcvZ/3CTyAGSE5qUOuqNi3/cnBFVPeT7uWk1uBZadobLW17kPVvESQ7TfGNWqHUq0FFX4TXsb7rajEPV7SuUUZ+BivfuHqH2HYTVH2IKbTJULpH9gsNsjZJAICXm1j3wFHK3/a1bQvaXgkORJ1y0sMxqJWoKQVPtp7vSssrlpgqvMQ8Xd4bPRvb9tBvV0v+erG2hKFX00ArU5Pt9eZKLwTy179QaJ8E2H6+v2plMKN0GMNqlrFpOJfu9cvh6fLSwwiodkpQWO+vvRtbnSYA+KW3fO8552xKr4wB0hNK6Yai1ojPOYv25RtiZjfStH6zTtpERTFNao20lRqt2tqg1RFF9LZWnypXOSqvXbMqZYVpSnlojypUR1RX23paKKVIqZF29nTlZZ2N2jWqvgKVgFGjYOSZ+vKlgAWxkw0DpCeULT0ZCtp5bOvNRu3mq1WjRkfdULR6YCgGVjYmyRnbXKmxNsUCtuehtSUMjqR30Amv1YNZuYTQtsBcKf/VVqtZ26iCd4kyQHpSKVcpqL1RO98D1JHj/qjdQLVq1Kh4E1QoRVC7+dqau87YC0avl5+mfjy0Od/V8kjxGrV3YnKx9gFq63WidB0qHQ/VJgNa3c8ceJ3oFYZqUbtXata7rsC9egNuWieA8q5BRR+E1SyLu/cfYdGhK5LzZN9Q5C8gpSvI2jeuoW2robinG9Iz9fh523nrViJD7WGhFEy8FVYVEMDiQ1eQkp4puYL8vnTfaBGEUsU8cD89E//bfVFyHrUqBdnJqm1u5KcNaVMVXu6uiL17H6ujbkjO45gqNnmVShVFt3rlICDwv10yx0qhzY7aOEjWnu/D2lVDUQ83PHyUhV92XMjz8moBsFyJitqYYG+FZbdfXH3sBuKT02TXYW9KgdVLTSoisGRR3H+UiZly57tCLukVnvpqVexK04a0qYpiHm64mZKGxTL3UkdQOv6li3ng5eBAuOiA33bGyC4v34RCuUScLMcAqQDqUMcf73WoAQCyAZJScbV6DbV1V9eHXR4P929NgKTWJkOJ3PRSxTwwrmt2j8Cr9x5gw8l4s3nUqisc8cY1oGUQKpfO7oUiFyApF6MrD5ynGBAolLaM75Z9rCIuJcgGSNayNrAKr+OPsf99SkIuQFIsfbAhYFTyQefH57s1AZJy0K8W1ElPddHBcL4nP8zEkoi8P/Qdcb6/3rwyGgT6AoB8gKTyFqRUgqS0sFL+5pzv526m2D9AsvJAtqlZ1nC+z9sXi4cZWWbzqJW2KbE2dor+qgsSH2TgxLUkDP49Ms/LW3qZafWZJCkMkJ5QSm2QHFldZQvFT40oFqHbow2S9XX6jqKWR4qfXlB8o7Ztj6x9gKr1UrSFXqFhqqMeGLZS7VmnUERoeyNteU5ZpQyF+xnUGnjbkirrqfVStIVQ7NanEnxbee54urnC39sVHZ8y/0ZgcU83hNUqq7i8Jfedle+0QKNKJa1KnyMwQHpCKZcwqC2rDWsvXLVxndSXV7uhWL9uW6iW81nZrsYZS99tbp+g8kKguKhW+WtLmxtbD5cT1sEovxAol6mpLetsbM4/KLwgqQTQjjgaa99thSplZMZlygMvmbH7tMJG2k5sZHgNyd8tubhebiI/JoktNxvA+k+hVPBVHuDOliBlSJj0uFG5bxRy6/j0GeXB/Gx5eLeqLj2+kiV5WLWs9DhW2csrB8BK6w+urP6GJneD7fJ0gMMa8b7fsaZ0Wiw4/spjTqkF0MrrL1HEuvfIUsVsGBFfpVRTbjwxofCvHGO61FLctC2Ni8PrSI9nZckVVL+ij+w0paBQ7X5Wp7z0+Fim65cSVlO5VESJ2lEc36225O9G9yyZ/XpdcRBZ2+6lk1+qj5esHOtMiSX3vMws5wpmGSA5sZHhNXHk4/A8LfN0eW8c+TgcT5X3ViqBtemN68RnnbBkcPM8pWvvh+2w/f/CFOexuueVyB747tD4DnkO3g6O65A9mJ+DrssFbzZDxEcd8rTMB51r4fiETijuKf9gVn/blle6uCeOfdIR/fM4mvKh8R3wW5/GDmmjIgQwvH11HByXt2NVwdcLER91QHBQKeUqZRteCA5/FI4N77XOU7q2vR+G/WPbK86jWrKl8HvzqqURMb4DyhSXHj1czsFxHfBO2+oOK0Wc+XoTRIzPWx6+1aYqoiZ0hK/CJ5YUH/hQPpbeRdwRNaEj3m1fPU/pihjfAXPfaJqnZSwlAAxpU031HDFVzMMVez9sh3a1/JRrCGy4v78cHIgJNo4gL71doFmVUorzPMpSaCCpAQZITq50Hm+Ari469WXU3jBUtlHUww0+Xu55SlfpYp7wdFMpPrWxmNzfuwhc8li6EeBjXp9uvvE8rdKIq4sOfiUs2EYuXu6u8CmqfHxteWAAQMliHvB0z1txtr93EbjYMPqv2pI6nc6y/MjFkuOr+kKgso0i7q7wVckPU6WKeqCIyvFVqwZVy0M/7yJwzeMd3JLja0sbJBcXHfy885aHnu6uisERoFKtqHI/AwBfC/LDVPbxtf5gWHLbKK9Sqm7K1UWHiiWLKs6j1B7PUSy5PwsIzOoXjJ9fkx9tmyVIZDNL34atHifHgnM0rxegJTdday9qYfT/8pUMztRmx5YAFch5YMjkr9orpGE+6/bZ2geociN8eZbmm3yjddtH0s7rOWBpHipNU9ofuXUYX//WbdsRFB+gFj5crS0Rt4QjAgprS+mV7lmW3M9UO+HITzJwVCmxj5c7nmsgP9p2MU+2QSKNjQyvoXhDeLqCep29I6hVKX0i01bIHo0w5VbRt3kl1Zuvv3feSvnsoXv9cooPDHt8DFOOXKAj9+2/3OQ+SOrIPBzXtY5iHlYqpfxG7ihKpTlCCEx4RvrDzvZ5lEuvxZKPNtujMW5evdSkosILgXDIx6bVqLWphABetfH7dMqrlz4e37xYT/EcKalSWqeFGn7FMSq8puz35bTCAKkAsrYkBMhug9CjofLNxLuIO45P6ITh7eTr7O35Bjq1V0Oc/rwz3FTqCwa2qqLavkHxjTqPj5aI8R3wZQ/1L9fvHtMOe8a0y9O6rR2XJ6RKKRz5OFw1GKlf0ReRH4crPszsWcJw5ONw/DNCuZ2ODtl5ndc2WUrU0hkxvgNeUXlIeXm44sRnnWQbzWZvJ28HRGn+L3o8jZOfdVKt8ukdUkn9fFdMg+KiZiI+6oAfXm6gOt+mkW3ynIfWlpjWDiiByI/DVR+cNf2z58sZa0lyO2rDKuTBvrHtseP/2qrON+nFenluk2V8zzKdqLzssU864tVmyi91Hm4uiJrQERNfkL+35bUjhkUlpgrTXmxcESNkOiVpid38CyBLr2Wpi8Tw1qqyEp+i7ijinj/xczFPNxRTKD0CHgc3Uu0blB8S8tURauvI2ZZaYOXp5oqStvRYygM318dtzOTbZGRPKFPcE9Y2ochrMGlJWzmdLvvGK9VmyNI8VGJLHnoXcYeXR/7cDr3cXVGiiGVtzCTPdwuzJq8N03PyRW39Hm4uKJVPpRCuLjrVhug5yS1T3NPqHrZ5DZBKFfWAh5vy/VFAZJ/vNuShcrtM899y7kNq57tvUQ94q5yD9qZUUuusnyFhCdITTOmkqybThdxRxeeWvpDIzVfOR7442x6lWUrrKOpu/wenPW4HwUHS3fXrV/S1w9rzmT0OiMI6gkpLn9dlimtb3SA3hECgQkNcBx8quDjgqWCPa7RRJV/J3xtrMLCgJfczd4UScXu0eVSaz1+m44JfCcc0B1DK38Gtq2D7+2GKw1444VBVABggFUx2KCru2aSi0adBAGBEhxpYNCjElpTJsnRsla2jw4zGf6pY0gufPPMUmlctbdHyprsvFKZZyqeoO6a80gAdavsZfvugcy2sfKeFlWu0zw1h4vP1MKKDcbH0yPAa+LCrfFWRURqsnqhs2ZDmhk8lANntR37t3djq8ZPsUY3UtW4AxnWtjaK5BqIb0aEGVr7T0rI05PF4WDr7undbGeVhyaLuGN+tNjrU8VNYyrJ0WXuOebq5YtprjYzai43pUgt/vR1q+Hfe89L2e9b4bnXMxoYbGV4Dnz0n3VYrL9uxpQRjxTst8EHnx2NLda0bgJ9ebahaKi6bFoVxkCxNZYc6fhjerrrR+f5lj6exerhl57s9+XsXURzTzZkxQHqCKd0QXF10GNq2mtFb2aiONY26nWoR1VcrWxwjwx8PGNi6RlkMbFVFcRn7vFErr+XFxhXxbK7eF8PaVTcaEl+L0Xp9irpjlMngiiPDaxo1dtfixSykamm8Hfb4e0ovNamI7vXLKS5jjyJ2pXXodDq8FVYNIbnGYRnVsSYCcxf7a5CHlUsXM8rD0GqlMaRNNeUefw5s0J7j2Qbl8WLjx4MFvtO2OoKDHh87Lb5GUqKIu9G9Acg+33MPOaLFddi4UkkMy9Ves0fDCqrtPB1d6q3T6fB/nWuh89MBht9eDw0yKom3R7W2vTjjaOcAA6QnTu77qlJ3SmvYUjyr+F0iGy+Odrm+AWTtjbuBk1ZLta5hPBK3rfeRZ3IFKhp9ckuSXJWvmtznVde6ykFYXin1qswLT5O2KrY+CjrleuhZq3a5EjavwxE6P208Eret53v7XKW+9vzGnK3rsra3Vu7Ndq1r+3lgP7ZllJPGRwU/QJo4cSJatGiBokWLwtfX16JlhBCYMGECypUrBy8vL4SHh+P8+bx/fV4rllY5vN22Gmb3C7bLNue90VS1l5KS3Bf2wXEdsODNZjanSacD5g1oiudzva0p9fpQugjrVvDBksHNZds55MXPrzXC9vfDVOay7I4wvW8TzLJTHs7qF4zJLz3upWRtzyIA2DyqDT61w2i7HZ/yx19vh6KyTBshwPJqpP4tgjD3DTud7wOaWtRLyRJ7xrTD/AH2GZF57hvB6G3hEA5KeVitbHH8+VYo2lj5KY3cAcLXL9TDllFtlNNi4QPwx14N7Xq+T8s1KKEt1ZFbR4fhewt6+KkJrVoay4Y0R60A+QDVqFmAkJ/2QqMKWDzY+iYRuV9MewUHYk5/+xx38+1YMI9Dtmy7Ah8gPXr0CC+//DKGDh1q8TLfffcdfv75Z8yYMQOHDh1CsWLF0LlzZ6SlpTkwpfZTp9zjcYoql5ZvzOnu6oLwp6S/jWSJ3Cdtu9p+eR7VW06ATxGbvnGUo2xxT7Sr5ac4unNI1cfVAk+rfJMptFppq8fEyX2smgaVVK1zV+vFlKO4pxs62pCHuRPW8Sl/iz8GWValtLCmfwk0VOhSbamqZYoZVd1IqeH/+FgqBbCuLjq0r22n872Wn+oxsJSfdxG0rWVZmyIlHm4uaF/bX3F059xViLUVHsJA9mcfqijcP5TkrgJsVMkXNfyVt2Xpd+mKeth2vufOw45P+VvcDkitF2p1v+JoYsE3DNVUKlUUISrtKZ/KdX8PrSY/r4uLDi2qlcnzKO9Svn2pPjrk+o5eXgMWV0e07HcCBb6b/+effw4AmD9/vkXzCyEwdepUfPzxx+jRowcA4Pfff4e/vz9WrVqFV1991VFJtVmJIm74uHsdo6LVRYNC8MfBy/jfrosA8q+oMvd2PupWB3UrGBcZz+kfjIMX72LWnkvZ8zsgDVLF3LnfikaG10D/0CDDv4e2rQY3Fx1+2HLOAanJlS6FyqvWNcqgR8MK8M/V9Xf9e62w/cwtQ7q0yMPRHWsanVc1/Utg4gt1sSTiCk5dT3ZcIlSqKj7oXMuoxOR/rzfBooNX8NM27Up8x3SphXom5/uCN5vh2JV7mLo1O12OyEPpQ/V4QyM61EDf5pUN/+4XGoT0TD0mb4q2f2Jyp0shDxtX8sXLwYFGLwybRrbBptPxmJJzvudT+UHurbzfsabRy2MFXy9817M+/jp6DRGXEhyWBrWquf/rVBODWj9uczm1V0P8cfByrvMqf46VR64eePMHNDXr3fz30FCcu5mKs3HJSHyYgaBcgXbZEp64nZKep+2xis1JXLp0CfHx8QgPf/wRWB8fH4SEhODAgQOyy6WnpyM5OdnoL7+8918vl0WDQtCraSWjt7eKJYtiXNc6eK1Z9mB4o2W+iG5vOQMVlvB0w+A2Vc3edDrU8cf//dezw9PNxerxSfLqmxfrA8gOjkaG1zR6Myzi7op3O9TA1F4NAQDvtK0mtQqHyHmb//7lBmZfyn66vA/e7VDD8NDtWi9/2hbknDMtqpXGex1qmJUC9AmpjF9eawwACLbD27OlXv/vIT+rXzCGtatuNICoX4kiGNWxpqHh/kfd6+RLmnICWg83F7zdphpa1zAuAQ2rWRbvtq8BNxcddLrs3mj5YcKz2T243gqrilEdaxqVenm4uWBYu+qY0Tc7Dwe0DMqXNAEwlC7+9Gojs1HdawWUwHsdahg+XJq7Mbgj5VTFN6rki3c71DAqiQeAV5oGGu4NaqXN9jTov3P5p1cbYnj7GkbfrCxd3BMjw2saOj18+qxlPfZsVcTdFT+83ADf9qyHtrX8zKrAm1QuhdeaVcLnPerip1cbGT2TNo1sgz8GPm5CIRUUFjUpyXbWcZAKfAlSXsXHxwMA/P2Ni3H9/f0N06RMmjTJUFqV30Z3rIl321dXHFfj6xfq4YPOtSWLsr/tWQ8f/n1ScuTUzk8H4NiVRJST+OzBy8EVMWXLOYTXMS/y9vJw/W/0a/nAx9PNFf9+0RkuOp1kr5wGgb44fT1JsRi5hcS0DzrXwuRN0YZgKLdXmgai41P+ikXmzzeqgLCaZSXneadtdayOuoFeweajLys1bC3u4YYA7yLI1Oslq2bWvdsKDzKyFAdnWzWsJVLTMyU/BDywVRXM2XvJEHTm1rZWWeyMvo3nJT7j8UWPuug755DZcAAAUMO/BI5P6CQ7Fg8ABJUphuMTOqG4xDx1ynmjuKeb6qdWAiWqLbvXK4f1J+PQN6Sy2bQvejyN0R1rKubhx93rYHi76pLz/NK7EYYvPobPJbp/t6/thx3RtyX3uVu9cpiw+jRaVS9jNs3DzcVwLstV6bq66HDq884QApKjwjevWgoHLyago8T1VMTdBWkZerSpYV71/HH3Ovhq/RnDwzu35xqUR+vqZRSPVZe65XDsk46SVTEDWlbBggOXJTt0VFFoFwYAQaWL4u79R6haxrw6+e+hLWTP5RxLBzdHSlqm5IeZh7Wrhl93xODjZ8wD4JxzR6oqbny3Onh5xgG81aaq2bRKpYvi+KedFBvfl/f1wvFPO6GYRFV0xZJeKFnUHUU93IxKWExJjXL/UpOK+OvINQxqbZ6uj7rXwTvtqitWQ37YpRYGta4iOWjmb70bo/+8CMnhDlpUK42Vx65LrlOtY0rPJtYFrqWKeRi9QEiVDm17PwwHL97FqGXHZedxCsIJffjhhwLZJaKyf2fOnDFaZt68ecLHx0d13fv27RMAxI0bN4x+f/nll8Urr7wiu1xaWppISkoy/F29elUAEElJSVbtY367n54h+XtGZpb452ScuJ2SJjn94aNModfrHZKmrCy9SMvIlJx2/d4DselUnOy2Hz6SXs4elNa959xtcf5miuS0jMws8Sgzy1HJkk1X8sNHYt3xG7J57MhjlZ6RJTKzpPPoTFySOBBzR3KaXq/XJA8zs/Tin5Nx4mbSQ9nlHHW+K+1zfNJDsfFUnMiSOZaOPlZy+7zvwm1xNi5Zclpmll6kZ+T/+X4/PUOsO35DpKRpc75nyFzj5+KTxb7ztyWnOfp8l0tTVpZerD9xQ1y790By+pm4JNlrwVaVP1wnKn+4TszcFaM6z+5ztxySBjlJSUkWPb91Qjhf7Hb79m3cvXtXcZ6qVavCw+NxxD1//nyMHDkSiYmJistdvHgR1apVw7Fjx9CwYUPD72FhYWjYsCF++ukni9KYnJwMHx8fJCUlwdtbm4+7EhEROaOgsesBZLdRHSxRogcA1+49wIVbqXbpxJAXlj6/nbKKrWzZsihb1vZeTlKqVKmCgIAAbNu2zRAgJScn49ChQ3nqCUdERETKlHpdVixZFBUVPqujtQLfSPvKlSuIiorClStXkJWVhaioKERFRSE1NdUwT+3atbFy5UoA2d1TR44cia+++gpr1qzByZMn0a9fP5QvXx7PP/+8RntBRET05BjWrhpqB5RAr6bm7TkLCqcsQcqLCRMmYMGCBYZ/N2qUPTDYjh070LZtWwBAdHQ0kpKSDPOMGTMG9+/fx5AhQ5CYmIhWrVph48aNKFJE+gN/REREZLkPOtfGB50t+y6ks3LKNkgFAdsgERERFTyWPr8LfBUbERERkb0xQCIiIiIywQCJiIiIyAQDJCIiIiITDJCIiIiITDBAIiIiIjLBAImIiIjIBAMkIiIiIhMMkIiIiIhMMEAiIiIiMsEAiYiIiMgEAyQiIiIiEwyQiIiIiEy4aZ2AgkoIASD7q8BERERUMOQ8t3Oe43IYIFkpJSUFABAYGKhxSoiIiCivUlJS4OPjIztdJ9RCKJKk1+tx48YNlChRAjqdzm7rTU5ORmBgIK5evQpvb2+7rZccg/lVcDCvChbmV8FR0PJKCIGUlBSUL18eLi7yLY1YgmQlFxcXVKxY0WHr9/b2LhAnGmVjfhUczKuChflVcBSkvFIqOcrBRtpEREREJhggEREREZlggORkPD098emnn8LT01PrpJAFmF8FB/OqYGF+FRxPal6xkTYRERGRCZYgEREREZlggERERERkggESERERkQkGSEREREQmGCA5mV9//RVBQUEoUqQIQkJCEBERoXWSCp1JkyahadOmKFGiBPz8/PD8888jOjraaJ60tDQMGzYMpUuXRvHixdGzZ0/cvHnTaJ4rV66ge/fuKFq0KPz8/PDBBx8gMzMzP3el0Pnmm2+g0+kwcuRIw2/MK+dy/fp19O3bF6VLl4aXlxfq1auHyMhIw3QhBCZMmIBy5crBy8sL4eHhOH/+vNE6EhIS0KdPH3h7e8PX1xcDBw5Eampqfu/KEy0rKwuffPIJqlSpAi8vL1SrVg1ffvml0ffLnvi8EuQ0li5dKjw8PMTcuXPF6dOnxeDBg4Wvr6+4efOm1kkrVDp37izmzZsnTp06JaKiokS3bt1EpUqVRGpqqmGet99+WwQGBopt27aJyMhI0bx5c9GiRQvD9MzMTFG3bl0RHh4ujh07JjZs2CDKlCkjxo0bp8UuFQoREREiKChI1K9fX4wYMcLwO/PKeSQkJIjKlSuLN954Qxw6dEhcvHhRbNq0SVy4cMEwzzfffCN8fHzEqlWrxPHjx8Vzzz0nqlSpIh4+fGiYp0uXLqJBgwbi4MGDYs+ePaJ69eritdde02KXnlgTJ04UpUuXFuvWrROXLl0Sy5cvF8WLFxc//fSTYZ4nPa8YIDmRZs2aiWHDhhn+nZWVJcqXLy8mTZqkYaro1q1bAoDYtWuXEEKIxMRE4e7uLpYvX26Y58yZMwKAOHDggBBCiA0bNggXFxcRHx9vmGf69OnC29tbpKen5+8OFAIpKSmiRo0aYsuWLSIsLMwQIDGvnMuHH34oWrVqJTtdr9eLgIAAMXnyZMNviYmJwtPTUyxZskQIIcS///4rAIjDhw8b5vnnn3+ETqcT169fd1ziC5nu3buLN9980+i3F198UfTp00cIUTjyilVsTuLRo0c4cuQIwsPDDb+5uLggPDwcBw4c0DBllJSUBAAoVaoUAODIkSPIyMgwyqvatWujUqVKhrw6cOAA6tWrB39/f8M8nTt3RnJyMk6fPp2PqS8chg0bhu7duxvlCcC8cjZr1qxBcHAwXn75Zfj5+aFRo0aYNWuWYfqlS5cQHx9vlF8+Pj4ICQkxyi9fX18EBwcb5gkPD4eLiwsOHTqUfzvzhGvRogW2bduGc+fOAQCOHz+OvXv3omvXrgAKR17xY7VO4s6dO8jKyjK6SQOAv78/zp49q1GqSK/XY+TIkWjZsiXq1q0LAIiPj4eHhwd8fX2N5vX390d8fLxhHqm8zJlG9rN06VIcPXoUhw8fNpvGvHIuFy9exPTp0zF69GiMHz8ehw8fxnvvvQcPDw/079/fcLyl8iN3fvn5+RlNd3NzQ6lSpZhfdjR27FgkJyejdu3acHV1RVZWFiZOnIg+ffoAQKHIKwZIRAqGDRuGU6dOYe/evVonhSRcvXoVI0aMwJYtW1CkSBGtk0Mq9Ho9goOD8fXXXwMAGjVqhFOnTmHGjBno37+/xqmj3P78808sWrQIixcvxtNPP42oqCiMHDkS5cuXLzR5xSo2J1GmTBm4urqa9a65efMmAgICNEpV4TZ8+HCsW7cOO3bsQMWKFQ2/BwQE4NGjR0hMTDSaP3deBQQESOZlzjSyjyNHjuDWrVto3Lgx3Nzc4Obmhl27duHnn3+Gm5sb/P39mVdOpFy5cnjqqaeMfqtTpw6uXLkC4PHxVroPBgQE4NatW0bTMzMzkZCQwPyyow8++ABjx47Fq6++inr16uH111/HqFGjMGnSJACFI68YIDkJDw8PNGnSBNu2bTP8ptfrsW3bNoSGhmqYssJHCIHhw4dj5cqV2L59O6pUqWI0vUmTJnB3dzfKq+joaFy5csWQV6GhoTh58qTRzWHLli3w9vY2e0CQ9Tp06ICTJ08iKirK8BccHIw+ffoY/p955TxatmxpNmTGuXPnULlyZQBAlSpVEBAQYJRfycnJOHTokFF+JSYm4siRI4Z5tm/fDr1ej5CQkHzYi8LhwYMHcHExDhFcXV2h1+sBFJK80rqVOD22dOlS4enpKebPny/+/fdfMWTIEOHr62vUu4Ycb+jQocLHx0fs3LlTxMXFGf4ePHhgmOftt98WlSpVEtu3bxeRkZEiNDRUhIaGGqbndB3v1KmTiIqKEhs3bhRly5Zl1/F8kLsXmxDMK2cSEREh3NzcxMSJE8X58+fFokWLRNGiRcXChQsN83zzzTfC19dXrF69Wpw4cUL06NFDsut4o0aNxKFDh8TevXtFjRo1CkzX8YKif//+okKFCoZu/itWrBBlypQRY8aMMczzpOcVAyQnM23aNFGpUiXh4eEhmjVrJg4ePKh1kgodAJJ/8+bNM8zz8OFD8c4774iSJUuKokWLihdeeEHExcUZrSc2NlZ07dpVeHl5iTJlyoj3339fZGRk5PPeFD6mARLzyrmsXbtW1K1bV3h6eoratWuLmTNnGk3X6/Xik08+Ef7+/sLT01N06NBBREdHG81z9+5d8dprr4nixYsLb29vMWDAAJGSkpKfu/HES05OFiNGjBCVKlUSRYoUEVWrVhUfffSR0dAXT3pe6YTINSwmEREREbENEhEREZEpBkhEREREJhggEREREZlggERERERkggESERERkQkGSEREREQmGCARERERmWCAREROKygoCDqdTutkWK19+/aoWLEi0tPTrVp+1apV0Ol0+PPPP+2cMiJSwwCJiDQRGxsLnU6Htm3bap0Uh1i/fj127NiB8ePHw9PT06p19OjRAw0aNMD48eORkZFh5xQSkRIGSETktLZt24YzZ85onQyrjB8/HmXLlsWgQYOsXodOp8PYsWMRExOD2bNn2zF1RKSGARIROa1q1aqhdu3aWicjz/bt24cTJ06gV69e8PDwsGldPXr0QIkSJTBjxgw7pY6ILMEAiYjy3WeffYYqVaoAAHbt2gWdTmf4e+ONNwzzSbVByl01d//+fYwePRqBgYHw8vJC48aNsXbtWsO8y5cvR0hICIoVKwZ/f3+89957ePjwoWSaHjx4gEmTJqFRo0YoXrw4ihcvjubNm2PBggV53r+c0p7XXntNcvr+/fvx/PPPo3LlyvD09ERAQACaNWuGsWPHIjU11WheLy8vPP/88zhx4gQOHTqU57QQkXX4sVoiynerVq3CwoUL8ffff8Pf3x9dunQxTGvVqpWhWiooKAiXL19G7ttUbGwsqlSpgtDQUOj1ely6dAlt2rTBnTt3sHv3buh0OmzcuBEnT57EmDFjEBYWBm9vb+zevRt3795F7969sWjRIqP03Lp1Cx07dsSJEycQEBCAxo0bQwiB/fv3IykpCcOHD8e0adMs3j8/Pz+kpqYiOTkZbm5uRtPWrl2L559/HkIINGvWDFWqVEFiYiLOnz+PmJgYXLp0CUFBQUbLzJ07FwMHDsQnn3yCL774wuJ0EJENBBGRBi5duiQAiLCwMNl5KleuLExvUznLARDt27cXqamphmnz5s0TAET16tVFyZIlxeHDhw3Trl+/Lvz8/AQAERMTY7TObt26CQBixIgRIi0tzfB7fHy8CA4OFgDEP//8Y9F+nTlzRgAQLVq0kJzepk0bAUD89ddfZtMiIiJEcnKy2e8nT54UAESbNm0sSgMR2Y5VbERUILm4uGD69OkoVqyY4bd+/fqhTJkyuHDhAoYNG4bg4GDDtPLly6NPnz4AgN27dxt+j4qKwoYNG9C0aVNMmTLFqMeZv78/Zs6cCQCYPn26Rek6ceIEAKBWrVqS02/fvg0ACA8PN5vWtGlTlChRwuz3nHZYUVFRFqWBiGzHAImICqSgoCDUrFnT6DcXFxdUrlwZANCpUyezZapWrQoAiIuLM/y2efNmAMDzzz8PFxfzW2JOm6SIiAiL0nXr1i0AQMmSJSWnN2nSBADw+uuv4/Dhw9Dr9arrdHNzQ4kSJZCcnIxHjx5ZlA4isg0DJCIqkCpUqCD5e/HixWWn50zLPXBjbGwsAOCjjz4yaiye+y81NRV37tyxKF1JSUkAIFkSBABff/01GjRogLVr16JZs2YoU6YMnnvuOcyePRtpaWmy6/X29gYAJCYmWpQOIrKNm/osRETOR6q0Jy/Tc+SU4LRq1QrVqlWzOV0+Pj4AgJSUFMnpgYGBiIyMxPbt27Fu3Trs2rULa9euxdq1a/Hdd9/hwIEDKF26tNlyOYGXr6+vzWkkInUMkIioUKtYsSKA7Cq2999/3+b1+fn5AQASEhJk53Fzc0OnTp0M1YCXL1/Gm2++ie3bt+Pbb7/Fd999ZzR/RkYGUlNT4e3tbfO4SkRkGVaxEZEmch70mZmZmqajY8eOAICVK1faZX0NGjQAAERHR1u8TOXKlfHhhx8CAE6dOmU2/ezZswCAhg0b2p5AIrIIAyQi0kSZMmXg7u6OmJgYZGVlaZaOkJAQdOzYEfv27cOwYcOQnJxsNs/x48exceNGi9ZXq1Yt+Pn5ISoqSjL4+/HHHxEfH2/2+4YNGwBkV8GZymkgHhYWZlEaiMh2DJCISBMeHh7o0qUL4uPj0aBBA/Tr1w+DBg3CvHnz8j0tCxcuRKNGjfDbb7+hcuXKaNeuHfr06YNnnnkGlSpVQsOGDS0OkACgW7duePjwoeTI159//jkqVKiAxo0bo1evXnjllVdQq1Yt/PTTTyhVqhT+7//+z2yZnTt3AgC6d+9u9T4SUd4wQCIizcyePRuvv/467t69i8WLF2POnDnYtWtXvqfDz88P+/fvx88//4ynnnoKx44dw19//YUTJ06gatWqmDx5smTgImfw4MEAgMWLF5tNmzZtGl599VU8ePAA//zzDzZu3Ag3NzeMHj0aJ06cQI0aNYzmf/jwIVatWoX69esjJCTEth0lIovxUyNERA7QqFEjXLt2DdeuXTMafDKvlixZgt69e+O3337D0KFD7ZhCIlLCAImIyAE2bNiA7t27Y9q0aRg+fLhV6xBCoFGjRkhNTcW///7LHmxE+YgBEhGRg7Rv3x7nzp1DTEyMVaVIq1atwgsvvIBly5bhlVdecUAKiUgOAyQiIiIiE2ykTURERGSCARIRERGRCQZIRERERCYYIBERERGZYIBEREREZIIBEhEREZEJBkhEREREJhggEREREZlggERERERkggESERERkYn/B7yxqsU5FUqoAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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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",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/100610.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/101006.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/101107.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/101309.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/101915.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/102008.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/102109.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/102311.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/102513.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/102614.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/102715.txt\n",
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+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/120212.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/120414.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/120515.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/120717.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/121416.txt\n",
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+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/121921.txt\n",
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+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/122620.txt\n",
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+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/123420.txt\n",
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+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/129634.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/130013.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/130114.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/130316.txt\n",
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+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/965367.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/965771.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/966975.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/969476.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/970764.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/971160.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/978578.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/979984.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/983773.txt\n",
+ "/vols/Data/HCP/Phase2/group1200/node_timeseries/3T_HCP1200_MSMAll_d15_ts2/984472.txt\n",
+ "/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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/iw3OcG50CGc/tocj39LFZXtDrPUexRf+7gjWb12HhLDW9ZMPcDzreUdx6YFd3LXSwa1f+jv0eks4j0uwsfEAJBhOnb0Jmxs9XD5cRof6NMXnwFdWwNLmGg+UiuXgiQPjdenxa7vIkg42z3wem/JvcWW1nl92KcNj/+tKbHzfKh75yEVYYsDaeo/Ms5YeX0YnWwWWgEMXreHgGQASYJZ178ylz0S6vD5WlD4nn4Xrsa4BWeN6Zxl6K5NvkmZfwDNWX4rlzSMYZi9Ap7MBqMOMAWx7B0c3n4nTxaM4j/+JKzd/EIf6F+Ois0tga88GX5kcaAy3nonk0nusc1JnrYuvHEzQ6a5gA36F+OJiDfexHOmLvoBDj7wI25vbSNHF0souUh5/mLFyftVYh9ewvrE2nrsFXwKUdmYbPTDQfcCUc6eeYX3vAyscq7LM98TyC7Aqmp1uC2v4ArsaN2z/FY4cW8WJoX8nvbq+jt6aPsYekxnWRqvobH0FyKG9TylDyJPvAq7+BWDYATrA7rrA50+NgP4zgEoV7hk4Y7KSYGPDNh46EGvlOn/06stw5qFzdIX3gNVDSxhspki6y9hY1vOTSLA62vS+t02SpTWc60ivqjUeY9PIZcD5u7dwzbddjo1OH90zn8Xw2r8flcVyvobVnvsQ79DGFjY6k28oO6tga6voVXp3IkdWnbiW5z3+WXztihdDsAS71MSgyBPsJjyfb4PJbe26wGEc33sm1lId3Fi5cgWDB5rvMDrfwfrGMhgxV/f3nocn774Eq72wQ69D6+e1NgCA/uqzsJI8hrPnn4+1tck+bShKUOtUIfHV4Q1448rHoAJ4RTrE11euwkn+f+Cc6KFXCFz2/IPY/toI3asew7PWr8J6ugs+0AG7IOkAbOtJnLzoevSSSf+S6UXWsRMiubgcuw9P1ht78dJaTmdvjZyfumsdLK8dxMbKifG1kwe+FavcPT+vMmB5LUGeXoaN9a8CAHbOX+7UR0258uJHsLN7AL215jO9jUvRk/3G9fvzJaysl31h88A6BsLYPJ4HLu5ehP5SeeDVYV30zpRzU9JdA++6Dya6bAlLmblOeGR9Cay9K28c2n1cH8MnHwa76oVgna9DgmO4ez22hleitwKcwwaWu4AUQHH4RTh05m8AALLzBNC5nMx/iTM8ulaO55UXrkJsX4mNSx5qpMuL9eb3Y8DFK9vYrvTDheukdjJ3Y8ybbroJd92lo/Yf/vCHcdNNN0XntXz+AdQqZY8tT0AsRTg4DpzvYGM3LUEsQ5hlPAzzS6LrY4rAQfxf/+ulYxALALbkCCuHdK0lWbIAC8UeellZwUxsI+U6zpg+uYTsPiBFisHxIZFBVY+uMvlw+6aEGYvm0rLjFHb0GDo9pT5idgPu1O4yvpxVpz7VB5IBXTMv2gFpSWa0XU59D0f5HWB4f5NltXbic+Uf6Qr6RYpc+t9BsCaWXHA3vvw3S9+Ld5x5Bd53+hnja7nkEEsH0Bn1cSg5qqQOXwC2N44Ep42RwaiDoncxAGCj9ziSboIrLvqSlqZAgkzIEsRShO2tQ4oXj3/3C7dSqUq+cr5xLd1sjofusKkoJr0CPPV/vyf+rosv3HUzHv3UC9Arql1KcR5MMhw8C1xxvotedwmMMRzky2BQx41b+coHDPzQCHwvxeboYsg8bjE/nZXzQD/Jca5zDbY3X4h/N/xp/NdTz0YXCY7zQ3j4+f8ACXfnK3hZ594ex1n5EL4m/wrpshsclcRES81Eo7w5jgaS4S/WOLZyBskAySU6Q/1brGYplrdO4HEeRsO+d/hMIN3DnugjSfS5i3GBzw6PQSZL+OjGq/EfT38XJPQ0Iwn8DRj+dCuHTCbzDmMMD58slbeN5Q5E/d6Mo8PC+moumgplLeYad7IYgIFhhQCBpPEdC1YyeAEg4QxZtwTmGSRGn9lC51COnfOXAgDWzjwMnBc49+4+uvfb2TzbA4FP9DN85FAXlywn6O1tldf7q5DgkOxScL6Kq7Z76NiWKDEAcvv6IcSlKJT94DkGSMYgU71enbSP0S7w2f+7Al0kkMDdL3l1n3OOzXPVxcJ+YCKY3v5nWdK4Nq7nSF8/vzo6iG5RIMflyIeHmw9U2RzMym85Elu4/9x7sFydgsvzk3lcYhmjwUUYdk/a56WqSTNpX/NNOTkU2BN7EFdNGMlnBn/neIKWDusClPrAFMU9M+sVrkOcut9yqASAVwx8ybt4fNeuxxVI8NXL/x7WVsMYyozoSpIBwrM+Mwhg92Pj33tiy1vWyT1Hv1Xmkc2L3Uw7vly2cb+QTR2KeXbqHqnZdgmhs6jSke1BhbGslGyWQ0dXwbJtLJ/+ChLqgzgkZBh0En3si84mhKIzyYCNJYPE1Z/5L3j2J9+Ny3b7SB3tM5AJtniTVVOIAw19HADYAXqsD47nkLzZ1yU28fHffhZixlY3bbJdtvtH0RcvxTCjyQZ7mcBIJJBdHZBJd8oDonOiBwaG48VHkD/nfoAB24e+AC4BXtj3Lk7h5QTzcHJAu7yzm0J65n2X7JwJ03v75+z7DVHQ5ScrHMvGvT0RACZL4NhGD2f7k/lz+wTNCKKks5JiNd2y9t++pPPKFOsRvmTpQ7uT6ytyfbzFcJzJjmWpxRzEpoQpOrtNRqP87FcgsmsgHrsE249Rlk/Aw8lzlAfsk8nSQOJAt8yjczBF/7zlQILYD3ZX0rFFx0LaS3QP2dnZwX333Yf77rsPAPDggw/ivvvuw8MPlzTmN7/5zXjNayamHj/1Uz+FBx54AD//8z+PL33pS/j1X/91/PEf/zF+7ud+rlWFD54pKYHLot2ibAOy+qPpzalODZuU/+0sw8al+mKgAUKq5LvoDScTSWKAEGJbYOtzO+gMPYpCMgGyJLObtZkLZ7frWGB270dHMYV0mRXFyvkdhsHQPD0MAbLaLV48M2bcvFkWNemoMnp4pIESPZ5i9WR5ciKSsm/m0q9MUEpx7lEUvz6wLELVRvVirS3Dv9PJ7vRgLiWFTDDolZuyNfllHPuWTXS3dIp2jhS55dSlOH09gJJ+vxcBXo5WTjWusZXmeOgOmkBC0T2A3gE/EHHFcyU2uru4Qv63ycVswkBlkuHomRTXbK9gs2Gl6H6XrM8hNs8BOUOxEzfekiTBqFogd1iG4+IYPlm8sLzH2dgU9+H0Moieez4RFUCSYRm9pFTCkuV4IFsQfXFvSM85O9XYHptFM46UTdqruzdEZzTAA6OwPvvp7UMQxWnsZFtgBuAjucTfbh/AX6/+E/zVWb9Se0LpLstJjnvPlON9bXkyZ0jGkXq+by2FA8gylbhtZJCcYWPYnPsYY0g6k/QFm5zXcw7sJQcAliBbPYbVY0+if/2XIMSkjisbDLKQOPsxeuOdIMFeARRSYlhIpMpiOujnALsImbwCF2dLYK65TwwgM3t/HuxerP2W1WGM6OgKJ7UB454NTQ10LadLkzOewm5OKSI2z2dG+rry6e1LwLCNYX4MYnio+UD1qTa3FWCFSfSTc+Xfw1VIVuolEhdBLmUAk1g6QK8P9Z5lFANk9TMUYgMPjt43vpYJt0k/JbYNilT4vTIPr5cpj33Oru9xVs0hScerN5zHCjrdMJcItk8vPeszALCTfwHITwEdYDD0u6z4+jnmqPtkjK55mCesV875mewDzGR9TAdkSQEcKC7G2vZB8hC5lqTlAaMmHLj4Wy5Gupqg1mF6AfqUJgH7wy7X57qio7dZqJqbVoejR/7uD3DAsqbVsiebdJQso8ENtgyky83+JguJUXFp47oQbqCTkg5vzvd72UE8euaF1mf2KtA+N9qrMzg57htLrIs+fxxnLv4U1r/vOLLOeXCJMISREpYDYPiqYTOwJXsA2u/fhjthwOseZSZfiRXIWpZYHumdaDsPoyOtJBwndydpTz8U/m1XNjkgEwgLkLVT0HUolONG3qM7v1B10Z1Jm4jR7PaEqrAAQoBL0h1i/s2GwD2fA3v4fpwhxhEAPJxfAbFUHaB4+uzBcwxpmuIyrCDPLGw7EshaOHafhUS34j333IMbbrgBN9xQ2ijffvvtuOGGG/DWt74VAPDEE0+MQS0AuOaaa/D+978fH/7wh3Hdddfh3//7f4//9J/+E17xile0qvCBE+XJzHIWzsxQxQZk7VjAgRj54qkmO6A/6KK7oTdzumRRhIpd9AYTpVYaNOV8q0C+k2Pwt3ZwCgDSzmTykg5jdG4yslKHKd/2V9BZUjZqxWwmLQ6OE+cFdgxTOuHpmhJJK18PAMAzQ5HNqMneXX7S6WCpmCiGLH8SaV4qMIKXC2MeUD+KkeUCsiRP8eCAphf3q5Oq1b7yfhFKw3k2pTmAQ/aScrHg+TlcefBvGvdzpMgsWiPbXYVkmwDbRBZxmjdYPt68uNx8vjNsbmQ5ErANv0J+cPXruDz9gH6qMnhMS8Oq/zXFPodJJJACGG4+3o43q0Q+OSMHODM6iMdwFdaXUhxe6yJXrMpFx70xq8mXW+na+D2SZXe/ovYc1BPbe3YQp8yn7BMiAbpVe61gFYkov9m5PHQdYOhnp7A9ONsAsgomIMGCQCwA+OKJyfjqyw76Rdl+XVUfZkkwkJUTFP9JrZsNKdLS/LbXaSrgSXfyLQsO1JtAzhm22Sb668/Abw9uRW/za1jpXwKuzA/LK+VacfTqAVl3bsxLqcpcyIChvAK7wyux5PN/IQbA0L6G7Z7V1+KaHVcYjCxzEwoA3GNGzas5mavtWtj9b1DsK9vbnRro77RbpMiK09jeuxYptcZUDIOLjz+Kmx9I8e1nj2AtuRiFevgkK1aW2ECflQBTesBSg2rTMnSYhpryZD+DFImmGIkWJ8Rc0nOIgHKINzK/eRgwIZEgH9rT1oys0ImS8ntJiQ3IotZs8vnj/x3Y+jP0h2e8abeHBezrweR6wj1t1ivnppE4D8F0cEROycgCgOJ8uWHtcsfmf0YEg6VndbV1tWfzkWGTEEYW01nbWWpsQluY+nQJfUKVbdFkOWUju6lZ7wD93QaE64uioBlULukiDrhmjKFW/7NEr3d3cBad6sBpMi6BExd9rLoGoIX/vVKGyFcvQ984rDjFlgHL/BOU606YDrF7yl6GDcjiywIrA50BujUK07M5gCe3J9/z9NfD35FxAIJZGVnncxoUyxV9gC/RAyg/X36/lHV08ErCz5qMBaMBsCmsfzg4lgYOP3kswYk+DYL2GMfXVl5S/XJPJlwAR/eW0duT4PIAmYYiRywtzwf8+2aT6NH/spfRzsNrec973kM+85nPfCa2KFKS81/Clds/iKV+u4nLBmTlxfTuwp7Ybi4450cphMF0SruWwZ5vY7mY2M4LuQcopw/ZuXICOfvJpsmUKp1kslAIjLQ8VGGGiU033SbTAQCTGbrKfD8rIKvLe3hsK8fBbhcHlOteRtYUShkf6d9DjpoqtWlaZEpnrQOcAVARQjr9yTcpKiUvC5i0C97sC7nDdCFfPgyxRbfNNtvAAQArezuKX7iw78TAvODhNFLwyYKcnG865syRYiQkuRdhYBDycoANkMFvpjHOM91GbynRnOOKng7WJqyLpKD6PUe+ugTAPiYAYJN9sXlx74Gww2/pUqA6WP/7uzi1fj8Y+96AzHRRLbfOyyGSrMBVArhypQueCaj9gnnC/RbVpHk2mZhG1if/NpESjUFFMbLO9t2gfJ1PzoAuOtgDsD5aAS9KkLIvwuftvcFxnCmuHDN8aslZjhi08PHtETjvQQiBYTF5Sd5V2oRxpIFAe+EIxkCdRmZJ6W6xwxOYTyZdDlT7qAJs7O+Kc+Cs2ECSHMXJ0RKOPP44Hlq+HkvKpmJ5aQCwLo6ufAwPye9vWP8x431UxnCaDfHoub+PlCVI0gRrhaN/FH3IBqgxkdGeYrrJJ1vZrLOibfWpDRgrmPNT1kqx9mqZA8gKNFmRnKFPrIn97CwS+Qx0htQ8XL5ZUoyQFCNc/MQubhw+D8NLJ+8vR4fBun8LIdawi5JhyjZG5huUUl0amr7THHXezQWE8V1lCySCSXqlkVA2TCMTiAjdoLiBpwQVEBwIOuTJEoLQFhsji1izbcLO3YutzR/0putnAmBLADkXKOPB02ay8gmVyR0IXKu9ggz0RxYiJdBtAeBntDdjHab5gOr1zwPrdhPThgToqF3oIOMo0ZkqbbhD3f4WsG4/GN8WHUjOtXcb9tes85app9cy2Ftt6BlFHq8Xd9Hc6Lt6GVPA/UGypo5wcJEjFcCQAXnW9JdUqhItgSzRx+n1ZzQun3bqUX7pnw8Dtkd7DBJdMDQBVVFYPl4vx8ruBNiU3XVkIqy8BMDj293KbLKDvB+uo3AOyIJZGVmnRsukZ4tCBRmX6O+UncnBQbNwO6yLwnEw10pagF+1dNiE0UlJsXIZ8oz+Hp1M4CP9q/EsoFJEPWVVoF6CHiR6YA3trPke3SW3/ruQMLkAeW0Sm3tAr9+OjcOsKxObys4aAPby5qZqq+hC8kAgK9vG0u426g6fF/qmfXgq7ERqiU8GR9EYTIqYQBZ3n8ykXYWR1d5KQBMpGDLBcHbUQ8ImM2vhnbzaK2VsZLQJEfHQB6SlaylGj0zYbku7k0U757VpoX94FcTpbuboh7sdmgYLAGcru/fl7XPK1XAga0bYJCmZdCtYGRJkjsWiKI5iJ7sWw8iADN1NvS2Lnr5p7TL6BFMyDtF1KxzLB7voUJvp4enAPZor/w62LvsMRDJ0TVpW0WYcBqRpAgaAD4qmgu/BgurDxpOqyrrkdohA+sgyvi9nDI8M3HT5Op+cA93qsGFtawnJqJwb90j/drSclIfw5GgF0mjPDPHKhKzA5totUq/DcZZPQE/JGDrBlHhpNdWhTPQGFaOME/e4srbkbKIDcg6czFfxSEWjH/Yuw1cHB5EIZaPY3cb6pV305GNYypvf1zxRVIGsTjZEwRh2C2DPp/Tle0Dfruxme5MOyRI2BihGXJ9DuozYgBFm4pqI8r7GyMrtYHVB+cMixra0bDbPyxX0cm5xVam3seyu4883bsbJ/sQMUfbLkxKZb2BLluCtXKUZH7Wj2FHg9ltUjOwGkNWaMdGUQp33Axys0+KLRFnpRRbfZaacTzbAfMwmoMHcrCWUkVXLmQCWzF4uHECTslG3ncRWIpb643Rm1GsxC99VlXAXSD9DPYIpc/PSzglHSqIanmHAGNCVOpA14DqQ5TvQpKTT99WTQXLd1Hiwa18HbT7x9ojIpLnP7YiZd8LQkc3DQebqZwo4skd47U4rP0tbedPhNZeyPSNL7OHL3abD7QFyFFPELds7E/6stATREDY9pDdCb2dyyC2Wwk0gGYCRZJDYhCQi2Duf5RIo7IysEyO6n+Tqt+nS8/XwZHk9JQC5ZB7x46bYa3Y8zxYdOzDOBjnOiVXIpIcY8LXbTSAIH2SS0Ne6Sy39xS1EkwsQyALQYuNRi1sPmG4Q7o7oySw3/FSltjDC+Ra4LNCrIloMC32RzU6HKYJdhdJb2E7N0PSR1UPTp5AqHcX/yqxQD1EpCpng6CimbcIDZE1zushN597Uabmn/GSFo/9o2c4pS7GyN1mssso5ax5AiS0IKm7mUJTPOqJJna5s7zujATq8VHJcEchUYWDBG4E2MrIARrXkMikZWRbZ23k+njx3LYbFuahy0w09z6yrK20dK7OPI/VsdlbWpxwDDhMXiQ7S2uSvBZBVGBOdk6zg+ex5taE7jqUxI0D2PCaBVJ2M34wxnPWYiNfg1wgS3SxFyjpY3eOQefkddwMjcgLAI/lBPDZYgTDac9RiPRlUY3tYzYOXX9Qd+yQDSmgqjfDtYI1WQ8whu6wsh8o9MYGsajfHGfDEaA1frECS0+nleKi/Aq6wd5Y7Z3Ho4hLo7WZNM3MpDBN5A8j68taTuPf4DnaET3vcA3bt5jfD3ck7qIDDIDF8TYrz4CaA5Fsiq9fVmC2ZneVZBG5kbb7rTxZrWLb6t9Mre27z2XhIDtFV3knslP7CZLaK7RrI4m6lehi4URxVfc40iwldM3ShG6BQzKik6YswcPqUDr1M3+SHfauRSO0Bd9TcrKaFcQeep0Z+ICsrAOvhnAxnZBXdyUFNIXWgY5aMLCYcc9ssgSzFg3Rv+zFHSqIanmHQWUkbUf9MIEu0AbK2/YBbznQ9rk+AUrWYLkDGzwwIE8VIIKuz0mLPo6xV24S1R1Idggzy5iEfF2gNZEm5h8+CAAgYfSAcKjunwtfpoqABpcJygCKW+kiUyCV5eiC4rHqWGYkD1nJtwpkEcglp0ekHlr2JCmRJiy/B0cnyOiPAu8Rj4umbv0iZBsgq3Lpdzu3jjjGGS1aXkPUujWJudDoJCsJPFuns3eXOZyHBcoECWe1XSmcYz0gFxZS9keVU3WDYqMwmJRFYFeq0VwE6/XzipC5FGuxgfUmhduawDxTzdbsyHMgSM3L2rqLUqQpk+Sj00wBZBhtADpoLq4+RxVc4Bo8NwcCQMIaV7clmKKuYZaM5MLJOFvaIJ2eLSZussGrBD5yAWy0wETLwnAZnMsGosNc1qaLnZMxt6mcKW9M3i9mSDg53Layo0lm3u024J9qfT6Rw+MiSHaxX5piSxSt+jZo5XsXHTqit53aRoF4yip5nASaAGZORFWQKVDuwhsDaVgeXbl8EDg4pdgCWYBQBZP3dzgFsFx0URnsWTKCbxC2FWxX43a8iScrUcCDPONIIBczqPJnoYnkFUlJsLa7M0SPICTOOAQ8PlvFoxYD7ZP+ZABgSRXHtdc7hotWvACh9nZhSGOYTiaJhdrIBPvPQGh44I3E+9/TXvA8he1a2w3BbAbKUPjIw/PIwSHRW9flT+g44q6qNhy7TN8uN5IE6wcjSlx8frtkDBxtmZLvpGgQkZKL4KcmWIdlFyPPeZB6wncZFmhbWTSUaY2h2SETNyJJgYCPz4wSuOY7NkbbJDzQtHLGU1sEamc+GkXViFGbuVVh1Gj1Kqkvypcn6aJowzZKRxWymVAiywgkXOdlM81Hkps9z2NpZbr7DgOuAQRtGVnfvSaczfADIFGbP+VPfBuEAa5mFkZUPOCTTgbesH2dm1yXaAHCPTLVNzhP+vtJiBA6OEc417iVSICi8HSE5ChSW/t/mO5XPdTDaiQCyMhr4KCyMLLG0p+ngBQsfg/U0P8g2reVan00kkElrcHmbqq0DWfRiKoVEB10I4iyKT+mYnZQpgCwu3UBW4TlgP9hJsNe5JBh83eklSJZS5CPie1E+shK7W4OFhMuFCWRNYdc2L0aWBMfAMpmZJ4okI0vZTKYVkKECWT6ke5xNyrE8UJQZaT/51mzvmULRt0hHrfeMTAu1+kgVyHLLNEAW6xsK0R41SXmczS/LckKXXXCZY2lvAmSNqrrNg5H1hC0iBqCxW1bGJh1PD2eCwwAga+gAR5f6EjwSbAAAua73/9GSbo7UtbCiJLhtHzMWlrhA8ZDKOUwLZYq1ShFv47NGGt/duQHyNKvK7pJVfy26boe2lMROGWqNBxDo9RNcWkUIlPk5yCROaa+dsmdEe/ZCNraKnNwrFaR+Vr5Vo3kZRxrBWrV9HknMIXWunOhkXHmP0kdWBXoxCaEoUp/fPVCmV0APDmBTfBaAbipdS27QjlJlHe4oLNezI3d/ld0evtD9dgC0EjncnpSjrlF7hIPprhE9U5K+qJT7lV47Bu49IG6os/ehZZ7dItwNTDLSNwrbSan87hoM7mzwPH0jYwWyKoYVQ9D8U38lQegWsQcbttT5OCDKEljR7vRZOoEs9fuEjeGcpVp0T5vY5vdQv2kAIJMl7AT6X7U6odd8ALn796h7bvx3Lk0gazpfQpo4hvhMD8WUMcKKOGfvPj+uXQJf3IO+AW3FTZQ5Op69xKia+yQ28ek/Ogq+ZO+PNtPCfKeAYLpp1HAvzP9SLR1LZDrm6GcqaHQub87hrBghZSkKNIEQLiTa+sjKiPyoOsWIJEwjnXUgWHAAICyMrLy7o9UsJBp7LfVzg/46smGcE3/OJDASVtNCG1EgFxOQsejaWfd7H8whhsTomMKflU2msXT31SbzOLNdFcBZdjAYc9gREo9zSfpMo0wLuSMY20LC5cIEsqZAUdxWOtMwsuxKgqn4pNTCpOyak8r/gESBtAI1eKAvmKTLsbyrAll2Bovq5iDEZ4TKyJqVs3d1cEuFCu8jfAlHe/uE9/VNuNxrovbUpKMKSxl6B3pIhimSbKAt/MOqblnA4poTQNbQAWQ90rdTjE9nk43e1Sf2cA2eiV6g1dS8GVl9z4JRAlkORlYBdCwUe5fkK+fGf6e9BILrC0fHYiIhwb3+SLjnvlccQJaQKTb3yo2fZG1MqcPbyneIlhPvaZpohhRdmMqAh12g3lYjsUkO8GwQDWTVkhOmhEsBG1tVHjlX9qPtIa1lScaROhiGplhbggKyagyGyF7d9GRQTmQtqCxXx5wy53EiOmhmAFmJCmSNJkrvqYFDOWPA6eVn4xPbV5TOrQnpn5+8g9oHdon0qTGEhCdioshr/2LVBQ9aHWpaSOn0gEdTMYCds6xc/0bGONk7cS2gRDUzfbyNRamqbQOjSv3mBTkHRs611iA61TcjTbjDynABWYmyJoS8cy08BMiyUOmEIxhLQ5LwA7fCdtijvL+PkZWlEyZlbrTbLBlZLlJNqOVAkBQTgJxFRCwG/BtgilE9MFjSbQGSjoeWNpIlMHHuyedACoB17OXYnL3n2wIjdrF2bbgbdxjf6cSjBKrqfyprjmspMiQWkzYuJCDauYbJhL3Ttf1OItJkL+vTY6jILEDWkr7/khFWP7zqQ3vbaxhZyrUJYxJylFudvdt8EKuMrKJjP3gYPDxouKYB4D03b7PPEFPsNZ0WWAAyDyMrzQscLw4iFHOQAAack1Es6T3l04NocKHLNx+Q5ezY0zCy7Juq3DBfIoEsZbOYKJ+F1xPRIGwC4F2Opb2Jw3gXkKUq8SFAFmcTHywz8werfA41dLAXyJpCKeO7Bp1zt3nSF3JysnxxD/nDEtnx49r1YQVQZC4/EpVQQNbIUrZMV3A2t/ezoUggK8X54IlH8LwvfAH86WJa6DCjA4CRTDDM3XXttDDlG61MzGW7B5rj26bHSTCvcmRzBhwsjug1UibYPH2yqssMTm0cr0I5Zlel0JgJFeuD2zcVtr5URC7aXFHC+uqEkwBpNoB0hYC31Y3RjCxvWHtDzvQLcM6xNUYwDNNCcHQ8/Vmrl6VtyHm2thYkHuHKhmgkJ/6ObPtflZGlAlmMKHiUGYws1bRwOAGyTvQdG07O8HVxOZ4c9UgGikSKfGhjZDXTm06JxZ4DDGdJs+18fZ/aeBBtaSPfuiI8m7YZp+sgIUaypH8IKVPe3VJn7RsHaHZjIIs4PY5fDegnsqKa96dgAzkZWdrniYjqFQJcW+b3UHNTIG6DndvaSPWR5SLWdjhEMlkrMsNktAiMlhYiInP061n6yCom8wrP4xhZXj+uRlvKZAmJMX203Wn49K5+9a0furcCohxbDyuQtZXjC9m1GCXPG19TzbJDxKbDuBhZarPuFM0+JYqBnTQqxIQWGykjaf/+bYGsIo8z2Rvu0mPUCmR19AO/GEZWbaO7e24Fo524+ZNzCQwLSEtEatv2LVe+TdGxs+47qy3n8xZzw1SkCc+jI98+ciTwud1L/BnVxUlgOy8ARsy1xImx6aNvIe3kmw7IcvVHOYXDQBdDaNRgZFHK8eSdEvVkvKqw3A0bSGmXgUEiHftpOmdNqy6QPGgjJ5FWTlJndeqmLkCFmLS/DyeTUyjGfM8PZPloKp0lhu6hDs5+bAts/Qnt3qCqWxHkIyscyMp7F5PXVZEdnSrNniZAVgHuZNCMZGJlNdTSy+P73GB5AjKmlza/c9eSp0TidbniZGSFsANczt5lgqXBDrp8DYXH5Jd8PsK00McGz7T5llXPxJtVxk4ZapUHyowguERn2Ifg8XNAL+Wtld6G8ATnCf96QMXIymMUdks/JBASUaWlCCNM2aDnkOO0tp1FYgOyCD9LJtFKjXiYiHxspXcuA6RtPWESe9WGWpDznH5CqvbbgkhvHvwXu/ZJRAWDJkCWe9KhyiTFAli5GVn6RuFEtW6MjKxWuikSbTNrG0hx7KR6jiiI0+NQf1O+KuUVkEUe9AWaoUgRCGTtFyMr5sAzIoCKFWgKdPZuHkbmDSBrhqaF0lGXmQJZE1YIiwRAfK7izNrLznpD7Wu7VriAIADoyw4kejj7cPltXZ5DbEDW6FwGgGEHk0jW/Vggy1JPJ5ClDIs9au4ohlbCQOuohQwYOIdSW0ZWXHsNd+gxGgxk8RhGVvnvzull9Lfjxi7jAhhk1vVHkC4LuBboI+/YSRCd1dmB4j5hYF6fc3bxMCOFG8hiALbkMna79mjxWmkMODPMwQgswWfls5D2cmECWdP4yHJOeO1NC11AVm4oMym1MCkOiBOtw5eLd3E+7J2Tan6po7ENhd0ESF20QxhZgETaLQfo7EwLJ38LFcjyUELb+ntgjIGZG8ztJoPAp8BIlmPvqs9i9cZdrBo+YfYUxdNHJc6JPjew9MO99BB5XcsvNajSvghi+yQMJaPMJiOZYFC4FZy0BZCVp9tIe2V7iovONO6nlvaRzKeKAtwTPcwrLkZWtQlZYesQDt8QweLqzj4Hwuo8UQNZzqAZ9GWTkeUbYxojSzErkEmBpMhaAVldywY2dt8OlOZkY0XePN1nHGkWA0BGMLJq00KKkVXR/RljpbP36jvZgEeNkaUySIk1dmiMv8SoXEfxYWcHssqxDoCMRielbqqibuLIpjCKESMHI0spb4xfeQIphAJZttHgBG+zyUZBJkvYrpRfau2T6se2MUE100JHuXWaKlFBBEyYlcpd580CfXxS0nRGPxHdyi+81jYH2loam4+sqKBA4XXKLECT1A487B3KHAu55JCKLjFLRhYAcAtIN1Nn7wbYy2K2LD59wfw0ySqGiWGOGV6aJtzTCHsiQcGOQIxkCdanju9qcakgBgIMDOdFaXYs0cHe2Uggy9o97fXJPECW4IOmjl0JL4p2jKwE2OV2M7DWPrIinZP3t+h5rMia5SdLiT5vg/a5aJWqnfNhirMPRzKymAT6I4ePLEr0dyv4gEwFAOly+/m8jSQtSSY+LX4o/YE4VjopHhCXB5UnpcSJfgZOMLKoQ8m5OJv+JpQLE8hq6SywFhs7wRXm2ScuYMVU6kkgSzEoUOnNonJ2mZ0Oe+ekWhA7rKoPk1a2laqPBc2vTE5MC1uACqQoGrdQnHY1/OkYUrgcZTuEU98+E41v76MALxUC2drXcfwZ/x0Hzp/T7vVVhdFzApNRPrIsQFaf2x29j/Mzou5QJkKUzJuRxSQgEgeQJTgGPp9CLX1S9Q5XwRMOPNzM0rLTDKGAu31k+esqCVr++F41FlZFD6IFI6shLtNCz6M5azKyXEwW2/xqjmlfuWo2IzFRqkQFPhQtgKy0RcAAm5wig0SUUgJZMSahFkCVKGJMJiLu1Zse3uEYCjZmZNnamivhqVVwkhF+TExLycSgPGgYoa2/MTmONCklsQkSBpClgjNEpqZpoWtdUgOmTPYXnvDcxDxAlWAr1bmfLSbMYLF0YPz3iJiTVIaltCm/sUBWlWceYALvF4v5U2W2KKcoQzqArERjZEU4Ug7wt2hjZMWYFsYg5DYgSz3wcK7Thp4n5AhgkzV31kCWFVSaoY+sxJiHqAAXNhEeHbUxd7AeMkM3b7vNtOkVE+HYya4s/+pyJ2PaxsgCSnD+XNGDRIpRci3ynbgam20wvh4IZEkwSGMdHiW74Bm9NnIh2ulxXGKb9LM3qUcbiQayztBjvxg1y0+Xk8Z4jQGy6j4kOMO5R+L2poyVjKzCUh656plgkeOQJ11pS/po951soLlXPPvIYeF3T7HS4aQJLVkcgJPDvNmWoBlZ9TiThNuEhYTLAsjSpH1zuIEVfeImN1OK90wVyCpESa0ePBm2mU065cOp6pfL6iBlwsTyOREtRSAd+8iaPSOrKFTTwvkwsuxf2JzQPH1BCjzv+AgJ62L9zAnt1q466fkYWcQEPbSUPeQb7joBGBlgUSiQNbszeHvueWI/URuKRFOQKDHN5UJFPPthLG12sbv6tcY928lpCJBl2+iUNwPa02EyU2/8VjKOHPaTMevzEWkLh7rOODNO9WogqwU7zmhrP5A1KXcoJqw0WUV6yVn8HGDbE7RhZH39nMqUMzdFcYwsW2RK6vC6bkaXaWHt9L1mYtkchOuMLLePrMKY81MjTVfZSFu/LRMYVgAWFf1NFMZmRRmG1Bs0fGQ51iU1NPi4ObzhuadTkZzMRdUficJWzQjlW/t+VmfvyqFQEJBVSk4Ekok+2LC+ZwKJxOLnKqwMJyMrUQG+8DrLAECbWdo5JmphjGS26Iaas3f78w12IjJIJQrfKJ+ds3fA3kdmxchijDU2omF6aiWRh60Cy8hhHra09L0UcCh+dnAEAJBQUcwVcamQXCYQ4BgmL8BZcdXMzDrdQJZxz3AZIVkOOaIj3nFRWEzKPcJzbFmCg5TSEsiKBNhtjCzVr2MtSY81wN7C4rOKEjZmU0dUsH6WuZ+l+7Xe0axBRQDwnq1Tzmcf0fag3efs3ee7FwB6CQ/G5gXKIVgQuj0NmlYZJ35m2ELssr/8wFnJFKaFwHwYWWaoYy1fY/KiDwNVIGsyanKxDZYcxPDJMPOitELBOqry40jPOxzFsAg8zBTjRXdmPrJUIEtMjD+8jKxAhNwUbl2guwAUGrtn9SjAcODkYzh2yfPB5Be0ezsRQNZIJo0PNLAs9CPmDxXcxwoOqhd8jiIqmbv1tpTIHdTwoag33sy6+LgWVpecOfwJ9F7xACSx87f5EJPgXgWCOc2SAsaHqw/XQNYwx3avBZBltKFL/89lB0mHoyCQRDP0NwsBsmymhSaQFWH5kXFerlY5IFkJEJlBNEIk1qm7Sx46J1DX0qTwS9j7MSmyAGXaLnI0Vuka0OUUaFNt7OvvNmFk2UwLFSBHw0paMLI0RpCtnYXOyDLNoTJdsVRN3qkIgmYxLkYWUzYtfLx+uVlzOVFmFCPLmbsCwigLsAn4lvcl+bc15xggaxaMLGeVugDBvgvO2uHHhqtz+owZWbCYjhcRQFaMc+dRACPLJaZeqwJZEhyD0Ww3S5xx8rtLKWfC8C7fxwCyIp73M7L031J2YHoVarvTKAP+2EHynlxBf8AB7CHx+GtzMbK44EACfD57DlKkAHasacm8bWbKjrVrZKw7MumCKdMok0DGzlrqK1B0ogxEq0JGOOtw3yIla6XExgJZnbV1SHTAjHWjGDXz4T0Js1J5BBArKwsF0UJnqR9x+UqU4JqzcWnMa8Kh3yY9S7t5dJ657zMa5bn01ASj3L/nX+ITZrtP6lQjol9RpoV1/UQaPzcPBgOMRjOw1oiUbreLXu/pBbxdoEDWlIwsq5PK9sqW05GmUhxPODiFykvVtHAyaEbFeawlHchigM3n9LB1/9DJhkoqxS4NfJekk1RAVsgUI5F2K8BhVows1dl7kQYDWaLlt7JuLo2F0HcSl1fo+rPuf6Bxb1c5WZUsceZEMbL6lmV+gBAgSweLeCCQ1erYJ0IYGEaOE7VhferOYSVcWk1qPCJ5gX7vcbpelixlgBEDbwmsjcXByBLVQri6u4t80x4G2SaNXu56GQHwbkICWU0lugJupLBbj1mUp9yolQ8LV6ekAXhlD8aQs7I92jCybAeibXq/NkcY80p56hw+R0rkAKWok2ZmpXDiXv29ah9AhZRgDFZFLFH90glMGFCE77jcGJem2Y8aVdTNyKrr1pzDiwaQpVZvOtNCFGxcswkjy60IUkAWdclWqrOPK31G3XCQpoVBjCyl3Bggi4xaGDsiJm3bvNWFkGkT/gkcHgXBGKuFExFVgySAGWF19j4n08KRBbCTqmmh81RS/ymQQWC5bHd2sJlgSrH1kVm5KWBA49A6xrTQ5/6iYVZXpA0gqw0jS6YrjYiRpqSnuhjtlGAI73r6k0M3Zzkbn3/wFkxBW9Q0VzQ101ei4F29Z0lgJ32cHN+8yCG7bfrhCOfQhdUEv7VpYdxzy4eXIbEKhnPa9ZwAstiSaIwFSt+3CQMDZ24wyvps9UjhfNQ8fDP7j33vwJZs7+Gpa+tt4+wZWTJdCdr3dBiDCDyUrFMNqbmcLKsCspI4tuxgMMA1V1+E40/G7w2mlY2NDRw5cgScc9x222247bbb9r0OplyYQNaUDtLsjKz2QFbmctysFJcs+ctIFF9B/Z2TWDtVRTY5ehIbvQM4/1nHBFOHW9cv2suqToNCGVnjiIvtIug2RJ0f8iIZG/jlXtZdu4nN7oRTbwCf7XztsLgzarJl+uok5vGRNSIavmAMkqWNKD19sdpIa8qONKJ+hTKy5g1kSYkht/vIqoEsyZj1FKUtkOUSW5hsKU2TOuJZj6Nor7h8ZFU70ZXtsyikP1qlT1zLcM4SJB3zjLEUZprg1G3SipElDLDYLeo8nYkESAoAKUasDGCRtWBkWee5abu/ebrPeBQjS1pM3IoMJL4FlGO2HC1KOfXnSTkYm3wuwVSUaiI6I0uOk1ARwkwgKzWBLKIeTSnGSh51GFGMTD8dqrlcKJBl64DKc2Mga87O3p0PEb7noDDVlEyFenJu82sTCWTVynmWzxbgaEpHA2MmEjboXIysRGNkRYAcAQd3bAaMrJiJJbPtOhVQxOlLyfSRhQyicmYsdJ72TIQCrFpwbez5U6aFEc/7GFmmyDwt/UEqj7XZdxfdTb/RSJ/j/D1lsAdfBE0nbpqxsVcM1oJZaetOLiZLLppAlv4scEY8QH6sRBQoWgAzAjl256D/0Q647ZKup5ByFWDntOsZYTDDekVjvBI+4d3lJRyCxxtjThhZrieNU2MjrXTot4Qv80DZX06Wy7RfdFaD9rEdAKG8jbq4AbGmkn2NSUACRaRp4Wg0wvEn9/DI5/4ZNtZnGI3WI1vbIxz71t/BI488go0Nv6ub/ZIL00fWtIwsGyFrClwvc7Ar1FZOl2zp1FDmk78HyXGc/stzZYreHuS6mzrsj7VmVK0yFQxaW6QYA1+zMi1Us1HDRefeb9wSobdF8TM2VT6TgIFjAVRNYHxRC0c2wCxprhR9YQeCxvUyfZEQJkJPiQhgD3bTwro9XXiamJYBRYitP0jQZhPas1MDWY45o1JKO8M+chlvWmgOD9f4zmSCxBLFy8rImoGPLK9vf6XonDHU9gsjWZottAGyrIEvZqxglUBWeH8VkjZxo1gFKnDVYNLWQFTCkKggkGUTkKjO3pXJmBERRHNDm0sK07QwgJElCwxE7SOLAPEN0EKdPgvKWWqEjyyRTe5NzDLd/TgLBS2sFjpOStbkL+Vb5eg0gBadkWVjPins5ihGFvGOscPB8ZoS3al0q4JgjNWiRo6NMeObCsiKYGTFzJKZre8WqrN3uzR9ZI2Qi5LFncvNiJqECQVaRfmw8hbA0DAtjLHWjmRkCZY0/NPZecd2GaWbEB6AhKUMwxMlG9Q032+Iw7RQjQPDIkEZwLVfsK9d5h1hMKOZlCgYzXTlRUGaiPsk9+harRlZkeAf44DkTcsIykcWljKSkWXTQSjpcNYK+KsJDdThz0TMw3tjD9TGB6zn8K6t/7y2s4otmAEADLuXBuWRivB618moSMAUOaI+7MwjGVm1bKx3sbGxtH//RYJmH/3oR3Hrrbfi6NGjYIzhve997/helmV44xvfiBe+8IVYXV3F0aNH8ZrXvAaPP05bz7jkmxPIsjqpbN8cIwcji2lAlkUJUkaKtrngAksHy7xFbxdFb7fxqF6Y9g/xQxcezciasWmhxshS/YR4Fq6WDCIrkGXSbD2vN8oZJLc4flTq5gWyLPel6UAzXdGc4VvzMwBVytcNLfNnZJlssclNjrxaRN3bvn1kZIF5WcdORlZIczoZWXU2slXEFrMdnd2ZMasfDm74kpnMnW2iFhrsGc82T52nBUQZhQvAQJ4GAIzaAFm27zLj7i/RZBO4RFhM3Kh5Tm01s2/Ubc9SXWG2OfRXoxZqlgTE/GsCWQ1GllRBBZvkGFYbLsqJd5EZQJbKMgow83NtXtXPMelb7j5IjXDqCVsuztwtjCzqp+6vJMC0MABCqVNkpO+O2YlEF4I66AtUIYSDMcZbM7JCUs2AkRUVtdByw3VIqpWl/yzkCP3s4irveQBZzXeLMf0Lyt+Yh+w+TpsiszgdtWBpwxKgDZDV5+sofAe9CtPZx8iCzY8VACWYL1gIem2Kjd3pDFKh/1SjB+9svhDSYfLPiyzKT1QtI0+Ar/0CsqQEBJrsGQrIkt0hzEEpwMBdwKQhacKD2LWm1OPE/azJwCLawvq8rX+4Kztofa7ecr/n6Md76eGgPNJR3tBdbTI+HAr0kVWPv7xFBO4LQXZ3d3HdddfhXe96V+Pe3t4e7r33XrzlLW/Bvffeiz/90z/Fl7/8Zfzjf/yPo8u5QE0L5xO1cBrTQpfTOPX0L7XawytAlmHD0TkA9E8CSI8AvW1nPSgE2onJ10BW0OJSzDR8PaADRpnShnNjZNnylebphPs9s0KWkSZEkyGXa5vPAEYW9SpGOFbZ3QwI6YxxRLBawk0Lg5JNIRJbZkSy+g5XfYrZv6ststs0YmvToKiFbEq2W5GUTsEJpUAoSmkyZeQ0AP7hYmv3xnivGFlOJ5r0ZdPvXQwjS0BCYlQO02rMjZyRYi15ztBHlipme0geZ1oo5IisAwVIaEAWZ/pyWGeSMA20swNZysPKByGdvTcYWTqLrCMF6vnOBWTVjCwKEBAGkKUOQ2r0l+udcnDg2Lyyql66j0ofI2s600J31EKVkaXXyfRdrM59Vjak+r0DJvT6a2cOxlOwOPe9nemiFrp8ZHGq8/slxGTU5iMrhpEVc1Zs3eQpjDknI8soqpAjbPUP4uIlYJSvB9cjXCgAdIZAFgOmcvbuCYPcMEtmaSNqYZujsx22jlwwp+YnldDk5mFRQxwMHtFXapi3YO7YgCzHm5tP1L4qZWcV/9/zL8d3Hv4CgC/R+RZFsLm2KgMfkNXS2buPOWcKY82I6RIJKEK16A4a46EAL4Esd4yRsaSctQJTx4ws17OMaR+T0nkZZzRpwZKtr6aZYFhKO5D5Pjkpd6y/WwhjZLFcYmk57BvU6z3ld5J09l6lH/HZRpR9usgtt9yCW265hby3ubmJD3/4w9q1//gf/yO+/du/HQ8//DCuvPLK4HIuUEbWdBtIW5ecCshysWWUxSK1htpVTAsL/f34RgaecgwTgV3P4Ks3xaHAxNg5sOPUR62jGmFlFkqLUECfXNm8F55v3NaykdtMyUwgy/NumQSkhQ4qlLzcNurA0FKO6Xeg6GwEMTyGbRlZcwaymADOW4AsFbRz6RVzMS20tGmIVwI+NbDGYHWApIwF3mKabqzfnqFqC/ZgMy20Opx2lJWZpoW+CDdKPhISQgwgO8DmVglkDcnNsVuszTD1VGayzeKcvQubZkvWSzGRM09V658JB1dNzSz9nKtglKKwUgC4uS8016lENZUjS0PlC8zOyErsLrKCnL0LM7SiWnbNctQ6lvsb5VP7yHLlr0aMUurEmoC+0IAsP6s4ZNavX11KDmacac6WkdWBaDFWaykyByNLO0wIr3WI817GLX7r5uQjy8riUfQJ54GjyeLDEHm+hIJdhTPbYZu2GJm7jyyC1Wr3cdoUEcvIkp0G86cN0+e8XKNiZYyFIwHjDJ2VUu+ZxkeW7KsmDTG1rPK2RrtxzKNGs9ZA1sm1b8VekcBFZedFEe0nCgD2fAfbLQ/7ZCyIzzD2OzcRem4T3T2Yg7JgPI6RxVnQoYQpE0aWk8ag/aJ0Xut8YyWDuEVCYqtFB2i9Hjn68dnsouBs1tOwOX/Ccm72CRI0ZTWQ1drp2DeUnD9/HowxHDhwIOq5CxTImlPUwimArKHT7IuNJ4QwRpaxIm3sYmm9iwxDjDo+GoM770ZyzrR/nSIFEm0SngWQNfk7U04es3kxsgjfLwCai6+HkZUXEoJTwIyej9e00DIEheEjS/BekEP2QQPICjz6mbNpoZQSZ3Ia+JNsUmdX754HI8tlWuhrEm7xoRIlFhq+Wq1kJiCj+2WsCovNR5ZTZaHzMs02YhhZBQSk2EOejrBx5gQAYNCKkdXKCCwgY/PklTmxvkbplvmOykKtafOzVXcTppsWWtgliQJYqWZ5lLN304dPkutzi8ostL26ah5cEHNsr7er+4xR3oEEsswoYwEOnrnWaD7TwmaZjuBDDXFtaLUdoVInKZuAvg7i+00LiwjTQgBI2JTkfCcjqwvRYqzWkjuYJpqPrIjNrC+kvc3UGogDsmI8IAhr4sD+as5BsvRA/ei5fzBV+1uLI33WzZCRBYZpGFl+H1n67xyUj6x4GcgOCoeZX1LphN21cu1nHh9ZLn+U+a6yboSqenru5FWXf8cGI6sy8d/CRnXfHlGFywJZC9Bp1zOftXU1EsvIAoBCmHosPbaK7i7ByGJOhp0pKZvOR5brWROkpUDbaAa7F8liODeY/YG0TWyseJmuYGfg9zlcy1LgTDA2LSTWLKqP1uNs1N57/lMiW1tb2n/DIRHtIFIGgwHe+MY34od/+IejHclfoEDWtIwsCwvGEzLXJUMH/b2QcqwYJbbTF9VHlkG7TC/ZQe9gBxAJhiiQOML1jhlZxgmvNX0NZAWh/gVSrjKyphfNtFA5ec29AMysgSzzdMKd/6iQKAhGlglc+UJ1Dy2AWYORxZa87AEAGBq22ax4ujh7lzg9soA2KiPLlcU8TAstaEoIO5O5jkCDu6eFkaUokkFkSfPxRn08mdiUqxaMLJvuZD7hZWQpjSiYxKh/D77W+0ukebloZkTUO69YKfHTzmammUpk1EJL36Y2MU4IsfpekrPGJ+0Qm3OugFFSZTMFMbIMIEs1lbPUT0oVyGp+v5Hs4uDliilVkGmhkr/LR1ZVP2aY8bkkC+wXNmDXnbuDkdVIqcw1tglBySIPMS3UcLT5eZmQMoWgfIgGDo+GuakiCVP7YPgY9g1NnnIIyy5uXoyskDzcUQv13wVKB0rDzB5kZbpaUcDyDIEshsahdVQADY9pYZNFmzaAxzZmXYXkzrWtBrI6q+WYYx7TQieoujt5RzGKVxRamRYaj9RBV7aqYEQ2UIlXc0zeok23PBv9tsdQ0UAWo0wL6bkz62yTpoUxlskpZ620Xl59Pzej0NjzUN/Nyryy9Ru3SAlsDdsAWW3nFYvFRfdAVKfhgf7n6rGRRfrIGlxgQNaxY8ewubk5/u/OO++cKr8sy/BDP/RDkFLiN37jN6KfvzB9ZMl8Kv3AHrWwPZBFOXerRUiJTsohcoHUSlFUTAuNk+5Tvc/hxuu/DafFExhkXXTWUhRnLNPbJK54UL0nQFZAYlkgnSMja6gxsiYmKGRVWq5coYws3wnPMJOak8uxcL0f+E6JM0vDC4NqKlgvyJ6yb4CxTITZooewvaYRJiV2ig5kJ22YO6o+slzrxVx8ZNlMC6U//id3cvl13z12oYEsVZFsx8iKfMjSD21KtM9Ru70YNgYUfA5xzSp1hwNc8fj9499Zm+AcVv9Cs7WtlWM2QSAl3dEPm3mrYtyvfpZAlq4El0wkg1WlmRYqPrIapbDGnJg2GFm2Ok5ENbUriDVzW6xidWWA03W7aUBWMz2P+G71psI8h3ZJZvNhaIgtF3eELgX4U9YJiWZYC9XZu920cCJmhFC6dNVEdUpF2lGcQAeCVDXD1pxiFGpaGMHI8jRPktp90+RR8074umplZKmAY0RRNSNrXkKDVrPUI1hD0YsxLfQ5e28wsgjznjYuLApw5A5VpTYH59WBtN9Hlv1WvpWPV5g2n9uq5Uhh/ZTm2p9VANVZUQKmViCrmgOGLRhG55DCRTlr6+wdLfwD5kVX3zlbzKaz7nl0DP2/YDyKPpKwdkBW7QvY/XrtGVntTQsZzvQL4IAn4YzEdphYdDajTHFDobcxIyvQR1b9RP8CA7IeeeQRjTW1tNTex1cNYj300EP4i7/4i2g2FvDNysiyTLRm+NEYsdPCy8WwtotOrfbwKiNLn7D7OIvH1+8GJEd/2EW6GuCoNHBir08HeEhPkAU6Ccehqw5EleES1fQiU1hthcydi1NMqG1VbAylRvQSj7I6zCVyRg1e09eWvU9JllqdmxeGUpWjGwhkGYvq08RHFhgvfUt2mk5npTKJu4JhinlELbT5TAswLWSsFZffEBuQNfluidM+iRazGb04pU1pNWg9QdHeAhVVhzsjMhvJO9g4c3z8uw0jy+ryYcq5rMGgYTwoOMM4vUWronQfdSNh3q7vUIwsUxgMRc/4IGqbUO3TYGQFmRaqrNvmHHtmtKo52dbNS5tCBzfxMBzU+56NMeXOI2aqdAK+NgYbkw1AX+0f1uitGiPLP2doMROjHJg3xe3TvoNCUPNcWEsWDp8qKpAV812Eh+bKEzuQJSLYa67gJY20VhpjO0ZWLgd0wlkJUd+ZMrKAqRhZ/sja+v2cAHPbnO8VknmArArAqgMt+XwmcVjX5+y80v9H8XrCLHxk1UFXTlfMP5veXjPR6LAmbtn2fPd9My1krDGXUYwsxoAsPU+aFsbg4Jy18uGPEGfv5h6KNN23+U5tOcylBE7szkJvDhMbkJUlcWBJ6NriCqBCOnsfA1kXFqdoY2ND+68tkFWDWF/96lfx53/+57joonC/Zap8cwJZ1pOG9s3hWjMLSLAq+ldqs4dXJupyg6BX8gH5UciCY2fYRbJiL4w8YXGcWo8nqpCT7QrIes7zL/GnDRR1ryfBxqc2ZX3cfsfaiNXULpaRVUhkFJBlaJNO00LTs7EihYHQF1gCY8zw79KUvrE55EVgdJA5M7KA6nQpXWtcVzcGrtMnPQT9bMQW1VGCe1cvNyMrVGxhdyf1SlvQD2MfsfvIaqQs82/h7N0sJ49kZAljTLRhZNn9jUyL5OrPC7C4TVdEWrdtYXlTMNMXVLM9uamkZuZY8ABZmT63qClsb6MqywVhyn8+72paie7svSlUP/SaPGm33d891AzGCtw5B6LGl9PvGI9pQKdt7KljK8RHlsb00cdW7GrgSi9kCkkxvgLXnHzkAG/UNozxvSOYG0Bg9oAfoQEAxhkFSvCcbcuywciaM5A1b9NCsAagwgIPdULq0Vxfmt+1zdGZkG5GFpM1I6sqz8fIApBY9g35zmReKAYtTAutJmL2FzCfGFZA1olR6TPWFvG7BvAGLTbtA4/P3BjAWBURaDKmlITcBLKIA7VkOQWYbPTDHCxK1WaslQ//se7hLku/Sfb1SNNCn0gpMcqlETnYL63nFYteNeJxUVyDgaw6CmE0I+vCArJCZWdnB/fddx/uu+8+AMCDDz6I++67Dw8//DCyLMM//af/FPfccw9+//d/H0VR4Pjx4zh+/DhGo7iolhdm64WyTKyyv4ysQsoxIytphLMfl679Sli3SQ0XHNv9DiTvW8uqT6hDh32saSFQ2m2XZUyvtJjzDGcdiDFQaacTtzYtzC2nAcYk4wM1+5lE1iWUcwO4cubjiFRh0tyzCvBIE4aRww/MoEgqv0ZlmvLEevLbKvNmZAFIOEOWrDa2NIKrzt7tFZmHjyybs3cxjvvieLadd1VdpIWRpShuqWjxeUw2k2+oxvrIctTIOZco9xq4SSOpcZrJ9eWqDSPLCmRN66S4waCJi1poC2BCMUfUb2m2UT2fS8Ya+3oTrGx88gYjS3kDon3SBkhuYRgpom4WKROtYZagSLrjHNQk1GERZVroA7LMmd4lFGhBKvGWbJymhSojy3D2bvYGHciy9WEya6tojKyGKhg347jKE6I7lW5VuIAs7XAjfAwLcCQpR25xNcAYK82ACJGMl+t8UNChmDrZTlhNQJrRAGljzp/9eqkVR1jPz5uRZVuvm88G1MNMQiySLr3eJgWY85CGVx6MeKfSowOGBu8kKCw+vxIkKFBA9Nt8bwuQFcXISiFZirNZqcs0rBsq4dWLDlkapJKqkkvhBKrbMrJkLJDFGLJc12CpuS3t2fzrcfAIp6cMTk+sVuG1aaGrzRo+sojyraC5lQ3ilPF44oknEspsxObrbSQjgSzLgXcjXfVvIThKGFNhqVPO3qv67VygUIxP7rnnHrz85S8f/7799tsBAK997Wvxi7/4i3jf+94HALj++uu15/7yL/8SL3vZy4LLaUVBete73oWrr74avV4PL3nJS/CpT33Kmf6d73wnnvOc52B5eRnHjh3Dz/3cz2EwmOK0aGpn75Zs2/hcqcRFURVygkCHMLIAIKFsZgVDlqcee3Cp/UP80KXazYRNruVgTjhH0uEzUVnMzQlX3zt0RxwhzIwIWUsEI4sxhn4mMCIizjXCpju0FMntk5cJZOU1kOWNeMI05+lc5gj5UvuAYyHhDDl3O8h3kXTkTBhQuth8pomAucCtYgS2qA3IUkxm0lxER4OaFvga59PobwGMLGd/U1gjnk1J88RcHy+jFhGH5tXTTeVeMtojk01sIC21X3W9QQ3USd5sP7MmiZkgM4GsSEaWUlk7I8ttWjjME2TK/KUxsgilnGYUEG2uvauK+LiYhQlp2kllb+X5Obsb7exdMgnzrKJQfWQF8ERCTsxVMKQJZEWKo7is6NEJAoeim5GltGEMI4sxd7Q4ZgeWBKRz7VYlxnePva/QYHUjFaHDzTKKYEPIjdmcGVkzBLJYngC951c/EmKta8fIKsCQuSImVu3GatPCAEaWK6p4zXQq9vbLtFB/t6FMIbsTUy3bm9f17CONVt99ml/bVb1N1MK8MPZmJCOLPuzPwSCi/DqisRYEPVcBLx4NS/tFpY2OWuiVijG+X8Zglg1FH6uR2YS9cb3uloxofY2gQdOKkTWFf+6ns7zsZS+DlLLx33ve8x5cffXV5D0pZRSIBbQAsv7oj/4It99+O+644w7ce++9uO666/CKV7wCJ06cINP/wR/8Ad70pjfhjjvuwBe/+EX89m//Nv7oj/4I//pf/+vYoicytY8s+rqYGyOrGNPY02BGFmGvX9EVXX6XwqIPKunH1QkzLayls9zBNNNZLeY8ozudteff9gSGW4Ask/bpAlQYY9gpGIYUCNFgZLn6lP0dMmPTnlV05iQkdG+iAFm5CFMu94ORxRhyYsMUatI7l6iFllP10keWu924dNBfQ7unJSw6U+a41BJZ0ZltA1hpSQW3mBY6TZEd765urDKvDxNdTHPb0QwZWVPPZEa+taIWurGzgbQUQ1G90oAZGQNYuVfymhaaDxtm13r65hhNc7tpoQ1IUcGBvGh+v0GW6BF8lM052V3amBYGiuTh/ctusOra0NKMLECiaJiqKtElA0wLQ0aWlssUuo+vwBLIIiTwkxRD+/qgm0DFfWPuALIY7ECWhAQCgawYpqcd9HKPY0uyKu0cgSyyvrMuzwSywnSA4Hl3q/qDd0ggtI3JWi4SZA7HRrWP3rGKF6D+OIGsykS76M8OyHK1sznUByJF3pkAWcLGyKpedA/EKYuzkv7osW2dvYtIB1SMlSbxqs5KMdD4ctmuZj+MBXBK08J43Y1X+mPhaGdT7yYjbdr6nTVbd3vW++QW7txaic1H1m4RB2QFn0tX6crWN4As8p3LB3anINEspIVp4Tve8Q785E/+JF73utcBAN797nfj/e9/P37nd34Hb3rTmxrpP/7xj+M7v/M78SM/8iMAgKuvvho//MM/jE9+8pPtaz01I8uioEzRmQonkAXwCsBKrPqiwcgiABBWIbpucIQyLSQ44LXEMLKUdi+BrOnFZJcyFghktSyPZRbwoTHJuCIlMQjJMKL8GzV8ZLlOfe33chPIqgCPNEABkLwLht2yriIvlVlfg+2HjyzOGmAEoFfNtb7ZHGJPI9wCpgjJvawGzrLpAUALI4urpoV5DtbzsTsaGUdVw6Yo25y9O1khgV0p9wBZXkZWi4hD1m864+4/2QQHMrJsvtoo9o/u3KghnDMIxrx7BZORxbMcusmUh5HViFroAZ1QhqavhTItHOSJ7qhcJU8xVl2YZD5X00ILm5Z6wmpK6ZxX1ae4dtWMOhgGZKnp4xhZaBh8z+5kI8t7WKLm7lBGlsPZO2O5QkKPBLKsh4ooGVm2iMKQzrVblThGliWt2U62zSVRpbniWEI2ypxlcSUjy3D2HmjiE8TIgpxMVDyxzLUtgCy43T/U35NVpoVBZAzX4VDBAA4MT8b5lSmztZkWOoAs45FHRuv40sq3KgnoyrKqs0QzskKArLbO3iNNC8udFEOpkO1VZRN7taXmGgqU79GLmFsZgKzFXFwzF21zGFW3GNNCiab/r/KGu65joEfYvcNS0pbpaTUt7K4De+H5xEYtLAhGFsX+Y9VakrUMtvIZ+b9jTUSCclPIjtwF8G68+MUvRpIkuO2223DbbbftW/k2idoJjEYjfPrTn8bNN988yYBz3HzzzfjEJz5BPvMd3/Ed+PSnPz02P3zggQfwgQ98AN/3fd/XvtZzMi2cHyNLjh3rpjYnd2aYYWJ1q1lDLn8T0c7ea4JFkLN3BcjqpTOhkZtkE93EwZF/S9CR5ZbFPsbZe9VoAzNCIJqnHK4TGNdpn8nIGkUwsjTTwiIP+077cELCLYwscxNnk7kwsiyKcTme3e3G4FIcQ49wLGcJysDo5M3gD95sY2sTccofmKM3v5GHadYIj26OiZk6e59OTICsHtvhjCxLP6R0RU9eLOGlaaPxrM9HFjNMprTbxnxIzUNq09rqqB74mCGqJRgGBdcyauAFxgVKUaWBLJu4TQtDJdQszFa2NNhUppP5QgOC/EBWNCNrWtMGR4HDrEs3UODmUzhcEU7DyHKZFjJm929Wtlvo3BNeJysLnPCRRZa0HwQpVUjkZ3YFMqBBY+CBQFb4aUqVP0stzt7j14tCMowCtii1SWGQaaErWuUORxdLKHbj9SMbkOXy/2aaFj7SX8F/O/3M8W+bzsvBIVlSRS2MaFfmd5DelpFFO+B2VqWSZSUPYu7slR2gGbWQR6naDKyVJ9aa0e9+PXOvQu0dLfm3RMjrrtP2e8UX2GztYuUITi4nSHzRQtVsgvW4UnIpYUazpE0LBWQSA+k9PeTuu+/GF77whacFiAVEMrJOnTqFoihw+PBh7frhw4fxpS99iXzmR37kR3Dq1Cm89KUvhZQSeZ7jp37qp5ymhcPhEMPhxNH51taWnsCl2QSIbS8zFSPL4yNrbFpoBSJMIIugORd1fi4FrGJkOUhYWvqqXmHO3vNxnp2lBFL53VYajCwquhFVlbYFZtM7e68n4xEFQpiMLOcGwV7GqAFkle0SAmQJ3hmXym1RGp8CSTiQUSGulU6030AWtypsVhVPeXZ6Z+9SdMkhpCrsaZZNfbLuUwStJ2+N6xWY7vCR5VJy1G9dmhY6FHTjd9EYE20YWRZTihnrVUU9tkPYkIAS4MK47gGyGPFhWcIARpgWGunIqYSnQDEk0uu/6HVsUjNb71BNHbLGBiDFUACyozLO9D4iGdPXb6ofkt2KKX+piI/949j8INGMLI+pLfmQ6iNLTS8bbMVCAc3d/ulKCWJkaT/MsRS3wrqiMwppY2CElVE4plk+DZDl2cgIycgsJWTEhBGxWQoEQ+2mhQQ7cU7AvU1YwLoZnheajKxQ08IAQI0xCZlX3FmeknOt1QG/Q0ogyzFGa0ZWDWBNaVo4fDjH0lJLC4kZmBaaYjvUT8ABniIDjxqqIdjofpkW1sVI9MYlUvsxuUStoVXoqigfWRJZiwFV6/2ulcJsM9JyP1rFcrfnBMjap3mJALJOrN2E+3EON3QuQRG4Pwq1hqgPI3IAUnb0wyULI0smTb/BC4mTuRtmfuQjH8Hb3/52/Pqv/zruvfde/Omf/ine//7345d/+Zetz9x5553Y3Nwc/3fs2DE9wZRRC22n5CEOnm3iZGQJCZ5w8JQ3NhdKDtov07SQIQFE7SPLNVnEGD5gvJuJZmQtpZjFcV/jFFIBh1zv2ZZKzCxhPc35znlSU31DyjTG9LPgZmRFAFmVg8mGg2ZChOKEnheBYOM+MLJsPrJUcW6IZmxayBzfJsS00M3IChDOAGFRQJV2SG3msA6JVhMCN2bjMdl2g6QU41T2iTqZjKxhCyCrsHX0tmFQazGab8LICjRBItY0BkYDWWrbE/cZZyiY/5NS+3ibg3ezdchnVWfvlvZU19i8cULZwRBSA2oa7xDAyKIaxc6Mc62N4aaFrUQDslRWqkRmtF+hnssHmBYGMbJ09CzgCZf4nqcYWWE5O6MWKkBWlLN3YBxJ2pKxg5ElEao6x/hYskctNOtmo0kEXpuVzHkvWlbdNC0M1QGC0A+FkZVAUGBRGx9ZknvWpirPtPTTpgH3nkco2frbHYhT7T6GzfRqGiDLppszMIB3SmBmxv2y7fIda1o4Lk/RRSgfWXKpDmZmMLKkrcVt0i5qYR3EKMZHFqnvWp636saeD1HrBdOqW6FCBYf4wvJlGEGMo4aGSCigXa+pmSxgWlvQZFJ5QTKyQuWjH/0obr31Vhw9ehSMMbz3ve/V7v/pn/4pvvd7vxcXXXQRGGO47777WpUTtfJffPHFSJIETz75pHb9ySefxGWXXUY+85a3vAU/9mM/hp/4iZ/AC1/4QvyTf/JP8Pa3vx133nknhCX85pvf/GacP39+/N8jjzyiJ3A5WZ5Cpopa6BiYtWlh2nNs4k0KtVGXhHUnwIlT/2pWxDVnxJkWKs7eu8lM1qKi4UwlzEdW25WQjYb0jQYjy7UAlJJR5p+mA0XXEHMsMiaQNcxrRpY9u1qEYlqYFKaRikX2YWHhjGFEOXtn6ibOLjMHslwRJcHg62NOZ+8hDcoA2Pw8KRMKE0V0d2+aFnrqY1NYHMwWe17+JACQe07DzCrpjCxGAsk+EQFmWW3EVMzqcR9sWsjojQPVPbSyKN2TlyiWOac3nL2TJdo20/p1ClDXfGTRuWimhU0fZx30oZuRNE1BDSCLcvZO6uMqQKeKg5EVY1oYnFJ9aPKUZlooQQBZk7Fi9U+nvFjIZknVWZoM5NkxsgBaLwkV1zShb/MiB7FjMWWwh66P8ZEVRz0Jy8PKziGBrDku7BQbdJa+NiWaenGoj6yQejAokVotEUqBaFpKIRmGgpFWFQAm3zkBegd6QWZaLkZWfj7H2Y9vR9VxnK81aqF90PlACNshMJMMkqeV2XSMaaE/SWtGVksgS3OHQSzSolMCWeb6n7Vw9g742aON5yqrJTcAE9LvPBWLlBrkjAeyWn4noqCHqgNoFmGPJmUYi7DWs4dCouHLmhwXEpKI5P6NIru7u7juuuvwrne9y3r/pS99KX7lV35lqnKiTAu73S5uvPFG3HXXXfiBH/gBAIAQAnfddRde//rXk8/s7e2BG36hksrjuU35WVpawtKS4+NOaVpozXaOPrIYZ0i7rvxNRpa5cUjRR83Icgiro2Uol5ynzvpzTlFOwzrdpFRkptRbGjiWzWeQmaylfsZtjCxDP3Ixvuo7GXXqFsHIcjXeUJkEZbI0XjhCGFnqhj80XPV+AFkJAzLi+6pKiKu2xYyBLMoP3bgekjs3AYwFgr8uYdIRV3qSN5fxapo5t3r1+sACNMXMqo/6QWDOGbbMMNaeXHJ1TPDEjpY4xMrImlJMwGXyxUJP8pprGgNrRK9rpCGy5wkvv3fDtNBcU9wZmk/7ylUv2Q52cnDUHy435k+JFFLqAFxjA8e59t1DGVn22y18ZBHZtzOVUBlZCpDFJHIhtXJM00Ib5yGmPhprbsqoSUy2WUL8Y0OCOycvjZEVOUs6N4esAl2JJCLCtDCmTcKjFlq+PhmwR2BehhfSdXo7A2FgDd8TwYysUJJT7cyKJVbH2Ba31lYpwFBIbn+qNq9KJJYOzGYTOzrd9nDfxsiyz4u+FdSm85aMLNoXmUvmaVrI08g9X1WMVPRYKkqj5IWafCw5YxBRpoWl8JSjKMIVHl7ZY7sMS5qMLEcFzMvWfN3foe5W+xS0sNmPGccuOAAJyrWZTcoo5vDOK2O3M4I39rKU+yEmBYrEEtX3G0BuueUW3HLLLdb7P/ZjPwYA+PrXvz5VOdFRC2+//Xa89rWvxbd927fh27/92/HOd74Tu7u74yiGr3nNa3D55ZfjzjvvBADceuuteMc73oEbbrgBL3nJS3D//ffjLW95C2699dYxoBUt0/qmCTB7iJXC8ayQEjxhSJdcza3XyTzNSVlnovg79S+CkeWaOOuohUGMrEm7dzpTOoetxCTlyVBn7zNnZBk/HavnmJFFmhbqzxUOsMRpWqi2gzLRhQFZBlU15NPuQ9RCzhgJZGn1cNybPSPLNWaZEyx1OQouJUxZkUOLcqIswIxFRvlBcyPrZ2RFXke5oaIOI9yBcur5htNAsKNsLeKlxX+RT9o47w0Sw1yihhpCt0DCYspBAVnaVEWxInjJamo6e9d/c5K9O6mxmtycH2jyh2paSCSAm5Elq8isWl9tOHsMcPZOtUmo2ZZ6aybO3qvzHuq+yshS6yclRkJoIY5VIMt26KS+dgjGYEAEZm7+DNTUfnsj4lpIzu5vwDSmWiyQ5Xb2TpqaoZxDwsGN8DpZDBQabRfjI6vsK3PyIBJ5iBErZU5GL3U5TNOeDamHALJ63k1gXY4C/RzWklcbVl+0aMYZugfDzIpcjKxpxKb3OwHDlowsAGOTPMnCR5AMaP/WzsOXUsSxw+pylEM18kC77rd6vTLEHcKP1+JkcgAU9JyoTQvtaZo+ssJ1OSnp7+dj5opxOmeyZj3ikk+eM4AsmfTGOlVBAv+0hPavurSBYDBrTap4TELwC8+00PRb7iUfzVmidwOvetWrcPLkSbz1rW/F8ePHcf311+ODH/zg2AH8ww8/rDGwfuEXfgGMMfzCL/wCHnvsMVxyySW49dZb8ba3va19rad19m65Tk5IgeJ29g4/I6thWmicniMdAydBPrIkcY2Q8UQV6SMrSfhMWOtm4DI9IqO7TdsIGw3I681Jxj9xZQSDz6STtnX2PlCpy0lvvIaF6DOmU+ygKXhfohbSDvI1NoLju84ayHIxssrTKnvLOf2rBIsEBpZ3UtshWUKUskWkNuGAhth3R0Y+zHbLf12tV0DzmUkydbli7ZzbWhlZU35O87CgNkvybWbGz5OmHIyc51SFk2RG8dK3lu+VyLlEzVAl7Bj1IB9V/rYBhqo5aGZo2aI2K1fb0lQ2TR9ZNmfvrtpJvaY2sQFZlLLuM6yjU9CmhQKytHhSild9ZNnMULVXDOh0ahrZYB95Zww9L8vGRs8v5JopPiBLpefFmu24309YmiDGtDDGb5fdR5aJQFvSUWbI8zQtJIbOLItjYDBN3HgoIytAn9EYWeBWn2ixi0N9qG31j6i0UXogbAs262AkE7GZFk7j7J02qmRAyaSufwRK2FzWroGKtANE+DqtS1H1fEFQe+pxZ86JOVj1PnH15SE+Reo6JmxsMutybdIAsqhmtoK7UZcnZYi67P0RMxq0TCekgIyHh3IQY2DKXfO6r+bE3lBYGFk5bxmo4SkU02/5HXfcgV/8xV98aiqDFkAWALz+9a+3mhJ+5CMf0QtIU9xxxx2444472hRFy5TO3m3DzcXC8UnDRE67x5CkHKmLxWTMIgkBZNWbANeiNo5aqGduf2AcXCtg5VcYWWUdpl9dC2OnJkWiTJ5Oaker8tiQBrJMDi416ZgyCnD27lxIHB9yqPqNSpfHQFaIZ7LciAwY0lL74XyRM4YhtehrJjF2mb2zd8fJoWROBcp7QhrUPSXkjqU/alY/y9Hd3Xw3rxWwdQ9ltkEIAuVoV9TqQDySlavgbgtGFkMNZFGMndlKLCOLAicYAME98zJFwghmZFHCiL8IRhb5WpPK2NZD1WOfycgSNcitrUXG+sDMFg0DstRn9Lo7TGhszt6ZX6ENEmkxi2MSI2NdzBV/fNZ5Scki1keWDzCaVtr7yHLXi6tO8GP1N8dhBGN2Z+9Fi41oiIRHLbTtIqkMCiAwEnS0kNjk7NqFSTTZaIG6f1DUQkhgqABZ1k17HECa12Vb6zCZP9hGYHvNCcmyO3u3t7PvEFlIGsJjYBDVuh0zgkIYWW0PtrM0KdfLwAxqLqZqOSJJ2lPt4kW/V4BFDZExzyDi4JQnDMjK7+d2ARawS7RNNW19ZNX/xn6vlt+XG99VKNYtIy4QyiESCDgZhOl30niA+hhMIufd/bO1nJE88sgj2NjYGP9+KtlYQEsg6ymXOfnIajhnixAX8CEEsLzagchdo1HfyHBjpk/AkdWO8lxAFijI278hDzItFAaQNRNGlnkqEOojqx17juX0Ai0bOy9//rRplLlwuZy9uxhZyokPX57UKmABMSMDhgFZ80eyOAOGPh9ZTkbWbMe9y9m7y1QYCGBkBbWnBLZ3LI8rbIkWUU2ajCxPfayvY9xQrb6YG+yz1St06jCrJJTvFWP2VUvCWXsTBI+Y7TDxtxJTntkyDJSG42s7llTRDj1zekLmpAJZmlZmSaVcU/qsDchSx3eDkVWbFqosLHMYBkQtpDextu9gbyN7H4tlZFlLUP7S58ChMREWUEzibYwsLWd/jVQfWSYjK5Y1EOdFqC4zpAT3PKxuuGOjFrqALMATtTA2ymuQWE8T9FQufKRR/vx2SPNWGRjQYAaFmhYGixAofeFxx6Y/rm9ntU5rA2CUhjO9QFhrMCfTQiuQ5QAMveZjtg2KxMS0MKx6VWVCGFnt9gOjgiNdTpHtBvar8atNyhMEAiqpAyiWIJeyOhwM+56T4iLmYs7GJrOu0d/cQzXbmfa7B/tUFQg4xk8dLYEzw15bJBPAZYAcS4EQSKjbIW1sGOPAFrUw5+kFB2RtbGxoQNZTLXMynp+zOE4Lpsp2TowsKTmWV1IkHUdzGzOAOX8kMhkDJ85JgGBkudPX/4QwsowIQTMBsghGllqGrSottCjXKYLJpA45oBlRpoWms3fHBOhScvtK3oUGZPkrZjKygmQ/GFmgGVnQohbaK1JM6xuvUR+Hjyy4QQ+/aWFAg0oJee6s9V4tebIclp9WujGufA9YoxYaJ+Iac8SWl72YYcUtd82XtiqpRPA2jKyea/6d8T5hYl7SHgxgAHLicdVsj4RsOINgvJWPLBu4bo4F2tn7JD+baaF6dVjoc0EhmoysZkCFdqaFzPK3S/O2ASOevam7cO2yAiSpDcokRobNfa4AWdLGnp6KkdU+amFIHyfn9ZApMsa0MFZcm0Om+3NTpfSRNfuohfbD0PaMLKsZ6iyktd+z0PybeikPPcQOWV/qRLwDSGYHsiLZJ2PmsK0ObdpoPjiWffw4TAt9YgOyGJTDqAgb1LA9frsGyiVHshKuS9RjT3WBQgJZRNAtyROAxfnpbPNWPGHAqI2PrPAKtD34FtWisx8H5wDAjd5TKEBWP8bCg4UxstS3auwFiSHFpEB2AZoWPt3kgmRkMcgS7Gw5Fmw09xBzMpsUDv9aQgAryx1sd1yLsMHIMnx0ppJPTNkcCyv9bo6GGkctjGVkxcZyocVkZKmRI901isdgnXRYbWJlTrZOLSMqWokxeeUtTQtHEgBLAZkjV4GsgDbPzGEdsmHYhxOBJICR5eQssnymirLVhwUqZ++OMWGxOlIzDxABtrtH31L64yiJNy1sZNd6srSjIW3o5VsixzKAvEV/U9mNMiZ2ciUHVlOcjy82SKRBL5mAbjFAltm93QEHrPlwVrFGzPz1CzGmheXa6AHQNEYWfeqsvs7IQOkK1I6Alc5hnOiY8yu5EfMxsgLZyjGn/NNOSypIK6TE0DhJLuRo/Ap2lo2SRwgjSwXSzDaL6nj+Pt7atNATSZrJXGmXyPnIpUcxoLDcL+bEyLK3UAOBpoVgT5g+YmYppM4wjWLezKxxJZiRFVMFngLgyGOobg7JfQfibeb0uTnJmoNpoQU9MU0LgyXg1duaFhYyQUzQuIlpoRvIGr+h2heqPUWrqkZ8f5YwIK9NCx2H9yaQRc35sUPC83L1OhMdmCMqtVqgcSCkAFl5DA1Kjv/PKbppoXHPYlqYtfT3eiHIzs4O7r///vHvBx98EPfddx8OHTqEK6+8EmfOnMHDDz+Mxx9/HADw5S9/GQBw2WWX4bLLLgsu58JkZE0rloWmLT0VcDMMhGRYSlP0lsOjFibGpNKRfEJZdkqTkeU806gZWSE+slQga06MLKECHe7wZ9FluZ5QD6D8k2x5f0R8D7MP2UIRl2K/l0GMTcrKrb/vCfVZXfkPaamQzz+tcMYwoE6vDLMam4gZMzFdoKCQ3Nm//VYsgehhZtloKBvZAV+K3gjGMrLC9aTpGFln89LXj+21XfmoYbvbAFkrvXltBppLSq1ARjGyiI9A6T7q0kDlz3iTSVfmr//2mxbSZZr3qGu29VDtl+ZBwBjMcR2qGC9BMolJfdwCwM6KkWXNJUw0MJ9JDAxGVsEUH1kWv2lqHwwyLbSUH/p8LUE9vCV7x8/IUoGNSP3NY65jU7dKk8xQZ+8RjCxCL6V0kThQY3+YD/Mpjnj3GZoWjtmevAMIRrJfq4RR+U6ALMs+oxWQFf9MWL4WxW8K00IbkxHA2Fw7JghBiGra1mVALngVuTBONCArdzCy1GqNgawWc2vE67GykLJuTkaWETXYokuQZVjDGbrrVpuzi8hB0HZaMaMWZkqEwKgI1hII+QiNACrqPYuz96yFLnuhyD333IMbbrgBN9xwAwDg9ttvxw033IC3vvWtAID3ve99uOGGG/DKV74SAPDqV78aN9xwA9797ndHlXPhtuAsD34qmca00Okjq/I/sb7qQF7NqIXG7N3JJ87Fw3xkqWYLfkZWkGmhxsiaDZCVm6aF6gmsE9SIL8upemo7rzAllXT2bjzrXtSdHxIyXQbL95Ah4sgIE2ZDjOwH1TcB0J/C2Xsxax9ZPkaW41k+iyMAKYChNeb6+K8h71ZmYO1P971Tm8200MxJBVGcij/deidHIzy328UoNKqUIqrPGtHCRxbvztnMhmJkTbEDYWCksuVThEtGFvMWbdLuq0InfyrvZBCEwhhZHhBu1PCRVZvOK33dXLsaUQuJb+rrW1qWrqiFtjFBXJty/lTLkhIYqutRgyzlj1oYsj5qmwljLYsBzoOAlbbN4/FNojLyYjez0gFkMVZFniKSFBAR43oWjCwjR1vZJCMrPDJXrMxbZ6D6ICvCI8x5pa4/SwHh6PGRvtd8/nRaRXZ8OvnI8uRpe38GtGJkhaRta1EgBEPRjUGJKkaRMjdQTJsxkEUwsmKqWh/ARH19ZX6wR+Ik5kuqES2dtWamxUq9rMVOHa2tf4QdyCpiKiHhD5gE/duaMy/pI4tJjL6BTQtf9rKXOdeJH//xH8eP//iPT13OhcvImsu8zlqzsgrH7FRUqNtKxwEwGMppYkwqnUJMfGQ58Y+akaWdp7seAADwoKiFqhIhZ8PIMgZ3kjw1jCydgR+2SR4SpoViRqaFAFB0DwIAMoWRFTKhZ41NiV+o04JZSwJgULiBLNcGbPZAlpuR5Vrn/DhKyE5SjKPLNO9N/hyyFNNOeL7TJ7sSbzI1mPXe5Lq9nC1kSNIUA2fgCzqfXGNkxQNZWeLoP1PGjTcPC4qxAhrByCLSUqCVphtT+XBGnsTGRi1UxWSL+HxkWRlZGvWeayfbE7MDpfMb7do0LYxnZFkr1MhmjqaF5h5CnQMhMVAU8MZ6H7BWh5w2a9+iUUgMayBohQm8Zqawj3PGFFYN4thPALzgQGGpXx7DyIoY//RhKMXIsmRA+ciaoxdh8rvPEtsi8uKhQFZQ1ML6jxRSqgE6rCkDivWvS08rRpblgzGHLzKvaaFFl2QSKFqAOSLI2Xu7BsokR+bynWlIXYruI4taDJuoheRtGFk12BqjR0zE3qcJFm7Y9DOV1PuM/eKJmq4HRiqQNXM4VR8bJqBLnj9JidGcIwZ/M8iFzciay6McbUIIuKKc5SFQruns3dgJdHMRNlmPJ31FwXMMwnE0lCDTwokSUTKypp/lTEYW482NDS2zBbL0Ayj3wlbXWEhentYpoGOTkeWapNzvkHUPogNgJCZ23SEn5XmjzABFYD9MC8GwJ6hTbpWRZWceCcVPzGzq4/rO7jE7E0aWKIAsR+11wSZ91sFy5HubyrI3ClmwDu9BUdylQEKiYAgDsgxRfWSJSDo2Y8Au+tFltpXapDgOyAq76G05ziomqDtlQq5zkwI1RpZJjKKebPjIakrzaoL6FKFe3yRjYAmDLCQRNckA1CgfQJ65zDgntaaLAUbaOHvXnjfmQG18GIDLrEwLVRWjYWayHz6ygqgW9om2EcltBvrIJPOKTU2NPyZbb5xdYoEUiEu2iZfKYb4Le7lyaR1pvuUFmxYGda4q05KRZePrShYxi2tAuw08D81MzWo+SNZ+OnsHJgcRMzctbNk+hWQYMiA0LjTlI6sgTAsnQbdUfakG8dogmS3SepiEcfvJsPr4I1rK6l9/0TMRY2MzVMKE5jH+VALXQy0SsOlmhgA8GQSGU8Aw539zgLy3f0DY7mAAAHjxi1+MJElw22234bbbbtu38m1y4QJZc5MEiIlmUInLR1YQkNWIzqL/TnMVLLELTRWeg48s/z4pSHJjvdTo/hbzxfZKpKPC6swaQSWXLAVTmGpBkUDGz7rLGSQHsAJgKCdAVohCk3kc5JJ12YeFhUNiJJIq7Lk64aubOLuIGUdgsqqnjINQ0TXhtrDEYwlhZBVANkI5DRsKuvJBBuj4gSizdOODeje2EWZU40eib5Ty5O4Ae5l/6XFFLRSRp1gHljvYlTtRz8SI6dvcRem3iwnS0JG0VGWJ0jNZwkos3njWzIqTg14BspSvb56y04e3fiCrOdEo37TaABWCI0k4iqJo9NsgZ+/kUmb7Hg5GljWSZ0wuYWICWZm2HoXlrlY3LGqhMgcb7OIYJk97RlbAU461jCXmWju7zT5j9jGcSxkMLEzv7H1aRtZ8F/Z5Alm0aeGQSElIECOrzj8BxGwYWSFAVruI29GPBGYsaAarg5HV1kcWw2QNj2mBEGOBNm5hJEoT/BhQbWJaqByqZc2y6/lTvSP3CcgaJ/UwTqWxL6W+a6yPLN9aUC9r+xW1kBn2fEMl2rWIOb0PVL+l9rcBZJFO+AQGLSJwP9Vy9913Y2Nj46muxlgWpoWNfNuhm2b0PVV8NvMAGsixCWR1Rgq45lzVqpMA2bxGSlsfWf7UQaLq670O9zCYamnZbR3NICMYWXpV9EmoEXLVdTrlacU+PwAAGBYKIytgARgZbRj0rfZhXanrIRPdJlwtOtYJ5DRic/Y+PjV0LF5erDPkPWSB8u2JPq+M/70WziDNTbh/dIeevPkZWb4e99BegpPbIewovU6F0uhFZJtsrHjmlSknNFMZrhX2OGfvVMbx4C1L2Jjj5CqAdPauOUKf/NlkZFEnDJNruaX/N/uhYi5atVUBVoYQB/xRC6kDGOq1yNpYEo/vWJy9E+/mPLAgWTVGGtVHFqvOcNjkd+NxYpOizaMBE7rL2fvMN85tnb0TPhVrMdsgPmqh+7YVjGYBD4cWogitK1BA1tOHkdXsyHMuzQGwxEtdWQ5k0g6YxAwGploUWEp9GjGyuIWRNa+ohWPdKqx6VdqQuaxN+yTIGZBFqPtjHVY1LSSaauwjS73GVR+QoeWN6VXRlfQdlDcDU9nz8l6r8/QxsqpCnipG1gCTvUeMaWEob0M3LTR0cGKoMUgMvoFNCz/60Y/i1ltvxdGjR8EYw3vf+17tvpQSb33rW3HkyBEsLy/j5ptvxle/+tXoci5cIGsacfbIlkCWp5fHqZVAYvT6Tj5ZwIOcvQeWXudFOs115M0Ym1pHMjd5q0tpGOjXctfpNMtr4ey9TGoAWaaJRoSNuil7bBMAMChCic+ltGJk7Ydp4Vhv1N8niJE1Bz2O2QZSpQAwxqybBi8jK6S+Ra0BUaDMJP/dFnNSbNTCVs6LW/jIAgCZMjy+F/9Oqvl2LCNracl9f0oXWY0GbuMji8qTmj98VU06vGT0eBlZHtPCaEbWRMJNCxUgq/q+BdiYbdOcs416EEAf+VpSfS/mSVzX1QJ0k9em60BmWZyxyYk6SbsjMtFO1/1laows059HxIIQFkWPbjW/RJgWRjrldq3NjLl5+b5N4jhdFJBF1qR5yca0sDh7n6eY337eekS4jyx/kknNE8hzI8e3ikE6/OtSm7UmsmsHizVwhMOE01d9q7N3ORkPMcBTmL+/NvNvgoI1uPBuYXWdwqIWqr1MtnL2Ls1s/M/UY9LHyGpkSrQhMafERU3VRVRI1n45e2cGIaTPJkCW7bCNlFDTQq0N/UAWILGNb1xn77u7u7juuuvwrne9i7z/q7/6q/i1X/s1vPvd78YnP/lJrK6u4hWveAUGlQljqFx4nLZaWJypTbi0WTFYQxEkkrjFZGQZ3uO7CiPL9easXC30kwCXOjW25Q6cXlVoempUXa9Vr1v6dkkt9yfSblV38ti09o4xLUy0fIV5yuFEHd3l7GIDAMMgnwA/IT2+wciyRBBTxYxKNg+pWYaSd4z+OfklLJ17lu5PanGbFo4TkcItfmrGErSTrFc2NyNrF2kc/R3Noek3LQzLRwM6rJOQuyiRhplum/nnyoXYqIVJOgPg0SHm98nbMLKIKxRLQP2WVP4s5SiEQMfsV0ZS7ukTGjBlnNTQzt4nfdYW/KQJsE54kfXmJhcJaleJ0sfIauEjS89wRj6yXDcDsjE3diUjqzLcIsY+Y5ThszI+Qo7OlCTzdvZOpgiZIomAKuM8jVuz9Vslp/JvOcklpg+FMrLsOTSvzN9HlqcKU+RtL3MWJpMTdjsHTpxDYT2YifiGaqe06TJPK0aWJWqhg5Hlq3/u6HL1nBozLkI4yd79FykchQQIy0B7Oah8ZCnlFYTJmKyIAerUXfeNufvIqoW7dSTTtJCsVughSqDU+6HYWamtoYZ50NVXdSKGcKqVmwYxFlcAFUkyFSVOyg7CevmFJ7fccgtuueUW8p6UEu985zvxC7/wC/j+7/9+AMB/+S//BYcPH8Z73/tevPrVrw4u55uTkeWUNuyHWSwypmnhpGMzcKSFDmTZKyMaiZzKzJiRFTe1MLCplRZTCep0mNNpvvpkK3HUVyrOdf3RqpRTlgYjy5i8pjAtHGAVuxf9Pa0+IW/eZGT5n5qH81pT6jVROhhZ1l44B0XOZlqonqpafQHMhJFVnwUS/U1ZEXdaKWm6tI1aOA8AcYAwPydmndRT0CLyDKZI3ODZtNsis30n/lbaNyADyIr56soThlzIxmbPrAltpqwAlQrIk5lAFvVkK2fvKiOrArKgmBY2ZgQD8AkEstS1xpihyXqWd2wOQoi0U3YgEzTjjCmmhQSQRU0ZSrowIEtlZJnrVhxvwC/tABXXBrVpXhk31pxRaRmDSBxrd+h6FAOCBNr22NYkOgjAvIEssxKzzJ3OLOhwIKgeZSIxSoG9oRXIitKNtEMwS36twhbGPxKWraV/0PQRAP5DMWvUQig+smKcvYccBLTSD0vTwlHMYdPYR5bKyKJOdWrTQmWv0MJH1riZYqIWMvMPWsx+TdaLerUposm39ZHVtvubjMPCWNP5jPcVrjWVHFLpEgYt3Rld6PLggw/i+PHjuPnmm8fXNjc38ZKXvASf+MQnovJaMLIa0g7Z94n01dcYcIkCXHX4in7S46LEjxcmdaKwTxq1MmiP2dIooGR8VcyvacScEHkqNSDLrkC0ntasd/RJxmdbrkxWhq+eBiPLlY9nEhVc4p7t73amoWTUMAMKoGZHl9JCavt4rnNFNNNCG3tsDkM9hJFF+aIBAO5TxEIW6qLqdJKK5FhXIMGelztDFG/+ntp2blwh8k9bEkr22Aicdb3+0BqMLOXvIvLAoS+HnnpNi0RAy792eDttt/UZilNgFE85skJ4C/eZFqqSG1EmrUBWdcNG23cBWWNn75KND5OZ0W+bJl3EO0QxshxAVkQAhPjeYyi5RgY1I6usB7XBIHcYk/wCamBGLdRyjJgvWvfxgDnSdEKvldsgA0XWxKWOeb9oWFkx+zULN6Z5xTrvPhWMLNMkda7FlWVaggDpFQnJqUq0Va7DwsbOiPKR5WdktTGTmsacy5mvNWphex9ZZdTsZmOWW4aalRT+PiLEw2erfseRM4ZRizGimk8WI+pdmvuwMSMrprLTfHZPaO2gwFTUga1r3nyaRS1MjPrkxjiKGlYh+Lm2pupCAVmyux5RgaePbG1tab+Xlpaw5PPfYcjx48cBAIcPH9auHz58eHwvVC5gRlb7keDqj7KVj6wQxktcCl5Men2X9cZofpnSdZRYnQQEMrJqJZkHM7LqDdr0jKxGu3GJPIR90nJRdz6lbdQiGFnM7SPLtWD7mF8FdRLvqRkADB0OctvWZRbCqtWr2WbKBszWp2YGxEzESqoKMS30jZeQ+o4ZWfr3kooSKHkCGUhr1sVgCHlBI8um3dwsqr6GWppiSCaxsew/QzHnOdX5ch4LZDG3J4xp9wkm2DCp6zQZMz2Ka12WDmU17vMOQ0ZoTeY7+hlZk/tZbkul1Xb8d24ZyOZlXZlWgKx6CHpNC5sbLkmWrZSj3W7hI4ty9u4CxKiLHoBOZ2Q16+jrr7F+ZZrVj5lxIjX8CHEBWdx09h5JH3XWyDuHBfrIimFkkV79A68BTwmQZX56abMpbpO15QNZmdSx+dfj63gdzXb6g1NdT6dyaln3OTGyrCZNjiAjvqFcSGZ9zzamhSE9qo1FgUSCAsCgxVSnMrIod2KSZGRVzt7bHBK0AVx8B+WNTCnQitiDOIkUbqmX5n3wZAKgycjKjRraDqrbiiuAChUUIOuuzrT8/ZJjx45hc3Nz/N+dd975lNbnwmVkzU3mw8jyinE6nuQKIwtdSDZxcumatOktr4ORVTu0dpzAGAWM/53WsWfjLaRpWmh7z7ZRCx3toBn2x/nIUsVcHJxRCz0LTc6a8FIQkNXwkeV/cF/C4Y6BLGMDEskkmJXYlcoA08JkBj6y6gAOkhvfhyknpXUPiPs+5l7e166tTAin0AGWlzjO7cU9o7KwTOajSzoJw0gWvhOMqcSMcNkmaiFVJ4qh6IaxUEYtDHB6R/L8lP6uAlnDBiPLDbLbgp80h0WTkZVLDpYUdSX0501FlNqIUdGBFAKxloNjnApr7PHmpWkZWebzaiwVQYHmFMCpYnUB84/aTCZgJKWf0VdLkI8sSlkIaDQnI6uxOM6OkeXPKpCRFbNhD5yEn16MLBNMnCXmQneQoPxDBmTVJ9mZHYDZvcRGgZEmI8uQ1k6r5wRk2d7axQH3+siSJVPP7HsMqmlheB1Dosu1YvhIjoxJDCJ039pHlsbIIp29U0BgzciKqWP1bwvTQjkLRhZ9YuXK1CljH1n7FJ2cCbdpYQzTMaTGNtPCsq2bZQ3TteDyn07yyCOPYGNjY/w7lo0FAJdddhkA4Mknn8SRI0fG15988klcf/31UXl9UzKynI+2slcNNS10JdAHHFdg/hSJftLjUsBAhH31KDMsYeE+stjkn+lJMg2KAHKhDv7A50JLc21aWkYtFF7TQhcjywdktVNChw2nggEfaj8QpOq0thHZUZvw6brOxdm7JVM5C0ZWiBQKkKXnPmZWosUJJiWtHIyCmrcY+WesdNMWDzOM5+c84gxmKeX+/jMtI8to33bO3k2wiF43tGmM+Kw85ciIW2ZNvFELVR9Zw+amxFUxe0QgE5jijb81RpaZPsDZe9wBy4yiFk65FprrBFc21sGMLOVayOZP9WPWPDKJiFoYnNKUANbYU+Ts3VezcHAjBsiiSm2+v509SwFZ83UibM5vUwH3Lctsn4/6gzvWyJjNrrp/aD7H2oYfnBeS1aJ/+Mxuc8nI92SYjNGYg9OQuSyWjVkKRyYlRgEHvbVMfGS5TQsnzt5VRlYF4szBuoAUT18zo0uS34Rcexx7Gh9br9pnxa+XLfu/ycgyyp31sNIYWVqfpL/FIF2ZbQX2STY2NrT/2gBZ11xzDS677DLcdddd42tbW1v45Cc/iZtuuikqrwUjy5CnzLSwEbUwH+ebykQ3x3JmJqsaqUpqAJAVvKDV+fvqESKmElQugrb7E5k9IyvG2buaixk9zQSynM7ePbNoxgS64Eg7HHnWdOJvk1Eb08J9WFsZY0h582QizLRw9vWxfmXVR5aNkUWeuKkSycjScx+bYU6ArLgmMJWStlELmzcUoMPKDHAXBQBpwMrTyF/KMkqoLKJMC7sVsOOSaaNgmcN8JttHCVCN6asrSxkKwZonDUZW3DvoTUaW8u2pclUgyzKQm1cJRhY4eG1SyI2WNH1cUKaFJB3MVnfHmmB11kxcc5r7229Nnjfei018wglqrvGZjYSwnZQ0JoHP6quQlJCEZKN5xRm10Ch2P00L58HICo9aaMmT2HTO3bRQG1ezXaSnWF4CGVlKIs7sQFZLH1n0LPT0YmRxhwkhY4wEN0J8ZJm+0wAAUmFkRdQxZC5r4+xdIkGOctwlnQTFKHzV1hhZpGmhsk8alxfPRpo4ew9+JMLZu/u3tdyWfZGBjZna++Ujy2RkZQ3yREzD+pNojCztAXod20kuTCArVHZ2dnD//fePfz/44IO47777cOjQIVx55ZV4wxvegH/37/4dnvWsZ+Gaa67BW97yFhw9ehQ/8AM/EFXOBQxktR8Jzv7YIkLYbPwLGaaFRQ6gU/4tuX5y7WRk1Q6k7XmbwhNupRhby2FsatNCUyQQ5iOrrTLg4ni1NC00gaxmJBC7+JTcEQqsgqOzmiAZcQz38qBXHxqmai4m2qQu+yOdhDdYFQ1n74TMzln5RKxZqt/UstD5GVkBbV4DWY3NGiuBMgnUfVFG8g3M9ppd1MKQ7+CvKU/iv6dkKB2Yijhn750AIGtaMTf+bRhZFIfKNFk0U5F6Zto8baXEz8hSgKzMZIs1v5/mI8s2jo3LOohdAVli4uzd7MfmWkuuW951qeQGkBXSsolhvkwn5ujmjI37LAVGUNOSOlZDTv11MwhzTo7YbAWlokwLZ8vImulphyerUB9ZUWyeUCDOivA8FYws5e+2bCObWLqHL2Cw61lVtDmMuYzpIr6hqjtQBJf2KMBcJPwAeyJhGgANZI39REWUR/mKbWTdZgKWvFqnGHg3DMiqTQs1H1kORpYm1aQdwjCbPNP4I1i8poWN+YYyYSfW+SlQ1f129m4ysgqjHWNeJaTK6nvpugrFrAW2k+XwClyAcs899+DlL3/5+Pftt98OAHjta1+L97znPfj5n/957O7u4p//83+Oc+fO4aUvfSk++MEPotfrRZXzTQlkuR9tsxjPgpGlDzgmBWo781QyHTBxI3HEFc8JPmfgMVELUSkB02vvjbwLJsGRQmDCSLNWIro8h6oiBCSSamH39AH1JNtrWmjPy6cMj1AA6ECkgOgC2HOAL+pzIgESZbMWIvu0snQS1ti0qb9tvnXmAbXZWl9qjCzLs7NgZGWjusBmzeq+OmZkRb6/kadXebIOKWKQ+p4JGJ7c52MMIDq7HIOMmQwHstJkTrsARUy8yfTF0D7fJpLlazmW0H2m0cuCTQsZsqCohZP8cstAbjp7Vw5oxj6yGDivTeQNICuAkUU1kLbh1tYBl2lhhK/E2HtGA5qgGQMwqpV+Rryj56Q8ZLZQP6npn0lGnVL5x1fbERhnWhg55qaiucyekUUzNQhGlsVBMWWCGvcd28ikLrNywu4vcUa6gNo23G5a2NpHFvXt2rbRjJ1ST8TByAI9j4R0KSqyZJlfvD4T5iOrTfskGBYVkJXGzR0qU7QYNe/XALLOyJocSobKeK1qQxzyOnsPiLBORsy15+kyGWVgtXeR/fORZXTWhrP3GQ8rLWqh9opE/2IM/VYWYBeOvOxlL3P3CcbwS7/0S/ilX/qlqcq5gH1kzUdM591hEhJpzzNwidUhZSUqmQi9Xi7MgY0prWHKOoDKtDDQ2fvkqZbHIEoOTdsh5LIAZyUTzZ59S0aWo76JyCDZalVu15mP3rJu00LXt/KdwA6rxTBPJE6lAwDhby75BGALUfz2aV0pGVmm+aXKyLIqk7Ovi52RpfrIoguejWlhqQFJYZbBxkyTNieYZXqTyeJJb/UXZm5wFaBjCiCLdGDtEQmMgayhDD+DSUOm9Cn7l9kWxVj/nIKRRbCxfI8AALP4HzPrSANZzcwTcOwZgAINZE0qY3X23rjSBLIKySbrGNfr2GBkEe9AmhZql9Ta29vA5uydNi2MFff4ZAwYjoEsIgKlZ2MbslFQozs2xn/E+Azq4y2dvYuGv8eJmE0Q6yPLWbzXLCcUyAoXmgFIgSEW4cRY8K5T04n67UkWzlR5060X1PIhjCwV+GXMATBEAFmazz+qzJa669wwQsc4t5QZBsQ0F10GOTYBjGJkBViLtItayFBU8wvrhPXd+vvVjGcJRs9R9fqlAhs1I6uNsh3zelVav7N343eoj6zWrEI2fvfYJmjd/T1RC2cNEKtguNdHFosDyRdil29ORpYz2zamhbNnZAHAEl9BVuwhFQZg4gDFJqaFKjjgMS3kk41zqDBGbcDjhHoLwSQ4S6tNXPNUp5S2CpO93Xieo5Ab4NjCqLg4OMcmI8tkT9jbSHom0UHVJzJe4KTsYxPrwfUCTwFR+2DyJ5+lo1yXdDhzmhZae+F+mhYq/ctGmvMzsgKkZmQRpoWTlqgUv8j3N1N7N7ahjCyN2tK+z5QsE99msflbsqRkq0T4gUvmdppdCtUMtcI+rWkhlbnXcb+FkdXwL+VhgNT3OTOjydqArMnfmQUka5gWSjbOrD5VzwQfA1mNfjwDH1l64hbO3omx6B5f/j7QYGQxhkEFNAkyDCNRLy0//3yh5tpgNMycyUOhfwFMiyhGVmSNXPOXD8gKnPuifGSRhwnUJtmWZ7MF5s/Imsg0JkcxwgP8twVtlNVEjDuYPy0ZWSSZpT0IMA9xW2LQnKyQtrWaFk5MOoLFztKfSBuDAimTSX1CGVnjtapOT+shFIA89pHVSpcNb7BwRlZAnpGMLFf/LqM9VmtaZBNQLhaCRJiMLOM7R+QZUmXdtNANZDFU/WiKoX3F/3E51tb2L/Lhzs4O8H8CL37xi5EkCW677Tbcdttt+1a+TS5cIGuKja2r37Rz9h4QtdCboKlwLKGHHQBJITXTQqcSJZuMLK9pYcLBWB42Uut1iE3PyGqWV2/+fN1y9owsXuTI5To6DOhn4UCW6aunyciy19VHh+7LcpM2QIFtjJCmHFlok/MOgH71w78KOHx+zlRKZ+8OIMvG5NhHIEtfgGzslhmaFpKMrJrSU4ESka9vziY+5cnWFV0MGmsDBgzPHBMfgI5aGfnKMTN1FGFa2MYfV4xwYj6WkACLNbahoDu3kPmP/Uu5n3U5+i0zqM37KCWsWTf1MMTm7N0EWFQGQz13FmDgXFr0cG78CmT6qHtWb+L6zmw2jyEMhiaQBfQLgXKr2QTrfFELQxhZWtTCho+soG1Os2CbEPUJwhocjCzWmH8iD7icII9vzQwrKwrIEmV6fWwRjCxbloQ/m/2MWrgfEQvLcmaVRmkvbuetR5kWevvF042R5TYtpCQEGqX6Qnk8VzGZIhSaMFuRNowsRYcIBLKkLEfoBGCn9yqUaWH9K8qsrk7ahpHlcV1iEhGob0Lq3W2xWAC1K+JY08K23T8x5vjMHEgt2tUlWtRCD5AFxuZiabIfcvfdd2NjY+OprsZYLlzTwrntTeblI8tTYYqRJcuNXioKUL5E6JpQ0e08jKwOJ0+2LRUl/2wn5nvUDAAfkNWu27pNC3MMi5LxtDvYdOajn3x7ohY6K+Q5MWESS8sp+pWPlHQ5CQZwY00L90tSzhqsCq09Ld8o54QjginF2vpK/ayMLN94CVmos2H5b2N4qiehnE7iE6P41j4J6CEa9wwhfZ6jm3jGsUkSkRJj00IRfgYzax/EjfyJa5KFbGoaTwXkbIBBxPfg43bVb5q5hTp7Z8R6QzOyJuVltqiFJiOLALHzyrSQJbyh6FGbStPMLiIYoxPMsDp7p645CVkB+oHJNGNAv6IihPrI0py9e0vU5wTzUEXMmMlDrkEhjKzCxcgyQdFY2HgKRlZwWTEgCAWeUUCWrV9SUQu/AU0LA/pm2HKnM7Lsa2SErzw1auFMGVntHvMJeRAwgzIZ4aKFycm8FKONhJgWtunlogWQNQaJvIys6hBIXarrQ8mIdm3zCcbP+MzPG0c6lDJBHFi1RlUZimrsxjPo2papHNZQfXLmjCx6TaXWi1lwQRZSSquV513veheuvvpq9Ho9vOQlL8GnPvUpZ/pz587htttuw5EjR7C0tIRnP/vZ+MAHPtCqwhOZTw9ow8gK2bCEnMma0quUuCQXGkDiDvUdH4L5wHWbEUCWlvFU0hjaY7ZXuUm1DfLWxboYWXmOQbEGiR76WXhI1NwEsswNgWMCDjnzZisMO6h8KS3Rm0pKpAoGBh19B2U7tSSsycgSxGbWlIz36RtTiB3YVNrYFrWQz4CRNaqArAbrgClOPut7cR/InCNCIv+Q+ZhAgrpxmcJJVl8W0U7YJZsoI4MIRhblO2aWYmdI8ODxWkogSG1lF+m5NLq3kZgLaq1QgayakRUPZIX6yFKZQPXfmeDgTIBxRijbfiDLpyWHMrJsId2pJ5w9jHrArLJxgTOGvaKo7jVjbtJVm1wM8cOiNpNosEJn7SOLArICHnMysqKzC6+A75XmYloYWBHrfLP/QJYq+8bImtHuT49ayBzO3sPzNA82m2W2lDlQsnxZTlcivTK1AXPygJHdxs2JGnnQ5+ajlomPrOo9sGRJSLEjVd0prI51Nm2YO15GVtBKGM/Iss0DDBj7JNsPZ++NelDtMeNhpZkWaq9Il23TMb6RZHt7G294wxtw1VVXYXl5Gd/xHd+Bu+++e6ZlRANZf/RHf4Tbb78dd9xxB+69915cd911eMUrXoETJ06Q6UejEb7ne74HX//61/Ff/+t/xZe//GX81m/9Fi6//PLpaj6NqZHz0acPI6tbhRVKi1w/6XF0/vEpumZa6HH2fpAhtczHRAHKn9MOQtuEV4Ew9h1iu9IcGxwGiUG2jIIdiwoj3TQtNE+2HVELA/rNbjfHliyBrFEqELzZZROzrTBn7/uDZCWM2JxqJxd0PYZ8Z+Z1CfKRZflEfuA3hJFVmxaaT/LxN6vH+rRQjO/7Wp3sN5AH5e/2OBaANr6r5HgeHET4yApZK6bp/XZAL9b/QYOuFP2IJlTUR0V8jKwxW4mYw0jTwiDwxM6iqadnCQZwCcYJZZtkZBkXSMqUpUJtfGRR1xyvHrJMUo/vVnYYlGkhvQQqp8EhLAbVtLDxrhEzTiz7JeLBwsXIYmZfitMLpjEdDS0rKmohgKkYWaTvtjmD+Boja8abMsug8gepCNseaPMV5w4dvS0ji4T7wdv4bJzHfneuviOJNUM2db8QCQLlo3MFhAJkiUhGVrkCJhDFajMJZ83k0PtGPKupBY3L832bQBbFyCKcvU/ByGrrI2sm1CXe1BlnvevRA6io/vKoA4l24+FCk5/4iZ/Ahz/8Yfze7/0e/vZv/xbf+73fi5tvvhmPPfbYzMqIRgTe8Y534Cd/8ifxute9Ds9//vPx7ne/GysrK/id3/kdMv3v/M7v4MyZM3jve9+L7/zO78TVV1+N7/7u78Z11103Xc3ntO82fUUEPTMnH1m9UQluJXmhTTpuUKxpWugDsgCA57MHCnxi32al5P2xtJ5I3V9hkK9hb3RNVC4+H1kuR5W+ExMAeBjb4xOpESuC8VuhTdohQNb+TKicuTentvYast2Z18X2xnpTWBhZBEvCyMVfgWHFMmtg2Kzxd4jzZlfpfmXQ0hdttMkZCOVbyiUSEx9ZgwjTwiIkAts8fC6yOEaWCTbanvT3BdsBgcH68WwIxyY8RDLaR5a/Gs05X/GRJVVFX5aMLGOOFKR5QAg1h9H3HeMiLqLQdApJk5EF7GTlxFAwipHVrJvKKgipjTrXmoysGCfhodyksGtGClfUQtO0MHoNa8/ICnf2HishQJbt2W8sRpb1nCQkImfQIYDOyLKtkVGsOrjHIAMDj2QiA3MhZHkBiWmKJP0qSjnxkRWRVxAjq0Vt1fVGBH4TfY1OkVPWG0pWulrByD89JUamn/QV3xxlWo9Q+8o2PrJc80Cd236YFjYZWcThZ0S2IXOK3UcWMY9jbjDG00b6/T7+5E/+BL/6q7+K7/qu78K1116LX/zFX8S1116L3/iN35hZOVGozWg0wqc//WncfPPNkww4x80334xPfOIT5DPve9/7cNNNN+G2227D4cOH8S3f8i14+9vfjqKYdoGdw8YD8ad6pfif8UaqoHxkDQcAgDTPDNNCByOLNZ0M+pRSxhh4FgoUqNpv4CPBUrHJxmZxlvdsATYCaESwMGV3tIJzu0ejsmyYFgaYwtjSUnIOQyV9uAiFkRVkwrFPBwMJmgCeugGxUY4H85jyQxhZNnyH8lsTlnmVwSSN2+d2Ox9ZJiPD5yzfdr/ZLwKUscC+5DsQbpo1YgJkOZgapogQ08IpupfdwjLW2XvYV1YVTlf+PsCL9jWjnCaPQ2XTSlizYkafo5JUedb+0XTTQqVsLsF5M5Yu6XcmxEeWtSlm4yOrTdQsZ54M2M5qRhYBmtMfYPxXCItBTSOkaSIf80IhvZzIL6CIIsLZe+wa5izem1cokDUt8yKCkUWZFs4iuq5L1DG7b6aFs5nPddNCbl8jI1Akv2kha2fqPg8ga58ZWcBkTo2ZXezRJCfSZvrVTAuDbf3UvxNkw+VmEku7qmUEs5rqF2vxqXwH5UGuTYgx0ZaRpc4PYtoFs1UFpgSyAtJo5vragkQxsgIjR17Akuc5iqJAr9fTri8vL+NjH/vYzMqJilp46tQpFEWBw4cPa9cPHz6ML33pS+QzDzzwAP7iL/4CP/qjP4oPfOADuP/++/EzP/MzyLIMd9xxB/nMcDjEcDjZwG9tbRGpphgIEo4O3IaR5e+M/shFRNTCfsnYSPMMUjkxcOU0XuQ1swGPs3dmRspxyfwmoMn86IP8W06kHuW8n61h4PV9BK0JGoysho8sRzaR7yGYDCaOqD6ywkwLMR/6uiGcMSJCltK3LVXdFXLm9ZvKR9a0poVqtg5G1tinxJTUat/Gts3on/aU2Gti0WAoyfFBQz/CtLAICNc+FSPLan/KEddpjTpYduZasxDjtr5vrjlmNSkfWVr6ai2h9o1UzZrzTPPMUQJY6SY4tr6EL5/e08Z+ob4vg8W0kFifzUtRSrI9bZSz91YlqGXpL8EAbI3KOaYAEeiCqJpaTpizd+XZxqebLQBCzrUhzJrcZVpoJo7V3dpPYOFr97RAVkwSAsjar3DEmAeQRfePENPCsNO7ST6Sces6EcXI8jh7BwCwDP6IveYzs1fOvAYB1N47sB50pFvV2Xv4HB0CZBVtGFkaOaCN9JD1mz5ZtDaS5J9zjEKprMORPrKowwtJsdm9uhTzNqjLUmVWYs5HkjAtnPWUpR4e6uxMYjww1opJ+HQQE5NZWlrC0lJzLKyvr+Omm27CL//yL+N5z3seDh8+jD/8wz/EJz7xCVx77bUzq8/coxYKIXDppZfiN3/zN3HjjTfiVa96Ff7Nv/k3ePe732195s4778Tm5ub4v2PHjhGp5jMS2jCyQp7xnnASm4rebtlZkmyoDwrnpovwtOAxrWl9MDM1I8s8SdePH6xN1pY+5FGARoEOI/UNgwlcGaYwjrrGTmIx5mWFevrgxVRYNKjWVjiIBVQ9qbE8tzWa/Xi3vrHaFpZEXtNC33hXy2hgYryRLnaomaX7h4zNrMKU2TGyvF2u4QNnYlq4V4QDWSFK8zS9y/YaMpKRFQpWagpxRP6mcEFsctWO4gSyiJ5h1p/aFEqJKzd6WONNRpY6VzImwBLCnx7RaUwTVUkBWSr5Qk/cTDt+JMI3TmwHMl7DrAVjwLAC7ylGFjl2PAfALimmMC0Mk3YjjOqitTR9ZMW99DQs5NCy5sPIspRFOntvEcQnQtg0nc6Xt40g5eoUtQR8XC0FZ/ZHohhZql8cqkwGQZgK++SpMS2cZlWkTQtroC8m5xB2aJvpqlAPw4J1FuXgBRdjuEMAWar6plxvZe0zfvXwDjA2LfRFLWxQ3qkOSzGyPOVbr6sH1vNHshr1JBhZMWtASFrN76SmS9Hp5+vBcH5y7NgxDaO58847rWl/7/d+D1JKXH755VhaWsKv/dqv4Yd/+IfB+ezgpyhG1sUXX4wkSfDkk09q15988klcdtll5DNHjhxBp9NBkkw60fOe9zwcP34co9EI3W638cyb3/xm3H777ePfW1tbBJg1JyBLxjroRVAP99aWODlL8yGW+WGko7MQvUDTQiLArc/hJ49iI6hHDFOuro1T4Do/PvlN7UnaMrI8J/WDTAJNprBTTGfuhcnIchTZDsgK+1Yi2tk72xdGVoIm2KezMui6nh5yEID/VGJjZGnRZSyNMrWPLHXMOY+nKiArcuGnUrvC/VodW5u6TlDpYR3J5yOrAcbJ0keS5CkR6dGVz3yVppmZFjIR1MAaoE0xsqr7DeDbZGQF+siiiFs0kBUSJp1hkzEkVZ+nnL0DZZuW078BsFAtGuLs3XbfwVixM7KIk+sp+5j5XvWGSXLWipEVK+Y6FucjK2CFacnIKlyMLAO4iX1/5/rrnZviD73CJATIss3VzW8m9pGRNftpls5wVqaFpo8se1btgCzyOcmQo5g/gyBE5qrvzc60MIRgO62PrNCtjIQc64QjcRH62wSzTjUhVBk6bUwLq+fbRS30AFmNtZUCsqjF33sC6b2+H4ysBlEiIohXW7GbFlqiFl6gjKxHHnkEGxsb498UG6uWZz7zmfirv/or7O7uYmtrC0eOHMGrXvUqPOMZz5hZfaK+bLfbxY033oi77rprfE0Igbvuugs33XQT+cx3fud34v7774dQtOGvfOUrOHLkCAliAWWjbGxsaP81ZQpTEBe40MrZewiQ5asvrXBsYBNJMdLQ/BBn70zb67iVGR7VltpIjXguJm/fN2g5IXkUoH4W6qNmIuZEZKooJmij32sBZAU2uearIYCRNVeus1YWtTmdCEmmSBl2BpEIY2BdvHesPtBn4Oy9zquxqisnV+MTzEi2AaWTOE7oImpL/mlL4pLYAxmJyrSQ0+vGUyVWk5pI00ITPAj5JnTu9VVJXh3/Jk0LFWW72gTLnIhcRFUuhJEFgEsgqQKZaA53VWfvXILzpskINWc2pi7PNG6ffYxsbIBB4DX3Pf1qg5FVX+cMeSCQFYCBWO+Z826USVrQ5EF8lBDQ1kEoMuGzaN3NBaR5D/dmH7WwKtjzG461mgJY5+0ja/LnfpkWhjGy/J1L88Dn2uRG6Eaank5WQUDERNytZQ7+rHw+sqYrkZinZUsgK+CgvQ0goPoFbLOTGWUHMTjX5IJoXUnbh6lAVmRhMenHjKw400LK0sfnW9VRvFPifWTFf1/zCTktIysgjXrgrDPwaL3lQjUtNPEZF5BVy+rqKo4cOYKzZ8/iQx/6EL7/+79/ZvWJRgRuv/12/NZv/RZ+93d/F1/84hfx0z/909jd3cXrXvc6AMBrXvMavPnNbx6n/+mf/mmcOXMGP/uzP4uvfOUreP/734+3v/3tuO2226as+jQgiuvZ+EUmyLTQV18LyHJwmFTPq7RMezYUI8un2fMoZWeGjCyreOjHLT+9z0fWXhafsc+U0HXyELv4FhGMrCLSR9Z+CWVaqPvIata14Mznp7+VWPuDpmVYGFmS2FxqEkELye1A1piRFfkNqdQuBpStjzSuqhesth+uminJPOlMwF6ympEV519k7qaFlveQjEe63jLRBDqVNhQovxayftxoPyMdaVqoPVCZFmbNdDQjy51dWbfSzx8rJNaXUuhRC/XMGG+eKlOnqtHO3rW/4529k2mnnGIbBx5V0aOEoVACfkzuU5sO47ejPPOeGbXQ547ASOxP0ZKRJRyMLJNJHg32O1vIw8gKjlo4rY5EbIBsZRHfbN5AlgpexURonarMACArKIKluumcAyOLFCaQi0j/WHOSuZ5dEqByGbWwBrLCJ8yQwBUuFx72fFVGVqjSMvmzP9rE7hnCqEljZE1E60ehxbVYV8bHWD5n7wFRC2nTwhkwsjw5NGQGpojTAlkhYvM7Sc4hrBnM5htRPvShD+GDH/wgHnzwQXz4wx/Gy1/+cjz3uc8dY0azkCjTQgB41atehZMnT+Ktb30rjh8/juuvvx4f/OAHxw7gH374Yc328dixY/jQhz6En/u5n8O3fuu34vLLL8fP/uzP4o1vfOPMXiJeXIvWnBhZvoFoOQE9eH6nUS+n+lXnE8PIkhE2++rENjUjyzaR8vqC5bH5RC08NyzAWBpgv61SZA3TQpNB4GL/xTp7R5jpUVkPdWiHv8+8hYMwLZTuBU6i6SB+FmJTEtSxZvXjzTJPs4YDWU3Gi8rIqoGsuCWPUkpc3c1qRuXQpKZ29u4z3zE35lX4bpnEMbLClOb2c5nVt2msjywrL0cX3bSwmaaevxrO3o10NJA1SZXkJVgrcwrIIp4MMPmRmIAa3ZQbEUvVE2sBxqv5UyksjJFFgSa2CrmALHrOiTUjDDrJNXGk6vcpDJHLJpBFLYGNcpijcKPRCmGae8/fR1aInxRRMKtJNGsAN7FAlkO8IPu8gCw/I8sOnDfHqZg3I0uT2eoRttx4AFvQd2hZ5j97RpbPgbiEQJb30CPuuWQeVlH+1yIHXWDuFtPCmi0UmAsQ5uy9zWxVtGDGqSDO7mAduyeb0d41F6vaUq2ub4HtWD/fQtkSHkZWw7SQWjapoB8zGOb74iOrcaHZHjFredB6pQFZSg0skZ+Fa43+BpHz58/jzW9+Mx599FEcOnQIP/iDP4i3ve1t6HRmB+hHA1kA8PrXvx6vf/3ryXsf+chHGtduuukm/M3f/E2bohwyhaLlAhdaGSOHMLI8/kgsldo4/UT1fHNzS2ckAann541aKGMcgqpA1nQzmmOLbPzbvM/TLkTuY8UYT/k2WhJA0gE8+aoTmpljg6HlAEZjgawCMnjS052Ouh/aT3Jr2T3NNlL+JuoqWMtx6a2LdZen/NmSkeVd9JT7DSagWmbtFNuTnT13JSsHgB/QFGW+AaD9jBhZMKJIlc7eOSSbAyNriu5l00XiD0WM2SRoM0aVW596uxPTpoUTSfrnynTFEMCqkRXFyDLyY82WkXIy7jqcafOUygpiFSNLMHOuaL4xT5sKuetzavccbWzFvohrsT7sGnkaPgrrPx8ebYEttzQtdCJZZvnm7xkDIC3bhyUMnbUORtvEYZvhIyuW4bwfQFa0BET8skdJpaIWztfZu9pQUwR+pcXSZ5jL3rR+NDJ/l2PsOEaWoqeThwwZRnkKJPP+Ln6Zr2mhg5EVmXGQs/dpGVltTBORgnc7KDJjbtIXmKnKqB+PGVp1EIxYZ++kX0QigERbZ+8aASB6+96mNxrvtx+mhUoqvX0pRtZ89jVPN/mhH/oh/NAP/dBcy3ha+BxsJ1OcoDtznRMjq+UqnxTlgqcp/C4IiDQ18QFZMYDQDBlZ1sfdpoVSMpwYtJgAfOHmGbCbx4JLbtPCWTt7DzUTFJqC6dWMo+oxjTDZ9D1jc/g8vobW+yC3BAFZdApO+a3RM/fcVvqiwchSF7f6z9htJc1gcCnrdH3bbPZCu5PX7UcDRCt9v4lIH1mxm5pYsTtf5ojxG9Ocp0MYWY78PG9ORy2c/JnunSrTZQQjiMjPnGeo0ovyI5b5c2ZlZIFJMNY8KKDA/yQxyvUxsrTb9jWhsNAgaNByOrExsr62u40+zjYfmHbKNp4vjOAJs2fyUN/E32r8oLSyUZo+suIaZZomDD2Eivbd03gH2kkw/ehTEbVQq8BcyxqXGeQjKyCfQGfvMT1FY2RRbhKkbKc2zyVsoe82cVgxReZMyvHBRAwTJsStQjtG1oTHEbI/o9b53sEmt04FCHVfxX69spmZjEuviJeRZR5eEGnIdmlrWqhtHfefkWUeik2fY1NsjCxybWItTCwXQkorRtbTQ+Y0EGQbH1khHdxR34BJSvMj5HygHBpRzt4LwgeHoybUn+3ERMzrbH0TDsNj2zkuvTiFKMIVNR8DgQM4sVfg6hV3PuprN6IWNkwL7d/KjHDokzyCkaW1oWeRnvlJqqssyEb/1YEsgpGFdj4QvHWxRS3UKOC2ZzPPuA0HsqTTR1b5HWOV31jTQoCTBA4zF4054tdXnOL3kWX+Lk0LBYtbtoKU5mm6l+3ZWNNCKcLWAqXfUpuNuq94gSySdTOpQLcCspJRv5mqpc+j8tmy76ecQfeRpbx87ezdYCpRawM3u4NnrtA33/Y1ISaSq+vVQyBiYTKyqr+fOLeCS48S7U/Uq/HaEZ2vYe4d45QwCDSgnL0HbFA3twG2St7jUX68LMUTcx7oS4aENW40C6PByCIAAet80xzPxbyjFtZtCMzcTsa2PocwssKqoazBjk1uDHNe2yxTh3IyLrRSLU+NaeFUuZNXKQ++PgkyLWzFyArdU1VCJFna7GL7USOZFWj265WNIlt0lvFw9AJZph5Oco3tBUwh0VEL5+QjKySQwPj52PIIPV6VcracbmBfs7OKDaxNlUeMbO2UrfDiF78YSZLgtttum4G/8+nlAgayppAZsmQABPnvmdYmWD/psafjxFLhNS0Ug/CKaB7sZrwSjpUiNv5JC4cQkpyY3Pl72oEBj27lXiBLFROMMn1muU5kY5uviPCRpbKe/IysuHpMI1w0lUa1jagvVBDMjFmInd1G7CjVu5yN6dt2ac/I0pJV5c/iAMtLCWesOU81ngk5VQzrUPHO3iUEEhTRjKz5IrW215ABZkJa+oZpoS2dVog1QQMeNZJyH0NV5uiyDml6TjKygk7O5YSRxZimTKuHAAyyHGMNZ++zZmTZN/qFNWphM//oHmZk3YiQWP3MigQpX0Emtp3Pt6qDIoVBCYtxEh7GOmwHfA7WjgPsMrpcA8jyOto2n+ccoOY81KwKe/3m5SNLBrSmnQG6/4wsDcia+TRrAbICfPHNkpEV81rCY65WiKRFxLb5SJiPrLbKoYWRNdbtw9sgBPRo06K5UL9VgBBNkW4Qbg5UFUllIXn0Skomc1P8dxDcvT8KAbKoOcXn38s+gyl6/lMRtZAA9uapG2r91sLIig3i9HSRu+++GxsbG091NcbyTWla6HrWZNiESQAjy1XfgDGq+8iy51U7sNQZWR4Ap9jzV2BcgAKQzXhBnryXn5GVC4k81pTAy8iSONcP8L9gW5xAAFuufCKHX8jJ1CRvlcbgYWTtJ5KlKDOTS8pmlvhEhQTklP7YKAnykUUIT0JoMz5/bErPcDh7r8eCyfTzCTnfuGz5JEifGa1GeOhpo3fHZrJWyjFTRDKyQmQaoND+HnGmhaFwrR9Cldq/kxv6b2/UQgC9QpKn3bSPLB8IWlWhWjcSA8jSfGTx0qTMrCEFVnDTjwfZQLZjcocuYLlOPeE6qArpWi7QkexDpMmH57dantFpG2YmMUyeoBekWtP/4CjdsbNijS/UZgzb/QS5M5tf1EL/R7TNKZSeJ+bNyNL0jP3RI4KCSgT1SUWXdVKewvW0wnPgXIik3Vozh6b1sbzoIgMrYlkzaj0mjpHl/97RJrwAcsXZe0jXpQCcZJWKoqv8rVyfykdWxKP12tw4HDGkYbZPfZU2rNeAukYzstqIuaYR4zjm7aJBLw/jjzHWqt8upCkXLpAVcipjEVfXaRMdLYTWOi3yqvvIcpwejdtFZWR5TAvzCEaWku+UzH5HCb7FrgSystjyfQwEANvDuEwbpoUNH1muk764SayQ4U/o/fhphPrLeNPCAjrYNSsJi1pILEAhQJavzVUQoYHeqaB1lTx6ESWuuSe+MCAr7BA7SGJNCwUkBEtQIM7Ze1DbTWFfa91YRtqDhEaKU9+H9olYpzOdr+s/aR9ZeqKlbI+OFEgCWQaw0MwdEnLc1CnTx1rTR5Ykgmc0hSdGucQBi2aOqd12sCFt35V4sWmXQnPdYNrfYX6SiC8SXv4UjKzWEjDkhMPctsmKbTEhWR9x5xXMyJqWqkeCmJayngpGlioz1getLTwjRpaWj8vZe8uohVQdiiJph/fNA8jyR1uZJncyO79u35QQf0ptztTVqIVt3Q9wAsjSSesqWMrINE5p82K1zhhrWkjpEoSzd1/d7UD7/kqDkUU6e29JDqDuG7c1vcky6L/RgayiKPCWt7wF11xzDZaXl/HMZz4Tv/zLvzzzqJUXsGnhFA3hfLQNthdiWjjdKq9OOq5OwKuBqVFaPadyPN8Jr4hOR5pKmq9RMwnc7SnBUAhgkAO9COtC30lerctwziEcoJc6oZlMmYZpocPPiu/ExJQ8gpOlT6K+p/ZvMmWCcJCvAVnNZ3JJs0KmrktLZ+9BjCzflypURpZrQ1b+HavPkOCNk6HBg/zuaLna8gv9VF4gq8nwEeDI5+AjayqV3bqxjPORBWOetnVP7boDVCl9PDnWCsK/oJm6t3cGglATqLETEvKeczamWSVMX9e0Ps4EOG9Ce5QymSQCOjOEKNhKr3I4e7cClMQ1x6vTtwy2oRO/IBa5kLFqX3qazzbWrRkDIC19qhUokFpABhPIijUtBByMLM8GJxjImtoREXWSb0tKbTrnvH1Umf/7QrMIY2SF5WMBGZopg/PU5jPivpSsnaPrOTi08kefi9MhfAkZ1LUpNJ+wbUYb/VAzLQxoXzIFdabG6LWolbP3FunrpF4gq2EZQQBZlGmhN0qPr2YtZAZTC+XsfZ4zln74Ts/jc4t++zSRX/mVX8Fv/MZv4Hd/93fxghe8APfccw9e97rXYXNzE//yX/7LmZXzzQlkORlNLRhZU5sWhjyvLJDWE0r1l1qeB8DJIoAs1UHm1KaF5ilw/Yfv1KZkZO3lEgdi3GQFOHsv/+CetJOaNZy7m0U6mqiQiJrbi/JIK0g0VtHTiJDFKEaWho1aGFlzmPBDnL2TYzNEqfRpaqrDWtO0UFV+WG1aGCdk6U4gi1ZQGn6qDLL8NBJ0INzYnHPkMo6R9ZT5yAIrx15g1xWNrbj/ZNOVteTu96ajf+k59rYeg8DVnlTVNXODSYHAykfn0KMWFkK1yagZWcb8Spl38AKqKuPbUIcysmzruo1pNo00Djy0KYg69Z+yT5NEHz5mBYoZM3kYNYMF7Gal1AMC6BLmU85ZL6u/KU+9AkGzeCaxE9Gsrtjq/FTEwFLqMmuGvhXJnxEjS6MXO5y9h2RViebLlspLtmHpYT5K3BwVQ9KUSoqxvhzDMC8CgqC0My2M9ZFF6UdEsgDTwuCzvtpHamB6tVa+KH3NqIX0yjYz2ed9SJORRZkWRjCyvHwAQ1dR9XjLOhDjLuZClI9//OP4/u//frzyla8EAFx99dX4wz/8Q3zqU5+aaTkXrmnhnDpAG2Q/BPyaNtyovkD6kawYH1msGEXUREUd5oQme01yGAohsTuK05xCohYCIYywiWibLwB5w9n7bKLhlHmHa0FCWzT9JpX7JbKgzIWUzSzxfrmU01gSWyWEQUJu+IIabAogSzUtrP6OPeyOjVoowcNO2gJQlOApNJaRBaBAgjzy/CVEWZkKiHAwsmKkwdq17eP0Y14qI9JSpuns3Q8uLJ19gPaRRTKyzHo2n9OALMY002xV8audvTeALNJHlvEeJPvH9rcLyKK/H3VyPe30ZDYxc/wCQGpucWYSlCg6RkhkOHdm/jQhDD626fCR5QLZA8X6iDuv4LKimTR+IMs+39BAlt+EbAqZIyPLFjwiyEdWwCFrKCMrZh7X2ObEvCnQDsiaxzdsxcgKz715SU50vZicw6IWRmRYSTYDZ+8+X4VaH2vJyGIMLeYRoPDoc0GmhcSc4u83tgTGqjbfsJmNWggqamEMkBVZvr5G0PN4Gxbx00G2tra0/4bDIZnuO77jO3DXXXfhK1/5CgDgs5/9LD72sY/hlltumWl9LmBG1hSqo8sMoAUjK0SpmRbIUsuw5aRtRJXyXKerjLFIVNiziYqRxsan+nc8wdralSMXlT+r9YjyvD6yyhoUkvRK0qgnQJkWkqsdqMaKD80dvp49bU0L0Xxv3UdW85lctgOYvWJjZGk+spr3w76BZ34qwoAsjE/kopGs5iWfAkKdOJqMqGno8WZ5LZ6R4DglD0Q+M1+ZmWlhIO/Op3wJKS1Kon6NF35GVme4BdGjQkdTHSxg86jUKzFMhrW5dGxaaAJZTeE8B7A0qQaRSFfS1TUsnpFFidu0MGQjxg1G1uQZajWiatYoJXKAccbHgHmUs3fL+qYLAXyGtEuxicQywMzn27AybOC9922Co47F1aepExAgciSQtV8iREjMxRhpz8gKw3Qn7eUEskKyqsTnJkHKdqDLPFjFYVELW4rl8KNNDy2k9Fa2TU1jGVmkv1Ri/tCSWcDSGHWWM+YlI2jlV/9GmxYy2dyTkebK7cb4/nOPjIMOipEVQZmPrb/uDojuJ3NyMz13OXbsmPb7jjvuwC/+4i820r3pTW/C1tYWnvvc5yJJEhRFgbe97W340R/90ZnW58IFsqYAhlwKVGwkubIqAUCWaxgErLqF5eRaz4e+7Fo+Es6Rs563/IkoQ29K00IK2gEm7WllloMhExLnBgVitHXalGYivCo/lwxdV0KlXmbkRFKZZox8mVaKd2A6Fen3bhj2m/JrLCjq+KHC8mZCTtvV6HoERS0MPxHXxDc/5Znyt98fW6yzdzJHH5BFsTxcmYQdvDkKdL8TzcjiuHv7SGABk5z8KaZZTyybYcZL9kBge5iMLFuNtKjx5GaJOQNU1sIDWTcUOE8zsgwfX1SZ6t9S6ows5fUZkwDh7J1anxNuvAfZcBOwRXsbxzgtrOupbeWypffDTpSb7okQA5MyGzU2GM4eTU5ryrdgWTOBTYIYWe1MC7N8Ax3rPOOK9homNlbArMgC8Wu8P73dHNLCYKLVj9lIlS+PjtAakbkhPmZ9qGhsGeemP/y9Cm3z2rwvZfxaDjw1jCz7PBqUO3FlMp/HrLcyYDFr5yNLKSPkvYgk5JiboWnhOG0bRpbnmaZbFGpNiXf27tiIGsnmOTERjCwyamFLEkdAgVqftPTP/Qf3ZiOPPPIINjY2xr+XlpbIdH/8x3+M3//938cf/MEf4AUveAHuu+8+vOENb8DRo0fx2te+dmb1uXCBrDl1gTbR0YIYWVMuvurTdoVFmSjVRdp1uso5Mk53QlJm6OzdWkTdntaDXoa8kDib56C9LVqkER1Ol3qaGwnpBLK0JvBELQTKzRdl3tfKOW2bsN+exWLeFF+iQO2n2jJUT82knFPUwgAgi8Qlg5As920XI4tyEjnHBX9SVAgja5bluW9Tyu4jxSV4chQOvDPEnX7OVuIKbhw4WBpbU77IzZLUTPhsiUlGFpGfGZwBoMHxMGfvyt/Qz0K0QBlMgDNZzqfKq1A+srgJuvgYWVJdWByMLIu3dNrZ+3QjowF4aFMQ5QiSAtMiNodk39R9lIUxrcKEHAne85UEu6N1rFgZWQbw22ag2xhZvrkp0Nws3twxgJFlK8vKyJrdd7QJJ8x2phHXGhtkWhgErqonAi5GVvg39PvIYi19ZLV4Zso8pzMtJNYMOWHYztydWosGytSohSE4FsXIYqw5vFQ2rbZdUhlZ4fUtGVnhUgfBKCKjFlJCOntv3Rf1hpr7rGRkTvkMi+mH0Yws7ZeNkfWUKadTycbGhgZk2eRf/at/hTe96U149atfDQB44QtfiIceegh33nnnTIGsC9NAE8B0tFfHrXn5yJpyyKoLpG0CslLOnYReBhkVzl49xph2EJrPS8v15nO5lBgVknaEa3vKowDVy8Uwwj1IHmJaaPkwbfoED4wYoveRpxcjy5x2tCiQhJaXyf1lZPnCJM/ER5bKyMrM8dlUeGIVP7JveYZK0FCyKGx6RqHzgo+R1ZTP710SmHcpoeOFPHkMFNvrikjTwjaRbSkVV0pJ18m4RvvIal4ifWRR2RvjiWZkqYctsDKySmfvArkD9B7naQBZlH8ctatq3dZlWmhjvlBtZM0lbHp1M7JI+hTxhB0MCxFzLQ1+PBY0GF/z9XeO8/1l610zamEb3a3tpiy0rOhly3zAYpJCPsosiss890oKI2v2YusfAQZqIWbOSv9z+8iKYWQp7WAB1NtFLYx/xJul95O1V7pIYElOvlxwzoHvLdEMIuQT1UdW0Mpryd7UMaymhQY/K1TazlG+qOiNiMCUkk2C4+585zLdzGDIUEDWMCaoSWQdhGq6alkv5rGveTrJ3t4euAGoJkkyNbHHlAuXkTUP789AK9PCoKiFU9ZXs7e1jSht/z1JIxyMLCEZ0qiqKRPztKaFxuP1K9bAoH29LxlZAMA4I60WSCF9wkyk/vL9XDhHhlotE7iKA7LaKN6l2ZCv6TVn7/vB5okS/fRWC1lNVDUTspXvOp/YneCrAylsE9kUz6DKlQALedMki43/Lt87VvklHXd6niF9ZDX54Er6qCoRBbpvk8zTyEKDIbU5DJFYH1kimJHlTxNi5hNqWkjNU7RpYYDvGnVoSV3BU+dOxgQYk43IrtRmJeEUkKWn06ursrPsa4LJBps8TYGH1mxAfyT9WtjKpF4i6mACO5E1Mn1xMbCpTG51aWNamGAkmGPMh7+vTazMH+/cFDqy4+YrWcZkdz9v0ycs5qDzPfMvc+eRgS3CxGJ5ELIJCgEaNSBrRqaFGiOrWX8hWaXvRPbWeZgWeuowFSOrOQVrjKzQwBSh46xs07h2HeXKtwphZFn6AUu4vrewnINqvTbic3JGr0M2qZMW3qiFxhpJHae0YmRZDoAC081O9BIpC5hBhNc276zTiFqo7NnJfeGF6yMrVG699Va87W1vw5VXXokXvOAF+MxnPoN3vOMd+Gf/7J/NtJwLF8iaYpJ1DZ82G5rpfWT5y1DtmW3FaUqZdhLgMp8AeFS0GUH+OQuRUgLMv3iVIblbSJCPLIa9TAKB1ktFgGmhjQoTG9UMKE/RWIBtuTZpP42cvZfFGQuop/iRkCjmECHTysjynTiFVMXrI0sBsjI7WFSPhVlsJ73O3gn2ksu00JbfzEz5KGU4Mm/axI6S9pW2M2HNTalbTBPwkEcp5VpK3YRPSaz/pMBOkpEVaFpoXvNgwEwIbb40WaSMicYpKgWqcehRd2nfNIojahuoZYhtyiFhKRfL235rUpaLTUWB+FTdYrowmdYAshqgyhRC5uPLnJftYsWadAWklZlGa8fFYc9Fs8Qa6QkQ2cYUNH3F2bOYnVQd3x0eJ17Kd7T0j5BD4SBGlqIjz8i0sPBsXlszsuZBm/e+1jRl0mvGGMia8euIcZnhm5JMcYIYBNjb5iFTb9LmbksfiwGmWFwfrL9bbNRCao9K7h3bzid2NXcuYmZPAXsDGc7I8vWQBpnWoywzRrPEvpHkP/yH/4C3vOUt+Jmf+RmcOHECR48exb/4F/8Cb33rW2dazgUMZM0namEba8sgIGtqHxpxzt71LYEDwBHCrrFToioRs+ZF1vuMmpFlq4K+HQrP33OSVzt7zzzvpX7KhsPEGEYWsVH3Sc3I8p0jaI4lvSaV+ysm69Fnqz8SYi6MGWb5zlrbkX4RQnL39CEVyIKE5keN8JEV7+zdz9hoSMghtpLFdD400DANapRFgSWxjKzA5NM5e7fkyXjkXiDsdNBXVyH9aq9dx23eMOc4wMbI8jNkVGCRCd3Zu87IKsCYaPrIohhZTA/97GMK66PLntbGyKIk2keWkW9jhlbXckrZJcZO44qj7i5G1sHkGThbPGB/uIWQ7Fdvk3EUBbOaQDXybMHate4jPHNTKGjWxljY/ZsGqW3RF+cuVTPNx7TQwsgK8ZEVlL0WXcKeLGLN0UylLfVqpTY/Bc7ep2PyE5krUQuD19vg9ZuFJ65kVqaFjbGnOXtXCQXtvmHsUWZdih/IMh+kdMb4qIXWu1Opi/FtZ74OBRoNIkwLyzU+vB4h+1QREJHzQpb19XW8853vxDvf+c65lnPhAllz2NgCLU7Q0NyYU0L5/okRzfzKksZmm+0yLUReQNrCM9E1Uf6cbgDKBhW4/NvvhLDlghBoWuj7VOpt03+8GcWwKpnMpxUazzynlXXeMT6ynmpGltlmxutlIt7/QZBYFWLlpK4188GjGo1GxoWEfKZt1MI2QjKynA9EXm/k7QOymhK7XwtlZE1lQmUpIjYYfcNHlo2Q4M3HAsAqf1vbhcicBLKIR02TH/L76aiUfiigTs+MgfOiETmQZmQNzUREfZXnAj+1DaygxmK0jyxjkTEdy+v9MbQPh0W9tEkNmHXP3ARsPhjee0MKouZaDyAhkaAQ3F4AM9swoB6NPGy7U/caGwpuzMXZO3VW9lQBWZXMBciyhq0OgB0CkqhAjXC0X0y30iwnSGZoPbZjTzBnv/77D6KI+8HAfjMtk5M5dfbO3uPbVDMtDEhvjXDq8JGlsf6UNDFfkzMGyWJ0iYqR5Y1aqD5hScufJqaFM2AxUqaFOSQ4Z2S0dHduftFNC5v3Z+Hs/cMH/i+srLtCk81W9tJy3/LiF78YSZLgtttuw2233bZv5dvkwgWy5mRd2mbDHBL9QbjqG1CkPulYslFPu7WTADvqLLM8lAhQPTDJt5zA/aCKPS8iazb5BkGMrAg02wdklSe8CWIsLRumhZRCZwGsTP8vIcJ4GIlfmyDn5E+urZj+rhpOJ6E3y6AQvoCTrSTEtJDcsAd9M08nyozNt9X8tBoLkX2FWpe9uHNQ1EJ17IXXhxJvZLDK1FiV6G3hPuzv7JhQ3OZOmDT3wKAejDGNESSkLP3oNeozkZh9rznHAZaxEzLPqN1HCC1v/bCGgbO8CfAQjZ0YQJbJjoqDEydiUzKpke0a7eQ9E6Bz4ReSiAjHmxNCjKk6XV+O1eQottLLwFmMahiiA1h29Z58C8HsrhQaPbzFqX1rb++hQFZktgFpSJDaMaDnOgdWFWaSt9UCSWFlpvQ9j4sIIHDfq81XLiArvAFDgKwidjEH5sPI8k4X1HweWg/KtLDhBdIvocAZi0iM8puORIJ68xPUd23zUEIgdoQInaoVLLWFd+gjdVfJPR+YdoOii5TUMVZLaeMbbgoJYWQBQJJyiFHInOL5CqaurCrhdn9A3nKfjnL33XcHRS3cL7mADTSniDLlGEuyBcsoZGgWTiXfn4NKWbaaMWjKr3rdcbKYZ0AeM7lM3qOGsdqKWepkrLu7ZWvH356NFq/a1Wcmot41GVg0CGY52f//t/fn8ZYV5bkA/FStPZ255266bWhEERAEGSRI1EQRNOqVxJjoJYrEeH8xjYokuUoSRa9Dg0mIGglEr4pfjMGY+0ESPyUX8SIOqC2KgiICgt0MPXefcQ9rqO+PPdVa661p7X1O94F6fr+Gs/deq6pWrRqfet73LTKIMbvFd9rZ+xFGZGUVWYZT6FYiioVWN0AtndcTpUPxkZUjsqTNaiqwQ/cE020BQG+29WnQiqz0PVbvYVivilIeLJpp4SBQEB6MOZlnZBVZ2veleS4BVT3101MrsvLfUwte0keWVdTCdF4yeRWl2j1HwMNcVFiKoA3QSH+RGe6yRFZvOjS0DfJQAoq+5cxYEIos+efUR6oj5DPMzlu6rkoVl4FjND4JPxF7EbCKgyLL4uFJRZbhPsbbiixLIquYjyz6a9MwZ7tMXDJFVnYznSnFYqEbL4KHQz4T1/gBZRbRfWzG3ZRaRqfIcolaKJurEWVIEruD7ywWZb9rSHMQ00Jahdon+uwV0LYPLuC2neWIpPHDZoWsFG9qfGSpFFkuYMzRiYOwU2Sl26FiDUMEFSnuVzCbTqFkCueoms+5duyUUjO8hOzPiXQARTt7X54k1pGI5UtkLVIktmKKLHM1DsolyIs0lSVgIV4kDCFyzqY1kB+Es8Kn3e20sp/bX3QHWCtFlkv+lqaFxqA4UsFsiCzVQqjIgqZNZJkvc1vUL+2AmvORlR2GMsVpxkn+yyFAFf1ImE7OhkFk5dqiXAfShr7zsl3NZsjNtqncznsulaLNDkZfTxRZ4ljGpTAtVOWQwC1qoWB2iqxc/jlT3cTs/8ShYJQk38ZHFoncM9G+HxMwBKyV22CQZckqsvLOPzJlMBeznZcKxAZVZ4ZGfZkpUl49l9LP2aU6sHkZQ6O1BYIBnJUdxgPzhTbEZxYCHFHMNG01S5wOjyAwpWWrtnQn1yyILOq74QTdc4YIBSqsCtEwX1sgdfpri6iFNkORfMCnf58ORJY8dxNspxCwMmPK3ed8hxlmemSQTQs1ZyS99bHtfGu7RG6/b5eGXkIoPZ7d2EFfoyey+n/LiiCXpX/R7mv0kWUTRpH0kaXP135PuLh7lOwdKm0ZKy3OAJkSZCmiFj7ZsWXLFjDGcv+GbY7oTQszKOQjy+Ke2OIUSX+/rMgyM1k5FQ4DORtWwgYQxkDZtiSSIou5DFp5qBRZLqaFLhO80UdWp85M/szkSThrdhOREaZUZmPabEgwZrc5l8nVI8/Zu5n8k9HszAgMHLoInK4oeuJoRwIYyhlnQ6Wr1B+uJ5hqGBVZxHPl77F4eGu1gqGfEUSZK5E1zMBrujwouJzkA0CSNZnRtE+9yF23+W9DucYlsqTMeosqsrLlkucyOTJpAo6AhR3iqp8SpYQJRD1T4Gye6ZuYpaFGrGg51LeDBXNhOUWIkcgivsrNp5ocqd8CsRpPhGUALQSo2M8LBRVZZgf5vGNaqNiAZH2CFZ1QC8CaNHMsk0mdDCgUWYfRR9aIGEfSGu4oy1QLVgDMxkDNpjgykaX1kWVftylVKXFbIor5clsM8YYp2MpAbrkoFW/SJ3OGHSfK3UdWgJa08Byms3cmq5iGoMjqeXCxRPdSo2kheVcaVKCwoubYA/m5LTC/ZtcjSp/EhOU+Bdc53kbx9mTH9u3bEUv77nvvvRcvfelL8drXvnao+SxjImsAaDcI7iI1m1O3REcmWMwYaf8higleLkbmEgZGbhzLrTrQioFRYxE66cqmhY4jrCnpTvkSMO3YUtjMzKjI6irC9MnIzSetoVDVB13eoqYQNnelfWQt9lbeDdkJjXT2LqEeJfQPA4KMpIVM+chdg0XiJglmri3KLT4/AQ5j4ZcwgwSXmOiz5q8pt1VKNYMlTFELibVpEX2D1Z5mEeiuBNxpM5BdNGoXftKD5U4ehWIUkr5UacWo4tpHLbQ5rEm/EXm+jVMn5ByMtZBkaoGMWpghsvI+sjKwfCcupoU60MOv9CXlm06uI/I0N5+oS7nIa5vHYpdY6Mwx1idblv6IKKmyfvPZVmSpRyzG4tSDDJXIMkZUtcurwPbL8Jk+yDpcpoUAIKYXa5OmmEOHpchKZEWW+hlcjs7SPv/ocjlHOEWxQ08jjGkWPzSknzDp+YW1XyNY5scAJwMjFiCSHs/OtFBBqGt8ZMkHPvLc5aTIYsyRBGrnaTYtTOVCp8QGE2Ck0lrybUg6Q9V8bttsjMXPVKFJkfVUsCxcu3Zt6vNVV12F4447Di960YuGms8yNi0cwEeWLtkCnc1OkeWebup+eRBUpJWOlmGXbqVZBxr2IUhTiqwBTQtz9daTHesVWfIbdBngTU5C+0SWaRHbR5TIcmFVnPDhEVmC2Q2ATkTWEk8wedNCPZqdHS4Zhn4AqEwL0yNEUVWQoVKziixFhJOuGkGlDnGByc8WVb3aO1TOeC1naFN5qAW/u4+sxdpwypnQXydOkYYAkWRNC3WloslWxhgSERtVm06KLMrZO9UvMjs3mr9JZywf0KQ2gWAIWJhXcJEKFQFeksqY69YqsoL+WplMrwz5J9MRzcagBkbTQUrlSyicMv1Rl29WDwoA8/NPQ9JJg7PycA1ElESWPuVEqH1kZVtYIaWLYkoZlo+swU0LiSsoXvMwmRYCwIDGBjQYlO3DijB3VWRpx0v7CjSt05Ok2F5gURRZpvXAILMiOWf06zmx3KDYlkAIt8jWQqQ1HFb5qKYRax9Z8r7Fvm458mO7Dt1cIlPUQhsfWXQIYEP+g55wDgfZw7ZYZRljTWS5HW6kohZSE8ZTgMiS0Wq18PnPfx5/+Id/WDzIigLLWJG1WKaFBRRZVtEfipSmD3nQUU4CqWJkTqUZXYZyqwHRoJa1CsiKLDbcvtgdrEUvUpXqXKdgrqaohT0fXfpk5AEr5SNLNVAq+OIiUQvBbE0Lpc2tsa8s7YiaG/DJU+j+S2j0Vn7DLafK5DLtI4vYOFul7qrIMpgWDuE4y7h4Ih5Ma1qoXK8MaZFK7d8dm4C9s/fi9avmhFyOnvOKrCLtnbG24Q1tJtqH2pImX+CsH0BApcjKOqvPXkDkljIt7H+fgIOz/LykGpt5mSPpHLFnfWTlnL1b1qvKpwbJyegSKqTIkpHvCNTGxuWUn5rxBSYAzLeLNGRFFjkHmQ6MumsxW9PCIueyygEiPQfRv5sxkDmNIh9nH1nOZleHH9qjTJtDbAtWMxXZWzNRuJiIpw42ySbPChGui6EYNj/WAIosorjykGW9nDF1w256ANz8CGSJLIv2oiKycmGEswVrYxBFllv/7Sqy9FelCVXFXEccmJjajaomB2nBxZa/WUUWXfCYcmhPwHXfaezny1iSNTMzk/pcrVZRrVa199x88804dOgQ3vSmNw29PMtXkTUIkaU7PS3QYWwaeJ+ccSxQLw+9ZBnI9gvNBlT+NopQajYgmKWhsLzZYup0bZAtYfdzz9m7ah2Tim5lD6Miy5LIyqSKXh24KrIKKoxsajw1aA/bIcGAyG44sso8+WOKJxz2cGUjbSR/t0pc/3OcVd9Iz5bys9Fd0g8+6ZlUBOQpSearVJUpCXXbTZ6N5tLue2U6S7FWUOSRMO7khy0bnNx+rc9Sf8cQijL1U1T2JEpFQG6miQWuicgCRWLL8xpLfc9ZK18WxZiZes+GqIXyLzpQBB5AP5dOxWt8j8QzpSJYWpoWZjPS5dskyssD+ZBqyGecBaIW9uYJ1QYy84RF1m5Fxwcq6AAF1zLlxnnSJIUo9GH0kbU4UDMYpnWcNWS1jI7Icph15Cir1OFTIkRBH1mL8X5NJ7aDrBv15PzQDru6uTE4sUMJKpnPVrnQ3+pMC1OqP8vCZdN3Ow/rjYuhybQwcxcFwYm+VnB5nG9O9hVSpOqyU6TKtNCWyHKJZg9kFFmLYht8+LB582ZMTU31/m3bts14z6c//Wm8/OUvx8aNG4denkI7w2uvvRZbtmxBrVbD2Wefje9///tW9914441gjOHCCy8skm0ag4YBVMK9SmxUXEnCBrJzl9lz1clj2rQw061UeScJqvOzQKBnU/uZy4vdRYpaaFjApogslxE+Sx5k0J10TFFlsnn2TAq1p7vmdGzQNi0013lKJbhofaUYcgO+7mLZbGrYiizFexZp9owoks0RoUmRlW2LJmfv9lC1D5MpH6kMye6rhvgOtESW8vTTLQ/bqIWDQLlwc2yxCRxMCzX1kwhB+9GRvlKbFuZ/iG0VWYokU8lnx85ehFqeKmDbtDAdjbD9PQ0uPVAuaqHcna3t79XzNdWPdK9KMcrQBezmIZWNnO+JxbfCUp9Ekxj7Fris07I3LbQbnCjiTT9GmhRWWaffia3X3lQiKiWC/jbbcTDrY9CiQIbP9PyjP4c8sg6yrKFoH1ZEls1UJzv7HpKz97R5dB6JKBq1cOkVWfQhjO0hFWVaKI/viwH79yRyiqziyedMC6WxOXW+kjqAt3+f7iuYriLLRGSp/LJKKZFmvKYS2baRRUZm/AgV80lERWakknMscGoP9iQzLdy5cyemp6d7/6644grt9b/61a/wta99DX/0R3+0KOVxZm2++MUv4vLLL8eVV16JH/7whzj11FNxwQUXYM+ePdr7HnnkEfzZn/0ZXvCCFxQubBrFu4Fue1HktMTqHq1zI3MCcYq8UZy4azqG8qckRpBEAK+orsje0E9TfWhmhRyP1SmkMCiyUhJdh/yMUQu7E4DrM3VXkYrVpMp3lq2fjXRedpt5uY7Mvg6WdkTN+cjS7RqY8sPAUJsWpna++fusduyGyTFS+8iSX1e3LIXMUDMwE1n5r7KnUDYLMFt/DroIlCoC6kg0LdQpslxGqPyi0a7wqWdkDLFqQpK+Vk/8+XttTQuzTpjpA9h0Wv1DoPT3CTgYKCJLdSpOft1J2e2Aof0TNxBdaQy0KKcUWalBwORDq/ONQ9+oU0RWyuDQQZFltf6h+rqtIkt5Ctf/01pRnsFiO3sfVJFFElnEd1ofWcuPyGo/oWJ+Fmafrmb3ayxzkYbIcjIt1B+ytqMWFqBxFuFAxnwgR/jhs02cuJClfrZcIzg8ttJHLYFEpE2nrcKUKIms7BdyoWjVn7uzd3eYfGQlkPyKKR/O3bRQhUFUSYXUtpnPkaKPR0P219aFvBx6sjl7n5ycTP0zmRV+9rOfxbp16/CKV7xiUcrjTGRdc801eMtb3oJLLrkEJ510Eq6//nqMjo7iM5/5jPKeOI5x0UUX4f3vfz+e/vSnD1TgHo4gRVZiocgSMQbaiMp5tCM66TuGtUlL93TLesfXH/KHrcjqTm6mAa+oaSEiO9NCo4Q0p3brlkfVDhQDaIGBXTC7Fiq3F2NbWOJ1btYsI7d4lzmdjCPrYULt7J0ui/a7HBxl+6kxRG7frPf/QRVpRiKLOJXONdGUWk2Vnu2JXAFFllXK7tcP14iijQRssKiF1vfKpDVrR/oztFvuUDBSkUXUmI1pYbZgfWIqT26XuL2PrNypuPxbqn7sIDSRolRKC2VaCiPLfqGIfif9TiuyzJtM3fTSiPP3pwJKCHsiy8Z/H7kVMymfu2OfciyQN4nFiCz1lGJYg1im77x5y12fv59s6lrzmOVHZLUfmy631bxtaFuMpa/RmxbaI5K7EMn1CyufulksyqbXNAcQeyz7+ZRapcp7mEWAg3/jOOfs3aJEqsM1nsk3ZVrY/7qohw8GR+Kr83+TaWH7QvogqQtBzDNFl6FLHbUwO+dEijkisqIxLfaFmXox9fNlOCoXQpIk+OxnP4uLL74YpdLiuGV3Ym1arRbuuusunHfeef0EOMd5552HO++8U3nf//pf/wvr1q3Dm9/8Zqt8ms0mZmZmUv/yWDxzKVenoTYTU6LzkWWxqch1NeMGW21ekfq6p1KyJbLkU1DmNsJmk8p+7nzR3dyoaiUd/cM+f2ZpWmhSZOWVBl1FltvJf7GohcKK0EmZohpI36U+GDBFLZTrN928hhy10EaRRZ6IWyTuaham8sfT9ZE1hFnPGLWQyj+roDHdoPs+A10kRmUbX7SohcNfVriaFmbnAVuuKdta4yShnb2z9HUkiDytTQstiKwcL9pzjpwdEzhJfqhMgLJ7iXytdP+0fCPa032iXJr2Y0Pz5O/R+8ginb07tOF5gshKq8AC2C617Vq5gcyjflWo9fr5SquifAOwg6Lo5qiFdu2okOraAHdn78sRTHlYnTUppWCnyOqnr/NXWti0kCh+IhjiApP5opgWGtcD1BhjB5LALfLcDv3HZd8WZxRZVrtJ1ZJEo8iSfUmmFFkO77Oo+zuTIqsNlvm/5pLeZ326S72fUCLTAUMF0RlavgtXIjJlRZUQeS9nSZYDvva1r2HHjh34wz/8w0XLw4ke27dvH+I4xvr161Pfr1+/Hj//+c/Je771rW/h05/+NO6++27rfLZt24b3v//9+otEsog9hsOFKLNRZLVVKMUnoyiTR1tumtn4yH/bThpdIst6JSSZFlreoUKuiJ3Fec/Zu6oEcmQYlyo1mRba+sjKftGpO5W0WWlaWITIgt34J6uejryohWnoiOCUImvYpoWqFia/LyJLO6G3o0PalLP3/p99UrcThn6A9Wwh08LcPVlFll5NooPOBFH1pl33a/amhQNAkUcC5rx4Z+iP63rfcdKfGdWiUvwrFUXtIyv/lTWRZTItJBRqqtkkBifnJKosYDx3qdxVioluXIwv9a/ZvKnO5yX3VdosgUg0c5ku2wVCnZxWgZUNKUj3WbRxMtKj0Relydn7IpoWmmC7w3Y86LMzLSRuNEYtXF7QrgCtfGQZSBoOyC4JdYSjk7P3VMAKulg2grIsFsPZuzHJIVu9yOs3W/cdbi3XgchK0ltfGzGC8orsZJoKzyjliXTbsC1t4Gxa2H5vkVWT0R8WAJ29ptyfjO1G9XX2UNSJpXRGttZUCrUQMWyoEJEk2pemEmZk/+6X76mB888/fyhR13VY1HOc2dlZvOENb8CnPvUprFmzxvq+K664IuVIbOfOnfmLFtWBtVu1xBTbmrtG5+zd/JJzAy29npeStFRk9WZV24WZbFoIsIGiMWQGtq4iq5cmXS9C8bc5N/3VXdNC0zIpN2D1nL27mRYW6drCcviXFwpGKf5Sr3MzpKzuidIlH+ZwpSHPjBtPi+RdFVmybw05mQJmUaoVamwiNCl2I7c5tiiFrVpBUx5HcePAMKkWC6XJuLN7mhRZW3Dyj1WHPNJ3LlELY+KghjYtNBSMKFP/ECiryGLkAQD1luLaWsNGT9WHdH1UPdZQEQq1j04VzRAxTc6DUhpQYdGzxLBuap4NCSJLztNBkWXVKTm1UTAdGHUPiFRXDIPIUuQ9JEWWe3CM7IBLEFnUXboN1jL0kQVAucbnNmO1jSJLvlynyHLxkSVbCxBlSIQ4YhRZlP+j1M8DzImmAFiLo4C2Xx9GWSLL5ial3041kZWKWii3DYc+Gbiq0Dt1azQtBGCzps6a7Rel/AZ644VuzhBZimcNLUUrrkVI+7U2r609isNJkbVmzRoEQYDdu3envt+9ezc2bNiQu/6hhx7CI488gle96lW975LOprpUKuH+++/Hcccdl7uvWq0anYcNuvvOscwpuG2YbXwdxbpWazGxZS0BdD5BgPymQrkv7JrbFVFkucaFzUDlSLp7UCtowUdKAacLe+6K7gLJyB7nRCpB52t95Lks4gIDmeB2p3Mpn2pLbZxuQHYTkD2dkxeO8knpMBVZTNvHGfln7yurYugmR0r6xMi/85rL4u9SZ8qnQv6USd48DtaudAqxYTl7t5blD9C0VGUqori0f8X9C1M+oBhDJBL6uaVxgDu0Bcogu4hpIa1kUjh7F4xWKhFz7Xx1AxjP9pS+si11xxBMC11bvTn4V75MRmfvlGmhQ9jCuYgyLZQ2XcKeGLKaX3g+PZNqPOtLMXd/lsgqcmqvilpouM/W95WrPyRbZ++59euTzUcWMNj8YtibZl+70EUtdHiHkWwtQCgO44S5BxMCFmXTa05ykMU9kR8tVNIn4yTacSGy0qaFdj6yFF/nTAsF9WeqSbrUbFBom8Usyfau9lFzrXlIskK2Pyi2d0NDdn4JFbm1rH1kGX7Pfk698MUhss6/v4nJ0aUb32cWWgCAs846C0EQYOvWrdi6deuS5a+CE5FVqVRwxhln4LbbbsOFF14IoE1M3Xbbbbj00ktz159wwgm45557Ut/91V/9FWZnZ/Gxj30MmzdvLl7yRfSRBUeCxkaRFYkAKlMcmwYdZQdpYzQ1ywdIXH1kpRVZQ53rOkUwDSvyeGjL0diQP92TDMJ9SAr5cnfSVm1+FN/HOr9pmrxtbpEfwXyCubRHA9mFoW6huFiKLGbrE4PsZxaNbmg+styfWVWbZtNCQhmSe1Y9yQfYn7YmnZp2U1U6wprIYvlAVtZQKUeZvYl3txgpRZY+x96VmVdi48vBRZEVEfMbSWQhv1A1oW9GnveRRSqyiDZ6gK0xuEhSKbJ0twxvrCHrIWWqQZhQGkwLKTVFztmsZpyaaeXpSXl8SIdm14MJ87um0mMdfbFKKd0lOVWKmJSvpIJEVtGpz1qRNfDUqnh2ziBkRkRDZGWDSCwHMDClIqv7u26eMQ+7GeJc5+zd4R2mTAuJGxMhjK4rKCyOaaG+HGaXFGqQxAhzP4B2qSkhAuv+HGacvWuFBh0o34HWtFDywyYrshyeLGDMSRXIIIDAcv5ivFPJ6vRzoomCbXHp6XQ7RVbTsp27HkzKo+4RpiUYGNu3b8fk5OThLkYPzi7kL7/8clx88cU488wz8bznPQ8f/ehHMT8/j0suuQQA8MY3vhGbNm3Ctm3bUKvVcPLJJ6fuX7FiBQDkvnfGEWRamFh49Iy0pEUBRRZ1UWpDY3MeDsnZu+UzZ5y9D2RamFXidHZnJh9Z8iQ5zFbAOySdaZLN12xHkaXykaV48UXKLqCISJZLm6c+HUnIbVqz7UD+Tfp7uIosXVrDyMexzuUxRHVyOWCxogJElr4rKH60XOjEEAg4Q0Qs7IdlWuhyeWFeXmXKydxbbPoOu7vTdzDEoJ29p00LFU9KEVmWPrIskso9UZ+kSY8JbVm+nWnh4/FqjGqcvSvrVFO9uo2DqwNvsqZSRaIIO/l+G4LZTXk7G+ZrUk4zESXY9gabbNVRBdW9zmyWJztSLko8Kg4QDFJO25p2VmRZHm6xgKV2SnrTQn2QmyMSDIaGZRitbaIWStCaFlrvBxjkl0etI5PE3j9UugyLsRM2SUwGWDeaIrZZqu2ciCyHfVsYF4laqPhaQ2TJ82Q6SJU9uLOPLEAQClgaNgSeW96qVU/+AHuRFVkZlyotxdjaEnbjozFqYeazPPYLatHwFHH2vhRwnv1///d/H3/zN3+D9773vTjttNNw991345Zbbuk5gN+xYweeeOKJoRc0j8WkOF1NC20UWbqJ14LIypaJWGil+oVGSJFOOCRu1kDq9MNWZHUnE5OUO217bJeXzdPxxDJqYc7/GO3npf+7YqNbQGGUwI7QST2DqY6W+LQge7KhO+lInSUPceDXmRYmhs2ulSJLV6nU/UpeQXbaPxh0PqlUGeT87sgfh0A2BYoNo+r005UacjnJLnrqrbpLCDbQUZz2zlQTTSuObHyw2J+ppDdn/W8VEcVkAoby05gzJ6dNC2OVIosoy8ONKdLZu6l8Wug2ts6vlOrv0gaHyEuw/raH8jfDiLDoWfJFudpQ9LmUs3cxWHCaXNpMdWaqIQxNzt5Th2rD9ZE1qNljFwP7yNIoslLQKrKWIZEFQNf+TLXqKMhCPART4mw/pu5LhCg0JZhciRSBcR1DEFm27Zkap1VmdsOCyS+XjFbOR1bxsS7XdBR9MaXIcnidnNkTf0Bn7ArsxkPV/JtOMEvUWRclndcga6Ei92buaSnaR2QrDnAsQorIGlyau2zx2GOP4Q/+4A+wevVqjIyM4JRTTsEPfvCDoebhrMgCgEsvvZQ0JQSA22+/XXvvDTfcUCTLPAZUZA3TR1ZscUQbD0xkpUF3PFrSqkNfkWVLZA3TR1a2MO3/dQd8lTJKpOTbsCu6xUjVVSmYoxZmNgy9qIWqDcLwBjHBhNJ/kIxUJM0jzLQw59g5e1Ij/5b6ZZhEli4tyc9F0YMU1/EpJcFQndwN9vxmRZY5jfSpokpJYQ+lLyzVDe7e0xfj0gzoMsUasymbMoikyJElQ5yItmIjA/l9qRRZucMFxQZPtdGXZzkbtxBCZVooFM7eiWynozIEy82Q0l+M/F4P3caWIOg0EOTcL31WkDCct98ltUETxFYw+50rkZVIR+Rxwjv+8MxrISGEkWBOHAOhAP02odoDMBb3HlKt+DJANWebmontcslxuLLxkQW4EVmJ0qjmyEVnBWj4XQNH8bFOgWn9CjMqGCrGTpyYXVcoMeDBcR4G1Rq5hrEtAFWf8sbeUpHlMP+5jAGtKPOubKIWWp+O0IosuTbbxJndwwWAW0XAnsjql0Gdfu6QqCiRld03OdxbxBoje9gWapLgAUdM+I1MwVWRZXKK9hTgtg4ePIhzzz0Xv/mbv4mvfvWrWLt2LR544AGsXLlyqPkUIrKOCBxBpoU2RFbbtLD4LJTzU2LYYFtn1Z1VbaX5OR9Zg/RG+t6e4kqRtLyZsWXJrbiuTnhVsyIr89mkyFLkXuSc1Hb6S23rFrWvuCN7OpErnfRzDODoMY4d84lWReUKrY+s1Pulatum0bkSWYqypDq1ZVqK60xKHUqRlB3aUlEUhxANS+XbSMWRD6pv0F5bcChTOnsvELUwtdi3vkMmbYAIMcrkPqL/pdpHlkptmvlaRWRJjsbIU8hc36cX0jEYmTelYo0Es1Ys2L5ivalRVlU2GBLFBixgDDEEWY+CUmTl+qoiP4WaO5H8brmYFtpA9YxWiiz1FVIyQ45aaMrZ1tn7oCOWIp88kaWebxK0lh2RBTD9usVwiEo5Ws+lL4HqE720bN8hy5Lx+UuSpBvIAs7di3OGpJCneBrm+ZvyrWZXF7QCxX1uc3naRHDrAb4VF1BkqQ7dMp1LSKSyMmqhA5HFwZy83DEG9cKKSN0uQc1nS1DO3hcTqTUK45rDlM6hkSE9V992RkXWU4DIuvrqq7F582Z89rOf7X137LHHDj2f5Te/dXEEEVmUM9wsBlZkZTuC0dl71qmWIo/IUZG1SFEL5bJ3mWzlQjwVtdAuL5un48J2ushm2j05VvjIUn7vPpIlljJY2dzVGEZ5sWeUDLKOVXWKrEgIPHvh8c77G97IrzvhMZFHi6/Ikv8s8sz0PaFpqqZ4h9ymnfUvVJ5Q6rORoQotrTTzc6wOly5W1LRQOU4V2K2ki1DgFJJ1o2Lp8+VK5nRAIktOiihDdvOU9ObOPJFFO3vP5xm3Q7kqy5H6ZB21ULexdQOtXEwvsil09yLWzt5zByx0eahouQFLR1OLHUwLbU7L1VEQ1ffGPdNCxRghLJ1EaaB24Ky/z3budh2/iR5DXsdzRJY6zQShUxmOHKjnK2OtGojG7OvTbXKt12kZMlUQkqz2epUVU5gM2bzQqBYeYI9FTQ9yframfC6+wVx8ZDXjjE9Gi3uUta/zkSW1gbwiyw4cwlGQlVj7yLIxLcw3f8OBqCKtnCLLYSItZpYokYhcr9nhhIJdnRqNbAll5SXpI2sZY2ZmJvWv2WyS1/3Hf/wHzjzzTLz2ta/FunXr8NznPhef+tSnhl6e5UtkLWokFrdGF1k00jDhajLJynFuZmluILJslQAs6eqCLJ856xhvICIrrSToQj7DoCAr4Gyjn9g0dG7JiqkUWeqJlNqIFOt6toEOU+TQEafIyvqS0GxmADx9x08wUuYY7hGGJi3jItimneiuIX5TXO5+oq9GZOwrhPIjVx7ZjEhlnuawSFP5yFJc78w1uRBZjkn37lPcmEBN+FjB8t4s75ooBwnZtFCVZzZxFZFl4SPLIv1u38+bFqp8ZOURAYTPSHpuseUJdZui/GvRtxwTmaEyiema3ZK+XyyiFqraD2ViwTlL3R8mdkSW7YY8KuAjq292qrpTdvY+ZEWW4dFtFVnuvV/djlPfOimy6E3GkQwGGNYthj5lWsvliCx1evZzcDZgBTF+FYhY2E9+yJthU9TCgdaN+jWvrc8gl9py8ZHVCDPO3m3mWtXZWo7Ikg775aiFprlRAS7crmeAgyKrO8ZqiKxsUqZFmHKbmznEMhVtQKQVWXoiy2Y7llC2whLya2XDC1/Gzt43b96Mqamp3r9t27aR1/3yl7/Eddddh2c+85n4r//6L7z1rW/F29/+dnzuc58banmWsWnhYDISXRMS4E6bGhsiK9aezpsmXWohS12nS5OWsvLYlciSFVkYaDSSSygTcz1iSOGiI63Isiu3ja8anjKbVPtQy9esQZFFEQQFF95pIkENWcE30GZ6EZB1lJttQql2IRKs2PcYxo9nGCbvrjNTLKKUyyfi2DFSsXrJPwdGwgS0wxA5pmRO0TqnyUJ1PXGPDqq1+bDmeJdkhr2uSFDEtLAPLRWqLGvbFM3AY4Er/Q9mvlARWYrSyfnmxknKdFWpyKKJa8qMP0oMJiXKytLUsINpoamRmWYR1bzR7RvUekQQRFbODFhRrpAoULYfhuCAtULZDDUZoCEQhN5HljxouqgxrGB8p3aDhTNvYXsA6eAjKxLLj8gCkD80lWCsfdOSOqsK1/R3WyLLxtl7b/zSzcMKDF+RpV+jUESWvUlgjv2AvH6zV2TZw2yK3Ecz4yPLKuqprUo8pcjqj1EyselEZDleDwDCksgq4uzdvE6iL8iOmTYueUxpaiEvZgyKLMqnqC45u+xZZ71MuwdYzqaFO3fuxOTkZO9ztVolr0uSBGeeeSY+/OEPAwCe+9zn4t5778X111+Piy++eGjlWcaKrAH5XG1vHL5pYSSYRqlgYHqJQZYqvry5sCEvGGPgzs7e02TPQDttqYzyHN3u/FxtGpEisixVCxbXqTZ3Wah9ZCkGcGqStA6Pm4ataWFqAX3EOXtPQ7vY75R9jCeF5PgqaE0LZdt2Uvlo0U60bSn/m/yKUq9ObjtDeHxtoACbUymw3iZfFUnHaZGmNC2kr19cRVbRCqafuJBpoZwqZQNG3pPWHCUJI59bHk9Vzt7zXxfwkaVKCvmj5UZnQ5FTZCX2poWRYDkH5mrapNim1FQGbVqGb82KLOJHSpFllS/QIhLMmvjGOgW5XAxbRZbyzFR9f86dQu7OxfORZYK1aaGjD1FrZ+/ZjRfRHrqIl6EiC8BgRKppLZdTZOn6u+V+gGWdvefL0PVTeSSYFhr79xBNCxlYqs/YqrZduk8C+zGgHlF7qmL1m7tNJrIEbV/iYlrIhIvGHYAQEIvo7L3ooJkVNA0SKdIGqbo37LdsOFCTai+vyAJ6ircnWdTCycnJ1D8VkXXUUUfhpJNOSn134oknYseOHUMtz/IlsoZ4WpiHo7N3i0YaJUwzuRZRZBkY3lySdB59FVKBiXVgRZakGsrkr1PMpE0L7fKyIvYSuU1piI5MXfajFqoUWVRmxcSQieVZcOQStXCJBVt5RVZW9SN/6BBZiDBMwk23aBH6jmRpWqgbn4j7UxFOVO178Od3WgxTZAgA3nt/inpwyELp7H1I73pJFFkqwh1sIDWkiCzvlRX0EIgT9ZloF7ZEljpqocK0UJ0UgHwUx0QEEKggW2Kljyxyo08Rd4q5xfYdaxVZbjAugBWdoGt2K4iDMkqRlV1fqHJtEOXJmvjGQCdqoQGWnSYuQGT1zE6VkU2HYVpo6ik0bNcdAzt7VxFZ2XJrTAtjNBzLcPjR7rPFoxaamq5L1MK4qCKLmuYHWC8Pm8hSzgE9DEJkUet5WZFlmc4AeepQj4jxwrDtUzaRrLN3aX3IJceD8ljg5H7B+so2GBPWPrJ6qTstlIqtabL9wSV6ZyEfWTKRZdpvWfQt01iebX4pIot61icXt0Xi3HPPxf3335/67he/+AWOOeaYoeazfImsxbSwdfRfFMbm69ubd1WZTc9iVx55g50z/SAGn/RiyEJayUqIS7X+HXwwRVbKhDl7wMjUglrZnNAUia0LG7UVz5pNKpCdMHumhco6JDYiAyiyrK6T6zZZTNLXHbmohdnNs/x3p+zjSQtLZVqYLgv1Tm0UWcOKWjjcIVrlk6qdV/ozGcUQDKx38jm4IktFKA7NzM+FtxsykZVgwPHRksjKZm8TvYmr2mfOtFAxTtn4dbRSBQOC1XKKrLaCOd/2syNZr3/YiibsLtNHLRyKqba0wVEqsrpXEvMHMWBkT5WVRBbBwmSHhVjYbbbsFVlqjZwKlI+hNGS1Q8FxUpG9SXFlOy67WxbmGBbyuuw4TkWx7CIWy4/IAgAkOtNCw/tx7KOxzkeWLUGSaRMx0c96FgQF5pqhK7IMrXMQH1k559YsfTChUnPn0nHoQdkDUh3qrfyYa1RkKaMWqtWR8tq7qLN3WI7F8g0icFzfapIfno+s9GcXs+siLT+tyDIQWRbt0XgglX0+CPSJrKcAa0Xgne98J7773e/iwx/+MB588EF84QtfwCc/+Uls3bp1qPksXyJrQEWWtlk5ygCtbX2VncV0fESZpuXzTI0vzga9Fk0hqCBiI6n8BvEHqSshU8fVSingrBVZFkdhNtcAlE+nriKL3pCQZMgAiiyteVgH6TD1pgF4aQfZrFNO7UKxsxAYDesYqiLL0rSQfHVWxdDUOdU3U8OZIP5ygKZ82v1Xjk3OX9I2bS1wiqeAq4+sQfUNw7pWhmqRGbO8AskpXfU+LlVYeQHOINTO3qXvzKfxvcTpr5Wmhf2/yStyJidAImp5/xkKZ+/tDOSH7469urenUGdp6kBLZKmTJ2GOdqRQvXXSTaj1BUFcZDdHqnZZJ47Cs3NK29+MxZxpvKKNSLgrskw+soYRtVCpXTRsbBZLkWW7bHNx9h6JulMZjhgMZFqo/zkftVBDqNqSlpnrqHcf9RqO+2xT1PRNnaBJqW+OjKq8NRdFNm1aaNt/XGbPRBkZNY/5kDAtLEoUZv2ISn0x5exdeufupoX2ZWNCICmVLa+28ZGl/UhcrhhTM58jFyarwDIq7exd3zYsvAMZ22xuX8gYehHtqXuXsbN3W5x11lm46aab8C//8i84+eST8YEPfAAf/ehHcdFFFw01n+Xr7H0R7aEEC5ySt3H2DmiSNNrzWyqyUg4/s2lShh5uELyMJq+haw3LGBvMtFCryAqU1VIkaqGNKolbmhbmTDh6UQtVi+L894utyIr7hwHpBf+RgJwEV+0klXWCEYy15nFoiYisVHQd8oJBFVmUzYEgfy4WtVB9z+CLYdY3LVT5yHKQnlsH1+ln73i9y7FfsbpRtaW2s3e3eSpFs4TqNiSUH9rEMKkjlK5TKlRzYxs9TimJLLkc2aQV18cUkQVakdUuE++PaQpSVa4BJhQ9VjfED9G00JSK2UcWMX8Q7do847cxTxFZmcdtHxbZPKmtIks13+lcCHTnVUXOUh0IB/846UTor01nO9YOwJ0PibLXW6pAdEQWlh+RxRi0RJaxVk1NN+sTjqnbu7VpYaYNCuJg1FrdRWCpTQsHWTeSpoXSmGptWeDwyC5rpYUWdUiiv8fWtBCs765FPsgqqshiiXDUVgjEgd3W3mS+3S5AnsninKkjcEp7j9TXmctjh4m0iAo6RSKafGRZrBNNRFb25/bzaebwpfbpcpjwyle+Eq985SsXNY9lrMga1LRQu4p1Sim0oXOha7iGCYN09k4psvrps0yvoxfychrmZxa8jAb6Tt00c78V5M6dfR6midWRMi20bAbclchyaAJ900JVO6A2IgWJLGZ3NpPy2+Y6Ai8FNA7yU8XpmBeMN+aszQGtsrdVZBHX2Tl7d/SRpYxaqFeHUdBqU7SKrMxHpWmhnshy6TzupoWujdWhLI4pd6FaBCWCFehb0uY8tLsjcx7cFjGaFuUqijbztWq0UfrI0imyWL7JMMYQiiqyBY4FUy+u5e8742iS3eSl8lEpstTQmaq5LkDpRbi0wVH5IesqskgiKy/Xy16mJLKi/LvL9sMIzEp+Mbizd/U82J/DqDE4/XnYPrJMsF0iuq9SM71ZkVGW1CB9pnUQJQvOpTjsEBhMkWVY82RrNdacqNgqsnKmhbl3x9yjWKaSH7Yiy7QuHGCPlbs1fTBh7ezdob5soxYKlBAS15pNCxVfK8x8s2Oj3JedTQsdKoIBSEpuRJY2vRxRV6wtZtu+i4+sQqszBx9ZsSbqay8NQxmyj9Oetzt7Q4qRfQoospYKy1eRNSiRZdrtuSiyLKNLqRN1j1pIlZ9rJiZq45KljkwQvIIFVKT8WIH40qkUNbmrz4tkRZYtq89i86LI1ieAKmqh2tn7EIksYRe1ML2IOrIUWQxdVUVCDuYpRVbU3s0HUcuZYDaXgkbK7zrVb6yILE1bIk0LC2u/3OAyeRKXJgJ908IhlE5pWqgiUBwnfzvH/N1rnZI2Imb2Y0quLGCF23vbt5CePBnUR5aNIovmb9JfMgBhUkMQpImZWHANmSRH8uyceCquLArd+Ow65ZFKgRRZrXD23rkta4oNgNyE5klIGrNhfj7I9sMIdlELbdFSmv1oosWJrlqAmEGzm8chn8mantzW1GfwqIU0cr4OtaaFy5DIYoBu3WLkYExGDpnq062QCjt7z7wShn5fLha10PkWA/RzE6XIsh0RTKaF9iaK9mOQdZ9Ejf7BRM7Y+jvgcefyTBRL+WxZn1M6+cTNtBBCILYksnrjr26cyvGxLGP9Y18uGU6mhUUgtV+TBUyTxTAaY3aVZkohWm7xhJ4ia5GcvV8db0UtHh88IUs04jkAn8dZZ52FIAiwdevWofu7KoJlTGQtno8s18VHaG1aqOoBBZy9k4OqRAxl0jROCBZjSsJLmIvRazWMYSCOpKizd9kJrK2zdxsiy9a0MGvOmPQUWfQ9wySyYgiJSlQjFRHS5PtriRVZDOityui6ka6N20QWT+KhMg36haRcAH0/U0LbLu0VWTrfHUWgWwznFp8Uycj6UQtVw6TL8Gkjukl/v3iNtXhNqxRZvECaolMWbn1qnW3LKp+NKYLY2rRQFbVQRWRJmxX6gvRHBrTiCkosHb45Ekwd9p5QZIncJkRBhKY+qetXb1pILVhd0U9Dpfjo9j9SkUVshZJM3xAKGw+ayEpflwjRZuyMkbzsnr01kI+s/GidzbdwUAxV9obnWoyoawA1bVgqsrh6fSNYrN2AHYlggkHvJNAAR7ezbUUWXYfWZqRZ4iJH2svjllWSaSx11MIBxALZMYsxllrrWZsWOuRJEv7UdRghvx9WsBfRMS3MWhDIrcvZR5bT+aNAFNjtL3rvRPPw+Z8E2v7VVAtA+ussb7XYRJa8RlEFVOlihrWw2rSz6rwH1RKUNhvunkY9udRX27dvx+Tk5OEuRg9PYdNCHRyjFtoSWcrN7XAUWelNXjavwRVZCStjPg4gSqxzBxvM2btMZOVKw5X1lVJkWeZvR2RZKrJyn1nq//nriY1IQR9ZscvZTKfdDBJ9ZlEggH7Y3/zTpOa3sAWgQ2QNcTKwNS2kpyabCVinyCKcqCr8IclvexjTvlMVUoosyAs0VYkWTwXlPGE5kWrF2pfW2bvzYq19PYc+8qBMOsqXaZ29y4os5aGKHUljZVpoqchqRlXMhelFUaI1LZSjX9F+lJjib1vofIbkNiGGDOj20f9O6SOr85gkkcWJTX7mMtWyZLqVvzfv/FpYqSZslCUMncMnkmzSmHR18qeKkTMtLOojS0EOmEhk2whpg5oWKvtf7uRPX16bADFHFBi0h9WmpzErsjLErab+1BE3M3lm2kR+6B/sHQzd2bupdQ4StTDnI4ulxjlrZZdDnrZ9MhIKRZahfpU/Z8einmlh1vervJZzUJrFws2cXbgTWdrUqaiF1PzTT5REnsgylU5KclAfWQbTwr02fgQFoOvD9Dvq1K/6pyc13ve+94Exlvp3wgknDD2f5avIGsTLOAB9K3LbLtmaFiq7omnCsDUtlOSe1BmutkQWO9yEl3EoHoEoxWARb0ctdPHYl81dp8iyNC20dvYemR3O2EYtzBFZRtPC/PemEwIV2lHj7BdWDImRyFrqqIUApDadr5uULi7qEFlxhGGO/FoiS5bAU3W9GKaFMpGVMjsasiJL69Qzd3HukrYj8e4pnsIc0mHB7arIWswNWfETWfrGBMVJZDNBIMg/0VHhkMo1KUlb00Kls3fFWCmXOruwE2iPNTIRzMDQiCqYD9On5LFQO3tPz4cWUQuVBybqfuwyPhvN0MgL5NNi+jlLQfd+O0WWEEmqblRz4yxBmudcjDExvOGWsY7PM070B41poc736CKbFppgu2l2Hb/z1w+uyAK64/4ykmQBWkWWsVZNPrIy42OsI1QLmhZmi2A38qgxbB9ZpgM5NoC5RX7MYqkx1Xbd7kLgxJZRCyNRJb831q/Kn2em6XQVWTzrM03620mRxZjT4sRFkdWPVq9LP3P4xIAEEaA0xlMcDmTaRLjoiiyZyNLXR11EKJU5Ik2AnfZ7UKdBP07XR5Y2+yc1nv3sZ+NrX/ta73PJ2uzVHsuXyLJw3l0ULosPAWYdiaSos3eyPKSz92xvkRcvJkWWGTHKOBBWIXgTwEi7Yy9ShGTGSsrf5ROyyJJI47FZps4T6RrdviiTZd+00MIUpptGUdNC4bBk5xyIAWEww11qGosB/Toh6iaSKpiHTQBAYOPB2qkMdoosasFhZ96mqXNiESeakmNKWRJtNHMkktcqeXTmVNlT1DwS9E0Lh/I6VESW2+XO6VMoTpIpFFmCWas8s2kxwQFLPxRUqQ3HFhoiy9a00ExkUVdkoyUyCNSjCqabedNCtbmYTDS3r8mZ4MqqNDlLy1esWwMUe6NqqDbRPUUWQehQzr2z/ZcqZ94Esw2q6du0PltfPzE42magmXlYqR4Eos5zJ8i7hM8rsooRWUrzaKu7zeTQIFHq+nkQ3zoSWVBbzg0VjLFC6olcOoBWkWWqd3Mg8HT9xZqxP++0XVUitQKnnan82b1dDNtHlnEdM4iPrByJlzYVtx1DXQgf2/cUJipFluFGa0UWbVooK6VjRyrTjcMUiGydvTPWOWFSX5Mj6gAI1gIUJppqRVb6h8UmsuT2a3MwVaoFWiLLBKqtis4MSS2XXA58lzNKpRI2bNiwqHksX9PCAU+X9E3IpVpcCAmVOYdJkWWXR3ZiSvsrIfLWWSISiFkZu5sjiNk8gPbGTwwwGOkUWZzVlIWKpYW9tbP3jomaDunNnQuZ2V1wK04iiO8HUmRZTfe42gAAhutJREFUX901LRxs0Td0CIF+H8s/TWr51GoA6JKMSxO10PzuB1VkEb81pUk3VknwixMbPbhUIbW5ZZIiS1Eel1NUW7l+F7qAFjTsry964K0iB0PGwZwPXLpEFjP0dNMOXD/eWyuylKaF9DObTAuzXzEwLEQV7JodS30f6Z5fXln31LD0pd1c8n/p4eYjy5CW4VuVIqtrfU6SIRkiiwV5fQW5WVISWcT3Q9xoRIIp1jHFCISh+chSmRYaBgMBQHDzZtFZkWUZejL//CZF1tJM8kF5iPkkS6fI0vmiLKrI6uRkda8VlIqgonmYnL1Tu2+7lLPtnmUVWZZUlgsPbBu1sBXRiiyjaaHtDz0fWeqDBVtFWheBC+nhYlooLOZPgo8VBVjxnCIrclgnFiF+pUPExMaVi43zYZ0iS3ODoNTFTw0eCw888AA2btyIpz/96bjooouwY8eOoeexfImsAZ296+FSLfbXKk8XDM9Cmzep9BIKEAsZs/4ijZiVMBeX0GT1/h1DUmTlfWTVlCd7ScpH1vBMC7nlptNVkTVMZ+8RszVqQG+T576ZXlwwAKIrNzApslrttsbjJVRkKf7u32tDZOkUWYSaQiayIvnkUiajbU+G7Z4th9yCjGi3MBNZLq/JVXlVxH26LYZ9QBaJoLhpoc5HFNJkiq25nIysMqp398DO3tUlEZSAhQHzrRJ2HkqfkseCK8230qa/+kOE/M3KDykoHc1r71Jdr9fHKUf0zpxNEoIZ4oJxZuX3R1X24m3ffCNDV5Hl5iMr6szzOh+6XSSWZkW2MKmKBITVAaMrF2hvWpj9wjDHLxGRFQezw0lIMC2RZeqErlELQ00zjqzNSPPXpTVYg00wFGFVGilh5TNXFkvPVInEutGWWKJNCyXfhraEmEOztSWyGrHCtNCoyFKdrmXKoXD2nlKkOTYFzu33mQwCLcvr++ONpkC5Vyk6poVuWHpFVn/tFVuM1bHVFKIjvNXXL/ajLjVmZmZS/5rNJnnd2WefjRtuuAG33HILrrvuOjz88MN4wQtegNnZIc0THSxjImtAo1NNv3WTqbv40lCVuUjUQuIrjSLLOCNYdLSoYxM9K1qd/Jh2rWGCTpElUFWbFkrVZa/IsvCRZWF+COSrUhQgsnQbJR0SYb1lkyr1yIpaCEDaBFBEVv9v3lFkBcmwfWSpkVqEkSa8NvJFemDvZJD/TiayQtmZtTtJoV2gOjQ7WqQhpKiFKq3J4jFZdo72zcmT1xZ29k6jBV7YtLDdZS3ft9Se+lGIiJSl92WryFI1GCo0exZkDoSz90cPjWEuTKtbImhUNoSPrJxRfSobWZFlSwZriCyCjNOnpU9ENaKzjtNg2rQwQ2QF+TSoN6TaiC6qaSEDoiSg36dmZ9w1LSQPE7KmdQXnU6VpoSE5AfQlcxq4mhaajhu74JnnT0ymhUswyXPO0WIHh5JW26XXIM7e9c+b5YS0poWWr5Be0w1xzUIQWatPWoXyeDEPMXlXJJnfSR9lluNn1rSQZRVZdu3RxbTQ9oi3ESukNwXn/9y6oWtamPOZJo35jvtXBx4LgEAY2LYJCyIrkzdjAkJHZKleWeb7cAD/yjaQDxF1PvC6sFIJ6hRZ5ON0bqCYy2WsyNq8eTOmpqZ6/7Zt20Ze9/KXvxyvfe1r8ZznPAcXXHABvvKVr+DQoUP413/916GWZ/n6yBrY4N9BS6mF/Qij7LZGRRZx0kNMapz0kdVNg1giybONxZgSdprL3mQOG9HZ0A/J2XuuykVVOazE0iqzHcLV4lTYxrTQctOZnVz7iiy6HMM0LSwWtdB0ury0YAIQrOMo0qDIYvU5AG0fZ8N1Sq9Tu+j7tBWZkuiILEqR1Sda41h2igqpqIuryLLZlCbyVUoSyqFFKa4dmiLL4fLCpoWKWm2BWQeQ6KfV9gjETLsnpb+VRF2mlGmhYs4R9NiWy97KtDA/NlN6k19N531tRAnXKNJkolfhI0uGUoakvkV30OCyuVLmL7V7pdlD5xrKxC6vyCICilDjjMo8SUFaDwsRmELBpCEQuoos4prh+cgq1ukTS0WWcw3mTAtV74vePCvBKE9jwwUvJ4hFQ+kC2gkC0Dl7N9asyVtHVtHX9RVEILJU+qgUWSL1ifrbDlRTDcYDCAcTrTQM5pcJcfhr7aQ9XRdZH1m2JXaZPWPL97QQqkwLDTdaHroJFnW+zpDN0vPHzmOrfXsppMjSFCfvfrKtyFKOJoq0slvFVmy3d+uVA8zNbQXcfGTpYot0oSsxRWT1RA6kjyxzfkcqdu7cicnJfqTpalXRpzJYsWIFjj/+eDz44INDLc8yVmQNRmTp25ALOeWwOFAOXiZn70R5KJFWlsgyqDlctR5Rh8jaFyUQ5fZiaqCohdLf2cdJUFUSBnKUSGvTwpYNkSUvnOzJgO6QrvaRRWwylsDZe/dKZvSFsPQjasK7y93804TyhrspmxYO00eWGvL7pU92zW1OW+fUrFbvt71YqcgaHLow4zkyQGHS3Hf2Tj+j04igXByqNnDuhl3WRSlc1YpxSrvsUSXVV2RpnfantkeWBxKyIktFsGXVpkM0LWx/mVdkUYgEU5sLEqaF+S1vilGj89NsJvSjrNB8yoNWpsm/03l1+yql6skTWVS++ZIpOT2qqi26jrWzd8HIQuoOJ8Lub9Ql2c1VQdNCpSLLZqNpCOneTsexPNamhdlN8uFXZAleR8wswthbJ7h4iqzUmMm41vm27U6DHjNo8qrQ2yBOWhhn4NVi6yLTuhAEkWVb7vwSh6VUMbbHsS5Elq36sR7SVKtJka36OcufddWRPNUesgpSR2W5yzJCCIS2PrLQPSzQZZ6Zs02KLNU+iCSyFhHSGie2UOy6OuDPZUefWAFQzQPLl8manJxM/bMlsubm5vDQQw/hqKOOGmp5ljGRtXjxLN2UHy6kl6KjFFBk0c5IMw5gUxOnYUNupchqTwAH62No1BbAGQY4DdIrspKkoqwvefyzjVrIQo1CpoNAMi3UpZodlLoEn9KnC/GdzcBKpsUcBC+sT2TpfUItsSZLAAnvyLuJGTrsTECCM1QancACsdk01AU68k4YFpzWZIoqC9LZe//5krC/CInduqgR+oiGmS+U5TephVxK5JZE4LozdFJkFSu48niCAdzylDiXWszsJWLy++gWhvKPJF2mMi3M+chSKrJsohbmCZ98NCsVmJLsT2+Cuoqs3EXmXIoqsoYyXPYTUc0FUWcLnRDqvKybAkqhTS3MncRpQyKyGBhiwWi/kJqxpK/IItLMrhcKDjqqRzQNcQICwsa00Llcdtfnhipucouw+HN8zBYQiYWhpafb7BunYKMiS0og4FqVZWRNulCKLCb9PRhIp+4BwKpFUzYECKAUWdamhenr8lELLZVdDoSPjfkYAMy1FJrBopLsTBlJRVZmjHdVZLm4PWAQaForstS+aqUE0/dwgQQaYYDSt3H6+2bsuod3ez+y+wONfqwH3SGvTRF0poXiSWZaaIs/+7M/wze+8Q088sgj+M53voPf/u3fRhAEeP3rXz/UfJavaeER4uzdRZFVlMgiWzx1OqMxLSRn9pT0xNyrws6zHpgfwfTaJ7AWTx9MkSUfimWeMRYV5WlmHEumhbacgsIZnYzAwvwQyG/2uoPkUvjIAhxOZ3oXGippyRVZAknnNJuqm7AjZBGcodJsL4zbjviHqcjSEFlSfdA150BkUZcSahgeJhDgYEgQpRRZwz1r0E3WWZWN2repvl25KbIUyh6V/y3WJpyGafbUz7PYfcpxnbktQFNpxSaCmd4gde8x1Y7SR1bm64EUWcQl2frQ1Y5yY8Ly/UO7JFbNM5o2pCMgXE0LCQvLVKGUSl4WAyiRYyTl7D0LkshScXpkVZs3GraKrEgE/QAflghj9bvNRe0raDandAdgeKwEgJ2zd8f+n3tlCkVW7vlNh6FLQGShhXBIRBYDDIo3/fOYommnqo8HQyGySBcgKZPFwRRZpECaM7BKsYnL5CKBxXbrYQrZ6mdgKYfbtgSvyzwf29iGAZhtKogsU5Eso0Z21UpMo0BzXXK7KrKalqSclTKOmLtiqA+WVa8s2yZcohZ2i+F0h7TGsdlvxUwMRIi4mhY+FfDoo4/i9a9/Pfbv34+1a9fi13/91/Hd734Xa9euHWo+nsiiknagSl18MygHZUMrJ0+mKSIrp8hKZUJc7yb3aHXk+4cWqphmD2MdezpEVJxY1jl7j5OKskiRNAvYKrJ4s2G8JogsiazM51B0FVmKRTFlWjiAvwrbSa1nWiiEs335YoIJIO4psgjTwiQBAt4mAjqNJIjDoR4qa6vQkI/JSap0IZ2YcnFWAtBC3HL3JWELbRvINSzFwq37vaoSHQYEdVumf4iZQDlgaFougpx8KiwKn1tQkRUBuooUig99ipEgMeRooErTwqyKStEGrBRZgyFWbWRTvla6ZEf22NgiA8371hHIrn3SpIZWEXZtRVapp0xK3Z31iUQSWXmo3glVFbGwOce2QygYTTZpGkmkCe2VV2QN2dm76ewHwirysKsiK9ffVD6ysqaFgUmRtfg7qQRNhGJueOnxQPlWjX4qTXO4dIHgetPCQRRZqZ416GROkRMcEOViCRtdTpCmhbbzbn4tkaofxqyYCScfWZZjwExDRWQZ3rPleqc7NusUWd3s7Hk6N0VWK3DzkaXd82ZJcJYgEWoiS5VS9lEXezciR2uPLMzAIySoGtqQrp7IgyNRah/KP8l8ZNnixhtvXJJ8Cs3+1157LbZs2YJarYazzz4b3//+95XXfupTn8ILXvACrFy5EitXrsR5552nvd4aA1Kc+jbkosjqXztSNnWCYoosisiinNtqnb2Tiiw3IisUZZQDhkSUkXRSF1Hx96AbxFtJBappTF7gRpYzAWuZFVkMQMDNA3s2y64TfBdFVlHTQsBhSuuZFpKSgB4WQdxiRH+TSiiyOkcbKVOoKHYijU3QKQnkvkX5qLKOnKc6BVf29zIEOIR0spik+rAlNA1Ed/Kc2zeq1BudFyNUPrIc2pNSKaDIO0GCsuUiTZfOwNdK0JrAWCyg0ol12n4kDCoKlRrOkti3PAhSmhYqFA9pZ+/ZtIjrNXmraBSR6p/t8tlGFjMd7nShI0Zch0uTKaLqtDjsmKgkFJEFC0UW0RGVRSHqLyFNi7K32Z3oq3xk6VpA2I1aSF2S4y2LzQ2q1YvJN6EArBRZVr62UunaNeSsGbRg+ne1FIdYbUXWEIks3dhpehzDspTLYyvnWlOvyHLHSZoWpu4dbOdKNkkOoET3f3OCJh9Zgyiy0uVpK7Lc+6hLq7U1DplpqoJr6OtQ6SMr8zkhTQuJdaSLuaCDolIIgYataWHX9YEu+UxSgiVa00KlJQ3R3JwU644njfJh2ywz+3CKB45aSBFZHWsdzaGMx+BwVmR98YtfxOWXX47rr78eZ599Nj760Y/iggsuwP33349169blrr/99tvx+te/Hs9//vNRq9Vw9dVX4/zzz8dPf/pTbNq0qXjJB1Zk6U67XRpdu6GWA4aTV4xi+171RK5e1OhP08iTTAtFVvoZCUWWI5HVEgFGKwGeMdIm1xhjA/GJcpbZMSqMy8r30Hb23r47snX23jArsgCgxJnRgXz2165STXX6SpGOgxBZ9r58mPSX7p4lHmQFEGuiFra6RJZU0+2N9/DKqecIpI0y1cAH9ZGljGRXAlBOEWXDPkdPmANNryKyDKaFToosx+8jtPvoYmDopoUABC+BITCa/vQLkQAC0Bx4dq6T/pSz7xJhZBt1V2SpoxaqFFlubVdX5UpFVorc7Th713vGp//WQBcFMedHzIb0yCfS+1M1F7SQgDGVs/dM7QaUIiufs1KRRTxCLFrmCHSWm/xIcGcfWVHXtJCowKEpspx/6Oa3OIqsfI9QKbIy+XDDgGGrIh4AMRoIk9mhpMUACK5ufaZadXL2zrl2E9v2R2uWD5mdvcvXFljNUGv+SoJStYTyWBmtWTfiyWhaSCpM7Eqd95Flr5iS4WLGbWtevBCqiCzrrDL3ZecDOyLLLT/7umMQaFjuL2wUWTkSjQvEOh9ZdkuLTtrMmux3fj3SGucQqxkvDwdcbVN3d4ks0uxwCMvYP//x5zFZs3O0PgzMNJq4CsBZZ52FIAiwdetWbN26dcnyV8F5ZLnmmmvwlre8BZdccglOOukkXH/99RgdHcVnPvMZ8vp//ud/xp/8yZ/gtNNOwwknnID//b//N5IkwW233TZg0QecmLWNyF2RtWG8ipH5ELWSxkmsMhF300LKHJxl/WakFvwmKbZ5MGklJVQCjqmII0o6m4wB+EQdXxTFZWWZ4xTRYJcXa9pF0ylZDC5Z5r0l1P6eAHryjwcxLbSVd0uTmfbkY6klWUIg6myRqDprdp29Z57T1h+LDfTO7xn5d//eAcceVX8XJQAVIEUGFFBkaRA7nOyp6qhvWqhSBdmjiCLriCOydL6WeBnMgbQWHRWMiPT1WKTLyrcoiaysj6wBTAvz9UL1JTVUY6QgfGRlFVkpJQRhepn7IQMdMeLqI4uG2bRQMIZqidO+X3Kbi8GILLqEYSbyFlEMi3QYgDBhtLmmpiqbXWfv1B4wS+QMW5FlSK5d7EUwLczVh6L/ZQYrE5FFKvKHjEjUETO7A0MjBBCziub3wWzSUptzHhgjltmQlvR4tbiKrLnS/YhLh1AeNVLO+fRsfODlMrVce+aILJ4b52w28i5KwsgysEozVlxnKo+tjyzKtJBS6zksOJyWPCKx9pFl5cMvewlPkAizhUs+LyptlwdzVWT1N6cHYCZ7Iov+oGuNlHFS1+sWqbtZHF8WS4Lt27fjZz/72RFBYgGORFar1cJdd92F8847r58A5zjvvPNw5513WqWxsLCAMAyxatUq5TXNZhMzMzOpfzkspo8sB6q0K81cVeaAAFaNqidfpY+sRK/IoiZIcnGXs2VOZZK/3lGR1RABKpwhaom+7H+A15D2kZV+oHpSIjeIAnkZuDlsLlM6J86ib1qoRi6MbId1V5845cs3kLN36wFdIrI0D+SmQBwGRC/SIzWYNzsa8awaagn81QJIT+7UQmpg08JY0WlEAJFRZKXezRAqINGdPGe/UDaLbqSbwcujSkGtyHIjslxKWLQX6EwABSuBO5DWcdzeCIpQ6FevimbRHZsosiWlgFUM3Hm1kZsiK52WrgRmqD005X1kaV2mKYksNXSKWddWb/KRpSNhqiWuFHDKGyhb00LlWyMqJuExuHEDb6vIYrRiRVGZAn0Cz2b/IywdPWehqg9TBCsh7BRZ7m0lC7p+edZHFtOrcZaKyALyZSsGgYirN5+mOdjo7D01KOh9ZAEALEy16PGKPogqchBB9XERRAgxg9JoEXfH5sV70TeZrX4OToyp5tSdfGRZEFltYkElNTeUR/Fzdv5POma+PDVJE4c4LpXr6E2h4ersXXd5VnHGY8TQEFmqbS75Mu0rwV2R1d9X7zNri9EaVJFFzbei6+yd6LtLYO79VIHT7L9v3z7EcYz169envl+/fj127dpllca73vUubNy4MUWGZbFt2zZMTU31/m3evDl3zaBTpf5+d0XWaCfFybJ6caM8yS3gI0uQpoWOiixHIquZBChz1j4OFZ2BYZCohVKm2aeJIMAYNfhwhNnqKmjbTsFGkZUjspJu1EL6ZupUdiAfWZYEgmzyoldkFS5KITDBen7FqJ7Y6s14OlPZActgex35nmwnPFV/VyuyhCinFg6pK4fwnnQLdlciSzURu0XIcnuoGMJNkeVSlqKKLM1vMS85KbKipL0ZFC1RTHLfu4fS8fe/G9i0UHG/PMzkriD9zakRKeotLbZqf8gdBRHkXj5HnSJLp9h0a7P0nrr/pc4sshwwxErzMul7oqqoE2Y1kUUVkqc3YxQsq6JtWuiiyAq0RFYWhU0LFX3MlKdgdiodV8Iid4iquD9HZHGTQmIpTAvbEQsL+WvKggERuf7r/myoWMPPKTUS52aVpcW7jihPLYL+u9DhIXGL4BGaOIDSiDuRxW16Vi46ph2y7TgAV45jOrioXykfTHlo6qkYj9U+KJf9NvKOaaFcB5Szd312ha8VnBvN3XvXdlPWVXM2qSBBrFFkqcbUQRVZrhYZ3cM6URpFyyKfaEC/2+Taurs3HFiNtvxx1VVXgTGGyy67bOhpDze2uwFXXXUVbrzxRtx0002o1dQ2q1dccQWmp6d7/3bu3LmEpXRTZCUdNQ7vtNRRzSSu5HyEXpFFRbijfHjko6npF+2uiqymKPU3kUnZ+j4VUvWceZwIMZigJh1CkWXIx7gYl9AlsnQL0OxPzZ6PLAdn7wOYFto+T8q0UHth4aIUg+grskjTwk5HSZLFU2QxTR/PKgeyJODgzt5VZl0cIimDlfrjQcrMcQjznvHkOQXFBtpkWuhQTrVpIf19BIHAYQGwFFELtaaFrGQ0z5IRxfPt/8/H2noUig99GovUuffAE5UqMP0ssWLhbxe1cLAOG5HjPyAvW7qHPLm5VbGBTM95OiWdxrQwc5uR9CC/lIgszehcLjEkKrWRfJulaaHSqbWCyDKqnS3nolAwOlKv8h0EPR9ZVoqsgktZxjlNvBj5u8XykWVXkNR7YchHsczA2kffAAhFe+xycUytAgMQ6syBDAyh0UeW9LuwcYxtcRgRUpYTStNC9zqi2mkShFgQu1EaKbKetDAtzHy29pGVuZMLnj+YsEjKNpgTYGcmJ7RuoQsyWQBYx0dhuluq3n33K5cDOYdrA/u2YBW1kFOKLJ0ClE5rUA8mzHGM765Rkuqk1fVNZh4jdU2M2t/3FVlWRXjSYvv27fjHf/xHPOc5z1mU9J1axpo1axAEAXbv3p36fvfu3diwYYP23r/5m7/BVVddhf/7f/+v8WGq1SomJydT/0gsEqHpclrSXUB1OaSKblOj/MndtJBaIOkVWZRpofSdxSjTSDjKXZ8kUalbkMJImRZmfguRgHbeyBFnRwzjYtseXZ+5WkuVzI/NRO/snSayBmm8tjOCnWnh4VC4tqxMC7PteZhRC9XIdYXs+st6oa64TmFaKASHSEpgQd/fScpH1hIrstTdqlshgxdIRXaoNsjupoWL37h1CrSEl5zabRS3g4W09rSsB6509h0SmLguFTxBRWRlblSZ96lMtVNbNZsDf81vKtVqKmphT5GlTildP+rDk1TeQ/SRRddD/0tdXqWAIU4Y2YbSiixijnExLSRLaHNkYndFpPCRpSQcWIA47m6yiJ8zSdmYFanACb+mpnEjEXaKLMtYNFK+2fqk61dWZHGLKK5LY1rYJrKyG99CEIAu1ICx1RmjFqYVWUbYKLIo4l2x8y1UQ0RSCWthLn4MvObe/rP7BfKaXJ52Jc9u3DmKRS0MHRiA0IrI0piYDbAk7xFZUl9M7b8GNC104YaFQ1RnO2IykzlPEAmNLzzFmE4RPS57bacIh0DPljEsTVhdHg7orighBvuu0EUQYZWX3qXL4cHc3BwuuugifOpTn8LKlSsXJQ+nkaVSqeCMM85IOWrvOm4/55xzlPd95CMfwQc+8AHccsstOPPMM4uXdqjQNSKXgaBDZHV0raVQZ66gUmgYTAspIovy/6qZmOgTejdF1mxY7gdHisrtKhzIR5Y60xiKgYtxipLT5uMyXJQsZozsFY2eaaG9IktlNmMD2+exVWQtta93CIFW1zSVqLNGh+hJVJvtIUBX+9n3lW+HNmF6OZSdSvVcMUeSlMHKfWK7yPZD9zp10ZnypmCqK/U+shy3+fTXirRDxG6KLBfn9ougyIpZAGaxCeqiFbWjfjV2twop8LrPa9qIq0wDBViKJYgUkQPtFFnZtN1AmuoAqfL1fGRlr1EIr5jqhwy0/c7xvdDvUSKyNA2vvSdh4ISZVYrIIpKIKEVW5/9jlYzqlCxiYFRc2VZFAk7PjyoLV/B+1ELL9IuCEREfTZsMwYSVCszKmbKcbs600KzIosqfS3cJFFmtpENkDYU0E2hhAGfvBrgqsnQKzS4o0l9l1qz0l6sDMU4kvIVQTIPX3CcLG2V50a12tt1zwQuRzS5+i7qkgQ5aRZbpYTXjdHcsTvktTF/gnp8EJ3cNNsRsN92eIkuDTDkTHiNO1MGzlAYHrqx+rhhurZF11tmt8pjV9dR8mYWu25IK6K7IgaqTYRD+hwlZH+bNptrUdOvWrXjFK16hdSc1KJxHlssvvxyf+tSn8LnPfQ733Xcf3vrWt2J+fh6XXHIJAOCNb3wjrrjiit71V199Nd7znvfgM5/5DLZs2YJdu3Zh165dmJubG7z0gzDomt+cTAt7iqx2S+Ut9dlwrOrIRmfv+ddE9QudImtwZ+8Mh6Jy37NRFEAwNpBkUq4Oao5QKbKIomlh5QuggwDdjaAa2UVIV5EVK9oN6SNrkKiFls1TXowfUdy/AMKuOSbPL1a7iizE6X6hMwd0hW4OyUXcyagdjI5mgxpEaQTKBb0yYhxHEpcgKn3ZdkqRNYTHt4nM0oMyP/3ix2V6dlW3tE0LFyn9giYxOsfQMSs7mTZHfBYVVJE09SWXf+tmzxjrjU0UkSV/wxWHJyxOIIJ+nwwV5n1KH1lSLjY+vnQ1Q5nqAOlxrbvBbBHOVKUbpL/tFFk6YiT3VIbXa6oFnVKBd6qAJLLkfElFVv4ddVVaJ5YyGxGiDQswGCObuQTGoQhdZeUE/YAylPo8y/cMosgiFAymdyYEIBQkrwznqIW5b1REVv9vbuHYc9GJLAaIjpNrWJjoGJNjAk2hJrKszfuV9/fbteAWazGLa0KKSFFE2NYdKKmQs8xjDIK31wp8pEh9WLynbD+zTDk79HMUO7x1IbJ0qtwu9ESWgbTX/Mw75JFMKqfXq9QY5qBGchhHkgJElhbZxTKPjcElyLyI/a9qz0TBPWp5e/9QL41b38FNi0vNO6NNCztjwvLlrEhs3rw55cd827Zt5HU33ngjfvjDHyp/HxacPQT+/u//Pvbu3Yv3vve92LVrF0477TTccsstPQfwO3bs6HVqALjuuuvQarXwu7/7u6l0rrzySrzvfe8brPSLBndFFiLRaeQM5RJHi4jFqey0Bh9ZpGkhJVZiSabDSJOoSZFlYMtFeRQiZj01hIhL7dYzAMtuvpN6D9R3hglIRRwQKNmw8pnP9bhrWqi6ftiKLMsBXVZkCaGupsOgyGp2NsitID/JtDptKomzxOzwwDVNIrsIy/um0LenpDwOBqE2QVQqshgSVgIrLdA5DeE9hU5RC1U13lVkqcKp2ZenTbjY3xAmsRMx5GRaWFSRpfkt5oGTaWHI5lFqls3lkfkYicjqLqZoIstsWshEAsErYGibDqj8VKkVWQzdGrGZGnSP2D7Bz5dTNtPoEk65qIWKDWQqP62STl2ynI8sw3OKRCA3hUv1p9uA8c4mglNLNaOzd7Ui68Qn7sMPpk7r/0AWIYBx0HHoXiSpo2gkqaiFxDiTfT2uyqdUWqRpoR4CQnHQJiccwHVQKWJaeCQosnhnrVHlU+T7ck5PAE2hMQMboiLL5mTQRn3XIoisdCn7+RDbAzMy6xBeYn0TrxH9HoKCkaQGRRjatee8IovlxzmLpEKHtXusO8zolmsQRZYGPdNCqZmkWsyAiiwntbjDSZ/VuMnSazTBw06ZGB1ZXmVaKK0NuogE0+ku08Vw9ZEVxwALcG/5pFy+KnDOkGgCmOnWk5Q/t+4clpBRC48oeYETdu7cmXL5VK3m/Rnu3LkT73jHO3DrrbdqfaIPA0VituLSSy/FpZdeSv52++23pz4/8sgjRbI4rHBpYAk4KgFPDVSVgCaylFONwbSQIrIIk1tCkSVlQZ31OfjISkpteWZPsRQHABOdwaXYIknOnlRkkQsDdwKIOyx6gs7GWuvsPfNbM2HtE0kLBUEXNqdHKqj80+jy1SsQCxelGCRFVoPTsl/BABGHqe+G6+xd/VvWn0HO2buhIHFpHEyEKCkVWXoiq8T6RJZKTFIUusgs1un31A+qTag9VAsD1cLNXZG1+L5hhIYkjljg5OwdANARK1ubE3Q6cFuR1f4qEYn2NF0VtbBNZPUXJS2ljyw1bd+FlRpOc0nbtJAgsmRn7yrTQhkyWUJJ2Qjo/FblGueAiiyd+qs71lARfI2KLIrIEsBUNcDGHT8FTjlNW0oBnvNTWAidgpLPqXH23gVJgGWJrAFMC6lTeLOKThjNzURQNbk/zd9jGbVQnpOMKgIoDjKHCMaBMh8DC0+BqA6hzQigriGydIosm4M+V0WWzTWUIktl3REmwt0eJjvslDiizoEDamH+emNyBZy9W87uWYIkEMXWvG6mhRYmohrzQ2OkP83PtGmhLCQgFFn63NLXurhTKGRaqHu49DvvBpZgjB6+Vf2PWjKECRyILLf2wxBjeuoUPM7G0FtUGZCNBpuFrp4oi6s46QS10pBjyxFa3+Ud3HXXXdizZw9OP/303ndxHOOOO+7AJz7xCTSbTQQOgQl0KERkHTFYNELTfiBIBEclI+0uKxYWMbG5AGBUZEWUaSEZyjy7gJCvIUYRueMZ5os4GAUgqUzjAIK3B24G5qZ86EC+h6wxcqFInZ6aFFn2C6s2+RBo08yOV40kAAK1AoE69RhEkWXr61re8HHNZnuph1jGGOodpccCG6Uv4gzJopoWak5dsvnkgu3oO0sYjCJImigpnQUo+nvEkPAApajvSDO1ibN+Uep6svEDYE6m+8PgRJbOLI9Mm9m3f8CRyCrIlOoIp5iZ/Qzl7tnfaR+axSvFxzCwntUq1VVSvIdSkSUguGxaqCCylKaFfQy6dabmvXYm+Q1CM9vqZO5K9Xo0r9toWpg/XNZfr/lW66heo8hKTSFEcSlHybEQWFVKUIpaKAcMYTewBlVCYWNaqP9ZvoQ0LdQosnRZZA8TbDaxKjDCtNA4LgmzIkuUaoDadYiqNOk0VIosWZFpsRdYbEUWY0A5eTbqrTGI2hBMC4XorRHo3wcbXeS1srCZUCzWa61E7yNLXle0ChBZWTKDl3hPkSUqTfBArybJw12RZXvomTMtFPnx3EaL3XQg0mPBOubQmvlY06aMhdH5yOopsiQiKzVJD6jIsr+U3BuqYCXayBFZ7fVJuz0qDiKJPSEVfKQZC4xZchnOpoVJjAafwEHMm6/t5mEcC3Rtizo46vhPphRZy1eQZYWXvOQluOeee1LfXXLJJTjhhBPwrne9a2gkFrDciSyX1SRxpwouPrKE4KgGPFWMsoIRjxKWNzEAzD6yiM2EjSIrveA3RC002IGEGSJLRByCJQMRWaksycGXIq0KKLJcTAs1PmZ6ZcgNWAyCl5WbH6rMyk2aBWwHdJE1LVReWLgohdHqLCjmBE1kCcaQRIunyOKa9p57u7m2qS9Ik491AocrHGrHiu8jIKkEKLf6RJbcoy2U821oKqqlWxjmlCb0+Nptz4nKPnORFVOL5SOr6Loi0Sqy3KIWAkBzt6v/ic4imvWJSuq5rUwLkwQJ78vAW0rTQtUCtg8bh8a6Om8pyAl58d0dcwUYSrUSokbUvah/vRC9MTNLE6igiyrb8SBgPW7S9WBHZHXNdxkVbUtadFObcdLUQQCToj2ujpY4prvm25SPLFFCIiJol4kOY7KLIksmiaihOmdaOIjC2YUZ7+UnjCowuR/ZIv+oCiJLytrOtNDd9MwJTKDRmEIrqg4lLwagHhfdnpjrg6fMAWycvZs3XdR4lYj+6C839VYUA4RJqxbZ87WAQ4gIo8FmhJhBeWwCzRl75tQmamFRZDfuPCHGOQ0R0kXLgbBsu27RW4gkOiLLCI0KsKvIkppJ+mmpDdviMBkuiqz+/KlBViTKQiMLSe0JqW1YIxL0ntimIAZwkSApV5wOS23GUhUoDrlnHk87tX5SY2JiAieffHLqu7GxMaxevTr3/aAovpte9tC1IvsWlgiOcuZEr6xYGCkX9kIvC6bMHKihmmUWEOlSEESWPLIYNh0tVuvc074ujgIILnpEVhHIkx2VAn3KWkCRFbsqsvRkJllTvKzc/DSJTcggpoWCAYHF4jtlWqglspZ+RO2eXs4kI/QFDBBRtl8Mr5yBrjqMzt71i6s6RhGyKpQnnhFNVOx/aB0eu3cdyk3ZtFBq7wMpQDpZ68ouhDESmvyDCOhxy4XUVhJZmlft4iPLyaluUaZUU5wQ7qaFotker3TO0tOKrI75mWFrIt/DY3qzyUSC2EaRpfSR1c8lW3xymNFUeawgUAQRtRAAghp1fWZ+SuWnztx4qp06JNLDpMjSObzt+xsiFFnybUQS1AlxJAQm4raj91F5ECTuF2BAZ+4cY6oQ5i6n/xSRpYqeaVJkpT+HhUkPWpFlmhITkSbbKMQ87zfEhNxaRtG45DZ9JCiywBK0WjU0mlUkwyCyBFBPdIosDalgk740LyQ2poU2PrIMiizZT05TcZilQqnCcnwbLzGw8HmoL5yIJg6COxJjdqaFxZRv2bu4AMIC6zcXRVabM9CPAzpFlkmZpyO8e7+llk/SHEFGYNdmlymcw6UOxLzVAUDG513Muj6yNIWiImwSD9FwcBbnts/sKLUDjZ89CoYupHsNOkUWReIdBv3AkxbLW5G1SPtvp6iFgqMSsJS32ZJiIFGa9SQGIovYTFBLBcbiTO/QK7LkWdZ0eN5kbcKhq2RJkiClyCoCRVGl3+18ZJkkykyxaaMQ9BbWbgomEZSV5g3zCUVkDcAhMyBgjPSBkoasyNI5+V76IbWr9DgU00SWYEASpU8Xh6vI0tUHdXIofTQs7hYwggprQnkyWKft9ZvTAWYXaihXZNNCuVyW0G6MofRtAAHwMkfc1C8e+4os+vTXhRcNBeUCVA+Xg0ydWmqghC1vCRl3Hh9ZrfPW7W2IO5fz3qkg1UJlYkxlbs2SBDHrE1nU5gxQV2lakUWXM32DjkyyUWRJG/pqSb4IQJ70TGenbuex4MM666LbtvQudGbmbaKXA5S/IJmfI529E346hcBEsz3+jEiVQa0PhOBgLYap8iqwOACqs8RzKIueKyipYFIoYxNpzUNekSOyBogCTJzCm9SvggkjuVGIyMo9rEKRJQ88ulOZDpJFJ7Ji1FtVLLSqEHD315QFRz8aNIlBTQvl+23GWQuVS5NYpyeC9VaxbWfk7XfVdNjAl6oCD4svYCP7/dT3rMSw0JzA7MIYVozejaD8dOs0O6UzXpGtGVvTwryzd1FozdtKEmu5hYAAWAUQalUatZfq3W/Ih2mig/ZMC2VFlqwK5sRW22EOcVrzuJgWdt6T7rWmXCcwtJ29J9DO3dSah1pyL4QOaimHRWI39ybhW9LqRgW0RBYxl0XdeYk2oXrKIetDfVhY5oqsxdmAu5ivJYKjnFmIKokspX9c/SKDmgDIfiGyiiwTkWVvWlhHh3Doh8WCgAAfgMgym54QpBUx25iScSGySp13oYvmQTHvglWUpxsLcd6dYZHTKRk2iix5QtOdYIpcyK/FR3fRdyCkF/sxY2i10solWyf3NtCaFuYUWdkr9AvAOVFDC1X1YnuB2BACqCbTCDJNJZ2C5fMbmobSoaUQ4OX+Sky9Hmr/EAeKBaPDa4qZyClaTXCxBHJRZBUhdLlh0RjB3UcWH+k4U7W06Oou8DhjvbEpIVaNcrNWmRYGSYRYypjanFU08nv5l1x9UnOWhjFQvQ8qaiEAoJovq9ZJrsY3pc60sF0G6W/tlWZFlk6dG7NOWzD5yCKSIKMWCmCqPg0AGJWOw2hTVA7BIiQzHElDpSZXFj13SVHTQurAJu0HhyEkDotsQSktTGOBAJAYFFlRAdPC/Iuk24bcrG0UWYnBD+ugECzGQqOGhWYZCVxNo4n0BB0FsAudHySbHaK8ljA57QfsggnQRFb/b1mE5aJEaQSPoi725NYhLGAIozJ27ptCQ+wtoMgyk5vZQzvbGTIbpY0nolDUwtjpkQQE9ORxrFH5JQayUktkEc7euVRZJJG1SFAdAJHX9l6C5tnkdUPAEXeJQlcii7h8znBomk3V/sr2tQ1HIquImXkX+qiF+eu9Imt4WN6KrAGga64uiqwYQc7UvaRYPIequcugyIoI8oZUZGk2baSPrMSeyFoQHdPCbmdlHAlLOoPc4IosKgWaUHQPlc0jB0VWb4OnIzqI7zSmhXOkImsAIosJq838IbYG3bgS7RNIRVTFRXafQaHZWVDsa9ELj3/ZPY1zskTWEI8wVBt5gCAxc4osfYubSWqYCipQKj7mZ8ivy619CEaemfpOblP2fmD05WOckQb9QgBBWXa+Qmuluv1S8Cbdbx1fUylgaOXiVKifwXatwRkcj2qKEFn631usBO5YIWKkDqCKCmkql0efyOK9zRK9B+w/n8q0EAASafFHqSKqGuJRftJcEyOqV2eeoOagZLMz6bCmIpW1O01lGkA6SfW8a+prgtnPevTU2v8y1CQWixhAGYIyiZEJPctOESUC4zP7AAAjSYjunECZ+ApwxGigjPZZW8BKiHOThZ2ecqJaQkxtsBTK2ETxjnu5yhbXQaWQmrIHisgyPFIshNFvUshs43Fp8lWUQz6MsDMtXNxJPmSHMFsfQT0sDcW0EIKhlWgG74FNC/sTTmJzkGJBDjQI1kU+y4qk0+eGw5r0YPKz9h88/cw8AFphGVHMwdiIlmghYWW2V2y7nb2LJwKhq4m9I6kgACSios0l0pCjicE/EtdwIrRpYR8xdbOLIsv+Uuu5AOh3I60bA8m0kAUMSTeCBVPvKayJrFZHcWxVWPcxvqk7DSSzMB1g6AQO+e+6iqwkydsNGaNkWuA7Z78ZY2PjA6dji/n5OQDX4qyzzkIQBNi6dSu2bt26ZPmrsLyJrEWS5lGqHxUSwXLElWo+UZoWmqIWWhNZGkUWpQ5xILLmRbW9VpTdaqE9gDOXVb2ElL9N4v6ECmdMRXAUen+BLOdrSY1upDlt1ELqO1ZSEqCzcX4Sc53UZdj6yPpu/Vgc3fmba4gsBxdiQ0NTlCBYCdMRvdjfOdfCCzK+pJj9IaYROkVWtjHnVB0s0a7vpqMa1gYVKGOvNxbIr6uzB1Fek74n7R/PdlFpWJCp2g5jYGVzuxQds6s4aJATiOvSt63Ism+EtkONTR8plLAEkyIrBEfNMWFRqyMoj4CVAyXXkhpqOhUeCIZIqMk1ea2qJXKlt0oRWRXNYFtGjO44M6iPLNVPKUWWtMGM5bbbI7IymcqJag6QTFFlXdbU9ShGXiiQJbLop427/YIgskyKrG455aQjITB5aA8AYCRuAqh18qGJrKbYjRqOBwBUWQ0LImMWbaGSFWBYN1qm1dQqIstoWih9G7ib8KWSoogs09jBzMr9lnVgeSlfW0WW/MHKtHBxiawZ8RBmG2cgEYxW/zuCgaGlUWsOqs5OmVbbKLIs2MImQWTJh2KRlOWCgyKrnuxt/5EdIwKgGXbWlvFasJJbndj5yEqnaZtDdi3MkwRRZq40puXKy0EgRlW7qdX5yEoM25hmaRY1rCB/I529Sw+YDEhkWdt0AkgclHn9MUxTGOlBeMARdRRZgvXnehNUh9DTjRi2RJZLdfUVWW5jsGnrr1PqUoqsqEvGk2PZ8tVkbd++HZOTk+YLlwjL3LRwcZBQ/igUiBOeI7ICxeZGSRgYFVmEjyyi02SJLBmCYAFcohbOJhVUMidXCes6hy7GKJoiBVOn4iSRZciHhfZS96ATQVJrWkD8lGgUWbNhvj01BiWyLNj8++cnsWPli5FUprQ+suLDYFooGMd/jb1ee005zPjIGmL+XONsNavIyjt719fXgaiKZlKhTzw1p36V2SYqU/JGkaEulWVYb0klnxaCpR0fqzbHJtPCAoosF9jyU85EVgGYumGrgI+suDaDynjVMK71W0N3U8fBEAmhJNcE62+TWbUK1YtKEVnE3FPVnFpWpHEmb67m+j5UZmcSkSWtPCP5BKlLZGXVlHKSmiArJj8jScq/lB4HW1Q+3buY9gS9yRJwRh/qyNWpFK/kNo8CIwvTAIDRUPbFR/vImol/1ftcosz3LAelyRKnVW4KIksmvSizx1RK3J0wSqVF1b/Fc5mchBcisnIMKd02XH1kLaoiiwHT8YO9eXMoHgAE0NTsKnXrGZtxhktzs41PIRvzwwZlWphSZPX/boZcb/Ysp5vsb/8RpJ+ZlYBG2M6z3lwDVl4EIitbz9ZZpPcFPEng4A6pnZWjYkUgQZzo+1yksVWMDeuFPfVRlCr0/X3TQtmeUEqbMHFzezr7q51MC7uKLG3W0lojYIjR6NyjPgyzbdutWFhfW0TCVnfU6iS8+OAVEhvasGtaSFSVKUiZhz2WN5E1kERDIxHU2FFn0YzLua6iqtRQGbJc/xyhpSKL63xkDajImotrOV82Ah1n7wX7fqo6qLWkpSLLtHDioYMiK+lGDFNfQ/nISlgZjzRohnomF1GJYU4jcTZBMKFW1WTwuX2nIqqs1C78ksNgWljmHNun12ivKYWLqchykKFlBVmaCVywAI81algQZZrIMnSW2ljf7FAEldRiznaDYPLvolznMJY2T1ASWQZn76YCZkARWbrWrY2WI8GVyKI2zCaYFFktVnKKsggAYXUa1fEKFti08hqqpFy0h3TVcydMoNw5rW0dtQUzJz+fvk46ZaXMZSpaIkvyu5Qzk6JsspVJKftK2tl7v3xNeX5SKLJSSSbqA47YcDTrosja3yAG2E5dCBaQ80kXLc5QK3HStDBlXqfs0+mPJWmtUYvqvb9pIouhLvaAd3zH8IiYs6y6DEMFDAHlJ6aoaaFMJA6oyKKYcZuxwOQjq1lIkZWFisiSrrDYfC2mj6xSGWDSu9X5F7UFE0BT58B/ULZManfCwpG7DZFFmULKY6BsTdgIS1ZrOB7w/qCVWQCxQKDR7ATNmV2p9alIY/EUWe17JSJLJHkrBMPjOxjGAGj32UgYiCyNIovyOyyjnnDUFW2yR4YrzgFjYuxzIuocLjU9R6oMNgmnFFkMsWjPG4Kpx5T84Z1GXWnR/zLFMF/byW/BlcgyVIeux1DCjLjXXoiElzf7YoXrrrsOz3nOczA5OYnJyUmcc845+OpXvzr0fJZ3VQ6ys9X0ChciqxGVc8sZ1fSr9JFlQFHTQhkUe87kmdVEZEUllDMbzgRt5p0VXLjIaxEqBSoKIGW+Zyh6MUWW4zppT7AJjzfoCHyx4KnFtiiPDmQfHXNhpcjqIuQjekWW61HZEJBtS+Q1uaiFwytnoGHvspN7TtWhmc6S2mokgqOelBUmw/pnqJYP9j+U0s6C7buZySxG/TtP2UUrCBGpIJSDWVen6aSLEp3S3XJV525a6N6+TN2wgcA52UZ5L6oTJYScNkHNlaFTH4FgCIXQlqnX73gJ9doEeY1sfkGaFmrafyWRiazMg5Pl0vjmUFniS5tKWeXTlN53z5Wjrq1ooluZfBiaFrwy6jFlqtYhsmqrUuouCmUFkSUXUWkKlyWyJHK9IileQ2J+4B0Sk3dMlkSTUEjbKJcABInoEWKARD5ZKbIIpJ59MEUW6SPL4v2anEM3DJtqCrn1jaJ+GWP9OgjMC8tEs+kcFK3SHnCM9T6bSGArCNaehxUmfbo52KZrMqkfWBFZhq1S25k3sTaVitmSlO9RbIiK2gGXSErBM2v4AFhotceFXQcnwEpuG4xCiiwn9OssiEVuzWsaOly3FQkTxqAPuuimsWEybzAgVCi6epFPpZ+5NDjGFMu4ODyWU9TCnopSc43sN4pxybRQ42Myd4CkK4StgsyhQXSvnLdxICjBNBdTkQm7iIi+EiZcOXYsX8NCezztaU/DVVddhbvuugs/+MEP8OIXvxivfvWr8dOf/nSo+SxvImuQkMKaThHlFDRqNKIySpnFmKrrxJT+36JvUv6UaNPCOPNZWtgTkxZvSgt5xYKyi/m4hHJmwZcg6ZwqFSNlTFELqUgxRUwLeUu9Ycmi1NmI6dKk5vY7F47VpitKI+TfRRB1zE1MWDFSxilrxhAHI1rZVaJzqrpIqFgsHqv1dHS/oso/CmrTQkb4xMpeoy5Io9JWmc0nCkWWobVOLkhEQEZpMDQeT1P1KWJKpciSiSzKp5bjcOBKONm2VpNaKosi1WvKow5308I6O4DaajdPMwwMLGmHeNeVqRy09WEJLyNSRPSRI8bVibFBZ/RYifuL29xVRAXr+rRyik5FLez/XSfEhHofWZow7Yb3KkfSs2k3Ikfct++aGz3BeG+ZMyTE4VpKiWOhXgPSBH6lY1oYcIaIOBRMOoE4RNC+Lm4m+fq0aKRCMPA4QSD13Fs3/Q5EZVK57oglxTJ1hawUSQo4VU+BJOPNMPlNaji4qOjnaz9W8O4JgIUiSyyiImtfsh1MyETWMBRZnQ22Itobj3SHk+b8Wcq0cAiKLIUn8FiSabSoOAkmcFk5llnfB/3D3rlGBcJg6poFs3L2XixqIZA+ACwSqMe1GQkkCE2mhZq1rknJ1BT0vgvoT0nymJzyTUgQWSbH4umL7c31Ioco0FbqyYyz965pYaIhsvKNW52PNqjDQGCYc0w7NohjdOrpiCC52s7eFXwCH4QkXh541atehd/6rd/CM5/5TBx//PH40Ic+hPHxcXz3u98daj7Lm8hixYksXfeNiVNoFRphkHMcrRqfooQ4hbEhsojO2CJF6DqVCUVk9U0LmMlHVlxCOUM+CHTsm4uaFsofiHqgBlnKYazG3VEbLkRWbFZkUYqTBxdodUMXSdBX18SlUevyUIhgp8g6cXUV60KBmI+mnZtK4OBmWckioGRBXozOHUp9Hi6RRfcVasGa95GlHndm+UoAwHxUohVZhsVjpS6bzKQXZfZKJ4Npoa7q5Q23ksjq1xEnIlu42v6TnKbuZVubFjoVw1lJBpi7zrzgNvvMPFbPIBtxT0Z2fOKcgydtZY2ua5UCBs4Z4lIZoWLz1fPJyDiprqhqNsWVuL/BzB5UuFcDfYc8L8iq3QVZpTOwaeHwFFkARYwJCFbCLv4M472lgCGh1iRSkmofWZm0JCKr3GpvSEqcHtHq8Tg4KyNkhzpJMVR4RiVqwa7HAuCRANBub0l1BX7GxtEaWaucvOX3SuUhV6fgFZTKHIGDk+MUiIHC5v2alA91gzqEQj5qoUY92x2rLRRZscYf3CAIShyH4gex98Czet+pNvsu6KWgJLLs13Tk/VI/sImOZyS7FGOpvKwuQmQJLr23zH4nW+48Wa6HjYl+VpHl9molIstkNkHAdYwVAFqK4EFd6BRZph5SF4naqoYyLZQqK6JMCx2GK9b7jxlOPrIcTQtl7j6Gev7MbXM12UzbGsw4KrJEUEXoGHgiMvQJnTthKn5DK+YQoKMKckcF5ZGEmZmZ1L9m0zwex3GMG2+8EfPz8zjnnHOGWp7lTWQNosjS9AkXRdZCWALPmGZxxWgfCp7vizZEFknesNzgwHP1oVdksfp8/4MhittCXMqRD4lo+8gqSmQZRGCkg1tKpWWaI3mzob9AQqnn7F0Nk5N68h7eV2FFAyqyWiy2UmTtKs9h/5omIo1pIXeY9IaJrLovi2qJ5/xYDZfIUo0dRLlypoXqghwQbSJrNlaYFhockkVhf+yJeUaRpb1Tvk5ft7oFFLNgf+Q+yMtUfbm9KMeDZGuazNaPXB9FiCx9HgusVOg0en7yEcShZh7KJFkplRDE7cWUrkwBZ+CsHQ48VDhV6ZImQrE5q2jacDlFZOnL3M5EmZTyR9lERY7quTdJMPm0tp/CfhPU9N1EPS+YTAtjxzYeEqrOf6n+EfZho/HecgBSkZUyr1PWVfpzSSIAqs226arqUGEBNVTZBGaSB/v3Z0+XbUwLBSCiBNPJagDA/EQ7lm5YGqG94CJtnkZttuRNeMIqKI8HCFThog2gCACbDXts8JG1UIDIIk86FUNyb6y2ILIWy9k7CxKUoxfhvp3re9/FRRZIcpqsr/JXKbICyb9b7n6bPKTF5zAUWapyyjxtI7NPiBU+JmUkrCn9nXmHWVcfDvOd7dnlIKaF8kEMuScylMHZtDARaBr2bqFGpBBp8mMBw0IkSIfeAEhn72lFVj7f2OGEiyWJ9cIndjjBExamhSkiS2pjiYbIclFs7J63HJschhUGBlGqIXZUPVHRe1O/6xRZVNTCmCGOFERWcBicEw8JmzdvxtTUVO/ftm3blNfec889GB8fR7VaxR//8R/jpptuwkknnTTU8ixvIkvY+z5yA4ewdBK3EJZyVKxqfKIllObRKaSUSWBg0gkkFXkn7eyd8JFV7/tgEZrFhyi1N9TZBW/EIjAOMNejk16ZpAGSUmQRdUMqsgwDHLNgi7sIOqYxekWWOyLpJDsMBiOyQiRWZlMRIjwkptEKakoFkk71sZjIRvrMYpTYlBQ51VNBVR9kQ8x9pZ7sdsXtjfR0VAbpyS7Wn/uFLYnIyimyhgTdgi1lWkhfKJsWUlxI4njA4CoItHX27mpaWGTJbsqiHcnUfXx8HD9CHKudWGeJiwpK4DHQSvRRHUu8bXoY8jJp7gBI71dFZGnacFXyu5R39p6/Xuf3TrWoTPvI6qMlgJETVrSvUSiyUrsjDZGlikBLlc2mNeYU1EJgy8gohIWf8oAz0i2BvLe23cSWJHVypUNkqUx7BRMosQnsi3/cI0d5Zr6wU2i0zbX31ldDsBJ2144CAIS8qlSoJinTQoMLAlZBo5qQ/vpsQClybMx+TATIfDwMRVakXFD2TQvN463eDKg4EsHxvftOTH1X1BdsF4xJ3psUYxQP7fwHKvOQDpniwLzWpw5QZQiFmXYiDYLNDJHVEIfAONMqCWNIa3SefociG0Xc5eDG8tqsVtPJtDClyMr/bkrLtRkJJtA09LlWQUVWUAkwGwJhZlIrBQGCIOiPxTLpIz0hdWhkMmNLQRAuLxTQBGbMobvH0r0LoVBkJVDPny7O3n910HIf77gAFkHNfFEGoSGTyJGkb8UcUThG/raciaydO3dienq69++KK65QXvusZz0Ld999N773ve/hrW99Ky6++GL87Gc/G2p5ljmRNcDkbGqPzC4STpxU8ooNxYa7lRBmXBYLJsq0EEDfwSBoIksGaVq40FdkMY08SnSIl6w5WxMLA5kWmuyzKd9NRZy9c1l5ZkAQmRVZRZTzkaSuaQ0YZanJYqNfIc4Y6p2pucGqyih9h4vIMqngR4jFOxsg0mMWaiKL2DA6OHv/VaNtYtpMAtrsRuvbA2jV+/lHfHFMC7X+r+XHV1wnL+oZ8SKbFhurVHHIJqgzLbRbh1sGw7HLU5WH1VXu7TZBiDBRL8SyJa0kHDwWiGI9uRYE7bqLeAmhwsePSZFVjdVtuBz2F7dZAoIc7nXKaKWKRD6gST/DL7qBPZSmhVKZYh2RpYfrErRBjAVhs4JDJfMhSxAIOgCNzMmpSOfM12WJAKg05jvpK14CE2BiAglaCCqi8xXPXmKNOOIIJ4/DHXwTAKAZjAAKZWxscPYuk9kxK2Mfr6v8ghtBEQA2DpMjE5HlEDSoi5ya9uBuWtmL/tgrrEwLF+fQN+k6ZZdAmdi4gLE+BSAURFbQ0hFZ5ncn+4cKK+b1mIm0TBSKLHnjm1VkzeMJBJUWWEn9bkLM9f6OWfo6kfFPGTspsiyvzZkWulFZXQQF1syu5+MJBOqRQZEVqd+jiciKE5Zy2A8A4AHGgxodtVC6lJprTWZsMljvP2aY1MQyYps1tewjS3q+SBMsxeXVHWrE4K6yfAMYgLjAPisyzPyRyo+N4oFbEUdYp13JxEU6xRGCbhTC7r9qVV3XlUoFz3jGM3DGGWdg27ZtOPXUU/Gxj31sqOVZ5kRW8cnZPI7bdYKEiEyjJ7Kskk1BRWRx6UTGqMgi/InxeYng0UxQXafTWZFMnc22zXeGELWQQkwM/pSzd10kCQDgM4esyxR0iAbdhG0TmjuLFutvSpt8QCJLRKgYFi1j1QDNzjtv8hqYwhyoaMTJQWFa4o9SzocNixQXKIksyowlV9cKpQiv4FEpciXVfhHqN65tK9h2fmGOyBoOdK/cyrRQKkh2n8EYQ93V9p9UHKgLKZhAyaKcrkovU8QaOg+bTNwXaSU+kjKbM6HSAngs0EqEVonGeXvSj3gZLYUz6jjpmvWoFFnqebfSkoisTHWS1asZu0OFD0y5dWWVU/t7NnzddLOKLPlmtXkSFWBFhquz94XMcyasBCYEHhfmQxbGgJg6Zk9FNLMzLSw3JSKrq8hStJcEgIjbp8lRMN0uSyZBm32tfM3PJl6Kmc64WOdVpX+BlI8s0h+oZFqIMvaKOoqec1AlEBYmVbGB3JgO3Z3QZyM1i52/BBJ6i90znbZRZC2SjyzKZ0xodFqqB5Peroog4q058nvrPCQCNSyZ12PGqIUKRVbKtLCVrpdGcgCH2L2I+Iwy3YbY28+DZ4is0iBEluV1Vg7hVXlIB+1F3HE4rnhiCDRDgyJL4/yb8jvcBeuQhs2MIicBA1qB5COr/7tsThkSa4DQQZHV80dsgcihHcwsrIQAQxhNqi+SX17KR5bOZUv28NcgtLAhshz3KtlgSTYw+dSiHLoDanceYcLRnKPL0VzGRNYgSJLEyqeWC5Y3kaVx1joohK0iiyKyFBN5k3JOY9E3WwqnNmlFliGRzITEGEPQkE61NIuPpGMWl13wNjDbnhALrltkQogaoymHu9mFXvs69YDAGUstWkwodTZpOrVYEUVWQyKyGgMSWXXEqBgmtZGKFHmMV8EUi+HDpcgy5TpCnIwkBTYGNBi4aoFmYVqoUmRFI2tTF5NEVkvvr606P4ek0l5UZBVZtpaVRh9ZWkWW9KONIquULlRQ5aQ/CB0YsaDTmfbEzC5YgKuLrMVw9g4AooBUpMT0ASGyJa3W2xHlTD6yOG+PiSFKaCkVWe37E8XmrBKq23Cl1SeHsocBCu8iyrRCBZkgt+8s2XewFaM8Uu418izxkvqkIbJMU5qrE9n5zPwa8xKqKyuYtzD54lzQfjtTiiy7cpSlSLBMCJQDhkDVPEWCVscsIkJ7s80yG0EbhYaQCvfzUj+zBq/aKbKoAUvaMMasgjoiOMToSedFJG9jpqVXPjBMFzh4CZO02wFxcF6p4u0eZIrg8JkWUuqrSGO+ZQPZtFCtyBqQyJJUbmHZQpFlkPcminJ2u317bE7XS4IqHo++gTp2kfdyzrEn3C7lkd78JZm510klajs5iqxpocukKvnIIhYvppHD1Q8hY0DdRGRp2mZLJwIvt+9rZBp8nDDsa8b9/ZekjpTVv5Qiq+XggoEligjRBDSisxya0RgEW49DTXW9iFTUQmncFTo/dW6LL8p9Tr4g9ukxAGHgHlSraXgnYaTaNyiujznqM/T40rBQ0i53XHHFFbjjjjvwyCOP4J577sEVV1yB22+/HRdddNFQ81neRJZG2mi+1/SzedMsECAAMXAmwEQ1P7G1Em7t30UG5ewdAJg0uRoVWZkO2j61l8qi2SXHnQ11dqgToiNrL9gfTYeekS2RpUnH1U/OyMJcJx+NzzCnFNuYTvpRDet8MEKmkUQoG56rJk16DVSVUQuXSpGVLa5pqTtCEG9xVMR5bh5csehsgyKysuZJdF0ulNemPreop5zZpy3b6NxBhJVVAKCMKmeGyaeMpm3Lz6poGnHKR1aWyHLfxLg2wZDFVkSWydw6V45FIrKSAkQWR01POGbK2u3HrVhoy8R4ew8TsnLbWTWxQWt2nFQrTQstiaysOwlSd6cYZysBV5KZsplPluSox0B5rNQ7bchVRUqRpTZPMplnhFLZbFrNw3Np5ZVAGTsqs4qr02BMkD6y5JNyVV3lFFkLafVHNWDKObLFIrRmV4CBo4VD7S8zc63NhqX7imsTZewS/TpfQEXp7L0lEXeUAlpWZEWdduriPFkG5eTZhqDTmRaK8iiKyO+jOLP5OtgAWvSGsWdaeBgVWSFRT624bG+6RoBLRJaKINIpsmzapKzItiKyDGmqytk1LeSUs++OD8TZ5BE60dpsypIiRnq/E5fTdR861HlRRZbLIW7KR5ab6BpAsVBeJtPChoblaepG8o76rZGRIIYCeHi22ScGZX9Ssn+0AYksQIDX7IhxF2fvDAz1+unYGd2ruUh6ZmmMjaAmslxRt2BhhYOP3FIi2qbrjmgZNrNZH2k9KNpyJIC5vfnxhXGGBedgRMsPe/bswRvf+EY861nPwkte8hJs374d//Vf/4WXvvSlQ81neLY6hwPJAB3JMJILQmmVRxVVRWOcqpUw28zH2xXItHkbRZbiuDUYCdBdX5o2bVliJne5hg2KOuq07FDMRIBgRBRXZMnjI2GOFBFHpdSJkC7cs+sWcvzQHujcfhVdoO2N+kTWPBuMyBLM3HHL0gULKCsVWU4xgAuCBww718/jGY0VqB9ony6b9hwjcZ6kjgyhla3Lo6s9G2fvTJAN5BBblfrcElm7uzrET76tLVuluYC50jpUAbQyREIRk1YKWuIoNTCoTI5kIisT6KIQkeX2XE0Wo2ThAMtZkVUgQpPNeCAKmBYyMdImeRTJNxECxGFLmAi1wgbt5s0Y0Or0AVGpgjXS8+hCXIUIKsrNWUWjKqxIKl+bdadqcVrRnD6nuCiigvhIWVIBZUlo6UOkJrLa6aofIHKc9H5yMMRvrQK6LrESVsYBy4M4xgXiFvFSU6oklclDGpW5Q+nPnCl9yTXQwg8e34DfWHU2GmI/RtsF75fL8hw0SYCRiTLuntiXKtA8qwBxAgGeU7nOSXM/vQeWNlSdoBgRswomn4MAA+Ms1RZtyHUd2ZmUaAe/xjRFn8gSKIHNNoDmAkDsyVxMCynXEsNAi0i2FZXa9VkweiED640dsWKtxCHAwZEUXHzKB3thUAWgdx5vcvZuVmTl74+iGhAA09FDWJcZboKAY0f8/0unlVFkRZn3HsJ+vWu7js0f2rm80/4YzIj6MSuyHLLqoGWIWjivUWzpFFk9IitMINdyIwJ21SM8nTAtlMco6lDTpP5JIU7ACHEEBRfKOkSCXXMr0CyrD1VkRZbs7CwS6j6T9aVo2ujOtAQmTHyyQ9MrJTHqRYgshT/CLlzNpiMA04/n31tQ4ZgRQZGzjmWFT3/600uSz/ImsuJmEfcjAMztx06RVVV60hovKwqmPSKmb4gUzh/4RBl4onMVabWoVmTl1r2aDhp2iKxsFkFYRmVSYGFeAFPK25Von3p2ThUJeXwsAgiUwaSh2VmR5bg55SLGRCUoZD6ow2Ot/sJ2UCILAEqGdlOSiSxWUSqyivo3c0FpVQl7RB1raqO9/kKGY5YwSqg+2kSWfoNpg0CnyLJx9q5QZO1JVqY+NzM95qvlGZx7/ImY+MV92vI12AQmATQLDs+m2tEpslKcuVKRJT1Xxs6fVbhFCTJ5Om5GGohQsjDPdeWcXX1y2OaRFJikhNArsvZHEZ5GzFGtBBjVNu+2t5eeqUO5AshEVqWKUhIhHlmt9E9TaaoPkLoOxAGgmRlzqMdhoq0ebWRCnVUCplyQp6MWEv21VuorvbKVKM05TKVSYYGRNKaUKCY8FrdwTOedxayEuqUxEGMCIWESI7c9lSmc3J+jCkdt9lDq9wpXE74RS1Arc9z90Ik49Vm3dhKU87frYCIRiEYFFjKbhDlUwEUEsClAHEz9Ni+RIJSLASaFVe+ayIYssVi10QiqJUT1fnuwebs6BUwUFCOy4qQGgaATLW4UwDzQoP2o9UwLuaVR2eBTZw5NwrawFZadx14Z8kFiMxiHqiYDxpFQ6zuLZ2RS5NVGyax8Njl7jxRm2F0BD6USC6MKgmqbZCyVOCJpDJyp3o358PF0Wjw97jYzSbYEg3WctqKKLNv00X9mzkpolscd7myjyHwcGQQIu2fVv8cEod1F0iGy6hlFViMS2FsPwavdCKISkSXNERSR1XAhsgQDKnbrCBcfWbNo4YnpKqqTL0Gzcht9UcrZu0xkqf078lxQEP1a9sBCjE2mJZ3DMrEUhVgoRGSpM2EMeWf/HQjFgBcCWDiQHzuCCsdsUfJCwgtf/AAmJ91NKItiZqZNXp511lkIggBbt27F1q1blyx/FZa3aeEgiqwhmBYmqKCqSGhUFUXIkYLV+VcRY/3BwbRwyJkWZjustMDPokdkZR61GgaojAu0dhULIyrPFyHLpxELDsHSEyBFZKkc8AEAd/CP1cVUSaA+aOgdCZtWVxGPrES3fvcXXm73YYo0zmVbdpYgUZEirqFhCqBear/bJ6QIPCY/lyNhfpJkQqDE3EPqZsG1E4hZkaXykbU7TLdV2TFoY4TjF2ji0fXrjeVbQCfyYYZwG9YeRLdJTyxMC1ORQ7NKyor7lCJ4vj51z7qAyBi1E3AnsgqZFlpcU4jI0kQsBIDdlBQCbZ8MJoKBs/7CWkhmNcmqdahvOQnlOESjvAoR0deeNcVRratPb4M47BEjM60ktcBTcT8TlfzYVNYMcPIcSvlPEtX+QQQlpgSgXfkIZg7K0pI2eLat5vFGv51HWvPmTHmQIIqINiQTWaq+Kv0dJy0EmaAfFSb6yh4Co9UAh+o1iHhNOz2p2XHLtUwcCdQJsmW2UwciSY+bAiXUE3391iQFQVe52hxAdVTKKElVGxMZ2YMKGVGBTVQbDOiueUSn/yn6W1eFb0tkufqtsUGDyLoZlgYjsiRF1rySxrJvf2Qe3X7AOeZUh84SYsMYvsBooqbnUodYtzZDaece9NVWQYnjsdb/y5ch45NoIdMzmg5LVhuz+6S2BtmImW78Pe/8N0C9NJH71ZSUq+oVAFqoQijWugIjaFFBMyRwxbwTl9v1tZBx2D8fJmglojdeiHKfIJWJLGqsqLuY+wqBxHJtFTl0vhnRRHhoFKP7nwWuIGPTUkHpACHRKLIypvCJwW/e7jlzXbi0vSBuYLbAXkEwKOfD2spye1xyGHZUB15BJXBfoB5B2L59O372s58dESQWsNyJLI2Pi0FBRSPMZY8qgpAebCuKMTi37jaZ1WiiOYQ1yZmiYWJqJAfSyVIdrErLqlodP2BZIitoldCo7ke8tyjp0y9zgxGmZIIhzmjqBbFx0XFOQeTuG2ISIfYvKHxKFVg8lSdDYCpEUl0BEdTwmBjM2TsAqCKm9y+QfCuwBBFXLawXfzCd6YSNPiiaKHdOlVS+cboYIU6hA5EgYEU3CH1Q/ip6IE0Ls9/RDe7xZvpkpBlO90bYHwdtEu+hifyCLou5TqSwRmZhofPb5gKdE1WrniyAMu88a8ZhpShAZDWp6LOaMs4jUkZaS6fhVo4ippuLpciKk5rWT8/eFofIDgKsHV5cWyaegDP0onCJcn+eq687Gk+sejpKcYg5vgKPs42pW1eMllAd24vJ2f3aslelzUAih7pVlGuM2EiWNFUmG5BRpoVJhUtrb0WmJkdiBjSlnmLbap6o99t5pFC7URBMoFEvgWXbUbW/yVSZ4aSmy9m8f74qEm1VdH0tztU3IAh4isiynQuTSGCW5/v4fOeUXmR8HyZsEomkQhdguTJWg76Cq94Zz5tu7q5TyJpE2wy1CxhVHjS2lPOtHq1IQLDOHBG3+6aYmyav5T0fWbbKvkJF0mKBWP82wpJ5XasBk46KZjVEljrol8UhR+cFi9oIZoi1ZxZzgV5RNA066ltvbUoSWf2xN+T9dxxWHifrLw76ayIeMMxFwGvOiHD+s9vPW3dYHxgPvksj2L/mxWCZA2+3t9pXZM0WcLwdFZiPE8Yh2ArytxjmtZfKoXqjs67J7jXmOuxh9wAwHuu/x5SPLGINEEHvz1IGYwyRBeEKtIMo2qKJBEHA0JqvYpI9g7wmKUljtxwpl4XqQ7PMyUpo8EC/dz4yK3wd2ne5tYDZgpYvXLG5eqLW5hsEsddWqedDVYgbS8f9HnZY3rUZm0NXK2HoM4kiNHkq+6QCFtKER1kR3SDfrE1ElnrB25A2jTrTwolgIxhvn/T0kiWUSiKgiaxmxyCMZcraaIzhF/gaeMIQDCCTLAcMDWIRGguGENnNfP7FxTpFVliAyIob2DtP31cquZl7cQZMszkcxBzC6iqEY0cNZUVpKkWL9SefJiK0VKLzJVBk7ZVOEvlkuw0yg/+MFQfzkXx4koAPg8jStNWEMAnJvi5KkSVYCbtbaYJyISkD5RhJmeN7zfYm8uFaFaKiJzLnovair5Ep54xtlFbT2KYYc8plDpQlNYrK5AgcY6wzVpTS/TYpMEHPsxbGHHxr1ZPIqEgE4BxYY7GcvZtO8ylEUUX/HhlDWEpfIBgQJwYiiwkwabKQiayDkxvxOF+NkkhwEFO4t3FU6tbVKwI8XKuitX6DtuwVaaHXkv5WqZFHiJcZaJh6OZ19YX48aMlkauaV9sgXXduwcM6vWqDq8Ih06txyUGQliHAoqqAWrEh/P9FX6tgosoLHfpH7vYJEy9uVKx0V8cwqsEAgkXZytj6y4ljgoMibis+BQQQVoJUufMTGEGd83VSm0puSGtvTKUSAQx2/BgsDEFmBbLbD7BxNJ4xB1FaRvzUop1YW2LsQIRFt0kR0/flkzEG74B3nZo3xJ+wSd1x3lGohpke+l0kinUadILLqrWBAIqtf/9NZ5/cSVO4JTE8pR/sWtVErX3V7ud5UdH+sV2TVs3aAAOrN/tq2jt29vx+Lv06m1SjtRWmk3S+CWgmH6gxPW3kQ5xz3ME4/poy6y3myqZLGN2B6yxqIASJsd9tKlY1gmiKyDGWYL9Cfg5JAS9D7mFCYzX2ZYmExH8iigf7fhxrtF9xtr+FoXzAgt8+GYqwMbBYyHcQlu/5rFQFQQqkz9pXDo8jfGyP98SUbWCJQlSlzstIMLcjlwDAnuiiyWvOYKWj5QhFZlVoJDyczbcGIwzjaEvR6jBU48F2O2LZtG8466yxMTExg3bp1uPDCC3H//fcPPZ9CtXnttddiy5YtqNVqOPvss/H9739fe/2XvvQlnHDCCajVajjllFPwla98pVBhc4gGILIMncKGyBKionSWzVoJxgnnfGGQJUj0BdGZFi5Ig4iubwVzJ6GKTWClft5y1JZ+ZvTiq9mpi6w52HRrAmVeRvOZv0A5Lj7hTSiicUSCo5XxD0a5cw01pAjXOCZWYao5h9lmktrwdVFx2IAAwLqpalsizYDZkXWYGVnnXB4KopxWPsgYrQQ4KJFH86KFiNGTuFhkIqtU5qkQ8z+r7G9P3hrHZpWAYWrfY7nvuUjAC24QUulohr3Z2tH5L7M+sohtTlJdiezKbDapAZjBr0bC3omNYAytDfSCoYeFCIIFqGfa2hOhnSl1y+C4VqXIOuW0fdi8pYnqyi7pSbeNCAxjnTDxWd92SblYe5ocyfYrHdFgVqB2r3NBMUWWOZOowDQbRvrxlHOBmRxLw+T/kUiYSJU5KfUXe7tKq9sulKs1PNhci/vm0ifYUa09ls6uXqMtm7xGsxl9q0SBdQ7re2ZfjOPhOrGBnKj0mo+sGirLvnC0qjUzkdVS+RzUYCFmPV8rLYdIlkln4i1jRer7aGy/dI3Cd4f0d/DQPbnfKyLWrx06UUkf2z8B8AgMrDd+lpWmKBkkCaYJM5qEA3F1DcRCmqBvihIOZdpesCJDZIm2/6Bk7GmYDdp9ZY5SdlqCV2Q3DcxKlRciRlRZTf5WLzhPPTETIu5uuMNOG5k9QF7LOMPoUVXUa4+Tv+eudyhHEHA8EP9/sDu6EyXpcGIhc4Q218z3gzAKEAfqqIImMNY2LQw4w4FITWQFBYJzAOm5I6nWcr7bKDxmeJ+7WvT6qusc+sBsvv3PS0TWdPwAGGNgo7OoJ3vItARLUF3ZIbKqHI3mCFaMtvdA5x73BBoOaznTtJWMrcFtj0doBukDULcZst1uxsUYDhBmXqa0nojdXccwLlCPFAfyCrIxdb9iTT3DBI4Z41g/mjYJO9AJtxd2eldzpE9Iymo2FZHFHSIMwvKQMHRJEwDvrtkaK8jfG7XdfZPLjAIfAT3mijD9dhcIIjeL2LBOcunupeYMDsByfsqAMrvl4wxgQGW0pFBkqZ6PkW1KFFwnLzd84xvfwNatW/Hd734Xt956K8IwxPnnn4/5+QG4GwLOK+wvfvGLuPzyy3HllVfihz/8IU499VRccMEF2LOHHny/853v4PWvfz3e/OY340c/+hEuvPBCXHjhhbj3Xk24T1tE+sky0RCypmaUC4NMwBTZcO1o/vcGy8rEDcO5hjjZF0UYXdWeYKnOxwCU2AgeeGwTwtYGRLwfepsTJneiSS+uGz0iK13WUFRQxTj2r/8W4mSv/jlyZeuXd6RG5xsJhkZm8HL1kRU0CxBZ9UPtMhK7qYpjlL+x8X7Zbq09C49WhkNk7S4vYEQhNZ4aKSGUWMc6ErQE3Z5FkdAwDiiPZFRFIkSwioMJoKJYNKyrsZz6D2g74u/5DRkAOiLrUOlp+S/HsvVMOLitrMh9Nx1XIVqP4OvN3anv963V+8laf/AJNMe3YG+GWF6IE3DdDr+bb6Jv81TY59GxAFMr9uJpm3egsqpTxyq5tOAY6w4fGUVWXND9W62WzqxsONG3CFrorAooRGRZXBMVcNrfaI5qyx+XQzzWzIzhLPU/EiJHZLXHdjG5AvWO8japjuDu2VWplNZOVHAA7cXHvqkV2rJPSD7P5hK5r9DPUyW+1vE8XQfvcW0NwiTfEGaDGLxLREhpV+UooJq6bYxsVP7WhRyq3aXVdLmJhgVR0kXUsecTcXqD1hjrq1YThdPBHsFVEgBxqFMRkVa5yDpEVhRzNDrvvxsso5TYHV4xjZPimdFjIeb667iYr8HP5vdh18G0qVYi+QOtjJXA0d5ALVSPwWMd34tNJKiNF9u88HFLklNCCwlawUryt4WC89R8mCBMOqRIo1OQVpP0JcA4Q/D0g7nvlXCY6lvVxxCirfgLy+21PQ8CfPNX6UOS2SbddmSFkSuYaB8VnbiyjH0aQp8nip2taUktrZXjmt17OiAYhMZh+eNNmuiKEoEqi/DEgfycuyARWbPJI3iwdB3uD/VRvoKV7fmWVwKsHu9PtqvG57BmqqL08ZSF6QBmtrYa9VhgPsjUv9MU2c5jvBVgrzIslhqPFBAqiCDBbIs2IVwIzXs6piCB9gvg1/f+BGcsPNojsu49FPRMa1uJAC9xNCt98+2UaaFi36AyY6NQsnT23nJc0nc1G9E8TQCCiR6BmlVkyXtKGUmGyJqrmxtOPdIXXAihXbvLKDVnMeMwx8pgxDuZ6fo+q6rNCFWg+qR4ipgW3nLLLXjTm96EZz/72Tj11FNxww03YMeOHbjrrruGmo9zbV5zzTV4y1vegksuuQQnnXQSrr/+eoyOjuIzn/kMef3HPvYxvOxlL8Of//mf48QTT8QHPvABnH766fjEJz4xcOF1RJYIGK751QHcz91NywDg4PxRPT8iKkzX9RvSKaoBZyKRQNCnbb2fNafDu+YF1j9jBQDajKaalDHBj8aO+hgOzKxDQ1pclBYI1cY8vTDYF49holoCIxwMl5P2gvOx+ldRclQrdVEbo0+3I3A0MoRiM86ffIU608K6ux+1Tb+6F5zREbEqBlvvLBYq/fx3o4zbmdnZtw0eSWYwrjDHGqnmy1hHlfZpMjyf9iRmx/KnnQ+WDwEAjp6kF5HrCDMUAKhGTQjLzZMOuslwT5zfxIYZopUyLaxnzH4A4IHGavx/Sxsxlznx/dVKevPTxYbH7se/s1fhALWbtzhte6ylN5X4lZjJSdpXr2n3Qc4TjG3o/Ka0++cY63j5zS5swoJxcHmln85oJcA015/Gli0WgM4WvEwdwU2FktpRSw+hI/nNWRmP7F5J+gPsYobN4/7ZdB11Te60iiwk2Fzpl7mryArX9Nt9SJiBTK7q37PT4OftmfX+PHNIMkVTDdMBsSHVkSu7Su3DgLriUODefQ2I3rlKvzKq8nvQRJt4rHq88rcumgWcvQNAd2u24HC8nHSI5yjMEFnVJ3qbcpU+rFe2kF4rVZJIS+otBP35a0+zrQAb6fgtYi1zu2aMIapq6rqyGdi/F4JNock342uH9mE2TlBvpQeSBelApDbez3dXdTPq0vgajxYzaQtH+qSCLRlQFxGafAX527yoYsVoGRum3OerQ41jAAAp394iPx4GJYaZTT9wTt8Ezjl2hH2riUfj/9t+jyhh/4IA64wZjDEsKHbNs8nDhfMvdVYqE1MxdjfVRFM2cEEfDJUxNaEpE1nNmp1yTjAgqtFKVFGqYTqiT3CaIce++R/isQP554hiDi758Ylt9KtTnf5Y4Vg/le5Xx6xOUNtk54vKNDfuKq0AAEyzdPvdNWdv7tdzbVIPsY849Nf11KTM0SiwOI15jIMNmnCcI8zQc6DM3KsB6mDYfNY4No8sAGDgpTLu2dXvk00hUJmSVJ0IMDbTIZlZoHbAXrVfcNiMS4wBLatTvj7izhqmNTuhNBcvrexKnGNU+Di682oDeb+LAACRXmdPL5jH5dmW/n0zMIwYTHy74GGj0MEkAMTEkPNo57BkpkI7FdDlNL4h3/aLuOB4MmB6ui3kWbWKNskvCqfabLVauOuuu3Deeef1E+Ac5513Hu68807ynjvvvDN1PQBccMEFyuu1KLUbsSiN4icr/huS6grlZmt/CWjEwJd2zGKW2PCPGBZhYVRBwggzow4EStjx6DHaNCZaCVaNlrF6tIzfmgTOWslxoJ45ZZj5Xuau9APtrakX1VHMUdpcxujqEaw9OjtIM2zZNYPmwmYAwEO7V+OBPWvBeYBqqYKRb9+WuhalMsTB/ESa1Fbj+9NrcPxkjazqiUabyIpZA9LuwQLt1ColjoOlrEqt83wiQZis7X0WKOM7v3oaxjMnEy2NmRrP1rcFRhZmsGWUoZWRanPGQCjElVg3VcFcdnEyJI+rggETU/nhkwFYNZrPY3+JYbSVH6GTRVRk1cZK+CnyjqH3J02U15ewRjHhrmvQp8xP/8V2sCEQWYGCWGhMHIf51kTulGwBDEFqDMlvG2cIZ6+bNjE8SjiS/dnEmNZ0pdqYx8aAbrctA9MSlALsNhzJxRAoT/Wfh3OG9Rv6C5KJzfrFaghgYmYaAENrcldqyIqKCSIwE8z1FHprJ8tGc4+1FtLsKHD3saFSOarAR83+VSJHH1kj7BiUSzVETD12hSzGwQRph++dPyey8n8JJSZQm++PG2GlvfGZn+wT7I2MX5TJWgn7gkO9z7+s1SDGaefGAHD8L3/Y+/tgq/8OHmjQdVVqRFg5mmk4RCTLLn4kRiAqk5jmbbOuo6eqWC+pE5oxh1hdxWg0DiGdY1RSfjq66afbkWAlPFTVz+sA0Ega6Fou7HeIjPuV3TPYUVrAo5ZmwkD7hP3UsQj1zAZN8ATVFe1CJIomFnZJKsLnIAA84/H7tKThHBpY1VEr3fnAegTlAJgOMMJHkVhsThhjeFxjZvYgXw2UJrBvdj2+Nf0EQtFCCfnDnoNSO6+Ntd9dPLYZ362kF8R7SsUiWdc5653EH3sivR7JYhYtzFdoFcNsXMXqlQHGVzZRcmTHZ5prkLCnIZmTXureR3p/tmpzAANGNwKNGm0JQYEFdu10ofbznhoLAOrJHrRGHsGuTrCCh2fafTooM6Xfuwb2IXA0cepifI4hKAEPsBlUK1WIUnuMEhl/sZWQJn7Y3gCVcwWqUzS5NLW6hGTVWiRrj8Kdxz3TulyNKn0AFUttMPuuheC448cvJi0JAKDE3JygR+Pt9ZQoBTj96PTa6tg1M5hfbbc+qmj4AFGZwMMdH5gHJcKeByX8Yp+LhUNnjR9VEBPr3lagnjsbBdcRIYuwb4F+uBlJNXf0GMf5fC8qmbWeyM5DAErjFRw1FqByLLDuOe1AIjOZA+16BMiBGdfyTag02/O34OqH2V2zP2i3GUVYwJ2iFgJAozsuiAC1gCYY2FR7XOUljhdvfiGeueJ5AID5ZKcy3ZL03AfnzHPF/rp5fCobXC60wRAXjhoL7Cqn113VkRKm0W6rv8ShdGTvDgTah6vjkl+F7lCwsDbAxHEVVCb79RFXlrdp4czMTOpfs2leBydJgssuuwznnnsuTj755KGWx+n8fN++fYjjGOszIeTXr1+Pn//85+Q9u3btIq/ftYteWAFAs9lMVUyXxds98QK0arPYMfpM/Iqtxm52FE7DA6iwzQBY22xCMDS4wHf317FprO1R6RsHDuDciREEot3gHopDrK+XwWpA1PF1ITiQsAiNOEKVjSBJOHYePBsrqhUw0eot9tr++xhmm8ehWuNYvaaBUjmBSBimD1XQbLRPxoUQgIhxRg2IxwQYK2Eti/DgwnGYHH0MtWh1uzDNGRxadT5m+AqUyk08WFqNk1pPYDw8hHK0gO80T8XR4203xALtU+3u2JsA+PFsA8//9Q3gjW9jpt4mvVrlFfh55SwcG/8Ms/s2YcskEPAA04cm8LNGE8cd2oGJlatRX7EKCEqY2XQ8pqsrsWp+FzB1MkbF4+DhHBhi7Ko9C6fUylgVN4A1DCjFCBYCMNGOuFGZW4m1wamYxwwOHHoAK2rP79DTArqht9mqYdOYwKpVCeYFAxSbvUdnVmPd2LFgLMHB8GisGhUYnZpBLR7Bvum2AZoQEWZbAUYzC7VKHKE5N4to42ZlOdpgEEEJolQGi2MAAidGu3FovozV1QQQAgHnGIvK+PH0NJ4+1X4HsUi7emKs3T5GRziCskCrPItx4baJdUHEpnHcipG2w1XRzn/TRBWHcBBrM/keFBwb9hwCXz2O7vlBIMqo748xsYIhjNpyYNV0k32T8ttlrPOfjmuRuAq0ggQP8xmsVizgHsF+HDU6grM3BQgjhplmDAFgKghx9I4dmNm0RTKKZ0BtBGDAipkSxPix+QRFpwJ0T9C5phauwY9WnIaAxXha+CCCuAnBgF9WXoCRsIWDa1oYTcqoLXAkUTu98gmjaO1tolRlmB15Rns8EAkAjnplFe4Jj8PGsfa1K0Y5qqMtTLN67j20ixLgkeedi1WP7myXqxQAcQzwACIoISlxRKUW1hI+66YbC1ihIB8ZF2gk81gjzMP6/sohbFxdQ7WcYMXKeSSijunpUTAGjKyZx4pzVmB6QpDlL2MEB1a+FEeHB7F/YifGz40RPzoKMKBZTehnNiLB+vXAzDQwWWsiMKTB0cDJq0o41Io6bVGgAoG4Y5Q6PsbRYPPOZVm7juPRg/l4SZ3m3S5pp1mOlYGENYx5NOZXYHX1ZCSC2tAzAByTYgJlUcMhfhBx8zgcv7qJA0LvEW50ZQMPJhzrO76KmonAy0b2Y75SQ0NRphERoTZWxuhohFJZ4KGZ43HMyS3sX30UpkZiYDRGs8VxHBOoRwnKnGFiRYiFlJoJ+OELzsczfn4veFNaiDPW7mIQeNHYDB7EBA40F3CgOoKIAQuNFkbWx5hFDCYYRuMSeKMd1e7UAFgYK+FgK0bAGQIo+k4HO1ecjd3hWjxvHcca0UKdlVASAqtqwJiIMF9tYfJQDaGIAC7AGUO90UCTt1+kiGewsP5l+EnjOJwSPICSSADE+CmehVnQ7V5GIEp4NHkc43wN9uzbhVMnaziQBGnXfwwYCxhWo4mDooQFwRGjie8daAcIXmvpJ4tx4OnBARycG8U6/lwwAHNloM6bqB4Xg60fxygTKBNl/k59F17EJsD374TYeEyHQO+erjOMsxaOjvYiKas3CKNTEWpCACLA3U/UcdLKEtjuKiLEnTlPPde3INCKm1irmN9DRPhm7bdxzO77UVvxDDAsIAmPwaYxgYABk7ztXzIWAqPH15DUE6zd9HMcCk7GI5OngiVhKu15MY9VaydQbXCIWABC5HyrcN6uBob2sNv1yVw+cQq1ah21EyKsrdu9m0eCFRifPAOlcKZTrwJRMIYFPolqeRYNFuGUTVXMzgSoRwkdeIsBAWOoBEASC8zON/FI/aVY8cC3gI1tUpXt2wms2YKQx3jkQBMbp1rYv+p+jEbHphPqjVwMJbYKSKYAPo1EAE/Ej2GV0PtnjMuHsK9+P8ZwYmc05QAYfnWI47H9DWwcBXYeWMDTRip45MA8No6WellXg37bCurH4UDpxxhPTnKSLFZ4CXNREz9tzWGyzLFmXYxdC2dgpLUP9694DiZEA6sae/EwfwbWP7EP5Y0rcz4fFx6dx4O1KRx9UhPVHflgZ6tHd2L7qb+J2VoNB0QLay3Ld1fl+Thq9bGoYQFchBiJDiEQLTw8fhJ+baqEMhKMRSF2tspYCBMwJiAE0Ey6aw8AEBhhwEaxgHVz+9CYOQHNyiwSBoSsBeQcnLffKcMEGIDW1CFMnLEJtfVNjLImZmb6RNhEsIBkdQ2Tz64gqTMkDdr8mQFY//SHcIg/G0lQBY9bgEjAkgjgHIfWnozpOsNaEeDxBrC+2gJPAsyFwKax9lgaS5Va5gwlzsAZQzVgGC8D9TBGMvNcrKxuwE9Kx5Hj6bcbu/ECvrZjqyWk0gnsTeJC64hARJidn8Te+RegzHe3nwsxElRwcG4SG8cSbGQRzvrV91GKQ4w+7UT8eGQ9Wp294Y7RAEfXq6mIwWxVGWetnsb07BiSaoDw4Az2zlfwzClgqpqgLBLEzQQ4fhqj0RYAAdbMjWNm5BkAGHZNHa98lrlkDuunptobu6Td//sZs3R0bwFUN1WUwfsYYygfEzjX2y5xCMeumgCrC/D6c8D4w+iv8NvvI1x3EOPPfhpWHgMszJWxGjU8Hj8DjaiJhDX6kWcZ2u+TAXyuDM5iAAwTQYLJsfY6qqvMZizt92p+oYFoogSO7l67/6AxY0gEQ3k6Aa+VoRtUygnwrfFzC65DgUaygNp4CSISYBWGvaMzqbT2JvOYyJBZjTDGf6/fg4kaw/5Va1EpA83KGO5bGMe+sIRTTnwA809fgbn7J4FyCY9tilJp1jvrK12k6iMJmzen99RXXnkl3ve+92nv2bp1K+69915861vfGnp5ChqCLC62bduG97///bnvT3jpHxyG0nh4eHh4eHh4eHh4eHh4eHgMF7Ozs5iaUvgqO4Kwc+dOTE72VfnVql4pd+mll+LLX/4y7rjjDjztaYQf4gHhRGStWbMGQRBg9+60I8fdu3djwwY6JPeGDRucrgeAK664Apdffnnv86FDh3DMMcdgx44dy+Ile3joMDMzg82bN+cGAw+P5Qjfnj2ebPBt2uPJBN+ePZ5s8G3a48kCIQRmZ2excaM50MyRgMnJSas+J4TA2972Ntx00024/fbbceyxhDXNEOBEZFUqFZxxxhm47bbbcOGFFwJo2z3edtttuPTSS8l7zjnnHNx222247LLLet/deuutOOecc5T5VKtVkuGbmpryA5bHkwa2g4GHx3KAb88eTzb4Nu3xZIJvzx5PNvg27fFkwJNRpLN161Z84QtfwL//+79jYmKi51JqamoKIyPF/Zhl4WxaePnll+Piiy/GmWeeiec973n46Ec/ivn5eVxyySUAgDe+8Y3YtGkTtm3bBgB4xzvegRe96EX427/9W7ziFa/AjTfeiB/84Af45Cc/ObSH8PDw8PDw8PDw8PDw8PDw8PA4fLjuuusAAL/xG7+R+v6zn/0s3vSmNw0tH2ci6/d///exd+9evPe978WuXbtw2mmn4ZZbbuk5dN+xYwe4FI3s+c9/Pr7whS/gr/7qr/AXf/EXeOYzn4mbb7556F7rPTw8PDw8PDw8PDw8PDw8PDwOD5bKeX0hZ++XXnqp0pTw9ttvz3332te+Fq997WuLZAWgbWp45ZVXGh2KeXgsB/j27PFkgm/PHk82+Dbt8WSCb88eTzb4Nu3h4QEATCyXeI8eHh4eHh4eHh4eHh4eHh4eTzHMzMxgamoKH7rza6iNjy1Zvo25efzlOefh+OOPRxAE2Lp1K7Zu3bpk+atQSJHl4eHh4eHh4eHh4eHh4eHh4fHkx/bt24+oAAvcfImHh4eHh4eHh4eHh4eHh4eHh8fhhyeyPDw8PDw8PDw8PDw8PDw8PDwGwh133IFXvepV2LhxIxhjuPnmmxclnyOeyLr22muxZcsW1Go1nH322fj+979/uIvk4ZHDtm3bcNZZZ2FiYgLr1q3DhRdeiPvvvz91TaPRwNatW7F69WqMj4/jNa95DXbv3p26ZseOHXjFK16B0dFRrFu3Dn/+53+OKIqW8lE8PHK46qqrwBjDZZdd1vvOt2eP5YbHHnsMf/AHf4DVq1djZGQEp5xyCn7wgx/0fhdC4L3vfS+OOuoojIyM4LzzzsMDDzyQSuPAgQO46KKLMDk5iRUrVuDNb34z5ubmlvpRPJ7iiOMY73nPe3DsscdiZGQExx13HD7wgQ+kIkX59uxxJMO00R1W+/3JT36CF7zgBajVati8eTM+8pGPLPajeXg85TE/P49TTz0V11577aLmc0QTWV/84hdx+eWX48orr8QPf/hDnHrqqbjggguwZ8+ew100D48UvvGNb2Dr1q347ne/i1tvvRVhGOL888/H/Px875p3vvOd+M///E986Utfwje+8Q08/vjj+J3f+Z3e73Ec4xWveAVarRa+853v4HOf+xxuuOEGvPe97z0cj+ThAaBtD/+P//iPeM5znpP63rdnj+WEgwcP4txzz0W5XMZXv/pV/OxnP8Pf/u3fYuXKlb1rPvKRj+DjH/84rr/+enzve9/D2NgYLrjgAjQajd41F110EX7605/i1ltvxZe//GXccccd+B//438cjkfyeArj6quvxnXXXYdPfOITuO+++3D11VfjIx/5CP7+7/++d41vzx5HMkwb3WG035mZGZx//vk45phjcNddd+Gv//qv8b73vQ+f/OQnF/35PDyeynj5y1+OD37wg/jt3/7tRc3niI5aePbZZ+Oss87CJz7xCQBAkiTYvHkz3va2t+Hd7373YS6dh4cae/fuxbp16/CNb3wDL3zhCzE9PY21a9fiC1/4An73d38XAPDzn/8cJ554Iu6880782q/9Gr761a/ila98JR5//HGsX78eAHD99dfjXe96F/bu3YtKpXI4H8njKYi5uTmcfvrp+Id/+Ad88IMfxGmnnYaPfvSjvj17LDu8+93vxre//W1885vfJH8XQmDjxo340z/9U/zZn/0ZAGB6ehrr16/HDTfcgNe97nW47777cNJJJ2H79u0488wzAQC33HILfuu3fguPPvooNm7cuGTP4/HUxitf+UqsX78en/70p3vfveY1r8HIyAg+//nP+/bssazAGMNNN92ECy+8EMDwxuPrrrsOf/mXf4ldu3b11hzvfve7cfPNN+PnP//5YXlWD49BcLijFu7cuTPl7L1araJarWrvzfbvYeKIVWS1Wi3cddddOO+883rfcc5x3nnn4c477zyMJfPwMGN6ehoAsGrVKgDAXXfdhTAMU+35hBNOwNFHH91rz3feeSdOOeWU3qYfAC644ALMzMzgpz/96RKW3sOjja1bt+IVr3hFqt0Cvj17LD/8x3/8B84880y89rWvxbp16/Dc5z4Xn/rUp3q/P/zww9i1a1eqTU9NTeHss89OtekVK1b0Nk0AcN5554Fzju9973tL9zAeT3k8//nPx2233YZf/OIXAIAf//jH+Na3voWXv/zlAHx79ljeGFb7vfPOO/HCF74wdXB2wQUX4P7778fBgweX6Gk8PJ482Lx5M6ampnr/tm3bdljLUzqsuWuwb98+xHGc2gQBwPr16z2L7nFEI0kSXHbZZTj33HNx8sknA0DvNGjFihWpa9evX49du3b1rqHae/c3D4+lxI033ogf/vCH2L59e+433549lht++ctf4rrrrsPll1+Ov/iLv8D27dvx9re/HZVKBRdffHGvTVJtVm7T69atS/1eKpWwatUq36Y9lhTvfve7MTMzgxNOOAFBECCOY3zoQx/CRRddBAC+PXssawyr/e7atQvHHntsLo3ub7JpuYeHhxmUIutw4oglsjw8liu2bt2Ke++9F9/61rcOd1E8PAph586deMc73oFbb70VtVrtcBfHw2NgJEmCM888Ex/+8IcBAM997nNx77334vrrr8fFF198mEvn4eGGf/3Xf8U///M/4wtf+AKe/exn4+6778Zll12GjRs3+vbs4eHh4bEomJycTBFZhxtHrGnhmjVrEARBLgrW7t27sWHDhsNUKg8PPS699FJ8+ctfxv/7f/8PT3va03rfb9iwAa1WC4cOHUpdL7fnDRs2kO29+5uHx1Lhrrvuwp49e3D66aejVCqhVCrhG9/4Bj7+8Y+jVCph/fr1vj17LCscddRROOmkk1LfnXjiidixYweAfpvUrTk2bNiQCzYTRREOHDjg27THkuLP//zP8e53vxuve93rcMopp+ANb3gD3vnOd/bMPHx79ljOGFb79esQD48nN45YIqtSqeCMM87Abbfd1vsuSRLcdtttOOeccw5jyTw88hBC4NJLL8VNN92Er3/96zkp8xlnnIFyuZxqz/fffz927NjRa8/nnHMO7rnnntTEfOutt2JycjK3AfPwWEy85CUvwT333IO777679+/MM8/ERRdd1Pvbt2eP5YRzzz0X999/f+q7X/ziFzjmmGMAAMceeyw2bNiQatMzMzP43ve+l2rThw4dwl133dW75utf/zqSJMHZZ5+9BE/h4dHGwsICOE8v4YMgQJIkAHx79ljeGFb7Peecc3DHHXcgDMPeNbfeeiue9axnebNCD49FxNzcXG//ALT93t199929w8Nh4Yg2Lbz88stx8cUX48wzz8Tznvc8fPSjH8X8/DwuueSSw100D48Utm7dii984Qv493//d0xMTPTs86empjAyMoKpqSm8+c1vxuWXX45Vq1ZhcnISb3vb23DOOefg137t1wAA559/Pk466SS84Q1vwEc+8hHs2rULf/VXf4WtW7cedhtkj6cWJiYmev7duhgbG8Pq1at73/v27LGc8M53vhPPf/7z8eEPfxi/93u/h+9///v45Cc/2QvDzhjDZZddhg9+8IN45jOfiWOPPRbvec97sHHjxl6knRNPPBEve9nL8Ja3vAXXX389wjDEpZdeite97nU+wpvHkuJVr3oVPvShD+Hoo4/Gs5/9bPzoRz/CNddcgz/8wz8E4Nuzx5GPubk5PPjgg73P3Y3uqlWrcPTRRw+l/f73//7f8f73vx9vfvOb8a53vQv33nsvPvaxj+Hv/u7vDscje3g8ZfCDH/wAv/mbv9n7fPnllwMALr74Ytxwww1Dy4cJIcTQUlsEfOITn8Bf//VfY9euXTjttNPw8Y9/3J8UeRxxYIyR33/2s5/Fm970JgBAo9HAn/7pn+Jf/uVf0Gw2ccEFF+Af/uEfUvLmX/3qV3jrW9+K22+/HWNjY7j44otx1VVXoVQ6ojlnj6cAfuM3fgOnnXYaPvrRjwLw7dlj+eHLX/4yrrjiCjzwwAM49thjcfnll+Mtb3lL73chBK688kp88pOfxKFDh/Drv/7r+Id/+Accf/zxvWsOHDiASy+9FP/5n/8Jzjle85rX4OMf/zjGx8cPxyN5PEUxOzuL97znPbjpppuwZ88ebNy4Ea9//evx3ve+txehzbdnjyMZt99+e2qj20V3ozus9vuTn/wEW7duxfbt27FmzRq87W1vw7ve9a4leUYPj2FjZmYGU1NT+NCdX0NtfGzJ8m3MzeMvzzkP09PTR5SPrCOeyPLw8PDw8PDw8PDw8PDw8PB4qqJLZL3n1n9fciLrAy99tSeyPDw8PDw8PDw8PDw8PDw8PDzs0Gg0cOyxx/Zc2CwlJicncdRRR4Fzjq1bt2Lr1q1LXoYsPJHl4eHh4eHh4eHh4eHh4eHhcQSj0Wig1Woteb6VSgW1Wm3J89XBE1keHh4eHh4eHh4eHh4eHh4eHssC3HyJh4eHh4eHh4eHh4eHh4eHh4fH4Ycnsjw8PDw8PDw8PDw8PDw8PDw8lgU8keXh4eHh4eHh4eHh4eHh4eHhsSzgiSwPDw8PDw8PDw8PDw8PDw8Pj2UBT2R5eHh4eHh4eHh4eHh4eHh4eCwLeCLLw8PDw8PDw2MIYIzh5ptvPtzF8PDw8PDw8PB4UsMTWR4eHh4eHh7LBnv37sVb3/pWHH300ahWq9iwYQMuuOACfPvb3z7cRfPw8PDw8PDw8FgClA53ATw8PDw8PDw8bPGa17wGrVYLn/vc5/D0pz8du3fvxm233Yb9+/cf7qJ5eHh4eHh4eHgsAbwiy8PDw8PDw2NZ4NChQ/jmN7+Jq6++Gr/5m7+JY445Bs973vNwxRVX4L/9t/8GALjmmmtwyimnYGxsDJs3b8af/MmfYG5urpfGDTfcgBUrVuDLX/4ynvWsZ2F0dBS/+7u/i4WFBXzuc5/Dli1bsHLlSrz97W9HHMe9+7Zs2YIPfOADeP3rX4+xsTFs2rQJ1157rba8O3fuxO/93u9hxYoVWLVqFV796lfjkUceWZS68fDw8PDw8PB4qsATWR4eHh4eHh7LAuPj4xgfH8fNN9+MZrNJXsM5x8c//nH89Kc/xec+9zl8/etfx//8n/8zdc3CwgI+/vGP48Ybb8Qtt9yC22+/Hb/927+Nr3zlK/jKV76Cf/qnf8I//uM/4t/+7d9S9/31X/81Tj31VPzoRz/Cu9/9brzjHe/ArbfeSpYjDENccMEFmJiYwDe/+U18+9vfxvj4OF72speh1WoNp0I8PDw8PDw8PJ6CYEIIcbgL4eHh4eHh4eFhg//zf/4P3vKWt6Ber+P000/Hi170Irzuda/Dc57zHPL6f/u3f8Mf//EfY9++fQDaiqxLLrkEDz74II477jgAwB//8R/jn/7pn7B7926Mj48DAF72spdhy5YtuP766wG0FVknnngivvrVr/bSft3rXoeZmRl85StfAdB29n7TTTfhwgsvxOc//3l88IMfxH333QfGGACg1WphxYoVuPnmm3H++ecvTgV5eHh4eHh4eDzJ4RVZHh4eHh4eHssGr3nNa/D444/jP/7jP/Cyl70Mt99+O04//XTccMMNAICvfe1reMlLXoJNmzZhYmICb3jDG7B//34sLCz00hgdHe2RWACwfv16bNmypUdidb/bs2dPKu9zzjkn9/m+++4jy/njH/8YDz74ICYmJnpKslWrVqHRaOChhx4atBo8PDw8PDw8PJ6y8M7ePTw8PDw8PJYVarUaXvrSl+KlL30p3vOe9+CP/uiPcOWVV+I3fuM38MpXvhJvfetb8aEPfQirVq3Ct771Lbz5zW9Gq9XC6OgoAKBcLqfSY4yR3yVJUriMc3NzOOOMM/DP//zPud/Wrl1bOF0PDw8PDw8Pj6c6PJHl4eHh4eHhsaxx0kkn4eabb8Zdd92FJEnwt3/7t+C8LTr/13/916Hl893vfjf3+cQTTySvPf300/HFL34R69atw+Tk5NDK4OHh4eHh4eHxVIc3LfTw8PDw8PBYFti/fz9e/OIX4/Of/zx+8pOf4OGHH8aXvvQlfOQjH8GrX/1qPOMZz0AYhvj7v/97/PKXv8Q//dM/9XxcDQPf/va38ZGPfAS/+MUvcO211+JLX/oS3vGOd5DXXnTRRVizZg1e/epX45vf/CYefvhh3H777Xj729+ORx99dGhl8vDw8PDw8PB4qsErsjw8PDw8PDyWBcbHx3H22Wfj7/7u7/DQQw8hDENs3rwZb3nLW/AXf/EXGBkZwTXXXIOrr74aV1xxBV74whdi27ZteOMb3ziU/P/0T/8UP/jBD/D+978fk5OTuOaaa3DBBReQ146OjuKOO+7Au971LvzO7/wOZmdnsWnTJrzkJS/xCi0PDw8PDw8PjwHgoxZ6eHh4eHh4eBiwZcsWXHbZZbjssssOd1E8PDw8PDw8PJ7S8KaFHh4eHh4eHh4eHh4eHh4eHh7LAp7I8vDw8PDw8PDw8PDw8PDw8PBYFvCmhR4eHh4eHh4eHh4eHh4eHh4eywJekeXh4eHh4eHh4eHh4eHh4eHhsSzgiSwPDw8PDw8PDw8PDw8PDw8Pj2UBT2R5eHh4eHh4eHh4eHh4eHh4eCwLeCLLw8PDw8PDw8PDw8PDw8PDw2NZwBNZHh4eHh4eHh4eHh4eHh4eHh7LAp7I8vDw8PDw8PDw8PDw8PDw8PBYFvBEloeHh4eHh4eHh4eHh4eHh4fHsoAnsjw8PDw8PDw8PDw8PDw8PDw8lgU8keXh4eHh4eHh4eHh4eHh4eHhsSzw/wdSc8vxpez87QAAAABJRU5ErkJggg==",
+ "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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9evXCqVOnPBoc8SyqfhFC2hM65xGpczsJy8vLQ1pamsNxlmVRW1vrkaCId9AJiRBCCJEOt5Owrl274s8//3Q4/sMPP6BPnz4eCYp4h3VPGCGEtHXUdkGkzu1ti+bMmYNJkyYhLy8PLMtizZo1OH36NL766iusX7/eGzESD6FKGCGEECIdblfCbr/9dqxbtw6///47AgMDMWfOHJw8eRLr1q3D6NGjvREj8RCaHUkIIYTYEvPz0O1KGAAMHToUmzdv9nQsxMtoOJIQQgixJeawdYuSMIHRaHT4YA8ODm5VQMR7rP+tKCEjhBBCxOX2cGRWVhbGjRuHwMBAhISEICwsDGFhYQgNDUVYWJg3YiQeYl1ypSSMEEIIEZfblbAHH3wQHMfhv//9L2JiYmj2iYyYzWbL99QTRgghxFOqqqpgMpkQGhoqdiiy4nYSdvjwYRw4cACdO3f2RjzEi2g40ndKSkrw6aef4tZbb0WPHj3EDocQQrxq2rRpuHLlCr7++msEBQWJHY5bxCxKuD0cOWDAAOTk5HgjFuJllIT5zldffYVt27Zh/vz5YodCCCFel52djaqqKpw7d07sUGTF7UrY0qVL8eSTTyIvLw/du3eHWq22ub1nz54eC454lnW2T8OR3nXy5EkAgF6vFzkSQgjxHfpscY/bSdiVK1dw/vx5TJ482XKMYRhwHAeGYWz6joi0UCWMENJSubm5+Omnn3D33XcjOjpa7HCIRFGfuHvcTsKmTJmCPn364Ntvv6XGfJmRaxJ2+vRprFmzBlOmTEFMTIzY4biE/i5IW/P+++/j6NGjKCkpwauvvip2OESi5Hjuk9U6YZcuXcLPP//sdBNvIm1yTcLefPNN5Ofno66uTjYnfyrJk7bm6NGjAICdO3eKHAmRMjr3ucftxvyRI0fi8OHD3oiFeJlck7D8/HwAQGZmpriBEEIIIR7kdiVs/PjxeO6553D06FH06NHDoTH/tttu81hwxLPkmoQRQgghbZHbSdiTTz4JAHjttdccbqPGfGmz/rehJIwQQggRl9vDkSzLNvpFCZi0WY/V078VIYRI0549e2i9rXaiVRt4E3mxrn5R86R3yXGGECFEfNnZ2Zg7dy78/Pzw008/iR2O2+izxT0uJWEffvghnnjiCfj5+eHDDz9s8r7Tpk3zSGDE86gnjBBCpO3SpUsAgOrqapEjIb7gUhL23nvv4YEHHoCfnx/ee++9Ru/HMAwlYRJGPWGEkPaEqjJE6lxKwrKyspx+T+TF+oRESRghhBBPo1YM97jdmP/aa6+hsrLS4XhVVZXTGZNEOqgnjBBCiDdREuYet5OwefPmwWg0OhyvrKzEvHnz3HquHTt2YPz48YiPjwfDMPjxxx9tbn/kkUfAMIzN18033+xuyKQeVcJ8h5JcQsRHCYHv0bnPPW4nYcJG3fYOHz6M8PBwt56roqICvXr1wscff9zofW6++WYUFBRYvr799lt3Qyb1rP846A+FEEKIp9Fni3tcXqIiLCzMUo1KT0+3ScTMZjOMRqNlIVdXjR07FmPHjm3yPlqtFrGxsW49L3GOKmGEEEK8iaqP7nE5CXv//ffBcRymTJmCefPmISQkxHKbRqNBx44dMWjQII8HuG3bNkRHRyMsLAwjR47EggULEBER0ej9TSYTTCaT5WeDweDxmOSKlqjwHToREUIIaY7LSdikSZMAACkpKRgyZAhUKu+v83rzzTfjrrvuQkpKCs6fP4+XX34ZY8eOxe7du6FUKp0+ZuHChW73prUXNBxJiDQUFhbizJkzuP7666FQuN0VQlwkx/McXcC1L27/9Q8fPhyXLl3C7Nmzcd999+Hy5csAgN9++w3Hjx/3aHD33nsvbrvtNvTo0QN33HEH1q9fj3379mHbtm2NPmbWrFnQ6/WWr5ycHI/GJGdUCSNEGubPn4/XX38de/bsETsUQoiI3E7Ctm/fjh49emDv3r1Ys2aNZabk4cOHMXfuXI8HaC01NRWRkZFN7qml1WoRHBxs80V4lHgRIg3COWzfvn0iR0IIEZPbSdhLL72EBQsWYPPmzdBoNJbjI0eO9PpVXW5uLoqLixEXF+fV39NWWSdhtIE3IYRIDw1Hti9uN3YdPXoUK1ascDgeHR2Nq1evuvVcRqPRpqqVlZWFzMxMhIeHIzw8HPPmzcOECRMQGxuL8+fP44UXXkBaWhrGjBnjbtgENDuSNK+srAwBAQE2F1iEEEK8w+1KWGhoKAoKChyOHzp0CAkJCW491/79+9GnTx/06dMHADBjxgz06dMHc+bMgVKpxJEjR3DbbbchPT0djz76KPr164c///wTWq3W3bAJaMV8X5Lj61tcXIwHH3wQr7zyitihEEJkRI7nO6lwuxJ277334sUXX8SqVavAMAxYlsVff/2FmTNn4uGHH3bruUaMGNHkP97GjRvdDY80gTbwJk3ZvXs3amtrceTIEbFDIcQjaGjPNygJazm3K2FvvPEGMjIykJSUBKPRiK5du2LYsGEYPHgwZs+e7Y0YiYfQ7EhCCCGeRssftZxLlTCDwWCZZajRaPD5559jzpw5OHr0KIxGI/r06YNrrrnGq4GS1qOeMN+hK3BCSEvIMYmx/jyhc597XErCwsLCUFBQgOjoaIwcORJr1qxBUlISkpKSvB0f8SCqhBFCCPE0qoS1nEvDkTqdDsXFxQD4bYRqa2u9GhTxDuoJ8x06ERFCCGmOS5WwUaNG4YYbbkCXLl0AAHfeeWejU9j/+OMPz0VHPEruw5FU5iaEtHVyv4Cj87R7XErC/ve//+HLL7/E+fPnsX37dnTr1g0BAQHejo14mNyTMLmfnAghvkXnDN+j19w9LiVh/v7+ePLJJwHwa3u99dZbCA0N9WZcxAvk3hNGV1iEEHfIMSGQ43lOjjFLhdvrhG3dutUbcRAfkHvzpBxjJoSIh5ID36DXueVcSsJmzJiB+fPnIzAwEDNmzGjyvu+++65HAiOeJ8fql1zRSYkQ8dHfIZE6l5KwQ4cOWWZEHjp0yKsBEe+ReyVMTidUOb6+hBDxyfHcQRf4LedSEmY9BEnDkW2DHP/Q5RgzIYS0dbRYa8u5vW1RY06dOoX09HRPPR3xArlXwuSETkSEkPbCeg1K+mxxj8eSMJPJhPPnz3vq6YgXUBLmO/T6EiI++jv0jbq6Osv3crwAFfN94rEkjMgLnZwIIYR4gnUSZl0VkwsxE0dKwtoRSrwIIUTarBMCuZyzrRMvOSZhYqIkrB2R+2KtciLHkjwhRFrkcp6mJKzlXF6sNSwsrMkPFutyJJEm643X5XKFRQgh7ZXZbIZSqRQ7jGbRBX7LuZyEvf/++14Mg/gCNeb7Dr2+hIhPjn+Hctzj1zpOubzmUonZ5SRs0qRJ3oyD+IDckzA5DfHJ8fUlpK2x76+S+jmE4zjU1NRYfpZLEmZ9vpPLcKRU1jajnrB2RO4lYzklNlI/2RPS3lgnN1JlMpmwaNEiy89yOU9LparkDqnETElYOyLHMjchhLSU9TmvqqpKxEhaRi7naevql1xipkoY8TmpZP6EEOIL1dXVlu8rKytFjKRl5HKetk7C5DJJTyrDppSEtSNyH46UE+srK3qtCRGHdeJVUVEhYiQtI5dzh3XiRUmYeygJa0doONJ3rF9ruZyU5HLVTYirrJMwGo70Huvlj+RyvpPKyJDLsyMFZrMZy5cvx5YtW3D58mWHN8kff/zhseCIZ1ElTBw1NTXQaDRih0FIu8JxnE31i4Yjvcc6CZPDBAhAOtU7t5OwZ599FsuXL8e4cePQvXv3dj0L7O233wbLsnjhhRfEDsUlUsn82wPr11ouJyVC2pLKykqYTCabn+VGKkNmzbE+x8nlfCeV/S7dTsJWrlyJ77//Hrfccos34pENo9GIzZs3AwCeeOIJhIaGihuQC+S+TpicyPGk1J4vqEjbY590ybEnjJIw77Gu3ll/72tu94RpNBqkpaV5IxZZsa50yOUPxX4IUg5DknJNHOV4UiKkLbHvAZNjJUzM5MAd1hVHuZzvrOOUVRL2//7f/8MHH3wgqw9Eb5NLBUGOSZj1H7ecWMct1/8HQuTMvvIlx0qYHBMa62VBpEwqfWxuD0fu3LkTW7duxW+//YZu3bpBrVbb3L5mzRqPBScXcklI7eOUS9wCuSS7ACVhhIjNvvIlx0qYXGZ0WidecjnfSeUc7XYSFhoaijvvvNMbsRAvs698ySEJsx7qlUO8AB+zddOnXE6khLQlbSEJk0vM1kmYXCph1q+tmK+z20nYsmXLvBEH8QH7JEYOw5FyTGDsY5bLSYmQtqQtDEfKJWY5Loqr1+st34v5OUOLtbYjchyOtP7jlkuTqv1JiJIwQnzP/u+wvLxcpEhazmg0ih2CSwwGg+V7uSRh1nFWVVWJNsGuRUnYDz/8gIkTJ+K6665D3759bb7csWPHDowfPx7x8fFgGAY//vijze0cx2HOnDmIi4uDv78/Ro0ahbNnz7YkZI+TQwJjT+6VsJqaGlnMRC0rK7P5WS5DCoS0JfYJjFwSGmtySRzlWAmzThwB8V5rt5OwDz/8EJMnT0ZMTAwOHTqEa6+9FhEREbhw4QLGjh3r1nNVVFSgV69e+Pjjj53evmjRInz44YdYsmQJ9u7di8DAQIwZM0YSlQU5JmFynB0pxyGFtrA+ESFyZ/8ha/+zHMglCbOOUy7Jrv37wf7i2VfcTsI++eQTfPbZZ/i///s/aDQavPDCC9i8eTOmTZtmM8bqirFjx2LBggVOG/05jsP777+P2bNn4/bbb0fPnj3x1VdfIT8/36FiRlwjxyRMjmv92Mcol5MSIW2J/eeRXq+XRSXdmlySMOtzXHl5uSw+W0pLS21+LikpESUOt5Ow7OxsDB48GADg7+9veZM89NBD+Pbbbz0WWFZWFgoLCzFq1CjLsZCQEAwcOBC7d+9u9HEmkwkGg8Hmyxus/5jl8IYD5JmEybESZp90ySFmOdu0aRPWrl0rdhhEYoqLi61+YsCyrMMHr9TJ5QLO+nOW4zhZxG2fdNm+X3zH7SQsNjbWEnxycjL27NkDgE+aPDlEV1hYCACIiYmxOR4TE2O5zZmFCxciJCTE8pWUlOSxmKxZJ2FyubqyT7rkELccK2H2JyC5XM3KEcdxeOedd7BkyRJcvnxZ7HCIhFi/H5SBYQCAoqIiscJxif05WQ7JjNlslkxVyR2yTcJGjhyJn3/+GQAwefJkPPfccxg9ejTuueceSawfNmvWLOj1estXTk6OV36PHLctso9TDpUwOTbXtoVeFLmwfg/L4b1BfKO6uhpXr161/KwJjgQA5OfnixWSS+wvMuVwAVdWVubwWWL92kuR2Wx2SLrEitntdcI+++wzyws+depUREREYNeuXbjtttvwz3/+02OBxcbGAuCvXOLi4izHi4qK0Lt370Yfp9VqodVqPRZHY+RYCbOP03pBUamSY1XJPulyt1dSLHLakUAgx8V85bofqpzYX3xrwuJQVXAGly5dEiki18ixlcHZyFRBQYEIkbjOWeJ45coVUWJxOwlTKBRQKBoKaPfeey/uvfdejwYFACkpKYiNjcWWLVssSZfBYMDevXvx1FNPefz3uUuOPWFyTMLsExixZrC4wz5GufShyDEhsH5PyyWJtN4iRS4xy8358+dtftaGxQMAzp07J0Y4LpPjRWdubq7DMalXHJ0Nl8qmMR8A/vzzTzz44IMYNGgQ8vLyAABff/01du7c6dbzGI1GZGZmIjMzEwDfV5aZmYns7GwwDIPp06djwYIF+Pnnn3H06FE8/PDDiI+Pxx133NGSsNs9+6RLDhU8+5KxWOP27rBPusrLy2WzEa/cyOUCyJr1EjtyTHzl4PTp0zY/a6M6AADOnDkj6feMfdJVU1MjiSWZmuKs5Sc7O1uESFznbOhRrOFIt5Ow1atXY8yYMfD398ehQ4csV3V6vR5vvPGGW8+1f/9+9OnTB3369AEAzJgxA3369MGcOXMAAC+88AKeeeYZPPHEExgwYACMRiM2bNgAPz8/d8MmcEzC5FAJsy8RS72xFrBNFJn6vzCp90gQ37Hu+5HjtlxycOzYMZuftaFxYFRaVFRU4OLFi+IE5QLryr+CUQKQfvXf2RCv1Id9nZ2PS0tLRflMdDsJW7BgAZYsWYLPP/8carXacnzIkCE4ePCgW881YsQIcBzn8LV8+XIAfKn+tddeQ2FhIaqrq/H7778jPT3d3ZC9Qo7DCPbb/ki9OmM2mx2SLqmXuevq6mwSx4BA/r80c48IrBMvqVc5BHKomgvKysocKjGMUgX/2DQAwJEjR8QIyyXWVfQA/xAA0k/CsrKyHI5duXJF0hNl7M/HSkYBjuNEuVh2Owk7ffo0hg0b5nA8JCRE8m8WT7JOwqx75KTMPumS+l6MRUVFDlcm+fn5kv5AuHz5ss0QU2B9Eib15FGurIeW5DK0Z514ySUJs/9AlfLfoFAM0IQn2BwPSMgAABw6dMjnMbnKuooeEBAKQLyGcVcYDAaH+ML9IgA49uVJif3EgQi/EKfHfaFF64Q5a27cuXMnUlNTPRKUHMhxhpN90iX1JMz+alap4GOW8swb+yZVXTD/X28tldLeWb+H5fJ3aN2Yb/29lNlfYEt52RUhCQtM6GJzPDCpGwDg8OHDkj33WVdoggP5ZTWk3IIh7OUc5R9tOZYQzK/NeebMGVFicoV99S4+KMrpcV9wOwl7/PHH8eyzz2Lv3r1gGAb5+fn45ptvMHPmTEnMWvQV6ytwKTd6WpNbEmbfVxAe7Py4lNj3m+hkELNALkmMNTkmNHKM2T4RkOpinBzH4cCBAwAA/4SuNrdpIxKh9A9CVVUVTpw4IUZ4zbKumIfUJwbC5DcpOnXqFAAgKbiD5VjH4BQAjpMjpKK6utphZCJRxyeRFy5c8Hk8bidhL730Eu6//37ceOONMBqNGDZsGB577DH885//xDPPPOONGCXJemhP6r1VAvs4pf4BYF8JCw/i/yvlqpJ9CT4ktOG41JMcOfY5Wr+H5TK0Zx2nXBrz7YecpDrR5MKFCygpKQGj0sA/1nZkhmEUCEzkq2H79+8XI7wm1dXV2ay5FRLMr5Up5fOdkISlhDa81ikh/PcnT54UJabmnDhxAizLIkwbZDmWGsIvYWI/ocMX3E7CGIbBK6+8gpKSEhw7dgx79uzBlStXMH/+fG/EJ1lyPJHaJ2FSTx7tTz5hMkjC7EvwwaEAw/DDOVLu7ZAr62quHGb7ArazI+WwDRfgmHRJdaKJkFwFxGdAoVQ73B6Y1N3mflKSm5tr8x6ODEsEwFfXpXgBx3GcJdHqGNKQhCWFdISCUeDq1auSPOcJEzM6hzdU79LCEsGAQUFBgc/f2y3uKNdoNOjatSuuvfZa6HQ6T8YkC9aNqnI4kXIc51D5knolzL5kHKJzflwqDAaDQ2xKFZ+IAdItz8uZHHeusF6GwGAwyKKdwX44sqn9e8X0999/AwACk7s7vT0gqRsABhcuXJBcNc++ih4WHAcFo0R5ebkkk978/HyUl5dDpVAjIahhj2atUot4HZ9ASvGcJ0zMSA9PthzzV/khJYTfmUdYt9RXXF4xf8qUKS7d77///W+Lg5ET60X1pNykKnDW/yXlJMxgMDgsXBimYwBwku2REE44gTqgwmoyWXgUoC/ly+BDhw4VKbrmWV9tm81mKJVKEaNxjRx3rrD+8Oc4DgaDAaGhoeIF5AL7yTBS/BvU6/WWXi9dck+n91H5B8EvJgXVRRewd+9ejBs3zpchNsm+iq5SqRERnoQrxRdx5swZxMTEiBSZc0JTfmJQElQK21SiY0gKcsuzcfbsWVx//fVihOeUwWCwvM7dImyHq7tHdsIFfT4OHDiAm266yWcxuVwJW758ObZu3YqysjKUlpY2+tVeWM8WksPSHM76ZaSchAkVpQCrdXlD6pd70Ov1ktxTTSjNh0XYHg+v/1mKV4XWrJMYOWyXAtjGLJfhSLntAsFxnMOsXym2BOzduxcsy0IbkQh1/Ybdzug69AYA7Nq1y0eRucbZ+SEmMrXR28QmJGEdQjo63JZcf0y4j1QcOnQILMsiUReNcL9gm9t6RPHryB08eNCnVXWXK2FPPfUUvv32W2RlZWHy5Ml48MEHER4e7s3YJM064ZRD8uksCZNyI7Nwkg8LAirrw9SoGQT6caio5m/PyMgQMUJHwokyNALItZoMGV7/eXD27FlJV5ish9j1er3kqzOAbV+j1Gf7CuyHwa5cuYJOnTqJFE3zSkpKHKr9+fn5qK6ultTuJcK2eYEde6PWcBVsndV7o7wYCpUGKl0YglL74urfa5CZmYny8nIEBQU19pQ+U1NT43Tpp7ioNBw7/Yckm9yFeJODOzrc1qH+2Llz58BxnGQm/Qi9gD2iHP/e0kIT4a/SwmAw4OzZsz77fHG5Evbxxx+joKAAL7zwAtatW4ekpCRMnDgRGzdulGTToLdZn0il1lvgjNySMGGphzC782NEsO3tUsFxnOWqL8zu2kQXDKjU/IlWyktVWCdhcqmEyXF2pH1/j5TXgQKcLLrp5w+WZUWZzt+YiooKHDjArw/mH5uGCytewsXv51huv/j9HFxY8RLqjKXQhMZCE56Auro67N69W6yQbZw7dw61tbXw09r2V8dFXwOAH6qU0kWG2Wy2XHR2CElxuD0hKBFKRgm9Xi+Z/kHr5Ut6RKY53K5UKC1DlL6cuOFWY75Wq8V9992HzZs348SJE+jWrRv+9a9/oWPHjpLeosAbrE+kUj+JAs6HHqU8q1O4yooOsT0eFcrY3C4VV65cgV6vB8MAwWG2tzEMEFp/TGpxW5PjSu7WyaIUh6jtGY1Gh6qSFPurrNn3KjERfG+SlIaa9uzZg7q6Wj7BCmm+dyootR8A4M8///R2aC4Retli7So0YSGx8NPqUFNTI6kV6M+dO4eqqir4qfyREJTocLtaqbEkZ4cPH/Z1eE5duHABxcXF0ChUNk351oQhSckmYTYPVCjAMAw4jpPNrCRPsm5UlUqm3xRnMzilmoSxLGs58UeH2ZaxY+qrTFLrkRCSq6AQwNloY2i47f2kSI7LPVj3Y8qhLcBZ9UjK7wnAMQlTRMY4PS4mIZkK6tQfDJof+gpK7Q+A7/+RQgFBSMLioq6xOc4wCks1TEoLzO7duxcA0CWiGxSM8zSia2R3m/uKTZg52zUyFRony5cAQK/6JOzUqVM2s5i9ya0kzGQy4dtvv8Xo0aORnp6Oo0eP4qOPPkJ2dna7WqaioqLC5h+ovLxc8sM3zhIuqSZh2dnZqKyshFrVMPwoiI/gT7Dnz5+XVLVG6NkIb6QfWGjWl2Jvh8A6CZPS0EdTrNchkuKaRPacLQZ55swZya7Zx3GcZUFOgSKSX0TU/rhYKisrLZULXX2Fqzna8HhowuJQV1cnepLAcRyOHz8OAIiLdhwmE5Iw4T5i4zgO27dvBwD0junb6P2E2/bt2yeJRFf4d+5tl+haC/cPQXJQDDiOsyRt3uZyEvavf/0LcXFxePPNN3HrrbciJycHq1atwi233CKbDaw9xdnMIPvZQ1LjrBIm1fXNjh49CgCIiwAUCtur2qAAQOfP9yRI5UMAaFhbJjzK+e0R9VurnTt3ThInJGfkOBxp3Qogh7YAh82j/fxQW1srqSqHtdzcXP6C0+ocr4iKs9wmhZnh+/fvR21tLdTB0dCGOw6NNSYohU/Y/vrrL2+F5pL8/HyUlZVBqVAhKryjw+3xMekA+EqYFPqvT5w4gdzcXGiUGvSI6oWrlVdQXNXQF11cdRVXK68gLjAB8boE1NbWYuvWrSJGzJ8bTp48CQYMekZdgyuVpbhaVWa5/WpVGa5UlsLMmtEvlt9zdMeOHT6JzeXsacmSJQgODkZqaiq2b9+OJ554AnfddZfDV3vgLAmz32JHapz1y0g1CRNWNE6IdBxWYBgGiVGMzf3EVlpaaumPiYlzfp+AQCAomB9qleJq3YBtf5XUK7sC68VxCwoKJPEh1ZiKigrHakYC/4bx1VW3u4T3KhMVbznG+PmBCeevKoRGZzEJS00EpfZ1axaeLpWv1Ozfv1/U5XqE6nhUREeoVI7DZLGRnaBglCgpKZHEoq2//PILAGBA3HWoqqvCK9tnYt7Oly23z9v5Ml7ZPhNlplIMSRwOAPj1119F/dsUEqqM8A7gwOH/bfsAs/78xHL7rD8/wf/b9gFKqg0YGMdvbXXgwAGfrAHqchL28MMP44YbbkBoaChCQkIa/WoPnM1wk/KsN8B5wiXFRmaO4yyVsKRo5yfUxPpqk1SSsO3bt4PjOISGAywLVFoVuiqN/MKtLAvE1S8qLfZVYWOsh9ilUOFoDsuyNv2YFRUVkk4e9+3bx/fPBjdM+VUk8cnN3r17JZlACgmOIsl2BpwymW8gF7uKZDabsW/fPgCArmNvtx6rjUiCShcOk8nkWKH0IaGiLww72lOpNIiK4LfYEbudQa/XWxKaoUkjmr3/oIQhUCvUuHDhgmixcxyHTZs2AQAGxjvfScFavC4KHYJjYTabfXKudnmdsOXLl3sxDHlx1lwrpenazjgbApPisFh2djZKS0uhVAKx4YCzzyU+OeNw6tRJ0dcq4jgOGzZsAADEJQAbf7S9/ff1/H/H3AEkpwBnjvMfxsXFxYiIsFvVVWRyq4SVlpY6TCAoKipCcHBwI48Ql7COFZOcCO4Y/4HExMcBSgVyc3ORlZWF1NTUpp7CpwoLCy0XOsqUdJj3N8wkVKZ2Rl3mbuzduxcGg0G01/zEiRMwGo1Q+ungF+3ea8cwDHTJPVF2Yhv279+P6667zktRNk2ootvPjLQWE5mKoqsXcPbsWYwYMcJHkTn6/fffUVtbi+TgDkgJ7YSrlU33YQZqdOgfNxC783bi119/RdeuXX0UaYMTJ04gOzsbGqUa18V1R0Vt873QwxL74OsTv+G3337Dbbfd5tV1ztpXM5cHWM/cs3b27FlJb5viLOEymUySa8AWeqsSIgGV0vkbP1TH94XV1taJ3qx64sQJZGVlQalsqHQ1JjgUiIjir95/++03n8TnDuuJGlKdtGHN2axkqc5ULisrs6xJpUhpmB7PaNRgEhMAwHK1LhU//fQTAECR0AGKQNsF+xQR0WAiolFXV4f169eLER4AfnYjAAQkdgXTgt5kYY9JsYZVzWYzsrKyAMBS7XImOqIjACdrtvmQ9QXn0KQbXH6ccN8dO3aIMvoixDwwthsC1K5dsA9O6Am1QoWsrCyvz8SnJMxNOTk5KC8vh9rqD16jUMBoNEp6SLKxyobUqmHCsEBSFKCv4GCoaCiFGSo46Cs4cByQHMMnaL7ebNWe8EGV2BFQa5q/fyrfY4tffvlFUglwZWWlzQw9X03Pbg1nk2GkOkFm/fr1qKurAxMZASbCdjVfRRf+TbFx40bJ/D1euXLF0vuj6jHA6X1UPfnja9asEa1yKsw2DYhv2erm/nHpABjk5+eLsn1UUVERTCYTFAoVFFDCYGxocDcYr0JffgUsa0ZEGH+FJ+ZnzNmzZ5GdnQ21Qo0BcQNdflxqaCfEBsbBZDJZqsG+UlFRYZnJOTyp8Zmc9gLV/rg2jq/aefuCmZIwNwn9Sp3CGtYiSKtfl0C4TYrsT5IB9QPRUtp83Gw2W4Y/IkIYfPELiy83NiRhX27k8MUvLMqrgOT62YZi9nKUlJRYTiqdOrv2mIRkQOvHP1ZKe9fZX+1dvHhRkj1K1pztmiC1nRQA/oNg7dq1AABFD8fhGCYxHggLRWVlJX788UcfR+fcf//7Xz45iEmAItFxRXQAUKZ2ARMWifLycvzvf//zcYS2q7b7xzku7eAKpTYA2gi+EinGbGthod5gXSSWr56Br9e+aLnt67UvYtmq6SivKEFYCD+Bo6SkRLQq9ZYtWwDwS0/4qwNcfhzDMLguYYjNc/jKtm3bYDKZEK+LxDVhzQxV2BGStm3btnl1EhslYW4SkoR0Yc0Bq++l0ijujH2yFahxflxMZ8+eRUVFBbRqx/XB7AmVsHPnzol2Fb5hwwaYzWaERzUsxtochRJIqe+/FSoNUiAM6wAAFPzJXup9js4WOZXiwqerV6/mK1yhIWBSHYecGIaBsk9Py33F/ps8cuQI/vjjDwCAetDIRvthGIUC6kEjAQA///yzz4fKcnNzYTKZwKi00ITEtvh5tJH8v4kY7x1htmNQYNP9oX7aQGg0ATaP8aW6ujps27YNADAwfrDbj782fhAA/r3ly/iFIf7hie7NnAWAzmEdEBsYgerqaq9W8CgJc4PZbLZUXrpENmyNkVG/jcehQ4cku9K4/Yldp+bfkFIadhKS2MQox/XB7On8GYQF8X0KzhbA9DaO47B582YAQIqbF+Ed6+9/+PBhSfQwcRxnc5LR1l8w+mqdnJaoqalxWrnIzc1FSUmJCBE5p9frsWbtGgCAsl/vRvuWmNQOQHgYKisrsWrVKl+GaINlWXz66acAAGVGL8uaYI1RJnSEIqUzWJbFZ5995osQLYShOW14fIv6wQTaiESb5/MlYQg0wL/5lQV0AfzeZ2LsVXzgwAGUlZUhSBNkWQnfHRH+kbgmrDM4jrMk+N6WnZ2NU6dOQcEoMDihp9uPZxgG1yf0AgDLud4bKAlzw8GDB2EwGBCk0aJTaMNwZKewSARr/WA0GiW5BlRdXZ3Dli4aFZ/kSOkDS2iyF9YBa45wPzGSsFOnTiE/Px9KFT/E6I6AQCCq/sLdVyekply8eNFmvS3/+klmf/75p2SHJA8ePMj3sAVYNeJF8rt27NmzR6SoHP3www+oqqwCIsLBpDT+RmEYBsr+vQHwfYZiLRHy119/8RUhtRrq/kPBsSzYcj3Y8oaLNeFnrn4iknrgCEChQGZmpk97NIWt45raK3Ls2LFYunQpxo4dC4ZhUFfpeNEpPN56KzpfES6O/bSBzd7X34+fHCFG5f/3338HAAyIGwSlwuVFFWxYD0n64rwinFt7RqUhRNuyHX0GJ/QEA8arFTxKwtywevVqAMCQpBSbSg3DMBhS3zch3EdKLl686LC/5+li/gRq/eErJo7jLOvIxDlZpNWZ+Po8WIxeDqFyFJcAOFlfsVlJHfn/ir3OEgDLrD2BJhGAgu9XcbYwsRQIHwro2LBFAdMhyvY2kZWVleHnn38GACj79Wp2OIRJTgQTFQGTySRKNYzjOHz//fcAAFX3/mD8A8BVlMO08lPUrF5muV/N6mUwrfwUXAWfDCiCQqDM4CsGwuN9QdimShXU+FDehAkTkJSUhAkTJoDjONSWO1aR1PWPF2PbK6HXSO3CrD1tfR+WrxfZrqystJwjBtUnUi3RL/ZaqBVqZGdne33ol+M4SyV/UHyPFj9PpH+oZbNvb232TkmYiw4dOoRDhw5BySgwJrWLw+1jOnWBklHgyJEjkquGNTXkKJVFOa9cuQK9Xg8FA0SHuvaY2LCGvjBfLw8iVFvi3ayCCeLqd1c5d+6cKMML1uz3zlNoAG39AulSqioJLl++bEleFekNw2XMNbEAw+D48eOS6A1bvXo1v/1TZDi/NhjLgis3gitvmAEp/MyxLBiGgaIvn8ysW7fO53+bhw8f5pffUaqg6ubaHowCVY8BAMPgwIEDPusNEypCSr/GqxyrV69GTk4OVq9eDYZhoA5y3NxVeLzRaPT5eUSYkaxUNH8lp6y/2vP1rOp9+/ahtrYW0QExSApufBmN5vir/dEjin9/e/vi88KFC8jLy4NaoUTv6PRWPde1sfxkGkrCRGQ2m7FkyRIAwI0p1yAq0PGPPjIgEKPr1x/49NNPJdUbZj8U6eptviTMagsLanx9MHthQYBSwe9z6Mt9AwsLC5GbmwuGAaKbbplplNYPECbYipm0FxUVOa0k+nXk/+utE09rfP/992BZFkx8GJiIhvWrmEA/MJ34STIrVqwQKzwAdlWwvvVVsIpK1K1cA/PqdZb7mVevQ93KNUAFX91gkhIs1bAffvjBpzF/9913fLzp3cH4uz77DQAUwaFQpnS2eR5vE/Y3VagaXxtmw4YNeOyxx7BhwwZwHAdVgGPvFVP/eI7jfH7eFpI+hmn+o5ip/7j2daJ4+PBhAECP6N6tXrS0R3Rvm+f0FuH5u0akwl+lbdVz9Yvhlz85ffq0V6qQlIS54JdffsHFixehU2twV33Z3Zk7O/dEkEaL7OxsSc18a6rvSypJmLC+U7gbC28rFAxCg2wf7wvCTMLwSEDjwtpgjRH2mbSZmehjwpIIartk0q8TAAVw5swZ0RfEtXb58mXLuj1MP8elExR9+WOW3iaRrFq1CtXV1fy6YMmubyptXQ37+eeffbZ21eHDh/n3oUIBVc9rW/Qcqt78ivM7duzwSTVMpeJ7k7gmkhKh96jJHiS2oVVDqVR6JjgXNSQ1rvdI+bpPU1jRv1No0zOQ7Pvv9KYyh/t0CuOnhp87d86r/x9Ca0t6WNNDFfYxl1U79tuF+4cgwi+k0YXaW4uSsGYYjUZ8/fXXAIAJXXpDp2k8qw7UaPGPLr0BAF9//bXoU80FTSVhYg+FCYRKVkige1daoYG2j/cFoXLV0iqYwDoJs+/Z84Xc3FxLtUZn1zah9GcQUF/FX7JkiSjxOfPdd9/xi57Gh0GR4LguCBOuA5PGN1p/8803vg4PAF8ptaw270IvmD0mKQFMdBRMJpPl3ONNZrPZMrNRmdELiuDQFj2PIiIayk4Z4DgOn376qdeThYAAvlrHmlq3CrvZxFc3tFqtz5Mw4fdxXPPVLZbjq3RC8ukrQkN6VEDjEyAAx/674irHz5YI/0gwYFBTU+PV4XbhAiw1NKHJ+9nHfLXKeetOp/rnERJST6IkrBnffPMNDAYD4oNCMLKj8w1WrY3okIbE4FCUl5eL9iFgr6kEpaKiQhIbeQt/kDp/9x4X6MfYPN7b6urqLMuUxMS37rnCIvlV9svLy72+NYY9juPw3nvvoa6uDtqk+mZ8O0HXAoyGr4YJSYWYLl++bNmCRNG/8X0CFf3423bt2uXzahjHcfj4449RW1sLJi4WTFLTHwLOMAwDxUB+ocgNGzbgxIkTng7Txrp16/jXSaOFuq/7a0BZU/UfBihVOHz4sNc3P46K4idi1BpbN8O7rv7xwvP5kpBQsWzzFznCMKQvkzCO4yzFhCBtUJP3te+/i/B37L9TKVQIqJ9g4M1ZnsJnWnOzIu1jjmxkqZDg+ueh4Ugfy8rKsgzXPNC9H5QKBcwsiysVRlytbGiuvVppxJUKI8wsC6VCgQe79wcgzgKGzjQ360eMqdn2hD9IrZvDe34a28d728mTJ1FZWQmNFghrYn1F+zJ3tZNFrhUKILp+qQpf7123bds2HDt2DIwKCBkKOCvWKAMYBNfvafz111+LOomD4zgsWbLEUgVj4sMavS8TFmiphn3yySc+reJt3LgRf//9N6BQQDnk2hb30ChiY8CkdwLHcXj77be9tkp6QUEBli3jZz6qBwwD49/8UglNUQSHQtWHX5hz8eLFXm13iIvjS8k1Za1ba89Uxp//4uNbeVXVAor69c1cqYQJ91G0Yk00d9XU1FiSPz9l0zM47fvvQrShTu+nrX8eb87yFPoFtcqmJzzYxxzq5zzRFJ5HeF5PoiSsEWazGe+++y5YlkX/uCT0iuGvaEuqKvHc5rV46Y+GTWtf+mM9ntu8FiVV/Juqe3Qcro1PBsuyeO+990QfymluQVAp7LcnvLnVLjblC9Qq28d7m9DwGR3rPHER2Je5rXJ2G8KQpi+3X2JZ1lKl1fUBVMGN/48EdAHUkfwJU9h6Rww//PADP6OKYaAY3PxsJ8XANEClxPHjx/HFF1/4pI/mwoUL+OSTT/jf3683mLDQVj2fcmB/IDAAeXl5+OCDDzz+/8BxHP7v//4P1dXVUMQlQVnfStFaql7XgomIhsFgsExo8oZOnToBAEzFOS4lMY0xXeWXYUlNbby6Kg2+X7PPej9ZjbLpK2SX+u8AqOufx/q5PU1IVOuaqTC6GrPwPN5IgCkJa8Tq1atx5swZBKjUmNSCRtWHeg5AgFqDs2fPiroCtsFgaLZKJIW1oIRp1yo3WzKUCtvHe5vQExDRzMiFfZk7oJGquPA858+f99msp/379yMnJweMGghsZgkdhmGgq9/39pdffhFl37qffvoJS5cuBQAorksDExnEL/dgqAJX3hAPV17FH2NZMEH+UAznl5JZvXo1vvzyS68mYuXl5Xjttdf4bXQS4qDo1a3Vz8n4aaG8gS9Tbt261eNJ8K5du/gKrEIJ9dAxrZ75JmAUSmiG3QwwDLZt2+a1mXDJycnw8/MDW1MFU3Fei5+nqoBvtu7c2cUNYD1IOG8plM0PMSrqF0n15QxOIT4GTIsXabWnVnh/qY2ICH6YQm9q5OrXTWWmcpvn9SRJJ2H//ve/wTCMzVdGRobXf29ubi6++uorAMADPfojzM3p2gAQ5heAh3sMAAD873//Q3Z2tkdjdJUrv1es2KwJ1UJ3PweECxNfVRuF/jpdM7M47cvcfo30ugnPU11d7ZMh1ZqaGsvwU0BXQKFp/gX36wgog/lEY+XKlV6O0Na3335rqS4xvTuA6Vk/28lognnFX2C/b1jHjP1+D8wr/gKMJgCA4ppYKAZdY3meJUuWeCUR4zgO77zzDj+sr9NBOXKoxxIaRVwMFNfx7Q1Lly716ExVYWFpVc8BUIS4uPmpixSRsZYFXNesWePR5xaoVCr06MFfRVTmtaxvrtZYgpqyQigUCstz+ZLJxL9XXVknTFVfQRIe4wvWfy+e+tuxzAf14kVReDj/fi6u9sy2fCXVBpvn9SRJJ2EA0K1bNxQUFFi+vLmRJsC/MT744APU1taiR3QchiV3avFzDUlKQa+YeNTW1uKDDz7w+fouQMP6W629j7cJr4271V7hs85XSZhQQlc0U7FztczNMA3/D94+uZrNZnzwwQe4cOECFH6ArhfAsRzqDBzqrPK/unKgzsCBY/nYGQWD4IH8bd99953P1g775ptvsHz5cj6GfilQDExzO7lR9OoAxfV8hePHH3/Exx9/7PGT/6ZNm/gVxRUKqEYNB+PX/Orn7lB0ywCT2gFmsxn/+c9/PPI+uXr1Kp/QMQxUXfu49Bj7PkeusTH2eqpufAn177//9lr/T//+fIJqzMps0eONF/nHde7cGUFBTTeee4M7K+ZrNPyVnC8nUvnVv5c5cKgxe2b4sKqOr15rta1bv6spHTrwi8rmlLd+1jzHccgtv2zzvJ4k+SRMpVIhNjbW8hUZ6TjjwpM2bdqEI0eOQKNUYnKvga26omUYBpN7DYRWqcKxY8ewceNGD0bqmgsXLjR7n9zcXK+Oz7uiqUqY/cm/oqrhQ1TYPcpXCW5oaCgAOG20b4kaE8Bx/HtFeG5vqKmpwaJFi/gtfRggdCTfeG82ApdXAFetdpu5+j1/zGz1GevfiUFAV/6E9MYbb2DTpk1eixXgZ+wJ1WjFwDQoB3RqeZN79yQoRnR1eF5PqKiowOeff87/nv69wUR5friCYRgohw4CAgNQUFDgkfYGYbIOExgMJtC15MO+z5Erb3oJHkX99F+WZb3WoD9kCL+NTlXhOdRVlLn9+PIL/ISY66+/3pNhuUxIqLTq5qeFazUBNo/xhYCAAISE8DMGCypav8VdjdlkWbrCmxMhhP6+bEPrJm0AwJWqUlTVmaBWq5GUlNTq57Mn+STs7NmziI+PR2pqKh544AGvDp0ZDAbLCXVCRi9Eu3hyakpkgA4TuvBl+aVLl/p8hllzSViAik9gLl265KOInLNUwpx8ztqf/A1WF9W+Ho5MTuaHw0o9tLxaaf1anDExMdC0ZuXXJhQXF+PFF1/Etm3bAAUQdiPgl+x+QhNyPeCfzv9bvfPOO1i6dKlXXvc9e/Y0NLj3T4WiT8dWP6ciIx6KYXwrw4oVKzx2QbRu3Tp+GDk0BIoeXV1+nGNVqelKEaPRQDmQ30po7dq1rf4gtvQVuZHY2vc5MkFNj8lz/NUFAO/1/0RFRaFr164AOBjO/e3WY2uNpajK5/vBhg4d6oXommephKlcqITVJ2q+3DuSYRhLr9yxy63v7Ttx9Rg4cIiKivLK0J5AiPlMSTZMrazgHbvKf4ampaV5ZXkQSSdhAwcOxPLly7FhwwYsXrwYWVlZGDp0aJO9MyaTCQaDwebLVV9++SXKy8uRFByKMZ0c94dsqTGpGUgODoPRaMSXX37psedtjtlsbjYJi9PxbwGx99oTPsydDUfan/yDrVr0fF0J69eP/yAszOMrWK1VkGv7vJ529OhRTJ06FSdOnACjASJuAfzTWlZRYhQMQm+ApVF/1apVePnllz16YXHq1Cm88cYb/LZEGfFOV8VvKUXXRDB9OwIAPvjgA+zbt69Vz8dxnGXdMmWv7mDcGEt3rCo1n1QxqR2BkGAYjUbs2rWrpWEDaFjegTPqwZlda/S273NkGpttIqiu4ku9AGJjY1sVb1NGjhwJADCc5fsDVbowpN7/JjpOfM1yn44TX0Pq/W9CpWtY2qT83N8AOHTv3h0xMU0vROotwqxulbr5CzBN/ZClryfHDBs2DACwK+9PmNnWTQrYkc2vHTd8+HCP9U06k5qaipiYGNSwdTh+tfnRoKYcLOK3dRs0aJAnQnMg6SRs7NixuPvuu9GzZ0+MGTMGv/76K8rKyvD99983+piFCxciJCTE8uVq+TAvLw+//vorAOChHgOg8uBUVKVCgYd78k36GzZs8NmSEHl5eTCZTFA38V6PD+L/P8Vez8yyh5qT2+xP/oH+DffydU9Yv379oNVqYSwHSlpZDTObgdz6AqQwrOIpHMdhzZo1eOGFF1BaWgpVGBB1F6BNbN2Jj2EYBF/LIGwUwKiAzMxMTJ061en+k+66ePEiZs+ezc8wTIqAYmiGx0/UigGdwFwTC7PZjNfmz8fRo0db/Fy5ubl8M75SCSbFvZ3cHatKza/PxTAMFJ34pNR+03V3RURE8D1QHAeupOl1BAWu9jkK2Kt8P058fLylt8gbhg0bBqVSCdPVbJhK8sEolFAHR0Id1DA0rA6KgDo4EoxVM6fh7G4AwI033ui12JojVCSVTENcDu0XlWUAGmZH+rq3eNiwYQgLC0Nx1VXszmt5T3ZW2Xkcv3oUCoUC48aN82CEjhiGsSRNO3NbXsErqTbg+NUsAO00CbMXGhqK9PT0Jqs2s2bNgl6vt3y5uvzCqlWrwLIsesXEo2uU56/aMiJj0Cc2ESzLNplEepLwOsXqGv8gi5dIJaxhI1vH25o8+fu4EhYQEGAZushq5TZiuZeA2hogOjoavXv3bn1w9cxmM95//318+umnYFkW/mlA5F2AKtRzCY1/GlP/nHyT9/PPP48dO3a0+PkKCgrw0ksv8VXu6GAobuoBRun50xPDMFCM6AomKQI1JhPmzJnT4ve+pQIYGABG3fzsNmuOVSXXZmAzocG2v7uFrGeas0Wt7/Vxhr3MLxvh7RntISEhGDCAv8g1nHGtQlh9NRum4lyoVGrRhiIB6/New9+mQ/uFsb5/r/5k5+t1J7VaLSZOnAgA+PnsWpjq3J8YwnEcfjjFz6y+8cYbfbIw7tixYwEAB4pOId/o2oWGvQ1Zu2HmzOjevbulFcXTZJWEGY1GnD9/3lJKd0ar1SI4ONjmqzmVlZX4448/AADjr+nusXjt3ZbOP/fWrVt90lwpVLfigxufyhcfzL8FLly4IPqisi0hnLq8Wdq2N378eABAziWgqoXtGRwHnOP3mMWtt97qsT3rhH6tDRs2AAwQPBgIvRFQNFUObSF1OJ+IaTvwjf8LFy5s0czJiooKzJ49m2/ejtBBOa4PGLXney8EjFIBxZieQFwoKisrMWfOnBZtlB0WVj+0VW4E5+Ziwe5WlSyPu1ps+7tboXt3/nxkLvBOny2bn23ze7xp9OjRAAD9md3gXNgCSH+Kr+gMGnSdKLMiBQ17Rza8DxzaL3T8YoJs/YK0vt7fEuDPUbGxsdCbyrA56zfL8TC/cLw+/G3Mvf4Ny7G517+B14e/jTC/hp6vw5cP4lzpGWg0GkyaNMknMXfs2BHXXXcdOHBYf76hghfuF4x3RjyLhUP/ZTm2cOi/8M6IZxHu15AvlNdUYGs2v0/wvffe67U4JZ2EzZw5E9u3b8fFixexa9cu3HnnnVAqlbjvvvs8+nsyMzNhMpkQExiEzhHRLj/OcQf2psfq08IiEasLRk1NDQ4ePNjasJslXOEn6Br/Z470V0Cj5Hvp8vJavuBha7U0ifLBQugOMjIy0K1bN3BsQyLlrqJ8QF/KXzQIV2ye8PXXX2PLli0AA4TdBOh6Ml5NUBUaBuFjgIAMPgF888033R6aXLp0KT9EH6iF8pY+YLTuVZVaglEpoby5NxAWiOLiYnz44YduP0diYiKuueYagONg3vW311fl54pLwR7n9xi94YYbWv18Qh8im5MFrtazs6O5SiPYojyb3+NNAwcOREhICMyVehgvHWnyvmytydI/dvPNN3s9tqYICZWZa0gcHdovAkIBACwrzgbeAKDRaDB58mQAwKasX2Ew8b3WSoUSkQFRNvtERvhHIjIgCsr6oV8za8aa0/yM3rvuusune3QKucJfeUeQW79chVKhRFRAGCL9Qy33i/QPRVRAmCVmAPjp3A6YzLVIS0uzLIXiDZJOwnJzc3Hfffehc+fOmDhxIiIiIrBnzx6P/yMKzeudI6Ld+sBy2IG9mXVzGIZBRn2S58rSEa3BcRyysvix7LgmkjAFwyBJAn1hwuvOuvk5Jtzdl5UwoOGP+8KZhmqYfwAw5g5g1K0N9xt1K3/Mer1fjgNO1n9O3HrrrS5Va12xa9curFixAgAQOhzwT/HNa8IoGIQM4xd1raurw/z5811eksBgMFiWu1CM7AYm0P21gxxnGro2XMJoVVCO7gEw/IzMlvRqPvXUU1AoFODOXwR72HMLqdrjKqtQt3krYDZjwIABuO6661r9nGlpaUhISADMdTCfajpxcVfdiUMAxyEjI8OrTfkCtVqNMWPGAADKjv3R5H0N5/4Ga6pEbGws+vRxbY00b3G2gXdjVVJOxEoYwDfTp6enw2Q2YVPWry4/bl/BHhRVFCAoKMgyrOkrGRkZGDp0KDhw+O7U7y4/rqiiBFsu8VWwxx57zLsXsl57Zg9YuXIl8vPzYTKZkJubi5UrV1r2C/MkYYZKgJt9HQ47sDc3WwhAgNo3qx6XlZVBr9eDARAT2PQ/c0L9cKWYy1Q0lOXde5zQCubrq8P+/fujS5cuMJuBU/W93QoFEKiDzRZFATr+mPU8j/xsfmkKPz8/3H333R6JJy8vD4sWLQIABHYHAjLcP2nYJzNmN4ZaGQWD0JENPWLz5893aXg7Pz+fb07210CR0LIp6/YXQyh3fWiQCdcB4fw/WEuWv+nWrRueeOIJAAC77yDYM56/kOFqalC34Xeg3Ii4uDg8//zzHtnDjmEY/OMf/wAA1GbuAeehxe84YznqjvEfYJ56f7ti3LhxYBgGlXknYSp13ufGcZwlSfNkG0BLWT7cXTjxCUmZry84BQzD4KGHHgIA7Mj+AxU1zW8JxHIsfju/DgDwj3/8A4GBrdsgviUmT54MpVKJw1fO4mRxlkuP+eHMFpg5M/r16+f1RF3SSZivCIvRNTecaM9xB/bmF9wTfofwO71FWAU/KoBpdlNsoTlfzJXzhSTK3bY0sZIwhmHw6KOPAgAungMMZa49zmwGjtbv1X3XXXd5pLdH2DanqqoKmjgguIWTeOyTGbObuygpNAzCbwYYDXD8+HH89NNPzT4mOrp++L+qBpyhZUmA/cUQglyficeZagF9pW0sbrrzzjstyYb5z11gczw3rM+xLMy/bweKSxEaGorXX3/do+eOm266iW84rq5E7V+bWj2kynEcanb8BtTWIiMjw+OzfpsSGxtrmcFWenSL0/tUFZyBqTgHWq3WUjkTk2WR6ua24AAsS6CI2bs7YMAAdOrUCSazCVsvNV9ZOnL5EAorChAYGGjppfW1hIQE3HLLLQCAlac2W3rrGnOhLA97C47bnOO9iZIw8A18AHCxrMStx7Wkufainv8d3tj+wJowKzQuqPk/7rj64UgxN/IWtrCoc3OSY63Z9vG+1KNHDwwZMgQcBxw54Npjzp8CKo38HmSeKs0XFRXxW9Ao+JXwmWaS7sbYJzPKFvQrq0IZBNfvd79li/MPQmvh4eHo25dfeIzd17IqkuNMQ9ffC+zBLKCORUpKSquq7FOmTOHXq2I5mLdstzTQtwbHcTDv2A0urwB+fn6YP38+P3zoQSqVylJZM184DfOp1i3IWZe5B2zeRWg0GsycOdPnVZs77rgDAGA4sxtmk2MpV0jObrzxRo+1AbSGsFOJyoXNsVVKtc1jxMAwjOWCY+ul31FjbnxEh+M4bDj/CwC+6ihGFUzw4IMPwt/fH1n6fPxd0Pg+oxzH4bvTmwHw7xFvjLzZoyQM/LgxwzAorChHcaX3Zi2WVlUiv5zfUJRf5dl7hI2mowKaPwlGBfBvg8uXL3u9ubgx/v58FbG21r3fX1tn+3hfe+yxx6BSqXC5gF/AtSmmauDUMf77KVOmeCzmq1f5BcsUWkDZ/Ih4o+yTGaX7+9YDAFT1yzMJW+M0Z/LkyXwv19lCsDnuJy8tn2loAHeEv/CYMmVKqxIGhUKBGTNm8EuN1NahbsMf4Ax2pcTAAKjuvQvKCQ0VAeWE8VDdexcQ6Phis/sPgTt7HgqFAq+88grS09NbHF9T0tPT8cgjjwAAandtsVmyggkMgvbef0IzYbLlmGbCZGjv/afDdkfm3CzUHeBnof3rX//yyhYvzenZsyc6duwIrq4G5Wdt11KrNZZY9oq8/fbbfR6bPY7j3Ns7Uu37vSOdGTZsGGJjY2GsLcefOdsbvd/pkpPI0p+HWq22JMdiCQ0NtQy9rzm7FeZGZtAeL76Ak8UXoVap8PDDD/skNkrCAAQFBVmSop053muY/7P+ubt27er1q7DCQn7PrEj/5v+Jw/z4FWhqampQUuJeNdBThKukajcv8oT7i3WVFR8fbzmhHz/UdGvH6WNAXS3fEO3JBSLT0tKg1WrBVgHVrWhJamkyY6+iPtF0dWmC9PR03HorP5uB3XoCXJX3r/S52jqYtxwHOA7XX389rr322lY/p1qtxpw5c5CSkgJUVaHul83gKhqqMYxCASZIByaoIVMWfrZfbd98+BjYTP6FnDZtmkfia8rEiRP5oUPWDNPmteCM5ZaYFUEhUAQ1DIEKP1vHzOpLULPlZ4DjMGbMGI/O+HUHwzCW91LZaduFRfWn/wI4Fj169LCMfoipsrLSslirn7b5qyd/Pz7pdWcXGG9QKpW45557AACbLvyC2ka2BfrlHN+OMHbsWK9uUeSqO++8E8HBwSisKMZfeY4TUTiOww+n+X7Bcbfe6rNdFCgJqyes4PvL2eO4UtF8w6G7rlYasf4sP3tKGJ/2JiGZCvNr/upepWAQomVsHudrQm9UhXvLLaGy/v7e3Py6Offeey8CAgKgLwPyGuntrqrkZ1ICfNXFE43VAj8/P0yYMAEAULYDqLksTjUTAMoPcag+z1eG3FlK5rHHHkNiYiJQaYJ542Fwdd7re+FYDuyW40BpBcLDw/HMM8947LkDAwPxxhtv8ItRGo2o+3UzODe3mTGfOAX2b34Jm0cffdQnCQ3DMJg5cyafnFRVwLRxNbga1yYPcVWVqNnwA1BjQpcuXfD00097N9hmjBw5EhqNBrVlDZs3cxwHw2l+hXwp9IIBsMwgVqv8oFQ2PynM388zC/V6wujRoxEVFYUyUxl25jpWw84Un8KZklNQqVQ+nZzRlMDAQEsLyLrzfzr0hh0vvoAL+jxotVqvrgtmj5KweiNGjEBGRgYq62rxzp4/oDd5bn8ug6kab+/ZisraGqSnp1v2OvMmo5FPJAPsFum0nwGnr2Zt7idWqVtYdsTg5q/XV/AJR0ubqj0hODgYd911FwC+2uWskHTuJD+JoFu3bpYeKE+677770LNnT3A1QPE6oPqibxMxjuWg38WhvH4EaMqUKfwaWi7y8/PD3Llz+YpmoR7spqPgzJ7fBYFjObDbToC7eAVqtRqvvvqqxxP48PBwvPnmm/x7ukwP88Y/wLm4gTV74RLYv/iNqO+//36fTukPCAjAvHnzEBYWBq7kMmp+/wlcM03gXF0tTJtWgzOUISYmBnPnzvXaRvSuCgwMxODBg22OmYpzUGu4DK1Wi+uvv16kyGwJSVhggGsTLQL96yeQlZWJvrC2Wq22VMM2XvgVdXZ7Sv56/mcAfMIr5rnZ3q233gqdToeiyhIcuWK7U8aGLD5JHzt2rEcmTLmKkrB6SqUSr7zyCiIiIpBbrseCPzd5pCJ2tdKIBTs3IddQhvDwcLzyyis+mRYtJFP+dkmY/Qy44ir+w1pIwpraHN2bhP6RYoPryQPHcSipDzcxMdEbYbns9ttvh1arhb4UKL5se1ttLZBV//d+zz33eKVZWaPRYN68eXwiVguUbAD0uziwbvbYtUSdnkPxz0BFfYV/0qRJLbr6TU5Oxr///W9otFpw2VfB/nqIn73oIVydGezvR8GdKYBCocBLL73ktd7MmJgYLFy4EMHBweCuFMO89c9mh3nZy1dg3sYPoY0fP95nPSnWYmNj8dprr8HPzw9s3kXU7tjQaNwcy6Jmy8/gLhdAp9NhwYIFPv3waop9ElaRza8j079/f9H6R+0Jw4p+WtdmwPjVD0dyHCd6XxjAJ1hhYWEorS7BwaJ9luO55dk4WXwcCoXC5+uCNcff398yErUluyHmwopiHLlyDgzD+LxfkJIwK9HR0fjPf/6DqKgoFBgN+PeODbiktx2eC/cPwHuj78SbIxtW5Hxz5K14b/SdCPe3ba7N1pfi3zs2IL9cj8jISCxatMgnCxcCDRvDquz+he1nwEXUb4YtbNMn1hWWUDW5ogdq6zgE+QOPjlNg0piGhGXSGAaPjlMgqP4cWmIAamr5mZHe2tfLVcHBwRg1ahQA4MJZ29tysvhesMTERMsed94QEBCAN954wzIVvOIIcGUVXxXzxoQLro5D+QEOV1YBNYX8CW727Nm4//77W/ycPXv2xPzXXoO/vz+4vFKY1+4DV+LkYkinhfL+IVBMbFi0VDHxOijvHwLoHGdHcuVVMP90ANyFy1CpVHj55Ze9XhFJSkrCvHnzoFarwV3KBXuw8QVRucoqmDdvA8xmDBo0CE899ZRo60Glp6dj9uzZ/IzJc8dRd3S/0/vVHfgLbPZ5aDQavPbaa6L/DVrr37+/zetXkcdvbeHt3jp3CE35Wo1rM2CUChVUKq3NY8Wk0Whw2223AQD+zN5mOf5nDv/99ddf77PPO3cI58dTJQ3rYu4t4FuFBgwY4JN9La1REmYnISEB77//PlJSUqA3VWHBn5twvvSq5XalQoGoQJ3NwqyRATpEBeqgtOrzySorxoKdm1BWXYUOHTrg/fff9+lsIaHaZr8Cvf0MuBA/hc39xFq8MCYmBpGRkWBZIO8qoFAwCAlkEBzYcCINDuSPKRT8sUtFfNBdunQRfdFFoOGPu9Bu4fWL9VWwcePGebQXzBm1Wo2nn34a8+fPR0REBMwGvipWvB6oueKYiCl1QPT9QKTVBWvkRP5YYzMtOZZD5RkOl1cC5fsArg7o3bs3Pv30U49shty7d2+8/fbb9cN5lTCv2Qf2bKHNfRiFAkywP5ighqoGE+TPH7N7jdnsqzD/8DdwxYCgoCC88cYbPtu0uWvXrpg2bRofx6EjYAuKHO7DcRzM2/8CKquQnJyMF154QfT384ABA/DUU08BAOr2bQd71fb1N+ddQl0mP3wzY8YMdOvWzecxNiUwMBCpqamWn01X+GbNHj16iBWSg9r6IWql0vU1DoVtdcRcpsLaLbfcAqVSiUuGhkVQDxbwFSax1gVrTnR0tMP7dV8hv2zFiBEjfB4PJWFOREZG4u2330a3bt1QVVeLt3ZtsSwt4YpCowFv/fU7Kmtr0LVrV7zzzjs+3S8LgOXD3r6tprEZcOb6LEyskz/DMJb9uc7nuVa1OZ/P38+b1SV3pKSkOPRBGfVAWQn/uvqiF1Bw7bXX4vPPP8fEiROhUqlQkwdcXQ2UbOJQp294fRkFA1UwA5XViIgqCFAFM2AUtpUYjuNQfYnDlR+Asj8As5Hv5XvppZfw5ptvenQ2UVpaGj766CN+yYc6M9gtx2D+6zQ41vU+MY7jwB7IAvtrJmCqRXp6Oj766CP06tXLY3G64qabbuI3mK5Ptji7xfC4M+fA5eZDo9HglVdeQUBAC9cG8bDx48fzySrLonZfw8bsHMehds9WAPyHsCf2sfSGjIyMhh84M0JCQnxe5WiK5ULZrfe0uFsX2QsNDXXYV9HEmhATE+OTjdtbatiwYTY/F1WWQK1Se2Q7MHdREtYIocchIyMDlbU1eH/vNlTXNd+fYqqrw3t7t8FY34T/+uuvIyioBatetpLwOytc7AkS7qfTtWKhqVYShofO5HJgm9lEsqKKQ+4V28dJwfDhw21+zq+vivXt29fnMzgDAwPx6KOP4osvvsCNN94IhmFQfQG4/B2g38mBNbk+RFl7lUPxOqDkN6CuhH+fTJkyBUuXLsUNN9zglaGz0NBQvPHGG5ZZltzRHLAbj4CrbX7InDOzYLeesCwAO27cOLzzzjuiDY/861//QmRkJFBuBHu8YbFIrqYO5vqZkJMmTZLE0gkChmHwxBNPQKVSgStqWASPzbsEruQyAgICLJs6S1FKSorNz6mpqaIN8TojJNs1ta5NAmNZM2pq+RmrUulrA+B0V4TBgwd7verfGs425O7arasoSx1J91WSgICAAMydOxcRERHINxrw7fGDzT5m5fGDyCvXIzw8HP/+979Fu6oVGmT1Ln7QCvcTs7G2T58+CAkJQZUJuFjY9H1PXuLAcfxQpJT6Duwbgq/U/3+IcYUliI2NxQsvvIBPPvmErxqy/Fpel1cClWeb7hdja/lZj1dWAzX5/HDn3XffjeXLl+Oee+6Bn5/rWwS1hFKpxCOPPILZs2dDo9GAu3QV5l8Pgauta/QxnJkFu7mhAX/atGmYNm2aqLP2AgICLFugcMdPW46zZ84B1SYkJiaKvqClM9HR0Q4XOeaLfNOjVFadb4x9j5q3dylxl3BRVlHp2mb3ldUGABwUCoXXt71zh7OExtkxKUlISEBERITNsd69e4sSCyVhzQgPD8fMmTMBAFuyzuDElcazg1NXi7A5iz/Bzpw50+Ef2ZeExfFKq5svdVfXcaius32cGFQqlWUR02NZjcfNcRyO1S/BcNNNN/kkNlfFx8fb/LuX1LcTivUHbi01NRULFizAwoULkZSUBLYKKNvCDy2yTlpMaks4XF1TP+uRA4YOHYovvvgCjz32mM+ru0OHDsWbb77JX9QUlIHdcMTpEhYcx4HdetyyBMXcuXMtawCKbfjw4fwFg1U/D3eGbxicMGGCz/c/dZWwH6OAzb/k9LjU2F+cSWkoEmiIx2C8CjPb/ChLqb4AAN8/K5XhSACIiIiwea0ZhvH6jjCtxTAMunTpYnNMrJgpCXNB3759LdNav8jcgxonMwhrzWZ8kbkHAHDzzTejX79+Po3RnrDHXKGx+SSsoP4+YWFhou7vBTQspHg+HzBWOa/Q5BfzMyO1Wq3D8J/YGIZB586dLT9zHD9z0tN7/rVG3759sXjxYjz00ENQKBSoOgsU/2J7H1Men4DVlfIn2QULFmD27Nk+W0XamW7dumHhwoX1MydLwO4643Af7mAWuHNFUKlUmDt3rqgVSHtKpZLvDbNmKIdWqxWlIdhVDs3slUYoFArJf9DaV+mktF4VwF/w6nQ6cBwLk6kCk+9+Hw/d+Zbl9ofufAuT734fQYH8hfGVYj75lVpFD+B7OAUJCQmS6Wtsiv3raD2Rw5coCXPRY489hoiICBRVlOOX+pXvrf167gQKjAaEhYXh8ccfFyFCW8IbrMCNJEwKU8w7duyILl26gOOAE40sOHr0PH98+PDhoieNztj/MUutFwXghxUffPBBvP322wgNDYXZaiWW2qtAya8Nsx4tw5gSkJGRgVmzZgEAuOO5YPMbAudKjWD387O0pk2bJpmYrTnrX+zXr5+kP7QiIiIcJhalpKRIqi/JGfu/OamsYSZgGMYyeaDwynmEBEUhWBdpuT1YF4mQoCgo6mdEFlzmLzqsL/KkwnqdRjH2DG0J68+70NBQ0YbWKQlzUWBgIJ544gkAwPqzx2EwNeyvYzSZsK4+MXviiSdEbW4XCA2++eWsZeZjY3INfGVPCkkYAMs2LUezHPuVTDUczuRyNveTGvuFY6V8UurWrRv+85//2JyAyrYBnJlPDubPny/qllDODBw40DLEyB24aDnOHrwIcBwGDx4sma1p7HXo0IFv0LcixWTRXqdOnWx+dmc3BKmQUh+VoGfPngCAnHzHC3trLMsip4Cf0OHr2b2usK6Qi1ktd4d124iYrUOUhLlh+PDhSE9Ph8lch9+zGoZCfr94BtV1tUhLS5PMsEJcXBwCAgJQywKXK5uuhl3U87enp6f7IrRmDR8+HP7+/tAb+aFHa6dzONSZ+YTRfkxfKuxPQlLrRbGXnJyMqVOnWn426/kPrJdeekn0LWga88ADD/B9MZetlo65xE+XFWOleVcxDOMwdV/4IJYy+5mG9kmZHEixai60rWQXHEddXeNrfxVeOYdqkxGBgYGSrIRZVxmldtHWGOs4xUzQKQlzA8Mwlv2ydlxq2Hdqe/1soXvuuUcy03IVCoUlqcoxNJ6EmVkOl/R8JUwqf9x+fn6WxTRPZ9tWwk7W/zx69GjJDfEJ7HtPpNaL4szAgQNtfr7lllskPfMtIiICffr0sT3I8cmBfcIgNdbxabVaySfpgGM1VypVc3d4eyZvS3Tq1AmRkZGoqzPhUv7RRu93PpvftWDAgAGSnMBhPVFHjCWZWsI6TjEvNqWRMcjIoEGDEBkZCWNtw1VLeW0NwsPDHZYnEJuQVDWVhF2uZFFj5qfQi73/ojVhAchzDcsTwVjFIa9+bTCpVBydsb/iFrPU7Sr7hFYKszmbY584NnZMaqyTrri4OMlcuDXFfqZhXFycSJG0nFqtFjsEBwzDWPoEz2X97fQ+HMfhbP1tUloT0Zp1T6PUewUFWm3D9mbe2NbNVdL/65cYpzOcwFdmpHaFIjR95hgaX9xSSNDS09MlNe25V69eCAoKgvX+zRcL+D+Uzp07S7q6JPWGYFfIoTrjbAZy3759RYjEPVLpRXGH/XtYzKVsWkqqlXMhsTqfc9Dp3r1Xii/CYLwCrVYr2f5B64RGihVHZ6TyfqAkrAWcVWGktlQC0JCEFVU0nuVn1/eD2WzxIQFKpdLhhHOpfts9KS074Aq59EhYk/JQpMB+TTa1Wi2597EzUulFcYf9+0GKVSW56tq1K8LDw1FTU4ncwhMOt5+7xO/F2L9/f8kmONbDeVK6mG+KdeIoZsyUhLVAhw4dbCoxERERoq0x0pTw8HCHmVj28o3S6gezZj8kliehhU/dYf3HLhdyiNl6ij/Az9iTQ3JgvdyDVCc+2JNalb8tUSqVlgvLrNxMh9vPXzoAQLpDkYBtki6Hv0HAthImZlWMkrAWsF8RuGvXrpIpbdoTmoBvT9dg3rCGcft5wwKwYHgALtdXyaS0Z53AfhaZqZb/A5fb9HipvjesabVaPPnkk5af5RAzYLsmmxQvhJyxTrzk0A8mV1qt1nGhWYkSdh+4lHvE5rjBeAXFZblQKBSSHYoEbBMvuZw7pILOAC1kPVtIyutACcmV3sQhwr/hnzvCXwEWgJnjx/CltP+iID4+3mERy9TUVNlcackJwzCyfF2tLx6kuJJ4WyGHyqg9hmFkMzTWq1cvaDQaGCtLbI5fyuVnTHbp0kXSsw7pYqLl6JVrIeshBSk3iQsN1iVVjjMkS+q3BZLqDC2GYRw+WKW+/ADxLevh9uaG3qVIzFlZ7pBrdUMur69Wq0W3bt0cjmfnHwPgfBIKaRuk98krE9Zj4FJuYhZmMelNjicj4ZiUZ+/Z77ko5aqjQKvVynIdJaHXTk6JrhzXJyLEGfteV47jkFd4EoA0V8knnkHdli0klyRMmIllcJKElcsgCbNfj0gOSycwDCPJymJzEhMTsWTJEkm/H+xZr0kkl/WJrMm1wiQXcnp97Xtg9cbLqKw2QK1WS2Y3E1fI6TWXAkrCWsh6qrCU+yWE2OqcrNdaV7+npJR7gew3Dpby0G9bIKcqGGDb5E4nf2JPLsORAD+7l2EYS8xFV/nN6NPS0mQzi5a4T36X6xJhnbhIOYmRO/vFLOWyuCXxDevGa0rCiJzZtzFcLc0BwCdhciKnxFcKKAlrIet1c6S8ho7wB9HU55OU/2jsF7OU8tAv8T1KvEhbYt3zWqYvAECzfts6SsJayDrxknIlrKqqCgCgdTJTW6tibO4jRfZ7ksllyjnxDTn23hHfkVuSbt1uYawsdTgmB3J7zcVGZ7AWsk4GpJwYVFRUAAD8VY5/GMIx4T5SZN17J6cPXDoR+Yb1356UK7qEuMK60l9t4s/LctnaKjY2Fmq1WjaLJkuFdMfRJM765C/l5KCyshJAI0mY2vY+UiTlod6mUELgG1LZeoQQT7Ce5FVXVwNAPhtif/jhh6iqqpJN0igV0s0erHz88cfo2LEj/Pz8MHDgQPz9999ih2STeEn55C8MNfo5yWX8lHzc1dXVvgzJLdbJjJRfZyIOhUJhGaaW27ANIfZszndOjklZSEiIJHdekTrJlxm+++47zJgxA0uWLMHAgQPx/vvvY8yYMTh9+jSddF0gJFgaJ5UwjQySMLPZbPleLicj4luffPIJampqoNPpxA6FkFapq6uzfK9QKB2OEe8Q87NF8pWwd999F48//jgmT56Mrl27YsmSJQgICMB///tfsUOzkHJyICQxSidFJKXC9j5SVFNTY/leyq8zEU98fLwkN6AnxF21tbWW75VKfm0w63Mg8Q4xR1kknYTV1NTgwIEDGDVqlOWYQqHAqFGjsHv3bqePMZlMMBgMNl/eJuVhMqGfwOTkYspUx9ncR4qsrwKlnCwSQkhrsWzDqtoKBeNwjLQ9kk7Crl69CrPZjJiYGJvjMTExKCwsdPqYhQsXIiQkxPLli70GpVyhEbZyqTY7xmgy295HiqxfWym/zoQQ6ZHbOcN6wpdlFEPCs+9J60k6CWuJWbNmQa/XW75ycnLEDklUwt6RpVUcNErgozE6fDRGB40SKKnir7CkPJslMDDQ8j2djAghbZlwLu6Y0AumGn6JCutzIGl7JN2YHxkZCaVSiaKiIpvjRUVFjc7C0Gq1kt7L0deE1ZYvV7KoYxsWaAWA/HI+CZNyP431Yq2UhBFC3CHlVhFnhMlmpYYC1NTyM9sjIyPFDIl4maQrYRqNBv369cOWLVssx1iWxZYtWzBo0CARI5OPiIgI6HQ6sBxQYLTtLcgzSj8Js+5Xk9vQAiGEuCM+Ph4AoC+/DIAfyaBKWNsm6SQMAGbMmIHPP/8cX375JU6ePImnnnoKFRUVmDx5stihyQLDMLjmmmsAAGdLGhrba8wcssr4n4Xbpcj6SlZOSZicYiWESENcXBw0Go3lZylfILcltERFE+655x68/fbbmDNnDnr37o3MzExs2LDBoVnf1wICAqDT6RAYGIigoCBRY2lOnz59AACnihuSsPOlZtSxfKXMF5MXPEFuQwuENKZfv34AgJEjR4ociesSExMBAD169BA5EtfJ7WJIqVTabNhNWwD5hpifLZLuCRM8/fTTePrpp8UOw4ZKpcJnn30GADZXLlLUq1cvAMDp4jqYWQ5KBYOTV/mErHfv3pTceAH1r5GmvPzyy8jPz5d0Fdre888/jxUrVmDKlClih+IyOZ7bOnTogLNnzwIAkpOTRY6mfRBz60HJV8KkLCIiAhEREWKH0axrrrkGOp0OVXVAVhnfB3b8Cr/+llAlkwM5Najec889AICbb75Z5EiIFOl0OqSnp8sqScjIyMBrr70mqyGyO+64AwAwYsQIUeNwR0JCgtPviefdcsstYBgGd999t2gxyKISRlpHqVSib9++2LFjB45frUN0IINsA5+MCcMiUvb0009j2bJlePzxx8UOxWXDhw9HQkKCzdACIcS3rrvuOnz44Yey+juMiopy+j3xvGeeeQYPP/wwwsLCRIuBkrB2on///tixYwdOXqlDvI4vgKampiI8PFzkyJo3fvx43HrrrbKqGlhPiCCEiINhGHTu3FnsMNzSo0cP6HQ6xMbG0v7IXqZQKERNwABKwtoNoZn2koG19IP17NlTzJDcIqcEjBBCWio2Nhbff/89FAoFnffaAeoJayfi4uIQFhaGOhb4M4ffJLZbt24iR0UIIcSeUqmkBKydoCSsnWAYBl26dLE5Zv8zIYQQQnyHkrB2xLo5NSAgQFazDQkhhJC2hpKwdsR6UdakpCQqdxNCCCEioiSsHcnIyLAkXl27dhU5GkIIIaR9o9mR7UhCQgKWLl2K0tJSZGRkiB0OIYQQ0q5REtbOJCYmWvaAI4QQQoh4aDiSEEIIIUQElIQRQgghhIiAkjBCCCGEEBFQEkYIIYQQIgJKwgghhBBCREBJGCGEEEKICCgJI4QQQggRASVhhBBCCCEioCSMEEIIIUQElIQRQgghhIigzW9bxHEcAMBgMIgcCSGEEELaOiHfEPKPprT5JKy8vBwAkJSUJHIkhBBCCGkvysvLERIS0uR9GM6VVE3GWJZFfn4+goKCwDCMR5/bYDAgKSkJOTk5CA4O9uhzewvF7BsUs+/IMW6K2TcoZt+RY9zeipnjOJSXlyM+Ph4KRdNdX22+EqZQKJCYmOjV3xEcHCybN52AYvYNitl35Bg3xewbFLPvyDFub8TcXAVMQI35hBBCCCEioCSMEEIIIUQElIS1glarxdy5c6HVasUOxWUUs29QzL4jx7gpZt+gmH1HjnFLIeY235hPCCGEECJFVAkjhBBCCBEBJWGEEEIIISKgJIwQQgghRASUhBFCCCGEiICSsBbYsWMHxo8fj/j4eDAMgx9//FHskJq0cOFCDBgwAEFBQYiOjsYdd9yB06dPix1WsxYvXoyePXtaFtIbNGgQfvvtN7HDctmbb74JhmEwffp0sUNp0r///W8wDGPzlZGRIXZYzcrLy8ODDz6IiIgI+Pv7o0ePHti/f7/YYTWpY8eODq81wzCYOnWq2KE1ymw249VXX0VKSgr8/f3RqVMnzJ8/36V98cRUXl6O6dOno0OHDvD398fgwYOxb98+scOyaO5zhOM4zJkzB3FxcfD398eoUaNw9uxZcYKt11zMa9aswU033YSIiAgwDIPMzExR4rTXVNy1tbV48cUX0aNHDwQGBiI+Ph4PP/ww8vPzfRIbJWEtUFFRgV69euHjjz8WOxSXbN++HVOnTsWePXuwefNm1NbW4qabbkJFRYXYoTUpMTERb775Jg4cOID9+/dj5MiRuP3223H8+HGxQ2vWvn378Omnn6Jnz55ih+KSbt26oaCgwPK1c+dOsUNqUmlpKYYMGQK1Wo3ffvsNJ06cwDvvvIOwsDCxQ2vSvn37bF7nzZs3AwDuvvtukSNr3FtvvYXFixfjo48+wsmTJ/HWW29h0aJF+L//+z+xQ2vSY489hs2bN+Prr7/G0aNHcdNNN2HUqFHIy8sTOzQAzX+OLFq0CB9++CGWLFmCvXv3IjAwEGPGjEF1dbWPI23QXMwVFRW4/vrr8dZbb/k4sqY1FXdlZSUOHjyIV199FQcPHsSaNWtw+vRp3Hbbbb4JjiOtAoBbu3at2GG45fLlyxwAbvv27WKH4rawsDBu6dKlYofRpPLycu6aa67hNm/ezA0fPpx79tlnxQ6pSXPnzuV69eoldhhuefHFF7nrr79e7DBa7dlnn+U6derEsSwrdiiNGjduHDdlyhSbY3fddRf3wAMPiBRR8yorKzmlUsmtX7/e5njfvn25V155RaSoGmf/OcKyLBcbG8v95z//sRwrKyvjtFot9+2334oQoaOmPvuysrI4ANyhQ4d8GpMrXPnM/vvvvzkA3KVLl7weD1XC2iG9Xg8ACA8PFzkS15nNZqxcuRIVFRUYNGiQ2OE0aerUqRg3bhxGjRoldiguO3v2LOLj45GamooHHngA2dnZYofUpJ9//hn9+/fH3XffjejoaPTp0weff/652GG5paamBv/73/8wZcoUMAwjdjiNGjx4MLZs2YIzZ84AAA4fPoydO3di7NixIkfWuLq6OpjNZvj5+dkc9/f3l3yVFwCysrJQWFhocw4JCQnBwIEDsXv3bhEjax/0ej0YhkFoaKjXf1eb38Cb2GJZFtOnT8eQIUPQvXt3scNp1tGjRzFo0CBUV1dDp9Nh7dq16Nq1q9hhNWrlypU4ePCgpHpPmjNw4EAsX74cnTt3RkFBAebNm4ehQ4fi2LFjCAoKEjs8py5cuIDFixdjxowZePnll7Fv3z5MmzYNGo0GkyZNEjs8l/z4448oKyvDI488InYoTXrppZdgMBiQkZEBpVIJs9mM119/HQ888IDYoTUqKCgIgwYNwvz589GlSxfExMTg22+/xe7du5GWliZ2eM0qLCwEAMTExNgcj4mJsdxGvKO6uhovvvgi7rvvPp9sRE5JWDszdepUHDt2TBZXgwDQuXNnZGZmQq/X44cffsCkSZOwfft2SSZiOTk5ePbZZ7F582aHK3Aps65o9OzZEwMHDkSHDh3w/fff49FHHxUxssaxLIv+/fvjjTfeAAD06dMHx44dw5IlS2SThH3xxRcYO3Ys4uPjxQ6lSd9//z2++eYbrFixAt26dUNmZiamT5+O+Ph4Sb/WX3/9NaZMmYKEhAQolUr07dsX9913Hw4cOCB2aESiamtrMXHiRHAch8WLF/vkd9JwZDvy9NNPY/369di6dSsSExPFDsclGo0GaWlp6NevHxYuXIhevXrhgw8+EDsspw4cOIDLly+jb9++UKlUUKlU2L59Oz788EOoVCqYzWaxQ3RJaGgo0tPTce7cObFDaVRcXJxDIt6lSxfJD6MKLl26hN9//x2PPfaY2KE06/nnn8dLL72Ee++9Fz169MBDDz2E5557DgsXLhQ7tCZ16tQJ27dvh9FoRE5ODv7++2/U1tYiNTVV7NCaFRsbCwAoKiqyOV5UVGS5jXiWkIBdunQJmzdv9kkVDKAkrF3gOA5PP/001q5diz/++AMpKSlih9RiLMvCZDKJHYZTN954I44ePYrMzEzLV//+/fHAAw8gMzMTSqVS7BBdYjQacf78ecTFxYkdSqOGDBnisMzKmTNn0KFDB5Eics+yZcsQHR2NcePGiR1KsyorK6FQ2H5UKJVKsCwrUkTuCQwMRFxcHEpLS7Fx40bcfvvtYofUrJSUFMTGxmLLli2WYwaDAXv37pV8T6wcCQnY2bNn8fvvvyMiIsJnv5uGI1vAaDTaVAmysrKQmZmJ8PBwJCcnixiZc1OnTsWKFSvw008/ISgoyNJTEBISAn9/f5Gja9ysWbMwduxYJCcno7y8HCtWrMC2bduwceNGsUNzKigoyKHPLjAwEBEREZLuv5s5cybGjx+PDh06ID8/H3PnzoVSqcR9990ndmiNeu655zB48GC88cYbmDhxIv7++2989tln+Oyzz8QOrVksy2LZsmWYNGkSVCrpn4LHjx+P119/HcnJyejWrRsOHTqEd999F1OmTBE7tCZt3LgRHMehc+fOOHfuHJ5//nlkZGRg8uTJYocGoPnPkenTp2PBggW45pprkJKSgldffRXx8fG44447JBtzSUkJsrOzLWtsCRdKsbGxolbwmoo7Li4O//jHP3Dw4EGsX78eZrPZ8hkZHh4OjUbj3eC8Pv+yDdq6dSsHwOFr0qRJYofmlLNYAXDLli0TO7QmTZkyhevQoQOn0Wi4qKgo7sYbb+Q2bdokdlhukcMSFffccw8XFxfHaTQaLiEhgbvnnnu4c+fOiR1Ws9atW8d1796d02q1XEZGBvfZZ5+JHZJLNm7cyAHgTp8+LXYoLjEYDNyzzz7LJScnc35+flxqair3yiuvcCaTSezQmvTdd99xqampnEaj4WJjY7mpU6dyZWVlYodl0dznCMuy3KuvvsrFxMRwWq2Wu/HGG0V/zzQX87Jly5zePnfuXMnGLSyn4exr69atXo+N4TiJL3tMCCGEENIGUU8YIYQQQogIKAkjhBBCCBEBJWGEEEIIISKgJIwQQgghRASUhBFCCCGEiICSMEIIIYQQEVASRgghhBAiAkrCCCGEEEJEQEkYIaTNunLlCp566ikkJydDq9UiNjYWY8aMwV9//QUAYBgGP/74o9vP27FjR7z//vueDZYQ0u5If+MyQghpoQkTJqCmpgZffvklUlNTUVRUhC1btqC4uFjs0AghBLRtESGkTSorK0NYWBi2bduG4cOHO9zesWNHXLp0yfJzhw4dcPHiRZw/fx4zZszAnj17UFFRgS5dumDhwoUYNWoUAGDEiBHYvn27zXMJp9GdO3di1qxZ2L9/PyIjI3HnnXdi4cKFCAwM9OL/KSFErmg4khDSJul0Ouh0Ovz4448wmUwOt+/btw8AsGzZMhQUFFh+NhqNuOWWW7BlyxYcOnQIN998M8aPH4/s7GwAwJo1a5CYmIjXXnsNBQUFKCgoAACcP38eN998MyZMmIAjR47gu+++w86dO/H000/76P+YECI3VAkjhLRZq1evxuOPP46qqir07dsXw4cPx7333ouePXsC4HvC1q5dizvuuKPJ5+nevTuefPJJS0LVsWNHTJ8+HdOnT7fc57HHHoNSqcSnn35qObZz504MHz4cFRUV8PPz8/j/HyFE3qgSRghpsyZMmID8/Hz8/PPPuPnmm7Ft2zb07dsXy5cvb/QxRqMRM2fORJcuXRAaGgqdToeTJ09aKmGNOXz4MJYvX26pwOl0OowZMwYsyyIrK8vD/2eEkLaAGvMJIW2an58fRo8ejdGjR+PVV1/FY489hrlz5+KRRx5xev+ZM2di8+bNePvtt5GWlgZ/f3/84x//QE1NTZO/x2g04p///CemTZvmcFtycrIn/lcIIW0MJWGEkHala9eulmUp1Go1zGazze1//fUXHnnkEdx5550A+OTq4sWLNvfRaDQOj+vbty9OnDiBtLQ0r8VOCGlbaDiSENImFRcXY+TIkfjf//6HI0eOICsrC6tWrcKiRYtw++23A+B7u7Zs2YLCwkKUlpYCAK655hqsWbMGmZmZOHz4MO6//36wLGvz3B07dsSOHTuQl5eHq1evAgBefPFF7Nq1C08//TQyMzNx9uxZ/PTTT9SYTwhpFCVhhJA2SafTYeDAgXjvvfcwbNgwdO/eHa+++ioef/xxfPTRRwCAd955B5s3b0ZSUhL69OkDAHj33XcRFhaGwYMHY/z48RgzZgz69u1r89yvvfYaLl68iE6dOiEqKgoA0LNnT2zfvh1nzpzB0KFD0adPH8yZMwfx8fG+/R8nhMgGzY4khBBCCBEBVcIIIYQQQkRASRghhBBCiAgoCSOEEEIIEQElYYQQQgghIqAkjBBCCCFEBJSEEUIIIYSIgJIwQgghhBARUBJGCCGEECICSsIIIYQQQkRASRghhBBCiAgoCSOEEEIIEQElYYQQQgghIvj//5rsAxwImkoAAAAASUVORK5CYII=",
+ "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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Y7777Dg0bNkTXrl3Ro0cPdO7c2ZjsUcWYdtfK2HUrY8xE1nCsK1liOed4an7mlWpRCw8Px7p164pt//jjjyv8Xr169UKvXr1K3a/RaDBt2jRMmzat1GMCAgKwdOnSCv9ue2MB63j8zMnZsFImUp+adUu5W9RycnIq9MYVPZ6IyB5kT95lj5+I7k65E7V69erhgw8+wJUrV0o9RlEUxMfHo3v37vjkk09sEiBRRbBSI2fDFjWie1u5uz63bt2Kt956C++++y5atGiBtm3bIjQ0FFWqVMGNGzdw8uRJ7Nq1C66urpg0aRJeeeUVe8ZNdsJEh0gsMl6TTC7tS8Zzgiqv3Inafffdh1WrViEhIQHff/89/vjjD+zcuRO3bt1CUFCQ8fFP3bt3h4uLiz1jJioVKwgi9TGRILKdCk8mCA8Px7hx4zBu3Dh7xEMqkz3RYQVBRETOpFLLcxARkWPIfvNERHeHiRo5FVZq5GzYSkx0b2OiRqVi0kNERKQuJmpkRva7dyaXRFQZspd95LyYqJEZ2RMdFrZERGRrUjxC6ujRo+V+0+bNm1cqGKK7xUSNSH0y3vDJGDPdG8qdqLVs2RIajabUk7lon0ajgU6ns1mARBXBwpZIfbxhIrKdcidqFy9etGccJCAmPUTqk/E6lDFmImvUvPkod6IWERFhzzhIELLfCbOCIFKfjOWIjDHTvaHCTyYwdfLkSSQkJCA/P99s++OPP35XQRFVFgtbIiJyJpVK1C5cuIAnnngCx44dMxu3VlRJcowaERHJhK3xJKpKLc8xatQoREZGIjU1FV5eXjhx4gS2b9+Otm3bYuvWrTYOkYiIiEg9UizPYWrXrl3YvHkzgoKCoNVqodVq0blzZ8yaNQuvv/46Dh06ZOs4iYjuSezOJ1KfmtdhpVrUdDodfHx8AABBQUFISkoCYJhwcPr0adtFRw7H5n8isfCaJLq3VapFrWnTpjhy5AgiIyPRvn17zJkzB+7u7li4cCHq1q1r6xiJyo2VGhEROZNKJWrvvPMOcnJyAADTpk1Dr1690KVLFwQGBmLFihU2DVBmTBocj91EROpj2UdkO5VK1GJjY43f16tXD3/99RfS0tJQrVo1VpRERDYkY5kqY8xEoqrUGLVvv/3W2KJWJCAggBenE+D/IRERkTgqlaiNGTMGwcHBeP755/Hrr79y3bRSMOkhorslYzeijDGTfcleH6p5TlcqUbty5QqWL18OjUaDZ555BjVr1kRcXBx27txp6/iIiEgyslfKRJakW57D1dUVvXr1wnfffYfU1FR8/PHHuHTpEh5++GFERUXZOkaicmMFQZbYuuN4/MyJbOeunvUJAF5eXoiNjcWNGzdw+fJlnDp1yhZxkUpMC1gWtkREZAusTyqvUi1qAHDz5k1899136NGjB2rVqoW5c+fiiSeewIkTJ2wZH1GFsDAgSzwniEhmlWpR69+/P9atWwcvLy8888wzmDx5MqKjo20dGxEREdE9rVKJmouLC1auXInY2Fi4uLjYOiaiSmPrCZH6OFaUnI1Usz4LCgqQnJyM+vXrM0kj4bCCIEuynxOyx0/kDKSa9enm5oajR4/aIxYiIiJVMCG2L36+lVepyQQDBw7E//73P1vHQkREREQmKjVGrbCwEF999RV+//13tGnTBt7e3mb7P/roI5sER0R0r+O4S8fg50zWqNkiWKlE7fjx42jdujUA4MyZM2b72LxJREREzkTNRL5SidqWLVtsHQcJSMY7TBljJiJydiybK6/SC94CwLlz57Bx40bcunULAP8jSH1s0SUiImdSqUTt+vXr6Nq1Kxo0aIAePXrgypUrAIChQ4di3LhxNg2QiIiI6F5VqURtzJgxcHNzQ0JCAry8vIzbn332WWzYsMFmwZG6ZGwhZYsaWZL9nJA9fiK6O5Uao/bbb79h48aNqF27ttn2+vXr4/LlyzYJjIiI5LxhIiLbqVSLWk5OjllLWpG0tDR4eHjcdVBElcVKjZwNW9TIGch+Hkv1ZAIA6NKlC77++mvjzxqNBnq9HnPmzMHDDz9ss+CIiEg+vGEiZyPd8hxz5sxB165dsX//fuTn5+PNN9/EiRMnkJaWhj///NPWMZJKZCxsZYyZyBoZz2nZW0+IRFKpFrWmTZvizJkz6Ny5M/r06YOcnBw8+eSTOHToEKKiomwdI6mEFQSR+nhOE93bKtWilpCQgLCwMLz99tsl7gsPD7/rwJyBjIkOEYlFxnJExpiJRFWpFrXIyEhcvXq12Pbr168jMjLyroMiIrIVJg1E6pP9OlQz/kolaoqilNgcn52djSpVqtx1UCQGGS8sdhMRkbOTsWyWnTQPZR87diwAQ8CTJ082W6JDp9Nhz549aNmyZaUC+eCDDzBp0iSMGjUKc+fOBQDk5uZi3LhxWL58OfLy8hAbG4vPP/8cwcHBxtclJCRgxIgR2LJlC6pWrYrBgwdj1qxZcHWtVK8uEZFQePNBpD5pErVDhw4BMGTzx44dg7u7u3Gfu7s7WrRogTfeeKPCQezbtw///e9/0bx5c7PtY8aMwS+//ILvv/8efn5+GDlyJJ588knjzFKdToeePXsiJCQEO3fuxJUrVzBo0CC4ublh5syZFY7D1mQsYE3v1GS8a5MxZrIvnhNEVBmi1IcVStS2bNkCABgyZAj+85//wNfX964DyM7OxoABA/DFF19gxowZxu0ZGRn43//+h6VLl+KRRx4BACxatAiNGjXC7t270aFDB/z22284efIkfv/9dwQHB6Nly5aYPn06JkyYgHfffdcskaTyEeXErCwZk2MiIhKPXq83fi/dgreLFi2ySZIGAHFxcejZsydiYmLMth84cAAFBQVm2xs2bIjw8HDs2rULALBr1y40a9bMrCs0NjYWmZmZOHHihE3iI7nImFwSWSPjOS3jDZOMMZPjSNOiViQnJwcffPABNm3ahNTUVLOsEwAuXLhQrvdZvnw5Dh48iH379hXbl5ycDHd3d/j7+5ttDw4ORnJysvEY0yStaH/RvtLk5eUhLy/P+HNmZma54iXxsbAlosqQMSEmx5FmjFqRYcOGYdu2bXjhhRdQs2bNSv0BiYmJGDVqFOLj4x0+U3TWrFl47733HPo7ZSF71ycRUWXwJo+ska5Fbf369fjll1/QqVOnSv/iAwcOIDU1Fa1btzZu0+l02L59Oz777DNs3LgR+fn5SE9PN2tVS0lJQUhICAAgJCQEe/fuNXvflJQU477STJo0yTiDFTC0qIWFhVX6b3EmsidqMsZMZA0TCKJ7W6XGqFWrVg0BAQF39Yu7du2KY8eO4fDhw8avtm3bYsCAAcbv3dzcsGnTJuNrTp8+jYSEBERHRwMAoqOjcezYMaSmphqPiY+Ph6+vLxo3blzq7/bw8ICvr6/ZFxkw0SESC69Jx+DnTNZI16I2ffp0TJkyBUuWLDFbS60ifHx80LRpU7Nt3t7eCAwMNG4fOnQoxo4di4CAAPj6+uK1115DdHQ0OnToAADo1q0bGjdujBdeeAFz5sxBcnIy3nnnHcTFxcHDw6NScdEdLLiI6F7BlkuyRroxav/+979x/vx5BAcHo06dOnBzczPbf/DgQZsE9/HHH0Or1aJfv35mC94WcXFxwbp16zBixAhER0fD29sbgwcPxrRp02zy+++WjImOjDGbkj1+IlIHyw4SVaUStb59+9o4DIOtW7ea/VylShXMmzcP8+bNK/U1ERER+PXXX+0Sz71I9jFqvCsmUh/LDiLbqVSiNnXqVFvHQQKSsbCVMWYia5hAEKlPugVvyXnJnujIWKnJGDM5juzXpCz4OZMlUXqYKtSiVq1atXJVKmlpaZUOyJnIWAGLcmIS2YqM16Ep2eMnortToURt7ty5dgqDRMFEzfH4OZM1Mp4fTC7J2UjTojZ48GB7xUECYgVBRETEMWokEBmTMyISC8sR++IN6b2FiRqZkb3rU8aYiZwNEwki22GiRmZkT9RYQRBRZbDsIEuinBNM1KhUMiZqMsZM9iX7OSFKZUF0N2Q/j9UsR5iokRlWakRikf2alAU/Z7JGumd96nQ6LF68GJs2bUJqair0er3Z/s2bN9skOHIsRVGQl5endhhENiV78i5j/Ex6yBmIcu1VKlEbNWoUFi9ejJ49e6Jp06bC/DF0d/Ly8vDWW28Zf5axsJUxZrIv2c8JGeNnnUDOwPQ8lq5Fbfny5Vi5ciV69Ohh63iI7gorCHI2PKeJ1CfdGDV3d3fUq1fP1rEQ3TUZWx+IrOE5TXRvq1SiNm7cOPznP/9hAeLkZPz/ZesDWZL9nJA9fiK6O5Xq+tyxYwe2bNmC9evXo0mTJnBzczPb/+OPP9okOKKKkjG5JCL1MSG2L9nLZmme9VnE398fTzzxhK1jIbprshcGRJZ4ThOpT7rJBIsWLbJ1HEQ2wbtisiR7osNz2jFkP0/I9kQ5J7jgrR2J8p98L2GlRs5GxnJExphZdpAl0/NYuhY1APjhhx+wcuVKJCQkID8/32zfwYMH7zowUp+Mha2MMZN9yV4Byx6/LFh2kDXSLc/xySefYMiQIQgODsahQ4dw//33IzAwEBcuXED37t1tHSNRubFSI2fDBMIxWHaQJVFa1CqVqH3++edYuHAhPv30U7i7u+PNN99EfHw8Xn/9dWRkZNg6RqJyY6VGzoYJBDkDGc9j0/pEuha1hIQEdOzYEQDg6emJrKwsAMALL7yAZcuW2S46UpWMSY+MhQGRNbwOHUOmz1mmWGUmdYtaSEgI0tLSAADh4eHYvXs3AODixYs8gUhVPP+I1MfrkJyNdC1qjzzyCNauXQsAGDJkCMaMGYNHH30Uzz77LNdXMyHjXaXsWEEQqY9lH1mSsWwWpeuzUrM+Fy5cCL1eDwCIi4tDYGAgdu7ciccffxyvvPKKTQMk9ch4YbGCIKLKYNlhXzLWJ6akW55Dq9VCq73TGNe/f3/079/fZkE5C174RHQvkrFSlilm1i2OIco5UekFb//44w8MHDgQ0dHR+OeffwAA33zzDXbs2GGz4IjKQ5SLicgeZKyUZYxZJizzHE+6yQSrVq1CbGwsPD09cejQIeTl5QEAMjIyMHPmTJsGKDPZLyZZ4i/qhgdYQVBxspzHpZE9fiJA/rJZuskEM2bMwIIFC/DFF1/Azc3NuL1Tp058KoETkaWCME3UZImZqLxkrOB4HZIzkHp5jtOnT+OBBx4ott3Pzw/p6el3GxNRhYhyMRGRAa9DItup9Dpq586dK7Z9x44dqFu37l0HRWIwbakSGe/eyRrZkwae32RJxnNaxphFWZ6jUona8OHDMWrUKOzZswcajQZJSUn47rvv8MYbb2DEiBG2jpFUIksFIcrFRGKS/ZyQsYIjsiTjdShKb02llueYOHEi9Ho9unbtips3b+KBBx6Ah4cH3njjDbz22mu2jpFUIsuFJcrFRGQPslyHspOp7OA54XjSLXir0Wjw9ttvY/z48Th37hyys7PRuHFjVK1a1dbxSU32i0mW+GWJk6gyZEogivCaJGcgSiNApRK1Iu7u7mjcuLGtYiHByFLYyt71KWNFLBMZP1+e044n4+dM9iXKdVihRO2ll14q13FfffVVpYIhscgymcAUKwhyBqLcyd9L+DmTJVGuwwolaosXL0ZERARatWrFyoWEIcpdD5Gt8Jx2PH7OZI00LWojRozAsmXLcPHiRQwZMgQDBw5EQECAvWIjlcnSosYClpwNEzXHY4saWSPNgrfz5s3DlStX8Oabb+Lnn39GWFgYnnnmGWzcuJGFiROS5f9UljhJHTKeH6Yxy3LDZErGz5xIVBVeR83DwwPPPfcc4uPjcfLkSTRp0gSvvvoq6tSpg+zsbHvESCphBUGkDtkfiyZj65Qsn7OiKFKWzbKTbsFb44u1Wmg0GiiKAp1OZ6uYSBAyFVxFZKwgyL5kPCfY9UmlycvLQ0JCgtphVBjP48qrcKKWl5eHZcuW4dFHH0WDBg1w7NgxfPbZZ0hISOA6ak5Glrs2JmpEYmGlTM5AlPO4QpMJXn31VSxfvhxhYWF46aWXsGzZMgQFBdkrNlKZjImaLDETOTPeMJElnhOVV6FEbcGCBQgPD0fdunWxbds2bNu2rcTjfvzxR5sER+qSJemRfTwPkTPgtUfWyJioiTIEoUKJ2qBBg6T8sKlyZEnUTMdHFhYWqhgJkW3IWM6KUqmRmGQ/J6Ra8JbuHbIkarm5ucbv8/PzVYyEyDa0Wm2J34tM9rGiMsZM9iXKzYccJQCpQpZEzTROWWImKi9ZEgjZW0yILIlyTquaqM2aNQvt2rWDj48PatSogb59++L06dNmx+Tm5iIuLg6BgYGoWrUq+vXrh5SUFLNjEhIS0LNnT3h5eaFGjRoYP348u8BsQJYlV0zjlCVmchxRCtvKkiVRk/0mSfbzhGxPlIlqqiZq27ZtQ1xcHHbv3o34+HgUFBSgW7duyMnJMR4zZswY/Pzzz/j++++xbds2JCUl4cknnzTu1+l06NmzJ/Lz87Fz504sWbIEixcvxpQpU9T4k8zIUsCWRpaC1zQ5kyVmovKSJYGQJc7SyF5ek+2J0ghQoTFqtrZhwwaznxcvXowaNWrgwIEDeOCBB5CRkYH//e9/WLp0KR555BEAwKJFi9CoUSPs3r0bHTp0wG+//YaTJ0/i999/R3BwMFq2bInp06djwoQJePfdd+Hu7q7Gn+YUZGmdEuViqghRxj6QmGQ8P2S/SZLlcybHMT2n1axbhBqjlpGRAQDGB70fOHAABQUFiImJMR7TsGFDhIeHY9euXQCAXbt2oVmzZggODjYeExsbi8zMTJw4caLE35OXl4fMzEyzLypOloJXxkQtLy/P+D0rCLIk47hLWeIsDVvUyJLpEComajBc5KNHj0anTp3QtGlTAEBycjLc3d3h7+9vdmxwcDCSk5ONx5gmaUX7i/aVZNasWfDz8zN+hYWF2fivcQ6yJD2mF5MsYxNl+WxJHaLcyVeE7ImarDdMspwfpmQ5VwoKCkr83tGESdTi4uJw/PhxLF++3O6/a9KkScjIyDB+JSYm2uX3yHrhF5GlABDlrqci1LzoSXwyjruUJU5nI+PnLkvdaFq3qFlmqzpGrcjIkSOxbt06bN++HbVr1zZuDwkJQX5+PtLT081a1VJSUhASEmI8Zu/evWbvVzQrtOgYSx4eHvDw8LDxX+F8ZCkAZGx9YKJG1sjYnS/7E0Jk7fqUpZyWcdylaTl9z3Z9KoqCkSNHYvXq1di8eTMiIyPN9rdp0wZubm7YtGmTcdvp06eRkJCA6OhoAEB0dDSOHTuG1NRU4zHx8fHw9fVF48aNHfOHOClZKggZn0xgGqcshRY5juxj1GSJ2RnI8lmbJsKylHmi1C2qtqjFxcVh6dKl+Omnn+Dj42McU+bn5wdPT0/4+flh6NChGDt2LAICAuDr64vXXnsN0dHR6NChAwCgW7duaNy4MV544QXMmTMHycnJeOeddxAXF8dWs7skS6JmegHJUmiJcqd2L5CxpUTGFjUZu2tNyZI8WJLxs5YlZlGuQ1UTtfnz5wMAHnroIbPtixYtwosvvggA+Pjjj6HVatGvXz/k5eUhNjYWn3/+ufFYFxcXrFu3DiNGjEB0dDS8vb0xePBgTJs2zVF/htPixWQ/pnGyG5QsyZg0yDgEwZSMCT0g52cty/ktSt2iaqJWnv+sKlWqYN68eZg3b16px0RERODXX3+1ZWgEeQoAUZqnK0LGmarkODKO55G961OWz9mSjJ+1LHWLKOe0MLM+STyyXEwyJj2iTPsmshUZr0NTbFGzL9NEWJaYRZkgw0SNSiXLxSRjBcFEjawR5RmDFSFjy7YzkKWcNiVLzGxRI+GxgrAfy8kEshRcMpKxS0vGc1rGGyZTMp4ngDzltIw3p6Kc00zUqFSyJA8yLnhr+ggpQJ6CixzD9HyQJekxPad5PjuOLGWejImaaZz5+fmqxcFEzY5k7L4wJUvMsldqAJCbm6tSJCQi0/NBlnPj5s2bxu8tz2+yH1nKPBkTtVu3bhm/V/M6ZKJmR6aDU2W5mEzJErOMBYBlRcaKjUyJUkFUhCitDxVhejMt62QCWW6oTc8JGctptqjdA2RJekzJUgAwUSNnY9o6Zfq9yEwrMlkSNRnLZUuylNOmn7Us54dpnGqW0UzUHETGAkGWAkDGxWMtW0lMW1CIZOz6FKX1oSJMywtZJxPIUrfIPpZYzURNiIeyOytZ1o3R6XS4evVqsRMxOzsbOp0OLi4uKkVWPqaVgiyFlmViJktlLDtFUaTo4pIxUROl9aEiTGNmomZfosygrAgmavcAUR4/UZarV69i8ODBxbbv2LEDV69eRUhIiApRlZ+MXS6WFz1b1OzH8mHQMiRqpueHjImaLNdhdna28XuRy2hrZIhbURSzMk6Wng/TYQdqltHs+rQjGddCkpHpRS/LnbzlRc9EzTFkaTUxTc5kOTdMY5blOpSx7LAckiJD3ZKXl4fNmzcbf5YlkTe99nJyclSLg4maHcnY1Csjy7ExMlTGHKOmDhnODUD+GXKytAKaxqwoihTltGWMMrSoWZKlvDNtcVVzUg8TNTuSsbCVkeWdsAx3a5Z3Z7LM7JOdLBNkZOxGNK18b926JUVSLOPsa8vETMa6Rc3WqYowjTMrK0u1OJio2ZGM6wrJyPLuXYbC1vKOUpaCS0amCYMslZqMZYfpdagoihSftYwLT1t+rjJ8zpZkuTHNzMw0fm/auuZoTNTsSMYxGzKScWC+ZWKmZiFwL5GlUhNltllFyJj0WCYMMiQQluewDN21lmQp70xb0XJyclTrZmaiZieWs1xkKLRkJdPAfJ1Oh+TkZNy4ccNse0pKipRjTWQgezeiLGWHZZwyxC1T2VHEMjGT5ebDlAyJml6vL9bdadrC5khM1OwkLy8Pp0+fNv4sw52abIqSHsuLKTExUdikp2gplPT0dLPte/bswdWrV9UJysnJOMjdNGGQpeyQcW1Ay89W5CEIReVdcnKy2fbr168LW94VsYxPhkQtMzOz2DjLjIwMVWJhouYgIhcAsipKeixbp2bMmMGkh4xkXDzW9OZDzUHMFeEMQxBELqeLyrspU6aYbf/666+FL+8sEzO1WqYqwrJeAQxJsRqYqNmJZSYuw4lJ6pJhlpyMZBzvZVpJpKenS3FuyNQ6VcQygZAlKZaNZf0nQ32YkpJSbJtaCTETNTuxvHNXq8mU5CFDxSYj2R5tlJOTY9YalZeXJ0UCIVPrVBFRxiA5O8uhHmlpaeoEUgGpqanFtpWUvDkCEzU7sTwxS2pGJTJlOfaEbMN0sLUMM+T+/vvvYtv++ecfFSKpGBlnMrOcdgzLxCw7O1voiT06nQ5nzpwptv38+fOqjAdkomYn165dM/tZ9DEEpL6kpCS1QyhTRkYGvvrqKyQmJqodSrmZJmcyzJC7fPlysW2XLl1yfCDlpNPpcOnSpWIVr8iTeopYlsssp+3DdGyXq8a12DbRXL16FfHx8cW2qzXpi4manVg2m6rVZErykCH5+eabb7BixQp89NFHaodSbrK1qJV0J3/27FkVIimfq1ev4pVXXim2fdWqVUInPoqiFIuvpO4uunumn3OAZwAAeetENZ5uwkTNTiy7Kq5fvy7NjDOyH2uJQkktKaLZsWMHAODkyZMqR1J+ls+CFZlOp8OxY8eKbT927JjwrVMy0el0OHXqVLEy+fLly1K0usrGdFhHgGcQAODKlStqhVMma+Mr1RiiwkTNTkwrXR93DyiKInT3BTlGSeOPily6dImVsY0pimI2Vkr0Ae6XL18usZxISEjAxYsXHR+Qk7p69SrGjBlTbHteXp5UNyGiK1r7zfTc9fPwAwChz2drN81q3FAzUbMT08K2tq/hxLxw4YJK0ZAozp07V+q+vLw8JCQkODAa55eXl2c29k/02ZN//fVXpfaJSo1uorslcgIhm5LWutyTtAtAyV38orBWTlvbZy9M1OzgypUrZpMJ6vpXB4ASuzTIPkTtvjh//rzV/adOnXJQJPcGy/NA9GUBjhw5Uql9opJhtqolXoOOceHCBWF7EKyNCVUjwWSiZmM6nQ7btm0z2xZa1RcAcOjQISkWrnQGolbIZd2NiV5JyHb+Ws6+FnkAs16vLzNRE7GFylpMZd2YqMXaenoydn3Kdl0Chv8DEVsv8/PzrbZeJyQkOHwZFyZqNnb16lUsWrTIbNsXhw1NvTdu3MDRo0fVCOueY1lBiyA9Pd1sVln37t3x5Zdfonv37tBoNADET9SK4pSF5ZInIrfwnDlzxmoFkJGRIeT5YW22sqizVU+cOFHqvtTUVClmYJuSYc26kojYy3Ts2LEyJx3t37/fQdEYMFGzsbLu2NevX++gSJyfaDNzymJZafXr1w9hYWHo16+f8Y44MTFR6Idwi9iiY43lHXtqaqqw49Q2b95c5jFbtmxxQCQVY62yPX78uJCtPfv27bO6v2h2sywsF+4VRVkJj4jd+X/88UeZxzj6/GCiZmO//fab1f07/vhDqrV6RF53yloyJmKiZjlRYNWqVUhMTMSqVaug0Wjg5mbYLvLdvGwtaqdPn77zg0+V4tsEkZ+fj02bNpV53ObNm4V7DJa1XoL09HThJlHl5eXh4MGDVo/Ztm2bkAlmaUTsQQDKHs919OhRocapFRYWlisJ279/v0NbMZmo2VBqaiq2WrnjresfhILCQixbtsyBUd0dkdd+s7YOj4ir/Fsmahs2bMCwYcOwYcMGKIoCH7+SjxOJaeIuekWWm5uLo6atPSGGD3jPnj0qRVS6rVu3Ggp+zyqlH+TlhZycHKFa1TIyMsqsjHft2uWgaMpn+/bt1ss1rQsuXrwoZEJfGlETNWutrVVcPZGTkyPU53zo0CFkZWWhqpuncZvlEJWgKv4oLCzEzp07HRYXEzUb+vbbb1Fo5e6gZ/3GAICNGzcKPVbGlOnDoUVjbU0yET9fywc+FyU6Rf96eJR8nEhcXFyM34u8eKxOp8P69euRa3r+hvgDMHQfihS7oihYs2YNAEBTP6rU4zQN6gIA1qxZI0yS/Oeff5YZS3m6khzpl19+sbq/akTzch0nEhEnyej1euzdu7fU/Q2q3QcAxSbfqakolubV6xu3WQ5RifSraXasIzBRs5Hz58+X2e1Z1z8QLYJrQafT4csvv3RQZHdH5PFSpl2Elnc9N27cEG4sUlmfpcvtrk9Rk+Nbt24hIyPD+LNoXVqmrly5ggULFphv/MNw556VlYWffvpJhahKdvLkScPsSBcXaKPqGLdbntOakGDA1RUXL160OhjekbZv3279AI0Gly5dEqaV+NKlS4YJGZrSqz7feh0AGCpi0RdILiLiKv9Hjx61Ovu+ZXAbAIYbJ1GWUzpw4AAAoHmNO4ma5RCVtjUbAQAOHz7ssLiZqNmAoihYuHAhFEVBy+BaVo99vklraDUa7Ny5E4cPH3ZMgHdB5NlEpt2bJQ3MF6VyKK+ihglRWkssWXZjiDhjq8iGDRus7v/uu++EGStaNMFIE1UHmqJmVZRwTuflQ3M7kfv111/VCNXMrVu3ip0DxWYyBwUDKHvwvqNs3LgRAOBVq6Fxm2XMLt5+cK9WE3l5eWUnooIQcUzujz/+aHV//YD74Ofhj4yMjHJNpLG3tLQ0pKWlQQMgwifYuN1yiEqUXxi8XD1QWFjosDqGiZoNHD9+HIcPH4abVove9ZsYt1sWAOm5t1DL1x9d6zQAYKgsRCdaq1SRW7dumd2tWd71ANa7RtVQvXp1q/vzbpXvODXk5eVh3rx5ZtuWLl0qZJfLwYMH8cMPP5R+QJAPbt26hffee0/1MZiKohin+mstuj0tz2mNj7fxmP3796ue0B8/ftwwZtHLx7jNMrnU+lYDYBj7ozadTmdMCHzqtjVut4y5MOs6/O7rBACIj49XJdaKunbtmurnsqkLFy4UGwtqWR9m5Wcipk4sAGDlypWqTyooSrqqe1WDe1H3BooPUdFogFo+NcxeY29M1GygaCxDp7C6CPD0Nm63LACu3TS0TvVq0AQaaHD06FHhW31EHS9lmYRZ3vUA4s2erFGjhtX9OdnlO87RFEXBN998Y3bX7lbDkCzPmzdP9QLW1KVLlzBjxgyrSYy2Y32gihvOnTuH2bNnqxp/UlKSYe00Fy00weYJuuU5rfHygqZGEODigoyMDNXP76KlFbQhd3oRLJNLl9p1ABi6wdRe2iUtLc2wjIVGC6/QBsbtxWZf+wTBO6IFALEW7C0rMRfpxvTnn38GADQNamHcZlkfXr91DQ+EPwwvVy/8/fffqvcweXoaJhAU6Mte6aBAV2j2GntjonaXcnJysOP2YNmukQ3M9lkWAEFeVQEAgZ7eaH27cCtrXJvaRF2fxzIuy7seAA5fPbosjRs3trr/1k3DYP377rvPQRGVLSkpCZMnT8b3339vtr1qSwAawwzK119/XYiFWC9duoQJEyYYxhUF+ZR6nKaqJ1xiWwBawxCEOXPmqJ9sal2gMZmoAZR8TmtcXAAXMYrtops4ze0nrwAlJJdBIQAMLbJqT+AoGl/p4ukDjdbVuN0yZlcvP7h6GWYI5+bmCrMcSlk3zaLc9Ofn5xu7jO+vFW3cblkfBnoGoYqrJ9qFGo75/fffVYm3SFBQEAAgPTfbarKmVxRcz80we429iXHFSyw/Px8FhYXQAKjl42+2z7IA8K9yJ/uufbtLQOQxYIC4j2IqT0ufaK2BzZs3h7u7OwCgfZc722N6AQ2bGb5v0qQJvL29S3i1YyUkJGDevHkYPnx4ieOLbvwGQAHgZngs1ujRo/H+++/jxIkTqnTJJSYmYsKECYYEPrAqtA82snq8pqY/tI82A7QabN26FXPmzFGlxadaNUM5gIICKFlllwVKdjaQbxjAHBAQYM/QymRcqkV7pxopllyaJJ9qr8lYVNZqtC5QcOccLTEh1roUe53arl69avy+pKeaiDJO7ezZs8jOzoaPuy/q+t3pzresD/08/AEA7Wq2B4Ay17azt2rVqqF69epQoOBwaulLzpy5cRlZ+Tfh5eWF2rVrOyQ2Jmp3yd/fH15eXlAApOaYj+cqqQAokpJjSCJq1bI++UBtoiZq5ZkZKdrsSXd3d7Rr1w4AcNVkaJdXVSD59moi0dHRJbzS/nQ6HU6cOIElS5bg1VdfxfDhw7F27VoUFhbCPaT01wU9DnjebgDcvn07xo4diyFDhmDBggXYv3+/Q8bN5ObmYvr06YYkLcgHLr3bQONxZ4yJZaWm3DS0kGgja5gla6tWrbJ7rJa8vLzQsmVLAIBu/+Eyj9ftN3Q3Nm/eHFWrVrVjZGUzLn5cYKWlzGSf2oslR0VFwd3dHYXZaci7ar31Keu84eYkJCTkTjKtMtNEraTJU6KMF3W7vXK3q9bV7P+8tPrQTetm9jq1aLVa9O7dGwDwx9+lj6n84+/DAIDY2FhUqWJl3UMbYqJ2lzQaDSIjIwEAq04dgR5ltyacv3ENB64YxhMUvVZU169fVzuEEnl5eZV5jAgtU5ZiYw2DZxMv3dmWkQakpwGurq7o2rWrw2JJT09HfHw8ZsyYgaeeegpjx47F0qVLDeNyNECVOkBgL8DvkdLfQ+sBVHtYg+pPGRI2jathqYDVq1fj7bffxlNPPYW33noLP/30k93u+P/73//i8uXLgJc7XHq0hKaKeYFfrFLLupM8aiNrQNvZkGl+9dVXZS7eag9Dhw4FACjnLkB/ufRxZ/qEv6GcPW/2GjU1b25Yb0yXVHrSo7+9LyoqSvXr0cfHBw8++CAAIPNs6YvwKlCQfmIrAKBHjx7QasWoJk3L4pImT4kyi9nHxzDsICs/E5l5GWUcDSRmGc4RX1/fMo60v+7du8PDwwP/ZF/Ds/c9illdXjXum9XlVQxr1gd/pV02S+ocwbXsQ6gsQ4cOxYQ338S+KwnwLSPDvn4zBx/t3ooCvQ7t27dHq1atHBRl+XTv3h39+vXDqlWrsGHDBly/fh06nc5soVMRFBUGd3uMo7Vt2xZBQUFmK4kn3H4cZXR0NPz8/Oz6+8+fP48//vgD+/btw7lz58z2aTwAj9pAlTDAIwJw8TRUAIWZd24+LM8P3U0Frr6AW5AG1R4G9J0V5CUCeQlA3t9AQXYBDhw4gAMHDuDzzz9HrVq10LZtW3Tq1AnNmjWzSSVYtEK4tvN90Hh5FNu/atUqY8wajcb4KCnj392oFjQXUqH/Ow27d+9GgwYNir2HPTVo0ABPPvkkfvzxR+j37Ic25kFo/HyhW2UYkO3SrzeUzCzoNxnG/fTt2xcNGza09pYO0b59e2i1WuhvlL4qvu5vw8ndsWNHR4VlVa9evRAfH4/sS4dLPSbzzC7kXr0ENzc3442VCEx7CDZs2ID169cbWohvt1CJsuZlSEgIGjZsiL/++gu/nLO+XmF2fjbWnDbM0C5KotXk6+uLF154AV9++SV+ubAD7UPvjCv2cffCT+cMi9z27dvXob1hYtwqSK5JkyYYM3YsAGDTxdLvyDPzcjFn1yZk5N1CZGQkJk6cKFwCZNn6oCiKkI8nKc/MSBGXuXBxcUFMTIzZtr8vG/61Z6WQkZGBiRMn4tVXX8WyZcuMSZprIFC1NRD0BBAyGAh4VAOvhhpjkmbJ8vzQWazeonXTwLOuBv4PaVBjAFD9WcC3A+BeE4DG8MSIn376CW+++SZGjBhhaAm7SzVrGlYKR37JY6CKz54snswpt19rfC8HGzZsGDp06ADoFej/3GNYA6CIRgP9jt2AXo/7778fL7/8sioxWvL39ze2qmmjGsG93xDjPvd+Q+D+2FNQ/jH8/3bu3FmVGC01bNgQvXr1AgBoq/ig9uNvGvfVeWYaavUcjesHDbP4hw0bBn9/fzXCLJHppIaSuhFFGeqh0Wjw+uuvQ6vV4vi10p8Dq1f0WHLsC2QXZKFOnTro16+fA6Ms3RNPPIE6deogu+AW1py58/SBDRd34+qtdAQFBWHQoEEOjYmJmo107doVAwcONP7cPerOYOYPHumFqQ88hs8P7MA/WRkIDAzEe++9V67uO0crqUnddGyEKGrXrl3m+ACRZk+askzUCvINA8Nbt25tt9/5yy+/mK1l5dkAqNEfqPG0Br73a+AerIFGW/YYomLLL1hptNRoNHCrpkHVlhoE9dEgeNDt2aK3Xbp0Cd98881d/FUGTZs2BQDo/zgN/V/FHx1mbayoklcA/cYjQGomNBpNmTNz7cXFxQUTJ05EVFQUcCsXuvitxn26TduAW7mIjIzEpEmThLq5Kyrz9BfNn9eo9fFD4SnDeLouXbqgTp06jg6tVMOHD0d4eDj0uVm4cfTOrHtXb39c270K0OvQrl079OnTR8UoiytrFXxRVvcHDF3dzz//vPHnZxsNMH4/tfNMzHjgX/j13FocTT0Md3d3jBkzBq6uYnTwubq6YuTIkQCA7f8cNm7fcNHQXf7KK684bFmOIkzUbGjgwIEYMMBwQq4/f2e5AjetK+bv34GUnCzUqFEDH374IYKDg0t7G1WVtB6ZKGMfTJkuY9HeZILfsw/f+V6E7qGShIWFFau4OnfubNcKuGPHjmZjhG6dAVKXAylLFaRtUJC5W0HOKQV5SQp0OUqpMzctzw+XEu419LkK8lMU3DyjIHOfgrTfFKSuUJDyNZB9+M5xrq6ueOQRKwPgyum5557D/fffD+j00G89Bd2WE1AKyp5hqKRkQLdqL5RL1+Dm5oYxY8aoOrnH09MT7733HgIDA4EMkxnLNzJQrVo1TJs2Tbibu2bNmqFNmzaAXo/CI3cWONVfT4H+8llotVqHtz6UpUqVKpg4cSK0Wi1yTLpA009sRd71RPj4+GDcuHGqT36wVNaNqWjnxsCBA429BD/8tcK4PdAzCHuv7MKupB3QarV46623hCurmzVrhgceeMBsW6GiQ/PmzdGlS5dSXmU/YqSwTkKj0WDQoEFwc3PD4sWLjdtXnjyI1JvZqFmzJmbPni1skgaU3PogYosaALRs2RJHjhxBqslyaTduz6SvU6eOUN0Wlpo3b45Lly4Zfy6a+WcvderUwddff42dO3fiwIEDOHnyJFJTU6HLBHQlrGKicQNc/RS4VgO0JmPALc+PgutAXoKCgmtAwQ2gMB1QrCw75efnh0aNGqFVq1bo3LmzTdYh8vb2xnvvvYcVK1bg66+/hv70FSgJpU+CUQp10O88A+WoYRBzcHAwJk+ejPr165f6GkepXr06Ro4ciffee89s+8iRI4VbCLnIoEGDcODAAehNbk4LjhpmTT700EMIDw9XK7RSRUVF4cknnzR7gsX1g+sAGLo8RZnpaaqsREy0RE2j0WDUqFG4ceOG2cPZz904jZ/PrgYAvPbaa6rNdC/Liy++WOwRYoMGDVIlgWeiZgfPPfcc/vrrL+zevRsAsCcpARqNBm+//bbQSVppRFuPrMj999+PJUuWINEkj7x8e2Jh0TIYomrcuDHWrl1r/Lmo+86eqlatim7duqFbt24ADOPWLl68iMuXLyMxMRFJSUlISkpCSkoK9AV6Q/JVxvDEGxtL3h4UFITQ0FDUqlULtWrVQkREBCIjIxEUFGSXgk6r1eK5555DkyZN8NFHH915SHV4IHA7adM+0wHIuAX9r4eBbMPMz5iYGPzf//2fUBNPoqOj0aRJE+OD1xs1aoROnTqpHFXpGjZsiJYtW5qtLK9cNoyBfPbZZ1WKqmwDBw7Eli1bjLMplcJ8NGrUyHh9iKasc9TeE5Eqw8XFBePHj8eIESOMY52/PvYVFCh49NFH0aNHD5UjLF2tWrXQqFEj42LewcHBDimnS8JEzU6GDh1qTNQAw2w5Ee7YK6NoRW/RREVFISAgwGytt8u3lxISPVELDQ01fu/t7a1KIevn54eWLVsWa80rKChAcnIyEhMTcenSJZw7dw7Hjx8v8Tzw8vJCkyZNUL9+fURERCA8PByhoaEOW1/IUvPmzbFgwQJ88803hlmUpi1rhTrot54A8gpRvXp1jBo1SsjzRKPRoEOHDsZELTo6WrhuOEv9+/cv9gigDh06CDU2zZKnpye6du2KlStXGrc9/vjjwizHYamsm3xR1+T09fXFmDFj8PbbbwMA0vNuoEaNGoiLi1M5srK1b9/emKh16NBBtevQaRK1efPm4V//+heSk5PRokULfPrpp4ZxKyqx7KZ4/PHHVYrk7uXk5KgdQok0Gg1at25t9uiR3HzDWA61BoWXl+mMVLUXLrXk5uaGsLAwhIWFGZdVUBQFx44dw/jx443HzZgxA61btxZqcDtg+P8fPnw4WrdujcmTJxsfD6VffwTIK0TDhg0xc+ZM1df1ssZ0iRAZbvBatGgBX19fs9b3hx9+2MorxNC2bVuzRK1t27ZWjlZXkyZNbn+nAW6v1/nCE7Px+44vceXqWYetkl8Zli1R3bp1c/iA/Mow7bZXswtfzFuHClqxYgXGjh2LqVOn4uDBg2jRogViY2OFGgQv6t1OeajxSKDyatOmTbFtLVq0UH2V67KYjicRLdEpiUajQbNmzdC9e3cAhu6udu3aCR17mzZtzBJL3MpHREQEpk+fLnSSBsBsfKXIYy2LaLXaYi2zoq0RWRLTJNjT01OIRVdLExwcfPum7k557OMdiBuZhm7+iIgIlSKrOFHW1SuLaSummsOWnCJR++ijjzB8+HAMGTIEjRs3xoIFC+Dl5YWvvvpK7dCMip7xSLZVtI5TWdvo7mk0GgwZMgRPP/00xo0bp3Y45WI5UPmFF14QujIuYtp17OFRfN03Ed1p8TEsnyPimClLpt2conZ5FtFoNKhXr57Ztszsa8jNy4abm5vwT7kxpdZ6hRVleh2q2QIo9plZDvn5+Thw4IDZ2lRarRYxMTHYtavkx4Tk5eUhMzPT7ItKJ3KrSVBQULEZWqKun+YM/Pz8MGzYMCFn8pVHSS2wIhI9aSiJ6XAPGSdNycCybEu5bnjyQ2RkpPC9CKZEm6FaGtMGFjXrQflKAwvXrl2DTqcrVjAEBweX+mzBWbNmwc/Pz/gVFhbmiFClJXrXS926dc1+joqKUikSEp0sFYQp0ScSFDFdasUWy644mgyfc7FE7dqFEreTbZgmZ2qeH9InapUxadIkZGRkGL8SE0t/EHJleXh4CD31uCJET9RMW3cCAgKkqIxNu7NkqCBk5eHhgf79+6sdRoWZnh+yDJswTc5ELzOKyNKtXMR0Ydjh/T9H6rWLxbaLyMPDA//3f/+ndhgVZtrdqWaLpfSzPoOCguDi4oKUlBSz7SkpKQgJCSnxNR4eHna/QDUajTCPxLhbgYGBaodglel4B1nGPpgmZ0zU7Eej0UjVJVQSWc4P2SbIAPJ8tkUCAwMRFBSEa9euIS39b1xNMzxLVfQWNVmvQ1HGMErfoubu7o42bdpg06ZNxm16vR6bNm0SdsVj0RStKQUAoVU1mPWwN2Y97A3/27ms6F3Dpt3eIj6InaiiZEsgAPPWKZFnisuuaELB6Qu7oNMVwMvLS+pVBURmmpypeU5Ln6gBwNixY/HFF19gyZIlOHXqFEaMGIGcnBwMGTJE7dCk4OLigmbNmgEArt1SEOCpgY+HBhm3HwUk+sBx09llsnS5kOPImPSYkiV+thI7RlGidvKs4fFGkZGRUk4+kYEoiZpT9M09++yzuHr1KqZMmYLk5GS0bNkSGzZsUH3mUdEFJdqCpiUJDQ2Fm5sb8gsKcO2mgux8BQoMiY/o0+xN4xM9VnK8opsQ06dBiM50XJps46jIvoomT+kVndnPopNh7LAlUW4+nCJRAwwPLR45cqTaYZjp2rUrrl27ZvcHbtuCi4sLIiMjcebMGSRk6pCdb7h7kGEGpWkiLMNq15bYTWRfzZo1w9SpU6VaELRKlSp49913oSiK8IvzloTntP1YdnOK/EQCU507d8avv/6KFi1aqB1KuZm2qKk5VttpEjURubq6YsCAAWqHUW5RUVGGRC1Dj5wCQ0FrucCiiEwXJZSxy0XGmGWi0WikWQndlMxjbGU6p7t06YI//vhDmsf8WU6YkqWl2N3dHR9++KHaYVSIRqPBZ599htzcXFV7a5iokVFR69nfWTrk3G5Rk6FZ3bSbSMY7ecsFe4lkJ9M5HRcXhzZt2uChhx5SO5Ry8fDwQEBAANLS0gCg1NUNyDZEeNYuRyCSUdEjSBIz9PgnSw9AjkTNlEx38iNHjoSvry+GDh2qdihENjF69Gh06tQJ3bp1UzuUcqtWrRq6d+8u1bAJ0+EeAQEBKkZCjsAWNTIqStTS8wytUm5ubpz2bUe9e/dGr169pEouiazp3r07unfvrnYY9xQZxzBSxbBFjYy8vb3N7s5CQ0OlWbiyUaNG0Gq1aNeundqhVAiTNCKqKFFmI5JjsEWNzISGhhrHPsjUmjZ79mxkZ2cL/xQFIiKiimCLGpmR8XFMgGGALZM0IroXFI0dNp3xTs6LLWpkxjTZYeJDRCSeF198EVqtFl27dlU7FHIAJmpkxnSMmkxT7ImI7hUhISF488031Q6DHIRdn2TG9EkEsi3NQURE5GzYokZmmjZtigULFsDFxUX4h7ETERE5OyZqVEzRempERESkLnZ9EhEREQmKiRoRERGRoJioEREREQmKiRoRERGRoJioEREREQmKiRoRERGRoJioEREREQmKiRoRERGRoJioEREREQmKiRoRERGRoPgIKQCKogAAMjMzVY6EiIiInF1RvlGUf1jDRA1AVlYWACAsLEzlSIiIiOhekZWVBT8/P6vHaJTypHNOTq/XIykpCT4+PtBoNDZ738zMTISFhSExMRG+vr42e197kjFmQM64GbNjyBgzIGfcjNkxGLPj2CtuRVGQlZWF0NBQaLXWR6GxRQ2AVqtF7dq17fb+vr6+Up2YgJwxA3LGzZgdQ8aYATnjZsyOwZgdxx5xl9WSVoSTCYiIiIgExUSNiIiISFBM1OzIw8MDU6dOhYeHh9qhlJuMMQNyxs2YHUPGmAE542bMjsGYHUeEuDmZgIiIiEhQbFEjIiIiEhQTNSIiIiJBMVEjIiIiEhQTNSIiIiJBMVGzg+3bt6N3794IDQ2FRqPBmjVr1A6pTLNmzUK7du3g4+ODGjVqoG/fvjh9+rTaYVk1f/58NG/e3LgQYXR0NNavX692WBXywQcfQKPRYPTo0WqHYtW7774LjUZj9tWwYUO1wyrTP//8g4EDByIwMBCenp5o1qwZ9u/fr3ZYpapTp06xz1mj0SAuLk7t0Eql0+kwefJkREZGwtPTE1FRUZg+fXq5nmGopqysLIwePRoRERHw9PREx44dsW/fPrXDMlNWXaIoCqZMmYKaNWvC09MTMTExOHv2rDrB3lZWzD/++CO6deuGwMBAaDQaHD58WJU4TVmLuaCgABMmTECzZs3g7e2N0NBQDBo0CElJSQ6Lj4maHeTk5KBFixaYN2+e2qGU27Zt2xAXF4fdu3cjPj4eBQUF6NatG3JyctQOrVS1a9fGBx98gAMHDmD//v145JFH0KdPH5w4cULt0Mpl3759+O9//4vmzZurHUq5NGnSBFeuXDF+7dixQ+2QrLpx4wY6deoENzc3rF+/HidPnsS///1vVKtWTe3QSrVv3z6zzzg+Ph4A8PTTT6scWelmz56N+fPn47PPPsOpU6cwe/ZszJkzB59++qnaoVk1bNgwxMfH45tvvsGxY8fQrVs3xMTE4J9//lE7NKOy6pI5c+bgk08+wYIFC7Bnzx54e3sjNjYWubm5Do70jrJizsnJQefOnTF79mwHR1Y6azHfvHkTBw8exOTJk3Hw4EH8+OOPOH36NB5//HHHBaiQXQFQVq9erXYYFZaamqoAULZt26Z2KBVSrVo15csvv1Q7jDJlZWUp9evXV+Lj45UHH3xQGTVqlNohWTV16lSlRYsWaodRIRMmTFA6d+6sdhh3ZdSoUUpUVJSi1+vVDqVUPXv2VF566SWzbU8++aQyYMAAlSIq282bNxUXFxdl3bp1Zttbt26tvP322ypFZZ1lXaLX65WQkBDlX//6l3Fbenq64uHhoSxbtkyFCIuzVv9dvHhRAaAcOnTIoTGVpTx19t69exUAyuXLlx0SE1vUqEQZGRkAgICAAJUjKR+dTofly5cjJycH0dHRaodTpri4OPTs2RMxMTFqh1JuZ8+eRWhoKOrWrYsBAwYgISFB7ZCsWrt2Ldq2bYunn34aNWrUQKtWrfDFF1+oHVa55efn49tvv8VLL70EjUajdjil6tixIzZt2oQzZ84AAI4cOYIdO3age/fuKkdWusLCQuh0OlSpUsVsu6enp/AtxUUuXryI5ORkszLEz88P7du3x65du1SMzPllZGRAo9HA39/fIb+PD2WnYvR6PUaPHo1OnTqhadOmaodj1bFjxxAdHY3c3FxUrVoVq1evRuPGjdUOy6rly5fj4MGDwo2HsaZ9+/ZYvHgx7rvvPly5cgXvvfceunTpguPHj8PHx0ft8Ep04cIFzJ8/H2PHjsVbb72Fffv24fXXX4e7uzsGDx6sdnhlWrNmDdLT0/Hiiy+qHYpVEydORGZmJho2bAgXFxfodDq8//77GDBggNqhlcrHxwfR0dGYPn06GjVqhODgYCxbtgy7du1CvXr11A6vXJKTkwEAwcHBZtuDg4ON+8j2cnNzMWHCBDz33HMOe7g8EzUqJi4uDsePH5fizvK+++7D4cOHkZGRgR9++AGDBw/Gtm3bhE3WEhMTMWrUKMTHxxe7mxeZaetI8+bN0b59e0RERGDlypUYOnSoipGVTq/Xo23btpg5cyYAoFWrVjh+/DgWLFggRaL2v//9D927d0doaKjaoVi1cuVKfPfdd1i6dCmaNGmCw4cPY/To0QgNDRX6c/7mm2/w0ksvoVatWnBxcUHr1q3x3HPP4cCBA2qHRoIqKCjAM888A0VRMH/+fIf9XnZ9kpmRI0di3bp12LJlC2rXrq12OGVyd3dHvXr10KZNG8yaNQstWrTAf/7zH7XDKtWBAweQmpqK1q1bw9XVFa6urti2bRs++eQTuLq6QqfTqR1iufj7+6NBgwY4d+6c2qGUqmbNmsUS9kaNGgnfZQsAly9fxu+//45hw4apHUqZxo8fj4kTJ6J///5o1qwZXnjhBYwZMwazZs1SOzSroqKisG3bNmRnZyMxMRF79+5FQUEB6tatq3Zo5RISEgIASElJMduekpJi3Ee2U5SkXb58GfHx8Q5rTQOYqNFtiqJg5MiRWL16NTZv3ozIyEi1Q6oUvV6PvLw8tcMoVdeuXXHs2DEcPnzY+NW2bVsMGDAAhw8fhouLi9ohlkt2djbOnz+PmjVrqh1KqTp16lRsiZkzZ84gIiJCpYjKb9GiRahRowZ69uypdihlunnzJrRa86rExcUFer1epYgqxtvbGzVr1sSNGzewceNG9OnTR+2QyiUyMhIhISHYtGmTcVtmZib27NkjxThdmRQlaWfPnsXvv/+OwMBAh/5+dn3aQXZ2tllLw8WLF3H48GEEBAQgPDxcxchKFxcXh6VLl+Knn36Cj4+PcYyDn58fPD09VY6uZJMmTUL37t0RHh6OrKwsLF26FFu3bsXGjRvVDq1UPj4+xcb9eXt7IzAwUOjxgG+88QZ69+6NiIgIJCUlYerUqXBxccFzzz2ndmilGjNmDDp27IiZM2fimWeewd69e7Fw4UIsXLhQ7dCs0uv1WLRoEQYPHgxXV/GL6N69e+P9999HeHg4mjRpgkOHDuGjjz7CSy+9pHZoVm3cuBGKouC+++7DuXPnMH78eDRs2BBDhgxROzSjsuqS0aNHY8aMGahfvz4iIyMxefJkhIaGom/fvsLGnJaWhoSEBOM6ZEU3UyEhIaq1BFqLuWbNmnjqqadw8OBBrFu3Djqdzlg/BgQEwN3d3f4BOmRu6T1my5YtCoBiX4MHD1Y7tFKVFC8AZdGiRWqHVqqXXnpJiYiIUNzd3ZXq1asrXbt2VX777Te1w6owGZbnePbZZ5WaNWsq7u7uSq1atZRnn31WOXfunNphlennn39WmjZtqnh4eCgNGzZUFi5cqHZIZdq4caMCQDl9+rTaoZRLZmamMmrUKCU8PFypUqWKUrduXeXtt99W8vLy1A7NqhUrVih169ZV3N3dlZCQECUuLk5JT09XOywzZdUler1emTx5shIcHKx4eHgoXbt2Vf28KSvmRYsWlbh/6tSpQsZctIxISV9btmxxSHwaRRF8+WgiIiKiexTHqBEREREJiokaERERkaCYqBEREREJiokaERERkaCYqBEREREJiokaERERkaCYqBEREREJiokaERERkaCYqBHRPevq1asYMWIEwsPD4eHhgZCQEMTGxuLPP/8EAGg0GqxZs6bC71unTh3MnTvXtsES0T1J/AfJERHZSb9+/ZCfn48lS5agbt26SElJwaZNm3D9+nW1QyMiAgDwEVJEdE9KT09HtWrVsHXrVjz44IPF9tepUweXL182/hwREYFLly7h/PnzGDt2LHbv3o2cnBw0atQIs2bNQkxMDADgoYcewrZt28zeq6iY3bFjByZNmoT9+/cjKCgITzzxBGbNmgVvb287/qVEJDN2fRLRPalq1aqoWrUq1qxZg7y8vGL79+3bBwBYtGgRrly5Yvw5OzsbPXr0wKZNm3Do0CE89thj6N27NxISEgAAP/74I2rXro1p06bhypUruHLlCgDg/PnzeOyxx9CvXz8cPXoUK1aswI4dOzBy5EgH/cVEJCO2qBHRPWvVqlUYPnw4bt26hdatW+PBBx9E//790bx5cwCGMWqrV69G3759rb5P06ZN8X//93/GpKtOnToYPXo0Ro8ebTxm2LBhcHFxwX//+1/jth07duDBBx9ETk4OqlSpYvO/j4jkxxY1Irpn9evXD0lJSVi7di0ee+wxbN26Fa1bt8bixYtLfU12djbeeOMNNGrUCP7+/qhatSpOnTplbFErzZEjR7B48WJjS17VqlURGxsLvV6Pixcv2vgvIyJnwckERHRPq1KlCh599FE8+uijmDx5MoYNG4apU6fixRdfLPH4N954A/Hx8fjwww9Rr149eHp64qmnnkJ+fr7V35OdnY1XXnkFr7/+erF94eHhtvhTiMgJMVEjIjLRuHFj45Icbm5u0Ol0Zvv//PNPvPjii3jiiScAGBKwS5cumR3j7u5e7HWtW7fGyZMnUa9ePbvFTkTOh12fRHRPun79Oh555BF8++23OHr0KC5evIjvv/8ec+bMQZ8+fQAYxppt2rQJycnJuHHjBgCgfv36+PHHH3H48GEcOXIEzz//PPR6vdl716lTB9u3b8c///yDa9euAQAmTJiAnTt3YuTIkTh8+DDOnj2Ln376iZMJiMgqJmpEdE+qWrUq2rdvj48//hgPPPAAmjZtismTJ2P48OH47LPPAAD//ve/ER8fj7CwMLRq1QoA8NFHH6FatWro2LEjevfujdjYWLRu3drsvadNm4ZLly4hKioK1atXBwA0b94c27Ztw5kzZ9ClSxe0atUKU6ZMQWhoqGP/cCKSCmd9EhEREQmKLWpEREREgmKiRkRERCQoJmpEREREgmKiRkRERCQoJmpEREREgmKiRkRERCQoJmpEREREgmKiRkRERCQoJmpEREREgmKiRkRERCQoJmpEREREgmKiRkRERCSo/wdn9Q2Wz/8kcQAAAABJRU5ErkJggg==",
+ "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",
+ " 2,\n",
+ " 3,\n",
+ " 4,\n",
+ " 5,\n",
+ " 6,\n",
+ " 7,\n",
+ " 8,\n",
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+ " 10,\n",
+ " 11,\n",
+ " 12,\n",
+ " 13,\n",
+ " 14,\n",
+ " 15,\n",
+ " 16,\n",
+ " 17,\n",
+ " 18,\n",
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+ " 20,\n",
+ " 21,\n",
+ " 22,\n",
+ " 23,\n",
+ " 24,\n",
+ " 25,\n",
+ " 26,\n",
+ " 27,\n",
+ " 28,\n",
+ " 29]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "history.epoch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "65e18286",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'loss': [0.7220436930656433,\n",
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+ " 'accuracy': [0.7648727297782898,\n",
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+ " 'val_loss': [0.4958552122116089,\n",
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+ " 0.3053490221500397],\n",
+ " 'val_accuracy': [0.8331999778747559,\n",
+ " 0.8384000062942505,\n",
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+ " 0.8863999843597412,\n",
+ " 0.885200023651123,\n",
+ " 0.8722000122070312,\n",
+ " 0.8859999775886536,\n",
+ " 0.8835999965667725,\n",
+ " 0.8867999911308289,\n",
+ " 0.885200023651123,\n",
+ " 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": {
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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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",
+ "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",
+ " [0.79609823 1.0192721 ]]\n",
+ "\n",
+ " [[0.92234135 0.7996243 ]\n",
+ " [0.7996243 1.0207424 ]]\n",
+ "\n",
+ " [[0.9045069 0.7845162 ]\n",
+ " [0.7845162 1.0037544 ]]]\n",
+ "[[[0.88976556 0.7850014 ]\n",
+ " [0.7850014 1.0120944 ]]\n",
+ "\n",
+ " [[0.8989827 0.7739572 ]\n",
+ " [0.7739572 0.9784459 ]]\n",
+ "\n",
+ " [[1.098712 0.78814816]\n",
+ " [0.78814816 0.9570935 ]]\n",
+ "\n",
+ " [[1.1058564 0.8175512 ]\n",
+ " [0.8175512 1.0108967 ]]\n",
+ "\n",
+ " [[1.0922778 0.8694079 ]\n",
+ " [0.8694079 1.1012552 ]]]\n",
+ "[[[0.94134605 0.80987793]\n",
+ " [0.80987793 1.0408254 ]]\n",
+ "\n",
+ " [[1.093068 0.78875166]\n",
+ " [0.78875166 0.95536584]]\n",
+ "\n",
+ " [[1.09097 0.7794325 ]\n",
+ " [0.7794325 0.9497656 ]]\n",
+ "\n",
+ " [[0.9290437 0.8224858 ]\n",
+ " [0.8224858 1.0627043 ]]\n",
+ "\n",
+ " [[0.9168434 0.8078131 ]\n",
+ " [0.8078131 1.0443711 ]]]\n",
+ "Number of states: 6\n",
+ "[[[1.1025289 0.81105536]\n",
+ " [0.81105536 1.002676 ]]\n",
+ "\n",
+ " [[1.0992348 0.7861176 ]\n",
+ " [0.7861176 0.96259874]]\n",
+ "\n",
+ " [[0.8994706 0.771886 ]\n",
+ " [0.771886 0.9884017 ]]\n",
+ "\n",
+ " [[0.92650026 0.8338887 ]\n",
+ " [0.8338887 1.0753859 ]]\n",
+ "\n",
+ " [[1.0062379 0.80014485]\n",
+ " [0.80014485 0.9716734 ]]\n",
+ "\n",
+ " [[0.9147214 0.7950424 ]\n",
+ " [0.7950424 1.019712 ]]]\n",
+ "[[[0.9029103 0.7746366 ]\n",
+ " [0.7746366 0.9868297 ]]\n",
+ "\n",
+ " [[0.89553756 0.7443281 ]\n",
+ " [0.7443281 0.9386706 ]]\n",
+ "\n",
+ " [[0.93892825 0.80205435]\n",
+ " [0.80205435 1.028882 ]]\n",
+ "\n",
+ " [[0.9468897 0.84350663]\n",
+ " [0.84350663 1.128863 ]]\n",
+ "\n",
+ " [[1.1176581 0.82541394]\n",
+ " [0.82541394 1.005693 ]]\n",
+ "\n",
+ " [[1.0946487 0.77416563]\n",
+ " [0.77416563 0.9443206 ]]]\n",
+ "[[[0.9161175 0.81267834]\n",
+ " [0.81267834 1.0434958 ]]\n",
+ "\n",
+ " [[0.9371134 0.7878498 ]\n",
+ " [0.7878498 0.98242277]]\n",
+ "\n",
+ " [[1.0801865 0.78495306]\n",
+ " [0.78495306 0.96006995]]\n",
+ "\n",
+ " [[1.0942332 0.8485199 ]\n",
+ " [0.8485199 1.0688753 ]]\n",
+ "\n",
+ " [[0.90466684 0.7862666 ]\n",
+ " [0.7862666 1.001132 ]]\n",
+ "\n",
+ " [[1.0735751 0.7846548 ]\n",
+ " [0.7846548 0.97497517]]]\n",
+ "[[[0.9374819 0.8263422 ]\n",
+ " [0.8263422 1.0667529 ]]\n",
+ "\n",
+ " [[0.8983345 0.80374223]\n",
+ " [0.80374223 1.0481212 ]]\n",
+ "\n",
+ " [[1.0901906 0.7727158 ]\n",
+ " [0.7727158 0.93249434]]\n",
+ "\n",
+ " [[1.0726608 0.7981091 ]\n",
+ " [0.7981091 0.9862327 ]]\n",
+ "\n",
+ " [[1.0789424 0.7869114 ]\n",
+ " [0.7869114 0.9710959 ]]\n",
+ "\n",
+ " [[0.9407348 0.8127351 ]\n",
+ " [0.8127351 1.0313928 ]]]\n",
+ "[[[0.9493842 0.7997072 ]\n",
+ " [0.7997072 1.0161539 ]]\n",
+ "\n",
+ " [[1.0872875 0.9011979 ]\n",
+ " [0.9011979 1.1571093 ]]\n",
+ "\n",
+ " [[0.90396553 0.7877316 ]\n",
+ " [0.7877316 1.0100045 ]]\n",
+ "\n",
+ " [[0.89578956 0.7730664 ]\n",
+ " [0.7730664 0.98428106]]\n",
+ "\n",
+ " [[1.1052877 0.7949801 ]\n",
+ " [0.7949801 0.9674929 ]]\n",
+ "\n",
+ " [[1.0856913 0.7629942 ]\n",
+ " [0.7629942 0.9151122 ]]]\n",
+ "Number of states: 7\n",
+ "[[[1.1021056 0.8982767 ]\n",
+ " [0.8982767 1.1350775 ]]\n",
+ "\n",
+ " [[0.8956594 0.7939985 ]\n",
+ " [0.7939985 1.0252453 ]]\n",
+ "\n",
+ " [[0.916886 0.7620164 ]\n",
+ " [0.7620164 0.9679563 ]]\n",
+ "\n",
+ " [[0.92897844 0.80735725]\n",
+ " [0.80735725 1.0388353 ]]\n",
+ "\n",
+ " [[1.051059 0.7524101 ]\n",
+ " [0.7524101 0.8689886 ]]\n",
+ "\n",
+ " [[0.9435898 0.8181365 ]\n",
+ " [0.8181365 1.0364859 ]]\n",
+ "\n",
+ " [[1.1064277 0.78433686]\n",
+ " [0.78433686 0.9683421 ]]]\n",
+ "[[[0.9463536 0.7354092 ]\n",
+ " [0.7354092 0.8937815 ]]\n",
+ "\n",
+ " [[0.9171668 0.7836797 ]\n",
+ " [0.7836797 0.992365 ]]\n",
+ "\n",
+ " [[0.94721687 0.7226219 ]\n",
+ " [0.7226219 0.8945146 ]]\n",
+ "\n",
+ " [[1.0808338 0.7809238 ]\n",
+ " [0.7809238 0.96382326]]\n",
+ "\n",
+ " [[1.0763447 0.8164317 ]\n",
+ " [0.8164317 1.0083678 ]]\n",
+ "\n",
+ " [[0.89992905 0.80587053]\n",
+ " [0.80587053 1.0476433 ]]\n",
+ "\n",
+ " [[1.0849026 0.906843 ]\n",
+ " [0.906843 1.1581697 ]]]\n",
+ "[[[0.9690579 0.72830003]\n",
+ " [0.72830003 0.8826607 ]]\n",
+ "\n",
+ " [[1.1101353 0.80389434]\n",
+ " [0.80389434 0.9926196 ]]\n",
+ "\n",
+ " [[1.0858461 0.7794871 ]\n",
+ " [0.7794871 0.96993834]]\n",
+ "\n",
+ " [[0.93614256 0.7641451 ]\n",
+ " [0.7641451 0.9591491 ]]\n",
+ "\n",
+ " [[0.92039365 0.8272848 ]\n",
+ " [0.8272848 1.0702317 ]]\n",
+ "\n",
+ " [[0.9261147 0.82936114]\n",
+ " [0.82936114 1.0604893 ]]\n",
+ "\n",
+ " [[0.91787225 0.8176582 ]\n",
+ " [0.8176582 1.0486727 ]]]\n",
+ "[[[0.924301 0.75347394]\n",
+ " [0.75347394 0.9421399 ]]\n",
+ "\n",
+ " [[1.0524554 0.74593186]\n",
+ " [0.74593186 0.88168347]]\n",
+ "\n",
+ " [[0.9499674 0.8387745 ]\n",
+ " [0.8387745 1.0969619 ]]\n",
+ "\n",
+ " [[1.0877323 0.82595843]\n",
+ " [0.82595843 1.0139198 ]]\n",
+ "\n",
+ " [[1.1184709 0.80465513]\n",
+ " [0.80465513 0.98803717]]\n",
+ "\n",
+ " [[0.91292226 0.8085382 ]\n",
+ " [0.8085382 1.0466415 ]]\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",
+ " [0.89900565 1.1580135 ]]\n",
+ "\n",
+ " [[0.91595525 0.7697255 ]\n",
+ " [0.7697255 0.9835607 ]]\n",
+ "\n",
+ " [[0.9020007 0.78764975]\n",
+ " [0.78764975 1.0130019 ]]]\n",
+ "[[[1.0908858 0.86337405]\n",
+ " [0.86337405 1.0829239 ]]\n",
+ "\n",
+ " [[1.0440573 0.7691612 ]\n",
+ " [0.7691612 0.97145104]]\n",
+ "\n",
+ " [[1.0812275 0.7605502 ]\n",
+ " [0.7605502 0.91883487]]\n",
+ "\n",
+ " [[0.91121876 0.71673125]\n",
+ " [0.71673125 0.8764679 ]]\n",
+ "\n",
+ " [[1.1192652 0.91376704]\n",
+ " [0.91376704 1.1442885 ]]\n",
+ "\n",
+ " [[1.094993 0.91519904]\n",
+ " [0.91519904 1.1672919 ]]\n",
+ "\n",
+ " [[0.9010709 0.7928878 ]\n",
+ " [0.7928878 1.0161046 ]]\n",
+ "\n",
+ " [[0.90103674 0.75275177]\n",
+ " [0.75275177 0.956106 ]]]\n",
+ "[[[1.0900388 0.7868064 ]\n",
+ " [0.7868064 0.972674 ]]\n",
+ "\n",
+ " [[0.8922494 0.77270436]\n",
+ " [0.77270436 0.98949605]]\n",
+ "\n",
+ " [[1.1205899 0.80705565]\n",
+ " [0.80705565 0.9700604 ]]\n",
+ "\n",
+ " [[0.9211841 0.7213676 ]\n",
+ " [0.7213676 0.87451786]]\n",
+ "\n",
+ " [[0.8841187 0.78384846]\n",
+ " [0.78384846 1.0270954 ]]\n",
+ "\n",
+ " [[0.9077612 0.7589635 ]\n",
+ " [0.7589635 0.9615318 ]]\n",
+ "\n",
+ " [[1.03309 0.8333379 ]\n",
+ " [0.8333379 1.0411042 ]]\n",
+ "\n",
+ " [[1.114002 0.9238987 ]\n",
+ " [0.9238987 1.1767256 ]]]\n",
+ "[[[0.89513147 0.72677225]\n",
+ " [0.72677225 0.91194934]]\n",
+ "\n",
+ " [[1.1261872 0.9245538 ]\n",
+ " [0.9245538 1.1620623 ]]\n",
+ "\n",
+ " [[1.092227 0.9024537 ]\n",
+ " [0.9024537 1.1502043 ]]\n",
+ "\n",
+ " [[1.0936671 0.78467953]\n",
+ " [0.78467953 0.9744951 ]]\n",
+ "\n",
+ " [[1.0964669 0.79312646]\n",
+ " [0.79312646 0.97518545]]\n",
+ "\n",
+ " [[0.937142 0.75748426]\n",
+ " [0.75748426 0.9241803 ]]\n",
+ "\n",
+ " [[0.867578 0.7824369 ]\n",
+ " [0.7824369 1.0396202 ]]\n",
+ "\n",
+ " [[0.9217296 0.7288364 ]\n",
+ " [0.7288364 0.90789264]]]\n",
+ "[[[0.90043074 0.79544365]\n",
+ " [0.79544365 1.0227047 ]]\n",
+ "\n",
+ " [[0.9246434 0.84118235]\n",
+ " [0.84118235 1.1442387 ]]\n",
+ "\n",
+ " [[1.1128544 0.78364897]\n",
+ " [0.78364897 0.9509447 ]]\n",
+ "\n",
+ " [[1.1191497 0.80643165]\n",
+ " [0.80643165 0.9808009 ]]\n",
+ "\n",
+ " [[0.9201129 0.7938978 ]\n",
+ " [0.7938978 1.0003242 ]]\n",
+ "\n",
+ " [[0.909699 0.73347354]\n",
+ " [0.73347354 0.90999967]]\n",
+ "\n",
+ " [[1.0924108 0.79094136]\n",
+ " [0.79094136 0.9784196 ]]\n",
+ "\n",
+ " [[0.9142059 0.81648827]\n",
+ " [0.81648827 1.0549946 ]]]\n",
+ "Number of states: 9\n",
+ "[[[0.95074123 0.8325831 ]\n",
+ " [0.8325831 1.0670731 ]]\n",
+ "\n",
+ " [[1.0765426 0.75886 ]\n",
+ " [0.75886 0.9285429 ]]\n",
+ "\n",
+ " [[1.0892979 0.8008286 ]\n",
+ " [0.8008286 0.980694 ]]\n",
+ "\n",
+ " [[0.90247893 0.7658782 ]\n",
+ " [0.7658782 0.98230106]]\n",
+ "\n",
+ " [[0.92199206 0.82028437]\n",
+ " [0.82028437 1.0608895 ]]\n",
+ "\n",
+ " [[1.1016724 0.79524195]\n",
+ " [0.79524195 0.9605984 ]]\n",
+ "\n",
+ " [[0.912925 0.7753818 ]\n",
+ " [0.7753818 0.9882154 ]]\n",
+ "\n",
+ " [[1.0787107 0.87446964]\n",
+ " [0.87446964 1.1129494 ]]\n",
+ "\n",
+ " [[0.92790204 0.7897976 ]\n",
+ " [0.7897976 0.99500936]]]\n",
+ "[[[1.102906 0.7810447 ]\n",
+ " [0.7810447 0.93838376]]\n",
+ "\n",
+ " [[1.1063184 0.7890468 ]\n",
+ " [0.7890468 0.95404834]]\n",
+ "\n",
+ " [[1.107973 0.8900043 ]\n",
+ " [0.8900043 1.1258235 ]]\n",
+ "\n",
+ " [[0.9166915 0.7006637 ]\n",
+ " [0.7006637 0.8614699 ]]\n",
+ "\n",
+ " [[0.93092287 0.7909375 ]\n",
+ " [0.7909375 0.9995565 ]]\n",
+ "\n",
+ " [[0.9435898 0.81312317]\n",
+ " [0.81312317 1.0224084 ]]\n",
+ "\n",
+ " [[0.92617387 0.7149954 ]\n",
+ " [0.7149954 0.8739951 ]]\n",
+ "\n",
+ " [[0.97343004 0.8659819 ]\n",
+ " [0.8659819 1.1578336 ]]\n",
+ "\n",
+ " [[0.88600254 0.8032057 ]\n",
+ " [0.8032057 1.057508 ]]]\n",
+ "[[[1.0780615 0.90799904]\n",
+ " [0.90799904 1.1637644 ]]\n",
+ "\n",
+ " [[1.0827413 0.7718213 ]\n",
+ " [0.7718213 0.9529378 ]]\n",
+ "\n",
+ " [[1.1387582 0.82011735]\n",
+ " [0.82011735 0.9898518 ]]\n",
+ "\n",
+ " [[0.9001697 0.80039054]\n",
+ " [0.80039054 1.0369468 ]]\n",
+ "\n",
+ " [[0.9522885 0.846554 ]\n",
+ " [0.846554 1.1082916 ]]\n",
+ "\n",
+ " [[0.9064803 0.7698696 ]\n",
+ " [0.7698696 0.9811515 ]]\n",
+ "\n",
+ " [[1.0812808 0.7738438 ]\n",
+ " [0.7738438 0.93978393]]\n",
+ "\n",
+ " [[0.89234096 0.7814731 ]\n",
+ " [0.7814731 0.99983764]]\n",
+ "\n",
+ " [[0.92652255 0.7070329 ]\n",
+ " [0.7070329 0.8685296 ]]]\n",
+ "[[[0.9374685 0.8106923 ]\n",
+ " [0.8106923 1.0316019 ]]\n",
+ "\n",
+ " [[0.93530124 0.77429146]\n",
+ " [0.77429146 0.9604364 ]]\n",
+ "\n",
+ " [[0.93732655 0.82991743]\n",
+ " [0.82991743 1.0657027 ]]\n",
+ "\n",
+ " [[0.91940045 0.82314986]\n",
+ " [0.82314986 1.0726796 ]]\n",
+ "\n",
+ " [[1.0798302 0.7641081 ]\n",
+ " [0.7641081 0.9411573 ]]\n",
+ "\n",
+ " [[1.0531902 0.74914044]\n",
+ " [0.74914044 0.90307885]]\n",
+ "\n",
+ " [[1.0734575 0.7847439 ]\n",
+ " [0.7847439 0.9730621 ]]\n",
+ "\n",
+ " [[1.0024395 0.87487704]\n",
+ " [0.87487704 1.157921 ]]\n",
+ "\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",
+ " [0.70666516 0.86549526]]\n",
+ "\n",
+ " [[0.9492726 0.824768 ]\n",
+ " [0.824768 1.1146137 ]]\n",
+ "\n",
+ " [[0.9601999 0.8260254 ]\n",
+ " [0.8260254 1.0321217 ]]\n",
+ "\n",
+ " [[1.0956397 0.79526615]\n",
+ " [0.79526615 0.98229986]]\n",
+ "\n",
+ " [[0.8909471 0.716595 ]\n",
+ " [0.716595 0.8982453 ]]\n",
+ "\n",
+ " [[0.95050144 0.82132065]\n",
+ " [0.82132065 1.0323883 ]]\n",
+ "\n",
+ " [[0.927769 0.83661586]\n",
+ " [0.83661586 1.0813875 ]]]\n",
+ "[[[1.1291575 0.9366514 ]\n",
+ " [0.9366514 1.1701603 ]]\n",
+ "\n",
+ " [[0.8946974 0.7995994 ]\n",
+ " [0.7995994 1.041776 ]]\n",
+ "\n",
+ " [[1.12785 0.798106 ]\n",
+ " [0.798106 0.9491125 ]]\n",
+ "\n",
+ " [[1.0851202 0.88981336]\n",
+ " [0.88981336 1.1315111 ]]\n",
+ "\n",
+ " [[1.0976489 0.7813601 ]\n",
+ " [0.7813601 0.95405513]]\n",
+ "\n",
+ " [[0.9151474 0.8248288 ]\n",
+ " [0.8248288 1.0621785 ]]\n",
+ "\n",
+ " [[0.99558944 0.73154896]\n",
+ " [0.73154896 0.8939125 ]]\n",
+ "\n",
+ " [[0.88816345 0.7401763 ]\n",
+ " [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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9pmPBqh6SlJCQILfbrdraWtPx2tpaJScnB71m1qxZ+uEPf6i77/7nA1r79eun+vp63XPPPXr44YcVE3NqzaJTp07q2bOnqqqqQv4eVD4AALCbRWs+PB6POnbsaBqnSz5iY2OVkZGhsrKyb8Lw+1VWVqbBgwcHveYf//jHKQmG2+2WJBlG8ATq+PHj+uSTT5SSkhLyj4PKBwAAdnNoq63X69WkSZM0cOBADRo0SMXFxaqvr1dOTo4kaeLEiUpNTQ20bsaMGaOioiJdddVVyszMVFVVlWbNmqUxY8YEkpAZM2ZozJgx6tatm7744gsVFBTI7XZr/PjxIcdF8gEAQJQaN26cDhw4oNmzZ6umpkYDBgxQaWlpYBFqdXW1qdIxc+ZMuVwuzZw5U3v37lViYqLGjBmjn/3sZ4FzPv/8c40fP14HDx5UYmKihg4dqvfee0+JiYkhx+UyTldHOYedKP9vp0OwRMOKFU6H0Gytx97odAjWaDhx9nPOAbu9m5wOodkunT/I6RCsERu8VH4ucXXu6nQIlogbMsH2zzju/b4l87QvetmSeZxG5QMAAJsZPNXWhAWnAAAgoqh8AABgNyofJo5XPr788ku98847+uijj05578SJE3ruuecciAoAAAv5/daMKOFo8rFjxw5dccUV+u53v6t+/fpp+PDh2rdvX+D9I0eOBLYDnY7P59PRo0dNw9dw0u7QAQBAEzmafDzwwAPq27ev9u/fr+3bt6tDhw4aMmRIWA+oCXar2cee+x8bowYAIEwOPFiuJXN0zce7776rN954QwkJCUpISNArr7yiH/3oRxo2bJjeeusttWvX7qxzBLvVrPHhWpsiBgCgCaIocbCCo5WPL7/8Uq1afZP/uFwuLVmyRGPGjNHw4cO1Y8eOs84R9Fazsa3tDBsAADSDo5WP3r1764MPPtAVV1xhOv7LX/5SkvT971tzUxYAAJwUhffzbBZHKx8333yz/vu/g9+N9Je//KXGjx/PLxgA4NzHmg8TR5OP/Px8rVu37rTvP/XUU/JH0dYiAMB5iuTDxPH7fAAAgPMLdzgFAMBmPNvFjOQDAAC7kXyY0HYBAAARReUDAAC7sXfChOQDAACbsebDjLYLAACIKCofAADYjcqHCckHAAB2Y82HCW0XAAAQUVQ+AACwGQtOzUg+AACwG20XE5IPAABsRuXDjDUfAAAgoqh8AABgN9ouJiQfAADYzCD5MKHtAgAAIioqKx/HZpc4HYIlumyqcjqEZnt8XaLTIViiLiY6Fov96tgep0Notqf/s6PTIVjC53I5HUKztdafnQ7BEmNqJtj/IVQ+TKIy+QAAoCWh7WJG2wUAAEQUlQ8AAOxG5cOE5AMAAJvRdjEj+QAAwGYkH2as+QAAABFF5QMAAJtR+TAj+QAAwG7GuX9fFyvRdgEAABFF5QMAAJvRdjEj+QAAwGaGn7bLt9F2AQAgii1evFhpaWmKi4tTZmamKioqznh+cXGxevXqpTZt2qhr166aPn26Tpw40aw5/xXJBwAANjP81oxwrV69Wl6vVwUFBdq8ebPS09OVnZ2t/fv3Bz3/hRde0IMPPqiCggJ9/PHHWrp0qVavXq2HHnqoyXMGQ/IBAIDNDMNlyQhXUVGRcnNzlZOToz59+qikpERt27bVsmXLgp7/7rvvasiQIbr99tuVlpamkSNHavz48abKRrhzBkPyAQDAOcLn8+no0aOm4fP5gp7b0NCgyspKZWVlBY7FxMQoKytL5eXlQa+55pprVFlZGUg2du3apXXr1mn06NFNnjMYkg8AAGxmVdulsLBQ8fHxplFYWBj0M+vq6tTY2KikpCTT8aSkJNXU1AS95vbbb9e8efM0dOhQtW7dWj169NC1114baLs0Zc5gSD4AALCZ4XdZMvLz83XkyBHTyM/PtyzOjRs3av78+Xrqqae0efNmrVmzRq+99poeeeQRyz5DYqstAAC2Mwxr5vF4PPJ4PCGdm5CQILfbrdraWtPx2tpaJScnB71m1qxZ+uEPf6i7775bktSvXz/V19frnnvu0cMPP9ykOYOh8gEAQBSKjY1VRkaGysrKAsf8fr/Kyso0ePDgoNf84x//UEyMOTVwu92SJMMwmjRnMFQ+AACwmVM3GfN6vZo0aZIGDhyoQYMGqbi4WPX19crJyZEkTZw4UampqYF1I2PGjFFRUZGuuuoqZWZmqqqqSrNmzdKYMWMCScjZ5gwFyQcAADZzKvkYN26cDhw4oNmzZ6umpkYDBgxQaWlpYMFodXW1qdIxc+ZMuVwuzZw5U3v37lViYqLGjBmjn/3sZyHPGQqXYVjViWqajz/+WO+9954GDx6s3r17a9u2bVq0aJF8Pp/uuOMOXXfddWe83ufznbLN6OjNN8oTc+53lLpsqnI6hGZ7POl7TodgiboYR3+bWOZXx/7sdAjN9nTrfk6HYAmf69y/3XZrRcfvizE1/237Z3w64HpL5knbssGSeZzm6N/QpaWlGjBggGbMmKGrrrpKpaWl+u53v6uqqirt2bNHI0eO1JtvvnnGOYJtO1q0uzpC3wAAgLMzDGtGtHA0+Zg3b55+8pOf6ODBg1q+fLluv/125ebmasOGDSorK9NPfvITLViw4IxzBNt2NO3SSyL0DQAAODurttpGC0eTj7/97W+aPHmyJOnWW2/VsWPH9IMf/CDw/oQJE/SXv/zljHN4PB517NjRNKKh5QIAQLRyfMGp6//6njExMYqLi1N8fHzgvQ4dOujIkSNOhQYAgCWa8lyWaOZoiSAtLU07d+4MvC4vL9cll3zTMqmurlZKSooToQEAYBmnnmrbUjla+Zg6daoaGxsDr/v27Wt6/w9/+MNZd7sAAIBzi6PJx5QpU874/vz58yMUCQAA9vHTdjFpcvLR0NCg/fv3y+8314G+3TYBAACs+fhXYScfO3fu1J133ql3333XdNwwDLlcLlMbBQAAOHeH05Yq7ORj8uTJatWqlV599VWlpKQEdqsAAACEIuzkY8uWLaqsrFTv3r3tiAcAgKgTTXcntULYyUefPn1UV1dnRywAAEQl2i5mId3n4+jRo4Hx6KOP6v7779fGjRt18OBB03tHjx61O14AAHCOC6ny0alTJ9PaDsMwNGLECNM5LDgFACA4ttqahZR8vPXWW3bHAQBA1GKrrVlIycfw4cMD/19dXa2uXbuessvFMAx99tln1kYHAACiTtjPdrn00kt14MCBU44fOnRIl156qSVBAQAQTQzDmhEtwt7t8vXajn91/PhxxcXFWRIUAADRhDUfZiEnH16vV5Lkcrk0a9YstW3bNvBeY2Oj3n//fQ0YMMDyAAEAQHQJOfn48MMPJf2z8vHXv/5VsbGxgfdiY2OVnp6uGTNmWB8hAADnOBacmoWcfHy94yUnJ0eLFi1Sx44dbQsKAIBoEk3rNawQ9pqP5cuX2xEHAABRizUfZmEnH9ddd90Z33/zzTebHAwAAIh+YScf6enpptcnT57Uli1btHXrVk2aNMmywJqj35/2Ox2CJVYkfM/pEJrtQ3d03PE2rdHtdAiW2Ll8otMhNNvlOc85HYIlDvzjiNMhNJs3eZjTIVhiTAQ+gzUfZmEnH48//njQ43PmzNHx48ebHRAAANGGtotZ2DcZO5077rhDy5Yts2o6AAAQpcKufJxOeXk5NxkDACAINruYhZ18/Pu//7vptWEY2rdvnz744APNmjXLssAAAIgWtF3Mwk4+4uPjTa9jYmLUq1cvzZs3TyNHjrQsMAAAEJ3CSj4aGxuVk5Ojfv366YILLrArJgAAogq7XczCWnDqdrs1cuRIHT582KZwAACIPn6LRrQIe7dL3759tWvXLjtiAQAA54Gwk4+f/vSnmjFjhl599VXt27dPR48eNQ0AAGBmyGXJiBYhr/mYN2+e/uu//kujR4+WJH3/+9+Xy/XND8IwDLlcLjU2RscdLQEAsIqfvbYmIScfc+fO1ZQpUwJPtwUAAKHxR1HVwgohJx/G/z0PePjw4bYFAwAAol9YW22/3WYBAAChiab1GlYIK/no2bPnWROQQ4cONSsgAACiTTRtk7VCWMnH3LlzT7nDKQAAQDjCSj5uu+02de7c2a5YAACISk62XRYvXqzHHntMNTU1Sk9P15NPPqlBgwYFPffaa6/Vpk2bTjk+evRovfbaa5KkyZMna+XKlab3s7OzVVpaGnJMIScfrPcAAKBpnGq7rF69Wl6vVyUlJcrMzFRxcbGys7O1ffv2oMWENWvWqKGhIfD64MGDSk9P1y233GI674YbbtDy5csDrz0eT1hxhXyTsa93uwAAgHNDUVGRcnNzlZOToz59+qikpERt27bVsmXLgp5/4YUXKjk5OTA2bNigtm3bnpJ8eDwe03nhPu8t5OTD7/fTcgEAoAmseraLz+c75c7iPp8v6Gc2NDSosrJSWVlZgWMxMTHKyspSeXl5SHEvXbpUt912m9q1a2c6vnHjRnXu3Fm9evXS1KlTdfDgwVB/FP+MI6yzI4AKCwAg2lh1e/XCwkLFx8ebRmFhYdDPrKurU2Njo5KSkkzHk5KSVFNTc9aYKyoqtHXrVt19992m4zfccIOee+45lZWV6dFHH9WmTZs0atSosO5wHtaC00jweDz685//rCuuuMLpUAAAaFHy8/Pl9XpNx8JdbxGqpUuXql+/fqcsTr3tttsC/9+vXz/1799fPXr00MaNGzVixIiQ5nYs+fjXH97XGhsbtWDBAl100UWS/tmvOhOfz3dKyckw/HK5WlxRBwBwnvJbtGfD4/GEnGwkJCTI7XartrbWdLy2tlbJyclnvLa+vl6rVq3SvHnzzvo53bt3V0JCgqqqqlp+8lFcXKz09HR16tTJdNwwDH388cdq165dSDtsCgsLNXfuXNOxdp6L1D4u0cpwAQBoMiee7RIbG6uMjAyVlZVp7Nix/4zD71dZWZnuvffeM17729/+Vj6fT3fcccdZP+fzzz/XwYMHlZKSEnJsjiUf8+fP1zPPPKOFCxfquuuuCxxv3bq1VqxYoT59+oQ0T7AS1OVdr7Y0VgAAmsOp1Yxer1eTJk3SwIEDNWjQIBUXF6u+vl45OTmSpIkTJyo1NfWUdSNLly7V2LFjA12Irx0/flxz587Vf/zHfyg5OVmffPKJ7r//fl122WXKzs4OOS7Hko8HH3xQI0aM0B133KExY8aosLBQrVu3DnueYCUoWi4AAEjjxo3TgQMHNHv2bNXU1GjAgAEqLS0NLEKtrq5WTIz578zt27frnXfe0euvv37KfG63W3/5y1+0cuVKHT58WF26dNHIkSP1yCOPhLX2xNEFp1dffbUqKyuVl5engQMH6je/+Q03MwMARB0nn+1y7733nrbNsnHjxlOO9erV67Q7T9u0aaP169c3OybHd7u0b99eK1eu1KpVq5SVlRXWVh0AAM4Ffv5hbeJ48vG12267TUOHDlVlZaW6devmdDgAAMAmLSb5kKSLL75YF198sdNhAABgKW6fadaikg8AAKKRk2s+WiK2hQAAgIii8gEAgM2susNptCD5AADAZk7c4bQlo+0CAAAiisoHAAA2Y7eLGckHAAA2Y82HGckHAAA2Y6utGWs+AABARFH5AADAZqz5MCP5AADAZqz5MKPtAgAAIorKBwAANmPBqRnJBwAANiP5MKPtAgAAIorKBwAANjNYcGoSlcnHZ1WvOR2CJQ794E6nQ2i2//hZrtMhWMI4uM/pECyx7d63nQ6h2T55/xmnQ7CEy33u//Hr//sXTodwzqDtYkbbBQAARNS5n3oDANDCUfkwI/kAAMBm3OHUjOQDAACbcYdTM9Z8AACAiKLyAQCAzVjzYUbyAQCAzUg+zGi7AACAiKLyAQCAzdjtYkbyAQCAzdjtYkbbBQAARBSVDwAAbMaCUzOSDwAAbMaaDzPaLgAAIKKofAAAYDM/tQ8Tkg8AAGzGmg8zkg8AAGxG3cOMNR8AACCiqHwAAGAz2i5mVD4AALCZ32XNaIrFixcrLS1NcXFxyszMVEVFxWnPvfbaa+VyuU4ZN954Y+AcwzA0e/ZspaSkqE2bNsrKytLOnTvDionkAwCAKLV69Wp5vV4VFBRo8+bNSk9PV3Z2tvbv3x/0/DVr1mjfvn2BsXXrVrndbt1yyy2Bc37+85/riSeeUElJid5//321a9dO2dnZOnHiRMhxtai2S319vV588UVVVVUpJSVF48eP10UXXXTGa3w+n3w+n+lYjM8nj8djZ6gAAITMqa22RUVFys3NVU5OjiSppKREr732mpYtW6YHH3zwlPMvvPBC0+tVq1apbdu2geTDMAwVFxdr5syZuummmyRJzz33nJKSkrR27VrddtttIcXlaOWjT58+OnTokCTps88+U9++fTV9+nRt2LBBBQUF6tOnj3bv3n3GOQoLCxUfH28ajy4qiUT4AACExLBo+Hw+HT161DT+9R/gX2toaFBlZaWysrICx2JiYpSVlaXy8vKQ4l66dKluu+02tWvXTpK0e/du1dTUmOaMj49XZmZmyHNKDicf27Zt01dffSVJys/PV5cuXbRnzx5VVFRoz5496t+/vx5++OEzzpGfn68jR46YxgPTpkQifAAAIirYP7gLCwuDnltXV6fGxkYlJSWZjiclJammpuasn1VRUaGtW7fq7rvvDhz7+rqmzvm1FtN2KS8vV0lJieLj4yVJ7du319y5c89awvF4PKe0WE421NkWJwAA4bJqt0t+fr68Xq/pmF3LDJYuXap+/fpp0KBBls/t+IJTl+ufy3dPnDihlJQU03upqak6cOCAE2EBAGAZvwxLhsfjUceOHU3jdMlHQkKC3G63amtrTcdra2uVnJx8xnjr6+u1atUq3XXXXabjX1/XlDm/zfHkY8SIEfrOd76jo0ePavv27ab39uzZc9YFpwAA4FSxsbHKyMhQWVlZ4Jjf71dZWZkGDx58xmt/+9vfyufz6Y477jAdv/TSS5WcnGya8+jRo3r//ffPOue3Odp2KSgoML1u37696fUrr7yiYcOGRTIkAAAs59Tt1b1eryZNmqSBAwdq0KBBKi4uVn19fWD3y8SJE5WamnrKupGlS5dq7NixpxQAXC6X7rvvPv30pz/V5ZdfrksvvVSzZs1Sly5dNHbs2JDjalHJx7967LHHIhQJAAD2ceoOp+PGjdOBAwc0e/Zs1dTUaMCAASotLQ0sGK2urlZMjLkJsn37dr3zzjt6/fXXg855//33q76+Xvfcc48OHz6soUOHqrS0VHFxcSHH5TIMI+qed3OybpfTIVji0A/udDqEZov/Wa7TIVjCOLjP6RAsse3et50Oodn6lD3gdAiWcLlbzHr/JvP//QunQ7BEXMZY2z/Dmxba/S/OpujTVZbM4zTH13wAAIDzy7mfegMA0MJFXYuhmUg+AACwGU+1NaPtAgAAIorKBwAANjNovJiQfAAAYDPaLma0XQAAQERR+QAAwGZ+2i4mJB8AANiM1MOMtgsAAIgoKh8AANiMtosZyQcAADZjt4sZyQcAADbjPh9mrPkAAAARReUDAACb0XYxi8rk46t31zgdgiXcbc79Mp1/8/86HYI1WrmdjsASqZcfdjqEZjt+X77TIVjiq3qX0yE0W8c7BzsdgjUyxtr+EbRdzGi7AACAiIrKygcAAC0JbRczkg8AAGzmN2i7fBttFwAAEFFUPgAAsBl1DzOSDwAAbMbt1c1ouwAAgIii8gEAgM24z4cZyQcAADZjq60ZyQcAADZjzYcZaz4AAEBEUfkAAMBmrPkwI/kAAMBmrPkwo+0CAAAiisoHAAA2M3i2iwnJBwAANmO3ixltFwAAEFFUPgAAsBkLTs1IPgAAsBlbbc1ouwAAgIii8gEAgM1YcGrmaOVj8+bN2r17d+D1888/ryFDhqhr164aOnSoVq1addY5fD6fjh49ahq+k1/ZGTYAAGExDMOSES0cTT5ycnL0ySefSJJ+9atf6f/9v/+ngQMH6uGHH9bVV1+t3NxcLVu27IxzFBYWKj4+3jQee+nNSIQPAEBI/BaNaOFo8rFz505dfvnlkqSnnnpKixYt0qJFizRlyhQ9/vjjevrpp7Vw4cIzzpGfn68jR46Yxk9+cF0kwgcAoMVbvHix0tLSFBcXp8zMTFVUVJzx/MOHDysvL08pKSnyeDzq2bOn1q1bF3h/zpw5crlcptG7d++wYnJ0zUfbtm1VV1enbt26ae/evRo0aJDp/czMTFNbJhiPxyOPx2M69mVrlrIAAFoOp3a7rF69Wl6vVyUlJcrMzFRxcbGys7O1fft2de7c+ZTzGxoadP3116tz58566aWXlJqaqj179qhTp06m86688kq98cYbgdetWoX3966jlY9Ro0ZpyZIlkqThw4frpZdeMr3/4osv6rLLLnMiNAAALOOXYckIV1FRkXJzc5WTk6M+ffqopKREbdu2Pe2ShmXLlunQoUNau3athgwZorS0NA0fPlzp6emm81q1aqXk5OTASEhICCsuR5OPRx99VGVlZRo+fLi6du2qhQsXatiwYbrnnns0fPhwzZkzRwsWLHAyRAAAWoygmyx8vqDnNjQ0qLKyUllZWYFjMTExysrKUnl5edBrXn75ZQ0ePFh5eXlKSkpS3759NX/+fDU2NprO27lzp7p06aLu3btrwoQJqq6uDut7OJp8dOnSRR9++KEGDx6s0tJSGYahiooKvf7667r44ov1v//7vxo9erSTIQIA0GxW7XYJtsmisLAw6GfW1dWpsbFRSUlJpuNJSUmqqakJes2uXbv00ksvqbGxUevWrdOsWbO0cOFC/fSnPw2ck5mZqRUrVqi0tFRLlizR7t27NWzYMB07dizkn4fjiyM6deqkBQsWUOEAAEQtq+7zkZ+fL6/Xazr2r+sem8Pv96tz58565pln5Ha7lZGRob179+qxxx5TQUGBpH8umfha//79lZmZqW7duunFF1/UXXfdFdLnOJ58AACA0ATbZHE6CQkJcrvdqq2tNR2vra1VcnJy0GtSUlLUunVrud3uwLErrrhCNTU1amhoUGxs7CnXdOrUST179lRVVVXI34PbqwMAYDPDov/CERsbq4yMDJWVlQWO+f1+lZWVafDgwUGvGTJkiKqqquT3f3NXkR07diglJSVo4iFJx48f1yeffKKUlJSQYyP5AADAZn7DsGSEy+v16tlnn9XKlSv18ccfa+rUqaqvr1dOTo4kaeLEicrPzw+cP3XqVB06dEjTpk3Tjh079Nprr2n+/PnKy8sLnDNjxgxt2rRJn376qd59913dfPPNcrvdGj9+fMhx0XYBACBKjRs3TgcOHNDs2bNVU1OjAQMGqLS0NLAItbq6WjEx39QhunbtqvXr12v69Onq37+/UlNTNW3aND3wwAOBcz7//HONHz9eBw8eVGJiooYOHar33ntPiYmJIcflMqLpZvH/58uXf+F0CJaoX/KK0yE0W9vRVzodgjVauc9+zjng+Et/cTqEZnPHOR2BNb6qdzkdQrN1vDN46f5c02Zi8N0iVhqWOsKSed7eW3b2k84BVD4AALAZT7U1I/kAAMBmJB9mLDgFAAARReUDAACbReHyymYh+QAAwGa0XcxouwAAgIii8gEAgM3CvTtptCP5AADAZqz5MKPtAgAAIorKBwAANmPBqRnJBwAANqPtYkbbBQAARFRUPliub9K/OR2CJT54fbbTITTbwSnR8ZC/dmnR8duk9eVJTofQbDcvP+x0CJaIhjL83JMdnA7BEkNqXrL9M9KTr7Fknj/XvGvJPE6j7QIAgM3YamtG8gEAgM380ddkaBbWfAAAgIii8gEAgM1ou5iRfAAAYDPaLma0XQAAQERR+QAAwGa0XcxIPgAAsBltFzPaLgAAIKKofAAAYDPaLmYkHwAA2Iy2ixltFwAAEFFUPgAAsBltFzOSDwAAbGYYfqdDaFFIPgAAsJmfyocJaz4AAEBEUfkAAMBmBrtdTEg+AACwGW0XM9ouAAAgoqh8AABgM9ouZo5WPn784x/r7bffdjIEAABs5zcMS0a0cDT5WLx4sa699lr17NlTjz76qGpqasKew+fz6ejRo6bhZz81AAAtluNrPl5//XWNHj1av/jFL3TJJZfopptu0quvviq/P7QEorCwUPHx8aZRV/+FzVEDABA6w6L/ooXjyUe/fv1UXFysL774Qr/+9a/l8/k0duxYde3aVQ8//LCqqqrOeH1+fr6OHDliGgntukQoegAAzs4wDEtGtHA8+fha69atdeutt6q0tFS7du1Sbm6ufvOb36hXr15nvM7j8ahjx46mEeNqMV8LAAD8ixb5t/Qll1yiOXPmaPfu3SotLXU6HAAAmsUvw5IRLRxNPrp16ya3233a910ul66//voIRgQAgPWcbLssXrxYaWlpiouLU2ZmpioqKs54/uHDh5WXl6eUlBR5PB717NlT69ata9ac/8rR5GP37t266KKLnAwBAADbObXVdvXq1fJ6vSooKNDmzZuVnp6u7Oxs7d+/P+j5DQ0Nuv766/Xpp5/qpZde0vbt2/Xss88qNTW1yXMG0yLbLgAAoPmKioqUm5urnJwc9enTRyUlJWrbtq2WLVsW9Pxly5bp0KFDWrt2rYYMGaK0tDQNHz5c6enpTZ4zGJIPAABs5kTbpaGhQZWVlcrKygoci4mJUVZWlsrLy4Ne8/LLL2vw4MHKy8tTUlKS+vbtq/nz56uxsbHJcwbD7dUBALCZVYtFfT6ffD6f6ZjH45HH4znl3Lq6OjU2NiopKcl0PCkpSdu2bQs6/65du/Tmm29qwoQJWrdunaqqqvSjH/1IJ0+eVEFBQZPmDIbKBwAA54hgN9YsLCy0bH6/36/OnTvrmWeeUUZGhsaNG6eHH35YJSUlln2GROUDAADbWXWDsPz8fHm9XtOxYFUPSUpISJDb7VZtba3peG1trZKTk4Nek5KSotatW5t2ol5xxRWqqalRQ0NDk+YMhsoHAAA2s2q3S7Aba54u+YiNjVVGRobKysq+icPvV1lZmQYPHhz0miFDhqiqqsr0iJMdO3YoJSVFsbGxTZozGJIPAACilNfr1bPPPquVK1fq448/1tSpU1VfX6+cnBxJ0sSJE5Wfnx84f+rUqTp06JCmTZumHTt26LXXXtP8+fOVl5cX8pyhoO0CAIDNnHoo3Lhx43TgwAHNnj1bNTU1GjBggEpLSwMLRqurqxUT800domvXrlq/fr2mT5+u/v37KzU1VdOmTdMDDzwQ8pyhcBnR9KSa/9M36d+cDsESH7w+2+kQmu3glF84HYIl2qVFx2+T1peH/odDS3Xz8sNOh2CJaLhV9tyTHZwOwRJDal6y/TPatOlmyTxffrnHknmcRtsFAABEFG0XAABsFoVNhmYh+QAAwGZOrfloqUg+AACwGZUPM9Z8AACAiKLyAQCAzah8mJF8AABgM1IPM9ouAAAgsgyE7cSJE0ZBQYFx4sQJp0Nplmj4HtHwHQwjOr5HNHwHw+B7tCTR8B0QXFTe4dRuR48eVXx8vI4cOaKOHTs6HU6TRcP3iIbvIEXH94iG7yDxPVqSaPgOCI62CwAAiCiSDwAAEFEkHwAAIKJIPprA4/GooKBAHo/H6VCaJRq+RzR8Byk6vkc0fAeJ79GSRMN3QHAsOAUAABFF5QMAAEQUyQcAAIgokg8AABBRJB8AACCiSD6aYPHixUpLS1NcXJwyMzNVUVHhdEhh+eMf/6gxY8aoS5cucrlcWrt2rdMhha2wsFBXX321OnTooM6dO2vs2LHavn2702GFbcmSJerfv786duyojh07avDgwfrDH/7gdFjNsmDBArlcLt13331OhxKWOXPmyOVymUbv3r2dDitse/fu1R133KGLLrpIbdq0Ub9+/fTBBx84HVZY0tLSTvm1cLlcysvLczo0WITkI0yrV6+W1+tVQUGBNm/erPT0dGVnZ2v//v1Ohxay+vp6paena/HixU6H0mSbNm1SXl6e3nvvPW3YsEEnT57UyJEjVV9f73RoYbn44ou1YMECVVZW6oMPPtB1112nm266SX/729+cDq1J/vSnP+npp59W//79nQ6lSa688krt27cvMN555x2nQwrL3//+dw0ZMkStW7fWH/7wB3300UdauHChLrjgAqdDC8uf/vQn06/Dhg0bJEm33HKLw5HBMs4+WubcM2jQICMvLy/wurGx0ejSpYtRWFjoYFRNJ8n4/e9/73QYzbZ//35DkrFp0yanQ2m2Cy64wPjVr37ldBhhO3bsmHH55ZcbGzZsMIYPH25MmzbN6ZDCUlBQYKSnpzsdRrM88MADxtChQ50Ow3LTpk0zevToYfj9fqdDgUWofIShoaFBlZWVysrKChyLiYlRVlaWysvLHYwMR44ckSRdeOGFDkfSdI2NjVq1apXq6+s1ePBgp8MJW15enm688UbT749zzc6dO9WlSxd1795dEyZMUHV1tdMhheXll1/WwIEDdcstt6hz58666qqr9OyzzzodVrM0NDTo17/+te688065XC6nw4FFSD7CUFdXp8bGRiUlJZmOJyUlqaamxqGo4Pf7dd9992nIkCHq27ev0+GE7a9//avat28vj8ejKVOm6Pe//7369OnjdFhhWbVqlTZv3qzCwkKnQ2myzMxMrVixQqWlpVqyZIl2796tYcOG6dixY06HFrJdu3ZpyZIluvzyy7V+/XpNnTpV//mf/6mVK1c6HVqTrV27VocPH9bkyZOdDgUWauV0AEBz5eXlaevWredcf/5rvXr10pYtW3TkyBG99NJLmjRpkjZt2nTOJCCfffaZpk2bpg0bNiguLs7pcJps1KhRgf/v37+/MjMz1a1bN7344ou66667HIwsdH6/XwMHDtT8+fMlSVdddZW2bt2qkpISTZo0yeHommbp0qUaNWqUunTp4nQosBCVjzAkJCTI7XartrbWdLy2tlbJyckORXV+u/fee/Xqq6/qrbfe0sUXX+x0OE0SGxuryy67TBkZGSosLFR6eroWLVrkdFghq6ys1P79+/Wd73xHrVq1UqtWrbRp0yY98cQTatWqlRobG50OsUk6deqknj17qqqqyulQQpaSknJK0nrFFVecc+2jr+3Zs0dvvPGG7r77bqdDgcVIPsIQGxurjIwMlZWVBY75/X6VlZWdkz36c5lhGLr33nv1+9//Xm+++aYuvfRSp0OyjN/vl8/nczqMkI0YMUJ//etftWXLlsAYOHCgJkyYoC1btsjtdjsdYpMcP35cn3zyiVJSUpwOJWRDhgw5Zcv5jh071K1bN4ciap7ly5erc+fOuvHGG50OBRaj7RImr9erSZMmaeDAgRo0aJCKi4tVX1+vnJwcp0ML2fHjx03/mtu9e7e2bNmiCy+8UJdccomDkYUuLy9PL7zwgv7nf/5HHTp0CKy5iY+PV5s2bRyOLnT5+fkaNWqULrnkEh07dkwvvPCCNm7cqPXr1zsdWsg6dOhwylqbdu3a6aKLLjqn1uDMmDFDY8aMUbdu3fTFF1+ooKBAbrdb48ePdzq0kE2fPl3XXHON5s+fr1tvvVUVFRV65pln9MwzzzgdWtj8fr+WL1+uSZMmqVUr/qqKOk5vtzkXPfnkk8Yll1xixMbGGoMGDTLee+89p0MKy1tvvWVIOmVMmjTJ6dBCFix+Scby5cudDi0sd955p9GtWzcjNjbWSExMNEaMGGG8/vrrTofVbOfiVttx48YZKSkpRmxsrJGammqMGzfOqKqqcjqssL3yyitG3759DY/HY/Tu3dt45plnnA6pSdavX29IMrZv3+50KLCByzAMw5m0BwAAnI9Y8wEAACKK5AMAAEQUyQcAAIgokg8AABBRJB8AACCiSD4AAEBEkXwAAICIIvkAzhPXXnut7rvvPqfDAACSDyDaTJ48WS6X65Tx85//XI888kjgvLS0NBUXFzsXKIDzFjfMB6LQDTfcoOXLl5uOJSYmnrMPeQMQXah8AFHI4/EoOTnZNEaMGBFou1x77bXas2ePpk+fHqiMAECkkHwA56E1a9bo4osv1rx587Rv3z7t27fP6ZAAnEdouwBR6NVXX1X79u0Dr0eNGmV6/8ILL5Tb7VaHDh2UnJwc6fAAnOdIPoAo9L3vfU9LliwJvG7Xrp3Gjx/vYEQA8A2SDyAKtWvXTpdddpnTYQBAUKz5AM5TsbGxamxsdDoMAOchkg/gPJWWlqY//vGP2rt3r+rq6pwOB8B5hOQDOE/NmzdPn376qXr06KHExESnwwFwHnEZhmE4HQQAADh/UPkAAAARRfIBAAAiiuQDAABEFMkHAACIKJIPAAAQUSQfAAAgokg+AABARJF8AACAiCL5AAAAEUXyAQAAIorkAwAARBTJBwAAiKj/D2b1s+Jeo+1bAAAAAElFTkSuQmCC",
+ "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": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "seaborn.heatmap(riemann_truth)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "5afe6c03",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "seaborn.heatmap(riemann_fit)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "87e10d1c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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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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",
+ "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%|â–ˆ| 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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dCg0N9evr1KlTs5WIAwcOqGfPnurXr5/uu+8+VVZW+j4rLy9XY2Oj33UTEhLUp0+fc173XEgqAAAwsrD8UVhYqIiICL9WWFjY7JKnTp1SU1OToqOj/fqjo6NVXV3dYphpaWkqKirSgQMH5PF4tGnTJq1du1bHjx/3jUlJSdHKlStVUlKiJUuW6MiRI7rlllv01VdfSZKqq6sVEhKirl27tvq658LpDwAALqK8vDy5XC6/PqfTacncL7zwgrKzs5WQkCCHw6H4+HhlZWX5lUvGjRvn+/egQYOUkpKivn376s0339T06dMtieN/sFIBAICRx2tZczqdCg8P92stJRWRkZEKDg5uduqipqZGMTExLYYZFRWl9evXq66uTkePHtX+/fsVFhamfv36nfPWunbtquuvv14HDx6UJMXExKihoUGnT59u9XXPhaQCAAAjr8e61kohISFKSkpSaWmpr8/j8ai0tFTDhw8/73dDQ0PVq1cvffPNN3r77bd1xx13nHPs119/rUOHDik2NlaSlJSUpI4dO/pdt6KiQpWVlRe8rhHlDwAA2gmXy6XMzEwlJydr2LBhKi4uVl1dnbKysiRJU6ZMUa9evXx7Mnbt2qWqqioNHjxYVVVVmjdvnjwejx5//HHfnI899pgyMjLUt29fHTt2TAUFBQoODtakSZMkSREREZo+fbpcLpe6deum8PBwzZo1S8OHD9dNN90UUPwkFQAAGNn0RM2JEyfq5MmTmjt3rqqrqzV48GCVlJT4Nm9WVlYqKOifRYb6+nrl5+fr8OHDCgsLU3p6ulatWuW36fIvf/mLJk2apL/+9a+KiorSyJEjtXPnTkVFRfnGPP/88woKCtKECRPkdruVlpamxYsXBxy/w+v1totnkXYI6WV3CG32co8f2h2CJabsnW93CJa49vp/tTuENjuw/jG7Q7BE0/q1dofQZsFJiXaHYI1/1NkdgSU6PVh0UeevK8y0bK6r8n5j2VztHXsqAACAJSh/AABgxAvFTCGpAADAKIBTG/gnkgoAAIxYqTCFPRUAAMASrFQAAGAU4CvL8S2SCgAAjCh/mEL5AwAAWIKVCgAAjDj9YQpJBQAARpQ/TKH8AQAALMFKBQAABl5Of5hCUgEAgBHlD1MofwAAAEuwUgEAgBErFaaQVAAAYMSRUlNIKgAAMGKlwhT2VAAAAEuwUgEAgIGXlQpTSCoAADAiqTCF8gcAALBEwEnFP/7xD23fvl2fffZZs8/q6+v129/+9oJzuN1unTlzxq95vWSFAIB2wuOxrl1BAkoqvvjiC914440aNWqUBg4cqNGjR+v48eO+z2tra5WVlXXBeQoLCxUREeHXvJ6vAo8eAICLweO1rl1BAkoqnnjiCQ0YMEAnTpxQRUWFunTpohEjRqiysjKgi+bl5am2ttavOYK6BDQHAABoXwLaqLljxw5t3rxZkZGRioyM1IYNG/STn/xEt9xyi7Zs2aKrrrqqVfM4nU45nU6/PofDEUgoAABcPFfYCoNVAlqp+Mc//qEOHf6ZhzgcDi1ZskQZGRkaPXq0vvjiC8sDBADgu+b1ei1rV5KAVioSEhL00Ucf6cYbb/Trf+mllyRJ//qv/2pdZAAA4JIS0ErFnXfeqf/4j/9o8bOXXnpJkyZNuuKyMgDAZYiNmqYElFTk5eXpvffeO+fnixcvlucKOz4DALgMkVSYwhM1AQAw4DHd5vBETQAAYAlWKgAAMGKlwhRWKgAAMPJY2AK0aNEixcXFKTQ0VCkpKdq9e/c5xzY2Nmr+/PmKj49XaGioEhMTVVJS4jemsLBQQ4cOVZcuXdSjRw+NHz9eFRUVfmPGjBkjh8Ph12bMmBFw7CQVAAC0E2vWrJHL5VJBQYH27NmjxMREpaWl6cSJEy2Oz8/P18svv6wXX3xRn332mWbMmKE777xTH3/8sW/Mtm3blJOTo507d2rTpk1qbGzUbbfdprq6Or+5srOzdfz4cV/7+c9/HnD8lD8AADCwa6NmUVGRsrOzfe/RWrp0qTZu3Kjly5dr9uzZzcavWrVKTz31lNLT0yVJDz/8sDZv3qznnntOr732miQ1W7lYuXKlevToofLyco0aNcrX37lzZ8XExLQpflYqAAAwsvBIaUtv5na73c0u2dDQoPLycqWmpvr6goKClJqaqrKyshbDdLvdCg0N9evr1KmTtm/ffs5bq62tlSR169bNr//1119XZGSkBgwYoLy8PJ09e7bVvy5fvAF/AwAAtFpLb+YuLCxsNu7UqVNqampSdHS0X390dLSqq6tbnDstLU1FRUU6cOCAPB6PNm3apLVr1/q9Qfx/83g8evTRRzVixAgNGDDA1z958mS99tpr2rJli/Ly8rRq1Srdf//9Ad8r5Q8AAIwsfI5jXl6eXC6XX5/xpZpmvfDCC8rOzlZCQoIcDofi4+OVlZWl5cuXtzg+JydH+/bta7aS8dBDD/n+PXDgQMXGxmrs2LE6dOiQ4uPjWx0PKxUAABh4PV7LmtPpVHh4uF9rKamIjIxUcHCwampq/PpramrOudchKipK69evV11dnY4ePar9+/crLCxM/fr1azZ25syZevfdd7VlyxZdc801573/lJQUSdLBgwdb+yuTRFIBAEC7EBISoqSkJJWWlvr6PB6PSktLNXz48PN+NzQ0VL169dI333yjt99+W3fccYfvM6/Xq5kzZ2rdunX64IMPdO21114wlr1790qSYmNjA7oHyh8AABjZ9Borl8ulzMxMJScna9iwYSouLlZdXZ3vNMiUKVPUq1cv356MXbt2qaqqSoMHD1ZVVZXmzZsnj8ejxx9/3DdnTk6OVq9erXfeeUddunTx7c+IiIhQp06ddOjQIa1evVrp6enq3r27PvnkE+Xm5mrUqFEaNGhQQPGTVAAAYGDXkdKJEyfq5MmTmjt3rqqrqzV48GCVlJT4Nm9WVlYqKOifRYb6+nrl5+fr8OHDCgsLU3p6ulatWqWuXbv6xixZskTStw+4+t9WrFihqVOnKiQkRJs3b/YlML1799aECROUn58fcPwkFQAAGNn4wu2ZM2dq5syZLX62detWv59Hjx6tzz777Lzzeb3nT5B69+6tbdu2BRTjubCnAgAAWIKVCgAADLw2rlRcytpNUlH7xAi7Q2izDpOn2x2CJa69/l/tDsESR774vd0htNnRUQ/bHYIldp2OsjuENvuXA3+yOwRLnD4SeuFBl4D4By/yBUgqTKH8AQAALNFuVioAAGgvKH+YQ1IBAIARSYUplD8AAIAlWKkAAMCA8oc5JBUAABiQVJhDUgEAgAFJhTnsqQAAAJZgpQIAACOvw+4ILkkkFQAAGFD+MIfyBwAAsAQrFQAAGHg9lD/MIKkAAMCA8oc5lD8AAIAlWKkAAMDAy+kPU0gqAAAwoPxhDuUPAABgCVYqAAAw4PSHOSQVAAAYeL12R3BpIqkAAMCAlQpz2FMBAAAsYctKhdvtltvt9uv75psmOTsE2xEOAAB+WKkwJ+CVis8//1wrVqzQ/v37JUn79+/Xww8/rGnTpumDDz5o1RyFhYWKiIjwa7/8r/2BhgIAwEXh9VrXriQBJRUlJSUaPHiwHnvsMQ0ZMkQlJSUaNWqUDh48qKNHj+q2225rVWKRl5en2tpav/bYiATTNwEAAOwXUFIxf/58/fSnP9Vf//pXrVixQpMnT1Z2drY2bdqk0tJS/fSnP9XChQsvOI/T6VR4eLhfo/QBAGgvvB6HZe1KElBS8emnn2rq1KmSpHvuuUdfffWVfvzjH/s+v++++/TJJ59YGiAAAN81r9dhWbuSBLynwuH49hcUFBSk0NBQRURE+D7r0qWLamtrrYsOAABcMgJKKuLi4nTgwAHfz2VlZerTp4/v58rKSsXGxloXHQAANvB6rGtXkoCOlD788MNqamry/TxgwAC/z//whz/o1ltvtSYyAABs4rnCyhZWCWilYsaMGbr99tvP+fmCBQv06quvtjkoAACuVIsWLVJcXJxCQ0OVkpKi3bt3n3NsY2Oj5s+fr/j4eIWGhioxMVElJSUBz1lfX6+cnBx1795dYWFhmjBhgmpqagKOnSdqAgBgYNdGzTVr1sjlcqmgoEB79uxRYmKi0tLSdOLEiRbH5+fn6+WXX9aLL76ozz77TDNmzNCdd96pjz/+OKA5c3NztWHDBr311lvatm2bjh07prvuuivg3xtJBQAABnYdKS0qKlJ2draysrLUv39/LV26VJ07d9by5ctbHL9q1So9+eSTSk9PV79+/fTwww8rPT1dzz33XKvnrK2t1bJly1RUVKRbb71VSUlJWrFihXbs2KGdO3cGFD9JBQAABlY+UdPtduvMmTN+zfiqCklqaGhQeXm5UlNTfX1BQUFKTU1VWVlZi3G63W6Fhob69XXq1Enbt29v9Zzl5eVqbGz0G5OQkKA+ffqc87rnQlIBAMBF1NKrKQoLC5uNO3XqlJqamhQdHe3XHx0drerq6hbnTktLU1FRkQ4cOCCPx6NNmzZp7dq1On78eKvnrK6uVkhIiLp27drq654Lrz4HAMDAyidh5uXlyeVy+fU5nU5L5n7hhReUnZ2thIQEORwOxcfHKysr65zlkouNlQoAAAw8XodlrcVXU7SQVERGRio4OLjZqYuamhrFxMS0GGdUVJTWr1+vuro6HT16VPv371dYWJj69evX6jljYmLU0NCg06dPt/q650JSAQBAOxASEqKkpCSVlpb6+jwej0pLSzV8+PDzfjc0NFS9evXSN998o7ffflt33HFHq+dMSkpSx44d/cZUVFSosrLygtc1ovwBAICBXe/scLlcyszMVHJysoYNG6bi4mLV1dUpKytLkjRlyhT16tXLtydj165dqqqq0uDBg1VVVaV58+bJ4/Ho8ccfb/WcERERmj59ulwul7p166bw8HDNmjVLw4cP10033RRQ/CQVAAAYeL32XHfixIk6efKk5s6dq+rqag0ePFglJSW+jZaVlZUKCvpnkaG+vl75+fk6fPiwwsLClJ6erlWrVvlturzQnJL0/PPPKygoSBMmTJDb7VZaWpoWL14ccPwOr9euX52/ujn32B1Cm3WY/LDdIVgifsQsu0OwxJEvfm93CG12dNTl8X9Tu05H2R1Cm/1L0pd2h2CJ00dCLzzoEhC/7/2LOv8ncRmWzTXovzdYNld7x0oFAAAGvPvDHJIKAAAM7NpTcanj9AcAALAEKxUAABi0j92Glx6SCgAADNhTYU67SSqWrwyxO4Q2mx7xut0hWOLA+sfsDsESl8PJib5/WmJ3CJbo9U7gR9Pam6BbZtgdgiWuqq258CCwp8Ik9lQAAABLtJuVCgAA2gvKH+aQVAAAYMA+TXMofwAAAEuwUgEAgAHlD3NIKgAAMOD0hzmUPwAAgCVYqQAAwMBjdwCXKJIKAAAMvKL8YQblDwAAYAlWKgAAMPDwoApTSCoAADDwUP4whaQCAAAD9lSYw54KAABgCVYqAAAw4EipOSQVAAAYUP4wh/IHAACwBCsVAAAYUP4wh6QCAAADkgpzKH8AAABLWLJS4fV65XCwqQUAcHlgo6Y5lqxUOJ1Off7551ZMBQCA7TwO69qVJKCVCpfL1WJ/U1OTFi5cqO7du0uSioqKzjuP2+2W2+3262v0NqmjIziQcAAAQDsSUFJRXFysxMREde3a1a/f6/Xq888/11VXXdWqMkhhYaGefvppv75/6TJQ6RGDAgkHAICLgnd/mBNQUrFgwQK98soreu6553Trrbf6+jt27KiVK1eqf//+rZonLy+v2arHq/3/LZBQAAC4aHhJqTkBJRWzZ8/W2LFjdf/99ysjI0OFhYXq2LFjwBd1Op1yOp1+fZQ+AADtBUdKzQl4o+bQoUNVXl6ukydPKjk5Wfv27ePkBwAAFlm0aJHi4uIUGhqqlJQU7d69+7zji4uLdcMNN6hTp07q3bu3cnNzVV9f7/s8Li5ODoejWcvJyfGNGTNmTLPPZ8yYEXDspo6UhoWF6Te/+Y3eeOMNpaamqqmpycw0AAC0Sx6b/mN5zZo1crlcWrp0qVJSUlRcXKy0tDRVVFSoR48ezcavXr1as2fP1vLly3XzzTfriy++0NSpU+VwOHyHJj788EO/v9P79u3Tj370I919991+c2VnZ2v+/Pm+nzt37hxw/G16TsW9996rkSNHqry8XH379m3LVAAAtBt27akoKipSdna2srKyJElLly7Vxo0btXz5cs2ePbvZ+B07dmjEiBGaPHmypG9XJSZNmqRdu3b5xkRFRfl9Z+HChYqPj9fo0aP9+jt37qyYmJg2xd/m51Rcc801uuOOO3TVVVe1dSoAAC47brdbZ86c8WvGxypIUkNDg8rLy5WamurrCwoKUmpqqsrKylqc++abb1Z5ebmvRHL48GG99957Sk9Pb3F8Q0ODXnvtNU2bNq3Z1oXXX39dkZGRGjBggPLy8nT27NmA75V3fwAAYGDlRs2WHqNQUFCgefPm+fWdOnVKTU1Nio6O9uuPjo7W/v37W5x78uTJOnXqlEaOHCmv16tvvvlGM2bM0JNPPtni+PXr1+v06dOaOnVqs3n69u2rnj176pNPPtETTzyhiooKrV27NqB7JakAAMDAyidhtvQYBeMJSLO2bt2qBQsWaPHixUpJSdHBgwf1yCOP6JlnntGcOXOajV+2bJnGjRunnj17+vU/9NBDvn8PHDhQsbGxGjt2rA4dOqT4+PhWx0NSAQDARdTSYxRaEhkZqeDgYNXU1Pj119TUnHOvw5w5c/TAAw/owQcflPRtQlBXV6eHHnpITz31lIKC/rnL4ejRo9q8eXOrVh9SUlIkSQcPHgwoqeAtpQAAGHjksKy1VkhIiJKSklRaWvrPODwelZaWavjw4S1+5+zZs36JgyQFB3/73Cev13+76YoVK9SjRw/dfvvtF4xl7969kqTY2NhWxy+xUgEAQDN2nf5wuVzKzMxUcnKyhg0bpuLiYtXV1flOg0yZMkW9evVSYWGhJCkjI0NFRUUaMmSIr/wxZ84cZWRk+JIL6dvkZMWKFcrMzFSHDv5/+g8dOqTVq1crPT1d3bt31yeffKLc3FyNGjVKgwYF9voMkgoAANqJiRMn6uTJk5o7d66qq6s1ePBglZSU+DZvVlZW+q1M5Ofny+FwKD8/X1VVVYqKilJGRoaeffZZv3k3b96syspKTZs2rdk1Q0JCtHnzZl8C07t3b02YMEH5+fkBx+/wGtdHbPJi7/vtDqHNpj8SancIlnAMG2l3CJaomvEfdofQZn3/tMTuECzxzTuL7Q6hzYJuGW93CJbw1tZceNAlIHTohIs6/297Wfc3aUrVa5bN1d6xUgEAgAHv/jCHpAIAAIN2sYR/CeL0BwAAsAQrFQAAGFj58KsrCUkFAAAG7Kkwh/IHAACwBCsVAAAYsFJhDkkFAAAGXvZUmEL5AwAAWKLdrFT8JbjJ7hDarPHT/7Y7BEsEHzthdwiW2HU6yu4Q2qzXZfAkSknqcMdP7A6hzb758F27Q7BEUO8b7Q7hkkD5w5x2k1QAANBekFSYQ/kDAABYgpUKAAAMeEy3OSQVAAAY8ERNc0gqAAAwYE+FOeypAAAAlmClAgAAA1YqzCGpAADAgI2a5lD+AAAAlmClAgAAA05/mENSAQCAAXsqzKH8AQAALMFKBQAABmzUNIekAgAAAw9phSmUPwAAgCVYqQAAwICNmuaQVAAAYEDxwxySCgAADFipMIc9FQAAwBKsVAAAYMATNc0hqQAAwIAjpea0Kamoq6vTm2++qYMHDyo2NlaTJk1S9+7dL/g9t9stt9vt1/eNt0kdHMFtCQcAANgooD0V/fv319/+9jdJ0pdffqkBAwYoNzdXmzZtUkFBgfr3768jR45ccJ7CwkJFRET4tZ21n5m7AwAALOa1sAVq0aJFiouLU2hoqFJSUrR79+7zji8uLtYNN9ygTp06qXfv3srNzVV9fb3v83nz5snhcPi1hIQEvznq6+uVk5Oj7t27KywsTBMmTFBNTU3AsQeUVOzfv1/ffPONJCkvL089e/bU0aNHtXv3bh09elSDBg3SU089dcF58vLyVFtb69duiugfcPAAAFwMHgtbINasWSOXy6WCggLt2bNHiYmJSktL04kTJ1ocv3r1as2ePVsFBQX6/PPPtWzZMq1Zs0ZPPvmk37jvf//7On78uK9t377d7/Pc3Fxt2LBBb731lrZt26Zjx47prrvuCjD6NpQ/ysrKtHTpUkVEREiSwsLC9PTTT+vee++94HedTqecTqd/IJQ+AABXuKKiImVnZysrK0uStHTpUm3cuFHLly/X7Nmzm43fsWOHRowYocmTJ0uS4uLiNGnSJO3atctvXIcOHRQTE9PiNWtra7Vs2TKtXr1at956qyRpxYoVuvHGG7Vz507ddNNNrY4/4COlDse3W2Lr6+sVGxvr91mvXr108uTJQKcEAKBd8chrWXO73Tpz5oxfM+4rlKSGhgaVl5crNTXV1xcUFKTU1FSVlZW1GOfNN9+s8vJyX4nk8OHDeu+995Senu437sCBA+rZs6f69eun++67T5WVlb7PysvL1djY6HfdhIQE9enT55zXPZeAk4qxY8fqBz/4gc6cOaOKigq/z44ePdqqjZoAALRnVu6paGkfYWFhYbNrnjp1Sk1NTYqOjvbrj46OVnV1dYtxTp48WfPnz9fIkSPVsWNHxcfHa8yYMX7lj5SUFK1cuVIlJSVasmSJjhw5oltuuUVfffWVJKm6ulohISHq2rVrq697LgGVPwoKCvx+DgsL8/t5w4YNuuWWWwIKAACAy1leXp5cLpdfn3ELgFlbt27VggULtHjxYqWkpOjgwYN65JFH9Mwzz2jOnDmSpHHjxvnGDxo0SCkpKerbt6/efPNNTZ8+3ZI4/kebkgqjX/ziF20KBgCA9sDKx3S3tI+wJZGRkQoODm526qKmpuac+yHmzJmjBx54QA8++KAkaeDAgaqrq9NDDz2kp556SkFBzQsSXbt21fXXX6+DBw9KkmJiYtTQ0KDTp0/7rVac77rnwmO6AQAwsHJPRWuFhIQoKSlJpaWl/4zD41FpaamGDx/e4nfOnj3bLHEIDv724IPX2/K1v/76ax06dMi3LzIpKUkdO3b0u25FRYUqKyvPed1z4YmaAAAY2PU8TZfLpczMTCUnJ2vYsGEqLi5WXV2d7zTIlClT1KtXL9+ejIyMDBUVFWnIkCG+8secOXOUkZHhSy4ee+wxZWRkqG/fvjp27JgKCgoUHBysSZMmSZIiIiI0ffp0uVwudevWTeHh4Zo1a5aGDx8e0MkPiaQCAIB2Y+LEiTp58qTmzp2r6upqDR48WCUlJb7Nm5WVlX4rE/n5+XI4HMrPz1dVVZWioqKUkZGhZ5991jfmL3/5iyZNmqS//vWvioqK0siRI7Vz505FRUX5xjz//PMKCgrShAkT5Ha7lZaWpsWLFwccv8N7rvWR79gTcZPsDqHNnvzh5XGcNvjqznaHYIl33gi3O4Q2u+uZwOqZ7VWHO35idwht9s2H79odgiWCet9odwiWcA740UWd/5G4Cz9zqbVe+O83LJurvWOlAgAAAy8vFDOFjZoAAMASrFQAAGBg5ZHSKwlJBQAABoEcBcU/Uf4AAACWYKUCAAAD1inMIakAAMCA8oc5lD8AAIAlWKkAAMCA0x/mkFQAAGDAw6/MIakAAMCAlQpz2FMBAAAs0W5WKqaFnrY7hDYLSQ3svfO4uP7lwJ/sDqHNgm6ZYXcIlrgcXsbVYej/sTsES3hOfWl3CJcEyh/mtJukAgCA9oLyhzmUPwAAgCVYqQAAwMDjpfxhBkkFAAAGpBTmUP4AAACWYKUCAAAD3v1hDkkFAAAGHCk1h/IHAACwBCsVAAAY8JwKc0gqAAAwYE+FOSQVAAAYsKfCHPZUAAAAS7BSAQCAAXsqzCGpAADAwMtjuk2h/AEAACzBSgUAAAac/jCHpAIAAAP2VJhD+QMAAFiClQoAAAx4ToU5rFQAAGDgkdeyFqhFixYpLi5OoaGhSklJ0e7du887vri4WDfccIM6deqk3r17Kzc3V/X19b7PCwsLNXToUHXp0kU9evTQ+PHjVVFR4TfHmDFj5HA4/NqMGTMCjp2kAgCAdmLNmjVyuVwqKCjQnj17lJiYqLS0NJ04caLF8atXr9bs2bNVUFCgzz//XMuWLdOaNWv05JNP+sZs27ZNOTk52rlzpzZt2qTGxkbddtttqqur85srOztbx48f97Wf//znAccfUPljz549uvrqq3XttddKklatWqWlS5eqsrJSffv21cyZM3XvvfdecB632y232+3X1+DxKCSIHAcAYD+7nlNRVFSk7OxsZWVlSZKWLl2qjRs3avny5Zo9e3az8Tt27NCIESM0efJkSVJcXJwmTZqkXbt2+caUlJT4fWflypXq0aOHysvLNWrUKF9/586dFRMT06b4A/ornpWVpUOHDkmSXn31Vf3bv/2bkpOT9dRTT2no0KHKzs7W8uXLLzhPYWGhIiIi/NrLfztk7g4AALCYx8Lmdrt15swZv2b8D2tJamhoUHl5uVJTU319QUFBSk1NVVlZWYtx3nzzzSovL/eVSA4fPqz33ntP6enp57y32tpaSVK3bt38+l9//XVFRkZqwIABysvL09mzZ8//S2pBQCsVBw4c0Pe+9z1J0uLFi/XCCy8oOzvb9/nQoUP17LPPatq0aeedJy8vTy6Xy6/vaPLdgYQCAMBFY+VGzcLCQj399NN+fQUFBZo3b55f36lTp9TU1KTo6Gi//ujoaO3fv7/FuSdPnqxTp05p5MiR8nq9+uabbzRjxgy/8sf/5vF49Oijj2rEiBEaMGCA3zx9+/ZVz5499cknn+iJJ55QRUWF1q5dG9C9BpRUdO7cWadOnVLfvn1VVVWlYcOG+X2ekpKiI0eOXHAep9Mpp9Pp10fpAwBwOWrpP6SNfwPN2rp1qxYsWKDFixcrJSVFBw8e1COPPKJnnnlGc+bMaTY+JydH+/bt0/bt2/36H3roId+/Bw4cqNjYWI0dO1aHDh1SfHx8q+MJ6C/5uHHjtGTJEknS6NGj9bvf/c7v8zfffFPXXXddIFMCANDuWHn6w+l0Kjw83K+1lFRERkYqODhYNTU1fv01NTXn3OswZ84cPfDAA3rwwQc1cOBA3XnnnVqwYIEKCwvl8fg/wmvmzJl69913tWXLFl1zzTXnvf+UlBRJ0sGDBwP5tQW2UvGzn/1MI0aM0OjRo5WcnKznnntOW7du1Y033qiKigrt3LlT69atCygAAADaGzs2aoaEhCgpKUmlpaUaP368pG/LFaWlpZo5c2aL3zl79qyCDCv9wcHBkv55D16vV7NmzdK6deu0detW32GL89m7d68kKTY2NqB7CCip6Nmzpz7++GMtXLhQGzZskNfr1e7du/Xll19qxIgR+q//+i8lJycHFAAAAPiWy+VSZmamkpOTNWzYMBUXF6uurs53GmTKlCnq1auXCgsLJUkZGRkqKirSkCFDfOWPOXPmKCMjw5dc5OTkaPXq1XrnnXfUpUsXVVdXS5IiIiLUqVMnHTp0SKtXr1Z6erq6d++uTz75RLm5uRo1apQGDRoUUPwBP1Gza9euWrhwoRYuXBjoVwEAuCTY9UKxiRMn6uTJk5o7d66qq6s1ePBglZSU+DZvVlZW+q1M5Ofny+FwKD8/X1VVVYqKilJGRoaeffZZ35j/2bYwZswYv2utWLFCU6dOVUhIiDZv3uxLYHr37q0JEyYoPz8/4Pgd3nby0viKhHF2h9BmfeawStOenH3jT3aH0GZhzz174UGXAM+hPXaH0GYdhv4fu0OwhOfUl3aHYAlnwuiLOv+Ya1IvPKiVtv5ls2VztXccuQAAAJbghWIAABh42sci/iWHpAIAAANSCnMofwAAAEuwUgEAgIFdpz8udSQVAAAYkFSYQ1IBAIBBO3nawiWHPRUAAMASrFQAAGBA+cMckgoAAAy8JBWmUP4AAACWYKUCAAADNmqaQ1IBAIABeyrMofwBAAAswUoFAAAGlD/MaTdJxaenr7Y7hDbr/be/2h2CJRydQu0OwRKnj1z693FVbY3dIVgiqPeNdofQZp5TX9odgiWCInvbHcIlgfKHOZQ/AACAJdrNSgUAAO0Fz6kwh6QCAAADD3sqTCGpAADAgJUKc9hTAQAALMFKBQAABpQ/zCGpAADAgPKHOZQ/AACAJVipAADAgPKHOSQVAAAYUP4wh/IHAACwBCsVAAAYUP4wh6QCAAADyh/mUP4AAACWYKUCAAADr9djdwiXJJIKAAAMPJQ/TKH8AQCAgdfrtawFatGiRYqLi1NoaKhSUlK0e/fu844vLi7WDTfcoE6dOql3797Kzc1VfX19QHPW19crJydH3bt3V1hYmCZMmKCampqAYyepAACgnVizZo1cLpcKCgq0Z88eJSYmKi0tTSdOnGhx/OrVqzV79mwVFBTo888/17Jly7RmzRo9+eSTAc2Zm5urDRs26K233tK2bdt07Ngx3XXXXQHHT1IBAICBR17LWiCKioqUnZ2trKws9e/fX0uXLlXnzp21fPnyFsfv2LFDI0aM0OTJkxUXF6fbbrtNkyZN8luJuNCctbW1WrZsmYqKinTrrbcqKSlJK1as0I4dO7Rz586A4iepAADAwMryh9vt1pkzZ/ya2+1uds2GhgaVl5crNTXV1xcUFKTU1FSVlZW1GOfNN9+s8vJyXxJx+PBhvffee0pPT2/1nOXl5WpsbPQbk5CQoD59+pzzuudCUgEAwEVUWFioiIgIv1ZYWNhs3KlTp9TU1KTo6Gi//ujoaFVXV7c49+TJkzV//nyNHDlSHTt2VHx8vMaMGeMrf7RmzurqaoWEhKhr166tvu65BJRUzJo1S//5n/8Z0AVa0lLW1uhtavO8AABYweP1Wtby8vJUW1vr1/Ly8iyJc+vWrVqwYIEWL16sPXv2aO3atdq4caOeeeYZS+YPVEBJxaJFizRmzBhdf/31+tnPfhZwBvM/Wsra1tZ9ZmouAACs5rXwf5xOp8LDw/2a0+lsds3IyEgFBwc3O3VRU1OjmJiYFuOcM2eOHnjgAT344IMaOHCg7rzzTi1YsECFhYXyeDytmjMmJkYNDQ06ffp0q697LgGXP/74xz8qPT1dv/zlL9WnTx/dcccdevfdd+XxtP5BIS1lbXdd1T/QUAAAuGyEhIQoKSlJpaWlvj6Px6PS0lINHz68xe+cPXtWQUH+f8qDg4MlfbsvpDVzJiUlqWPHjn5jKioqVFlZec7rnkvAD78aOHCgxo4dq1/84hdat26dli9frvHjxys6OlpTp05VVlaWrrvuuvPO4XQ6m2VpHR3BgYYCAMBFYeb5ElZwuVzKzMxUcnKyhg0bpuLiYtXV1SkrK0uSNGXKFPXq1cu3JyMjI0NFRUUaMmSIUlJSdPDgQc2ZM0cZGRm+5OJCc0ZERGj69OlyuVzq1q2bwsPDNWvWLA0fPlw33XRTQPGbfqJmx44ddc899+iee+5RZWWlli9frpUrV2rhwoVqamJ/BADg0mXXEzUnTpyokydPau7cuaqurtbgwYNVUlLi22hZWVnptzKRn58vh8Oh/Px8VVVVKSoqShkZGXr22WdbPackPf/88woKCtKECRPkdruVlpamxYsXBxy/wxtAOhYUFKTq6mr16NGjxc+9Xq82b96sH/3oRwEHsjZmcsDfaW/+5amudodgCUenULtDsMSx4k/tDqHNeq14yO4QLOHoFG53CG3XIcTuCCwRFNnb7hAs0TGy30WdPyriBsvmOllbYdlc7V1AKxV9+/b1Lae0xOFwmEooAABoT+wqf1zqAkoqjhw5crHiAACg3fCQVJjCW0oBADBgpcIcnqgJAAAswUoFAAAGdp3+uNSRVAAAYED5wxzKHwAAwBKsVAAAYMDpD3NIKgAAMPCyp8IUyh8AAMASrFQAAGBA+cMckgoAAAw4/WEO5Q8AAGAJVioAADBgo6Y5JBUAABhQ/jCHpAIAAAOSCnPYUwEAACzBSgUAAAasU5jkvULU19d7CwoKvPX19XaHYtrlcA9e7+VxH5fDPXi93Ed7cjncg9d7+dwHzHF4vVdG4ejMmTOKiIhQbW2twsPD7Q7HlMvhHqTL4z4uh3uQuI/25HK4B+nyuQ+Yw54KAABgCZIKAABgCZIKAABgiSsmqXA6nSooKJDT6bQ7FNMuh3uQLo/7uBzuQeI+2pPL4R6ky+c+YM4Vs1ETAABcXFfMSgUAALi4SCoAAIAlSCoAAIAlSCoAAIAlSCoAAIAlroikYtGiRYqLi1NoaKhSUlK0e/duu0MKyJ/+9CdlZGSoZ8+ecjgcWr9+vd0hBaywsFBDhw5Vly5d1KNHD40fP14VFRV2hxWwJUuWaNCgQQoPD1d4eLiGDx+uP/zhD3aH1SYLFy6Uw+HQo48+ancoAZk3b54cDodfS0hIsDssU6qqqnT//fere/fu6tSpkwYOHKiPPvrI7rBaLS4urtn/LhwOh3JycuwODd+xyz6pWLNmjVwulwoKCrRnzx4lJiYqLS1NJ06csDu0Vqurq1NiYqIWLVpkdyimbdu2TTk5Odq5c6c2bdqkxsZG3Xbbbaqrq7M7tIBcc801WrhwocrLy/XRRx/p1ltv1R133KFPP/3U7tBM+fDDD/Xyyy9r0KBBdodiyve//30dP37c17Zv3253SAH7+9//rhEjRqhjx476wx/+oM8++0zPPfecrr76artDa7UPP/zQ738PmzZtkiTdfffdNkeG75y97zO7+IYNG+bNycnx/dzU1OTt2bOnt7Cw0MaozJPkXbdund1htNmJEye8krzbtm2zO5Q2u/rqq72vvvqq3WEE7KuvvvJ+73vf827atMk7evRo7yOPPGJ3SAEpKCjwJiYm2h1Gmz3xxBPekSNH2h2GpR555BFvfHy81+Px2B0KvmOX9UpFQ0ODysvLlZqa6usLCgpSamqqysrKbIwMtbW1kqRu3brZHIl5TU1NeuONN1RXV6fhw4fbHU7AcnJydPvtt/v9/8el5sCBA+rZs6f69eun++67T5WVlXaHFLDf//73Sk5O1t13360ePXpoyJAh+vWvf213WKY1NDTotdde07Rp0+RwOOwOB9+xyzqpOHXqlJqamhQdHe3XHx0drerqapuigsfj0aOPPqoRI0ZowIABdocTsD//+c8KCwuT0+nUjBkztG7dOvXv39/usALyxhtvaM+ePSosLLQ7FNNSUlK0cuVKlZSUaMmSJTpy5IhuueUWffXVV3aHFpDDhw9ryZIl+t73vqf3339fDz/8sP793/9dv/nNb+wOzZT169fr9OnTmjp1qt2hwAYd7A4AV56cnBzt27fvkqx/S9INN9ygvXv3qra2Vr/73e+UmZmpbdu2XTKJxZdffqlHHnlEmzZtUmhoqN3hmDZu3DjfvwcNGqSUlBT17dtXb775pqZPn25jZIHxeDxKTk7WggULJElDhgzRvn37tHTpUmVmZtocXeCWLVumcePGqWfPnnaHAhtc1isVkZGRCg4OVk1NjV9/TU2NYmJibIrqyjZz5ky9++672rJli6655hq7wzElJCRE1113nZKSklRYWKjExES98MILdofVauXl5Tpx4oR+8IMfqEOHDurQoYO2bdumX/3qV+rQoYOamprsDtGUrl276vrrr9fBgwftDiUgsbGxzRLSG2+88ZIs5Rw9elSbN2/Wgw8+aHcosMllnVSEhIQoKSlJpaWlvj6Px6PS0tJLsgZ+KfN6vZo5c6bWrVunDz74QNdee63dIVnG4/HI7XbbHUarjR07Vn/+85+1d+9eX0tOTtZ9992nvXv3Kjg42O4QTfn666916NAhxcbG2h1KQEaMGNHsePUXX3yhvn372hSReStWrFCPHj10++232x0KbHLZlz9cLpcyMzOVnJysYcOGqbi4WHV1dcrKyrI7tFb7+uuv/f7r68iRI9q7d6+6deumPn362BhZ6+Xk5Gj16tV655131KVLF9+eloiICHXq1Mnm6FovLy9P48aNU58+ffTVV19p9erV2rp1q95//327Q2u1Ll26NNvLctVVV6l79+6X1B6Xxx57TBkZGerbt6+OHTumgoICBQcHa9KkSXaHFpDc3FzdfPPNWrBgge655x7t3r1br7zyil555RW7QwuIx+PRihUrlJmZqQ4dLvs/LTgXu4+ffBdefPFFb58+fbwhISHeYcOGeXfu3Gl3SAHZsmWLV1KzlpmZaXdordZS/JK8K1assDu0gEybNs3bt29fb0hIiDcqKso7duxY7x//+Ee7w2qzS/FI6cSJE72xsbHekJAQb69evbwTJ070Hjx40O6wTNmwYYN3wIABXqfT6U1ISPC+8sordocUsPfff98ryVtRUWF3KLCRw+v1eu1JZwAAwOXkst5TAQAAvjskFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBIkFQAAwBL/D5QuU5mOOjGNAAAAAElFTkSuQmCC",
+ "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, ?it/s]\n",
+ "/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:427: UserWarning: Calculation of Riemannian distance failed for eps=0\n",
+ " warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}')\n",
+ "Computing Riemannian distances: 0%| | 0/8 [00:00, ?it/s]\n",
+ "/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:427: UserWarning: Calculation of Riemannian distance failed for eps=1e-09\n",
+ " warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}')\n",
+ "Computing Riemannian distances: 0%| | 0/8 [00:00, ?it/s]\n",
+ "/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:427: UserWarning: Calculation of Riemannian distance failed for eps=1e-08\n",
+ " warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}')\n",
+ "Computing Riemannian distances: 0%| | 0/8 [00:00, ?it/s]\n",
+ "/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:427: UserWarning: Calculation of Riemannian distance failed for eps=1e-07\n",
+ " warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}')\n",
+ "Computing Riemannian distances: 0%| | 0/8 [00:00, ?it/s]\n",
+ "/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:427: UserWarning: Calculation of Riemannian distance failed for eps=1e-06\n",
+ " warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}')\n",
+ "Computing Riemannian distances: 0%| | 0/8 [00:00, ?it/s]\n",
+ "/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:427: UserWarning: Calculation of Riemannian distance failed for eps=1e-05\n",
+ " warnings.warn(f'Calculation of Riemannian distance failed for eps={eps}')\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "Riemannian distance failed for all eps values!",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[4], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m riemannian_cor_truth \u001b[38;5;241m=\u001b[39m twopair_riemannian_distance(correlations_truth,correlations_truth)\n\u001b[1;32m 3\u001b[0m riemannian_cov_truth \u001b[38;5;241m=\u001b[39m twopair_riemannian_distance(covariances_truth,covariances_truth)\n\u001b[0;32m----> 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%|â–ˆ| 8/8 [00:00<00:00, 43\n",
+ "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 27\n",
+ "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "seaborn.heatmap(fisher_cor_truth_HMM_reordered)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "0b2619c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "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": [
+ "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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R3XnnnZo6dapOnTpV6/YHDx7UjBkz9MADD+j48ePKyMhQRkaGTp48aem8JAMAAJg5dGvhlClTdM8992jgwIG6+eab9fTTT6tLly46dOhQrduvWrVKP/zhD/Xoo48qMTFRS5cu1fDhw2seBthYJAMAALSgQCCgioqKkBEIBBrcr7q6Wlu3blVVVZXS0tJq3SYvL08TJkwImZs4caLy8vIsxUgyAACAWdC+4ff7FRMTEzL8fn+dpz5x4oS6dOkir9erBQsWaPv27RoyZEit25aWlio2NjZkLjY2VqWlpZYul7sJAAAwsfOhQz6fT1lZWSFz5jf3/rVBgwapoKBA5eXl2rZtm2bPnq19+/bVmRDYgWQAAIAW5PV66/3xN4uIiFBCQoIkKTU1VUeOHNGqVau0fv36a7aNi4tTWVlZyFxZWZni4uIsxUibAAAAMxvbBM0OJRisc41BWlqa9uzZEzKXk5NT5xqDulAZAADAxKl3E/h8Pk2aNEn9+vXTpUuXtGXLFuXm5mr37t2SpFmzZqlPnz41aw4WLVqksWPHasWKFZo8ebK2bt2qo0ePasOGDZbOSzIAAICZQ08gPH/+vGbNmqWSkhLFxMQoKSlJu3fv1l133SVJKi4uVocOfynqp6ena8uWLfrZz36mn/70pxo4cKB27NihoUOHWjovyQAAAK3ESy+9VO/3ubm518xNmzZN06ZNa9Z5SQYAADAxXPZuglaTDFzd9aHTITRb+I+al5m1FuWDE5wOwRZdf/yfTofQbJXFDzgdgi2CX1U6HUKzdegZ5XQItrh6uqzhjcCLigAAgLu0msoAAACtBW0CAADczmXJAG0CAABcjsoAAAAmtAkAAHA5kgEAAFzObckAawYAAHA5KgMAAJgZHqcjuK5IBgAAMKFNAAAAXIXKAAAAJkaQNgEAAK5GmwAAALgKlQEAAEwM7iYAAMDdaBMAAABXoTIAAIAJdxMAAOByhuF0BNcXyQAAACZuqwywZgAAAJdzpDIQCAQUCARC56qD8oaRmwAAnEdloAEfffSRNm/erI8//liS9PHHH+vBBx/U3LlztXfv3kYdw+/3KyYmJmT86vjnVkMBAKBFGIZ9oy2wlAzs2rVLt956qx555BGlpKRo165dGjNmjAoLC1VUVKS77767UQmBz+dTeXl5yMhK+W6TLwIAADSdpWTgqaee0qOPPqo//vGP2rx5s/7xH/9R8+fPV05Ojvbs2aNHH31Uy5Yta/A4Xq9X0dHRIYMWAQCgtTCCHttGW2DpF/jUqVO6//77JUn33nuvLl26pH/4h3+o+X7mzJn6/e9/b2uAAABcb4bhsW20BZb/HPd4/nxhHTp0UGRkpGJiYmq+i4qKUnl5uX3RAQCAFmcpGbjpppv06aef1nzOy8tTv379aj4XFxcrPj7evugAAHCAEbRvtAWWbi188MEHVV1dXfN56NChId+/9957uvPOO+2JDAAAhwTbSHnfLpaSgQULFtT7/TPPPNOsYAAAwPXHEn4AAEycWkDo9/s1cuRIRUVFqVevXsrIyNDp06fr3Sc7O1sejydkREZGWjovyQAAACZO3Vq4b98+ZWZm6tChQ8rJydHVq1d19913q6qqqt79oqOjVVJSUjOKioosnZcXFQEAYGLnkwNrewS/1+uV1+u9Zttdu3aFfM7OzlavXr2Un5+vMWPG1HkOj8ejuLi4JsdIZQAAgBZU2yP4/X5/o/b939v1u3fvXu92lZWV6t+/v2688UZNnTpVp06dshQjlQEAAEzsfHKgz+dTVlZWyFxtVQGzYDCoxYsXa/To0dfcvffXBg0apE2bNikpKUnl5eV69tlnlZ6erlOnTqlv376NipFkAAAAEztvLayrJdCQzMxMnTx5UgcOHKh3u7S0NKWlpdV8Tk9PV2JiotavX6+lS5c26lwkAwAAtDILFy7U22+/rf379zf6r/v/FR4erpSUFBUWFjZ6H9YMAABg4tSthYZhaOHChdq+fbv27t2rAQMGWI69urpaJ06csPREYCoDAACY2Hk3gRWZmZnasmWLdu7cqaioKJWWlkqSYmJi1KlTJ0nSrFmz1KdPn5pFiE899ZRuv/12JSQk6Ouvv9by5ctVVFSkefPmNfq8JAMAALQSa9eulSSNGzcuZH7z5s01bw0uLi5Whw5/KexfvHhR8+fPV2lpqbp166bU1FQdPHhQQ4YMafR5SQYAADBx6t0ERiNKErm5uSGfV65cqZUrVzbrvCQDAACYWO31t3UsIAQAwOWoDAAAYOLUAkKnkAwAAGDi1JoBp7SaZKDjEOv3UrY6V684HYEtwibMdDoEW1R+scDpEJqtc5+6X0zSllQWvuN0CM1mXCx1OgRbhEdYe7WtW7FmAAAAuEqrqQwAANBa0CYAAMDlXLZ+kDYBAABuR2UAAAAT2gQAALgcdxMAAABXoTIAAIBJ0OkArjOSAQAATAzRJgAAAC5CZQAAAJOgyx40QDIAAIBJ0GVtApIBAABMWDMAAABchcoAAAAm3FoIAIDL0SYAAACuQmUAAAAT2gQAALic25IB2gQAALicLZUBwzDk8bhrsQUAoP1iAWETeL1effTRR3YcCgAAxwU99o22wFJlICsrq9b56upqLVu2TD169JAk/epXv6r3OIFAQIFAIPQY31bL2zHMSjgAAMAGlpKB5557TsnJyeratWvIvGEY+uijj9S5c+dGtQv8fr+efPLJkLmfTkzVE5NGWAkHAIAWwbsJ6vHMM89ow4YNWrFihe68886a+fDwcGVnZ2vIkCGNOo7P57umylC98SdWQgEAoMW47KWF1pKBf/3Xf9X48eN13333acqUKfL7/QoPD7d8Uq/XK6/XGzL3J1oEAIBWglsLGzBy5Ejl5+fryy+/1IgRI3Ty5EnuJAAAwAZ+v18jR45UVFSUevXqpYyMDJ0+fbrB/d544w0NHjxYkZGRGjZsmN59911L523S3QRdunTRyy+/LJ/PpwkTJqi6urophwEAoFUKejy2DSv27dunzMxMHTp0SDk5Obp69aruvvtuVVVV1bnPwYMHNWPGDD3wwAM6fvy4MjIylJGRoZMnTzb6vB7DMJrVGvniiy+Un5+vCRMmqHPnzk0+zp9WLWhOGK2CJzHJ6RBsEZY42ukQbOHpFOV0CM3Wuc8Yp0OwRWXhO06H0GzGxVKnQ7BHRKTTEdjCO3hsix7/jfiZth1rWsl/NnnfL7/8Ur169dK+ffs0Zkzt/z6YPn26qqqq9Pbbb9fM3X777br11lu1bt26Rp2n2Q8d6tu3r/r27dvcwwAA0C7Vdjt9bWvnalNeXi5J6t69e53b5OXlXbMof+LEidqxY0ejY+RxxAAAmARtHH6/XzExMSHD7/c3HEMwqMWLF2v06NEaOnRonduVlpYqNjY2ZC42NlalpY2vZvGiIgAATOx8cmBtt9M3piqQmZmpkydP6sCBA/YFUweSAQAAWlBjWwJ/beHChXr77be1f//+BlvxcXFxKisrC5krKytTXFxco89HmwAAAJOgPLYNKwzD0MKFC7V9+3bt3btXAwYMaHCftLQ07dmzJ2QuJydHaWlpjT4vlQEAAEycegJhZmamtmzZop07dyoqKqqm7x8TE6NOnTpJkmbNmqU+ffrUrDtYtGiRxo4dqxUrVmjy5MnaunWrjh49qg0bNjT6vFQGAABoJdauXavy8nKNGzdO8fHxNeO1116r2aa4uFglJSU1n9PT07VlyxZt2LBBycnJ2rZtm3bs2FHvokMzKgMAAJg49erhxjz6Jzc395q5adOmadq0aU0+L8kAAAAmbns3AckAAAAmbntrIWsGAABwOSoDAACYOLVmwCkkAwAAmLhtzQBtAgAAXI7KAAAAJm6rDJAMAABgYrhszQBtAgAAXK7VVAbC/uZ+p0NoturtLzodgi2u5OQ4HYItgl9VOh1Cs1UWvuN0CLaIGvg3TofQbKVTvud0CLYIXm4fd9B739rXosenTQAAgMu5LRmgTQAAgMtRGQAAwKR9NFMaj2QAAAATnkAIAIDLsWYAAAC4CpUBAABM3FYZIBkAAMDEbQsIaRMAAOByVAYAADDhbgIAAFzObWsGaBMAAOByVAYAADBx2wJCkgEAAEyCLksHaBMAAOByVAYAADBx2wJCkgEAAEzc1SQgGQAA4BpuqwywZgAAAJejMgAAgAlPIAQAwOXcdmths5KBqqoqvf766yosLFR8fLxmzJihHj16NLhfIBBQIBAwTV6R1xvRnHAAAEATWFozMGTIEH311VeSpLNnz2ro0KF6+OGHlZOToyVLlmjIkCE6c+ZMg8fx+/2KiYkJGf++7pWmXQEAADYzbBxW7N+/X1OmTFHv3r3l8Xi0Y8eOerfPzc2Vx+O5ZpSWllo6r6Vk4OOPP9a3334rSfL5fOrdu7eKiop0+PBhFRUVKSkpSU888USDx/H5fCovLw8Zjy2YZSlwAABaStDGYUVVVZWSk5O1Zs0aS/udPn1aJSUlNaNXr16W9m9ymyAvL0/r1q1TTEyMJKlLly568skn9aMf/ajBfb1er7xeb8hcgBYBAMDlJk2apEmTJlner1evXuratWuTz2v51kKP589LLC9fvqz4+PiQ7/r06aMvv/yyycEAANAaBGXYNgKBgCoqKkLGNevmmunWW29VfHy87rrrLv3ud7+zvL/lZGD8+PEaPny4KioqdPr06ZDvioqKGrWAEACA1szONQO1rZPz+/22xBkfH69169bpzTff1Jtvvqkbb7xR48aN07Fjxywdx1KbYMmSJSGfu3TpEvL5rbfe0h133GEpAAAA2jOfz6esrKyQOXOrvKkGDRqkQYMG1XxOT0/XZ599ppUrV+rXv/51o4/TrGTAbPny5VYOBwBAq2Tn44hrWyfXkkaNGqUDBw5Y2oeHDgEAYNKWHzpUUFBwzZq+hpAMAABg4lQqUFlZqcLCwprPZ86cUUFBgbp3765+/frJ5/Pp3LlzeuWVPz+b57nnntOAAQN0yy236PLly3rxxRe1d+9evf/++5bOSzIAAEArcfToUf3gBz+o+fy/aw1mz56t7OxslZSUqLi4uOb7K1eu6Cc/+YnOnTunG264QUlJSfrggw9CjtEYJAMAAJg49QrjcePGyTDqrktkZ2eHfH7sscf02GOPNfu8JAMAAJgYbXjNQFNYfs4AAABoX6gMAABg4lSbwCkkAwAAmLTlWwubgjYBAAAuR2UAAAATd9UFSAYAALgGbQIAAOAqVAYAADDhbgIAAFzObQ8dIhkAAMDEbZUB1gwAAOByracycLnK6QiaLVj6pdMh2MLjjXA6BFt06BnldAjNZpSfdzoEW5RNTXA6hGbrteNTp0OwxVeLRzkdQptAmwAAAJejTQAAAFyFygAAACZBgzYBAACu5q5UgDYBAACuR2UAAAATt72bgGQAAAATt91aSJsAAACXozIAAICJ254zQDIAAIAJawYAAHA51gwAAABXoTIAAIAJawYAAHA5w2WPI6ZNAACAy1EZAADAhLsJAABwObetGaBNAACAy5EMAABgYtj4Hyv279+vKVOmqHfv3vJ4PNqxY0eD++Tm5mr48OHyer1KSEhQdna25eslGQAAwCQow7ZhRVVVlZKTk7VmzZpGbX/mzBlNnjxZP/jBD1RQUKDFixdr3rx52r17t6XzsmYAAIBWYtKkSZo0aVKjt1+3bp0GDBigFStWSJISExN14MABrVy5UhMnTmz0cSxVBo4dO6YzZ87UfP71r3+t0aNH68Ybb9T3v/99bd26tVHHCQQCqqioCBmBK1eshAIAQIsxDMO2UetvXiBgS5x5eXmaMGFCyNzEiROVl5dn6TiWkoE5c+bos88+kyS9+OKL+vGPf6wRI0boiSee0MiRIzV//nxt2rSpweP4/X7FxMSEjH/f+BtLgQMA0FKCNo7afvP8fr8tcZaWlio2NjZkLjY2VhUVFfrmm28afRxLbYJPP/1UAwcOlCS98MILWrVqlebPn1/z/ciRI/X0009r7ty59R7H5/MpKysrdPKz31kJBQCAFmPni4pq+83zer22Hd8OlpKBG264QRcuXFD//v117tw5jRo1KuT72267LaSNUBev13vNP4hARISVUAAAaBNq+82zS1xcnMrKykLmysrKFB0drU6dOjX6OJbaBJMmTdLatWslSWPHjtW2bdtCvn/99deVkJBg5ZAAALQ6Tt1NYFVaWpr27NkTMpeTk6O0tDRLx7FUGfjlL3+p0aNHa+zYsRoxYoRWrFih3NxcJSYm6vTp0zp06JC2b99uKQAAAFobp15UVFlZqcLCwprPZ86cUUFBgbp3765+/frJ5/Pp3LlzeuWVVyRJCxYs0OrVq/XYY49p7ty52rt3r15//XW98847ls5rqTLQu3dvHT9+XGlpadq1a5cMw9Dhw4f1/vvvq2/fvvrd736ne+65x1IAAADgz44ePaqUlBSlpKRIkrKyspSSkqKf//znkqSSkhIVFxfXbD9gwAC98847ysnJUXJyslasWKEXX3zR0m2FUhOeM9C1a1ctW7ZMy5Yts7orAABtglMvKho3bly9VYnani44btw4HT9+vFnn5aFDAACY2Hk3QVvA44gBAHA5KgMAAJgEHVpA6BSSAQAATNyVCtAmAADA9agMAABg4tTdBE4hGQAAwIRkAAAAl3PqCYROYc0AAAAuR2UAAAAT2gQAALgcTyAEAACuQmUAAAATty0gJBkAAMDEbWsGaBMAAOByVAYAADChTeCQ6nf/0+kQmq1Dwk1Oh2ALo/S80yHY4tvCC06H0GzhEZ2cDsEW1X8KOh1Cs331UKrTIdii68r/63QItvj2ly17fNoEAADAVVpNZQAAgNbCbc8ZIBkAAMAkyJoBAADczW2VAdYMAADgclQGAAAwoU0AAIDL0SYAAACuQmUAAAAT2gQAALgcbQIAAOAqVAYAADChTQAAgMvRJgAAAK5CZQAAABPDaPuv3baCygAAACZBGbYNq9asWaObbrpJkZGRuu2223T48OE6t83OzpbH4wkZkZGRls9JMgAAgIlhGLYNK1577TVlZWVpyZIlOnbsmJKTkzVx4kSdP3++zn2io6NVUlJSM4qKiixfL8kAAACtxK9+9SvNnz9fc+bM0ZAhQ7Ru3TrdcMMN2rRpU537eDwexcXF1YzY2FjL5yUZAADAxM42QSAQUEVFRcgIBALXnPPKlSvKz8/XhAkTauY6dOigCRMmKC8vr85YKysr1b9/f914442aOnWqTp06Zfl6SQYAADCxs03g9/sVExMTMvx+/zXnvHDhgqqrq6/5yz42NlalpaW1xjlo0CBt2rRJO3fu1KuvvqpgMKj09HR98cUXlq6XuwkAAGhBPp9PWVlZIXNer9eWY6elpSktLa3mc3p6uhITE7V+/XotXbq00cexlAw89NBDuvfee3XHHXdY2e0agUDgmhJJ9bfV8nYMa9ZxAQCwg51PIPR6vY368e/Zs6fCwsJUVlYWMl9WVqa4uLhGnSs8PFwpKSkqLCy0FKOlNsGaNWs0btw43XzzzfrlL39ZZ9miIbWVTJ7d+/smHQsAALsZNv6nsSIiIpSamqo9e/bUzAWDQe3Zsyfkr//6VFdX68SJE4qPj7d0vZbXDLz//vu655579Oyzz6pfv36aOnWq3n77bQWDjX9Ag8/nU3l5ech45M4kq6EAANCuZGVlaePGjXr55Zf10Ucf6cEHH1RVVZXmzJkjSZo1a5Z8Pl/N9k899ZTef/99ff755zp27Jjuu+8+FRUVad68eZbOa3nNwLBhwzR+/HgtX75c27dv16ZNm5SRkaHY2Fjdf//9mjNnjhISEuo9Rm0lkz/RIgAAtBJWnw9gl+nTp+vLL7/Uz3/+c5WWlurWW2/Vrl27ahYVFhcXq0OHv/wdf/HiRc2fP1+lpaXq1q2bUlNTdfDgQQ0ZMsTSeT2GhSvu0KGDSktL1atXr5D54uJibdq0SdnZ2Tp79qyqq6stBSFJf1o+1/I+rU5UlNMR2MIorfvhFm3J1Y+a1sZqTTpZWADUmlX+5AmnQ2g276CuTodgi67PH3U6BFt8e+Vcix7/OzGDbDvWl+WnbTtWS7Hl1sJ+/frpF7/4hc6cOaNdu3bZcUgAAHCdWGoT9O/fX2FhdZfzPR6P7rrrrmYHBQCAk5xqEzjFUjJw5syZlooDAIBWw85bC9sCHjoEAICJ2yoDPI4YAACXozIAAIBJ0MLDgtoDkgEAAExoEwAAAFehMgAAgAl3EwAA4HJWXjDUHtAmAADA5agMAABgQpsAAACX424CAADgKlQGAAAwcdsCQpIBAABM3NYmIBkAAMDEbckAawYAAHA5KgMAAJi4qy4gyXCJy5cvG0uWLDEuX77sdChN1h6uwTDax3W0h2swDK6jNWkP12AY7ec63MZjGO5ojFRUVCgmJkbl5eWKjo52OpwmaQ/XILWP62gP1yBxHa1Je7gGqf1ch9uwZgAAAJcjGQAAwOVIBgAAcDnXJANer1dLliyR1+t1OpQmaw/XILWP62gP1yBxHa1Je7gGqf1ch9u4ZgEhAAConWsqAwAAoHYkAwAAuBzJAAAALkcyAACAy5EMAADgcq5IBtasWaObbrpJkZGRuu2223T48GGnQ7Jk//79mjJlinr37i2Px6MdO3Y4HZJlfr9fI0eOVFRUlHr16qWMjAydPn3a6bAsW7t2rZKSkhQdHa3o6GilpaXpvffeczqsZlm2bJk8Ho8WL17sdCiW/OIXv5DH4wkZgwcPdjqsJjl37pzuu+8+9ejRQ506ddKwYcN09OhRp8NqtJtuuuma/y08Ho8yMzOdDg2N1O6Tgddee01ZWVlasmSJjh07puTkZE2cOFHnz593OrRGq6qqUnJystasWeN0KE22b98+ZWZm6tChQ8rJydHVq1d19913q6qqyunQLOnbt6+WLVum/Px8HT16VHfeeaemTp2qU6dOOR1akxw5ckTr169XUlKS06E0yS233KKSkpKaceDAAadDsuzixYsaPXq0wsPD9d577+kPf/iDVqxYoW7dujkdWqMdOXIk5H+HnJwcSdK0adMcjgyN5ux7klreqFGjjMzMzJrP1dXVRu/evQ2/3+9gVE0nydi+fbvTYTTb+fPnDUnGvn37nA6l2bp162a8+OKLTodh2aVLl4yBAwcaOTk5xtixY41FixY5HZIlS5YsMZKTk50Oo9kef/xx4/vf/77TYdhq0aJFxve+9z0jGAw6HQoaqV1XBq5cuaL8/HxNmDChZq5Dhw6aMGGC8vLyHIwM5eXlkqTu3bs7HEnTVVdXa+vWraqqqlJaWprT4ViWmZmpyZMnh/z/o6359NNP1bt3b333u9/VzJkzVVxc7HRIlv32t7/ViBEjNG3aNPXq1UspKSnauHGj02E12ZUrV/Tqq69q7ty58ng8ToeDRmrXycCFCxdUXV2t2NjYkPnY2FiVlpY6FBWCwaAWL16s0aNHa+jQoU6HY9mJEyfUpUsXeb1eLViwQNu3b9eQIUOcDsuSrVu36tixY/L7/U6H0mS33XabsrOztWvXLq1du1ZnzpzRHXfcoUuXLjkdmiWff/651q5dq4EDB2r37t168MEH9S//8i96+eWXnQ6tSXbs2KGvv/5a999/v9OhwIKOTgcA98nMzNTJkyfbZH9XkgYNGqSCggKVl5dr27Ztmj17tvbt29dmEoKzZ89q0aJFysnJUWRkpNPhNNmkSZNq/ntSUpJuu+029e/fX6+//roeeOABByOzJhgMasSIEXrmmWckSSkpKTp58qTWrVun2bNnOxyddS+99JImTZqk3r17Ox0KLGjXlYGePXsqLCxMZWVlIfNlZWWKi4tzKCp3W7hwod5++219+OGH6tu3r9PhNElERIQSEhKUmpoqv9+v5ORkrVq1yumwGi0/P1/nz5/X8OHD1bFjR3Xs2FH79u3T888/r44dO6q6utrpEJuka9euuvnmm1VYWOh0KJbEx8dfk0gmJia2yZZHUVGRPvjgA82bN8/pUGBRu04GIiIilJqaqj179tTMBYNB7dmzp032eNsywzC0cOFCbd++XXv37tWAAQOcDsk2wWBQgUDA6TAabfz48Tpx4oQKCgpqxogRIzRz5kwVFBQoLCzM6RCbpLKyUp999pni4+OdDsWS0aNHX3Ob7SeffKL+/fs7FFHTbd68Wb169dLkyZOdDgUWtfs2QVZWlmbPnq0RI0Zo1KhReu6551RVVaU5c+Y4HVqjVVZWhvy1c+bMGRUUFKh79+7q16+fg5E1XmZmprZs2aKdO3cqKiqqZs1GTEyMOnXq5HB0jefz+TRp0iT169dPly5d0pYtW5Sbm6vdu3c7HVqjRUVFXbNWo3PnzurRo0ebWsPxyCOPaMqUKerfv7/+53/+R0uWLFFYWJhmzJjhdGiWPPzww0pPT9czzzyje++9V4cPH9aGDRu0YcMGp0OzJBgMavPmzZo9e7Y6dmz3Py3tj9O3M1wP//Ef/2H069fPiIiIMEaNGmUcOnTI6ZAs+fDDDw1J14zZs2c7HVqj1Ra/JGPz5s1Oh2bJ3Llzjf79+xsRERHGd77zHWP8+PHG+++/73RYzdYWby2cPn26ER8fb0RERBh9+vQxpk+fbhQWFjodVpO89dZbxtChQw2v12sMHjzY2LBhg9MhWbZ7925DknH69GmnQ0ETeAzDMJxJQwAAQGvQrtcMAACAhpEMAADgciQDAAC4HMkAAAAuRzIAAIDLkQwAAOByJAMAALgcyQAAAC5HMgAAgMuRDAAA4HIkAwAAuNz/A7bryigkZVlCAAAAAElFTkSuQmCC",
+ "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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",
+ "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",
+ "2\n",
+ "10\n",
+ "13\n",
+ "17\n",
+ "16\n",
+ "3\n",
+ "27\n",
+ "11\n",
+ "13\n",
+ "18\n",
+ "10\n",
+ "6\n",
+ "26\n",
+ "23\n",
+ "21\n",
+ "17\n",
+ "17\n",
+ "31\n",
+ "16\n",
+ "20\n",
+ "8\n",
+ "43\n",
+ "13\n",
+ "18\n",
+ "19\n",
+ "17\n",
+ "6\n",
+ "18\n",
+ "11\n",
+ "25\n",
+ "11\n",
+ "20\n",
+ "18\n",
+ "10\n",
+ "10\n",
+ "25\n",
+ "20\n",
+ "16\n",
+ "36\n",
+ "12\n",
+ "17\n",
+ "21\n",
+ "5\n",
+ "14\n",
+ "10\n",
+ "5\n",
+ "22\n",
+ "36\n",
+ "18\n",
+ "18\n",
+ "9\n",
+ "10\n",
+ "20\n",
+ "14\n",
+ "18\n",
+ "12\n",
+ "12\n",
+ "17\n",
+ "22\n",
+ "11\n",
+ "13\n",
+ "10\n",
+ "6\n",
+ "13\n",
+ "25\n",
+ "12\n",
+ "15\n",
+ "13\n",
+ "16\n",
+ "35\n",
+ "6\n",
+ "8\n",
+ "7\n",
+ "25\n",
+ "9\n",
+ "12\n",
+ "18\n",
+ "18\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(100):\n",
+ " posterior_hard = np.argmax(posterior[i],axis=1)\n",
+ " truth_0 = np.argmax(truth[i*1200:i*1200+1200,:],axis=1)\n",
+ " print(np.sum(truth_0!=posterior_hard))"
+ ]
+ },
+ {
+ "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 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_mouseover(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+ "File \u001b[0;32m/well/win-fmrib-analysis/users/uap971/conda/skylake/envs/osld/lib/python3.10/site-packages/matplotlib/_api/deprecation.py:454\u001b[0m, in \u001b[0;36mmake_keyword_only..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m 449\u001b[0m warn_deprecated(\n\u001b[1;32m 450\u001b[0m since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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%|â–ˆ| 8/8 [00:00<00:00, 46\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "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%|â–ˆ| 8/8 [00:00<00:00, 50\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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Zf/nllyVJP/zhD+2LDAAAXBWWKgM/+tGP9G//9m8Nvvbyyy/r7rvvbjNZEAAAl8QCwkvLzs7Wtm2XvkRi+fLlCgaDzQ4KAABHuSwZ4KZDAACYWLmNcHvAswkAAHA5KgMAAJi5rDJAMgAAgJnLlr/RJgAAwOWoDAAAYOK2BYQkAwAAmLksGaBNAACAy1EZAADAzGULCEkGAAAwcduaAdoEAAC4HJUBAADMaBMAAOBubmsTkAwAAGDmssoAawYAAHA5KgMAAJgYLqsMtJpk4OXItv87nxZsNb+dzfLYsPucDsEWu/e+7nQIzbYgJdvpEGyREWz7RcgtZ3o6HYItjia5qxfeZG3/I8mStv83FAAANEv7+CoLAICNaBMAAOB2LksGaBMAAOByVAYAADChTQAAgMuRDAAA4HJuSwZYMwAAgMtRGQAAwMzwOB3BVUUyAACACW0CAADgKlQGAAAwMYK0CQAAcDXaBAAAwFWoDAAAYGJwNQEAAO5GmwAAALgKlQEAAEy4mgAAAJczDKcjuLpoEwAAYGIEPbYNq5YtW6aEhARFRUUpNTVVxcXFl9x20qRJ8ng89cZtt91m6ZgkAwAAtBIbNmyQz+dTTk6O9uzZo6SkJGVkZOjUqVMNbr9p0yadPHmybuzdu1fh4eG64447LB3XkWQgEAioqqoqZNQatU6EAgBAPU5VBpYuXaqsrCxlZmYqMTFReXl56tSpk/Lz8xvc/tprr1VcXFzd2LFjhzp16tTyycD+/fu1Zs0aHThwQJJ04MABzZkzR7NmzdJ7773XqH34/X5FR0eHjM8qD1kNBQCAFmEY9o2GvgAHAoF6x6ypqVFJSYnS09Pr5sLCwpSenq6ioqJGxb169Wrddddd6ty5s6XztZQMFBQUaOTIkXr00Uc1atQoFRQUaMKECTp8+LCOHTumW265pVEJQXZ2tiorK0NGYvQgS4EDANAWNPQF2O/319uuoqJCtbW1io2NDZmPjY1VWVnZFY9TXFysvXv3avbs2ZZjtJQMLFq0SI899pj+/ve/a82aNbrnnnuUlZWlHTt2qLCwUI899piWLFlyxf14vV5169YtZIR7wi0HDwBAS7CzTdDQF+Ds7GzbY169erWGDx+uMWPGWH6vpWRg3759euCBByRJd955p86dO6d//dd/rXv93nvv1X//939bDgIAgNbEMDy2jYa+AHu93nrHjImJUXh4uMrLy0Pmy8vLFRcXd9l4q6urtX79ej344INNOl/LawY8nm8XQ4SFhSkqKkrR0dF1r3Xt2lWVlZVNCgQAADeLjIxUcnKyCgsL6+aCwaAKCwuVlpZ22ff++7//uwKBgO67774mHdtSMpCQkKDPP/+87ueioiL169ev7ufjx48rPj6+SYEAANBaGEH7hhU+n0+rVq3SunXrtH//fs2ZM0fV1dXKzMyUJM2YMaPBFsPq1as1bdo09ejRo0nna+kOhHPmzFFt7T8vARw2bFjI6++8845uvvnmJgUCAEBrEXToqYXTp0/X6dOntXDhQpWVlWnkyJEqKCioW1R4/PhxhYWFfo8/ePCgPvjgA7377rtNPq7HMFrHTRfv6f8jp0NotrSgtUs5WqtXLx5xOgRb7N77utMhNNuCFPsXGTkh40Lbv7/Zh1Ht4171P72u/MobtQF9Py688kbNcGjov9i2r0H7C2zbV0vh2QQAAJgYDlUGnEIyAACACU8tBADA5VpHA/3qafuNPAAA0CxUBgAAMKFNAACAyzl1aaFTaBMAAOByVAYAADDh0kIAAFyOqwkAAICrUBkAAMDEbQsISQYAADBx25oB2gQAALgclQEAAEzctoCQZAAAABPWDDjkhb5nnQ6h2aLiv3I6BFvcHxnldAi2WJCS7XQIzfab3X6nQ7BFTd5Cp0NotvTR33c6BFs8+ZMPnA7BFr9p4f2zZgAAALhKq6kMAADQWtAmAADA5Vy2fpA2AQAAbkdlAAAAE9oEAAC4HFcTAAAAV6EyAACASdDpAK4ykgEAAEwM0SYAAAAuQmUAAACToMtuNEAyAACASdBlbQKSAQAATFgzAAAAXIXKAAAAJlxaCACAy9EmAAAArkJlAAAAE9oEAAC4nNuSAdoEAAC4nC2VAcMw5PG4a7EFAKD9YgFhE3i9Xu3fv9+OXQEA4Ligx77RFliqDPh8vgbna2trtWTJEvXo0UOStHTp0svuJxAIKBAIhM4Fg/KG0bUAAOBqs5QM5ObmKikpSd27dw+ZNwxD+/fvV+fOnRvVLvD7/Xr22WdD5nx9++vRft+xEg4AAC2CZxNcxuLFi7Vy5Uq98MILuvnmm+vmIyIitHbtWiUmJjZqP9nZ2fWqDGf+ZaqVUAAAaDEue2ihtWTgiSee0OTJk3Xfffdp6tSp8vv9ioiIsHxQr9crr9cbMldNiwAA0EpwaeEVjB49WiUlJTp9+rRSUlK0d+9eriQAAKANa9KlhV26dNG6deu0fv16paenq7a21u64AABwTNBlX3KbVZu/6667tHv3bm3atEn9+/e3KyYAABxl2DisWrZsmRISEhQVFaXU1FQVFxdfdvuzZ89q7ty5io+Pl9fr1aBBg7Rt2zZLx2z2TYf69OmjPn36NHc3AAC43oYNG+Tz+ZSXl6fU1FTl5uYqIyNDBw8eVM+ePettX1NTox/84Afq2bOn3nzzTfXu3VvHjh2rd9XflfBsAgAATOxcQNjQvXUaWkgvfXufnqysLGVmZkqS8vLytHXrVuXn5+uJJ56ot31+fr7OnDmjDz/8sG5Bf0JCguUYWcIPAICJnXcg9Pv9io6ODhl+v7/eMWtqalRSUqL09PS6ubCwMKWnp6uoqKjBON966y2lpaVp7ty5io2N1bBhw7R48WLLa/moDAAA0IIaurdOQ1WBiooK1dbWKjY2NmQ+NjZWBw4caHDfR44c0Xvvvad7771X27Zt0+HDh/XTn/5UFy9eVE5OTqNjJBkAAMDEzjsQXqolYIdgMKiePXtq5cqVCg8PV3Jysk6cOKHnnnuOZAAAgOZw4g6EMTExCg8PV3l5ech8eXm54uLiGnxPfHy8IiIiFB4eXjc3dOhQlZWVqaamRpGRkY06NmsGAABoBSIjI5WcnKzCwsK6uWAwqMLCQqWlpTX4nnHjxunw4cMKBv+55PHQoUOKj49vdCIgkQwAAFCPU48w9vl8WrVqldatW6f9+/drzpw5qq6urru6YMaMGcrOzq7bfs6cOTpz5ozmz5+vQ4cOaevWrVq8eLHmzp1r6bi0CQAAMHHq2QTTp0/X6dOntXDhQpWVlWnkyJEqKCioW1R4/Phxhf2PZ/n07dtX27dv14IFCzRixAj17t1b8+fP1+OPP27puCQDAACYOPnUwnnz5mnevHkNvrZr1656c2lpafroo4+adUzaBAAAuByVAQAATKz2+ts6kgEAAEycWjPgFNoEAAC4HJUBAABM3FYZIBkAAMDEcNmaAdoEAAC4XKupDFSe6uh0CM32j7PWHhnZWlWc6ex0CLbICLb9XLcmb6HTIdgi8qFFTofQbOfnzHI6BFtkfN3wPe4RijYBAAAu57ZkoO1/dQIAAM1CZQAAABMnb0fsBJIBAABMuAMhAAAux5oBAADgKlQGAAAwcVtlgGQAAAATty0gpE0AAIDLURkAAMCEqwkAAHA5t60ZoE0AAIDLURkAAMDEbQsISQYAADAJuiwdoE0AAIDLURkAAMDEbQsISQYAADBxV5OAZAAAgHrcVhlgzQAAAC5HZQAAABPuQAgAgMu57dLCZiUD1dXV2rhxow4fPqz4+Hjdfffd6tGjxxXfFwgEFAgEQuZqgrWKDAtvTjgAAKAJLK0ZSExM1JkzZyRJX375pYYNG6YFCxZox44dysnJUWJioo4ePXrF/fj9fkVHR4eMlV8dadoZAABgM8PG0RZYSgYOHDigb775RpKUnZ2tXr166dixYyouLtaxY8c0YsQIPfXUU1fcT3Z2tiorK0PGj6+5vmlnAACAzYI2jragyW2CoqIi5eXlKTo6WpLUpUsXPfvss7rrrruu+F6v1yuv1xsyR4sAAABnWE4GPJ5vl1heuHBB8fHxIa/17t1bp0+fticyAAAcwgLCK5g8ebI6dOigqqoqHTx4UMOGDat77dixY41aQAgAQGvmrlTAYjKQk5MT8nOXLl1Cfn777bc1fvz45kcFAACummYlA2bPPfdcs4IBAKA1aCsL/+zCTYcAADBhzQAAAC7nrlSABxUBAOB6VAYAADBhzQAAAC5nuKxRQJsAAACXIxkAAMDEyWcTLFu2TAkJCYqKilJqaqqKi4svue3atWvl8XhCRlRUlOVjkgwAAGASlGHbsGLDhg3y+XzKycnRnj17lJSUpIyMDJ06deqS7+nWrZtOnjxZN44dO2b5fEkGAABoQYFAQFVVVSEjEAg0uO3SpUuVlZWlzMxMJSYmKi8vT506dVJ+fv4l9+/xeBQXF1c3YmNjLcdIMgAAgIlh4/D7/YqOjg4Zfr+/3jFrampUUlKi9PT0urmwsDClp6erqKjokrGeP39e/fv3V9++fXX77bdr3759ls+XqwkAADCx8w6E2dnZ8vl8IXNer7fedhUVFaqtra33zT42NlYHDhxocN+DBw9Wfn6+RowYocrKSj3//PMaO3as9u3bpz59+jQ6RpIBAABakNfrbfDD3w5paWlKS0ur+3ns2LEaOnSoXnnlFf3yl79s9H5IBgAAMHHipkMxMTEKDw9XeXl5yHx5ebni4uIatY+IiAiNGjVKhw8ftnRs1gwAAGBi2PirsSIjI5WcnKzCwsK6uWAwqMLCwpBv/5dTW1urTz/9VPHx8ZbOl8oAAAAmTt2O2OfzaebMmUpJSdGYMWOUm5ur6upqZWZmSpJmzJih3r171y1AXLRokb7//e9r4MCBOnv2rJ577jkdO3ZMs2fPtnRckgEAAFqJ6dOn6/Tp01q4cKHKyso0cuRIFRQU1C0qPH78uMLC/lnU/+qrr5SVlaWysjJdc801Sk5O1ocffqjExERLx/UYhtEqbsBc2v+HTofQbAP/t9MR2OPvu/7hdAi2yDvT0+kQmm3hK2OdDsEWF1590+kQmq3Liktf592WrB250OkQbJH1f19v0f1nJtj3D/qav/7etn21FCoDAACYuO2phSwgBADA5agMAABgEmwdHfSrhmQAAAATd6UCtAkAAHA9KgMAAJjY+WyCtoBkAAAAEyt3DmwPaBMAAOByVAYAADBx230GSAYAADBhzQAAAC7HmgEAAOAqVAYAADBhzQAAAC7XSh7oe9XQJgAAwOWoDAAAYMLVBAAAuJzb1gzQJgAAwOWoDAAAYOK2+wyQDAAAYOK2NQO0CQAAcDlLycCePXt09OjRup9fe+01jRs3Tn379tWNN96o9evXN2o/gUBAVVVVIaPGqLUWOQAALcQwDNtGW2ApGcjMzNQXX3whSXr11Vf1k5/8RCkpKXrqqac0evRoZWVlKT8//4r78fv9io6ODhn5lYebdgYAANgsaONoCyytGfj88891ww03SJKWL1+uF198UVlZWXWvjx49Wr/61a80a9asy+4nOztbPp8vZO7AsLuthAIAQIthAeFldOrUSRUVFerfv79OnDihMWPGhLyempoa0ka4FK/XK6/XGzIX6Qm3EgoAALCJpTbBrbfeqhUrVkiSJk6cqDfffDPk9Y0bN2rgwIH2RQcAgAOCMmwbbYGlysCvf/1rjRs3ThMnTlRKSopeeOEF7dq1S0OHDtXBgwf10UcfafPmzS0VKwAAV0VbWfhnF0uVgV69eumTTz5RWlqaCgoKZBiGiouL9e6776pPnz76z//8T02ZMqWlYgUAAC3A8k2HunfvriVLlmjJkiUtEQ8AAI5rK+V9u3AHQgAATNx2NQF3IAQAwOWoDAAAYBJ02QJCkgEAAEzclQrQJgAAwPWoDAAAYMLVBAAAuBzJAAAALscdCAEAgKtQGQAAwIQ2AQAALscdCAEAgKtQGQAAwMRtCwhJBgAAMHHbmgHaBAAAtCLLli1TQkKCoqKilJqaquLi4ka9b/369fJ4PJo2bZrlY5IMAABgYhiGbcOKDRs2yOfzKScnR3v27FFSUpIyMjJ06tSpy77vr3/9qx599FGNHz++SefrMVpJY6T3Nd91OoRm2/3dnk6HYIu+xYecDsEWR5OGOB1Csy09fZ3TIdgi42unI2i+LyPaR1f1gdJFTodgi4iY61t0/0lxY23b11/KPmz0tqmpqRo9erRefvllSVIwGFTfvn318MMP64knnmjwPbW1tZowYYJmzZqlP//5zzp79qy2bNliKUYqAwAAtKBAIKCqqqqQEQgE6m1XU1OjkpISpaen182FhYUpPT1dRUVFl9z/okWL1LNnTz344INNjpFkAAAAE8PGX36/X9HR0SHD7/fXO2ZFRYVqa2sVGxsbMh8bG6uysrIG4/zggw+0evVqrVq1qlnn2z7qXgAA2ChoYwc9OztbPp8vZM7r9TZ7v+fOndP999+vVatWKSYmpln7IhkAAMDEzjsQer3eRn34x8TEKDw8XOXl5SHz5eXliouLq7f9F198ob/+9a+aOnVq3VwwGJQkdejQQQcPHtSAAQMaFSNtAgAAWoHIyEglJyersLCwbi4YDKqwsFBpaWn1th8yZIg+/fRTlZaW1o0f/vCHuummm1RaWqq+ffs2+thUBgAAMLGzTWCFz+fTzJkzlZKSojFjxig3N1fV1dXKzMyUJM2YMUO9e/eW3+9XVFSUhg0bFvL+7t27S1K9+SshGQAAwMSpBxVNnz5dp0+f1sKFC1VWVqaRI0eqoKCgblHh8ePHFRZmf1GfZAAAgFZk3rx5mjdvXoOv7dq167LvXbt2bZOOSTIAAICJU20Cp5AMAABg4lSbwClcTQAAgMtRGQAAwIQ2AQAALkebAAAAuAqVAQAATAwj6HQIVxXJAAAAJkGXtQlIBgAAMDFctoCQNQMAALgclQEAAExoEwAA4HK0CQAAgKtYSgYefvhh/fnPf272QQOBgKqqqkKG2y7jAAC0XkHDsG20BZaSgWXLlmnSpEkaNGiQfv3rX6usrKxJB/X7/YqOjg4Z5y5UNGlfAADYzbDxV1tguU3w7rvvasqUKXr++efVr18/3X777frDH/6gYLDx3+yzs7NVWVkZMrpGxVgNBQAA2MByMjB8+HDl5ubqb3/7m15//XUFAgFNmzZNffv21VNPPaXDhw9fcR9er1fdunULGR4PyxcAAK2DYRi2jbagyZ/AERERuvPOO1VQUKAjR44oKytLv/vd7zR48GA74wMA4KoLyrBttAW2fB3v16+fnnnmGR09elQFBQV27BIAAFwllu4z0L9/f4WHh1/ydY/Hox/84AfNDgoAACe1lfK+XSwlA0ePHm2pOAAAaDXayiWBduEOhAAAmLitMsASfgAAXI7KAAAAJm3lKgC7kAwAAGBCmwAAALgKlQEAAEy4mgAAAJdrKw8YsgttAgAAXI7KAAAAJrQJAABwOa4mAAAArkJlAAAAE7ctICQZAADAxG1tApIBAABM3JYMsGYAAACXozIAAICJu+oCkgyXuHDhgpGTk2NcuHDB6VCarD2cg2G0j/NoD+dgGJxHa9IezsEw2s95uI3HMNzRGKmqqlJ0dLQqKyvVrVs3p8NpkvZwDlL7OI/2cA4S59GatIdzkNrPebgNawYAAHA5kgEAAFyOZAAAAJdzTTLg9XqVk5Mjr9frdChN1h7OQWof59EezkHiPFqT9nAOUvs5D7dxzQJCAADQMNdUBgAAQMNIBgAAcDmSAQAAXI5kAAAAlyMZAADA5VyRDCxbtkwJCQmKiopSamqqiouLnQ7Jkj/96U+aOnWqevXqJY/Hoy1btjgdkmV+v1+jR49W165d1bNnT02bNk0HDx50OizLVqxYoREjRqhbt27q1q2b0tLS9M477zgdVrMsWbJEHo9HjzzyiNOhWPLMM8/I4/GEjCFDhjgdVpOcOHFC9913n3r06KGOHTtq+PDh2r17t9NhNVpCQkK9/xcej0dz5851OjQ0UrtPBjZs2CCfz6ecnBzt2bNHSUlJysjI0KlTp5wOrdGqq6uVlJSkZcuWOR1Kk73//vuaO3euPvroI+3YsUMXL17ULbfcourqaqdDs6RPnz5asmSJSkpKtHv3bt188826/fbbtW/fPqdDa5KPP/5Yr7zyikaMGOF0KE3y3e9+VydPnqwbH3zwgdMhWfbVV19p3LhxioiI0DvvvKPPPvtML7zwgq655hqnQ2u0jz/+OOT/w44dOyRJd9xxh8ORodGcfU5SyxszZowxd+7cup9ra2uNXr16GX6/38Gomk6SsXnzZqfDaLZTp04Zkoz333/f6VCa7ZprrjFeffVVp8Ow7Ny5c8YNN9xg7Nixw5g4caIxf/58p0OyJCcnx0hKSnI6jGZ7/PHHjRtvvNHpMGw1f/58Y8CAAUYwGHQ6FDRSu64M1NTUqKSkROnp6XVzYWFhSk9PV1FRkYORobKyUpJ07bXXOhxJ09XW1mr9+vWqrq5WWlqa0+FYNnfuXN12220hfz/ams8//1y9evXS9ddfr3vvvVfHjx93OiTL3nrrLaWkpOiOO+5Qz549NWrUKK1atcrpsJqspqZGr7/+umbNmiWPx+N0OGikdp0MVFRUqLa2VrGxsSHzsbGxKisrcygqBINBPfLIIxo3bpyGDRvmdDiWffrpp+rSpYu8Xq8eeughbd68WYmJiU6HZcn69eu1Z88e+f1+p0NpstTUVK1du1YFBQVasWKFjh49qvHjx+vcuXNOh2bJkSNHtGLFCt1www3avn275syZo5/97Gdat26d06E1yZYtW3T27Fk98MADTocCCzo4HQDcZ+7cudq7d2+b7O9K0uDBg1VaWqrKykq9+eabmjlzpt5///02kxB8+eWXmj9/vnbs2KGoqCinw2myW2+9te6/R4wYodTUVPXv318bN27Ugw8+6GBk1gSDQaWkpGjx4sWSpFGjRmnv3r3Ky8vTzJkzHY7OutWrV+vWW29Vr169nA4FFrTrykBMTIzCw8NVXl4eMl9eXq64uDiHonK3efPm6Q9/+IN27typPn36OB1Ok0RGRmrgwIFKTk6W3+9XUlKSXnzxRafDarSSkhKdOnVK3/ve99ShQwd16NBB77//vl566SV16NBBtbW1TofYJN27d9egQYN0+PBhp0OxJD4+vl4iOXTo0DbZ8jh27Jj++Mc/avbs2U6HAovadTIQGRmp5ORkFRYW1s0Fg0EVFha2yR5vW2YYhubNm6fNmzfrvffe03e+8x2nQ7JNMBhUIBBwOoxGmzx5sj799FOVlpbWjZSUFN17770qLS1VeHi40yE2yfnz5/XFF18oPj7e6VAsGTduXL3LbA8dOqT+/fs7FFHTrVmzRj179tRtt93mdCiwqN23CXw+n2bOnKmUlBSNGTNGubm5qq6uVmZmptOhNdr58+dDvu0cPXpUpaWluvbaa9WvXz8HI2u8uXPn6o033tB//Md/qGvXrnVrNqKjo9WxY0eHo2u87Oxs3XrrrerXr5/OnTunN954Q7t27dL27dudDq3RunbtWm+tRufOndWjR482tYbj0Ucf1dSpU9W/f3/97W9/U05OjsLDw3X33Xc7HZolCxYs0NixY7V48WLdeeedKi4u1sqVK7Vy5UqnQ7MkGAxqzZo1mjlzpjp0aPcfLe2P05czXA2//e1vjX79+hmRkZHGmDFjjI8++sjpkCzZuXOnIanemDlzptOhNVpD8Usy1qxZ43RolsyaNcvo37+/ERkZaVx33XXG5MmTjXfffdfpsJqtLV5aOH36dCM+Pt6IjIw0evfubUyfPt04fPiw02E1ydtvv20MGzbM8Hq9xpAhQ4yVK1c6HZJl27dvNyQZBw8edDoUNIHHMAzDmTQEAAC0Bu16zQAAALgykgEAAFyOZAAAAJcjGQAAwOVIBgAAcDmSAQAAXI5kAAAAlyMZAADA5UgGAABwOZIBAABcjmQAAACX+39m4Aa47ElaLQAAAABJRU5ErkJggg==",
+ "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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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utH9RbZbBYKBnz54lLkVNsSdPnqROnTqlWjZv3gzAH3/8gcPhYMSIEURGRqrLtm3bOHToEJGRkUycOPGG759wD0maxN+aTqejd+/efPvtty5NLWfOnGHhwoXccccdatPRX5ttvL29adSoEfn5+YBzML2//sd900034ePjo5apKHa7vVgTUEhICHXr1nU5t5eXV7FyAPfccw9paWkuT0AVFhby8ccf4+3tTbdu3Uody1NPPcWaNWuYOnUqtWrV4u67777mPp06dSImJobZs2ezfPnyYtsLCgp4+eWXAeeHbatWrZg3bx4XL15UyyQnJ7NmzRruueeeUsdaWl9++SVZWVnq70uXLuX06dPqtRXVvlxe26IoSrGhAcrqr+85rVarNgcXva733HMP27dvZ8uWLWo5q9XKZ599RoMGDYiKirqhGMrbjb5XS7rX27Ztc7l+QH0i8fL3SNG5unfvzqxZszh9+nSx418+fML19Glq3rw5y5YtK7Y0a9aMiIgIli1b5tKXTlQv0qdJ/C188cUXrFq1qtj6l156if/7v/8jISGBO+64g3/84x/o9XpmzZpFfn4+77//vlo2KiqK7t2707ZtWwIDA/n111/Vx6YBDh06xF133cWjjz5KVFQUer2eZcuWcebMGR5//PEKvb6srCzCwsJ4+OGHadmyJd7e3qxdu5ZffvnFpZarbdu2LFq0iNGjR9OuXTu8vb257777GDp0KLNmzWLQoEHs2LGDBg0asHTpUn7++WemTp1aqg7aRfr378+YMWNYtmwZL774YqkHbPzyyy/p3bs3Dz30EPfddx933XUXXl5e/P7773z99decPn1aHavpgw8+4O6776ZTp04MGTJEHXLAz8+vQubjCwwM5I477mDw4MGcOXOGqVOn0qhRI5577jnA2bx400038fLLL/PHH3/g6+vLN998c90PBBR59tlnuXDhAnfeeSdhYWEcP36cjz/+mFatWql9ll599VW++uor7r77bkaMGEFgYCDz5s0jJSWFb775plTNgH+1ceNGevTowfjx48v9ft7oe/Xee+/lf//7Hw8++CB9+vQhJSWFTz/9lKioKLKzs9X9zWYzUVFRLFq0iJtvvpnAwECaN29O8+bN+eSTT7jjjju49dZbee6552jYsCFnzpxhy5YtnDp1iqSkJIDr6tMUFBRE3759i60vGquppG2iGnHTU3tCVIqix6SvtJw8eVJRFEXZuXOnEh0drXh7eyuenp5Kjx49lM2bN7sc6//+7/+U9u3bK/7+/orZbFZuueUW5Z133lEKCgoURVGUc+fOKbGxscott9yieHl5KX5+fkqHDh2UxYsXXzPOokfj09PTS9xev379qw45kJ+fr7zyyitKy5YtFR8fH8XLy0tp2bKl8u9//9tln+zsbKV///6Kv7+/Arg80n3mzBll8ODBSlBQkGI0GpVbb71VmTNnjsv+RY9gX2tYhXvuuUcBit3Da8nJyVGmTJmitGvXTvH29laMRqPSuHFjZfjw4crhw4ddyq5du1bp3LmzYjabFV9fX+W+++5T9u3b51LmSvd14MCBipeXV7Hz//Wx8KLH9L/66ivltddeU0JCQhSz2az06dPHZRgBRVGUffv2KT179lS8vb2VoKAg5bnnnlOSkpIUwOU+XuncRdsuf02WLl2q9O7dWwkJCVGMRqMSERGhPP/888rp06dd9jty5Ijy8MMPK/7+/oqHh4fSvn17ZeXKlS5liq5lyZIlLuuLXtPLY/zuu+8UQPn000/VdVcavqPouH995P5KbvS96nA4lHfffVepX7++YjKZlNatWysrV64sdu8URVE2b96stG3bVjEajcWGHzhy5Ijy9NNPK6GhoYrBYFDq1aun3HvvvcrSpUtLdR1lJUMO1AwaRbmOXo9CCHEVDz74IHv27CnVSNNVWVGNy5IlS3j44YfdHU6lGTNmDF999RWHDx/GZDK5Oxwhqgzp0ySEKFenT58mPj6+1KN7i6pnw4YNvPXWW5IwCfEX0qdJCFEuUlJS+Pnnn5k9ezYGg4Hnn3/e3SGJ6/TLL79c137Z2dku/YpKEhwcfMVhC4So6iRpEkKUi8TERAYPHkxERATz5s0r9YCeouaYMmUKb7/99lXLpKSklGoeRiGqIunTJIQQolwcPXr0mtOx3HHHHaUa4V6IqkiSJiGEEEKIUpCO4EIIIYQQpSB9msqJw+EgNTUVHx+fKw7fL4QQQoiqRVEUsrKyqFu37jUHg5WkqZykpqa6zFEmhBBCiOrj5MmThIWFXbWMJE3l5PIJHovmKhNCCCFE1WaxWAgPDy/VdFGSNJWToiY5X19fSZqEEEKIaqY0XWukI7gQQgghRClI0iSEEEIIUQqSNAkhhBBClIL0aRJCCCHKwG63Y7PZ3B2GKAOj0XjN4QRKQ5ImIYQQohQURSEtLY2LFy+6OxRRRlqtlsjISIxG4w0dR5ImIYQQohSKEqaQkBA8PT1lIONqomjw6dOnTxMREXFDr5skTUIIIcQ12O12NWGqVauWu8MRZRQcHExqaiqFhYUYDIbrPo50BBdCCCGuoagPk6enp5sjEdejqFnObrff0HEkaRJCCCFKSZrkqqfyet0kaRJCCCGEKAVJmoQQQgghSkGSJiGEEKIGGzRoEBqNBo1Gg8FgIDIykjFjxpCXl+fu0IrZuHEjGo3mmsM65OXlMWjQIG699Vb0ej19+/atlPjk6bkqzm53kJdlo9DmwC/Y7O5whBBCVEMxMTHMmTMHm83Gjh07GDhwIBqNhvfee8/doV0Xu92O2WxmxIgRfPPNN5V2XqlpquJOH85k7qs/E/9JkrtDEUIIUU2ZTCZCQ0MJDw+nb9++9OzZk4SEBHW7w+EgLi6OyMhIzGYzLVu2ZOnSper2ohqg+Ph4WrRogYeHBx07diQ5OdnlPJs2baJLly6YzWbCw8MZMWIEVqtV3f7f//6X2267DR8fH0JDQ+nfvz9nz54F4NixY/To0QOAgIAANBoNgwYNKvF6vLy8mDlzJs899xyhoaHldZuuSZKmKs7k6awMzMspdHMkQgghLqcoCjkFhZW+KIpyQ3EnJyezefNml9Gx4+Li+PLLL/n000/Zu3cvo0aNYsCAASQmJrrs+8orr/Dhhx/yyy+/EBwczH333acOx3DkyBFiYmLo168fu3fvZtGiRWzatIlhw4ap+9tsNiZNmkRSUhLLly/n2LFjamIUHh6u1hodPHiQ06dPM23atBu61vImzXNVXFHSlJ9jQ1EUedxVCCGqiFybnahxqyv9vPsmRuNpLNvH98qVK/H29qawsJD8/Hy0Wi0zZswAID8/n3fffZe1a9fSqVMnABo2bMimTZuYNWsW3bp1U48zfvx4evXqBcC8efMICwtj2bJlPProo8TFxfHkk08ycuRIABo3bsz06dPp1q0bM2fOxMPDg2eeeUY9VsOGDZk+fTrt2rUjOzsbb29vAgMDAQgJCcHf3/96b1GFkaSpivPwco5c6ihUKLQ5MBh1bo5ICCFEddOjRw9mzpyJ1Wrlo48+Qq/X069fPwAOHz5MTk6OmgwVKSgooHXr1i7ripIqgMDAQJo0acL+/fsBSEpKYvfu3SxYsEAtoygKDoeDlJQUmjZtyo4dO5gwYQJJSUlkZGTgcDgAOHHiBFFRURVy7eXJrUlTXFwc//vf/zhw4ABms5nbb7+d9957jyZNmgBw4cIFxo8fz5o1azhx4gTBwcH07duXSZMm4efn53KsuXPn8q9//YtDhw7h6+vLI488wieffKJu3717N7GxsWqV4vDhwxkzZozLMZYsWcJbb73FsWPHaNy4Me+99x733HNPxd+IqzCYdGi0GhSHQr61UJImIYSoIswGHfsmRrvlvGXl5eVFo0aNAPjiiy9o2bIln3/+OUOGDCE7OxuA+Ph46tWr57KfyWQq9Tmys7N5/vnnGTFiRLFtERERWK1WoqOjiY6OZsGCBQQHB3PixAmio6MpKCgo8zW5g1uTpsTERGJjY2nXrh2FhYW8/vrr9O7dm3379uHl5UVqaiqpqalMmTKFqKgojh8/zgsvvEBqaqpLB7V//etffPjhh3zwwQd06NABq9XKsWPH1O0Wi4XevXvTs2dPPv30U/bs2cMzzzyDv78/Q4cOBWDz5s088cQTxMXFce+997Jw4UL69u3Lzp07ad68eWXfGpVGo8HkqScv20Z+jg3vgNK/gYUQQlQcjUZT5mayqkCr1fL6668zevRo+vfvT1RUFCaTiRMnTrg0xZVk69atREREAJCRkcGhQ4do2rQpAG3atGHfvn1qcvZXe/bs4fz580yePJnw8HAAfv31V5cy5TXdSYVRqpCzZ88qgJKYmHjFMosXL1aMRqNis9kURVGUCxcuKGazWVm7du0V9/n3v/+tBAQEKPn5+eq6sWPHKk2aNFF/f/TRR5U+ffq47NehQwfl+eefL1XsmZmZCqBkZmaWqnxZ/PetzcqM59cpfxzKKPdjCyGEuLbc3Fxl3759Sm5urrtDKbOBAwcqDzzwgMs6m82m1KtXT/nggw8URVGUN954Q6lVq5Yyd+5c5fDhw8qOHTuU6dOnK3PnzlUURVE2bNigAEqzZs2UtWvXKnv27FHuv/9+JSIiQv1sTUpKUsxmsxIbG6v89ttvyqFDh5Tly5crsbGxiqI4P+ONRqPyyiuvKEeOHFG+/fZb5eabb1YA5bffflMURVFOnTqlaDQaZe7cucrZs2eVrKysK17X3r17ld9++0257777lO7duyu//fabepy/utrrV5bP7yr19FxmZiaA2hHsSmV8fX3R653ZfUJCAg6Hgz/++IOmTZsSFhbGo48+ysmTJ9V9tmzZQteuXV2eFIiOjubgwYNkZGSoZXr27OlyrujoaLZs2VJu13e9TJ7Ofk35OTY3RyKEEKIm0Ov1DBs2jPfffx+r1cqkSZN46623iIuLo2nTpsTExBAfH09kZKTLfpMnT+all16ibdu2pKWl8d1336mfrS1atCAxMZFDhw7RpUsXWrduzbhx46hbty4AwcHBzJ07lyVLlhAVFcXkyZOZMmWKy/Hr1avH22+/zauvvkrt2rVdnrz7q3vuuYfWrVvz3XffsXHjRlq3bl2sD1a5u2ZaVUnsdrvSp08fpXPnzlcsk56erkRERCivv/66ui4uLk4xGAxKkyZNlFWrVilbtmxR7rrrLqVJkyZq9turVy9l6NChLsfau3evAij79u1TFEVRDAaDsnDhQpcyn3zyiRISElJiLHl5eUpmZqa6nDx5ssJqmlZM+02Z8fw6Zf/m1HI/thBCiGurzjVN5aGopikjI8PdoVyXGlfTFBsbS3JyMl9//XWJ2y0WC3369CEqKooJEyao6x0OBzabjenTpxMdHU3Hjh356quv+P3339mwYUOFxRsXF4efn5+6FLXPVoQ/hx2QsZqEEEIId6kSSdOwYcNYuXIlGzZsICwsrNj2rKwsYmJi8PHxYdmyZRgMBnVbnTp1AFweVQwODiYoKIgTJ04AEBoaypkzZ1yOWfR70UiiVypzpZFGX3vtNTIzM9Xl8ubA8lbUPJcnzXNCCCGE27g1aVIUhWHDhrFs2TLWr19frO0U/nzyzWg0smLFCjw8PFy2d+7cGXCOHlrkwoULnDt3jvr16wPOcSV+/PFHddRScPaFatKkCQEBAWqZdevWuRw7ISHBZUyKy5lMJnx9fV2WiiI1TUIIIdype/fuKIpSJQecrExuTZpiY2OZP38+CxcuxMfHh7S0NNLS0sjNzQX+TJisViuff/45FotFLVP0OOLNN9/MAw88wEsvvcTmzZtJTk5m4MCB3HLLLeocNv3798doNDJkyBD27t3LokWLmDZtGqNHj1Zjeemll1i1ahUffvghBw4cYMKECfz6669X7YRWWaQjuBBCCOF+bk2aZs6cSWZmJt27d6dOnTrqsmjRIgB27tzJtm3b2LNnD40aNXIpc3lz2JdffkmHDh3o06cP3bp1w2AwsGrVKrUZz8/PjzVr1pCSkkLbtm355z//ybhx49QxmgBuv/12Fi5cyGeffaZOVLh8+XK3jtEEkJVnIzUnH5CaJiGEEMKdNIpygzP/CcBZK+bn56cOiVBeth09z5szttM3x0RoQz/6jWlbbscWQghROnl5eaSkpBAZGVmsm4io+q72+pXl87tKdAQXV+bnaSDv0hy90jwnhBBCuI8kTVWcv9lIvsZZGZgnzXNCCCGE20jSVMX5mQ3kXXqV8q02pDVVCCGEcA9Jmqo4D4MWh87ZPuewKxTaHG6OSAghhPh7kqSpitNoNJi9DDhw1jDlW6WJTgghROkNGjQIjUaDRqPBYDAQGRnJmDFjyMvLc3doxWzcuBGNRsPFixevWe6BBx6gTp06eHl50apVKxYsWFDh8ekr/Azihjk7gxfiqTg7g3sHmNwdkhBCiGokJiaGOXPmYLPZ2LFjBwMHDkSj0fDee++5O7TrsnnzZlq0aMHYsWOpXbs2K1eu5Omnn8bPz4977723ws4rNU1V3LHMY+T5LqZAbwVkrCYhhBBlZzKZCA0NJTw8nL59+9KzZ08SEhLU7Q6Hg7i4OCIjIzGbzep4hUWKaoDi4+Np0aIFHh4edOzYkeTkZJfzbNq0iS5dumA2mwkPD2fEiBFYrVZ1+3//+19uu+02fHx8CA0NpX///pw9exaAY8eOqYNSBwQEoNFoGDRoUInX8/rrrzNp0iRuv/12brrpJl566SViYmL43//+V163rESSNFVxmQWZZBp+JN+QCciwA0IIUWUoChRYK3+5wQeCkpOT2bx5M0ajUV0XFxfHl19+yaeffsrevXsZNWoUAwYMIDEx0WXfV155hQ8//JBffvmF4OBg7rvvPnWKsiNHjhATE0O/fv3YvXs3ixYtYtOmTS4za9hsNiZNmkRSUhLLly/n2LFjamIUHh7ON998AzinRjt9+jTTpk0r9XVlZmYSGBh4vbelVKR5rorzM/oBkG/Idv4rNU1CCFE12HLg3bqVf97XU8HoVaZdVq5cibe3N4WFheTn56PVapkxYwYA+fn5vPvuu6xdu1adb7Vhw4Zs2rSJWbNm0a1bN/U448ePp1evXgDMmzePsLAwli1bxqOPPkpcXBxPPvkkI0eOBKBx48ZMnz6dbt26MXPmTDw8PHjmmWfUYzVs2JDp06fTrl07srOz8fb2VpOekJCQMs1zt3jxYn755RdmzZpVpvtSVpI0VXH+Jn8A8vWSNAkhhLg+PXr0YObMmVitVj766CP0ej39+vUD4PDhw+Tk5KjJUJGCggJat27tsu7ySewDAwNp0qQJ+/fvByApKYndu3e7dMhWFAWHw0FKSgpNmzZlx44dTJgwgaSkJDIyMnA4nE+EnzhxgqioqOu6tg0bNjB48GD+85//0KxZs+s6RmlJ0lTF+Rh9AA0FeuckxnnSPCeEEFWDwdNZ6+OO85aRl5cXjRo1AuCLL76gZcuWfP755wwZMoTsbOeX8vj4eOrVq+eyn8lU+gePsrOzef755xkxYkSxbREREVitVqKjo4mOjmbBggUEBwdz4sQJoqOjKSgoKPM1ASQmJnLffffx0Ucf8fTTT1/XMcpCkqYqTqfV4aH1Jl+fA0hNkxBCVBkaTZmbyaoCrVbL66+/zujRo+nfvz9RUVGYTCZOnDjh0hRXkq1btxIREQFARkYGhw4domnTpgC0adOGffv2qcnZX+3Zs4fz588zefJkwsPDAfj1119dyhT1s7Lb7de8jo0bN3Lvvffy3nvvMXTo0GuWLw/SEbwa8NL7XpY0SU2TEEKIG/PII4+g0+n45JNP8PHx4eWXX2bUqFHMmzePI0eOsHPnTj7++GPmzZvnst/EiRNZt24dycnJDBo0iKCgIPr27QvA2LFj2bx5M8OGDWPXrl38/vvvfPvtt2pH8IiICIxGIx9//DFHjx5lxYoVTJo0yeX49evXR6PRsHLlStLT09VasL/asGEDffr0YcSIEfTr14+0tDTS0tK4cOFC+d+sy0jSVNU57PjqPcnXOZvnpKZJCCHEjdLr9QwbNoz3338fq9XKpEmTeOutt4iLi6Np06bExMQQHx9PZGSky36TJ0/mpZdeom3btqSlpfHdd9+ptUMtWrQgMTGRQ4cO0aVLF1q3bs24ceOoW9fZWT44OJi5c+eyZMkSoqKimDx5MlOmTHE5fr169Xj77bd59dVXqV27tsuTd5ebN28eOTk5xMXFUadOHXV56KGHKuBu/UmjyGRm5cJiseDn50dmZia+vr7ld+CUH4ld9QwnczsQfWgIoQ396DembfkdXwghxDXl5eWRkpJCZGQkHh4e7g6n0m3cuJEePXqQkZFRpqfaqoqrvX5l+fyWmqaqzhyAv90uzXNCCCGEm0nSVNWZA/BzOMjXS/OcEEII4U7y9FxVZw7E3+4g38NZ0yRDDgghhKhs3bt3R3rzSNJU5dlzCgg7qaexl3PuHkehQmGBHb1R5+bIhBBCiL8XaZ6r4vJ//53I7z14em0ODpwjp0oTnRBCCFH5JGmq4nSXnlLwzYWCS8MO5FmliU4IIYSobJI0VXG6gAAAvPKQUcGFEEIIN5KkqYrT+fkBzheqUCvDDgghhBDuIklTFacxGNCYnJ2+7RqpaRJCCCHcRZKmakDv7Ry9VFEkaRJCCCHcRZKmakDn4wmARpGxmoQQQpTNoEGD0Gg0aDQaDAYDkZGRjBkzhry8PHeHVszGjRvRaDRcvHjxquUOHjxIjx49qF27Nh4eHjRs2JA333wTm61iPx9lnKZqQOfnA6Sjtcuo4EIIIcouJiaGOXPmYLPZ2LFjBwMHDkSj0fDee++5O7TrYjAYePrpp2nTpg3+/v4kJSXx3HPP4XA4ePfddyvsvFLTVA3o/JwTCBps0hFcCCFE2ZlMJkJDQwkPD6dv37707NmThIQEdbvD4SAuLo7IyEjMZjMtW7Zk6dKl6vaiGqD4+HhatGiBh4cHHTt2JDk52eU8mzZtokuXLpjNZsLDwxkxYgRWq1Xd/t///pfbbrsNHx8fQkND6d+/P2fPngXg2LFj9OjRA4CAgAA0Gg2DBg0q8XoaNmzI4MGDadmyJfXr1+f+++/nySef5KeffiqvW1YiSZqqAV1AIABGm/ONJzVNQgjhfoqikGPLqfTlRqczSU5OZvPmzRiNRnVdXFwcX375JZ9++il79+5l1KhRDBgwgMTERJd9X3nlFT788EN++eUXgoODue+++9QmsSNHjhATE0O/fv3YvXs3ixYtYtOmTQwbNkzd32azMWnSJJKSkli+fDnHjh1TE6Pw8HC++eYbwNn8dvr0aaZNm1aqazp8+DCrVq2iW7duN3Jrrkma56oBfa0gADzyc8AA+VZJmoQQwt1yC3PpsLBDpZ93W/9teBo8y7TPypUr8fb2prCwkPz8fLRaLTNmzAAgPz+fd999l7Vr19KpUyfAWZOzadMmZs2a5ZKIjB8/nl69egEwb948wsLCWLZsGY8++ihxcXE8+eSTjBw5EoDGjRszffp0unXrxsyZM/Hw8OCZZ55Rj9WwYUOmT59Ou3btyM7Oxtvbm8BAZyVBSEgI/pcGd76a22+/nZ07d5Kfn8/QoUOZOHFime5LWUnSVA3oatUGwJyXQ563NM8JIYQomx49ejBz5kysVisfffQRer2efv36Ac5ampycHDUZKlJQUEDr1q1d1hUlVQCBgYE0adKE/fv3A5CUlMTu3btZsGCBWkZRFBwOBykpKTRt2pQdO3YwYcIEkpKSyMjIwOFwTg924sQJoqKiynxdixYtIisri6SkJF555RWmTJnCmDFjynyc0pKkqRrQ1a4HgFduDnlAnjTPCSGE25n1Zrb13+aW85aVl5cXjRo1AuCLL76gZcuWfP755wwZMoTs7GwA4uPjqVevnst+JpOp1OfIzs7m+eefZ8SIEcW2RUREYLVaiY6OJjo6mgULFhAcHMyJEyeIjo6moKCgzNcEziY9gKioKOx2O0OHDuWf//wnOl3FTGrv1j5NcXFxtGvXDh8fH0JCQujbty8HDx5Ut1+4cIHhw4fTpEkTzGYzERERjBgxgszMTJfjFD1Kefny9ddfu5TZuHEjbdq0wWQy0ahRI+bOnVssnk8++YQGDRrg4eFBhw4d2L59e4Vcd1npaoUA4GuVjuBCCFFVaDQaPA2elb5oNJobilur1fL666/z5ptvkpubS1RUFCaTiRMnTtCoUSOXpSgpKbJ161b154yMDA4dOkTTpk0BaNOmDfv27St2jEaNGmE0Gjlw4ADnz59n8uTJdOnShVtuuUXtBF6kqJ+V3W4v83U5HA5sNptae1UR3Jo0JSYmEhsby9atW0lISMBms9G7d2+1p31qaiqpqalMmTKF5ORk5s6dy6pVqxgyZEixY82ZM4fTp0+rS9++fdVtKSkp9OnThx49erBr1y5GjhzJs88+y+rVq9UyixYtYvTo0YwfP56dO3fSsmVLoqOji72g7lA0/5xvtjNpchQqFBaU/Q0lhBBCADzyyCPodDo++eQTfHx8ePnllxk1ahTz5s3jyJEj7Ny5k48//ph58+a57Ddx4kTWrVtHcnIygwYNIigoSP28HTt2LJs3b2bYsGHs2rWL33//nW+//VbtCB4REYHRaOTjjz/m6NGjrFixgkmTJrkcv379+mg0GlauXEl6erpaC/ZXCxYsYPHixezfv5+jR4+yePFiXnvtNR577DEMBkP537AiShVy9uxZBVASExOvWGbx4sWK0WhUbDabug5Qli1bdsV9xowZozRr1sxl3WOPPaZER0erv7dv316JjY1Vf7fb7UrdunWVuLi4UsWemZmpAEpmZmapypdFQVqasq/JLcruW25Rpj+/Rpnx/DolOyOv3M8jhBCiZLm5ucq+ffuU3Nxcd4dSZgMHDlQeeOCBYuvj4uKU4OBgJTs7W3E4HMrUqVOVJk2aKAaDQQkODlaio6PVz+MNGzYogPLdd98pzZo1U4xGo9K+fXslKSnJ5Zjbt29XevXqpXh7eyteXl5KixYtlHfeeUfdvnDhQqVBgwaKyWRSOnXqpKxYsUIBlN9++00tM3HiRCU0NFTRaDTKwIEDS7ymr7/+WmnTpo16nqioKOXdd9+94utztdevLJ/fGkW5wWcXy9Hhw4dp3Lgxe/bsoXnz5iWWmT17Nq+99hrp6enqOo1GQ926dcnPz6dhw4a88MILDB48WK3C7Nq1K23atGHq1KnqPnPmzGHkyJFkZmZSUFCAp6cnS5cudamhGjhwIBcvXuTbb78tFkd+fj75+fnq7xaLhfDwcDIzM/H19b3BO+HKkZ/PwZatAPj+rvfwsHvz+Lj21KrrXa7nEUIIUbK8vDxSUlKIjIzEw8PD3eFUuo0bN9KjRw8yMjJK9VRbVXO1189iseDn51eqz+8q0xHc4XAwcuRIOnfufMWE6dy5c0yaNImhQ4e6rJ84cSJ33nknnp6erFmzhn/84x9kZ2erndHS0tKoXbu2yz61a9fGYrGQm5tLRkYGdru9xDIHDhwoMZa4uDjefvvt673cMtGaTGgMGhSbQqE2B+zeMlaTEEIIUcmqTNIUGxtLcnIymzZtKnG7xWKhT58+REVFMWHCBJdtb731lvpz69atsVqtfPDBByX24C8vr732GqNHj3aJ768d5sqTzstI4cV87BqZSkUIIYRwhyoxIviwYcNYuXIlGzZsICwsrNj2rKwsYmJi8PHxYdmyZdfs5NWhQwdOnTqlNp+FhoZy5swZlzJnzpzB19cXs9lMUFAQOp2uxDKhoaElnsNkMuHr6+uyVCSdt7M6UVHkCTohhBCVq3v37iiKUi2b5sqTW5MmRVEYNmwYy5YtY/369URGRhYrY7FY6N27N0ajkRUrVpSqLXnXrl0EBASo40t06tSJdevWuZRJSEhQB+kyGo20bdvWpYzD4WDdunUuA3m5k97HCwBNUdIko4ILIYQQlcqtzXOxsbEsXLiQb7/9Fh8fH9LS0gDw8/PDbDarCVNOTg7z58/HYrFgsVgACA4ORqfT8d1333HmzBk6duyIh4cHCQkJvPvuu7z88svqeV544QVmzJjBmDFjeOaZZ1i/fj2LFy8mPj5eLTN69GgGDhzIbbfdRvv27Zk6dSpWq5XBgwdX7k25Auekvano7M6kKU9qmoQQQohK5dakaebMmYCz2u9yc+bMYdCgQezcuZNt25yjrRaNZFokJSWFBg0aYDAY+OSTTxg1ahSKotCoUSP+9a9/8dxzz6llIyMjiY+PZ9SoUUybNo2wsDBmz55NdHS0Wuaxxx4jPT2dcePGkZaWRqtWrVi1alWxzuHuortUJaorzAGd9GkSQgghKptbk6ZrjXZQ1IZ6NTExMcTExFzzXN27d+e33367aplhw4a5zMZclegCawFgLMgBs/RpEkIIISpblegILq5NVysYAI+Coo7gUtMkhBBCVCZJmqoJXZDzKT5z3qUhB6QjuBBCCFGpJGmqJnQhzpmnvXKd8/JJ85wQQghRuSRpqiZ0wc6aJh9r0dNzUtMkhBDi2gYNGoRGo0Gj0WAwGIiMjGTMmDHk5eW5O7RiNm7ciEaj4eLFi6Xe5/Dhw/j4+FTKGFKSNFUT+oAAAHytMrilEEKIsomJieH06dMcPXqUjz76iFmzZjF+/Hh3h3XDbDYbTzzxBF26dKmU80nSVE0UDTngme9MmhyFCoUFdjdGJIQQorowmUyEhoYSHh5O37596dmzJwkJCep2h8NBXFwckZGRmM1mWrZsydKlS9XtRTVA8fHxtGjRAg8PDzp27EhycrLLeTZt2kSXLl0wm82Eh4czYsQIrFaruv2///0vt912Gz4+PoSGhtK/f3/Onj0LwLFjx+jRowcAAQEBaDQaBg0adNXrevPNN7nlllt49NFHb/QWlYokTdWExtMTdKCz5+HAmSzJE3RCCOE+iqLgyMmp9OVaQ/FcS3JyMps3b8ZoNKrr4uLi+PLLL/n000/Zu3cvo0aNYsCAASQmJrrs+8orr/Dhhx/yyy+/EBwczH333YfN5mz5OHLkCDExMfTr14/du3ezaNEiNm3a5DKUj81mY9KkSSQlJbF8+XKOHTumJkbh4eF88803ABw8eJDTp08zbdq0K17H+vXrWbJkCZ988skN3Y+yqDIT9oqr02g06Dz12LMKKdTmYnR4k5djw8vf5O7QhBDib0nJzeVgm7aVft4mO3c4v0iXwcqVK/H29qawsJD8/Hy0Wi0zZswAID8/n3fffZe1a9eqU4c1bNiQTZs2MWvWLLp166YeZ/z48fTq1QuAefPmERYWxrJly3j00UeJi4vjySefZOTIkQA0btyY6dOn061bN2bOnImHhwfPPPOMeqyGDRsyffp02rVrR3Z2Nt7e3gQGBgIQEhJy1T5K58+fZ9CgQcyfP7/C5369nCRN1Yjey4Q9qxC7JgfwlpomIYQQpdKjRw9mzpyJ1Wrlo48+Qq/X069fP8DZkTonJ0dNhooUFBTQunVrl3WXz8caGBhIkyZN2L9/PwBJSUns3r2bBQsWqGUURcHhcJCSkkLTpk3ZsWMHEyZMICkpiYyMDBwOBwAnTpwgKiqq1Nfz3HPP0b9/f7p27Vq2G3GDJGmqRnQ+npBmRVEujdUkSZMQQriNxmymyc4dbjlvWXl5eanTkX3xxRe0bNmSzz//nCFDhpCdnQ1AfHw89erVc9mvaOL70sjOzub5559nxIgRxbZFRERgtVqJjo4mOjqaBQsWEBwczIkTJ4iOjqagoKBM17N+/XpWrFjBlClTgD+TM71ez2effeZSo1WeJGmqRnQ+XkA6GkWeoBNCCHfTaDRlbiarCrRaLa+//jqjR4+mf//+REVFYTKZOHHihEtTXEm2bt1KREQEABkZGRw6dIimTZsC0KZNG/bt21dsrtgie/bs4fz580yePJnw8HAAfv31V5cyRf2s7ParP+i0ZcsWlzLffvst7733Hps3by6W+JUn6Qhejej8/QDQ2i8lTTIquBBCiOvwyCOPoNPp+OSTT/Dx8eHll19m1KhRzJs3jyNHjrBz504+/vhj5s2b57LfxIkTWbduHcnJyQwaNIigoCD69u0LwNixY9m8eTPDhg1j165d/P7773z77bdqR/CIiAiMRiMff/wxR48eZcWKFUyaNMnl+PXr10ej0bBy5UrS09PVWrC/atq0Kc2bN1eXevXqodVqad68OQGXhuipCJI0VSO6S28EfaHUNAkhhLh+er2eYcOG8f7772O1Wpk0aRJvvfUWcXFxNG3alJiYGOLj44mMjHTZb/Lkybz00ku0bduWtLQ0vvvuO7V2qEWLFiQmJnLo0CG6dOlC69atGTduHHXr1gUgODiYuXPnsmTJEqKiopg8ebLavFakXr16vP3227z66qvUrl3b5cm7qkCj3OiziwIAi8WCn58fmZmZFdaT//yUNzk7+xsS292P3SuaFj3C6PLYzRVyLiGEEH/Ky8sjJSWFyMhIPDw83B1Opdu4cSM9evQgIyOjUkbeLm9Xe/3K8vktNU3ViC4oBABTflFNkzTPCSGEEJVFkqZqRB/irOI050vznBBCCFHZ5Om5akQX7HwiwCs3h/NAnnQEF0IIUQm6d+9+wyOR1wRS01SN6C7VNPnkSE2TEEIIUdkkaapGip6e88xzJk150qdJCCGEqDSSNFUjWh8f0MiQA0IIIYQ7SNJUjWg0GrRmLQabM2lyFCoUFlx91FQhhBBClA9JmqoZvacBnT0fBeckhzLsgBBCCFE5JGmqZnTeHmhQsGuck/bmSROdEEIIUSkkaapmdD7OySEVRQa4FEIIISqTJE3VjM7Px/mDJE1CCCFKYdCgQWg0GjQaDQaDgcjISMaMGUNeXp67Qytm48aNaDQaLl68eNVyx44dU6/p8mXr1q0VGp8MblnN6P39ANDac0AvT9AJIYS4tpiYGObMmYPNZmPHjh0MHDgQjUbDe++95+7QbsjatWtp1qyZ+nutWrUq9HxS01TN6AICgcuGHZBRwYUQQlyDyWQiNDSU8PBw+vbtS8+ePUlISFC3OxwO4uLiiIyMxGw207JlS5YuXapuL6oBio+Pp0WLFnh4eNCxY0eSk5NdzrNp0ya6dOmC2WwmPDycESNGYLVa1e3//e9/ue222/Dx8SE0NJT+/ftz9uxZwFl71KNHDwACAgLQaDQMGjToqtdVq1YtQkND1cVgMNzorboqSZqqGV1gEIA67IDUNAkhhHsoioIt317py41OZ5KcnMzmzZsxGo3quri4OL788ks+/fRT9u7dy6hRoxgwYACJiYku+77yyit8+OGH/PLLLwQHB3Pfffdhszk/h44cOUJMTAz9+vVj9+7dLFq0iE2bNjFs2DB1f5vNxqRJk0hKSmL58uUcO3ZMTYzCw8P55ptvADh48CCnT59m2rRpV72W+++/n5CQEO644w5WrFhxQ/elNKR5rprRBYUCYCrIoQDp0ySEEO5SWODgs5cSr12wnA2d1g2DSVemfVauXIm3tzeFhYXk5+ej1WqZMWMGAPn5+bz77rusXbuWTp06AdCwYUM2bdrErFmz6Natm3qc8ePH06tXLwDmzZtHWFgYy5Yt49FHHyUuLo4nn3ySkSNHAtC4cWOmT59Ot27dmDlzJh4eHjzzzDPqsRo2bMj06dNp164d2dnZeHt7ExjobE0JCQnB39//itfj7e3Nhx9+SOfOndFqtXzzzTf07duX5cuXc//995fp3pSFJE3VjC7YOf+cOS+HLCRpEkIIcW09evRg5syZWK1WPvroI/R6Pf369QPg8OHD5OTkqMlQkYKCAlq3bu2yriipAggMDKRJkybs378fgKSkJHbv3s2CBQvUMoqi4HA4SElJoWnTpuzYsYMJEyaQlJRERkYGDodzzMETJ04QFRVV6usJCgpi9OjR6u/t2rUjNTWVDz74QJIm8SddaDgAXnnSPCeEEO6kN2oZOq3btQtWwHnLysvLi0aNGgHwxRdf0LJlSz7//HOGDBlCdnY2APHx8dSrV89lP5PJVOpzZGdn8/zzzzNixIhi2yIiIrBarURHRxMdHc2CBQsIDg7mxIkTREdHU1BQUOZr+qsOHTq49NOqCJI0VTO6EOcb2itXhhwQQgh30mg0ZW4mqwq0Wi2vv/46o0ePpn///kRFRWEymThx4oRLU1xJtm7dSkREBAAZGRkcOnSIpk2bAtCmTRv27dunJmd/tWfPHs6fP8/kyZMJD3dWAPz6668uZYr6WdntZZ8ibNeuXdSpU6fM+5WFWzuCx8XF0a5dO3x8fAgJCaFv374cPHhQ3X7hwgWGDx9OkyZNMJvNREREMGLECDIzM0s83vnz5wkLCytxjIeNGzfSpk0bTCYTjRo1Yu7cucX2/+STT2jQoAEeHh506NCB7du3l+fllgudnx+gqE/PyYjgQgghyuqRRx5Bp9PxySef4OPjw8svv8yoUaOYN28eR44cYefOnXz88cfMmzfPZb+JEyeybt06kpOTGTRoEEFBQfTt2xeAsWPHsnnzZoYNG8auXbv4/fff+fbbb9WO4BERERiNRj7++GOOHj3KihUrmDRpksvx69evj0ajYeXKlaSnp6u1YH81b948vvrqKw4cOMCBAwd49913+eKLLxg+fHj536zLKW4UHR2tzJkzR0lOTlZ27dql3HPPPUpERISSnZ2tKIqi7NmzR3nooYeUFStWKIcPH1bWrVunNG7cWOnXr1+Jx3vggQeUu+++WwGUjIwMdf3Ro0cVT09PZfTo0cq+ffuUjz/+WNHpdMqqVavUMl9//bViNBqVL774Qtm7d6/y3HPPKf7+/sqZM2dKdS2ZmZkKoGRmZl7/DSml/S1uUba17anMeH6d8vkrP1X4+YQQ4u8uNzdX2bdvn5Kbm+vuUMps4MCBygMPPFBsfVxcnBIcHKxkZ2crDodDmTp1qtKkSRPFYDAowcHBSnR0tJKYmKgoiqJs2LBBAZTvvvtOadasmWI0GpX27dsrSUlJLsfcvn270qtXL8Xb21vx8vJSWrRoobzzzjvq9oULFyoNGjRQTCaT0qlTJ2XFihUKoPz2229qmYkTJyqhoaGKRqNRBg4cWOI1zZ07V2natKni6emp+Pr6Ku3bt1eWLFlyxXtwtdevLJ/fGkW5wWcXy1F6ejohISEkJibStWvXEsssWbKEAQMGYLVa0ev/bF2cOXMmixYtYty4cdx1111kZGSoPe/Hjh1LfHy8y3gSjz/+OBcvXmTVqlWAsy20Xbt26tMEDoeD8PBwhg8fzquvvnrN2C0WC35+fmRmZuLr63u9t6BUDne6FUuuL1s6TkKr1/DijB4Vej4hhPi7y8vLIyUlhcjISDw8PNwdTqXbuHEjPXr0cPlsrU6u9vqV5fO7So3TVNTsVvTI4ZXK+Pr6uiRM+/btY+LEiXz55ZdotcUvacuWLfTs2dNlXXR0NFu2bAGcTwjs2LHDpYxWq6Vnz55qmb/Kz8/HYrG4LJVF72lUx2lyFCoUFpS97VcIIYQQZVNlkiaHw8HIkSPp3LkzzZs3L7HMuXPnmDRpEkOHDlXX5efn88QTT/DBBx+ondP+Ki0tjdq1a7usq127NhaLhdzcXM6dO4fdbi+xTFpaWonHjIuLw8/PT12KOrVVBp2PBzp7PgrORzWlM7gQQghR8apM0hQbG0tycjJff/11idstFgt9+vQhKiqKCRMmqOtfe+01mjZtyoABAyop0j/Pm5mZqS4nT56stHPrfLzRoKCQC0hncCGEEBWre/fuKIpSLZvmylOVSJqGDRvGypUr2bBhA2FhYcW2Z2VlERMTg4+PD8uWLXOZW2b9+vUsWbIEvV6PXq/nrrvuApwDX40fPx6A0NBQzpw543LMM2fO4Ovri9lsJigoCJ1OV2KZ0NDQEmM2mUz4+vq6LJVF53/pXA4ZdkAIIYSoLG4dp0lRFIYPH86yZcvYuHEjkZGRxcpYLBaio6MxmUysWLGiWAeub775htzcXPX3X375hWeeeYaffvqJm266CXCOYPr999+77JeQkKCObGo0Gmnbti3r1q1TH510OBysW7fOZc6cqkJ3KdPXOnJAK0mTEEJUlir07JQog/J63dyaNMXGxrJw4UK+/fZbfHx81P5Dfn5+mM1mLBYLvXv3Jicnh/nz57t0uA4ODkan06mJUZFz584B0LRpU7Ua8YUXXmDGjBmMGTOGZ555hvXr17N48WLi4+PV/UaPHs3AgQO57bbbaN++PVOnTsVqtTJ48OBKuBNlowtwdpTXFeZg18uo4EIIUdGKWjhycnIwm81ujkaUVdGI4zrdjQ1G6takaebMmYCzrfRyc+bMYdCgQezcuZNt27YBFBthNCUlhQYNGpTqPJGRkcTHxzNq1CimTZtGWFgYs2fPJjo6Wi3z2GOPkZ6ezrhx40hLS6NVq1asWrWqWOfwqkAXFAKAwZaD3QPyrVLTJIQQFUmn0+Hv78/Zs2cB8PT0RKPRuDkqURoOh4P09HQ8PT1dnry/HlVqnKbqrDLHabL+sJAToyax89bHuVirC+36NKD9fQ0r9JxCCPF3pygKaWlpxWacEFWfVqslMjJSnablcmX5/Ja556ohXXBdADzypCO4EEJUFo1GQ506dQgJCcFmk24R1YnRaCxxHMeykqSpGtKHOsej8pSkSQghKp1Op7vhvjGieqoSQw6IstEF1wPAWFiUNMk3HiGEEKKiSdJUDWlMJjAo6G1S0ySEEEJUFkmaqimdWYv+Uk1TrrXAzdEIIYQQNZ8kTdWU3qzHcClpypGkSQghhKhwkjRVU3pvk1rTZMu1uzkaIYQQouaTpKma0vl4YrjUp0kphEKbJE5CCCFERZKkqZrS+Xqjs+ejKA5AOoMLIYQQFU2SpmpK5++HBgUU52TFMpWKEEIIUbEkaaqmdP4BAGgdMlaTEEIIURkkaaqm9LWCANAVylhNQgghRGWQpKma0tUKAf4cFTxPapqEEEKICiVJUzWlC64DgLHgUk2T9GkSQgghKpQkTdWUrnY4AOY86dMkhBBCVAZJmqqpoqTJKPPPCSGEEJVCkqZqSusXAnpFHRVckiYhhBCiYknSVF1ptWhMqEmTdAQXQgghKpYkTdWY3kOLodA5uGWONd/N0QghhBA1myRN1Zjey4D+Up+mnGxJmoQQQoiKJElTNab3NmMotALSp0kIIYSoaJI0VWN6H0+1T1NhrsPN0QghhBA1myRN1ZjOzxfDpeY57BoKbXb3BiSEEELUYJI0VWM6fz909nxQnLVM0kQnhBBCVBxJmqoxXWAtNChoHM4n6GQqFSGEEKLiSNJUjekCg5z/2mUqFSGEEKKiSdJUjemCQwHUfk3SPCeEEEJUHEmaqjFdSF0ATAVS0ySEEEJUNEmaqjFdSBjw56S9eVLTJIQQQlQYSZqqMW2tuihambRXCCGEqAySNFVjGg9/tCbHZX2apHlOCCGEqChuTZri4uJo164dPj4+hISE0LdvXw4ePKhuv3DhAsOHD6dJkyaYzWYiIiIYMWIEmZmZapnz588TExND3bp1MZlMhIeHM2zYMCwWi8u5Nm7cSJs2bTCZTDRq1Ii5c+cWi+eTTz6hQYMGeHh40KFDB7Zv315h114utFq0Jo1a0yTzzwkhhBAVx61JU2JiIrGxsWzdupWEhARsNhu9e/fGanXOp5aamkpqaipTpkwhOTmZuXPnsmrVKoYMGaIeQ6vV8sADD7BixQoOHTrE3LlzWbt2LS+88IJaJiUlhT59+tCjRw927drFyJEjefbZZ1m9erVaZtGiRYwePZrx48ezc+dOWrZsSXR0NGfPnq28G3IdDJ46NWmyZue5ORohhBCi5tIoiqK4O4gi6enphISEkJiYSNeuXUsss2TJEgYMGIDVakWv15dYZvr06XzwwQecPHkSgLFjxxIfH09ycrJa5vHHH+fixYusWrUKgA4dOtCuXTtmzJgBgMPhIDw8nOHDh/Pqq69eM3aLxYKfnx+ZmZn4+vqW6bpvxIl7W5GSGUVys2fxq29kwGt3VNq5hRBCiOquLJ/fVapPU1GzW2Bg4FXL+Pr6XjFhSk1N5X//+x/dunVT123ZsoWePXu6lIuOjmbLli0AFBQUsGPHDpcyWq2Wnj17qmX+Kj8/H4vF4rK4g87bjL7o6Tmr9GkSQgghKkqVSZocDgcjR46kc+fONG/evMQy586dY9KkSQwdOrTYtieeeAJPT0/q1auHr68vs2fPVrelpaVRu3Ztl/K1a9fGYrGQm5vLuXPnsNvtJZZJS0srMZa4uDj8/PzUJTw8vKyXXC4M/t4YLjXP2XIdbolBCCGE+DuoMklTbGwsycnJfP311yVut1gs9OnTh6ioKCZMmFBs+0cffcTOnTv59ttvOXLkCKNHj67QeF977TUyMzPVpagpsLLp/PzUPk126dIkhBBCVJiS27gq2bBhw1i5ciU//vgjYWFhxbZnZWURExODj48Py5Ytw2AwFCsTGhpKaGgot9xyC4GBgXTp0oW33nqLOnXqEBoaypkzZ1zKnzlzBl9fX8xmMzqdDp1OV2KZ0NDQEmM2mUyYTKYbuOryoQsIQF94FACNXUOhzY7eoHNzVEIIIUTN49aaJkVRGDZsGMuWLWP9+vVERkYWK2OxWOjduzdGo5EVK1bg4eFxzeM6HM5mqvx85yP4nTp1Yt26dS5lEhIS6NSpEwBGo5G2bdu6lHE4HKxbt04tU1XpAmqhL8wD5dI1ywCXQgghRIVwa01TbGwsCxcu5Ntvv8XHx0ftP+Tn54fZbFYTppycHObPn+/S4To4OBidTsf333/PmTNnaNeuHd7e3uzdu5dXXnmFzp0706BBAwBeeOEFZsyYwZgxY3jmmWdYv349ixcvJj4+Xo1l9OjRDBw4kNtuu4327dszdepUrFYrgwcPrvT7Uhb6WsFoUNDZc7Drvcm3FuLl5/4aMCGEEKKmcWvSNHPmTAC6d+/usn7OnDkMGjSInTt3sm3bNgAaNWrkUiYlJYUGDRpgNpv5z3/+w6hRo8jPzyc8PJyHHnrIZZiAyMhI4uPjGTVqFNOmTSMsLIzZs2cTHR2tlnnsscdIT09n3LhxpKWl0apVK1atWlWsc3hVowt2Ttqrt+U6kyYZFVwIIYSoEFVqnKbqzF3jNNn3/8ihB5/nlzZjyPKtT59/tKBBi6BKO78QQghRnVXbcZpE2WmD6qBoLp+0V2qahBBCiIogSVM1p/GqhcaoqGM15UlHcCGEEKJCSNJU3Xn4ozU51JomGRVcCCGEqBiSNFV3Wi16Dw2GS1OpZGfnuDkgIYQQomaSpKkG0Jv1ak1Tdnaum6MRQgghaiZJmmoArZdJTZpysvPdHI0QQghRM0nSVAPofDwxFDprmPJyCtwcjRBCCFEzSdJUAxj8fdDbioYcsLs5GiGEEKJmkqSpBjD4+atDDhTmylilQgghREWQpKkG0AUGqH2ayJeXVAghhKgI8glbA+gCg9SkSWPXUmiTJjohhBCivEnSVAPogkPRF+aB4gAgX0YFF0IIIcqdJE01gC64DhoU9JeeoMu3StIkhBBClDdJmmoAXXA9AJm0VwghhKhA15U0nTx5klOnTqm/b9++nZEjR/LZZ5+VW2Ci9DTeQShGh/oEnTTPCSGEEOXvupKm/v37s2HDBgDS0tLo1asX27dv54033mDixInlGqAoBXOAc9Jem9Q0CSGEEBXlupKm5ORk2rdvD8DixYtp3rw5mzdvZsGCBcydO7c84xOl4eGH3uhQRwXPzs5zc0BCCCFEzXNdSZPNZsNkMgGwdu1a7r//fgBuueUWTp8+XX7RidLR6tB7aNEXWgGwZFndHJAQQghR81xX0tSsWTM+/fRTfvrpJxISEoiJiQEgNTWVWrVqlWuAonS0ngYMl5rnsrNy3ByNEEIIUfNcV9L03nvvMWvWLLp3784TTzxBy5YtAVixYoXabCcql9bLQ316zmqV5jkhhBCivOmvZ6fu3btz7tw5LBYLAQEB6vqhQ4fi6elZbsGJ0tP5eKE/40ya8qzSEVwIIYQob9dV05Sbm0t+fr6aMB0/fpypU6dy8OBBQkJCyjVAUTp6Px+1I7gMOSCEEEKUv+tKmh544AG+/PJLAC5evEiHDh348MMP6du3LzNnzizXAEXpGGoFqEMO2HIdbo5GCCGEqHmuK2nauXMnXbp0AWDp0qXUrl2b48eP8+WXXzJ9+vRyDVCUjiGwljq4pSNP4+ZohBBCiJrnupKmnJwcfHx8AFizZg0PPfQQWq2Wjh07cvz48XINUJSOrlaI2hFcU6BzczRCCCFEzXNdSVOjRo1Yvnw5J0+eZPXq1fTu3RuAs2fP4uvrW64BitLRBYWqSZPWrsNukyY6IYQQojxdV9I0btw4Xn75ZRo0aED79u3p1KkT4Kx1at26dbkGKEpHH1wXfWEeKM5kKU+mUhFCCCHK1XUNOfDwww9zxx13cPr0aXWMJoC77rqLBx98sNyCE6Wn8Q0Ggx19YS6FBi/ycwrx8jO5OywhhBCixriupAkgNDSU0NBQTp06BUBYWJgMbOlO5gA0RgV9YY4zaZKxmoQQQohydV3Ncw6Hg4kTJ+Ln50f9+vWpX78+/v7+TJo0CYdD+tK4hWcgOpNDfYIuN6fAzQEJIYQQNct11TS98cYbfP7550yePJnOnTsDsGnTJiZMmEBeXh7vvPNOuQYpSsHDD6PRro7VlGnJBmSgUSGEEKK8XFfSNG/ePGbPns3999+vrmvRogX16tXjH//4hyRN7qDVofPQqaOCO5MmIYQQQpSX62qeu3DhArfcckux9bfccgsXLlwo9XHi4uJo164dPj4+hISE0LdvXw4ePOhynuHDh9OkSRPMZjMRERGMGDGCzMxMtUxSUhJPPPEE4eHhmM1mmjZtyrRp04qda+PGjbRp0waTyUSjRo2YO3dusTKffPIJDRo0wMPDgw4dOrB9+/ZSX0uV4GlUhx3IyspxczBCCCFEzXJdSVPLli2ZMWNGsfUzZsygRYsWpT5OYmIisbGxbN26lYSEBGw2G71798ZqtQKQmppKamoqU6ZMITk5mblz57Jq1SqGDBmiHmPHjh2EhIQwf/589u7dyxtvvMFrr73mEl9KSgp9+vShR48e7Nq1i5EjR/Lss8+yevVqtcyiRYsYPXo048ePZ+fOnbRs2ZLo6GjOnj17PbfILbReHmrSZLXmujkaIYQQombRKIqilHWnxMRE+vTpQ0REhDpG05YtWzh58iTff/+9OsVKWaWnpxMSEkJiYiJdu3YtscySJUsYMGAAVqsVvb7k1sXY2Fj279/P+vXrARg7dizx8fEkJyerZR5//HEuXrzIqlWrAOjQoQPt2rVTky2Hw0F4eDjDhw/n1VdfvWbsFosFPz8/MjMz3TbA5+nYriQdasWRm/ri1czGoOHRbolDCCGEqC7K8vl9XTVN3bp149ChQzz44INcvHiRixcv8tBDD7F3717++9//XlfQgNrsFhgYeNUyvr6+V0yYispcfowtW7bQs2dPlzLR0dFs2bIFgIKCAnbs2OFSRqvV0rNnT7XMX+Xn52OxWFwWd9P5+6o1Tfk5hW6ORgghhKhZrnucprp16xbr8J2UlMTnn3/OZ599VubjORwORo4cSefOnWnevHmJZc6dO8ekSZMYOnToFY+zefNmFi1aRHx8vLouLS2N2rVru5SrXbs2FouF3NxcMjIysNvtJZY5cOBAieeJi4vj7bffLu3lVQpDQKA65EBBjgz9IIQQQpSn66ppqgixsbEkJyfz9ddfl7jdYrHQp08foqKimDBhQollkpOTeeCBBxg/frw6H15Fee2118jMzFSXkydPVuj5SsMjKEgdcsCe5+ZghBBCiBrmumuaytOwYcNYuXIlP/74I2FhYcW2Z2VlERMTg4+PD8uWLcNgMBQrs2/fPu666y6GDh3Km2++6bItNDSUM2fOuKw7c+YMvr6+mM1mdDodOp2uxDKhoaElxmwymTCZqtY0JfrgUAyFl/pt5VeZfFgIIYSoEdz6yaooCsOGDWPZsmWsX7+eyMjIYmUsFgu9e/fGaDSyYsUKPDw8ipXZu3cvPXr0YODAgSWOEdWpUyfWrVvnsi4hIUHtxG40Gmnbtq1LGYfDwbp169Qy1YEuuI7ap0lbUDyxFEIIIcT1K1NN00MPPXTV7RcvXizTyWNjY1m4cCHffvstPj4+pKWlAeDn54fZbFYTppycHObPn+/S4To4OBidTkdycjJ33nkn0dHRjB49Wj2GTqcjODgYgBdeeIEZM2YwZswYnnnmGdavX8/ixYtd+j2NHj2agQMHctttt9G+fXumTp2K1Wpl8ODBZbomd9L6h6BzOIdr0Dn02G0OdAapcRJCCCHKQ5mSJj8/v2tuf/rpp0t9vJkzZwLQvXt3l/Vz5sxh0KBB7Ny5k23btgHQqFEjlzIpKSk0aNCApUuXkp6ezvz585k/f766vX79+hw7dgyAyMhI4uPjGTVqFNOmTSMsLIzZs2cTHf3nI/mPPfYY6enpjBs3jrS0NFq1asWqVauKdQ6v0swB6PU5oDhAoyUvx4aXX9VqQhRCCCGqq+sap0kUVxXGaeLc7xy87242Rn1EocGLJ8Z3ILCOl3tiEUIIIaqBCh+nSVRR5kAMRofaryk7W6ZSEUIIIcqLJE01iYcfRqNDHavpwsXMa+wghBBCiNKSpKkm0enRmHXqWE0XLVluDkgIIYSoOSRpqmnMRgyFzsl6s7Ksbg5GCCGEqDkkaappvD0v69OU6+ZghBBCiJpDkqYaRuPjrSZNudYCN0cjhBBC1BySNNUwOn8/DJf6NOXnFLo5GiGEEKLmkKSphtEHBqo1TQW5djdHI4QQQtQckjTVMB7BIeqQA/ZcGbdUCCGEKC+SNNUwptp/Ttqr5GrcHI0QQghRc0jSVMPoAmqjtzuHGtDml2lqQSGEEEJchSRNNYzGMxCd1lnTpCuUyXqFEEKI8iJJU01jDsCozQZApxiw2xxuDkgIIYSoGSRpqmk8A/HQW0FxJku51nw3BySEEELUDJI01TTmADyMhegvTaVyIVMm7RVCCCHKgyRNNY2HPwaTQ32C7rwkTUIIIUS5kKSpptHpwUOnjtV00WJxc0BCCCFEzSBJUw2kmD3QX5pKxZJldXM0QgghRM0gSVMNpHh7YrjUpyk7O9fN0QghhBA1gyRNNZGPj9qnKSc7z83BCCGEEDWDJE01kM7fT02a8qw2N0cjhBBC1AySNNVAusAgDJf6NNlyZXBLIYQQojxI0lQDGYOC1Zome44kTUIIIUR5kKSpBjLXrou+8NJTcznujUUIIYSoKSRpqoGMvkHoNM5sSZOvd3M0QgghRM0gSVNNZA7AqLk0aa/N6OZghBBCiJpBkqaayByAUedMmvR2DzcHI4QQQtQMkjTVROZAvC4lTVqM5OTJAJdCCCHEjZKkqSYyB+Clt4LifHIuPeO8mwMSQgghqj9Jmmoisz96kx19oXM08POZF90bjxBCCFEDSNJUE+kM4KFTx2rKsGS5OSAhhBCi+nNr0hQXF0e7du3w8fEhJCSEvn37cvDgQXX7hQsXGD58OE2aNMFsNhMREcGIESPIzMx0Oc6IESNo27YtJpOJVq1alXiu3bt306VLFzw8PAgPD+f9998vVmbJkiXccssteHh4cOutt/L999+X6/VWJoenB4ZLSZPFku3maIQQQojqz61JU2JiIrGxsWzdupWEhARsNhu9e/fGanUOzJiamkpqaipTpkwhOTmZuXPnsmrVKoYMGVLsWM888wyPPfZYieexWCz07t2b+vXrs2PHDj744AMmTJjAZ599ppbZvHkzTzzxBEOGDOG3336jb9++9O3bl+Tk5Iq5+Arm8PJCb3PexwuWzGuUFkIIIcS1aBRFUdwdRJH09HRCQkJITEyka9euJZZZsmQJAwYMwGq1ote7Dtw4YcIEli9fzq5du1zWz5w5kzfeeIO0tDSMRue4Ra+++irLly/nwIEDADz22GNYrVZWrlyp7texY0datWrFp59+es3YLRYLfn5+ZGZm4uvrW5bLrhB/vN6dLftjOFO7PWea7WHi8JfcHZIQQghR5ZTl87tK9WkqanYLDAy8ahlfX99iCdPVbNmyha5du6oJE0B0dDQHDx4kIyNDLdOzZ0+X/aKjo9myZUtZLqHK0Pj745N9CoCcNIUcm8ynIoQQQtyIKpM0ORwORo4cSefOnWnevHmJZc6dO8ekSZMYOnRomY6dlpZG7dq1XdYV/Z6WlnbVMkXb/yo/Px+LxeKyVCW6wCB8sk4AEJRVj+1p290ckRBCCFG9VZmkKTY2luTkZL7++usSt1ssFvr06UNUVBQTJkyo3OBKEBcXh5+fn7qEh4e7OyQX+qDa+GSdBMCnoBY/Hv7ZzREJIYQQ1VuVSJqGDRvGypUr2bBhA2FhYcW2Z2VlERMTg4+PD8uWLcNgMJTp+KGhoZw5c8ZlXdHvoaGhVy1TtP2vXnvtNTIzM9Xl5MmTZYqpopn8gjFqczDnOK9p/8EUqlD3NSGEEKLacWvSpCgKw4YNY9myZaxfv57IyMhiZYqefDMajaxYsQIPj7LPpdapUyd+/PFHbDabui4hIYEmTZoQEBCgllm3bp3LfgkJCXTq1KnEY5pMJnx9fV2WqsTDNwidyaHWNhku+HAw4+A19hJCCCHElbg1aYqNjWX+/PksXLgQHx8f0tLSSEtLIzfXOVdaUcJktVr5/PPPsVgsahm73a4e5/Dhw+zatUvdd9euXezatYuCggIA+vfvj9FoZMiQIezdu5dFixYxbdo0Ro8erR7jpZdeYtWqVXz44YccOHCACRMm8OuvvzJs2LDKvSnlRO8ViM7kwDfb2a8pODuCH0/96OaohBBCiOrLrUMOaDSaEtfPmTOHQYMGsXHjRnr06FFimZSUFBo0aABA9+7dSUxMvGqZ3bt3Exsbyy+//EJQUBDDhw9n7NixLuWXLFnCm2++ybFjx2jcuDHvv/8+99xzT6mupaoNOcDxLZwY1J9TubfyW6uRZJnOkxz9LfPvme/uyIQQQogqoyyf31VqnKbqrMolTekHSX/xTtL2h/Bjlw8BmHvb66we8D2BHlce0kEIIYT4O6m24zSJcmQOwD8yB70jV+0MHmQN4+c/5Ck6IYQQ4npI0lRTefhj8HLgXSdf7QwenB1B4qnizZhCCCGEuDZJmmoqvZECnSf+DXPwvTTIZUh2GJv/2IzNYbvGzkIIIYT4K0maajCNZyDedfPQFjib58Iy65Nly2LX2V3uDUwIIYSohiRpqsH0XoFotHA80DnnnsERgIfNS4YeEEIIIa6DJE01mMbsHLjzWP1aamfwRmlh0q9JCCGEuA6SNNVkvnUBqOdzAcWeCcBtx8JJyUzhpKVqTfsihBBCVHWSNNVkjXoC0Eu3kwNGEwB1siLQ2RV+/EOa6IQQQoiykKSpJmvUE7R6Gmn+4ESgDoAcczhtjijSr0kIIYQoI0maajKzP9TvDMCtxl8ByPcIpMduT35J+4UcW44bgxNCCCGqF0maarpb+gDQy7CNLI1zfKYG58PxzShgy+kt7oxMCCGEqFYkaarpmtwNwG3aQ5zRFwKQ7R3Bnbsd0kQnhBBClIEkTTWdfwTUvhUdDgL0xwHI8omgR5LCphM/IvM1CyGEEKUjSdPfwS33AH/2a8ryq09QFtTbe5b9F/a7MzIhhBCi2pCk6e/gUhNdD+M6APKMARQYvOi5S5GBLoUQQohSkqTp76BOK/Cth7/uInnaXACyvCNoc1jht73r3RubEEIIUU1I0vR3oNGotU0+hhMAWG9uj06B0I37OJd7zp3RCSGEENWCJE1/F5eSpijDTgBy67cC4K4kB5tOylN0QgghxLVI0vR30aALitGHSONeAM7meGLzNBKSCUfWLnNzcEIIIUTVJ0nT34XehKZxT4L1RwGwZRdij44BIDhhFza7zZ3RCSGEEFWeJE1/J03uwajNxVN3BgCvbo8D0PpgITsPSIdwIYQQ4mokafo7adwLRaOjnuEAAKdyvDnXMBC9A04tnu/m4IQQQoiqTZKmvxNzAJoGnQkxOJvoDh+4gPaBaACC1ybJ6OBCCCHEVUjS9HfT5B6CDUcAsKblcOtjL5BrhODzNo6uX+Hm4IQQQoiqS5Kmv5sm96idwY0FCoriy6F2tQE4/dWX7oxMCCGEqNIkafq7CaiPoU5D/HWnANiy4zSGvs656fy27KcwI8Od0QkhhBBVliRNf0Oay5ro9iWf47Zuj3E0FPR2hfT/LXFzdEIIIUTVJEnT31GTe9TO4BdPWajvW59dHYMBSF+0UDqECyGEECWQpOnvqG5rgnydzXD+OQWkZeZhvqc3eQYwnDhD7s6dbg5QCCGEqHokafo70mgIad4UcGBymNiYdJrbG/dkc1MNABmLF7s3PiGEEKIKkqTpb8rYPBp/XSoAO5PSaBvSlp9v8wQg84cfsGdmujM8IYQQosqRpOnvKrILQcbjACjHj6NBR2i7LhwPBk2BjczvVro5QCGEEKJqcWvSFBcXR7t27fDx8SEkJIS+ffty8OBBdfuFCxcYPnw4TZo0wWw2ExERwYgRI8j8Sy3IiRMn6NOnD56enoSEhPDKK69QWFjoUmbjxo20adMGk8lEo0aNmDt3brF4PvnkExo0aICHhwcdOnRg+/btFXLdVYLeRO16BgDqF+Tx28mLdA3vxrpWzrfExcWLpUO4EEIIcRm3Jk2JiYnExsaydetWEhISsNls9O7dG6vVCkBqaiqpqalMmTKF5ORk5s6dy6pVqxgyZIh6DLvdTp8+fSgoKGDz5s3MmzePuXPnMm7cOLVMSkoKffr0oUePHuzatYuRI0fy7LPPsnr1arXMokWLGD16NOPHj2fnzp20bNmS6Ohozp49W3k3pJKFNLsZAI9CbxIPpnNHvTv4qZmGAj3kHzpE3u7dbo5QCCGEqDo0ShWqTkhPTyckJITExES6du1aYpklS5YwYMAArFYrer2eH374gXvvvZfU1FRq13aObP3pp58yduxY0tPTMRqNjB07lvj4eJKTk9XjPP7441y8eJFVq1YB0KFDB9q1a8eMGTMAcDgchIeHM3z4cF599dVrxm6xWPDz8yMzMxNfX98bvRWVouBCOv95PQnQsrvRBWa9/DD94/vTdc4uuiUr+PV7iLrvvOPuMIUQQogKU5bP7yrVp6mo2S0wMPCqZXx9fdHr9QBs2bKFW2+9VU2YAKKjo7FYLOzdu1ct07NnT5fjREdHs2XLFgAKCgrYsWOHSxmtVkvPnj3VMn+Vn5+PxWJxWaobY2Awfh7OoQcanNnP2aw8uoR1UZvoMpctlyfphBBCiEuqTNLkcDgYOXIknTt3pnnz5iWWOXfuHJMmTWLo0KHqurS0NJeECVB/T0tLu2oZi8VCbm4u586dw263l1im6Bh/FRcXh5+fn7qEh4eX7YKriNp1nclnE3sOPx06R7ewbhwIg/VtDeBwkDZuPOc+nSX9m4QQQvztVZmkKTY2luTkZL7++usSt1ssFvr06UNUVBQTJkyo3OBK8Nprr5GZmakuJ0+edHdI1yW4aSQAJrsv2/cfoWlgU4I9Q/i0l4Pc/n0ASJ86lbOTJ6M4HO4MVQghhHCrKpE0DRs2jJUrV7JhwwbCwsKKbc/KyiImJgYfHx+WLVuGwWBQt4WGhnLmzBmX8kW/h4aGXrWMr68vZrOZoKAgdDpdiWWKjvFXJpMJX19fl6U6CrmlPgDnbDehO5KAQ4GuYV1Bo2FNTBC1X3P257ow70tSX30VxWZzZ7hCCCGE27g1aVIUhWHDhrFs2TLWr19PZGRksTIWi4XevXtjNBpZsWIFHh4eLts7derEnj17XJ5yS0hIwNfXl6ioKLXMunXrXPZLSEigU6dOABiNRtq2betSxuFwsG7dOrVMTRUU7gMoZDuC6Fiwh6RTF+kS1gWAjSc3EvD009R9/z3Q67Gs+I6Tw4bhyM11a8xCCCGEO7g1aYqNjWX+/PksXLgQHx8f0tLSSEtLI/fSh3JRwmS1Wvn888+xWCxqGbvdDkDv3r2JioriqaeeIikpidWrV/Pmm28SGxuLyWQC4IUXXuDo0aOMGTOGAwcO8O9//5vFixczatQoNZbRo0fzn//8h3nz5rF//35efPFFrFYrgwcPrvwbU4mMHnoCaukAuNmey0/7/6BTnU546Dz4I/sP9p3fh9/99xP+yQw0Hh5YE3/kxDNDsF+86N7AhRBCiErm1qRp5syZZGZm0r17d+rUqaMuixYtAmDnzp1s27aNPXv20KhRI5cyRX2IdDodK1euRKfT0alTJwYMGMDTTz/NxIkT1fNERkYSHx9PQkICLVu25MMPP2T27NlER0erZR577DGmTJnCuHHjaNWqFbt27WLVqlXFOofXRMENgwDILgwnY986PA2edA/vDsD3Kd8D4N2tGxFffI7W15fc337j+FNPY/tLc6YQQghRk1WpcZqqs+o4TlORXWtP8PPSw0SatnHB9xfuHvsVSRd+5qUNLxFiDmHNw2vQaZ21UXkHD3Hy2WcpTE/HULcu4Z/PxlRCs6oQQghRHVTbcZqEe4TU9wEg3XYTPbU72fS7c3RwH6MPZ3PPsvPsTrWsR5Obqf/VVxjr18eWmsrxJweQm7zXXaELIYQQlUaSJuHsDK6BbEcQvg4HR5I2YdQZ6VW/F/BnE10RY1g96i9cgEdUFPYLFzgxcCDWrdvcEboQQghRaSRpEs7O4LU9AUgvvAnf42twOBTujrwbgDXH1mCzuw41oK9Vi4gv5+HZoQMOq5WTzz2HZc2aSo9dCCGEqCySNAkAgiOcTXRnbTfR2f4LyamZtKvdjiBzEJYCC5tTNxfbR+ftTfhns/Dp1QvFZuOPkaNk2hUhhBA1liRNArg8aWpEU+0JdibtQqfVEdMgBijeRFdEazJRb+pH+D/yiEy7IoQQokaTpEkAEFLf+cTAGfstADgOOJOkoia6DSc3kGPLKXFfjU5H6MS3qfX884Bz2pXjTz1F9k8/SfIkhBCixpCkSQAQFO4NGsgt9CXH7sctmZu4mFPArUG3EuYdRm5hLomnEq+4v0ajIWTUSGq//joag4HcX3dw8rmhHOv3MJbVa2TeOiGEENWeJE0CcO0MfrbwJtpr9rNt3xE0Go1a2/T90ZKb6C4X+PRT3LQ2gcBBg9CYzeTt28cfL73E0Xvv4+Ky5TJ3nRBCiGpLkiahCr40XtMx5Tb0GgfnflsJwD2R9wCwKXUTmfmZ1zyOoXZtar86lkbr1xH0j3+g9fWl4OhRTr/2Goejo7mwYAGOvLyKuxAhhBAVQikoIHf3bjJXrPhbzgohSZNQhUQ4+zWd17cFoNYf6ziQZqFRQCNuDriZQkcha4+vLfXx9AEBBI8YTqP16wh5+Z/ogoIoTD3NmUn/x+G7enLuP//Bnp1dIdcihBDixtnOnsWyZg1n3v+AY/2f5GC79hx79DFSx4zl6H33/+2GmpFpVMpJdZ5GpUjq7xdZ9uFOvHw0DPLqS4Gi4yXtq7w4ZCjbM5Yybec02oe25/Poz6/r+I68PC7+739cmP05ttRUALS+vgQOeJKAp55CHxBQnpdTupjy89Ho9Wh0uko/d02i2O3YTqdhO3mCghMn1X+V/HwCnn4K786d3R1ijZJ/5AjZGxMxhIfhdXtndN5e7g6pSlEKCjg36zOyNqzH79778H/0UblHpaDYbOQdOEDub7vI3eVciv6vvpzOzw+tnx+2EycACOj/BCFjx6I1mSo75HJRls9vSZrKSU1ImgryCvnPqB9BgSc7fY1/yiJyFSMv8jqPP9GbV7Y9jgYNax9ZS4hnyHWfR7HZyIyP5/xn/6Hg6FEANGYzAY8+iu+9fdCYTGgMBjQGIxqD/tLPlxa9HnQ6NBpN8eMqCkpODoUZGdgvXKDwwgXsFzKwZ1z284UL6nb7hQs4cnLQBQQQPHoU/v36odFK5euVOPLysJ065ZIUFZw4ge3ECQpSU+Eq/dV877mHkFfHYgi5/vdNVeXIzSUvORnjTTehDwysuPPk5ZG1Zg0ZixeT++uOPzcYDHi1uw3v7t3x7tYNY/36FRZDdZC7J5nTr79O/u+/q+u0fn4EPtnfbV/Oqip7ZibW7dsvJUhJ5CUno+TnuxbSajE1boy5VSvn0rIlxsgGUFhI+vTpnP/PbABMTZpQ76N/YWrYsPIv5AZJ0uQGNSFpAlg4YSsZaTn0eTGKsN3D0B9JIEsxM8TxJvmtN3E0ay9j2o3hqainbvhcisNBVsJazs+aRd6+faXfUaNREyiNwQBGAxqNFntmZvE/+DIwt2pF6NsT8GjS5LqPcS25e/eSPn06+fsPYKxfH2OjmzA1aoSpUWNMjW5CX6tWhZ27rBRFIWfbdi7MnUve/v0UXqP/gsZgwBAWhiEiHGN4BMaICAqOHyfjq6/A4UDr7U3wyJEEPPF4jajZyz96lIyvvyZz+bc4LBbQavFs0wafXj3xvqsnxrB65XOew4fJWLyYzG9X4Mi81KdQq8WrY0cKTp1Sv+0XMUZGqgmUZ9s2zr+RvwFHfj7nPvk35z//HOx2dIGBBDzxBJb4eAqOHQNA4+GB/yOPUGvwIAx165Z7DIrNRuGFDPQhwSV+sasqbGfPcmHOXDIWLULJcR1KRuvnh7llC8ytWuHZujUet7a4ai1d9k+bSB07FvuFC2jMZkLfegu/B/tW6ev/K0ma3KCmJE0Jc/ZyaNsZ2t8XSbveodjnP4Lu+E9cVLx4wLsvF0J+onmt5nx171fldk5FUbBu+pnzX3xOwZGjKIWFKDabcykshMLCMh1PYzKhCwxEHxCALjAQXWAA+oDAP38ODEQXEIg+MACtnx+Z337Luekf48jJAZ2OwKefJnhYLFqv8qvOLzhxgvSp07B8f/UnEHUBAZhuuglj40aYbmp0KaG6CV2tWpX2n5CiKFg3b+bcv2eSu2OHyzatt7czKYqojzE8/LIEKRx97dolJkO5e/eSNuFt8vbsAcCjWTNCJ0zAfGvzSrme8qQUFJC1di0ZXy8iZ/t2db3Wz+/PhOYSU9Om+PS8C5+evTDd3LhMr58jL4+s1avJWLSY3J1/Tpitr1sH/4cfxv+hhzCEhqIoCgUpx8hOTCR740Zyduxw+XvRenvjdccdeHfvhnfXrtdVE+bIz8d+MRN75kUcFgsas9n5NxQYWGWaY3KTkkh9/Q0KjhwBnDWbtd98A31gIIrd7vxy9p//kLf30uTiej1+995LreeexXTTTTd0btuZs1g3/UR24o9YN2/GkZ2N+ba21H7lFcwtW97opZUr2x9/cP7zz7m49BuUggIAjA0a4NmunbMWqXUrjA0alLnG3Xb2LKljx5KzZSsAvvffR+i48dWmSVSSJjeoKUlT0rqTbFryOw1aBNHnHy0gPxvHf/uiPfULB7V+PBzhDxqF+AfjifCNqJSYFIcD/pJIFfu5sBCdnx/6gAA0np5lTjBsaWmceTeOrEudGvWhodR+/TV8evW6oWSl8Nw5zv17pnN6mcJC0Gjwvfde/B95mMLTp8k/fIT8w4fJP3wY26lTcIU/R52/P6ZGjTA2ugmvDh3w7tYNrafndcdVEkVRsP70E+c++Te5SUkAaIxG/B95BL/77sVQvz46f//ruh+K3c7FxYs5+6+PcGRlgUZDQP/+BI98CZ2PT7leR0UoOPUHF5cs4eI332A/d865UqvFu3t3Ap54HK/OnbGlniZ7/TqyEtY6k5fLxiYzRETgc9dd+PTqibllyyvWtOUdOsTFJUvJ/PZS7RWATod3j+4EPPooXp07X7WWzp6VhfXnn8nemEj2jz9iv3Dhz40aDeYWLfDu0R2PZs1xWK3YMzMvLRexZ2biyMy8lCD9uShXedJV6+WFrlYtNYnS17r0haRW0ZeUwEvbaqEPDCj3Wi9HXh7pH3/MhTlzweFAFxRE6Phx+PbqVaxs0ZeB8/+ZTc7Wrep67553ETR0KOYWLUp1TqWwkNxdu8j+8Seyf/yR/AMHrljW5+4YQkaNwhhROf9XXkl+Sgrn/zObzBUr1KTa3Lo1QS88j1fXruXyhUyx2zn/n9mkf/wx2O0Y6kdQ71//wtys2Y0f22bDum07llU/YKxfn6DnnrvhY15OkiY3qClJk9oZ3N/EoMmXOu/mXkSZey+aM3sYVLsuOzz19Ah5iul3j3FvsBUg+8cfSZs4yZnAAN7dulH7rTcxhoWV6Tj2bCsXvviC83PnqtXfXl26EDJ6FB5Nm5a4jyM3l/yjRyk4coT83w+Tf8SZUNlOniyWTGnMZry7d8M35m68u3ZBazZfx9U6KYpC9oYNnPv3TPKSk53HN5kIePwxAp8ZgqF2+fVDKkxP58z7H2D57jsAdMFB1H71VXzvueeG/uN21rikYN26lfxDhzCEhmKMbIipYSSG+vXRGo1lP6bdTvaPP3Lx60Vk//ij+hrog4Pxf+Rh/B95BEOdOiVf54ULZG/YSNbatVh//ln9Vg+gCwrCp0cPfHr1xLNjR7DbsaxazcXFi8n97Te1nKFuXfwffQS/Bx+6rtdAsdvJ27OHrMREsjcmkr9/f5mPodJqnZ1/fX1QcvMovHCh7DXARiNeXbrgG90b7x49bjhZztn5G6ffeIOClBTAWbtR+7XXStVnKTcpifOzZ5OV8OfTwJ4dOlBr6HN43X57sfei7exZrD9tIvvHS7VJWVmXXZgGj1tvxbtLF7y7dkEfHEz6jE/IXLbM+Z4xGAh44nGCXnyx0vtT5R08yPlZs7CsWq0m8Z6dOhL0wot4tm9XIbXXOTt38sc/X6bw9Gk0BgMhr7xCwFMDynwupbCQnF9+wfL9D2QlJGC/eBEAQ/0Iblq1qlxjl6TJDWpK0nR5Z/DB79+Bp++lDxvrOZQ59/Bt3ineCq6FJr8WY1vM48mONa/TqSMvj3OzZnF+9udgs6Hx8CDohReo9cxgNNf48FUKCshYtJhzM2eq3/I9br2VkH/+E6+OHa47noKjR8k/coS8ffvJWrvWmUhdovH0xKdHD3zvjsGrS5dSN5koDgdZa9dybuan6geqxmwm4IknqDV4EPrg4OuKtzSsW7aQ9vZEta+J1+2dCB03DmODBqU+hi0tDeuWreRs3YJ167Yr97nSajGEhWGMbIApsiHGhpGYIiMxNmyILjCw2H++henpXPzmGzIWL6Yw9bS63uv2Tvg//jg+PXqUqcbEYbWSvelnstauJXvjRpcPXK2XF2i1f67T6fC58078H30Ur9s7lWvfL1taGtmJP5KdmIjt5Am0vn7o/C5b/P/8Wevnh87PX12n9fJyabJRFAWHxXLpAYtLD1qcv+B86OL8ZeuK/s3IALtd3V9jMODVuTM+0dH43NkDnZ9fqa/DkZtL+tRpXPjyS1AU9MHBhL49AZ877yzzPck/coTzsz8n87vv1CTQo1kzaj33HPrgoD9rk/6ScOr8/Z3Nnl274HXHHSU2e+YdPMjZKR9i/eknwNlUWuv5oQQ+9RRaD48yx1oWubt3c+7TWWSvX6+u8+7enaAXnsfcqlWFnhvAfvEiqW++Sfbadc5z9+hBnXffuWbSqNjt5PzyK5ZVP5C1JsGlplQXGIhP7174xtyNZ4f2kjRVdzUlaYLLOoPHtqDBrUF/brCcxjInmh6+Dgq0GvRHhxDb836e7VL9npYojfyjR0l7eyI527YBYLzpJkLHjcOrQ/tiZRWHA0v896RPm6bWUhkbNCB45Eh8onuX6x+4oijk7d2H5YfvyfphlcsjwVovL7zvuhPfmLvxuqNziTUsit1O1po1nPv3TPUJI62nJwFPPkng4EEV+gTY5RwFBZyfPZvzn85CKShAYzRSa+hQaj33bImJX2FGBjnbf8G6dQs5W7aqCVcRjdGIuXVrzC1upfBsOvnHUig4muJaK/AXWj8/TA0aYGzYEGODBuTtdyalRR+gOj8//B56iIDHHi1TQnclSkEB1l9+cSZQa9dRmJ4OgCEszNkM+mDfGvmEoeJwkH/oEFlr1mBZtVp9ahZwPv3XqSO+0dF433nnVT9Yc379ldQ33sB23Nn53a9vX2q/9mqZkq6S2FJTOT93LheXLEXJzS1eQKPBo3lzZ21St654NG9e6oTWunkzZz6YoiZe+jp1CH5pBH7331+uT+sqikLOL79w/tNZWDdvVuP2iYkm6Pnn8bjllnI7V2njyViwkLPvvYdis6EPDaXelA/wvO0213J2O7k7d2L5YRWWNWv+bP7GmZz69OqF790xeLZv73x6ugJI0uQGNSlpcukM3ifSdWPGcUYu6cM6k4b7MmBl2psM7d2GYXc2dk+wFUxRFCwrV3Jm8nvYz58HwO+B+wkZMwZ9rVpqJ/az//rXn/8pBgcTFBuLf7+HKvzJJUVRyNuzB8v3P2BZtYrCtDR1m9bHB5+77nLWQHXqBDodlu9/4Nynn6odZrXe3gQ+/ZRbH8UuOH6ctEn/h3XTJsBZ/R46bhyerVuTs2MH1i1bsW7dQv7+A67NlFotHs2b49WxI16dOmJu3brYN3hFUbCfO0d+ijOBKkg5qv5s++OPK/YhM7duTcDjj+ETE1NhnZ0Vh8P5iHdhIeZWrf5Ww13k//47ltVryFq92mVoAHQ6vDp0wCcmGp+ePdUE3pGTw9l/fUTGggXO2qXatakz8W28u3Ur17gKMzLI+O98MhYuBEVx1iZ163rF2qTSUhwOLN99x9mp0yg87ay9NN1yCyGvvHzDY5jZs7LI2bGD85/958+HBnQ6/O67j1pDn3P7EAB5+/fzx6jRzi85Wi1Bw2IJGjqU3KQkLD+sImv1avXLAzi/yPj06un84tehfaU8/SlJkxvUpKSpWGfwv1iz50v+ufMD6hQW8u5xMwMLXmdg9+a8Et2kWj1mWhb2zEzOTp3Kxa8XgaKg9fWl1nPPYv15s9qpVOvtTa1nnyXw6afKvZN2aSgOx6X/iH4ga9VqCs+eVbdp/fzQ+fiotWBaX18Cn36awKcG3PC39PKgKApZq1dz5p13//wPVK8v1m/G1LgRnh074dWpI5633YbuBv7WHHl5FBw/QUHKUQpSUsg/moLO3x//h/tV6LATwlX+0aNkrV6NZfUa107VWi2e7dvj1fl2Li5eojZJ+z3cj9pjx1b4AwSKopT7/2eOvDwy5s/n3KzP1BpQrzvuIOTlf16xJkhRFArPpv85NtrJE9hOnKTg5ElsJ06ofX3A2ezp1+8haj37bJn7YVYkh9VK2sRJZH77LeDsUnD5UAdaHx98evZ0fsHr2PGa3SDKmyRNblCTkqbUwxdZNmUnHl4Gnhjf4c9+TZfkFebR/euuWO25fJmaRl5OIwbZxvDE7Tcz/r6oGps4gbOvwOkJE8jf92cfB43BQMCTT1Lr+aFVZuA8xeEg97ffnDVQa1ZjT3dWeev8/AgcPJiAJ/tXyafW7NnZpE+fTsb8BeBwYKhXD89OHfHq0BGvjh0qtJ+VcL+CY8ewrEkga/XqP4cHuERfpw51Jk7Eu8sdboqu/BRmZHD+00+5sPAr56CwGg1+DzyAT0w0ttTUy5Ki4xScPHXVJxjB+XCBX58+BD7zTLk+uFHeLi5fTtrESSg5OWi9vfG560587r4br9tvv66HNcqLJE1uUJOSJrvNwcK3t2I5l0etMG8eHN0ak6drFekbm95gxZEVPJadx5vpZ9lgb8lQ2z/p1y6Sdx68FZ225iZOSmEhGQu/4sLcuXi2b0/w8GEY6pXPQIYVQbHbyfl1B4Xp6Xh3714txk6xnTmLYiuoUt+WReUqOHmSrDVrsP78M6bGNxM0fBg6b293h1WunOO3TcXy/Q9XL6jVYqhTx2VcNMNl/1aHv+kitj/+ID/lGJ7tbqsy43xJ0uQGNSlpArh4Jof/fbiTXEsBoQ39uP+lVhhMf3Z83PTHJl5c+yKBBh/WHT2KvjCXH+ztGWYbzr2twvnwkZbodX+fPhpCCHG9cpOSSJ/xCYVpp53JUNHAsRGXfq5bt9KbrP5OJGlyg5qWNAGcO5XF8n/9Rn5OIRFRgdzzjxbo9M5EyOaw0XNJTy7kXeDTqOfpvOptsBewzN6F0bbnebJjA/6v761uvgIhhBDi6sry+S1VAeKKgsJ86BPbEr1Ry4l9F0j4Yh8OhzPHNmgN9KrvHHX3+/zT8Mhc0Oh4UPcTk/RzmL/1ON/sOOXG6IUQQojyJUmTuKo6N/lx9/O3otVpOLLzLBsXHKCocrJPwz4ArDuxjrxGd8FDnwEaBujX8ar+K15ftoe9qZlXOboQQghRfUjSJK4polktej3TDI0G9v98ms3/O4KiKLQMbkkdrzpYbVZ+PPUj3Pow3DcNgBf0KxmsLOeF+TvIzLG5+QqEEEKIGydJkyiVRm1D6D7AOY7IroQT7Fx9HK1GS0xkDAA/pFx6+qPtQOg1CYBXDV9zR+ZKRi76TW3WE0IIIaorSZpEqUV1rsvt/RoBsHX5UZITT9En0tlE9+OpH8kquDRdRecRcMdoAN7Rf4HX7yuYvv73Eo8phBBCVBeSNIkyad0rgrZ3OyfpTfz6EJrDfjT0a0iBo4B1J9b9WfCucdB2MFqNwr8M/+a39UvZcODsFY4qhBBCVH2SNIky63B/Q27tVg8UWDd3PzHah4HLmugANBro8yE0ewijxs6nho+Y8/VXnDifc4WjCiGEEFWbJE2izDQaDV0eu5mb29d29lVaE0adzJvYdnob53L/nKEarQ4enIXjprswawqYoUzmvblLyC2wuy94IYQQ4jq5NWmKi4ujXbt2+Pj4EBISQt++fTl48KBLmc8++4zu3bvj6+uLRqPh4mWTExbZuXMnvXr1wt/fn1q1ajF06FCys7Ndypw4cYI+ffrg6elJSEgIr7zyCoV/mQx048aNtGnTBpPJRKNGjZg7d255X3KNodFquHNgUxq0CMJRqNDn0AsEZNVhzbE1rgX1RrSPzaegbnt8NTlMsLzFR4t/QMZUFUIIUd24NWlKTEwkNjaWrVu3kpCQgM1mo3fv3litVrVMTk4OMTExvP766yUeIzU1lZ49e9KoUSO2bdvGqlWr2Lt3L4MGDVLL2O12+vTpQ0FBAZs3b2bevHnMnTuXcePGqWVSUlLo06cPPXr0YNeuXYwcOZJnn32W1atXV9j1V3c6nZboZ5tRt7E/+kIjffa/yIbdm4sXNHpifGoJ1oBbCNZk8tShkfwv8ZfKD1gIIYS4AVVqGpX09HRCQkJITEyka9euLts2btxIjx49yMjIwN/fX13/2Wef8dZbb3H69Gm0WmcOuGfPHlq0aMHvv/9Oo0aN+OGHH7j33ntJTU2ldu3aAHz66aeMHTuW9PR0jEYjY8eOJT4+nuTkZPXYjz/+OBcvXmTVqlXXjL0mTqNSWgW5hSz91y9knMwl25jBQy+3pXFEg+IFs86Q+e878cs9xe9KPXKeXEnLmxtWerxCCCFEkWo7jUpmpnP06MDAwFLvk5+fj9FoVBMmALPZDMCmTZsA2LJlC7feequaMAFER0djsVjYu3evWqZnz54ux46OjmbLli1XPK/FYnFZ/q6MZj0PvtSWXJ9MvAsCSPjkINkZ+cUL+tTG97mVXNTVorHmD/RfPcq5C+crP2AhhBDiOlSZpMnhcDBy5Eg6d+5M8+bNS73fnXfeSVpaGh988AEFBQVkZGTw6quvAnD69GkA0tLSXBImQP09LS3tqmUsFgu5ubnFzhsXF4efn5+6hIeHl/5iayCzt5GIxxWyTOdRMg0sjvuFPw5mFCunCYzEMOhbLPjQTPmd058+RGF+8fsrhBBCVDVVJmmKjY0lOTmZr7/+ukz7NWvWjHn/396dx0dVHfwf/9zZJ8lMkkkyWclGIBBCwiYYFURBAi4Vl1arVay2ikLrUrWuVbs8+Ph72qfP07o8ba1a96XiroisgiBrICwJJBCy78kkmWTWe39/3DBhSCBBgQl63q/Xfc125+bMyZ253znn3DMvvcQf//hHwsLCSEhIICMjg/j4+KDWp5PtwQcfxOFwBJaqqqpT9rfOFIW5s/gk9zlawmrp6fDw/p+3s/WzCpSjZgMPHzEex1Wv0a0YGe8pouy5a8HvO8ZWBUEQBGF4GBahafHixXz00UesWrWKlJSUE37+ddddR319PTU1NbS0tPD444/T1NREZqY6XiYhIYGGhoag5xy+nZCQcNx1rFZroLvvSEajEavVGrR830UaI8nPzGFp7p9oTNmPoqgzh3/yXDEuZ/Dvz40YP4Od05/DregY07aa6n/9HIbP8DpBEARB6CekoUlRFBYvXszSpUtZuXIlGRkZ32p78fHxRERE8Oabb2IymbjooosAKCgooLi4mMbGvhmply9fjtVqJScnJ7DOihUrgra3fPlyCgoKvlWZvm8WT1xMmNnMuyl/ZU/uSjQ6iYqdzby9ZDNNlZ1B6549+0o+HPU7/IpEyqF3aXvvfhGcBEEQhGErpKFp0aJFvPLKK7z22mtYLBbq6+upr68PGkNUX19PUVERZWVlgHpmXFFREa2trYF1/vrXv7Jt2zb27dvH008/zeLFi1myZEngLLs5c+aQk5PDDTfcwI4dO1i2bBmPPPIIixYtwmg0ArBw4UIOHDjA/fffT0lJCc888wxvvfUWd9999+mrkO+AbFs2L859kbiwONZa3mf15BcJs+noaHbx76e2svvLmqA5mub/eCF/i1brOHrH33CvfFIEp+8ARVF4ft1BfvjcV6zb3zz4E76Huj0+/nv5Pn774R5cXjHhqyCcCUI65YAkSQPe/8ILLwTmWXr88cd54oknjrvOjTfeyMcff0xXVxdjxozh3nvv5YYbbgha/9ChQ9x+++2sXr2a8PBwFixYwJNPPolOpwuss3r1au6++2727NlDSkoKjz76aNB8T8fzfZ5yYCDVndXcuvxWqjqrSNSlcEvTozSXuADIPjuB86/LRm/QAtDU6eaVP9/P3f4XAfCMmY9h/l/ANPzqsdvjY39DF6UNnRxocqIoCkadBkPvYtRp1etaDUa9ennk/cbeJSbCiC3cEOqXc0p0e3zc/85OPtqpnoghSfCri0Zzx8wsNJqB3/PfN2v3NfHwe8VUtapfEC8cY+e5n0zGoBsWIyYE4XvlRI7fw2qepjOZCE39Nfc0c9vy29jXtg+L3spDhv+ickUPigIxyeHMvXU8UfFhAGw91Man//gNv9a8il7y4whLJez6V9An54ek7F6/zMFmJ6X1nexr6KSk97KytfukNIRJEpydEcPlE5KYl5tIZJj+2290GDjU4uS2l7dSUt+JTiNxTlYsa/c1AWow+NOP8okK+26GxaFodXr43Ud7WLq9BoCkSBOt3R5cXpm54xL463UT0WlFcBKE00mEphAQoWlgDreDX6z8Bdsbt2PSmvhdxh+pfV9DT4cHvUnLhTeMJWuyHYBNB1t5/d/vcG/nkyRLLXjQc2DKb8i+eDHSKToTUlEUqtt6KK3vpLShMxCSypu68PoHfmvERhjITrCQFReBXqvB45fx+GTcvr5Lt8+Pxyfj8cu4vfIR6/hx+2Tau/sGxhu0GmZmxzF/YjIXjrFj0mtPyWs91VaXNvLL17fT4fIRG2Hk2Z9M4qx0G29tqeLR93bh9smkRJt59vrJjE+JDHVxTytFUVi6vYbffbSHtm4vkgQ3nZPOvXOy2XqojZ+9tAWPX+YH+Un89zUT0IoWOUE4bURoCgERmo6tx9fDPavvYV3NOnSSjt9O/A+8y+zUlamTmebPGkHBlSPRajX4ZYWl64uJX3En09kGwFdhFxJ33bOMSkk4aWWqbuvm7Q2VlKytxdPtY5PJi/OoXBZu0DI6wcKYBAuj4y1k917GRhi/9d+vau3mw521vL+9ltKGvgHyEUYdheMSmD8xiXNGxp60g6dfVvDLyinp/lEUhWdWl/Nfn5eiKDAxNYpnr59MQqQpsM7uWge3v7KNytZuDFoNj/9gHD+eOmLALvruDg91Ze0Yw/Ukj4pCOsMDRFVrNw8tLebL3rFdYxIsPHlVHhNGRAXW+WJPAwtf2YpPVvjh5BT+86o80ZUpCKeJCE0hIELT8Xn9Xh5e9zCfVnyKhMTDUx9hxJ5JbF9eCUBCZiSFP88lIloNJB09bra89ltmVD6DTpIpV5L4dOyTXHfZvG88Fsjt8/PFnkbe/PoQXXvaKXDpCVPUA5NXA+0ZZuImxzIm0Up2goXkKPMxx92dTCX1HbxfVMsHRbXUtPedBBEbYeSy/EQun5BMfkrkoGXxywq17T0caunmYIuTQ81OKlqcVLR0U9nSjYLCFROTuXXGSLLsESel7F1uH/e9vYNPd6mTxP54aiqP/yAHo65/a5mjx8uv3trBF3vVqT2umpTC7+fnopUVave3U13SRnVJGy01fT+2HRFtZExBImMKEoiMCzspZT5dfH6Zf64/yJ+W78PllTHoNNw5axS3zshEP0AX3CfFdSx+bRuyAj85O5XfXZ57WvY/4eTw+GRWlTZSXO1gZnYck9Oixf/vDCFCUwiI0DQ4v+xnyaYlvFn6JgC/nPhLLvRfzsqX9uJx+TFb9Fx4w1jSxscEPmzqildheu9nRPubcSl6lki3kDbrNm44J33AA89ASus7eXNzFUu3VRPn8DPDpSdaVp8bFtZNuN5DkyMKgOgYmPGDOFImZoIh/ORXwnHIssLWyjbeL6rh4511tB3RhZceE8YPJiTzg/wkjDpNIBhVNDs51OLkYLOTqtYePH55wG2bZAhXJFo0CpIGCnMSWDhzZFBrx4k62Ozk1n9tYX9jF3qtxBM/yOW6aamDvsbnVpXz+qf7GeHVMEajJ9Yt9ZsANSY5nK42N+7uvklPk0ZFMaYgkZGT4jCYdEdveljZVePggXd3sqtG/XmlgswY/uPK8WTEhiPLCj2dHrodHpwON1q9hoSMSPRGLe9tr+Hut4pQFLjlvAweuWTs9+7A6/PLtHZ7aO700Nzlpq3bQ35KFOmxp/f9OBSKolBc4+DfW6v5YEdt0Ht2wogofjY9g7njEsQ4tWFOhKYQEKFpaBRF4a9Ff+VvO/8GwI05N/LztEV89rddtFSrLQyRcWbGnpvImLMTCY8ygrOFtldvIrp2LQD/9p/H85G/4N5LJ3JBtn3Ag0qX28eHO2p5c3MVRVXtpPg0nN+jJ8mvfniZdV1MDXuZHPMXSCjs7bmQDZ034FLUsTYjTes5N/ZdLDYzWJPUJTKl77o1Wb00Wk5JPXl8MuvKmnhvey3L9zTQM8RT0vVaiVRbGOkx4aRbzCR1yehqXTirnCiygsuk4Wvc7Db4cGrUg/ntM0cyfVTsCR2cV+xt4K43i+h0+Yi3Gnnm+slMTosecF1FVmip7aK6pI2qvW3UlrXjcwe/Hp1Vz+i8OFLGRJOSHY3ZYsDn9XNwRzMlX9VRubcVej+pdEYtWZPtjC1IJDGrrwWu1elhfVkz+xo6GZ8cyTlZsUQYT1+4kv0yrS0unv9iP19sqyPMDzFaHecmRxFv0NPt8NDtcNPd6e0XEjUaCXu6leTRUZTh5fEN5fgkuGPmSO4rzD6twckvK7R3e2hxemjp8tDq9NDqdNPc5aG924MkSepZoHotJr0Gk06L6fD1I+6TXH48TS6663rorHMie2Xip8YhJ5po6fLS1OWmuctNS5cajtTFQ1u3p9/JFnqtxC8vHMXCmSOH/GXpVKp3uFi6vYZ3t1Wzv7GvZdRuMTIxNYpVpU14fOoXmJRoMz89N4NrzhpxWvdHYehEaAoBEZpOzMt7XuapzU8BMD9rPg9PfoQtH1SyZ30t3t4DqiRBWm4MY89JIi03GmnD/8Kq36NR/OyXk7nDeycJWRN49NIcRsdbUBSFbZVtvLGpio+L6+j2+LH5JWa7JNK8arefTuphYvh7TAj7AIPWC5nnQ9Ik6KrH1drCprJcdrWcjYIGneRicvg7TAx/H610jJ95GXsZzH1SDVSniNPt44u9Dby3vYa1+5vRShIjbGYyYsNJiwknPTac9Bg1KEVptBza2Uz5tkZq9rUHHZw1OgnZp95WJCjX+dlh8HFQJ5OTbGXh+SO5eHziccdRybLCX1aW8d9f7ANgSlo0z/xkEnaLKWi9zlYXVXtbqdrbSk1pGz2dwTPCmy16YjKtfNHiYK2jC4dW4bbzM7lvTvaA38q72lyUbKyn5Ks6HE19XZiGKAOdCUa+knvY2tQZdLDVayWmpNmYmR3HzGw7o+MjTnr46G7vZsdrn7KvREuXxwIMcfsSmC0GwiMNuJxeulqP+oFrDVRLfip1MtOmJXH71TmBKTq+rZL6DjYdbKW5Sw1Drb3hqMWpBqSBQstgTDIk+DXq4tOQ6NcQoQxcF+U6PyvNXtq1x/4jkgS2MAOxEUY0Gom9dWqLXU6ilaeuziM3+fSfSNDj8bNsdz3/3lbN+rJmDr+1jDoNheMSuHJSMudlxaLTamjqdPPyxkO8svEQrU4PABajjh9PS+Wmc9JJiur/KxPfRW6fn8YON3UOF/UdLuodPdQ5XMiywrjkSCaMiGJkXMSgYze72tw0HHTQcLCD8Cgj+bNO7m+9itAUAiI0nbj3y97nsa8ew6/4uXDEhTx1/lNIXi3l2xrZu76OunJHYF2zRU/22YnkZNQTufIW6KqnVmPiCeUKVutGMSndRGNDKgcb1QNurOzmOrkHY1c8oEHCT455OWdFvEl4+hgYfzXkzIeIuH7laq7uZO1re6k70NvyZfVx3oQDpEfsho7a3qUGXL3lM0TABQ/B1NtAO/g3ya42Nz2dHiJsRkzh+hM6kLu8fvRaTdCHjNPh5sD2Jsq3NVK7vz3ogBeXamHkpDhGTrQTFmmgbKtat/UH+uq2S6NQrPdRbPATZTdz64xMrpqU0u8svg6Xl3ve7BuTdGNBGo9ckoNBp8Hr9lO7v53KPS1U7Wmlrb476Lk6o5akrChGjI0mZUw0MUkRSBoJr1/mPz8t4R/rDgIwLcPGX66b2C+EgdpKubeugy+/qqG2qJmoZi+G3pCioFChk2m164nOimRLTTuHWoLLkBhp4vzRcczMjuPcrFgspm8+zUNHSw+b31zH/mIZv9K3HQk/Oo0TY6SJ2JQEwqJMhFsNhEcZCYs0Eh5pIMxqJMyqR9MbDhVFobPFRXVpG7X72qnZ10ZXW/8QlZgZSfLoaJJHR5GQGYnuBEKUoihsKG/hubUHAlNADCYqTI8t3EBMuAFbuAFbuBFbuB68Mv4WD0qrG02bF0OHD4Orf7ewDLTrFRp1CrUaPxa/xCSXFi0SsgSOVDPaXCtxUWZiItSAdHixhRsC+7iiKLxfVMvjH+6mvduLTiNx+8yRLL4wa8CxcyeTLCtsrmjl39uq+aS4ni5335ens9KjuWpSCvPGJ6DzKLTVd9Ne76SzVe1y1Ru1SDqJoroOlu9vpKrDhUdSkLUS08fa+cl56eRnRAf2g4H4/TI+j4zP4+9dZLy9l4dv+9w+fG0NyAYLsmRAlhWU3kX2K4Hbsl9BkdXXJMsKil9GVsAcrmdEjo2krCi0+qG34nV7fGoYcrioc7ho6HBR5+gJut3c5Rl0O+EGLbnJkeSPiCI/JYocewSGDh8NFR00Huyg/mAHzva+90PsiAiueXjqkMs5FCI0hYAITd/MysqV3LfmPjyyh6kJU/mfC/4Hj+yhqbuJysoGarc66dqlhZ6+MNISWcXumHXsi92GT9v3plRkPWmto7mxNY/21gn4FPXAm2HcSEH6JqLPmgm5V0LU8cfdgPpBvX9zA+v/XUa3Q/0b6XmxnPfDrL4ByQ274aN7oGqjejthPFz6P5AyObAdr8dP06FOGg52qN+UKjqCDog6gwaLzYQlxtR3GWPCYjNjsZkIjzQMePZYV5uL8t6gVFfuCHRdAdjTLIycbGfkRDuRcQN/o22tdbLnq1pKN9bj6uprBTqk87PT4KctWsdN0zO4/uxUrCY9ZY1d3PryFg40OTHoNPzu8nHMSoymcrfamlRb1h5oxQK1pcCebmVEjo0RY2zEZ1jRHufMvU+K67j/nZ10uX3EWYw8fd0kpmbYaOhwsW5/M+vKmvlyfzPNXX11p1dgitbIZNmAub3vYGYw68icGIcly8ouv5u1Zc1sKG/B7es7sOs0EpPSotVWqNF2xiZaBgyv7d0eKlq6OdTipKK5m9rKDqylDUQ59IB6wI7RlVNrqmWccROzdRvQSr1dj9YUmHITTFoAEfZjvvajKYpCR3MPNfvaWbm2kq5KJ5ajWm00OomEjEgy8mPJmhwfOIHiaD6/zGe76/m/NQcorlGDskaCGaPjSI4yExNuCEy0GhNuwBahBqToMENQN1hHSw/7vm6gbGsjLbVdQfvbYZF2M/Y0K/HpVuxpFmJTLf1ax9obuvnyzX1U7lF/0SEi2si5V49i5KS4Qb88NHW6eeyDXXxSrJ50MDo+gqeuzv9W4/KO5VCLk39vq2Hp9urABKQaBcZazFyUEsN4qxmlwxcISh7XN5/RXdJKGM06NWRJBAUiWT59h2edUUtKdjRpuTGk5cZgsZlQFIWmTjdljV2UN3VR3uQMXK9zuIa0XYNOQ2KkiXiricRIEwmRJvx+hZ01DnZVOzC5ZBL9GpJ6Wyjj/BKao1psJQ3EJEcQnxFJ4shIsqedvDOpQYSmkBCh6ZvbVLeJX676JU6vc8DHNbKG1PYcxjSeTWpbDpreg5VX46batpV223qaImowtZ3DlOp5hHnVcUYx5gpmnN1F0oWFEJf9jcrmcfnY8nEFO1ZUIcsKWp2GiXNSmTQ3TT0gyDIUvQLLf4PS3U67P4mGlFtoCL+I+koXLTXOfuNXFGQ8+h6M3sEHtmq0EhHRxkCoCrMaqN3vCGopAojPsJI12U7mxDisMUcEJW8PlK2A3Uuh9YAa7FLOghFTIWYUfj8c3NnMnvW1VB0xbqhHUtij91MeoXDOlCQ+3FGL3ONjgt7E/MQYuiq76OkI/hYZYTOSmhPDiLE2UsZEYwo/sZac8qYubn9lK/sautBqJDJiwyk7YrwIgFmvZVqmjfOyYpk+Ki7Q5eZo6qZkQz0lG+uCurqM4ToyJ8SRmh9LlU5mbXkza0qbONAcvK/ZLUbOHx1HQqSp92xD9axDR48aKBN8Eue5JTK8feEk2bCD9ogaViTNIT4+nuunpTLR4oAtL8D2l6G7pfefqIecH8BZP4PUAjVRnoA/Livh5S8OkOrTcFmiDV2LJxDkAZAgeXQUo89KIHNiHKZwPS6vn7e3VPH3Lw9S2aq2uJn0Gn40ZQQ/Oy+T1JjBz0R09/go39ZI6cZ6ave3Bz0WYTNiT1PDkT3dij3VgnGIE7QqisLBHc2se3s/nS3qgTdlTDTTrxmNLXHw98QnxXX85v1dNHd50Ejw8+mZ3H3R6G89v1mHy8snO+t4d0sVlQccxPo1xMgSdkVLsk6HvkceMCyC+i+1xpqJTgjDEmtG9it43T68Lj9et7p4XH68bh+uHvV+6QSOvArg04AXBY96C7ummVipFZ3kRiN50UleNMhIyGgkPxJ+/JKGLl0ULqMNf7gdbVQC4REWrGEGwkxaNBqJ9sYeKne10H3U+7nLKFGm9VEq+ajWycgD7LYRRh0Jkb1hyKoGosO31ZBkJjpMbU139/joaOrB0dRDS21X7xfJDjw9/Yc+dEoKdTqZWq1MnU6mQSsTbzOTPyKKs9Kiuencb/c7tUcToSkERGj6dna37GbRF4tocakHGpvJRpw5jriwOOxhduLM6mWUPxZ/SQSN2z10NfW9yfVSD15FDQsOUxMbUz+kNq6EH2b/kJtzbyYurH833Iloq3fy5Zv7qNrbBqgHjXOuzMJg1tFwwEFDWTMN5a24ff2nQwiL1OOPc7JDs5FSXRFNEVX4tB60so4fJl3HNUk/weNQ6Gx10dniClx2tbv7Ba4jJY6MZOQkNShZbEd0Z3l7oOwL2P0e7PsMPF0Db8AYqbaKpUyFlLPoCBtPyTYne76qw3lEa1idVkYDxPuDW4p0Bg3J2dGk5tgYkRVGlLYaqWU/NO+DplJo3g8d1eonviSpXxcDl70LR94GGQ0tTi8dbpk9ShpP++djSBofCEmT0qKO2yWj9E5fULatkfJtjUFjqUzhejInxpE12Y4/xsiX5U2sLm3iq/KWgQfaK5Dm0zDDpyfB3ffaM40byR5Zif3qO4lIGjtwQbwu2PM+bP4HVG/qu9+eA2fdAnnXDPkkAkVR+I9P9vL3Lw8iSfBfV+cxKyWGyj2tlG1pCOrG1mgl/PEmVrm62OH34JMgOkzPjQXp3FiQRswgc4z5/TJVu1sp/bqegzub8Xt7W+ckSB4dTfa0BNJyYwizHmPaD0UBVzu0HYL2SmjvvWw7BCgw9VbImg2ShM/jZ9uyQ2xbVonfJ6PRSOTNGsFZl6QPenZkq9PDbz/czXtFtQBkxobz1NV5TEm3DalOA69XVli9s54VX1ZSd8BButdLjE+PxMB/X2/SEh0fRnRCOFHxYUQnhBGVEEZUXNgJdW0BVDc7eXldBR9srcbj8mNQQELCKyl4Aa+k4AO8ktrViQQRdHOH7gNu0X6KUfIiI7HeMpc1SbfS5nQT2bqTlO5djJP3kScdwCz17x4rlxMpUrLYJY2mNiKXg9p0Dra4iPZAhk9LpldDkl8T1NrjlcAZpcM4IpzksdGMzoxmZFx40Oz+sqzgbHfjaOqho7kncNnR1IOjuQe3c+BxoTq9hrg0C/EZkSRkWIlKiaDS5WZHVTs7qx0UVbdzoMmJEQ+zNNuwx8Xx+N2/PKG6HowITSEgQtO35/V7aXG1EGOKQa89/rdWRVGoK3Ow96tayrY04PMqmMNgyqVZdGRV8tyu59jRtAMAg8bA1aOv5ubcm4kPj//G5VMUhYNFzXz59r7+A3d7aXVgNxwknh1YDfv5OlPHqxYHzR41bFkNVq4dcy1un5uX9rwEQLo1nf+c8Z/kxOQEbUv2yzgdniOCVA9d7R5siWFkTrAHd8kcLyhZUyDnckieBPXFUL0ZaraBr4d+YkcjJ59FFdPZUzmCA6We3k9sVVyKmREjPIyIriFRuxttW4kaktqrOPw13AfsN+gpMhpxaDVc2enE7v9m3RcKEtK4K2DmAyfcWigfDlBbGzmw/agAFaFnZG+AsqVb2VbVztr9TXS5faTZzMQ6ZLqL2uioVVukNPgYbVrLxBE7sF1+F2TNGnpB6nbA5ueh+G3w9o6zMkRA/rUw5RaIz+n/HL9X/Z/63ODrQfH28OwXu/li5yHMkpc7Z45g6ogIMETQ4Ylm6w6ZnVs60XX11bNXAnNGBBcWpjMyN/aYY2cURaHxUCelX9dTtqUhqJ6iE8MZc3YCo86K7wvm7s6+IHRkKDocktwdx6+P1HNg9mOQejYAjqYe1r29n4qd6uSfYZEGzrkyi9FT4wftslu+p4GHlxbT2OkOzLJ+X2E2YYaBQ48sK7TWOtlZ1MCOokZc9d1Yff3/hlHqJE5fTrSummhTK9FjxxF97mWEZZz8Mxm73D7e2lzFSxsqqHe4sFuNxFtM2K1G7BYTcRYj9ggd+U0fkln8Z3Q9vT+AnT4dCv8DEvP6bbPD5aWmpZOOih0o1ZsJb9yOvaOYeG9Vv3Xdip4DSiJljKA1fCRe22j0MeMwk4iu0UN7eUe/EzlsSeGMGGvD75MDAamzxYV8jF9ROMxsNRAZayIqPoz4dCvxGZHYksPRHmtcl6JA9WbcW19Bu2cpOk8HTdETiLtzzdAqd4hEaAoBEZpCx9Pjo7m6k9gUCwaz+mGpKAob6jbwbNGzFDUVAWp4umr0VdycezMJ4d+8T9zb+w25eHU15ghD75tfXWJSImjtruPlFb/i7bZiunp//iVeG8aN+bdx9ZhrCdOrXSMbajfwyLpHaOxpRKfR8cuJv2TBuAVopCF+Yw0EpaWwb9nAQWncFZA8GY7+GRq/Fxr3QNUmqN6itoa0Huj3J7q1SRw0zEcnuRjhW0GY51C/dToliZ0mI0URkWwPs1Cs8dNN38E7Um/hsfELuShhGiiy+kGoyOrCEdcV+q77emDri+prA0CC8T+E838NsVlDq58jyH6ZmsMBalsTLmffQcBs0TNyop2Rk+10tbrYtuxQYCC7Djdjw5YzwbYW65zbYPJPhzTYf0A97bDjdbX1qaWs7/6oNJD96mv2usDnAuWbhcxDnmxKPRdQ656G0xvV9xpNPrKyPIwebyR+pA3JEE5HYwf7dnSxb5eftra+1juz0cPoxEqy40qJNRxC8nSqQcndAa4OcDsG+MtHCbdDdJo6fjCq97KlDDb9Hfy9XzhGFcKsR9UuY6CiuJl1b+0PnB2ZmBXJjGuziU05/kSsjh4vv/9oD29vrQYg1RbGk1eN55yRsbi7vTQc7KDugIPqfe3UVzjAe/QhT8amqyZBX0KCYR+JWdFETi1E6qpTw277Eft85ky1m3X0vG++HxyHoij9Q1n5Klj2MDTuVm/bRsKc30P2vBPu6qW7FWq24jv0NZ5DmzDUb0Pn7Rx4XZ0Z4kajxI6lSTeRSkcmh2oiaKh0HfPsSo1WwhprxhprIjLWjDXOjDXWTGScGUuMaejzq7VXwo431fdLa3nf/dZk9cvGBY/0/0z7FkRoCgERmoYnRVHYWLeR53Y8x7ZG9WdZ9Bo9V466kp+N/9m3Ck9Hq3BU8OLuF/mg/AO8snpQHomBnzbVcXGXE31MFlzyJ3Wag17trnYe3/A4KypXADAtYRq/P+/3xy7XYEFp3Hz1rMCBgtJgnC1Qs6U3SG2Gmq39uvYUoNqWSlFUAkUmI9tlJ2XuFpSjBntE6CPIj8unxdVCSWsJAJePvJwHpj5AhOEEZiOv3wWrl0DJR+ptSQP5P4YZ94Htm41r8PtlakvbKdvaQHlR04DdBgbJyfiwT8iL+Iywgmvh/PvBPPA8VCdMUeDgGjU8lXwyeEDSmQKLojdR74Qml4QPHeH0YJM6sNGJ9ohBMooiUe/NZp9rBmU95wTmHwOwausJ17RS5+1r4dLiJtO0iWzzakYYitBIA0+SGmC2qUEoKBilqbcjR4DhGGOmHDWw5j9h+yt9rzv3avXs05iR+L0yRSsq2fJJBT6PjCRB7swURk6Iw+P24+nx4XX58LjU6x6XP3C7obWbQ/VdSD4FoyIRppHQDFS1kocEfSkp+j0kGkqI1+/DmDxa7TIdf3XwoH1ZhvIV6v9q3zICg5osSTDlpzDpRrB8u8+QDk8HKytXsuLQCnyKj/OSz+P8lPNJcXXD54/A/mXqiqYotcV1yi2gO0k/ei3LaihsKoHGverStBea9vWF26O4NHaqDIXUynkYIqOITI7Dmp5OZKo6r943/vkfdyfs+UANShVf9t2vD4OxP4AJP4b0GSc1LB0mQlMIiNA0vCmKwqb6TTy741m2NmwF1PB0RdYVXDPmGmLNsVgMFvSaEz8NvbipmH/u+icrKlcEwsNE+0Ruzr2ZGcnT0ex5Hz57ALrUU/XJuwYufES93tOO0tPG0qovePLQR/QoPqySnsfDx3CRbISeNnWMSE+7euly9LbQ9IocEdyidDK7DmQ/NJVQUvYpX/fUUeRto6jjAM29486OlBKRwkT7RCbYJzDBPoGRkSPRarR4/V6e3fEs/yj+BwoKyRHJLJm+hIn2iSdWltoiNTzt+0y9rdHBhOvU8DSEsyGPxe+XqSmupeyrMspLPGj8nUw0f0Bu2DIM2TOg8A8QO+obb39QnfVqC5/OBHoz6IzqN3y9Sb3UGfv9T31+mV++sZ1PiuvRSHBJXhK3TU8n16aAsxm6m4+4bMHf1UJ1lZ59NQkcaMnEJx8xmD3iANlxJYxMqMMQblLHWRktYLQecd0Cpt7bBosaEkwn9hnX7e1mX5s6t5deo0frqEW75Xm0ZcvRKaCTtGhzr0Rb8Et01iRcDh/b3q/h4Lb++9qJ6tJ4CNdXkW/YwFjDFmy6KjUUWlMg70fq+9E+Jug5DrcDjaTBYjhi3FnbIbX1c9u/1LoFdT8cc6na+pR+3pDff93eblZXrebTik9ZX7M+8CXrSCM9Xs7v7uZ8l5e88T9BN/NBCDuxMVvHoygK9c56JEnq/yVN9kNbRXCQaiyBlv3gP8Y0AuF2iB/Xu+Sql3HZ6j58LLIfDq5Vg9LeD/u6r5EgY7r6BWnsD8B4cn726VhEaAoBEZrOHJvrN/NM0TNsadjS7zGzzozFYMFqsAYWi8GC1WgN3H/40if7eLP0TTbV9w30PT/lfG7OvZlJ8ZOCN+xywMrfq90TxzgFp0Kn4wF7DLuN6ofMlZ1d/LqljbCj36KnMij1UhSF9bXreb74+X71pNPoyInJYULcBCbaJ5Iflz/oQPutDVt56MuHqHXWopE03JJ7C7dPuP3EQ2r1Vlj9H2prG6hnpk26Eab/CiKTB3++36ceAGq2qt2SNdtobinlJWsEb1oi6NFqMCswIjyJtLhxpFpSSbOmkWpVL2NMMad1du5j8fplVuxtICcxckhnwh3mcfvYvmk/ri4vk6aNDj6B4BTwyT7e3f8uTxc9Taur9YSfn+wYzZSqeVjkKMLDzcRYo4iKsGIwa9GbdBhMWgwmHQZz73W9gsFdS13lLvbs2cRY3zam6Pb0bdBoVc9kzLsW0s4NarVoc7Wx/NByPq/4nM0Nm1EUhZFRI9UvAnHql4FUSyqS36O2iGx5Hio39G07Nlsd5J9/LZj6T77p8rlYV7OOTw9+ytrqtbj8fafsj4wcydy0izDVFrG2Zh3bDFr8R+xnkcbIQAvUOUnnEGk8sck9vX4vBxwHKGktoaS1hNK2UkpbS+nw9E4aGpNDYXohc9LmkGI5zkS9fp8a8pt6w1TDbnVpPcCAn2uSFmJHHxWmcsDjVIPSzrfUOe8Os41UW5TyroWokzuB5fGI0BQCIjSdeTbXb+bvO//Ozuadx5zuYCh0ko6LMy/mpnE3MSp6kFaJmm3w8a+gdhtojWCOUrt9TFFgjsJrtPKM3MTz3eUoQKohmv8cfQO5ceMD6xARf0qCEqgHueWHlvN88fOUtpWqr0+j49ykcwMtSeNixmHSnfjBttPTyZObnuSD8g8AGBczjiXTl5AR+Q262So3wqr/ULu5ALQGdbzR9Hv6uksUBRxVQQGJuqLAt9lmrYZ/Rlp52xKBq/fgKXHMs8oBCNeHk2pJJdWaGghUadY0UiwphOvDMWgMaDWndsLFE9HuamdXyy6Km4spbipmV/Mu2tzqSQnnJJ3DbXm39Q/4J4GiKKyrWccft/yRcoc6JsVmshGmC8Ov+PHLfnyKD7/ixyf78Ps9+PxeBhiT3U+aNY2LUmdzUfQ4xrpcSE29B+/Gveq4qaO6OxVJi5Q1G/KvgeyL1Ra9Xg63gxWVK1hWsYyv677GP0hXabQxmnx7vvpeiJtAjg9M219Wx98c/gzR6NSWQ0XBi8JXRj2fhRlZaTbQfURIS/V6mdvVzVxnN6O8wS1NHfHj+GrSVaz2NLGuZh2OI8aRaSUtk+IncX7K+cxImUG6NT0oyDvcDkpbS4PCUbmjHJ/cvxtaJ+mQkZGPaL3OjcllbsZc5qTNITEi8bj1EeBxqi1RDbv6glTDLrV1fDCmSMi9CvKvg5Qpp+yz7XhEaAoBEZrObD7Zh9PrpMPdQYe3gw53B52eTjo86uXh64eXTk8nPb4epiVM48acG4f+4RL4g+7jNltvrt/MQ+seot5Zj07ScceEO7g59+ZTdkB2+928X/Y+L+5+kapO9Qwbs87MD0f/kBtybjipY7+WVSzjtxt+S4enA5PWxL1T7uVH2T/6Zi04FevU8HRovXpbZ1LHpThb1LDkbOz3lEaTlX8mjuAdnLh7Tw0cHzuehfkLKUgsoKarhsrOSg51HOJQxyEqOyqp7Kyktqu239itgegkHXqtHoPWgEFjwKA1oNfoMWqNgesGrSHweLg+HHuYXZ1aIywOu1m9jDHHnFBLnMvnoqS1hF3Nu9jZvJNdzbsC/8ug8ml0KIoSCAhTE6ZyW95tnJVw1klpRSttLeWPW/7Ihjq1FSbKGMXC/IX8KPtHx389igL7P0de8QT+ht14JQl/eCyugjv4WiuzvGYt67oqeucpUiV7fcxxdnORs5tcj0c9Sd4UpbZq2HPUQebZFwfN/N/p6WRV1So+O/gZG+o2BIWJsbaxzM2YS2F6IUatkR1NO9jRuIOipiJ2N+/GIwd3Tek0OnJsOUyw5TDB2cGEfauIbixlk8nEZxFhrAgz06Hte88m+nzM7eqm0Okkx+Pt/6M7EfFq1/2E66H3ve6Tfexs2sma6jWsrV5LWXtZ0FNSLalMS5xGU08Tpa2l1DnrBqxei8HCGNsYsqOzybZlM8Y2hszITDo9nayoXBFoYTsyQOXF5VGYVsic9Dkn/hmgKOqvJxwOUIfDVLPaTcuoi9Tut+x5gc9CWZHxyT58sg+v7FUXv3p5+D69Vk9mZOaJlWUQIjSFgAhNwsnmcDv43cbfsaxCHQg6OX4yS85bcuIB7Ti6PF28WfomL+95OTBHVpQxiuvGXsePs39MlCnqpP2tIzU4G3hk/SNsrFNnU5+RMoMnznmCWHPsiW/s8MDqlX8InhMJ1G/98eMgeTL1caP5Z88B/l21MnDwy4/L5/b82zkn6ZxBA4PH76G6s1oNUr2hqrKjkkOdh6h31p94uQchIWEz2QYMVPYwO5HGSA46DqohqWkn+9v241P6tyakW9PJjc0lNzaXvNg8sm3ZNHY38vyu53mv7L1AaJhkn8RtebdRkFTwjcJTY3cjTxc9zdL9S1FQ0Gv0XD/2en6e93OshhP4TJRl2P2u2p3ddjDoIack8WWYmc/Dw/jSbMZ1xKDjREM0s0dcwJxR88mz5wedher0OlldtZrPKj7rN4ZodPRoCtMLKUwvJM2adsxiefwe9rbupaixiB1NO9jeuJ3mw6f/H8GkNeI6YhB1nNHGnOTzmJsyk7zoMWg0WtT5yaT+l6aoQc/Kq+qsYm31WtZWr2Vz/eYBx0MlRySrAcmWTXa0GpASwxMH/b829zTzxaEvWFaxjK0NW4O+JEy0T6QwvZCL0i7CHnb8Ge6dXicN3Q00djfS2N1Ig7Oh77azgXZ3O16lLxz5ZB9ev3fA/fdoE+0T+de8fw263okQoSkERGgSTgVFUfjwwIf8YeMf6PZ1Y9FbmD9qPpmRmaRb00mPTP9G42yae5p5de+rvFnyJp29pxwnhCdw07ibuCLrisC0CKeSrMi8uvdV/rz1z3hkDzaTjccLHueC1Au+2QYVRZ39fN9n6pl1yVMgMY86dzvP73qed/e/GzjATLJPYmH+Qs5OPPuktK54ZS8ev6dvkftf9/q9eGQPbr9bvd37nA5PB03dTTT1NNHU3URjTyPN3c1DOoAczWaykRebR25sLuPjxjMuZtxxx7/UO+t5vlitm8NBMi82j9vyb2N68vQh1U23t5uXdr/EC7tfoKd37q/C9ELunHQnIyzfYlyK36vOrL7haZB9YB+njoex50D8OLqtSayv38jyiuWsrl4d+NsAdrOd2WmzGWMbw5c1X7K2ei3uI4JMZmQmc9PVFqXMqG/WaqEoCjVdNRQ1FQWC1L62fciKTLQxmtlps5mXMY9J9kmnrIXY6XWysXYjRU1FJIQnkB2dzWjb6BMLqcfQ1N3E8kPLWVaxjO2N2wMBSkJion0iF6VdhE6jU0NRd0NQSPo2wx2OJCGh1+jRa/XqpUbPuNhx/OXCv5yU7R8mQlMIiNAknEpVHVU8sO4Bdjbt7PeYRW8hPTI9EKIOX6ZZ0zBqg7sAqzqreGn3SyzdvzRwkMyMzOTm3Ju5OPPib3T24Le1v20/D3z5QODsqqtHX819U+771sGtpquGfxT/I6g1ZUr8FG7Pv/2kdUWdKrIi0+pqDYSpxu7GQKBq6lZvt7haGGEZ0ReSYseTEJ7wjVuJXtj1Au/seycwQHmsbSy35d/GBSMuGHDuML/s54PyD/jr9r/S2KN2g+bF5XHflPuYYJ/wrV7/iXL5XHxV+xXLDy1nddVqurz9Z8FPs6ZRmF7I3PS5ZEVlnZL/v9PrpLqzmsyozJC8l06VBmdDIEAdnvduMBH6iEArqT3MTnxYfOAy2hQd6KrWaXSBQHRkONJr9KdtfKAITSEgQpNwqvlkH58e/JQ9LXuo6KigwlFBTVfNMcfZSEgkRSSRHplOhjWDFlcLyyqWBcYs5MXmccv4W5g5YubQJ9Q8RTx+D3/Z/hde3P0ioI7T+Nn4nxGmDwuMCwoaD3TEfUePHaruquYfxf/gg7IPAq010xKmcVu+Om5HOLbmnmb+tftfvFH6RqDlZlT0KG7Nu5WLUi8KHMQ21m3kvzb/V+BkgeSIZO6afBeFaYUhD6Mev4eNdRv5vOJzytvLmZo4lbnpcxljGxPysn0X1Dvr+bzic9bXrseoNRIfFk98eHy/gBSuH/x3BIcLEZpCQIQmIRTcfjeVHZWBEHX48mDHQTo9A8/0e27yudySewtT4qcMu4PI13Vf8/C6h2nobjgp2ytILGBh/sJTcobYd1mbq42X97zMayWvBbpaMiMzuX7s9YEByaC2ct6adyvXjb0Og/YkTbgoCKeZCE0hIEKTMJwoikKrq5WKjgoOOg5S4ajAI3u4IusKxsYc40dmhwmH28GzO57lQPsBPLI6Fsjtd/cbG3R4vNBAp1Kfm3QuC/MXnvZuou8ah9vBq3tf5ZW9rwSFcJ2k45ox13Bb3m1Em07STOmCECIiNIWACE2CEBqyIgcNxNZK2lN21t/3Vaenk9dLXuet0rfIjc3lrkl3kR6ZHupiCcJJIUJTCIjQJAiCIAhnnhM5fod29KcgCIIgCMIZQoQmQRAEQRCEIRChSRAEQRAEYQhEaBIEQRAEQRgCEZoEQRAEQRCGQIQmQRAEQRCEIQhpaFqyZAlnnXUWFosFu93O/PnzKS0tDVrnb3/7GzNnzsRqtSJJEu3t7f22s2/fPi6//HJiY2OxWq2cd955rFq1KmidyspKLrnkEsLCwrDb7dx33334fMGT4q1evZpJkyZhNBrJysrixRdfPNkvWRAEQRCEM1RIQ9OaNWtYtGgRGzduZPny5Xi9XubMmYPT2fcLyd3d3cydO5eHHnromNu59NJL8fl8rFy5kq1bt5Kfn8+ll15KfX09AH6/n0suuQSPx8NXX33FSy+9xIsvvshvfvObwDYOHjzIJZdcwgUXXEBRURF33XUXP/vZz1i2bNmpqwBBEARBEM4Yw2pyy6amJux2O2vWrGHGjBlBj61evZoLLriAtrY2oqKiAvc3NzcTFxfH2rVrmT59OgCdnZ1YrVaWL1/O7Nmz+fTTT7n00kupra0lPj4egOeee45f//rXNDU1YTAY+PWvf83HH3/Mrl27Atu+9tpraW9v57PPPhu07GJyS0EQBEE485yxk1s6HA4AbDbbkJ8TExNDdnY2//rXv3A6nfh8Pv7v//4Pu93O5MmTAdiwYQPjx48PBCaAwsJCOjo62L17d2Cd2bNnB227sLCQDRs2DPh33W43HR0dQYsgCIIgCN9dulAX4DBZlrnrrrs499xzyc3NHfLzJEniiy++YP78+VgsFjQaDXa7nc8++4zoaPWHJOvr64MCExC4fbgL71jrdHR00NPTg9lsDnpsyZIlPPHEEyf8OgVBEARBODMNm5amRYsWsWvXLt54440Tep6iKCxatAi73c6XX37Jpk2bmD9/Ppdddhl1dXWnqLTw4IMP4nA4AktVVdUp+1uCIAiCIITesGhpWrx4MR999BFr164lJSXlhJ67cuVKPvroI9ra2gJ9kc888wzLly/npZde4oEHHiAhIYFNmzYFPa+hoQGAhISEwOXh+45cx2q19mtlAjAajRiNxhMqqyAIgiAIZ66QtjQpisLixYtZunQpK1euJCMj44S30d3dDYBGE/xSNBoNsiwDUFBQQHFxMY2NjYHHly9fjtVqJScnJ7DOihUrgraxfPlyCgoKTrhMgiAIgiB894S0pWnRokW89tprvP/++1gslsD4osjIyEDrTn19PfX19ZSVlQFQXFyMxWIhNTUVm81GQUEB0dHRLFiwgN/85jeYzWb+/ve/B6YQAJgzZw45OTnccMMNPPXUU9TX1/PII4+waNGiQGvRwoUL+etf/8r999/PzTffzMqVK3nrrbf4+OOPh/RaDp+EKAaEC4IgCMKZ4/Bxe0iTCSghBAy4vPDCC4F1HnvssUHX2bx5szJnzhzFZrMpFotFOfvss5VPPvkk6G9VVFQo8+bNU8xmsxIbG6v86le/Urxeb9A6q1atUiZMmKAYDAYlMzMz6G8Mpqqq6pivRyxiEYtYxCIWsQzvpaqqatBj/bCap+lMJssytbW1WCwWJEk6qdvu6OhgxIgRVFVViTmgjkHU0fGJ+hmcqKPBiTo6PlE/gxuOdaQoCp2dnSQlJfUb6nO0YTEQ/LtAo9Gc8CD2E2W1WofNTjZciTo6PlE/gxN1NDhRR8cn6mdww62OIiMjh7TesJlyQBAEQRAEYTgToUkQBEEQBGEIRGg6AxiNRh577DExL9RxiDo6PlE/gxN1NDhRR8cn6mdwZ3odiYHggiAIgiAIQyBamgRBEARBEIZAhCZBEARBEIQhEKFJEARBEARhCERoEgRBEARBGAIRmoa5p59+mvT0dEwmE9OmTWPTpk2hLtKw8fjjjyNJUtAyZsyYUBcrpNauXctll11GUlISkiTx3nvvBT2uKAq/+c1vSExMxGw2M3v2bPbv3x+awobIYHV000039duv5s6dG5rChsCSJUs466yzsFgs2O125s+fT2lpadA6LpeLRYsWERMTQ0REBFdddRUNDQ0hKvHpN5Q6mjlzZr/9aOHChSEq8en17LPPkpeXF5jAsqCggE8//TTw+Jm8/4jQNIy9+eab3HPPPTz22GNs27aN/Px8CgsLaWxsDHXRho1x48ZRV1cXWNatWxfqIoWU0+kkPz+fp59+esDHn3rqKf73f/+X5557jq+//prw8HAKCwtxuVynuaShM1gdAcydOzdov3r99ddPYwlDa82aNSxatIiNGzeyfPlyvF4vc+bMwel0Bta5++67+fDDD3n77bdZs2YNtbW1XHnllSEs9ek1lDoC+PnPfx60Hz311FMhKvHplZKSwpNPPsnWrVvZsmULF154IZdffjm7d+8GzvD9Z8i/SCucdlOnTlUWLVoUuO33+5WkpCRlyZIlISzV8PHYY48p+fn5oS7GsAUoS5cuDdyWZVlJSEhQ/t//+3+B+9rb2xWj0ai8/vrrIShh6B1dR4qiKAsWLFAuv/zykJRnOGpsbFQAZc2aNYqiqPuMXq9X3n777cA6e/fuVQBlw4YNoSpmSB1dR4qiKOeff75y5513hq5Qw0x0dLTyj3/844zff0RL0zDl8XjYunUrs2fPDtyn0WiYPXs2GzZsCGHJhpf9+/eTlJREZmYm119/PZWVlaEu0rB18OBB6uvrg/apyMhIpk2bJvapo6xevRq73U52dja33347LS0toS5SyDgcDgBsNhsAW7duxev1Bu1HY8aMITU19Xu7Hx1dR4e9+uqrxMbGkpuby4MPPkh3d3coihdSfr+fN954A6fTSUFBwRm//4gf7B2mmpub8fv9xMfHB90fHx9PSUlJiEo1vEybNo0XX3yR7Oxs6urqeOKJJ5g+fTq7du3CYrGEunjDTn19PcCA+9ThxwS1a+7KK68kIyOD8vJyHnroIebNm8eGDRvQarWhLt5pJcsyd911F+eeey65ubmAuh8ZDAaioqKC1v2+7kcD1RHAddddR1paGklJSezcuZNf//rXlJaW8u6774awtKdPcXExBQUFuFwuIiIiWLp0KTk5ORQVFZ3R+48ITcIZa968eYHreXl5TJs2jbS0NN566y1uueWWEJZMOJNde+21gevjx48nLy+PkSNHsnr1ambNmhXCkp1+ixYtYteuXd/7sYLHc6w6uvXWWwPXx48fT2JiIrNmzaK8vJyRI0ee7mKedtnZ2RQVFeFwOHjnnXdYsGABa9asCXWxvjXRPTdMxcbGotVq+51R0NDQQEJCQohKNbxFRUUxevRoysrKQl2UYenwfiP2qROTmZlJbGzs926/Wrx4MR999BGrVq0iJSUlcH9CQgIej4f29vag9b+P+9Gx6mgg06ZNA/je7EcGg4GsrCwmT57MkiVLyM/P53/+53/O+P1HhKZhymAwMHnyZFasWBG4T5ZlVqxYQUFBQQhLNnx1dXVRXl5OYmJiqIsyLGVkZJCQkBC0T3V0dPD111+Lfeo4qquraWlp+d7sV4qisHjxYpYuXcrKlSvJyMgIenzy5Mno9fqg/ai0tJTKysrvzX40WB0NpKioCOB7sx8dTZZl3G73Gb//iO65Yeyee+5hwYIFTJkyhalTp/LnP/8Zp9PJT3/601AXbVi49957ueyyy0hLS6O2tpbHHnsMrVbLj3/841AXLWS6urqCvskePHiQoqIibDYbqamp3HXXXfz+979n1KhRZGRk8Oijj5KUlMT8+fNDV+jT7Hh1ZLPZeOKJJ7jqqqtISEigvLyc+++/n6ysLAoLC0NY6tNn0aJFvPbaa7z//vtYLJbAOJPIyEjMZjORkZHccsst3HPPPdhsNqxWK7/4xS8oKCjg7LPPDnHpT4/B6qi8vJzXXnuNiy++mJiYGHbu3Mndd9/NjBkzyMvLC3HpT70HH3yQefPmkZqaSmdnJ6+99hqrV69m2bJlZ/7+E+rT94Tj+8tf/qKkpqYqBoNBmTp1qrJx48ZQF2nYuOaaa5TExETFYDAoycnJyjXXXKOUlZWFulghtWrVKgXotyxYsEBRFHXagUcffVSJj49XjEajMmvWLKW0tDS0hT7NjldH3d3dypw5c5S4uDhFr9craWlpys9//nOlvr4+1MU+bQaqG0B54YUXAuv09PQod9xxhxIdHa2EhYUpV1xxhVJXVxe6Qp9mg9VRZWWlMmPGDMVmsylGo1HJyspS7rvvPsXhcIS24KfJzTffrKSlpSkGg0GJi4tTZs2apXz++eeBx8/k/UdSFEU5nSFNEARBEAThTCTGNAmCIAiCIAyBCE2CIAiCIAhDIEKTIAiCIAjCEIjQJAiCIAiCMAQiNAmCIAiCIAyBCE2CIAiCIAhDIEKTIAiCIAjCEIjQJAiCcIpIksR7770X6mIIgnCSiNAkCMJ30k033YQkSf2WuXPnhrpogiCcocRvzwmC8J01d+5cXnjhhaD7jEZjiEojCMKZTrQ0CYLwnWU0GklISAhaoqOjAbXr7Nlnn2XevHmYzWYyMzN55513gp5fXFzMhRdeiNlsJiYmhltvvZWurq6gdf75z38ybtw4jEYjiYmJLF68OOjx5uZmrrjiCsLCwhg1ahQffPDBqX3RgiCcMiI0CYLwvfXoo49y1VVXsWPHDq6//nquvfZa9u7dC4DT6aSwsJDo6Gg2b97M22+/zRdffBEUip599lkWLVrErbfeSnFxMR988AFZWVlBf+OJJ57gRz/6ETt37uTiiy/m+uuvp7W19bS+TkEQTpJQ/2KwIAjCqbBgwQJFq9Uq4eHhQcsf/vAHRVHUX6pfuHBh0HOmTZum3H777YqiKMrf/vY3JTo6Wunq6go8/vHHHysajUapr69XFEVRkpKSlIcffviYZQCURx55JHC7q6tLAZRPP/30pL1OQRBOHzGmSRCE76wLLriAZ599Nug+m80WuF5QUBD0WEFBAUVFRQDs3buX/Px8wsPDA4+fe+65yLJMaWkpkiRRW1vLrFmzjluGvLy8wPXw8HCsViuNjY3f9CUJghBCIjQJgvCdFR4e3q+77GQxm81DWk+v1wfdliQJWZZPRZEEQTjFxJgmQRC+tzZu3Njv9tixYwEYO3YsO3bswOl0Bh5fv349Go2G7OxsLBYL6enprFix4rSWWRCE0BEtTYIgfGe53W7q6+uD7tPpdMTGxgLw9ttvM2XKFM477zxeffVVNm3axPPPPw/A9ddfz2OPPcaCBQt4/PHHaWpq4he/+AU33HAD8fHxADz++OMsXLgQu93OvHnz6OzsZP369fziF784vS9UEITTQoQmQRC+sz777DMSExOD7svOzqakpARQz2x74403uOOOO0hMTOT1118nJycHgLCwMJYtW8add97JWWedRVhYGFdddRV/+tOfAttasGABLpeL//7v/+bee+8lNjaWq6+++vS9QEEQTitJURQl1IUQBEE43SRJYunSpcyfPz/URREE4QwhxjQJgiAIgiAMgQhNgiAIgiAIQyDGNAmC8L0kRiYIgnCiREuTIAiCIAjCEIjQJAiCIAiCMAQiNAmCIAiCIAyBCE2CIAiCIAhDIEKTIAiCIAjCEIjQJAiCIAiCMAQiNAmCIAiCIAyBCE2CIAiCIAhDIEKTIAiCIAjCEPx/acSAynMklkEAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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2t912G8nJyXzzzTfu7qKLKa/LxX5moaGhHq1MNeH8LllFUTh48KC7lXHnzp3s37+fmTNn8sADD7jLXapbrbL0ej233XYbt912G06nk8cff5zp06czceJEmjdvTuPGjS/6WUDlf3bXWnV/V+fOnUvTpk354YcfPMpMmjTJo9zFti9vzdLpdJdtRTp+/HilWq8AVq5cecF8UA0aNPDoNl22bBnR0dHuPxJE3SMtTeK6ptFoGDx4MAsXLvToajl9+jTffvstvXr1cncdnd9t4+fnR/PmzbFYLIBrMr3S0lKPMs2aNcPf399d5mpxOBwXdAGFh4fTqFEjj2P7+vpeUA7glltuISMjw+MKKLvdztSpU/Hz86Nv376Vrstf/vIXfvnlF95//31CQkLcX4KX0rNnT4YMGcJnn33GggULLlhvtVp55plnANeXbYcOHZg5cyZ5eXnuMqmpqfzyyy/ccsstla5rZX311VcUFha638+dO5f09HT3uZW3vpzb2qIoygVTA1TV+b9zarXaHdTKf6633HILGzZsYN26de5yZrOZTz/9lCZNmpCQkHBFdahpV/q7WtFn/ccff3icP+C+IvHc35HyY/Xr14/p06e7xxid69zpE6o7pqkis2fPZuPGjUyYMKFSrYyidpKWJnFd+OKLL/jpp58uWP7kk0/yz3/+k5SUFHr16sXjjz+OVqtl+vTpWCwW3nrrLXfZhIQE+vXrR+fOnQkODmbTpk3uy6YB9u/fz4ABA7j33ntJSEhAq9Uyf/58Tp8+zZ///Oeren6FhYVER0dz99130759e/z8/Fi2bBkbN270mHuoc+fOzJ49m6effpquXbvi5+fHbbfdxiOPPML06dMZPXo0mzdvpkmTJsydO5fff/+d999/v1IDtMuNGDGC5557jvnz5/PYY49VesLGr776isGDB3PXXXdx2223MWDAAHx9fTlw4ADfffcd6enp7rma3n77bW6++WZ69uzJmDFj3FMOBAYGXpX78QUHB9OrVy8efPBBTp8+zfvvv0/z5s15+OGHAVf3YrNmzXjmmWc4efIkAQEBzJs3r9oXBJT761//Sk5ODjfddBPR0dEcO3aMqVOn0qFDB/eYpRdeeIH//ve/3HzzzYwfP57g4GBmzpzJkSNHmDdvXrW+oFetWkX//v2ZNGlSjX+eV/q7euutt/LDDz9w5513MnToUI4cOcInn3xCQkKCx/xHJpOJhIQEZs+eTcuWLQkODqZt27a0bduWjz/+mF69etGuXTsefvhhmjZtyunTp1m3bh0nTpxg+/btANUe0/Trr7/y2muvMXjwYEJCQli/fj1ffvklQ4YMqXAuM1GHeOmqPSGuifLLpC/2OH78uKIoirJlyxYlKSlJ8fPzU3x8fJT+/fsra9eu9djXP//5T6Vbt25KUFCQYjKZlNatWyuvv/66YrVaFUVRlKysLGXs2LFK69atFV9fXyUwMFDp3r278v3331+2nuWXxp85c6bC9Y0bN77klAMWi0V59tlnlfbt2yv+/v6Kr6+v0r59e+Xf//63xzZFRUXKiBEjlKCgIAXwuKT79OnTyoMPPqiEhoYqer1eadeunfLll196bF9+CfblplW45ZZbFOCCz/ByiouLlXfeeUfp2rWr4ufnp+j1eqVFixbKE088oRw8eNCj7LJly5Qbb7xRMZlMSkBAgHLbbbcpu3fv9ihzsc911KhRiq+v7wXH79u3r9KmTRv3+/LL9P/73/8qL774ohIeHq6YTCZl6NChHtMIKIqi7N69Wxk4cKDi5+enhIaGKg8//LCyfft2BfD4HC927PJ15/5M5s6dqwwePFgJDw9X9Hq9Ehsbqzz66KNKenq6x3aHDh1S7r77biUoKEgxGo1Kt27dlMWLF3uUKT+XOXPmeCwv/5meW8cff/xRAZRPPvnEvexi03eU7/fcS+4v5Up/V51Op/LGG28ojRs3VgwGg9KxY0dl8eLFF3x2iqIoa9euVTp37qzo9foLph84dOiQ8sADDyiRkZGKTqdToqKilFtvvbVGpgM4ePCgMnjwYCU0NFQxGAxK69atleTk5AumShB1j0pRqjHqUQghLuHOO+9k586dlZppujYrb3GZM2cOd999t7erc80899xz/Pe//+XgwYMYDAZvV0eIWkM6VoUQNSo9PZ0lS5ZUenZvUfusXLmSiRMnSmAS4jwypkkIUSOOHDnC77//zmeffYZOp+PRRx/1dpVENW3cuLFa2xUVFV32vmphYWEXnbZAiNpOQpMQokasXr2aBx98kNjYWGbOnFnpCT1F/fHOO+/w6quvXrLMkSNHKnUfRiFqIxnTJIQQokYcPnz4srdj6dWrV6VmuBeiNpLQJIQQQghRCTIQXAghhBCiEmRMUw1xOp2cOnUKf3//i07fL4QQQojaRVEUCgsLadSo0WUng5XQVENOnTrlcY8yIYQQQtQdx48fJzo6+pJlJDTVkPLbTBw/ftx9rzIhhBBC1G4FBQXExMRU6nZREppqSHmXXEBAgIQmIYQQoo6pzNAaGQguhBBCCFEJEpqEEEIIISpBQpMQQgghRCXImCYhhBCiChwOBzabzdvVEJWk0+lq7H6HEpqEEEKISlAUhYyMDPLy8rxdFVFFQUFBREZGXvE8ihKahBBCiEooD0zh4eH4+PjIRMZ1gKIoFBcXk5mZCUDDhg2vaH8SmoQQQojLcDgc7sAUEhLi7eqIKjCZTABkZmYSHh5+RV11MhBcCCGEuIzyMUw+Pj5eromojvKf25WORZPQJIQQQlSSdMnVTTX1c5PQJIQQQghRCRKahBBCCCEqQUKTEEIIUU+NHj0alUqFSqVCp9MRFxfHc889R2lpqberdoFVq1ahUqkuO6VDaWkpo0ePpl27dmi1WoYNG3ZN6gdy9Vytp9jt2LOzwW5HFxXl7eoIIYSoY4YMGcKXX36JzWZj8+bNjBo1CpVKxZtvvuntqlWLw+HAZDIxfvx45s2bd02PLS1NtVzeDz9wsG8/Mib/09tVEUIIUQcZDAYiIyOJiYlh2LBhDBw4kJSUFPd6p9NJcnIycXFxmEwm2rdvz9y5c93ry1uAlixZQmJiIkajkR49epCamupxnDVr1tC7d29MJhMxMTGMHz8es9nsXv/111/TpUsX/P39iYyMZMSIEe75k44ePUr//v0BaNCgASqVitGjR1d4Pr6+vkybNo2HH36YyMjImvqYKkVCUy2XoTQgLWYA+7MDvF0VIYQQ51AUhWKr/Zo/FEWpdp1TU1NZu3Yter3evSw5OZmvvvqKTz75hF27dvHUU09x//33s3r1ao9tn332Wd599102btxIWFgYt912m/sS/kOHDjFkyBCGDx/Ojh07mD17NmvWrGHcuHHu7W02G5MnT2b79u0sWLCAo0ePuoNRTEyMu9Vo3759pKen88EHH1T7PK8W6Z6r5ZadOIW62V34F+z3dlWEEEKco8TmIOHln6/5cXe/loSPvvJf34sXL8bPzw+73Y7FYkGtVvPRRx8BYLFYeOONN1i2bBk9e/YEoGnTpqxZs4bp06fTt29f934mTZrEoEGDAJg5cybR0dHMnz+fe++9l+TkZEaOHMmECRMAaNGiBR9++CF9+/Zl2rRpGI1GHnroIfe+mjZtyocffkjXrl0pKirCz8+P4OBgAMLDwwkKCrqSj+iqkdBUy6nDDXAA7NpAFJsNlU7n7SoJIYSoQ/r378+0adMwm8289957aLVahg8fDsDBgwcpLi52h6FyVquVjh07eiwrD1UAwcHBtGrVij179gCwfft2duzYwaxZs9xlFEXB6XRy5MgR4uPj2bx5M6+88grbt28nNzcXp9MJQFpaGgkJCVfl3GuahKZaLrJZFBm/27AYgrBlZaG/wvvmCCGEqBkmnYbdryV55bhV4evrS/PmzQH44osvaN++PZ9//jljxoyhqKgIgCVLlhB13sVGBoOh0scoKiri0UcfZfz48Resi42NxWw2k5SURFJSErNmzSIsLIy0tDSSkpKwWq1VOh9vktBUy7WKCiGDDJwaA3lHTxEuoUkIIWoFlUpVpW6y2kCtVvPSSy/x9NNPM2LECBISEjAYDKSlpXl0xVVk/fr1xMbGApCbm8v+/fuJj48HoFOnTuzevdsdzs63c+dOsrOzmTJlCjExMQBs2rTJo0z5OCuHw3FF53g1yUDwWq5l5nrU9kIADu896t3KCCGEqPPuueceNBoNH3/8Mf7+/jzzzDM89dRTzJw5k0OHDrFlyxamTp3KzJkzPbZ77bXXWL58OampqYwePZrQ0FD3HEnPP/88a9euZdy4cWzbto0DBw6wcOFC90Dw2NhY9Ho9U6dO5fDhwyxatIjJkyd77L9x48aoVCoWL17MmTNn3K1gFdm9ezfbtm0jJyeH/Px8tm3bxrZt22r0c6qIhKZaTh8Ui8qZB0Dm8dPerYwQQog6T6vVMm7cON566y3MZjOTJ09m4sSJJCcnEx8fz5AhQ1iyZAlxcXEe202ZMoUnn3ySzp07k5GRwY8//uhuHUpMTGT16tXs37+f3r1707FjR15++WUaNWoEQFhYGDNmzGDOnDkkJCQwZcoU3nnnHY/9R0VF8eqrr/LCCy8QERHhceXd+W655RY6duzIjz/+yKpVq+jYseMFY7CuBpVyJdcuCreCggICAwPJz88nIKAGpwc4vYtpLy7DqW9HsH4z9334bM3tWwghRKWUlpZy5MgR4uLiMBqN3q7ONbVq1Sr69+9Pbm5urb2q7XIu9fOryve3tDTVdgGNUGlyALAV1d5+XiGEEKK+k9BU2xmDUGlzAVCsMt2AEEII4S11a9j/9UilQmMsxF4CCj7ero0QQojrTL9+/a5oFvL6RFqa6gC9nwUAh1pupSKEEEJ4i4SmOsA3xPVjsumDcFosXq6NEEIIcX2S0FQHRDRqAIBDa6L4RIaXayOEEEJcnyQ01QGNI5qgtpsByNh/0su1EUIIIa5PEprqgIZhLVA78gA4fvC4dysjhBBCXKckNNUB2qAYUPIAyE7P9W5lhBBCiOuUhKa6ICAKRe2a4LI0r9TLlRFCCCGuTxKa6gKfUFQaVwuTvcTLdRFCCFFnjB49GpVKhUqlQqfTERcXx3PPPUdpae37A3zVqlWoVCry8vIuW+6OO+6gYcOG+Pr60qFDB2bNmnVN6iiTW9YFajXoC8ABiuP6uueREEKIKzNkyBC+/PJLbDYbmzdvZtSoUahUKt58801vV61a1q5dS2JiIs8//zwREREsXryYBx54gMDAQG699daremxpaaojND7FADhVfl6uiRBCiLrEYDAQGRlJTEwMw4YNY+DAgaSkpLjXO51OkpOTiYuLw2Qy0b59e+bOneteX94CtGTJEhITEzEajfTo0YPU1FSP46xZs4bevXtjMpmIiYlh/PjxmM1m9/qvv/6aLl264O/vT2RkJCNGjCAzMxOAo0eP0r9/fwAaNGiASqVi9OjRFZ7PSy+9xOTJk7nhhhto1qwZTz75JEOGDOGHH36oqY/soiQ01RGGINfNem26IO9WRAghhIuigNV87R9XcEuT1NRU1q5di16vdy9LTk7mq6++4pNPPmHXrl089dRT3H///axevdpj22effZZ3332XjRs3EhYWxm233YbNZgPg0KFDDBkyhOHDh7Njxw5mz57NmjVrGDdunHt7m83G5MmT2b59OwsWLODo0aPuYBQTE8O8efMA2LdvH+np6XzwwQeVPq/8/HyCg4Or+7FUmnTP1REBkb4UHAeH1ofS7AKMIXJLFSGE8CpbMbzR6Nof96VToPetdPHFixfj5+eH3W7HYrGgVqv56KOPALBYLLzxxhssW7aMnj17AtC0aVPWrFnD9OnT6du3r3s/kyZNYtCgQQDMnDmT6Oho5s+fz7333ktycjIjR45kwoQJALRo0YIPP/yQvn37Mm3aNIxGIw899JB7X02bNuXDDz+ka9euFBUV4efn5w494eHhBAUFVfr8vv/+ezZu3Mj06dMrvU11SWiqIxo1iiXdXoJDayLvcDqREpqEEEJUQv/+/Zk2bRpms5n33nsPrVbL8OHDATh48CDFxcXuMFTOarXSsWNHj2XloQogODiYVq1asWfPHgC2b9/Ojh07PAZkK4qC0+nkyJEjxMfHs3nzZl555RW2b99Obm4uTqcTgLS0NBISEqp1bitXruTBBx/kP//5D23atKnWPqpCQlMd0Ti8BdvseTi0Jk4fSieyaytvV0kIIa5vOh9Xq483jlsFvr6+NG/eHIAvvviC9u3b8/nnnzNmzBiKiooAWLJkCVFRUR7bGQyGSh+jqKiIRx99lPHjx1+wLjY2FrPZTFJSEklJScyaNYuwsDDS0tJISkrCarVW6XzKrV69mttuu4333nuPBx54oFr7qCoJTXVEWGgLcG4CGnLyWDrtvV0hIYS43qlUVeomqw3UajUvvfQSTz/9NCNGjCAhIQGDwUBaWppHV1xF1q9fT2xsLAC5ubns37+f+Ph4ADp16sTu3bvd4ex8O3fuJDs7mylTphATEwPApk2bPMqUj7NyOByXPY9Vq1Zx66238uabb/LII49ctnxNkYHgdYQqMApFlQdAwZlC71ZGCCFEnXXPPfeg0Wj4+OOP8ff355lnnuGpp55i5syZHDp0iC1btjB16lRmzpzpsd1rr73G8uXLSU1NZfTo0YSGhjJs2DAAnn/+edauXcu4cePYtm0bBw4cYOHChe6B4LGxsej1eqZOncrhw4dZtGgRkydP9th/48aNUalULF68mDNnzrhbwc63cuVKhg4dyvjx4xk+fDgZGRlkZGSQk5NT8x/WeSQ01RX+kShlE1xaimxerowQQoi6SqvVMm7cON566y3MZjOTJ09m4sSJJCcnEx8fz5AhQ1iyZAlxcXEe202ZMoUnn3ySzp07k5GRwY8//uhuHUpMTGT16tXs37+f3r1707FjR15++WUaNXINlA8LC2PGjBnMmTOHhIQEpkyZwjvvvOOx/6ioKF599VVeeOEFIiIiPK68O9fMmTMpLi4mOTmZhg0buh933XXXVfi0PKkU5QquXRRuBQUFBAYGkp+fT0DA1RmkPf2RMdjVIzFZD/DQF49elWMIIYS4UGlpKUeOHCEuLg6j8fqaZHjVqlX079+f3NzcKl3VVptc6udXle9vaWmqS0yupkqnUrf60IUQQoj6QEJTHaLxc90ryK6R6QaEEEKIa02unqtDTKF6LNng0Pphs9jRGeTHJ4QQ4urq168fMpLHRVqa6pCghhFoHBYACo9nebk2QgghxPVFQlMdEhXZHJ3VdQVd3tFML9dGCCGEuL5IaKpDGkfGo3LkAZB+JMO7lRFCCCGuMxKa6pAGYc1xkgdA5qls71ZGCCGEuM5IaKpLAhqhqFzdc+acYi9XRgghhLi+SGiqS3QmnLo8AKzFciWDEEIIcS15NTQlJyfTtWtX/P39CQ8PZ9iwYezbt8+9PicnhyeeeIJWrVphMpmIjY1l/Pjx5Ofne+wnLS2NoUOH4uPjQ3h4OM8++yx2u92jzKpVq+jUqRMGg4HmzZszY8aMC+rz8ccf06RJE4xGI927d2fDhg1X5byvhKJ3nbvDeX3NSCuEEEJ4m1dD0+rVqxk7dizr168nJSUFm83G4MGDMZvNAJw6dYpTp07xzjvvkJqayowZM/jpp58YM2aMex8Oh4OhQ4ditVpZu3YtM2fOZMaMGbz88svuMkeOHGHo0KH079+fbdu2MWHCBP7617/y888/u8vMnj2bp59+mkmTJrFlyxbat29PUlISmZm16yo1lU8JAA6Vv5drIoQQorYbPXo0KpUKlUqFTqcjLi6O5557jtLSUm9X7QKrVq1CpVKRl5d3yXL79u2jf//+REREYDQaadq0Kf/3f/+HzXYN7suq1CKZmZkKoKxevfqiZb7//ntFr9crNptNURRFWbp0qaJWq5WMjAx3mWnTpikBAQGKxWJRFEVRnnvuOaVNmzYe+/nTn/6kJCUlud9369ZNGTt2rPu9w+FQGjVqpCQnJ1eq7vn5+Qqg5OfnV6p8dX326lDlo0eXKx89ulyxWx1X9VhCCCFcSkpKlN27dyslJSXerkqVjBo1ShkyZIiSnp6upKWlKfPnz1cCAgKU5557zttVu8DKlSsVQMnNzb1kuUOHDilffPGFsm3bNuXo0aPKwoULlfDwcOXFF1+86DaX+vlV5fu7Vo1pKu92Cw4OvmSZgIAAtFrXbNjr1q2jXbt2REREuMskJSVRUFDArl273GUGDhzosZ+kpCTWrVsHgNVqZfPmzR5l1Go1AwcOdJc5n8VioaCgwONxLRjDA1E7rAAU5pZck2MKIYSouwwGA5GRkcTExDBs2DAGDhxISkqKe73T6SQ5OZm4uDhMJhPt27dn7ty57vXlLUBLliwhMTERo9FIjx49SE1N9TjOmjVr6N27NyaTiZiYGMaPH+/uOQL4+uuv6dKlC/7+/kRGRjJixAh3b87Ro0fp378/AA0aNEClUjF69OgKz6dp06Y8+OCDtG/fnsaNG3P77bczcuRIfvvtt5r6yC6q1oQmp9PJhAkTuPHGG2nbtm2FZbKyspg8eTKPPPKIe1lGRoZHYALc7zMyMi5ZpqCggJKSErKysnA4HBWWKd/H+ZKTkwkMDHQ/YmJiqnbC1RQS1QyDJQ+AgmNnrskxhRBCXEhRFIptxdf8oVzBLU1SU1NZu3Yter3evSw5OZmvvvqKTz75hF27dvHUU09x//33s3r1ao9tn332Wd599102btxIWFgYt912m7tL7NChQwwZMoThw4ezY8cOZs+ezZo1axg3bpx7e5vNxuTJk9m+fTsLFizg6NGj7mAUExPDvHnzAFf3W3p6Oh988EGlzungwYP89NNP9O3bt9qfS2XVmpuXjR07ltTUVNasWVPh+oKCAoYOHUpCQgKvvPLKta1cBV588UWefvpp9/uCgoJrEpyiI1tx2p4HhJOflgVdm1z1YwohhLhQib2E7t92v+bH/WPEH/jofCpdfvHixfj5+WG327FYLKjVaj766CPA1WvyxhtvsGzZMnr27Am4WnLWrFnD9OnTPYLIpEmTGDRoEAAzZ84kOjqa+fPnc++995KcnMzIkSOZMGECAC1atODDDz+kb9++TJs2DaPRyEMPPeTeV9OmTfnwww/p2rUrRUVF+Pn5uXuZwsPDCQoKuux53XDDDWzZsgWLxcIjjzzCa6+9VunPpLpqRWgaN24cixcv5tdffyU6OvqC9YWFhQwZMgR/f3/mz5+PTqdzr4uMjLzgKrfTp0+715U/ly87t0xAQAAmkwmNRoNGo6mwTPk+zmcwGDAYDFU/2SsU16gNm5SfAEhPO027a14DIYQQdUn//v2ZNm0aZrOZ9957D61Wy/DhwwFXK01xcbE7DJWzWq107NjRY1l5qALXMJpWrVqxZ88eALZv386OHTuYNWuWu4yiKDidTo4cOUJ8fDybN2/mlVdeYfv27eTm5uJ0OgHXFfAJCQlVPq/Zs2dTWFjI9u3befbZZ3nnnXd47rnnqryfqvBqaFIUhSeeeIL58+ezatUq4uLiLihTUFBAUlISBoOBRYsWYTR6Xmrfs2dPXn/9dTIzMwkPDwcgJSWFgIAA9w+hZ8+eLF261GO7lJQU9y+AXq+nc+fOLF++nGHDhgGu7sLly5d7NC3WBr4hcTjKJrjMzcy/TGkhhBBXi0lr4o8Rf3jluFXh6+tL8+bNAfjiiy9o3749n3/+OWPGjKGoqAiAJUuWEBUV5bFdVRoGioqKePTRRxk/fvwF62JjYzGbzSQlJZGUlMSsWbMICwsjLS2NpKQkrFZrlc6nXHnvTkJCAg6Hg0ceeYS///3vaDSaau2vMrwamsaOHcu3337LwoUL8ff3d48fCgwMxGQyUVBQwODBgykuLuabb77xGHAdFhaGRqNh8ODBJCQk8Je//IW33nqLjIwM/u///o+xY8e6f+B/+9vf+Oijj3juued46KGHWLFiBd9//z1Llixx1+Xpp59m1KhRdOnShW7duvH+++9jNpt58MEHr/0HcymGAJzqPABKCqr3iyaEEOLKqVSqKnWT1QZqtZqXXnqJp59+mhEjRpCQkIDBYCAtLe2yY4LWr19PbGwsALm5uezfv5/4+HgAOnXqxO7du93h7Hw7d+4kOzubKVOmuMPOpk2bPMqUj7NyOBxVPi+n04nNZsPpdF7V0OTVKQeACh9ffvmloihnLz+s6HHkyBH3fo4eParcfPPNislkUkJDQ5W///3v7ikJyq1cuVLp0KGDotfrlaZNm7qPca6pU6cqsbGxil6vV7p166asX7++0udyraYcUBRF+eTBe5WPHl2ufPrXb676sYQQQtTtKQfuuOMOj2U2m02JiopS3n77bUVRFOUf//iHEhISosyYMUM5ePCgsnnzZuXDDz9UZsyYoSjK2e/iNm3aKMuWLVN27typ3H777UpsbKx7ap/t27crJpNJGTt2rLJ161Zl//79yoIFC9xT+WRmZip6vV559tlnlUOHDikLFy5UWrZsqQDK1q1bFUVRlBMnTigqlUqZMWOGkpmZqRQWFlZ4Tt98840ye/ZsZffu3cqhQ4eU2bNnK40aNVJGjhx50c+hpqYcqFXzNNVl1zI0/fvxW5SPHl2uTPvr/Kt+LCGEEPUrNCmKoiQnJythYWFKUVGR4nQ6lffff19p1aqVotPplLCwMCUpKck9Z2J5aPrxxx+VNm3auBsWtm/f7rHPDRs2KIMGDVL8/PwUX19fJTExUXn99dfd67/99lulSZMmisFgUHr27KksWrTIIzQpiqK89tprSmRkpKJSqZRRo0ZVeE7fffed0qlTJ/dxEhISlDfeeOOSP5uaCk0qRbmCaxeFW0FBAYGBge55pK6mfz8/CCX/RVCc/O3jm9Boa83MEUIIUS+VlpZy5MgR4uLiLhhbW9+tWrWK/v37k5ubW6mr2mqjS/38qvL9Ld+2dZAuxIjKaQOVGnO+xdvVEUIIIa4LEprqIL+IGAwW15VzRVnmy5QWQgghRE2Q0FQHhUW1Qm/NA6AgLdu7lRFCCFGv9evXD0VR6mzXXE2S0FQHxTaMR+0om6vphIQmIYQQ4lqQ0FQHNY5uh4M8ADJPZXm3MkIIIcR1QkJTHWTwj8BeNsFlYXaRdysjhBBCXCckNNVFajUOnat7rrRE5eXKCCGEENcHCU11lMNQCIDdUbV7EAkhhBCieiQ01VEOn2LXs8oPp8Pp5doIIYQQ9Z+EproqQIPK6QCVmmK5ca8QQghx1UloqqP0oWHorWUTXObKrOBCCCEuNHr0aFQqFSqVCp1OR1xcHM899xylpaXertoFVq1ahUqlIi8vr9LbHDx4EH9//2s2h5SEpjrKr1FTjBbXYPDCzEIv10YIIURtNWTIENLT0zl8+DDvvfce06dPZ9KkSd6u1hWz2Wzcd9999O7d+5odU0JTHRURHY+ufFbwkznerYwQQohay2AwEBkZSUxMDMOGDWPgwIGkpKS41zudTpKTk4mLi8NkMtG+fXvmzp3rXl/eArRkyRISExMxGo306NGD1NRUj+OsWbOG3r17YzKZiImJYfz48ZjNZ2/19fXXX9OlSxf8/f2JjIxkxIgRZGZmAnD06FH69+8PQIMGDVCpVIwePfqS5/V///d/tG7dmnvvvfdKP6JKk9BUR8VFt8Op5AGQK6FJCCGuOUVRcBYXX/OHoijVrnNqaipr165Fr9e7lyUnJ/PVV1/xySefsGvXLp566inuv/9+Vq9e7bHts88+y7vvvsvGjRsJCwvjtttuw2azAXDo0CGGDBnC8OHD2bFjB7Nnz2bNmjWMGzfOvb3NZmPy5Mls376dBQsWcPToUXcwiomJYd68eQDs27eP9PR0Pvjgg4uex4oVK5gzZw4ff/xxtT+L6tBe06OJGtO4UTxrVK7uueyMXC/XRgghrj9KSQn7OnW+5sdttWUzKh+fSpdfvHgxfn5+2O12LBYLarWajz76CACLxcIbb7zBsmXL6NmzJwBNmzZlzZo1TJ8+nb59+7r3M2nSJAYNGgTAzJkziY6OZv78+dx7770kJyczcuRIJkyYAECLFi348MMP6du3L9OmTcNoNPLQQw+599W0aVM+/PBDunbtSlFREX5+fgQHBwMQHh5+yTFK2dnZjB49mm+++YaAgIBKfw41QUJTHaXW6rFr8wAoLrR5tzJCCCFqrf79+zNt2jTMZjPvvfceWq2W4cOHA66B1MXFxe4wVM5qtdKxY0ePZeWhCiA4OJhWrVqxZ88eALZv386OHTuYNWuWu4yiKDidTo4cOUJ8fDybN2/mlVdeYfv27eTm5uJ0uqbLSUtLIyEhodLn8/DDDzNixAj69OlTtQ+iBkhoqsNsetfVc1ar/jIlhRBC1DSVyUSrLZu9ctyq8PX1pXnz5gB88cUXtG/fns8//5wxY8ZQVOS6FdeSJUuIiory2M5gMFT6GEVFRTz66KOMHz/+gnWxsbGYzWaSkpJISkpi1qxZhIWFkZaWRlJSElZr1abNWbFiBYsWLeKdd94BzoYzrVbLp59+6tGiVdMkNNVhdqPrl92OL06nglott1QRQohrRaVSVambrDZQq9W89NJLPP3004wYMYKEhAQMBgNpaWkeXXEVWb9+PbGxsQDk5uayf/9+4uPjAejUqRO7d+92h7Pz7dy5k+zsbKZMmUJMTAwAmzZt8ihTPs7K4XBcsh7r1q3zKLNw4ULefPNN1q5de0Hwq2kyELwOc/o5QHGCSkNJoUxwKYQQ4vLuueceNBoNH3/8Mf7+/jzzzDM89dRTzJw5k0OHDrFlyxamTp3KzJkzPbZ77bXXWL58OampqYwePZrQ0FCGDRsGwPPPP8/atWsZN24c27Zt48CBAyxcuNA9EDw2Nha9Xs/UqVM5fPgwixYtYvLkyR77b9y4MSqVisWLF3PmzBl3K9j54uPjadu2rfsRFRWFWq2mbdu2NGjQoOY/sHNIaKrDVA2CMMgEl0IIIapAq9Uybtw43nrrLcxmM5MnT2bixIkkJycTHx/PkCFDWLJkCXFxcR7bTZkyhSeffJLOnTuTkZHBjz/+6G4dSkxMZPXq1ezfv5/evXvTsWNHXn75ZRo1agRAWFgYM2bMYM6cOSQkJDBlyhR391q5qKgoXn31VV544QUiIiI8rryrLVTKlVy7KNwKCgoIDAwkPz//mo3m/88nj2D8tTsFAXHc/Gg7mnYMuybHFUKI601paSlHjhwhLi4Oo9Ho7epcU6tWraJ///7k5uZes5m3a9qlfn5V+f6WlqY6LCCqFQZLHgAFpwu8WxkhhBCinpPQVIc1im2Lxl52K5VTMleTEEIIcTVJaKrDmsW0x04eALnp2d6tjBBCiHqpX79+KIpSZ7vmapKEpjqsYYMIbBpXC1N+drGXayOEEELUbxKa6jCVSoVV57p6rqREfpRCCCHE1STftHWcTe8aAG53mlCcciGkEEIIcbVIaKrjbL4WUJwoKi0lRXIPOiGEEOJqkdBUxzkCfNBbCwEw58kEl0IIIcTVIqGpjlMFh7nnairKLfVuZYQQQoh6TEJTHWeMaIzB4rqCTm6lIoQQQlw9EprquKDGbTCWzwqeke/dygghhKhVRo8ejUqlQqVSodPpiIuL47nnnqO0tPb1TKxatQqVSkVeXt4lyx09etR9Tuc+1q9ff9XrqL3qRxBXVWxsezKcmwEoTM/xcm2EEELUNkOGDOHLL7/EZrOxefNmRo0ahUql4s033/R21a7IsmXLaNOmjft9SEjIVT+mtDTVcS0btcSmzgMgL1PuPyeEEMKTwWAgMjKSmJgYhg0bxsCBA0lJSXGvdzqdJCcnExcXh8lkon379sydO9e9vrwFaMmSJSQmJmI0GunRowepqakex1mzZg29e/fGZDIRExPD+PHjMZvN7vVff/01Xbp0wd/fn8jISEaMGEFmZibgaj3q378/AA0aNEClUjF69OhLnldISAiRkZHuh06nu9KP6rIkNNVxwT6+WLR5ABQVOr1bGSGEuI4oioLN4rjmD0Wp/px8qamprF27Fr1e716WnJzMV199xSeffMKuXbt46qmnuP/++1m9erXHts8++yzvvvsuGzduJCwsjNtuuw2bzTXVzaFDhxgyZAjDhw9nx44dzJ49mzVr1jBu3Dj39jabjcmTJ7N9+3YWLFjA0aNH3cEoJiaGefPmAbBv3z7S09P54IMPLnkut99+O+Hh4fTq1YtFixZV+zOpCumeqwcsetdYJqvdgKIoqFQqL9dICCHqP7vVyadPrr58wRr2yAd90Rk0lS6/ePFi/Pz8sNvtWCwW1Go1H330EQAWi4U33niDZcuW0bNnTwCaNm3KmjVrmD59On379nXvZ9KkSQwaNAiAmTNnEh0dzfz587n33ntJTk5m5MiRTJgwAYAWLVrw4Ycf0rdvX6ZNm4bRaOShhx5y76tp06Z8+OGHdO3alaKiIvz8/AgODgYgPDz8kve58/Pz49133+XGG29ErVYzb948hg0bxoIFC7j99tsr/blUh4SmesBict13TkGLxWzH6Hf1myiFEELUDf3792fatGmYzWbee+89tFotw4cPB+DgwYMUFxe7w1A5q9VKx44dPZaVhyqA4OBgWrVqxZ49ewDYvn07O3bsYNasWe4yiqLgdDo5cuQI8fHxbN68mVdeeYXt27eTm5uL0+nqHUlLSyMhIaHS5xMaGsrTTz/tft+1a1dOnTrF22+/LaFJXJ7dT4expBCb3p+ivFIJTUIIcQ1o9Woe+aDv5QteheNWha+vL82bNwfgiy++oH379nz++eeMGTOGoqIiAJYsWUJUVJTHdgaDodLHKCoq4tFHH2X8+PEXrIuNjcVsNpOUlERSUhKzZs0iLCyMtLQ0kpKSsFqtVTqfinTv3t1jnNbVIqGpHnAEBmHIy3WFplwLodH+3q6SEELUeyqVqkrdZLWBWq3mpZde4umnn2bEiBEkJCRgMBhIS0vz6IqryPr164mNjQUgNzeX/fv3Ex8fD0CnTp3YvXu3O5ydb+fOnWRnZzNlyhRiYmIA2LRpk0eZ8nFWDoejyue1bds2GjZsWOXtqkoGgtcD6tBI91xNMsGlEEKIS7nnnnvQaDR8/PHH+Pv788wzz/DUU08xc+ZMDh06xJYtW5g6dSozZ8702O61115j+fLlpKamMnr0aEJDQxk2bBgAzz//PGvXrmXcuHFs27aNAwcOsHDhQvdA8NjYWPR6PVOnTuXw4cMsWrSIyZMne+y/cePGqFQqFi9ezJkzZ9ytYOebOXMm//3vf9m7dy979+7ljTfe4IsvvuCJJ56o+Q/rPBKa6gFTw+Znb6WSU+LdygghhKjVtFot48aN46233sJsNjN58mQmTpxIcnIy8fHxDBkyhCVLlhAXF+ex3ZQpU3jyySfp3LkzGRkZ/Pjjj+7WocTERFavXs3+/fvp3bs3HTt25OWXX6ZRo0YAhIWFMWPGDObMmUNCQgJTpkzhnXfe8dh/VFQUr776Ki+88AIREREeV96db/LkyXTu3Jnu3buzcOFCZs+ezYMPPljDn9SFVMqVXLso3AoKCggMDCQ/P5+AgIBreux5f/yIaeIyjjS9g5btAxj0WJdrenwhhKjvSktLOXLkCHFxcRiNRm9X55patWoV/fv3Jzc395JXtdVml/r5VeX7W1qa6oGmUW1xkAdA4ek8r9ZFCCGEqK8kNNUDLcIaYdG6btpbkCdjmoQQQoirQa6eqwf8DAZKda4JLkssWpngUgghRI3p16/fFc1CXp9IS1M9UWooBMCp6LCW2L1cGyGEEKL+kdBUT1j8VGhtrsszZdoBIYS4OqTFpW6qqZ+bhKZ6wurve3auJhnXJIQQNUqnc91pobi42Ms1EdVR/nMr/zlWl4xpqiccQSEYTuVR5BeNWVqahBCiRmk0GoKCgsjMzATAx8dHxo7WAYqiUFxcTGZmJkFBQWg0VzaDu4SmekITHoNhq+sKuqLcUi/XRggh6p/IyEgAd3ASdUdQUJD753clJDTVEz7R8Rgs+wEozDJ7uTZCCFH/qFQqGjZsSHh4ODabzdvVEZWk0+muuIWpnISmeiIyrj066wYAijJyvVwbIYSovzQaTY19CYu6RQaC1xMtIppgU7vCUqHcf04IIYSocRKa6olmIeEUa/MAMEvvnBBCCFHjJDTVEwatllJDAQB2p0xwKYQQQtQ0CU31SImPHa3dNReFzNUkhBBC1Cyvhqbk5GS6du2Kv78/4eHhDBs2jH379nmU+fTTT+nXrx8BAQGoVCry8vIu2E+TJk1QqVQejylTpniU2bFjB71798ZoNBITE8Nbb711wX7mzJlD69atMRqNtGvXjqVLl9bo+V5tJX4mDGUTXMpcTUIIIUTN8mpoWr16NWPHjmX9+vWkpKRgs9kYPHgw5nMG5RQXFzNkyBBeeumlS+7rtddeIz093f144okn3OsKCgoYPHgwjRs3ZvPmzbz99tu88sorfPrpp+4ya9eu5b777mPMmDFs3bqVYcOGMWzYMFJTU2v+xK8SS2CAOzQV5clcTUIIIURN8uqUAz/99JPH+xkzZhAeHs7mzZvp06cPABMmTABg1apVl9yXv7//RSeumjVrFlarlS+++AK9Xk+bNm3Ytm0b//rXv3jkkUcA+OCDDxgyZAjPPvssAJMnTyYlJYWPPvqITz755ArO8tpxBkdgOFA+waW0NAkhhBA1qVaNacrPzwcgODi4yttOmTKFkJAQOnbsyNtvv43dfnYg9Lp16+jTpw96vd69LCkpiX379pGbm+suM3DgQI99JiUlsW7dugqPZ7FYKCgo8Hh4mzaiydn7z8ms4EIIIUSNqjWTWzqdTiZMmMCNN95I27Ztq7Tt+PHj6dSpE8HBwaxdu5YXX3yR9PR0/vWvfwGQkZFBXFycxzYRERHudQ0aNCAjI8O97NwyGRkZFR4zOTmZV199tUr1vNoCGidisLha74oyvR/ihBBCiPqk1oSmsWPHkpqaypo1a6q87dNPP+1+nZiYiF6v59FHHyU5ORmDwVCT1XR78cUXPY5bUFBATEzMVTlWZcXEtERt/w6AojNFXq2LEEIIUd/UitA0btw4Fi9ezK+//kp0dPQV76979+7Y7XaOHj1Kq1atiIyM5PTp0x5lyt+Xj4O6WJmLjZMyGAxXLZBVV8uwaPaVTXBZVOjwbmWEEEKIesarY5oURWHcuHHMnz+fFStWXNCFVl3btm1DrVYTHh4OQM+ePfn11189brCYkpJCq1ataNCggbvM8uXLPfaTkpJCz549a6RO10JsUDBmg2uMltWuxWaR4CSEEELUFK+GprFjx/LNN9/w7bff4u/vT0ZGBhkZGZSUnL13WkZGBtu2bePgwYMA7Ny5k23btpGTkwO4BnC///77bN++ncOHDzNr1iyeeuop7r//fncgGjFiBHq9njFjxrBr1y5mz57NBx984NG99uSTT/LTTz/x7rvvsnfvXl555RU2bdrEuHHjruEncmW0GjVmkw2N3TUI3CwTXAohhBA1R/EioMLHl19+6S4zadKkS5bZvHmz0r17dyUwMFAxGo1KfHy88sYbbyilpaUex9q+fbvSq1cvxWAwKFFRUcqUKVMuqM/333+vtGzZUtHr9UqbNm2UJUuWVPpc8vPzFUDJz8+v1mdRU/49ooPyxf2zlI8eXa4c35Pt1boIIYQQtV1Vvr9ViqIoXklr9UxBQQGBgYHk5+cTEBDgtXr869EbicwdQW5wPANGxdO6Z0Ov1UUIIYSo7ary/V2r5mkSV84a2OCcuZqke04IIYSoKRKa6hklpBEGS9ms4DKmSQghhKgxEprqGX1Uy7P3n8uSuZqEEEKImiKhqZ5p0DheQpMQQghxFUhoqmeaNIxDUVzdc+Z8q5drI4QQQtQfEprqmVahURTr8gAotWiw22SCSyGEEKImSGiqZxoGBFBoKkHtcLUyyQSXQgghRM2Q0FTPqNUqinw1Z6+gk2kHhBBCiBohoakeMvsaZK4mIYQQooZJaKqHSvz9z2lpKvVybYQQQoj6QUJTPWRtEOqedsAsLU1CCCFEjZDQVA8pYbFn52rKKfFuZYQQQoh6QkJTPeQT1RJjefdctkxwKYQQQtQECU31UHhs87MtTdI9J4QQQtQICU31ULOIxlhVeQCUlKhw2J3erZAQQghRD0hoqodahjWi0FiEymkDZIJLIYQQoiZIaKqHgn1MFPipz+mik2kHhBBCiCsloakeUqlUFPrq8DOfAuDUgXwv10gIIYSo+yQ01VNFfkZCsncBcCw1y8u1EUIIIeo+CU31VElAIKHZqQBkHCmgpNDq5RoJIYQQdZuEpnrKFhyJwZqPqSQNFDi2K9vbVRJCCCHqNAlN9ZQqogkADXJdrU3HdkpoEkIIIa6EhKZ6KiAqDoDIDFdoStuVjcMh8zUJIYQQ1SWhqZ6KjG6BUwWBBWkYDXaspQ7SD8pVdEIIIUR1SWiqp1qEx5DnCyoUogwHATi2U66iE0IIIapLQlM91Tw0kqwAFQCBBb8CcFTGNQkhhBDVJqGpngow6tkTZQDA58R21GrIO11MXmaxl2smhBBC1E0SmuqxPbEhAKjSnTSMLAHkKjohhBCiuiQ01WO5ce2waUBnVhOj2g7AURnXJIQQQlSLhKZ6rFNcV/ZFucY1hR79CYBTB/Kwltq9WS0hhBCiTpLQVI/d3eYGdjZxhSaOpREYosXpUDi+J8e7FRNCCCHqIAlN9VibiMbsjvUDwJxpoElDV1iSq+iEEEKIqpPQVI+pVCoscW0pNoDKqqZh4W+Aa74mxal4uXZCCCFE3SKhqZ7rFtuJXbGuLjq/PT+jM2ooKbSRmVbo5ZoJIYQQdYuEpnpuSPPu7nFNpceKiW1uBOQqOiGEEKKqJDTVc23D2rCziQaA4jN6YoOOATJfkxBCCFFVEprqOZPWhDOqKTl+gFNF+PGloIIzaYWY8yzerp4QQghRZ0houg50i+rk7qKzb99ARJMAAI6lSmuTEEIIUVkSmq4D3Rt1dIemkjQLTZprARnXJIQQQlRFtULT8ePHOXHihPv9hg0bmDBhAp9++mmNVUzUnMSwxLODwXN0xOh2AnB8by52m8ObVRNCCCHqjGqFphEjRrBy5UoAMjIyGDRoEBs2bOAf//gHr732Wo1WUFy5WP9YrA0CORECoMKwbRG+gXrsFgen9ud5uXZCCCFE3VCt0JSamkq3bt0A+P7772nbti1r165l1qxZzJgxoybrJ2qASqWiY0R7d2tT0dZdNE4MBWR2cCGEEKKyqhWabDYbBoMBgGXLlnH77bcD0Lp1a9LT02uudqLGtD+ni85ywkaTpq7lx1KzUBSZHVwIIYS4nGqFpjZt2vDJJ5/w22+/kZKSwpAhQwA4deoUISEhNVpBUTMSwxLZFavCqQJroZaIgl/RaNUUZJWSm17s7eoJIYQQtV61QtObb77J9OnT6devH/fddx/t27cHYNGiRe5uO1G7tAttR6lRzcGGrvdFq5YS1aoBIFfRCSGEEJWhrc5G/fr1Iysri4KCAho0aOBe/sgjj+Dj41NjlRM1x1/vT9PApuxssp+WpxRKduynSVIIabuyOZaaTaekxt6uohBCCFGrVaulqaSkBIvF4g5Mx44d4/3332ffvn2Eh4fXaAVFzTl36gHLSTuxje0ApB/Kp9Rs82bVhBBCiFqvWqHpjjvu4KuvvgIgLy+P7t278+677zJs2DCmTZtWoxUUNScxLJH9USpsWnCUatCmLiK4kS+KUyFtt1xFJ4QQQlxKtULTli1b6N27NwBz584lIiKCY8eO8dVXX/Hhhx/WaAVFzUkMS8SuVbEn2tXalPXLzzRp5xq4LzfwFUIIIS6tWqGpuLgYf39/AH755Rfuuusu1Go1PXr04NixYzVaQVFzmgU2w0frw/a4svvQ7TlC43au+ZqO7crG6XB6s3pCCCFErVat0NS8eXMWLFjA8ePH+fnnnxk8eDAAmZmZBAQE1GgFRc3RqDW0C2139ua9GQ4iggox+GqxmO1kHCnwcg2FEEKI2qtaoenll1/mmWeeoUmTJnTr1o2ePXsCrlanjh071mgFRc1KDEvkWASUGMFpV3Pmf98SmyBddEIIIcTlVCs03X333aSlpbFp0yZ+/vln9/IBAwbw3nvv1VjlRM1LDEtEUanY09g120T+quU0SXSFJpmvSQghhLi4as3TBBAZGUlkZCQnTpwAIDo6Wia2rAPahbYDYGOck077QHXgOLEJIajUKnJOmSnILiEgxOTlWgohhBC1T7VampxOJ6+99hqBgYE0btyYxo0bExQUxOTJk3E6ZTBxbRZiCiHaL9o9rslxRkHJTyOyqWssmnTRCSGEEBWrVmj6xz/+wUcffcSUKVPYunUrW7du5Y033mDq1KlMnDixpusoalhiWCKZDVSY/QFFxdE5M2lSdhXdUQlNQgghRIWqFZpmzpzJZ599xmOPPUZiYiKJiYk8/vjj/Oc//2HGjBk1XEVR0xLDEgHY18QAgG3dGndoOrkvF5vF4bW6CSGEELVVtUJTTk4OrVu3vmB569atycnJqfR+kpOT6dq1K/7+/oSHhzNs2DD27dvnUebTTz+lX79+BAQEoFKpyMvLq7A+I0eOJCAggKCgIMaMGUNRUZFHmR07dtC7d2+MRiMxMTG89dZbF+xnzpw5tG7dGqPRSLt27Vi6dGmlz6UuaR/musHyH81c7/VH02nQ0Af/ECMOu5MT+3K9WDshhBCidqpWaGrfvj0fffTRBcs/+ugjEhMTK72f1atXM3bsWNavX09KSgo2m43BgwdjNpvdZYqLixkyZAgvvfTSRfczcuRIdu3aRUpKCosXL+bXX3/lkUceca8vKChg8ODBNG7cmM2bN/P222/zyiuv8Omnn7rLrF27lvvuu48xY8awdetWhg0bxrBhw0hNTa30+dQVrRq0wqAxsKns3nNKHpxI3XFOF51cRSeEEEKcT6UoilLVjVavXs3QoUOJjY11z9G0bt06jh8/ztKlS923WKmqM2fOEB4ezurVq+nTp4/HulWrVtG/f39yc3MJCgpyL9+zZw8JCQls3LiRLl26APDTTz9xyy23cOLECRo1asS0adP4xz/+QUZGBnq9HoAXXniBBQsWsHfvXgD+9Kc/YTabWbx4sXvfPXr0oEOHDnzyySeXrXtBQQGBgYHk5+fXiQk+H/jfA2zN3MqXn9rxzYb8P91Co3tf4sep2/ENMjAq+QZUKpW3qymEEEJcVVX5/q5WS1Pfvn3Zv38/d955J3l5eeTl5XHXXXexa9cuvv7662pVGiA/Px+A4ODgSm+zbt06goKC3IEJYODAgajVav744w93mT59+rgDE0BSUhL79u0jNzfXXWbgwIEe+05KSmLdunUVHtdisVBQUODxqEsSQ10tggeb+gGg3baBRi2D0OrVmPMsZJ0outTmQgghxHWnWqEJoFGjRrz++uvMmzePefPm8c9//pPc3Fw+//zzau3P6XQyYcIEbrzxRtq2bVvp7TIyMggPD/dYptVqCQ4OJiMjw10mIiLCo0z5+8uVKV9/vuTkZAIDA92PmJiYSte5NigfDL6lpREA3+NZ2BSFmHhXYD0mXXRCCCGEh2qHppo2duxYUlNT+e6777xdlUp58cUXyc/Pdz+OHz/u7SpVSXloWt0wH9QKFMP2VWtk6gEhhBDiImpFaBo3bhyLFy9m5cqVREdHV2nbyMhIMjMzPZbZ7XZycnKIjIx0lzl9+rRHmfL3lytTvv58BoOBgIAAj0ddEukbSbhPOMU6BUu469egcOlsGrd13VLl9NECigus3qyiEEIIUat4NTQpisK4ceOYP38+K1asIC4ursr76NmzJ3l5eWzevNm9bMWKFTidTrp37+4u8+uvv2Kz2dxlUlJSaNWqFQ0aNHCXWb58uce+U1JS3APd66PyqQeOtXR9BgG7t+EbZCAs1h8USNslrU1CCCFEuSrde+6uu+665PqK5lC6lLFjx/Ltt9+ycOFC/P393eOHAgMDMZlc9z/LyMggIyODgwcPArBz5078/f2JjY0lODiY+Ph4hgwZwsMPP8wnn3yCzWZj3Lhx/PnPf6ZRo0YAjBgxgldffZUxY8bw/PPPk5qaygcffOBxc+Enn3ySvn378u677zJ06FC+++47Nm3a5DEtQX2TGJpIyrEUUhOCaPlrDgEZeRw5XUDjdiGcSSvk6I4sWvds6O1qCiGEELVClVqazh34XNGjcePGPPDAA5Xe37Rp08jPz6dfv340bNjQ/Zg9e7a7zCeffELHjh15+OGHAejTpw8dO3Zk0aJF7jKzZs2idevWDBgwgFtuuYVevXp5hJ3AwEB++eUXjhw5QufOnfn73//Oyy+/7DGX0w033MC3337Lp59+Svv27Zk7dy4LFiyo0qD0uqZ8XNOK8ELUWidYYdtPy2jaPgyAIzuyMOdZvFlFIYQQotao1jxN4kJ1bZ4mgBJ7CTd8ewN2xc73sxyQprD/hm7c8cVMfnhnM+kH8+mUFEvPO5t7u6pCCCHEVXHV52kS9YNJa6JlcEsAMuJdA94jDu2m1Oag4+DGAKSuPom1xO61OgohhBC1hYSm61z5JJd72rtCU0BWEev2nqJJ2xAaRPpgLXWwa80pb1ZRCCGEqBUkNF3nysc1rQ21ozE6UDng4E8pqNQqOgyKBWD78uM47E5vVlMIIYTwOglN17nyaQf25B9CF+26mDJgw88oikKrbpH4BOox51k4sPH0pXYjhBBC1HsSmq5zMf4xBBmCsDqtFCY2ASDuxEGOZJnR6NS0v8l1e5itKWnINQNCCCGuZxKarnMqlcrdRbe3o2tyUb+8Yn7degSANn2i0Bk15JwycyxVJrsUQghx/ZLQJNyDwTcFq9H721ApcHrZMgAMJi1tekcBsC0lzWt1FEIIIbxNQpNwtzTtyDuAMdYAQOyuVRRbXVMNtL8pGrVaxcn9eZw+UuC1egohhBDeJKFJ0Da0LSpUnCw6CR1bAdAy8xjrDrm64/waGGnZLQKArSnHvFZPIYQQwpskNAn89f40C2oGwIHObUCl4FNoYf2GPe4y5dMPHN56hrzMYq/UUwghhPAmCU0CONtFtz3IgLGBDYCCVSuxls3PFBLlR+O2ISgKbF923Gv1FEIIIbxFQpMAzg4G31FwBN/GRgC6nNjI/1LT3WU6lrU27VmXTkmh9dpXUgghhPAiCU0CONvStDNrJ6Yurtet04/x3crd7jKNWgYR3tgfh83JjlUnvFJPIYQQwlskNAkAmgY2xVfnS4m9hJM9umEItKG1O4n99X/sOJEHuOZ0ct/Id9VJbBaHF2sshBBCXFsSmgQAGrWGtqFtAdjpH0hIgmuw9/BDq/h61T53uaYdwwgIM1FqtrFnbXqF+xJCCCHqIwlNws09rqnwCAGD+qPzseNvLcG2eBFZRRYA1GoVHQe6bq2ybVkaTofcyFcIIcT1QUKTcCu/ee+OMztQdXuQ4PgiAO7ct4Lv1h52l2vdsyFGPx2F2aUc2nrGK3UVQgghrjUJTcKtXVg7AA7nH6YgugtBHULQGByEl+RxaPZ8bGWtSlq9hsT+0QBs/UVu5CuEEOL6IKFJuAUbg4nxd3W9pWbvRt1tFMGtzAAk7fiFn3eecpdt2zcKrU7NmbRCTu7L9Up9hRBCiGtJQpPw4J7kMms7dBxJgxYlqHVOGhee5o9ZC93lTH564m9sBLham4QQQoj6TkKT8OAeDH5mBwQ0QtNmMA2au1qbOq1ZRGrZ9AMAHQbGoFJB2u4csk4UeqO6QgghxDUjoUl4OHcwuKIo0Hm0q4tOrdA6N42fZy11lw0INdGsczgAW1OktUkIIUT9JqFJeGjZoCUGjYECawHHCo5B84FowyIJauqat6nh0u/JMZ+9hUr5rVUObMykMKfUK3UWQgghrgUJTcKDTqOjTUgbALaf2Q4aLXS8n5DWRSgq6Hh6H4vnrnCXD28cQFSrBihOhe3L5Ua+Qggh6i8JTeIC5YPBd5zZ4VrQ6S/o/ZwExrpam1TffoX9nEktOw12tTbtWnOKUrPt2lZWCCGEuEYkNIkLlIemLZlbXAuCYqH5AELKJrvsdGwbK1M2usvHJAQTEuWH3eJg128nr3l9hRBCiGtBQpO4QNeIrqhVag7mHeRE4QnXws6jMQbZMUU7UKOQ+Z/P3OVdN/J1tTZtX3ECu01u5CuEEKL+kdAkLhBkDKJzRGcAVh5f6VrYcgj4hhPeOgeAdrvXsWfHAfc2zbuE49fAQEmBlX3rM655nYUQQoirTUKTqNBNMTcB54QmjQ46jsQn1IYSqUenONj1wXR3eY1GTfsB5TfyPY7ilFurCCGEqF8kNIkK9Y/tD8Dm05vJK81zLez0AAAxrVwtSU3Xp5BzKtO9TUKvRhh8tOSdLmb376cQQggh6hMJTaJCUX5RtGrQCqfiZPWJ1a6FwU0hri9+kaUUB/thcljZ8K9P3NvojVo6JTUG4LfZBzh9tMAbVRdCCCGuCglN4qJuinV10a1IOzsvE51HoVJBbLt8AEJ/WYitsMi9uuOgWJokhuKwO/lp+k6KC6wIIYQQ9YGEJnFR5aFp7am1lNhLXAtb3wo+IYRGnCbP3x9fazEbP/rCvY1KrWLggwkERfhQlGvh5/+k4jhnTichhBCirpLQJC6qVYNWNPJtRKmjlHWn1rkWag3Q/j5UagjpZABAM++/OK1nW5QMJi23PNYOnVHDqQN5rJ130BvVF0IIIWqUhCZxUSqVyt3a5L6KDqDzaACaRaSSa/QjoCiP/d9877Ftg0hfBo5OAGDHihPs+0OmIRBCCFG3SWgSl9Q/xnUV3arjq7A77a6FoS2g8Y1o1E5snRoBkP/5FygOz0ktm3YIo8stTQBY+c1ezqQVXqtqCyGEEDVOQpO4pE4RnQjQB5BnyWNb5rZzVowCoFuz/RTqTARkp3N68U8XbN/t1jgatw3BYXPyv092UlIkA8OFEELUTRKaxCVp1Vr6xfQDYMXxc66iS7gdjEGYbKc40iYegOMfT0NRPCe1VKlVDHoogcAwE4U5pfzy2S6cMjBcCCFEHSShSVxW+ezgK9JWnA1FOhO0/zMAPboVUarR4Zd2iMLf1lywvcFHx82PtUNr0HBiby7rFhy+ZnUXQgghaoqEJnFZPRv1xKAxcLLoJPtz959dUdZFF1u0ht+bue5Vd/iDjyvcR0gjPwaOcrVIbUtJ48DG01e30kIIIUQNk9AkLstH50PPRj2B87roIhIguhsqp522/QOwqTQYdm2neOvWCvfTrFM4nYa4Zgxf8dUesk7IwHAhhBB1h4QmUSnuG/imrfRc0dnV2nQjq1gZ2wmAtI8+4WK6396U2IRg7GUDw0vNtqtTYSGEEKKGSWgSldI3pi9qlZo9OXtIL0o/u6LNnWAIQJd/FH3fBJyoUH7/lZLUXRXuR61WMWhMGwJCjRRklfLL57twOpUKywohhBC1iYQmUSnBxmA6hHUAzuui0/tCu3sAuLvhDn6Lag/A8XHjsJ85U+G+jL46bv5bIlq9muO7c/hjoQwMF0IIUftJaBKV5p4d/CJddKEnUljT/w5O+IXhyMjg+ONjcZaUVLiv0Gg/bnrANTB8y8/HOLg58+pVXAghhKgBEppEpZWPa9p0ehP5lvyzKxq2h0YdUTmsTE48yqs3/JV8vQ+lO3dy6rnnUZwVz8vUoksEHQfFArD8qz1knyy66ucghBBCVJeEJlFpMQExtGjQAofi4NcTv3quLJt+IO7YXMaN7MvkbqOxqTUUpqRw5l//uug+ewxrSnTrBtgtDpbKwHAhhBC1mIQmUSXl96LzuIEvQLu7QecL2Qf4c+RJ+tw9iPc63gtA9mefkztnToX7U2vUJP21Lf4hRgrOlLB02g6Kci1X9RyEEEKI6pDQJKqkfFzTmpNrKLWXnl1h8Id2w12vN8/g2cGt8B16K9+0GgRAxiuvYl63rsJ9Gv103Py3dmj1atIP5vPd5D/YvyHjgluyCCGEEN4koUlUSUJwAhE+EZTYS/gj/Q/PlZ1Gu553L0RtyeOde9qzN+lPrIzuCA4Hx58Yj+XQoQr3Gxbjzz0vdiW8sT+WYjspX+zm509TKSmUG/wKIYSoHSQ0iSpRqVTu1iaPqQcAojpBRDuwl8LqtzDqNPxnVFfmDRjNruAmKEVFpD3yN+zZ2RXuO7ihL8Of60y32+JQq1Uc2nqG/772B0e2Vzx1gRBCCHEtSWgSVVYemlYdX4XD6Ti7QqWCgZNcr9f/Gw6vIthXz3/+egMf9HuYdJ8Q7CdPcHzsOJyWisctqTVqug6N4+4XuhDcyJeSQhtLp+1k+czdWErsV/nMhBBCiIuT0CSqrHNEZ/z1/uSU5rD9zHbPlS0GQZeHXK8XPA4leTQN8+Pdh/sx+ca/UqgzUbptG+kvvnTRqQgAwmL9uefFLq4pCVSwd10G303+gxN7c67imQkhhBAXJ6FJVJlOraNPdB+ggqvoAAb/E4KbQsFJWPosAN3igpkwZhCvd3sAu0pNwdKlZH300SWPo9VpuGF4c+78eycCQo0U5VhY+P42fp29H5vVcclthRBCiJomoUlUS/lEl8vTll94lZveF+78FFRq2Pk9pP4AwB0dohg0cihTO9wNQNa/p5G3YMFlj9WoeRB/+r9utO0TBcDOlSeY/c8NZBzOv8yWQgghRM2R0CSq5caoG9Gr9RwvPM6hvAquiIvpCr2fcb1e/BQUnAJgbP/mBA2/i9ktXKHr1P9NpHjjxsseT2/U0ndEK24b3x7fIAP5mSX88PZm1i04hMN28W4+IYQQoqZIaBLV4qvzpUejHkAFV9GV6/scNOwApXmwcCwoCiqVitfvbMeB20eyplE7VHY7aWOfwHr0aKWOG5sQwp8ndqNV90gUBbb8dIw5UzaRdaKwRs5LCCGEuBivhqbk5GS6du2Kv78/4eHhDBs2jH379nmUKS0tZezYsYSEhODn58fw4cM5ffq0RxmVSnXB47vvvvMos2rVKjp16oTBYKB58+bMmDHjgvp8/PHHNGnSBKPRSPfu3dmwYUONn3N9Ut5FtyLtIqFJo4O7/gNaIxxaARs/A0CnUfPv+7syf8jD7G0Qg1KQz7FH/oY9N7dSxzX66hj4YAJDHm2LyV9H9ski5iRv4pfPd3FyX65MiimEEOKq8GpoWr16NWPHjmX9+vWkpKRgs9kYPHgwZrPZXeapp57ixx9/ZM6cOaxevZpTp05x1113XbCvL7/8kvT0dPdj2LBh7nVHjhxh6NCh9O/fn23btjFhwgT++te/8vPPP7vLzJ49m6effppJkyaxZcsW2rdvT1JSEpmZmVf1M6jL+sb0RYWKXdm7yDBnVFworCUMes31+peJcGY/AIEmHZ8+fCMf3fQ3TpuCsKcd4/gT41GslZ/MslnHcP48sTtx7UNxOhQObDzNgve2Muvl9Wz5+RjFBTIxphBCiJqjUmrRn+VnzpwhPDyc1atX06dPH/Lz8wkLC+Pbb7/l7rtdg4f37t1LfHw869ato0cPV/eQSqVi/vz5HkHpXM8//zxLliwhNTXVvezPf/4zeXl5/PTTTwB0796drl278lHZFV1Op5OYmBieeOIJXnjhhcvWvaCggMDAQPLz8wkICLiSj6FO+cvSv7DtzDb+0f0f/Ln1nysu5HTCN3fB4ZXQqCOMSXG1QgHbj+fx7Ns/kLziQ3zsFnx79yLsiScwJSZWqR6ZxwrYteYUBzacxmZxXVmnVqto0j6UhBsbEZMQjFqtuqJzFUIIUf9U5fu7Vo1pys93XQ0VHBwMwObNm7HZbAwcONBdpnXr1sTGxrLuvPuYjR07ltDQULp168YXX3zh0UWzbt06j30AJCUlufdhtVrZvHmzRxm1Ws3AgQMvOI7w5J4d/GJddABqNQz7NxiD4NRW+PUd96r2MUE88+gtvNHtARwqNebf1nD03j9x5E9/In/xkkq3PIU3DqD/yNaMfvNG+v+lNRFxATidCoe3nmHxR9v5+v/WsmHxEQpzSi+/MyGEEKICWm9XoJzT6WTChAnceOONtG3bFoCMjAz0ej1BQUEeZSMiIsjIONsd9Nprr3HTTTfh4+PDL7/8wuOPP05RURHjx4937yciIuKCfRQUFFBSUkJubi4Oh6PCMnv37q2wvhaLBcs5s1oXFBRU+9zrsptib+Jfm//FxoyNFFgLCNBfJKUHNIJb/wVzH4Jf33ZNghndBYDBbSI5/tCdPGHw586Dv9L/5DZKt+/g1PZnyAwLI+i+P9PgT39CGxJy2frojVoSbmxEwo2NyD5ZxO7fT7FvfQZFORY2Lj7CxiVHiE0IoU2vRjRODEGjqVV/NwghhKjFak1oGjt2LKmpqaxZs6bK206cONH9umPHjpjNZt5++213aLoakpOTefXVV6/a/uuKxgGNaRbYjEP5h/jtxG8MbTr04oXbDoe9SyF1LvzwCPztN9ecTsBDNzbBpEvizZ8a80XuUG4+up7hJ/7A98wZsj6cSva0TwgYOpQGf7kfU5s2lapbSJQfve9tSc87m3F46xl2rznFyf15pO3KJm1XNqYAPfE9I4lJCEFn0KDVq9HpNWj1rtdavUa69IQQQrjVitA0btw4Fi9ezK+//kp0dLR7eWRkJFarlby8PI/WptOnTxMZGXnR/XXv3p3JkydjsVgwGAxERkZecMXd6dOnCQgIwGQyodFo0Gg0FZa52HFefPFFnn76aff7goICYmJiqnLa9cZNsTdxaOchVqStuHRoAhj6DhxbCzmHXAPDb/0X4BqXNqJ7LEPaRvL2z/v4zuTPnJb9uSlzF2NObyDgyD7yFywgf8ECTJ06EfyX+/EfOBCVTnfZ+ml1Glp2i6Rlt0jyThezZ+0p9qzLoKTAypaf09jyc9pFt9Xo1BeEKd05ocon0IBvoB7fQAM+gXp8gwz4Bhow+elQSeASQoh6xauhSVEUnnjiCebPn8+qVauIi4vzWN+5c2d0Oh3Lly9n+PDhAOzbt4+0tDR69ux50f1u27aNBg0aYDAYAOjZsydLly71KJOSkuLeh16vp3Pnzixfvtw9mNzpdLJ8+XLGjRtX4TEMBoN7/9e7m2Jv4j87/8Oak2uwOqzoNfqLFzY1cI1v+noYbPocWt3s6qorE+yrJ/mudtzXLYaXF+7iF3V7folsz02JmTyRvxXj76so2bKFk1u2oI2MpMF99xF07z1oGzSoVF2DInzoeWdzut3elGM7stmzLp38zGJsFgd2qxO71YH9nMkyHTYnDpsTi7lqNwtWq1X4BOrPhqqyMHVusPIN0mP01aFSSbgSQoi6wKtXzz3++ON8++23LFy4kFatWrmXBwYGYjKZAHjsscdYunQpM2bMICAggCeeeAKAtWvXAvDjjz9y+vRpevTogdFoJCUlhWeeeYZnnnnG3X125MgR2rZty9ixY3nooYdYsWIF48ePZ8mSJSQlJQGuKQdGjRrF9OnT6datG++//z7ff/89e/fuvWCsU0Wu16vnAJyKk0FzBpFZksm/B/yb3tG9L7/R/16AP6aBXwQ8tg58Lxyv5HQqzNtygjd/2ktWkWtA+F2xBsYW7cCx8Acc2dkAqAwGAm4dSsDNt+DTrStq/SVCWyUoTgW73Ynd4sBmPSdMWZ1l78tel9oxF1gpzrNgzrdiznc9lxRaoZL/qtRaFb4BrgDlG2jAJ+hsy5Xrveu1wUcr4UoIIa6Cqnx/ezU0XexL4Msvv2T06NGAa3LLv//97/z3v//FYrGQlJTEv//9b3e32U8//cSLL77IwYMHURSF5s2b89hjj/Hwww+jVp8d5Ltq1Sqeeuopdu/eTXR0NBMnTnQfo9xHH33E22+/TUZGBh06dODDDz+ke/fulTqX6zk0Afxz/T+ZvW82d7e8m0k9J11+A1sJTO8LWfsg/na49yu4yO9DfomN95ft56t1x3A4FQxaNeN6NebPxfsomjWL0l273GXVPj749uqFX//++PXtg7bsSsxryeFwUlJgxZznClLFZWHKnGdxBauykFVaZKv0PjU6tbvFqkGkLyFRvoRE+RES5YfR9/JdlEIIISpWZ0JTfXK9h6a1J9fy6LJHCTGGsOLeFahVlbgq7dQ2+GwAOO1w53Rof5F5nsrszShg0sJd/HEkB4DYYB8mDo3nRks6+fPnU7RqFfYzZ85uoFZj6tABv/798O/fH32zZrWqtcZhd1JccG6YKgtZeRbM5yy/XNegb5ChLEC5glRotB9BET5otHJloBBCXI6EJi+43kOTzWGjz+w+FNmK+Prmr+kQ3qFyG/76Nqz4JxgC4LHfISj2ksUVRWHxjnReX7KHjALXnEv9WoUx6bY2NAk2UbprN0UrV1C4chWWPXs8ttXFxLgC1E034dO5c6UGkdcGdpuD4rKWqqJcCznpZrJOFJFzqoiCrIrnnVKrVTRo6ENwI1eIConyIyjChMFHh8GklUHqQghRRkKTF1zvoQnguV+f439H/seDbR/k6c5PX34DAIcdvrwZTmyAxr1g1I+uyTAvw2yx89HKg3z222FsDgWdRkXflmHc0q4hAxMiCDDqsKWnU7RqFYUrV1K8/g+PiTLV/v749S7rxuvTB01gYHVP26usJXayT5nJPll0zsOMteQSrVMqMJi0GHy0rhBV/uyrxXj+Mh8tRt+yZz8dOoOmVrXWVVdpkY28zGLyTheTe7qY/NPF5GWWAApGPx1GXz1GPx0mPx1GX93Z1+e8ry+fhRDXOwlNXiChCX46+hPPrn6WxgGN+XHYj5X/Qsk+BJ/0BpsZBr4CvZ6q9DEPnyni1R93s3r/2W45vUZN7xah3NyuIYMSIgg06XCazZjXraNwxUqKVq92DyIHQKPBp1tX/AcMxH/ATegaNqz08WsjRVEoyrWcDVEnisg6aaYwuwS71Xn5HVyCWqvC5KvD6KfH6KetMFyc+2zy06Ezeidc2K0O8jJLyDtdTF6mKxjllr2u6tWQFdFo1Rh9tRj99Jj8dfgE6vELMriujix7+AUZ8AnQo77CSVTtVgfFhVZKCm2UlD1bim3oTVr352wsexh8dDK/WB3i7qbPt6DTawgINaEzaLxdreuKhCYvkNAERdYies/ujd1pZ+EdC2ka1LTyG2/6EhZPcL2+YTwMmASays+IsS+jkCU701m6M52DmUXu5TqNihubh3JLu4YMToggyEeP4nRSumMHhStXUbRiBZYDBzz2ZWzTBv+BA/AbMABDixb1qjXBYXNiKbFjKbZRanY9W4rPeS5bVuqxzFXWYa9e4FJrVBdtrTH56c97f7YVB8Buc2IrdWAttXs+W8rfO7CV2rFaHNjKXpeUtSIV5VguWS+/BgaCInwICvchKMKHwHATGo2aErNrkH75o8Rc9lxkw2K2UVJoq9pnoQIff71HkHK9di1TqVTuIFRcaKW00EqxOxy5lpffT7GyxzP6nP08DeeFKqOvzlWfBq66GP3q37QXDrsTa4kdp1PB5K/3Sog8/4IQc57lgjGMxQUWSgovvCDEFKAnMNRIQJiJgFATgec8+wTo693Py9skNHmBhCaXx5Y9xpqTa/hLwl94rutzld9QUWDZK/D7+673cX3h7i8rnIrgcg6cPhug9p8+G6C0ahU3NA9laLtIBidE0sDXNTWBNS2NwuUrKFy+jJLNW1x1KaOLjcV/wAD8Bw7A1KEDKs31+RegoijYrU5KiqxYzHZKisqChbksTJwTLkrNZwPHuXNeVYVao0JxKlzp/04GH+05wchEUIQvQREmAsN8qv3XfEWfRUmhzfVlWPYoKnsuzrfidNbMf7FqrQoffz0mf1fLlsGkxWpxeAQ6S3HVW9DUWhW+geeEuQYGjxYzv7J5xTS6q3dhgaIoOO0Kdtu5U3u4pvqwWR3YShxYSuxYS+xYS+1nX5c9LCWOc17bcZzze6dSgU+AK6D6uM9Tf85r1/kZfC89rYfT4aT03N/9ss+9tOznX/66uNBGcb6FkiJb5ace0bjmdbNZHJdtBdXq1GfDVKiJgDAj/iEmDCYNOoMWnUHjehg16PSaKo9fdDqclBS5/jgoKbCWtXC6HsVly0oKrZSabaBSoVarUGvKHmWvVWoVao36guXqsuXgCpVOu+J6dig47U4cdgWnw4mj/P25z2Xlm3UOZ8AD8VU6p8uR0OQFEppc1pxcw2PLHsOkNfHL8F8IMgZVbQepP8DCca6uusBY+NPX0KhDtetzMLOQpTszWLoznb0Zhe7lGrWKG5qFcEu7hrSPDkJX/g86Lxdl3Rocv67CvmE9nDsOqkEwPv364TdgAH433oDWaLjgP1lFUXCai3Hm52HPy8OZn48jLw9H+XOe61nt64OpU2d8unRGd4nZ7es6m9VxttXG7PlFU2q2u75wzlvnOD9oqUBn0KA3aNAZteiNZV8IhvLX2rJ1GvRG11itwDATQRE+Xm9FUZwKJUU2jyB1frgCMPnr8fHXuQORyR2Oyrr+/PWV6uZ0OJwXhNqzn/nZ1yWFVopySyts5bgYVxekAY1WjUpVPkOICpX67PQxrotmVa71ahWuIq735WHTFYTK5z47G46uyjeRikoHF41WfXa+tEA9Tody9vMqsroCaRXrWD7JrXtC2/IJb8uOU1FgsxTbKMgqJf9MCQVZJR7PRTmlVf6ctHr12SBVHqqMGvcyh93pCkQFtrNhqBanguZdwkn6a9sa3aeEJi+Q0OSiKAr3Lr6XvTl7ebz94zzW4bGq7+T0bpg9EnIOg9YIt74PHe674rodOlPE/3ams2RnBnvSL3+DZaPdQqfM/dxwaifdTu/B31biXlei0bMjrDkGvZYQZymB1mJMpWa0xYWo7FX7a18XFYVPl86YunTBp3MX9HFNruvm9/KgpdaoXP+xV+OvZVE5Drvz7HQXeRaKcksvaC0z51mr3TVbHSq1yn2bIp1ejUanQW/UYDBp0Zu06H20GIxlr01aDCaN+7XrfdlrowbKuj7dQTX/3Ndnz7vUXPnwaPDVYvLTu7s8Xc+ucFve5Vweioy+NXs7JYfDSWF2KQVZJRScKSE/q5SCMyUU5pRis7i6p20WBzbLlYVQlQrXufjr8Qk4J7yXvfbxd3Wrg6tlyulQXA+ncs5rJ4pDKWslUlDOWaegoNGq0WhdrVEajQq1Vo1Go0atVbmey5dpXa1T5c96kwaT35VNYHw+CU1eIKHprJ+O/MSzvz5LoCGQX4b/go/Op+o7KcmDHx6GA7+43nd7BJLeAE3NTBNwJMvM0p3p/Lwrg1N5JTicCg6nglMBu9OJ0wkOxbUMQON00C77MD1PpdIzI5WwkvxL7t+q1lJi8sPu648qIBB9gyB8w4IJjAjFEByEIyub4s2bKd29G5yeX0iakBB8OrtaoUydO2Ns3brOdAsqioKzsBB7Vhb2M1mu8TydOqHS1orbXIpqUBSFUrPtbHejQ0FRyrpOFVBQUJyucuXlUcp6ucvKKYqCSqXyuBn2+fdxLF+mucJB89XhsDndM/qXByqNVn1OMCofe6e94kH914KiKDhsTneAcj9Ky1+7wpW11OE6zwCdOwyZygLR9XQxgYQmL5DQdJbD6eC2BbdxvPA4z3Z5lgfaPFC9HTmdsHoKrH7T9T72BrhnBvhf/rY2NUVRXEHKFahcIcrmcGLdvRvzpk3k2NVkoOe4Xcdhi4Z9JWr2m1WUXOL+e40CjTQKMuFj0BKkWGl8+jCxx/cRmbaXBscOoLF7/tXr9PFFSWiHtkNHTJ07ERYVgUanRaXVusKURotKq3G9Llvmfl2J6Rsqw1laWhaEzmDPysJRForsWVnYs7OxZ53BUfb+3KkdADShoQTeeiuBdw7DeM7tkoQQojaQ0OQFEpo8zdk/h9fWvUa4Tzg/3fUTuitpIdq7FH54BKyF4N8Q7v0aYrrWXGVrmMXuIC27mENnzBzOKuLwGTOHzxRxOMtMXvGluwF0Djst847TJvswbbOP0Cb7CD72S18Fdklq9dkAdYkuv0v+J+B0opRWPInmRQ/r7482NBRHbi6OvDz3ckN8PEHD7iDg1lvRhlR9kL8QQtQ0CU1eIKHJk9VhZci8IZwpOcNrN7zGnS3uvLIdZh2A70a67lWn1sEtb0OXB2umstdQjtnK4TNFnCm0UGx1UGy1Y7Y6XK8t5a/t7nXFJTaCMo4Se3w/TdMP0CQ7DYPdilpxolGcaJwONIoTrXL1x5yoDAa0oaFoQ0PRhIWWvQ5zPYeFog0JQRMahjY0BLXRCIBitVK0Zg358xdQuGoV2MpCo0aDX+/eBA4bht9N/a/4JsveoigKhRY7JVYHYX6Ga9KloSgK6fmlHM0yE+pvIDbYB6OubnTfClEbSWjyAglNF5qROoN3N79Lk4AmLLhjARr1Ff7HbimEBY/Bnh9d7zs9ALe8A1rDlVe2jnA4FY5kmdmdXsDuUwXsOpXP7lMFZBdZzgYpxYnG6XqO9NPSOsyXVqEmIv312J0KFrsTq92Jxe4oe3a9L3UoWO0OLHbXc/k6u8NJQEQIzRpHEN8okNYN/WkW5oeuimM77Lm5FCxdSv6ChZTu3Olerg4MJOCWmwkaNgxjYmK1BsErDgeOnBxX96DdgTEhvlrjwBRFoaDETrbZQo7ZSlaRlRyzlRyzhWxz+evy5RZyzTasDldg9TNoaR3pT3zDgLKHP60i/fHRV388l9OpkJZTTOqpfHadKiD1pOs5x+zZBRrub6BxiA8xwT40DvYlNsREbLAvscE+hPrJvD5VUWpzcOB0EQWlrp+tze7E5ijrlrc7XcvcDwWr3fN9+U3FTXoNRl35Q41Jp8GkO2+ZXoNRq3E/B5gu3SIsrg4JTV4goelCZpuZQXMHUWgt5F/9/sWgxoOufKeKAmv+BcsnAwpEdYF7v4LAqCvfdx2lKApnCi3sKgtSu08VsDu9gCNZ5qt2TL1GTfNwP3c4SCgLCuVzX12O5dAh8hcsJH/RIuynT7uXO6JjKegzmBNd+uIIDsG/1IxfcQEmcz7GwlwMBflo83NQ5+WiystByc7Gnp2NIyfHY34tbUQEgbffhnHobZgbxpBdZCW32BV4cs1Wcopt7tCTY3atyy5bZ6/GvEquS+orXt4kxJf4hv7ER5aFqUYBNAo0XvDlaHc4OZxldgej1JOuQFxoufBqTI1aRWywD1lFFgpLL321po9eQ2ywj/vROMSHMH8jJr3mnC9yNUad5uwXvVaNtg4MeL5SVruT/acL2XEin50n89hxIp99GYXV+h2oCSG+em5oHkqv5iH0ahFGVJDJK/W43kho8gIJTRWbunUqn+74lISQBL4b+l3N/RV1YBnMGwOleeAbBvfMhCY31sy+64kii5296a4AtetkAVlFFgw6NUatBoNOjaHs2Xjes0Hr+hItf9aoVRzLLmZPegF70gvYm15Y4Rc5QESA4ZyWlgCigozkmm1kmy3uVpvsIlerTVaRlZyCEmKO7qL/sU3ckL4To8PVfedEhQJoqjBhjBMVZpMfWocNk/XsGKz9QdEsj+nM6uiO5Bv8KrUvf4OWYD89wb56Qnxdz8G+hrOv/VzLQ/xcyzRqFYfPmN2f0e70AvakF5JVVPF4tECTzt0q5XAqpJ7KZ096AaUVTAaq16qJj/SnTVQgbRsF0qZRAK0i/THqNCiKQn6JjWPZxRzLKeZ4TjHHss2k5RSTll1MekHV5/Upp9OoXEGqrGXEpNNg1GsI9tER6mcg1N/gevbTlz27Xjfw8c4M3Jdjczg5cLrIHY5ST+azJ73Q3VJ4rmBfPeH+BnQaNTqNCp1GjV6rRq9Ru5ZpXcvd7zVqdFrXe5VKhcXuoNTqoNTmpMTmoNTmoMTmwFL2vnyZ6+Gk2GqnopwWF+pLr+ah3Ng8lJ7NQgg01dxNxsv/2LI6nASadPgZrt9WLglNXiChqWI5pTkkzU2i1FHKp4M+pWejnjW48yMw+344nQoqDSTe65qaIKpTzR1DXEBRFE7klpQFg/JHIWk5xVe033C1nQFndtH78AbiTu5zHassCBWY/Mkz+JNr8CdL78sZnZ/7fa7RnzyDH/l6X5xqDTqHjW4ZexhwfDNdT+9xj/dyqNUcbJLI4Y59yO/QncAgP4J9dDTw1RPiayDIR0dIWVAyaGtmjNCZQss5n5Hrczp0puiiLRk+eg1tGgXQplEgbaNcAal5eNW7QstZ7A5O5JaQll1MWk4xx7KLScsxk222Umpzur+4S875Ar9SGrWKYF/9eYFKj59Bh91Z1r1lV9xdWtaybi2b/dz3TuzlXWJlN+Q2al2hzahVu7u33F1dWnXZOo3HOrPFTurJfHaUtdpZKphvKtCkIzE6kHZRga7n6KAKWwKvJkVxdZvvOJHPmoNZrDlwhu0n8t1TngCoVZAYHUTvFq4Q1Sm2AXrtpX8vbA4nJ3NLOJZTTFq22R2uy38fSmxnb8+jVkGASUfgOY9z3wcYPdcFmnT4GjT4GbT4GbWYdHX3BtYSmrxAQtPFJf+RzLd7v6V7ZHc+S/qsZnduNcOPT8LOOWeXRXeFbo9Cwh2grZsDjOuiIoudfRkF7E4vdIeEM4UWd4tMsK+eED89ob4GdzgJ9TNUGFTsWVkoTifa4OAK53hyOhVKbA7MVtcgbLPFQYnNjtniwO50EuTjagkKsBThXP4LBQsXUZqa6t5eHRBAwM03E3jHHZg6drim/9lb7K4xM3vSC9ibUYhWraJNWUCKC/H1aiuNs2zMW3mQOrdFpMTqahFxjelytRy6ns++vtzVod7mb9TSLiqQdtGBJEYFkRgdSHQDU638si8otbH+UDa/H8zit4NZHD7j2d1u0mno3jSYXs1D6RgbxJlCK2k5Zo5mu0LRsRwzp/JKPYLX+dQq0KrVFba2VZVaBb56Lb4GrStMGXX4GTT46rX4GVzL/Yyu1wFGrfv/hFA/VytukMl7c0NJaPICCU0Xd6roFEN/GIpdsfPtLd/SLqxdzR/kxGbYMN11GxZn2X/cvuGuK+y6PAT+9fdWJaJyLAcPkr9w0QXjqHSxsQTecTuBd9yBPjraizWs+6x2pztUnSmykF0erAotmK0O9GVdXa7uLfXZ92XLPN5r1Oi1KrRqNXan09XVZXVQane4W8ksNgel9rLlZa/LQ57F5kSrUdGmUQDtooNIjAokNtinVnYdVsapvBLWHMzi97JHVpH18hsBRp26bDybL41DXGPaXGPbfIkKMqHXqim1OcgvsZFfYqOg7Pn8R0GJ/YL1ZoudIqu9Rm6Bo1ZR1h1ucHeBh5Z3i5/THd4oyEh0g2pMmHwJEpq8QELTpf1jzT9YdGgRA2IH8H7/96/egYoyYfMM2PQFFKa7lqm1rlanbo9CTLfym2aJ65TicFC8YQP5CxZSkJKCUny2W9H3xhsJHfs4Pp2ki1fUXk6nwr7Tha5WqANZ7M0oIDLASGyIL42DfYgN8aFxWTAK97+6U2EoiqvVt6jUTpHF1dpbZCl/7flcZLFTVOoKXzlm18UX2UUWCi5zMcO5ktpEMP0vXWr0HCQ0eYGEpks7lHeIYQuHAbDwjoU0DWp6dQ/osMGeRfDHp3B8/dnlDdu7wlPb4aAzXt06iFrPaTZTuGwZ+QsXYl633n0JnE/PHoSNG4dP585erqEQ9Z/V7nRdwVpkPW+6D0vZMle4yjFbGRAfwcRbE2r0+BKavEBC0+U9ueJJVhxfwe3Nbuf1Xq9fuwOnb4cNn8LOuWAvu6rKJwQ6jXJ13QXFXLu6iFrLeuIE2Z/+h7wffoCymy779OhB2Lix+HSp2b9shRC1h4QmL5DQdHk7zuxg5NKRaFValt61lIZ+Da9tBYpzYMtM2Pg55B93LVOpofGNEB4PYa0gtBWEtQbfUOnGu05ZT5wk+9NPyZs/3z2DuU/37q7w1LX23r5HCFE9Epq8QEJT5Yz5eQwbMjYwMn4kL3R7wTuVcNhh///gj+lw9LeKy5iCXSEqrCxEhbZ0PQc0kjB1nbCdPElWectTeXjq1o3QcWPx7dbNy7UTQtQUCU1eIKGpctaeWsujKY9i1Bj55e5faGBs4N0KndkPJza67ml3Zh+c2Qu5x7joLWz1/hBWFqDKA1VYKwiMBXX9n0H5emQ7dYqsTz8lb9454alrV0LHjcO3u4QnIeo6CU1eIKGpchRF4U+L/8SenD08mvgo4zqO83aVLmQrcd0g+My+sjC11/U65zA4L3KVh87H1RpV3s0X1tr1CGosYaqesJ06RdZ//kP+3Hko54ansWPx6d6tVs71I4S4PAlNXiChqfJ+OfoLf1/9d/z1/qTcnYKvztfbVaocu9UVnM7shaz9kLnHFaayD4DjInOmaE3ntUyVhaoGTeBKb2AsvMKWnk72f/5D3py57vBk6tgRU6eO6KOj0UVHo4uKRhcdhVovk6sKUdtJaPICCU2V53A6GLZwGEcLjvJMl2cY1WaUt6t0ZRx2yD1S1iK1FzLLWqay9oOj4nuPYQiEpn2g2U2uR4MmV69+xTmuLkiAZgNAc+EM26LqbBkZrqvt5sxxhycPKhXa8HB00dFnw1R0NProKHQxMWjDw1FJK6QQXiehyQskNFXNDwd+YNLaSYSbwvnf8P+h19TDv8gddsg7VtYitffsI+vA2akPygU3PRugmvQGYzV/hxTF1Rp2/A9IW+96PrP37PoGTaDXU9D+PtAaqn1q4ixbRgaFKcuwHk/DduIktuPHsZ486TFpZkVUOh26Ro0wtmlDwNBb8O3dW1qmhPACCU1eIKGpamwOG0N+GEJmcSaTek7i7pZ3e7tK147TAae2waEVrseJDZ5jpVQa18zl5SGqUceLd+XZrZCxoywgrYe0P8CceWG5kOZQkgvF2a73AVFw45PQ6QHQmWr8FK93iqLgyM3FduIE1uPHXWHqxAlsJ09gPX4CW3q6ey6ocuqAAPwHDSTw1lvx6dYNlUa6b4W4FiQ0eYGEpqr7atdXvL3pbWL9Y1k0bBGa63WMT2kBHF1zNkTlHPJcbwyEpv1cASr2Bsg9CmnrXK1IJzdf2Gql0UPDDhDbHWJ7Qkx317xTVjNsnglrPzx7ixnfcLhhnGuST4P/NThZAaDY7dhPn8aalkbR6l8pWLoUe+bZsKsJCyVgyM0E3joUY2KiDDIX4iqS0OQFEpqqrthWzOB5g8m35PN237cZ0mSIt6tUO+QehUMrXQHq8Gqw5F+6vKkBxPRwhaSYHq6WqUvdIsZWCttmwZr3IT/t7D56PA7dHgFTUA2diKgsxeGgeNNmCpYsofDnn3Hkn/2Z62JiCBh6C4FDh2Jo0cKLtRSifpLQ5AUSmqrn39v+zbTt02gd3Jrvb/1e/qI+n8MOp7aebYU6uck1jUFsD1cLUmwPCGlRvWkNHDbY8T389u7Z1i1DAHR72BWgfENr9lxqksPumtU957DrkXv07OuSPAhtARFtIbItRLRxXb1YE92QiuJqpctIdXWLnk6FjJ1QmAFNerluDN3qZlcIre4hrFaKfv+dgiVLKVyxwmNslKFlSwJuvZWAW25BHx115ecjhJDQ5A0SmqonrzSPwfMGU2Iv4ZOBn3Bj1I3erlLtpig1PyO50wG75rvCU+Zu1zKdj6vLruc4CKjk7W6cTrAUQGl+2SPP1aql0bkGnWsMoNV7Pmv057zWeZ6b3eKaaNQdjI6cfZ2XdvE5syqiUrvCZUQb1yOynes5IOrin6fD5roCMmPn2cfp1LPjwi5GrXN1pybcAa2Hgk9w5et5Hmfx/7d35+FRlHkewL993925L3JwBBNCIBySGHEcNUjAYwWZFZWZJwjKisEVHHUfZ1fR59lZHJ1n13F1WE/wmRFR5hkOjxExQMaRIxCIEEgCxEAISSfpQN9397t/VLqSJoF0IKQ68Pvw1FNnun9dFPQ3b1W95YRt1y5Yv/oa9u+/5zvXBADVlClQ3zwdigkToJyQB/norGt7N57PzZ3iVcVQdxnkukKhSQAUmq7c76p+hz/X/Rk3J9+MdXPWCV3OjSsY5B4v8/c3uNYtgAs1U3/JtdS4zOGBKGxs4a7NulRP6pGSKLiAJZZyF65f7vWkSu5uwLix3cMYIHYMoIzh7hhsPwa0H+VahVzn+38NZQzXIhUKUz5nT0DqrO+//y2RhGvJSpnU3Zo1iQtGJ7YDx7f2BM/QtmNuBybOA3Lvu6rWu4DZDOuOHbB+9TWc+/dzAbp3WWo1lDfdBGXeBD5IKW4aH35HXjAImE8DlnPc3xkfcq3d0+Ze05bw6dC+EMuAmExu3/NDVs+00nDFn5FEyOvgjtHWGqCthvslImE8dy1j2lQgKY/7ZYREhEKTACg0XTmjw4i5f50Lf9CPP839E6YkTRG6pBsbY0BjBVD5BndH3mBJldwXp9LATQf9XKtRwNs99nB3/QW8AAtc/rXkWi4MhYJRbK9pXWpkpyUZA+ztXHhqr+0OU7VcK9JArVUKfU8wSukeJ064/DVjnSe48HR8KxfaQkTi7lN484AJ9wPapIFrvwRfewfslbvhPn4c7ro6eBpOgLndfTeUSKAYFQ9lshxKnRNKWQsUWgsksmv4374qlgtPMVl9g5U+nb7MB4sPSId7QpLpBMCCl/4ZiZwLTmlTgbQpXJgSKkh5HRc9YaGBW6ZJADSJ3Fjdazo0r9AN23M+KTQJgELT1Xn5h5ex+dRm3DbqNvyx5I90bVM0YAw48wNQvZ47VaWK6QlDSgPXSqO8eJnh8oHiYsFA3yAVCleh/0Sv1bHg93D/gYdCVMdxrgf3lEk9ISkm6+rev6sROL6FC1BtP/ZaIQKyZnKn8DJvARRa7rmGCi0XNAf5nszeBW91BdzVP8BdVwf36TZ42r0IePsPlTKDCKpUJZTpOqiyEqAYnQKJIa7n71Ch757Wd093z8vU3PVbF073DOYzPdOOzgEqFQHaZCAmAzBkAIZ0rtXKkM7Nx2QI31LFGOAwcX8HCt3w9mfmsXMBqa1m4ICkTekJRbGjuZbR1houXLnNfbeXyLnW1NQp3M+kTeV+ARiqIOW6wP3CwD/Hs3sI3WwyWBJFT4jSJHYHqwRg1DQgf8HQ1NyNQpMAKDRdnSZLE+ZvnY8AC+A3Rb/BI7mPCF0SIUPrfBNQtw04tgVoPXTp7USS8BAl13Atbgpd91jLjcVS7ovSeIQ7PXMRxgB/IBZuNgZuRwzcJgZ3cxf8HaZ+3lME+bixUE3Mh3LSJKjyJ0IxYQLEikEGBo+9V4g60zdcXdw9Rn8U+p4AFQpTmkTuFKgqrnscyw1Xcm1VwAdYW7kbCcxnu8fNveZbwnvylyi4fa/Q9YRIhf6iZbqe5TIld/2Xz8k9x9Ln6B67uBYWn6t7nbNn2ts9b2lBv6ekewekUOjRpfT/+Rjj9nUoQIUC2KWClD6N+2VBquBulpAquOAuVfQslyq5z8UvVwIQhT9Wyt5+6X2uTuh+jFQOkJDDBWOniQvZji5uzM+buH1xKfkLgF98dOn1V4BCkwAoNF29j499jN8f/D2kYinWz1mPgsQCoUsi5NowNwPHt3Eh6sJpLmz4HFf3mjGZQMrk7lay7rEhvU+rlf/CBe603tFauGqPwl17DH6jse/rSaVQjB8PVX4+lPn5UE3Kh2L8eIhksiurjzHuInpzMxcOeoeW0PSlrj27FKXhoiAVFx6uROK+72Vru/ypLQCACFd9fd6VGkxAihRj3HEWClChMOUeoDuTwdKP6glGvUOSJn5wr+N1cOHJaeLGoTDl6OSO7YKFQ1o2hSYBUGi6eowx/Lry19hxZgeS1En4/L7PEa8a5D82QkaqYID7svDauRDltXHzHnv3MluvdXbu9GL8uO6AlH9V3Rz4Ozvhqq3lgtQxbhw43zfAiORyqKZOhaa4GJpbi6GcOHFoey73OriQYz7LndaxtHCDw8QFKud57oaEgfouG4hEHn5K0JDZc8owJqP7rkoxt889Nu5i+NC029LPsu4L5j1WrpVJpuJOZcpUgFzdMy27eFrFtSTKVFyrTkzG1QekSIWClKOTa/Hye7iWQH7wXGJ597qAj7tOLTGXC0YJ46/88U8Co9AkAApNQ8Phc+CRrx5Bk6UJhSmFePfudyEV0wNmCRlOjDH429rgOloLd21Pi1TQZgvbTqzXQ1NUBM2txdAUF0OWlTU81yMGfFx44oPUhV7TvZYFfIBhVN9wpEm6sr7NyHWJQpMAKDQNnZ/MP+Hhrx6Gy+/CkvwlWDV9ldAlEXLDY8EgvKdPw7F3Lxx798K5v6pPiJKmpXa3Qt0KzS23QBpPLcUk+lFoEgCFpqH1zelv8Hzl8wCAN+98EyWZJQJXRAjpjfn9cB87xoWoPXvhPHw4rPNNAFDk5vKn8hQ5OWBeL4JOJ5jLhaDLhaDThaCre97ZvSw073Ij6HKBud2Qjx0LTVEhVFOnQqyiB0yToUWhSQAUmobe6wdex5+O/wlamRYb79uILH2W0CURQi4h6HTCWV0Nxx6uJcpTXz/0byKTQVUwGZrCIqiLiqCaUjD4O/wIuQiFJgFQaBp6vqAPj29/HIc6DiE7Jhuf3PMJ1DK10GURQiLg7+qCY9++7paoPfC3tkGkVEKsUkGsUkGkVkGsUvedVyohVqsgUnHzIqmEa9HaX9XnLr/QhenqokJoioqgmjQJIjl1nkkGh0KTACg0XRudzk489OVDMLlMuGfMPXjtZ69Rx5eEjEAsGLyqZ+MxxuBrboZj/34491fBUbUfgc7wPqdEKhXUU6dCXVQETVEhlPn5EEmj+0aSoNcL1+EaOKsPQpacAv3994U/+oZccxSaBECh6dqpbq/G0u1LEWABvFj4Ih6d8KjQJRFCBMYYg7epCc79++HYXwVnVVXfbhLEYoiVSq7VSqEIHyuVXMuXUgmRSgmxQgmxSgmRUgWxSglZWhoUubmQZw3tg5BZMAh3XR2c+/Zx14JVV4c9AkealIS4xYsRu/AhiDWaIXvfoeL56SfYvt0B54EDAGNcv10yKUQyWfggvWi+9zYqFaSxsZD0HvT6oe2+YhAoNAmAQtO11bvjy3Wl6+j5dISQMCwYhOfUKTj3V8FZtR+OqgMIWq6+88bQg5AVuTlQ5k6AckIuFDfdFPEF6Ywx+M6e5a712rcPzn37EDCbw7aRJCZAffPNcB06DH8717O2xGBA7C9/idhfLoI09sr74LpajDG4jx+HbccO2HZ8B29j47V5I5EIEr0+PEjFxnDhKiaGXyZLT4fyppuG9K0pNAmAQtO1xRjDc5XP4dsz31LHl4SQAbFgEH6TCcztRtDlBvN0j90uBN2e8LHLjaDHDdY9Djoc8J4+A09DA5jH0/fFxWLIs7K4ANUdpJS5uZAmJgIIv57LuXcffOfOhf+4RgN1YSF3Z2HxLZBnZ0MkEoF5vbB88QW63nsf3jNnAHChLXbhQsQtXgxZ8pU/5HkwWDAIV00NbN/ugG3HjvD6ZTJoim+B9o47INHpwLw+MF+vwe8H83n5efj9Peu6tw06HAiYzfCbLyBwwYyg1RpxbbrZs5H+1h+G9PNSaBIAhaZrjzq+JIQMJ+b3w3vmDNz19fDU18NdVw93fT0Cpn6e3wdAEh8PSUxM39YYmQzqggKoby2G5pZiqCblX/ZxNCwQgO3bb2F673146uoAACKZDIb58xH/+FLIMzOH7DPy7+nzwVFVxbUoVVSEXS8mUiqh/dnPoJt9N7Q//zkkQ/wdx3w+BCwWBMxmBC5cgP8CF6YCFy5wg7l7mdkCzcxbkfTMM0P6/hSaBEChaXj8ZP4Jj3z1CJx+J3V8SQgRhL+zE+76Brjr6+Cpq4e7oQHepiYg2PNMu959VKmnT4dYPfg7fxljcHz/PUzvvQfXwWpuoVgM/dy5iF/2BJQ5OVf1OQJ2B5z793EtSrt3h53OFOt00N55B3R33w3tbbdd1/1jUWgSAIWm4bP99HY8V/kcAODNO95ESRZ1fEkIEVbQ5YLn5En4TV1QFUwe8t7QnQcPwvTee3D8/Xt+mfaOOxC/bBnU06byyxhj3Kmvzs6wIWAycdMdPcuCTmfYe0ji4qArKYFu9mxoigpvmO4bKDQJgELT8Ap1fKmRabDx3o0YbRgtdEmEEHLNuY8fh+n992H7Zjv30F0Ayvx8QCzmwpDJ1Kdn9suRpqZCd/cs6O++G6pp0wS7g01IFJoEQKFpeA2m40u7145z9nNosbfgnK17bD+Hc7ZzaHW0Ii8+D7//+e+RoEoY5k9BCCFXxtPUhK4PP4Rl67Z+Q5IkJgbSxARIExP7DJKE0PIkSLTR163BcKPQJAAKTcOvd8eXc0bPwbzseWixtfQEJPs5nLOfg8Uz8G3HYwxj8MHsD5CkHp67UwghZCj4jEY49u6DRKcNC0XUQWbkKDQJgEKTMHp3fHk5sYpYjNKOwijdKG6sHYV0XTpUUhVe+PsLMDqMyNJn4YPZHyBFkzJM1RNCCBEahSYBUGgSzqYTm/D24bcRp4xDujYd6bp0PhiFQpJGdukm6BZbC5ZuX4pWRyvSten4sPRDpGnThvETEEIIEQqFJgFQaBrZ2uxtWLJ9CVrsLUjTpOGD0g+QocsQuixCCCHX2GC+v4fugTqEjGCp2lSsm7MOWfostDpa8dg3j6HZ2ix0WYQQQqIIhSZCuqVoUrCudB3GGMag3dmOx755DE2WJqHLIoQQEiUEDU1r1qzBjBkzoNPpkJSUhHnz5qGhoSFsG7fbjfLycsTHx0Or1WLBggVo736gYUhzczPuvfdeqNVqJCUl4fnnn4ff7w/bZvfu3Zg2bRoUCgWys7Oxfv36PvW88847GD16NJRKJYqKilBVVTXkn5lEt0R1Ij4q/QjZMdnocHXgsW8eQ6P5Gj2gkhBCyIgiaGiqrKxEeXk59u3bhx07dsDn82H27NlwOBz8NqtWrcIXX3yBTZs2obKyEq2trXjwwQf59YFAAPfeey+8Xi/27NmDjz/+GOvXr8fLL7/Mb9PU1IR7770Xd955J2pqarBy5Uo8/vjj2L59O7/NZ599hmeffRarV6/GoUOHUFBQgNLSUnR0dAzPziBRI0GVgA9LP0RObA663F1Ysn0JTlw4IXRZhBBChMaiSEdHBwPAKisrGWOMmc1mJpPJ2KZNm/ht6urqGAC2d+9exhhjX3/9NROLxcxoNPLbrF27lun1eubxeBhjjL3wwgts4sSJYe+1cOFCVlpays8XFhay8vJyfj4QCLC0tDS2Zs2aiGq3WCwMALNYLIP81CRamd1m9s/b/pnlr89nt316G6vrqhO6JEIIIUNsMN/fUXVNk6X7YYFxcXEAgOrqavh8PsyaNYvfJjc3F5mZmdi7dy8AYO/evZg0aRKSk5P5bUpLS2G1WnHs2DF+m96vEdom9BperxfV1dVh24jFYsyaNYvf5mIejwdWqzVsINcXg8KAD0o/wKSESTB7zFi6fSmOdR0TuixCCCECiZrQFAwGsXLlSsycORP5+fkAAKPRCLlcjpiYmLBtk5OTYTQa+W16B6bQ+tC6y21jtVrhcrlgMpkQCAT63Sb0Ghdbs2YNDAYDP2Rk0O3p1yO9XI93734XBYkFsHqteGL7EzjSeUTosgghhAggakJTeXk5amtrsXHjRqFLiciLL74Ii8XCD2fPnhW6JHKN6OQ6vHv3u5iWNA02nw3LdizD4Y7DQpdFCCFkmEVFaFqxYgW+/PJL7Nq1C+np6fzylJQUeL1emM3msO3b29uRkpLCb3Px3XSh+YG20ev1UKlUSEhIgEQi6Xeb0GtcTKFQQK/Xhw3k+qWRabB21loUphTC4XPgX3b8Cw4aDwpdFiGEkGEkaGhijGHFihXYvHkzdu7ciTFjxoStnz59OmQyGSoqKvhlDQ0NaG5uRnFxMQCguLgYR48eDbvLbceOHdDr9cjLy+O36f0aoW1CryGXyzF9+vSwbYLBICoqKvhtCFHL1Hi75G0UpxbD5Xdh+XfL8buq32FX8y5YvXRNGyGEXO8EfYzKU089hQ0bNmDr1q3IycnhlxsMBqhUKgDA8uXL8fXXX2P9+vXQ6/V4+umnAQB79uwBwHU5MGXKFKSlpeH111+H0WjEr371Kzz++OP4r//6LwBclwP5+fkoLy/HkiVLsHPnTvzrv/4rvvrqK5SWlgLguhwoKyvDu+++i8LCQrz55pv4/PPPUV9f3+dap/7QY1RuHJ6AByt3rcQ/zv2DXyYWiZEbl4vClELMSJmBaUnToJVrBaySEEJIJEbMs+dEIlG/y9etW4fFixcD4Dq3/PWvf41PP/0UHo8HpaWl+OMf/xh22uzMmTNYvnw5du/eDY1Gg7KyMrz22muQSqX8Nrt378aqVatw/PhxpKen46WXXuLfI+Ttt9/GG2+8AaPRiClTpuCtt95CUVFRRJ+FQtONxRf0YWfzTuxv248DxgM4bT0dtl4sEiMvLg8zUmegMKUQ05KmQS1TC1MsIYSQSxoxoel6QqHpxtbh7MAB4wF+aLaFP7dOIpJgYsJEviUqU5cJh88Bm9cGm9cGu8/Oj+1eO2w+Gzf22vhpu9cOV8CF/Ph8lGSW4K7Mu5CoThToExNCyPWBQpMAKDSR3owOIw4YD6DKWIUDxgM4Zz835O8hggiTEyejJLMEJZklyNRnDvl7EELI9Y5CkwAoNJHLabW38gHqoPEgutxd0Mq00Ml10Mq00Mp7pnVyHTcvCx9r5VqIIcae1j3Y2bwTR0zh/UWNjx3PB6ic2JxLnv4mhBDSg0KTACg0keHW7mjHzrM7UdFcgYPGgwiwAL9ulHYUH6AKEgsgEUsErJQQQqIXhSYBUGgiQrJ4LKhsqcR3Z77DntY98AQ8/Lo4ZRzuzLgTt6bdihRNCpLUSYhXxUMmlglYMSGERAcKTQKg0ESihdPnxJ7WPahorkDl2UrYfLZ+t4tTxiFRlYhEdWKfcZIqCYnqRApX5IYRCAaw37gfW09txa6zu5CoSsS87Hm4f9z9SNH038kxuT5QaBIAhSYSjXwBHw4YD6CiuQJ15+vQ6eqEyWmCn/kj+nkRRIhVxkItVUMukXODWN7/tEQOmVgGhUTBr1NKldDINNDJdfw4dN2WRqaBRqaBWBQVDyYgN6jTltPY1rgN2xq3od3Z3me9WCRGcWox5mXPw52Zd0IhUQhQ5aUxxuDyu6hLk6tAoUkAFJrISBFkQZg9ZnQ6O9Hh7IDJZUKHswOdrk50Oju58SDD1ZUSQQSNTMNd6C7T8hfFa2VaJKmTkKHLQIYuA+m6dKRp0iCTUKtXNGOMwc/88AV88AW5wR/k5kUiEQwKA7QyreA3Kdi8Nmw/vR1bT21FTWcNv1wv12PumLm4b+x9OGM9g82nNqO6vbrP+vnj5yMvLk/Qz8EYQ2VLJd4/8j6OmI4gSZ2EKYlTMCVpCqYkTkFuXC79e4kQhSYBUGgi15sgC+KC+wJMLhNcfhd8QR88AQ+8AS+8QS837h4uXucLcPNuv5vre6q7/6ne/VD5g4MLZGKRGCnqFD5EpevSw0KVXk7/7oaK2+9Gq70VLfYWnLOfwznbObTYW9DmaIPH7+EDET/0CkkDkYgkMCgM0Mv1MCgM3CDnxnqFnp/uvTxBlXDVLSmBYABVxipsObUFFc0V/HV/YpEYM9Nm4oHsB3BHxh19WpLOWs9iS+MWbGvcBqPDyC8fHzse88bNw33j7kOcMu6qahvs59hxZgfeP/o+Tlw4ccntFBIF8hPy+SBVkFiAWGXssNU5klBoEgCFJkIixxiDN+jlOvT02rmOPkOdeHZ39NnmaMNZ21m02FrQYmuBO+C+7GsaFAZkaDOQoc9ATmwOJsRNQG587rB+oQ0Xq9eKJksTAEAqlkImlvHji6dD871bRQLBANqd7ThnP4cWGxeMWuwtOGc7h3P2c+h0dQ5ZraEaAiwQdoPCYIVaH/sbktXJ3A0Oyvg+d4qesZ7B1lNb8cVPX4SFnnGGcXgg+wHcN/a+iDqJDV3ztOUkF7q8QS8AQCqS4vb02zEvex5uS7/tml0D6Av68GXjl/io9iP+CQRqqRoLcxfioZseQpujDT92/oiajhrUdNbA4rH0eY3R+tEoSCzgW6PGxoy96tPjQRbk/81aPVZYvd1D97TNawubt3qtUElVmBg/EfkJ+ZiUMAkpmhRBW+0oNAmAQhMh1w5jDCaXCS32Fpy1neXDVGj6vPv8JX82SZ2EvLg85MbnIjcuFxPiJiBVkyr4KaJIWb1W1HXV4VjXMRzvOo7jXcdx1nZ20K8jFUkhk8ggFUnhCrgGbOnTyrQYpR3FDbpRSNemI02bBo1MEx7OJLL+w1r3e/Xez26/G1avFRaPhRu8Flg9Vn7a4rHA7DFzy3rNu/yuiD6jWCRGgjKBD1Pn3ef7Pf02L3seJsZPvOJjwOKx4Jumb7Dl1BbUdtXyy+OV8bhn7D24JfUWTEmaMiStn26/G5tPbca62nVoc7Txn+OXE36JRyc8CoPC0OdnGGM4bT2Nmo4aPkg1Whr7bKeT6ZCmTQML/emOA4yxPssA8PMMDEEW5J9iEGTBq/qMcco4TEqYhPyEfG6Iz0eMMuaqXnMwKDQJgEITIcJx+px8kGqyNqH+fD3quur6PM4mxKAwIDeWC1G58VyQGq0fHdZK4Q/64fa74Q64uXHv6YvG/qAfOrkOsYpYGJQGxChiEKuIhUqqGtQX82ACUoomBVKRNOwaIn/QH/FpMqlY2hOKtKOQrkvnxlpubFAYoiZYOnwOtDvb0eHs6Hdod7ajy9UV1ldZyECn367WyQsnseXUFnz505dh4V0EEbJjszEtaRqmJk3FtKRpSNWmRvy6Dp8Dnzd8jo+PfYwudxcALpSVTSzDQzkPQSPTDKpOi8eCI51HUNNZg5qOGhw1HY04jEZCIVFAJ9dBL9dzg0LPT/PLu5dZPBYcNR1FrakWJy+c7PfayQxdBh+gJiVOQm5cLlRS1ZDV2xuFJgFQaCIk+ti9djRcaOBDVP35ejSaG/v9T1olVUEj0/BhaLDXXPVHJpYhRhGDGGUMN1bEwKAwcOFKwYWr8+7zfEi6VEAapR2FvPg8TIyfiLz4POTF5/XbwhDCGEOABcKCFB+oAj4opUokqhKvq05PA8EAzrvP8yGqw9kBACjJLBmWZzT6gj583/I9dp3dhcMdh3HGeqbPNimaFExLmsYFqeSpyI7J7nN6zOKxYEPdBvy57s+weq0AgFRNKh7Lfwzzs+dDKVUOSb3+oB8nLpyA2W3mFoi4oCcSicD/6Q7NInSPRaKwbXqHoSsNo26/G/Xn61FrqkVtVy1qTbX97juJSILsmGyUZJVgecHyK3qvS6HQJAAKTYSMDN6AF6fMp1DXVYe681yQOnHhxGV/61ZKlFBKuwfJRWOpElKRFFavFWaPmRvcZv6al8EKBaTeIelyAYlEJ5PLhJqOGhzqOITD7YdRd76uT0uYTqbDlKQpmJY8DfkJ+djTugef1X8Gp98JgLsGaUn+Etw37r4bqr80i8eCY13HUGuq5VukTC4TAOCfxv0Tfnvbb4f0/Sg0CYBCEyEjVyAYQLOtGZ6Ap08oUkgUgz5NFeo7x+Kx4ILnAsweM399jtlt5sOVxWOBWqbmQ1JeXN6wXstBho/T58RR01Ecaj+EQx2H8GPnj5cM6jfF3oQnJj+BuzPvvq5aA68UYwztznbUmmqRoErAlKQpQ/r6FJoEQKGJEEJIpPxBPxouNOBw+2Ec6jiEo6ajSNOkYUn+EtyefnvUXE92I6DQJAAKTYQQQsjIM5jvb3p+ASGEEEJIBCg0EUIIIYREgEITIYQQQkgEKDQRQgghhESAQhMhhBBCSAQoNBFCCCGERIBCEyGEEEJIBCg0EUIIIYREgEITIYQQQkgEKDQRQgghhESAQhMhhBBCSAQoNBFCCCGERIBCEyGEEEJIBCg0EUIIIYREQCp0AdcLxhgAwGq1ClwJIYQQQiIV+t4OfY9fDoWmIWKz2QAAGRkZAldCCCGEkMGy2WwwGAyX3UbEIolWZEDBYBCtra3Q6XQQiURD+tpWqxUZGRk4e/Ys9Hr9kL729YD2z8BoHw2M9tHAaB9dHu2fgUXjPmKMwWazIS0tDWLx5a9aopamISIWi5Genn5N30Ov10fNQRaNaP8MjPbRwGgfDYz20eXR/hlYtO2jgVqYQuhCcEIIIYSQCFBoIoQQQgiJAIWmEUChUGD16tVQKBRClxKVaP8MjPbRwGgfDYz20eXR/hnYSN9HdCE4IYQQQkgEqKWJEEIIISQCFJoIIYQQQiJAoYkQQgghJAIUmgghhBBCIkChKcq98847GD16NJRKJYqKilBVVSV0SVHjlVdegUgkChtyc3OFLktQf//733H//fcjLS0NIpEIW7ZsCVvPGMPLL7+M1NRUqFQqzJo1CydPnhSmWIEMtI8WL17c57iaM2eOMMUKYM2aNZgxYwZ0Oh2SkpIwb948NDQ0hG3jdrtRXl6O+Ph4aLVaLFiwAO3t7QJVPPwi2Ud33HFHn+PoySefFKji4bV27VpMnjyZ78CyuLgYf/vb3/j1I/n4odAUxT777DM8++yzWL16NQ4dOoSCggKUlpaio6ND6NKixsSJE9HW1sYP//jHP4QuSVAOhwMFBQV45513+l3/+uuv46233sL//d//Yf/+/dBoNCgtLYXb7R7mSoUz0D4CgDlz5oQdV59++ukwViisyspKlJeXY9++fdixYwd8Ph9mz54Nh8PBb7Nq1Sp88cUX2LRpEyorK9Ha2ooHH3xQwKqHVyT7CACeeOKJsOPo9ddfF6ji4ZWeno7XXnsN1dXVOHjwIO666y488MADOHbsGIARfvwwErUKCwtZeXk5Px8IBFhaWhpbs2aNgFVFj9WrV7OCggKhy4haANjmzZv5+WAwyFJSUtgbb7zBLzObzUyhULBPP/1UgAqFd/E+YoyxsrIy9sADDwhSTzTq6OhgAFhlZSVjjDtmZDIZ27RpE79NXV0dA8D27t0rVJmCungfMcbYz3/+c/bMM88IV1SUiY2NZR988MGIP36opSlKeb1eVFdXY9asWfwysViMWbNmYe/evQJWFl1OnjyJtLQ0jB07FosWLUJzc7PQJUWtpqYmGI3GsGPKYDCgqKiIjqmL7N69G0lJScjJycHy5cvR1dUldEmCsVgsAIC4uDgAQHV1NXw+X9hxlJubi8zMzBv2OLp4H4V88sknSEhIQH5+Pl588UU4nU4hyhNUIBDAxo0b4XA4UFxcPOKPH3pgb5QymUwIBAJITk4OW56cnIz6+nqBqoouRUVFWL9+PXJyctDW1oZXX30VP/vZz1BbWwudTid0eVHHaDQCQL/HVGgd4U7NPfjggxgzZgwaGxvxm9/8BnPnzsXevXshkUiELm9YBYNBrFy5EjNnzkR+fj4A7jiSy+WIiYkJ2/ZGPY7620cA8OijjyIrKwtpaWk4cuQI/u3f/g0NDQ3461//KmC1w+fo0aMoLi6G2+2GVqvF5s2bkZeXh5qamhF9/FBoIiPW3Llz+enJkyejqKgIWVlZ+Pzzz7F06VIBKyMj2cMPP8xPT5o0CZMnT8a4ceOwe/dulJSUCFjZ8CsvL0dtbe0Nf63g5VxqHy1btoyfnjRpElJTU1FSUoLGxkaMGzduuMscdjk5OaipqYHFYsFf/vIXlJWVobKyUuiyrhqdnotSCQkJkEgkfe4oaG9vR0pKikBVRbeYmBjcdNNNOHXqlNClRKXQcUPH1OCMHTsWCQkJN9xxtWLFCnz55ZfYtWsX0tPT+eUpKSnwer0wm81h29+Ix9Gl9lF/ioqKAOCGOY7kcjmys7Mxffp0rFmzBgUFBfjDH/4w4o8fCk1RSi6XY/r06aioqOCXBYNBVFRUoLi4WMDKopfdbkdjYyNSU1OFLiUqjRkzBikpKWHHlNVqxf79++mYuoyWlhZ0dXXdMMcVYwwrVqzA5s2bsXPnTowZMyZs/fTp0yGTycKOo4aGBjQ3N98wx9FA+6g/NTU1AHDDHEcXCwaD8Hg8I/74odNzUezZZ59FWVkZbr75ZhQWFuLNN9+Ew+HAY489JnRpUeG5557D/fffj6ysLLS2tmL16tWQSCR45JFHhC5NMHa7Pew32aamJtTU1CAuLg6ZmZlYuXIl/vM//xPjx4/HmDFj8NJLLyEtLQ3z5s0Truhhdrl9FBcXh1dffRULFixASkoKGhsb8cILLyA7OxulpaUCVj18ysvLsWHDBmzduhU6nY6/zsRgMEClUsFgMGDp0qV49tlnERcXB71ej6effhrFxcW45ZZbBK5+eAy0jxobG7Fhwwbcc889iI+Px5EjR7Bq1SrcfvvtmDx5ssDVX3svvvgi5s6di8zMTNhsNmzYsAG7d+/G9u3bR/7xI/Tte+Ty/vd//5dlZmYyuVzOCgsL2b59+4QuKWosXLiQpaamMrlczkaNGsUWLlzITp06JXRZgtq1axcD0GcoKytjjHHdDrz00kssOTmZKRQKVlJSwhoaGoQtephdbh85nU42e/ZslpiYyGQyGcvKymJPPPEEMxqNQpc9bPrbNwDYunXr+G1cLhd76qmnWGxsLFOr1Wz+/Pmsra1NuKKH2UD7qLm5md1+++0sLi6OKRQKlp2dzZ5//nlmsViELXyYLFmyhGVlZTG5XM4SExNZSUkJ+/bbb/n1I/n4ETHG2HCGNEIIIYSQkYiuaSKEEEIIiQCFJkIIIYSQCFBoIoQQQgiJAIUmQgghhJAIUGgihBBCCIkAhSZCCCGEkAhQaCKEEEIIiQCFJkIIuUZEIhG2bNkidBmEkCFCoYkQcl1avHgxRCJRn2HOnDlCl0YIGaHo2XOEkOvWnDlzsG7durBlCoVCoGoIISMdtTQRQq5bCoUCKSkpYUNsbCwA7tTZ2rVrMXfuXKhUKowdOxZ/+ctfwn7+6NGjuOuuu6BSqRAfH49ly5bBbreHbfPRRx9h4sSJUCgUSE1NxYoVK8LWm0wmzJ8/H2q1GuPHj8e2bduu7YcmhFwzFJoIITesl156CQsWLMCPP/6IRYsW4eGHH0ZdXR0AwOFwoLS0FLGxsThw4AA2bdqE7777LiwUrV27FuXl5Vi2bBmOHj2Kbdu2ITs7O+w9Xn31VTz00EM4cuQI7rnnHixatAjnz58f1s9JCBkiQj8xmBBCroWysjImkUiYRqMJG377298yxrgn1T/55JNhP1NUVMSWL1/OGGPsvffeY7Gxscxut/Prv/rqKyYWi5nRaGSMMZaWlsb+/d///ZI1AGD/8R//wc/b7XYGgP3tb38bss9JCBk+dE0TIeS6deedd2Lt2rVhy+Li4vjp4uLisHXFxcWoqakBANTV1aGgoAAajYZfP3PmTASDQTQ0NEAkEqG1tRUlJSWXrWHy5Mn8tEajgV6vR0dHx5V+JEKIgCg0EUKuWxqNps/psqGiUqki2k4mk4XNi0QiBIPBa1ESIeQao2uaCCE3rH379vWZnzBhAgBgwoQJ+PHHH+FwOPj1P/zwA8RiMXJycqDT6TB69GhUVFQMa82EEOFQSxMh5Lrl8XhgNBrDlkmlUiQkJAAANm3ahJtvvhm33XYbPvnkE1RVVeHDDz8EACxatAirV69GWVkZXnnlFXR2duLpp5/Gr371KyQnJwMAXnnlFTz55JNISkrC3LlzYbPZ8MMPP+Dpp58e3g9KCBkWFJoIIdetb775BqmpqWHLcnJyUF9fD4C7s23jxo146qmnkJqaik8//RR5eXkAALVaje3bt+OZZ57BjBkzoFarsWDBAvz3f/83/1plZWVwu934n//5Hzz33HNISEjAL37xi+H7gISQYSVijDGhiyCEkOEmEomwefNmzJs3T+hSCCEjBF3TRAghhBASAQpNhBBCCCERoGuaCCE3JLoygRAyWNTSRAghhBASAQpNhBBCCCERoNBECCGEEBIBCk2EEEIIIRGg0EQIIYQQEgEKTYQQQgghEaDQRAghhBASAQpNhBBCCCERoNBECCGEEBKB/we4BoCvVSKdTAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "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 = [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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Computing Riemannian distances: 100%|██████████| 16/16 [00:00<00:00, 316.14it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "index_fit_2_first_second: {'row': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'col': [1, 0, 7, 3, 14, 2, 9, 4, 5, 10, 6, 13, 15, 8, 11, 12]}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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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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",
+ "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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",
+ "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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",
+ "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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",
+ "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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OHjwYVf1XVVUpLS2t0TFR+QMAYGIY9g2/36+kpKSocbzkbxiGpk+frmXLlum9995T165do/b3799fcXFxKi4urt9WUVGh3bt3Kzs7u9HXR+UPAICJnZW/FXl5eVqyZInefPNNJSYm1s/jBwIBtWvXToFAQLfccovy8/OVnJyspKQkzZgxQ9nZ2Y1e6S+R/AEAiBmFhYWSpBEjRkRtX7hwoSZPnixJevzxx9WqVStNmDBB4XBYY8eO1fz58y2dx3Ly/+STT1RaWqrs7Gz16NFDn376qZ544gmFw2HdcMMN9fcdnsixVj6GIxH5WzELAQBwnlOVv2EYDX6nbdu2KigoUEFBQZPPYynbFhUV6fzzz9fs2bN1wQUXqKioSMOGDdP27du1a9cujRkzRu+9916DxznWysenvtjV5IsAAMBOds75xyJLyf++++7T7bffrq+++koLFy7UddddpylTpmj16tUqLi7W7bffrrlz5zZ4nGAwqOrq6qgx/cwuTb4IAADQeJaS/0cffVQ/53D11Vfr0KFD+sUvflG///rrr9eHH37Y4HGOufKRlj8AIEY49WKfk8XynL/P98OFtGrVSm3btlUgEKjfl5iYWP8oQgAATlXNfSxvrLNUbp911lnatm1b/ecNGzYoKyur/vPu3buVnp5uX3QAAMB2lir/adOmqa6urv7zv581/G9vv/12o1b7AwAQy+x8tn8sspT8p06desL9Dz74YLOCAQAgFkRo+wMAADfhCX8AAJi4fcEfyR8AAJNYvUXPLiR/AABMYvXJfHZhzh8AAI+h8gcAwIS2PwAAHsOtfgAAwFWo/AEAMOFWPwAAPIbV/gAAwFWo/AEAMHH7gj+SPwAAJm6f86ftDwCAx1D5AwBg4vYFfyR/AABMmPM/Sb45GO90CM3WIaG90yHYo3VrpyOwRUn3RKdDaLZxO753OgRb/N/0b50Oodlqvm7ndAj2aH/q/39xMjDnDwAAXCVmKn8AAGIFbX8AADzG5ev9aPsDAOA1VP4AAJjQ9gcAwGNY7Q8AAFyFyh8AAJOI0wG0MJI/AAAmhmj7AwAAF6HyBwDAJOLyG/1J/gAAmERc3vYn+QMAYMKcPwAAcBUqfwAATLjVDwAAj6HtDwAAXIXKHwAAE9r+AAB4jNuTvy1tf8Nw+dMQAABwEVuSv9/v1yeffGLHoQAAcJwhn20jFllq++fn5x9ze11dnebOnauOHTtKkn7/+983PzIAABwSic2cbRtLyX/evHnq16+fOnToELXdMAx98sknat++vXy+hv/GwuGwwuFw9LZIRP5W3HwAAEBLs5T8H3zwQT3zzDN67LHHNHLkyPrtcXFxWrRokXr16tWo44RCId17771R22Z0Olu/OaOblXAAAGgRbn+2v6VS+3//93/16quvatq0aZo9e7aOHj3apJMGg0FVV1dHjV91PLtJxwIAwG6GjSMWWe6zDxw4UGVlZfryyy81YMAAbd26tVGt/v/m9/uVlJQUNWj5AwBiRcTGEYuadJ9/QkKCFi9erKVLl2r06NGqq6uzOy4AANBCmvWQn2uuuUZDhgxRWVmZunTpYldMAAA4KmKxo32qafYT/jIzM5WZmWlHLAAAxIRYnau3CxPtAAB4DM/2BwDAJFYX6tmF5A8AgInbn/BH2x8AAI+h8gcAwMTtT/gj+QMAYMJqfwAA4CpU/gAAmLh9wR/JHwAAE7ff6kfbHwAAE6fe6rdu3TqNGzdOGRkZ8vl8Wr58edT+yZMny+fzRY1LL73U8vWR/AEAiBG1tbXq16+fCgoKjvudSy+9VPv27asfr7zyiuXz0PYHAMDEqTn/3Nxc5ebmnvA7fr9faWlpzToPlT8AACYRG0c4HFZNTU3UCIfDTY5t7dq1SklJUffu3TVt2jR99dVXlo9B8gcAoAWFQiEFAoGoEQqFmnSsSy+9VC+88IKKi4v10EMPqaSkRLm5uaqrq7N0HNr+AACY2LnaPxgMKj8/P2qb3+9v0rGuueaa+j/36dNHffv21TnnnKO1a9dq1KhRjT4OyR8AABPDxjl/v9/f5GTfkLPPPludOnXS9u3bLSV/2v4AAJyi/vnPf+qrr75Senq6pZ+Lmcq/VWsXPEm5GQs4Yknky385HYItag60dTqEZlue5Y7/ps76+ydOh9Bs23r2cjoEe3x9wOkITglOPeTn8OHD2r59e/3nnTt3qry8XMnJyUpOTta9996rCRMmKC0tTTt27NAdd9yhbt26aezYsZbOEzPJHwCAWOFU8t+8ebMuvvji+s//XiswadIkFRYW6sMPP9TixYt18OBBZWRkaMyYMbr//vstTyuQ/AEAiBEjRoyQYRy/E75q1SpbzkPyBwDAxAUT0SdE8gcAwIS3+gEA4DG81Q8AALgKlT8AACZur/xJ/gAAmLh9wR9tfwAAPIbKHwAAE1b7AwDgMW6f86ftDwCAx1D5AwBg4vYFfyR/AABMIi5P/7T9AQDwGCp/AABM3L7gj+QPAICJu5v+JH8AAH7E7ZU/c/4AAHgMlT8AACY84e8Eamtr9dprr2n79u1KT0/Xtddeq44dO9oVGwAAjnD7rX6Wkn+vXr20fv16JScna8+ePRo2bJi+/vprnXfeedqxY4fuv/9+lZaWqmvXric8TjgcVjgcjt4WicjfilkIAABamqVs++mnn+r777+XJAWDQWVkZGjXrl3auHGjdu3apb59++quu+5q8DihUEiBQCBqPP3lzqZdAQAANjNsHLGoyaX2hg0bdM899ygQCEiSEhISdO+992r9+vUN/mwwGFR1dXXUmHrGibsFAACcLBEbRyyyPOfv8/2wCuLIkSNKT0+P2nfmmWfqyy+/bPAYfr9ffr8/atsBWv4AAJwUlpP/qFGj1KZNG9XU1KiiokK9e/eu37dr1y4W/AEATnks+Psvc+bMifqckJAQ9XnFihUaOnRo86MCAMBB7k79zUz+Zo888kizggEAAC2Ph/wAAGASqwv17ELyBwDAhDl/AAA8xt2pnxf7AADgOVT+AACYMOcPAIDHGC5v/NP2BwDAY6j8AQAwoe0PAIDHuP1WP9r+AAB4DJU/AAAm7q77Sf4AAPwIbX8AAOAqVP4AAJiw2h8AAI9x+0N+SP4AAJi4vfJnzh8AAI+Jmcrf18oFLZbWrZ2OwBatkgNOh2CLeH+N0yE024HKBKdDsMXH5/RxOoRm++VX7qgFV3ZKczqEUwJtfwAAPMYdv+odH21/AAA8hsofAACTiEHbHwAAT3F36qftDwCA51D5AwBg4vZn+5P8AQAwcfutfrT9AQDwGCp/AABM3H6fP8kfAAAT5vwBAPAY5vwBAICrUPkDAGDCnD8AAB5juPzxvrT9AQDwGCp/AABMWO0PAIDHuH3On7Y/AAAxYt26dRo3bpwyMjLk8/m0fPnyqP2GYei3v/2t0tPT1a5dO40ePVrbtm2zfB6SPwAAJoaN/1hRW1urfv36qaCg4Jj7H374YT355JN6+umn9f7776t9+/YaO3asjhw5Yuk8tP0BADBxas4/NzdXubm5x9xnGIbmzZunu+++W1dccYUk6YUXXlBqaqqWL1+ua665ptHnofIHAKAFhcNh1dTURI1wOGz5ODt37lRlZaVGjx5dvy0QCGjw4MHasGGDpWNZSv4ffPCBdu7cWf/5xRdfVE5Ojjp37qwhQ4Zo6dKljTrOMf8iIm5fXgEAOFUYhmHbCIVCCgQCUSMUClmOqbKyUpKUmpoatT01NbV+X2NZSv433XSTduzYIUl67rnn9Ktf/UoDBgzQXXfdpYEDB2rKlCl6/vnnGzzOsf4iCvfvbPDnAAA4GSI2jmAwqOrq6qgRDAZP8hVFszTnv23bNp177rmSpPnz5+uJJ57QlClT6vcPHDhQDzzwgG6++eYTHicYDCo/Pz9q296cn1sJBQCAFmPni338fr/8fn+zj5OWliZJqqqqUnp6ev32qqoqnX/++ZaOZanyP+2003TgwAFJ0hdffKFBgwZF7R88eHDUtMDx+P1+JSUlRQ1/K5YfAABwPF27dlVaWpqKi4vrt9XU1Oj9999Xdna2pWNZyri5ubkqLCyUJA0fPlxvvPFG1P7XXntN3bp1sxQAAACxJiLDtmHF4cOHVV5ervLyckk/LPIrLy/X7t275fP5NHPmTP3ud7/TW2+9pS1btujGG29URkaGxo8fb+k8ltr+Dz30kHJycjR8+HANGDBAjz32mNauXauePXuqoqJCpaWlWrZsmaUAAACINU692Gfz5s26+OKL6z//e4p80qRJWrRoke644w7V1tbq1ltv1cGDBzVkyBAVFRWpbdu2ls5jKflnZGTo73//u+bOnasVK1bIMAxt3LhRe/bsUU5Ojv76179qwIABlgIAAAA/GDFixAl/8fD5fLrvvvt03333Nes8lh/y06FDB82dO1dz585t1okBAIhVvNgHAACPsXO1fyxiiT0AAB5D5Q8AgEnEoQV/JwvJHwAAE3enftr+AAB4DpU/AAAmrPYHAMBjSP4AAHiMU0/4O1mY8wcAwGOo/AEAMKHtDwCAx/CEPwAA4CpU/gAAmLh9wR/JHwAAE7fP+dP2BwDAY6j8AQAwoe1/kqTdPsjpEJotsuefTodgi/hb7nY6BFukpT7vdAjNlpYYcDoEWxhfVjkdQrOtTE13OgRbPPLrTU6HYIvf7mrZ49P2BwAArhIzlT8AALHC7ff5k/wBADCJMOcPAIC3uL3yZ84fAACPofIHAMCEtj8AAB5D2x8AALgKlT8AACa0/QEA8Bja/gAAwFWo/AEAMKHtDwCAx9D2BwAArkLlDwCAiWFEnA6hRZH8AQAwibi87U/yBwDAxHD5gj/m/AEA8BgqfwAATGj7AwDgMbT9AQCAq1hK/jNmzNBf/vKXZp80HA6rpqYmaoSPft/s4wIAYIeIYdg2YpGl5F9QUKARI0bovPPO00MPPaTKysomnTQUCikQCESNR1b8rUnHAgDAboaN/8Qiy23/d955R5dddpkeffRRZWVl6YorrtDKlSsViTT+gQjBYFDV1dVR4/ZxF1kNBQAANIHl5N+nTx/NmzdPe/fu1UsvvaRwOKzx48erc+fOuuuuu7R9+/YGj+H3+5WUlBQ1/HGsPQQAxAbDMGwbsajJC/7i4uJ09dVXq6ioSJ9//rmmTJmil19+Wd27d7czPgAATrqIDNtGLLJltX9WVpbuuece7dy5U0VFRXYcEgAAtBBLvfYuXbqodevWx93v8/l0ySWXNDsoAACcFKvtertYSv47d+5sqTgAAIgZsXqLnl1YZQcAgInbK3+e8AcAgMdQ+QMAYBKrq/TtQvIHAMCEtj8AAHAVKn8AAExY7Q8AgMfE6gt57ELbHwAAj6HyBwDAhLY/AAAew2p/AADgKlT+AACYuH3BH8kfAAATt7f9Sf4AAJi4Pfkz5w8AgMdQ+QMAYOLuul+S4RFHjhwx5syZYxw5csTpUJrMDddgGO64Djdcg2FwHbHEDddgGO65DrfzGYbLJzb+v5qaGgUCAVVXVyspKcnpcJrEDdcgueM63HANEtcRS9xwDZJ7rsPtmPMHAMBjSP4AAHgMyR8AAI/xTPL3+/2aM2eO/H6/06E0mRuuQXLHdbjhGiSuI5a44Rok91yH23lmwR8AAPiBZyp/AADwA5I/AAAeQ/IHAMBjSP4AAHiMJ5J/QUGBzjrrLLVt21aDBw/Wxo0bnQ7JknXr1mncuHHKyMiQz+fT8uXLnQ7JslAopIEDByoxMVEpKSkaP368KioqnA7LssLCQvXt21dJSUlKSkpSdna23n77bafDapa5c+fK5/Np5syZTodiyT333COfzxc1evTo4XRYTfLFF1/ohhtuUMeOHdWuXTv16dNHmzdvdjqsRjvrrLN+9O/C5/MpLy/P6dBwHK5P/q+++qry8/M1Z84cffDBB+rXr5/Gjh2r/fv3Ox1ao9XW1qpfv34qKChwOpQmKykpUV5enkpLS7V69WodPXpUY8aMUW1trdOhWZKZmam5c+eqrKxMmzdv1siRI3XFFVfoo48+cjq0Jtm0aZP++Mc/qm/fvk6H0iQ/+clPtG/fvvqxfv16p0Oy7Ouvv1ZOTo7i4uL09ttv6+OPP9Zjjz2m008/3enQGm3Tpk1R/x5Wr14tSbrqqqscjgzH5eyrBVreoEGDjLy8vPrPdXV1RkZGhhEKhRyMqukkGcuWLXM6jGbbv3+/IckoKSlxOpRmO/30043nnnvO6TAsO3TokHHuuecaq1evNoYPH27cdtttTodkyZw5c4x+/fo5HUaz3XnnncaQIUOcDsNWt912m3HOOecYkUjE6VBwHK6u/L/77juVlZVp9OjR9dtatWql0aNHa8OGDQ5GhurqaklScnKyw5E0XV1dnZYuXara2lplZ2c7HY5leXl5uvzyy6P+/zjVbNu2TRkZGTr77LN1/fXXa/fu3U6HZNlbb72lAQMG6KqrrlJKSoouuOACPfvss06H1WTfffedXnrpJd18883y+XxOh4PjcHXyP3DggOrq6pSamhq1PTU1VZWVlQ5FhUgkopkzZyonJ0e9e/d2OhzLtmzZooSEBPn9fk2dOlXLli1Tr169nA7LkqVLl+qDDz5QKBRyOpQmGzx4sBYtWqSioiIVFhZq586dGjp0qA4dOuR0aJZ8/vnnKiws1LnnnqtVq1Zp2rRp+s1vfqPFixc7HVqTLF++XAcPHtTkyZOdDgUn0MbpAOA9eXl52rp16yk5PytJ3bt3V3l5uaqrq/XGG29o0qRJKikpOWV+AdizZ49uu+02rV69Wm3btnU6nCbLzc2t/3Pfvn01ePBgdenSRa+99ppuueUWByOzJhKJaMCAAXrwwQclSRdccIG2bt2qp59+WpMmTXI4OusWLFig3NxcZWRkOB0KTsDVlX+nTp3UunVrVVVVRW2vqqpSWlqaQ1F52/Tp07Vy5UqtWbNGmZmZTofTJPHx8erWrZv69++vUCikfv366YknnnA6rEYrKyvT/v379dOf/lRt2rRRmzZtVFJSoieffFJt2rRRXV2d0yE2SYcOHXTeeedp+/btTodiSXp6+o9+cezZs+cpOYWxa9cuvfvuu/rlL3/pdChogKuTf3x8vPr376/i4uL6bZFIRMXFxafkHO2pzDAMTZ8+XcuWLdN7772nrl27Oh2SbSKRiMLhsNNhNNqoUaO0ZcsWlZeX148BAwbo+uuvV3l5uVq3bu10iE1y+PBh7dixQ+np6U6HYklOTs6Pbnv97LPP1KVLF4ciarqFCxcqJSVFl19+udOhoAGub/vn5+dr0qRJGjBggAYNGqR58+aptrZWN910k9OhNdrhw4ejqpmdO3eqvLxcycnJysrKcjCyxsvLy9OSJUv05ptvKjExsX7NRSAQULt27RyOrvGCwaByc3OVlZWlQ4cOacmSJVq7dq1WrVrldGiNlpiY+KO1Fu3bt1fHjh1PqTUYs2fP1rhx49SlSxft3btXc+bMUevWrXXttdc6HZols2bN0kUXXaQHH3xQV199tTZu3KhnnnlGzzzzjNOhWRKJRLRw4UJNmjRJbdq4PrWc+py+3eBk+MMf/mBkZWUZ8fHxxqBBg4zS0lKnQ7JkzZo1hqQfjUmTJjkdWqMdK35JxsKFC50OzZKbb77Z6NKlixEfH2+cccYZxqhRo4x33nnH6bCa7VS81W/ixIlGenq6ER8fb5x55pnGxIkTje3btzsdVpOsWLHC6N27t+H3+40ePXoYzzzzjNMhWbZq1SpDklFRUeF0KGgEXukLAIDHuHrOHwAA/BjJHwAAjyH5AwDgMSR/AAA8huQPAIDHkPwBAPAYkj8AAB5D8gcAwGNI/gAAeAzJHwAAjyH5AwDgMSR/AAA85v8BAvoqM9SQQJwAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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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",
+ "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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",
+ "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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bLF0fyR8AgBYUDocVCATiRjgcbvTnYrGYZs2apfz8fOXm5kqS9u3bJ0maN2+epk+frpKSEl155ZUaO3asPvnkk4Rjou0PAICJnav9Q6GQCgsL47b5/f5Gfy4YDKqysjKuqo/FvllBePfdd+v222+XJF1xxRXasGGDXnrppYR+qZBI/gAAnMYw7DuW3+9PKNn/rRkzZmjdunXavHmzsrOzG7ZnZWVJkvr37x/3/X79+unAgQMJH5+2PwAAJkbMZ9uwdF7D0IwZM1RcXKy3335bPXv2jNvfo0cPdevW7bTb/z7++GN179494fNQ+QMAkCSCwaCWL1+u1157Tenp6aqurpYkBQIBtW3bVj6fT3PmzNHcuXOVl5en73//+1q2bJk++ugjrVq1KuHzWE7+H374ocrLyzV8+HD17dtXH330kZ588klFo1HdcsstGjNmTKPHiEajp610PGXUK9WXYjUcAABs59QT/oqKiiRJo0ePjtu+ZMkS3XbbbZKkWbNm6eTJk5o9e7aOHTumvLw8lZaW6tJLL034PJaSf0lJiSZNmqT27dvrq6++UnFxsW699Vbl5eUpFotp/PjxevPNNxv9BSAcDuuRRx6J2zY6I1d/12GglXAAAGgRds75WztvYid+6KGH4u7zt8rSnP/PfvYzzZkzR3/+85+1ZMkS3XTTTZo+fbpKS0u1YcMGzZkzRwsWLGj0OKFQSJFIJG6MDFze5IsAAACJs5T833///Ya2w5QpU3T8+HH9wz/8Q8P+m2++Wf/1X//V6HH8fr8yMjLiBi1/AECycGrB3/liec7f5/vmQlq1aqU2bdooEAg07EtPT294GhEAABcqpx7ve75Yqvx79OgR9wShrVu3Kicnp+HzgQMHGu5BBAAAyclS5X/vvfeqvr6+4fO3jxv81htvvJHQan8AAJKZ1VfxXmgsJf977rnnnPvnz5/frGAAAEgGMdr+AADATXjCHwAAJm5f8EfyBwDAJFlv0bMLyR8AABOnnvB3vjDnDwCAx1D5AwBgQtsfAACP4VY/AADgKlT+AACYcKsfAAAew2p/AADgKlT+AACYuH3BH8kfAAATt8/50/YHAMBjqPwBADBx+4I/kj8AACbM+Z8n6S74i+7sP+l0CLY4Fm3jdAi2uPWiIU6H0Gy/PrTF6RBs8fxFf+d0CM22PrXO6RBskdm3xukQLgjM+QMAAFdJmsofAIBkQdsfAACPcfl6P9r+AAB4DZU/AAAmtP0BAPAYVvsDAABXofIHAMAk5nQALYzkDwCAiSHa/gAAwEWo/AEAMIm5/EZ/kj8AACYxl7f9Sf4AAJgw5w8AAFyFyh8AABNu9QMAwGNo+wMAAFeh8gcAwIS2PwAAHuP25G9L298wXP40BAAAXMSW5O/3+/Xhhx/acSgAABxnyGfbSEaW2v6FhYVn3F5fX68FCxaoU6dOkqRf/vKXzY8MAACHxJIzZ9vGUvJftGiR8vLy1KFDh7jthmHoww8/VLt27eTzNf43Fo1GFY1G47adMuqV6kuxEg4AAGgCS8l//vz5eu6557Rw4UKNGTOmYXtaWpqWLl2q/v37J3SccDisRx55JG7b+IwBuiYw0Eo4AAC0CLc/29/SnP9DDz2klStX6t5779UDDzygurq6Jp00FAopEonEjXEZlzfpWAAA2M2wcSQjywv+hgwZooqKCn3++ecaPHiwKisrE2r1/y2/36+MjIy4QcsfAJAsYjaOZNSk+/zbt2+vZcuWacWKFRo3bpzq6+vtjgsAALSQZj3k5yc/+YmuuuoqVVRUqHv37nbFBACAo2IWO9oXmmY/4S87O1vZ2dl2xAIAQFJI1rl6u/BiHwAAPIZn+wMAYJKsC/XsQvIHAMDE7U/4o+0PAIDHUPkDAGDi9if8kfwBADBhtT8AAHAVKn8AAEzcvuCP5A8AgInbb/Wj7Q8AgIlTb/ULh8MaMmSI0tPTlZmZqcmTJ6uqqurMMRqGJkyYIJ/Pp9WrV1s6D8kfAIAkUVZWpmAwqPLycpWWlqqurk7jx49XbW3tad9dtGiR5bfqfou2PwAAJk7N+ZeUlMR9Xrp0qTIzM1VRUaGCgoKG7bt27dLChQu1Y8cOZWVlWT4PyR8AABM75/yj0aii0WjcNr/fL7/f3+jPRiIRSVLHjh0btn311Ve66aabtHjxYnXt2rVJMdH2BwCgBYXDYQUCgbgRDocb/blYLKZZs2YpPz9fubm5Ddtnz56tESNGaNKkSU2OicofAAATOyv/UCikwsLCuG2JVP3BYFCVlZXasmVLw7Y1a9bo7bff1nvvvdesmEj+AACYGDbO+Sfa4v9bM2bM0Lp167R582ZlZ2c3bH/77be1d+9edejQIe77119/vUaOHKlNmzYldHySPwAAScIwDM2cOVPFxcXatGmTevbsGbf/oYce0l133RW3bcCAAXriiSc0ceLEhM+TNMn/g1bRxr+U5C6Jtnc6BFv8oW290yHYYtOJ/3Y6hGZLTUmaf6LNctfnG50Oodm6tOvgdAi22L2tv9Mh2GJ0Cx/fqYf8BINBLV++XK+99prS09NVXV0tSQoEAmrbtq26du16xkV+OTk5p/2icC4s+AMAwCRm47CiqKhIkUhEo0ePVlZWVsNYuXKlDVf1V+4oKwAAcAHDsP4+wab8DMkfAAATt7/Sl+QPAIAJb/UDAMBjeKsfAABwFSp/AABM3F75k/wBADBx+4I/2v4AAHgMlT8AACas9gcAwGPcPudP2x8AAI+h8gcAwMTtC/5I/gAAmMRcnv5p+wMA4DFU/gAAmLh9wR/JHwAAE3c3/Un+AACcxu2VP3P+AAB4DJU/AAAmPOHvHGpra/Xqq69qz549ysrK0o033qhOnTrZFRsAAI5w+61+lpJ///79tWXLFnXs2FEHDx5UQUGB/vKXv6h3797au3evHn30UZWXl6tnz57nPE40GlU0Go3bVm/UK8WXYv0KAACAJZbm/D/66COdOnVKkhQKhdStWzd9+umn2rZtmz799FMNHDhQDz/8cKPHCYfDCgQCcWNX5KOmXQEAADYzbBzJqMkL/rZu3ap58+YpEAhIktq3b69HHnlEW7ZsafRnQ6GQIpFI3Ph+oG9TQwEAwFYxG0cysjzn7/N9swri5MmTysrKitt38cUX6/PPP2/0GH6/X36/P24bLX8AAM4Py8l/7NixSk1NVU1NjaqqqpSbm9uw79NPP2XBHwDggseCv78xd+7cuM/t27eP+7x27VqNHDmy+VEBAOAgd6f+ZiZ/s1/84hfNCgYAALQ8HvIDAIBJsi7UswvJHwAAE+b8AQDwGHenfl7sAwCA51D5AwBgwpw/AAAeY7i88U/bHwAAj6HyBwDAhLY/AAAe4/Zb/Wj7AwDgMVT+AACYuLvuJ/kDAHAa2v4AAMBVqPwBADBhtT8AAB7j9of8kPwBADBxe+XPnD8AAB6TNJX/d5TidAjN1qv1cadDsEV9NN3pEGxR3aar0yE0W+tWSfNPtFmmtO7hdAjN9ujnW5wOwRYDRhxxOoQLAm1/AAA8hrY/AABwFSp/AABMYgZtfwAAPMXdqZ+2PwAAnkPlDwCAiduf7U/yBwDAxO23+tH2BwDAY6j8AQAwcft9/iR/AABMmPMHAMBjmPMHAACuQuUPAIAJc/4AAHiM4fLH+9L2BwDAY6j8AQAwYbU/AAAe4/Y5f9r+AAAkiXA4rCFDhig9PV2ZmZmaPHmyqqqqGvYfO3ZMM2fOVJ8+fdS2bVvl5OTovvvuUyQSsXQekj8AACaGjX+sKCsrUzAYVHl5uUpLS1VXV6fx48ertrZWknTo0CEdOnRIjz/+uCorK7V06VKVlJTozjvvtHQe2v4AAJg4NedfUlIS93np0qXKzMxURUWFCgoKlJubq9/97ncN+y+99FI99thjuuWWW3Tq1CmlpiaW1kn+AAC0oGg0qmg0GrfN7/fL7/c3+rPftvM7dux4zu9kZGQknPgli23/nTt3av/+/Q2f//3f/135+fm65JJLdNVVV2nFihUJHScajaqmpiZunDLqrYQCAECLMQzDthEOhxUIBOJGOBxuNIZYLKZZs2YpPz9fubm5Z/zO0aNH9eijj+qnP/2ppeuzlPxvv/127d27V5L0wgsv6O6779bgwYP18MMPa8iQIZo+fbpeeumlRo9zpr+IHZEPLQUOAEBLidk4QqGQIpFI3AiFQo3GEAwGVVlZedbCuqamRtdee6369++vefPmWbo+S23/Tz75RJdddpkk6ZlnntGTTz6p6dOnN+wfMmSIHnvsMd1xxx3nPE4oFFJhYWH8tgHWFisAANBS7HyxT6It/r81Y8YMrVu3Tps3b1Z2dvZp+48fP65rrrlG6enpKi4uVlpamqXjW0r+3/nOd3T06FF1795df/rTnzR06NC4/cOGDYubFjibM/1FpPpSrIQCAIDrGIahmTNnqri4WJs2bVLPnj1P+05NTY2uvvpq+f1+rVmzRm3atLF8Hktt/wkTJqioqEiSNGrUKK1atSpu/6uvvqpevXpZDgIAgGQSk2HbsCIYDOqVV17R8uXLlZ6erurqalVXV+vEiROSvkn839769+KLL6qmpqbhO/X1ia+ds1T5//znP1d+fr5GjRqlwYMHa+HChdq0aZP69eunqqoqlZeXq7i42NKFAgCQbJx6sc+3Bfbo0aPjti9ZskS33Xabdu7cqXfffVeSTiu29+/frx49eiR0HkvJv1u3bnrvvfe0YMECrV27VoZhaNu2bTp48KDy8/P1hz/8QYMHD7ZySAAA8D8a+6Vj9OjRtvxiYvk+/w4dOmjBggVasGBBs08OAEAy4sU+AAB4jJ2r/ZMRz/YHAMBjqPwBADCJObTg73wh+QMAYOLu1E/bHwAAz6HyBwDAhNX+AAB4DMkfAACPceoJf+cLc/4AAHgMlT8AACa0/QEA8Bie8AcAAFyFyh8AABO3L/gj+QMAYOL2OX/a/gAAeAyVPwAAJrT9z5MF/6e/0yE0W7R4o9Mh2OLyXy1yOgRb/K9n/83pEJotduSU0yHYInX4pU6H0Gz3RTo5HYItdj3y/5wOwRYjWvj4tP0BAICrJE3lDwBAsnD7ff4kfwAATGLM+QMA4C1ur/yZ8wcAwGOo/AEAMKHtDwCAx9D2BwAArkLlDwCACW1/AAA8hrY/AABwFSp/AABMaPsDAOAxtP0BAICrUPkDAGBiGDGnQ2hRJH8AAExiLm/7k/wBADAxXL7gjzl/AAA8hsofAAAT2v4AAHgMbX8AAOAqlpL/zJkz9c477zT7pNFoVDU1NXEjWneq2ccFAMAOMcOwbSQjS8l/8eLFGj16tHr37q2f//znqq6ubtJJw+GwAoFA3PjF6ub/UgEAgB0MG/8kI8tt/zfffFM/+tGP9PjjjysnJ0eTJk3SunXrFIsl/kCEUCikSCQSN+ZMHmk1FAAA0ASWk/+AAQO0aNEiHTp0SK+88oqi0agmT56sSy65RA8//LD27NnT6DH8fr8yMjLihj+NtYcAgORgGIZtIxk1ecFfWlqapkyZopKSEu3bt0/Tp0/Xf/zHf6hPnz52xgcAwHkXk2HbSEa2rPbPycnRvHnztH//fpWUlNhxSAAA0EIs9dq7d++ulJSUs+73+Xz64Q9/2OygAABwUrK26+1iKfnv37+/peIAACBpJOstenZhlR0AACZur/x5wh8AAB5D5Q8AgEmyrtK3C8kfAAAT2v4AAMBVqPwBADBhtT8AAB6TrC/ksQttfwAAPIbKHwAAE9r+AAB4DKv9AQCAq1D5AwBg4vYFfyR/AABM3N72J/kDAGDi9uTPnD8AAB5D5Q8AgIm7635JhkecPHnSmDt3rnHy5EmnQ2kyN1yDYbjjOtxwDYbBdSQTN1yDYbjnOtzOZxgun9j4HzU1NQoEAopEIsrIyHA6nCZxwzVI7rgON1yDxHUkEzdcg+Se63A75vwBAPAYkj8AAB5D8gcAwGM8k/z9fr/mzp0rv9/vdChN5oZrkNxxHW64BonrSCZuuAbJPdfhdp5Z8AcAAL7hmcofAAB8g+QPAIDHkPwBAPAYkj8AAB7jieS/ePFi9ejRQ23atNGwYcO0bds2p0OyZPPmzZo4caK6desmn8+n1atXOx2SZeFwWEOGDFF6eroyMzM1efJkVVVVOR2WZUVFRRo4cKAyMjKUkZGh4cOH64033nA6rGZZsGCBfD6fZs2a5XQolsybN08+ny9u9O3b1+mwmuRPf/qTbrnlFnXq1Elt27bVgAEDtGPHDqfDSliPHj1O+3/h8/kUDAadDg1n4frkv3LlShUWFmru3LnauXOn8vLydPXVV+vIkSNOh5aw2tpa5eXlafHixU6H0mRlZWUKBoMqLy9XaWmp6urqNH78eNXW1jodmiXZ2dlasGCBKioqtGPHDo0ZM0aTJk3S+++/73RoTbJ9+3b9+te/1sCBA50OpUkuv/xyffbZZw1jy5YtTodk2V/+8hfl5+crLS1Nb7zxhj744AMtXLhQ3/3ud50OLWHbt2+P+/9QWloqSbrhhhscjgxn5eyrBVre0KFDjWAw2PC5vr7e6NatmxEOhx2MqukkGcXFxU6H0WxHjhwxJBllZWVOh9Js3/3ud40XXnjB6TAsO378uHHZZZcZpaWlxqhRo4z777/f6ZAsmTt3rpGXl+d0GM324IMPGldddZXTYdjq/vvvNy699FIjFos5HQrOwtWV/9dff62KigqNGzeuYVurVq00btw4bd261cHIEIlEJEkdO3Z0OJKmq6+v14oVK1RbW6vhw4c7HY5lwWBQ1157bdy/jwvNJ598om7duul73/uebr75Zh04cMDpkCxbs2aNBg8erBtuuEGZmZm64oor9PzzzzsdVpN9/fXXeuWVV3THHXfI5/M5HQ7OwtXJ/+jRo6qvr1eXLl3itnfp0kXV1dUORYVYLKZZs2YpPz9fubm5Todj2e7du9W+fXv5/X7dc889Ki4uVv/+/Z0Oy5IVK1Zo586dCofDTofSZMOGDdPSpUtVUlKioqIi7d+/XyNHjtTx48edDs2Sffv2qaioSJdddpnWr1+ve++9V/fdd5+WLVvmdGhNsnr1an3xxRe67bbbnA4F55DqdADwnmAwqMrKygtyflaS+vTpo127dikSiWjVqlWaNm2aysrKLphfAA4ePKj7779fpaWlatOmjdPhNNmECRMa/nvgwIEaNmyYunfvrldffVV33nmng5FZE4vFNHjwYM2fP1+SdMUVV6iyslLPPvuspk2b5nB01r344ouaMGGCunXr5nQoOAdXV/6dO3dWSkqKDh8+HLf98OHD6tq1q0NReduMGTO0bt06bdy4UdnZ2U6H0yStW7dWr169NGjQIIXDYeXl5enJJ590OqyEVVRU6MiRI7ryyiuVmpqq1NRUlZWV6amnnlJqaqrq6+udDrFJOnTooN69e2vPnj1Oh2JJVlbWab849uvX74Kcwvj000/11ltv6a677nI6FDTC1cm/devWGjRokDZs2NCwLRaLacOGDRfkHO2FzDAMzZgxQ8XFxXr77bfVs2dPp0OyTSwWUzQadTqMhI0dO1a7d+/Wrl27GsbgwYN18803a9euXUpJSXE6xCb58ssvtXfvXmVlZTkdiiX5+fmn3fb68ccfq3v37g5F1HRLlixRZmamrr32WqdDQSNc3/YvLCzUtGnTNHjwYA0dOlSLFi1SbW2tbr/9dqdDS9iXX34ZV83s379fu3btUseOHZWTk+NgZIkLBoNavny5XnvtNaWnpzesuQgEAmrbtq3D0SUuFAppwoQJysnJ0fHjx7V8+XJt2rRJ69evdzq0hKWnp5+21qJdu3bq1KnTBbUG44EHHtDEiRPVvXt3HTp0SHPnzlVKSopuvPFGp0OzZPbs2RoxYoTmz5+vKVOmaNu2bXruuef03HPPOR2aJbFYTEuWLNG0adOUmur61HLhc/p2g/PhV7/6lZGTk2O0bt3aGDp0qFFeXu50SJZs3LjRkHTamDZtmtOhJexM8UsylixZ4nRoltxxxx1G9+7djdatWxsXXXSRMXbsWOPNN990OqxmuxBv9Zs6daqRlZVltG7d2rj44ouNqVOnGnv27HE6rCZZu3atkZuba/j9fqNv377Gc88953RIlq1fv96QZFRVVTkdChLAK30BAPAYV8/5AwCA05H8AQDwGJI/AAAeQ/IHAMBjSP4AAHgMyR8AAI8h+QMA4DEkfwAAPIbkDwCAx5D8AQDwGJI/AAAeQ/IHAMBj/j/KC4aY5n/SmAAAAABJRU5ErkJggg==",
+ "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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",
+ "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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1+vRpzZkzRydOnNCQIUNUVVWlyy+/PO7zkPwBADBz6D7/eF+O9+ijj8bc528VyR8AABO3P9uf5A8AgJnLX1DLan8AADyGyh8AABPD5ZV/wiT/36dYewBCIurzTOWFv3QJmJPndAT2GLHhpNMhtNn3vjLA6RBsUWuEnQ6hzU74uzgdgi1u/O3LTodgj2/+S/se3+XJn7Y/AAAekzCVPwAAiYK2PwAAXuPy5E/bHwAAj6HyBwDAhLY/AAAeQ/IHAMBj3J78mfMHAMBjqPwBADAzfE5H0K5I/gAAmND2BwAArkLlDwCAiRGl7Q8AgKfQ9gcAAK5C5Q8AgInBan8AALyFtj8AAHAVKn8AAExY7Q8AgMcYhtMRtC+SPwAAJm6v/JnzBwDAYyxX/u+++65qamo0cuRIDRgwQO+9956eeeYZRSIR3XnnnRo7duwFjxGJRBSJRGK2fW40q6MvyWo4AADYjsr/CyorK/XNb35TDz/8sIYOHarKykoVFBRo3759OnTokCZMmKB169Zd8DihUEiBQCBm1ITfafVFAABgJ8OwbyQiS8n/Jz/5iR555BH97W9/U3l5ub773e9q5syZqqqq0tq1a/XII49o3rx5FzxOSUmJwuFwzPhWYFCrLwIAAMTPUvLfs2ePZsyYIUmaOnWqTp48qX/7t39r2X/HHXfo//7v/y54HL/fr7S0tJhByx8AkCiMqM+2kYgsz/n7fH+/kA4dOqhTp04KBAIt+1JTUxUOh+2LDgAAB7j98b6WKv8+ffro/fffb/m8efNm5eTktHw+fPiwsrKy7IsOAADYzlLl/8ADD6i5ubnlc25ubsz+N954I67V/gAAJDK3P9vfUvK///77z7v/ySefbFMwAAAkgihtfwAA4CY83hcAABO3L/gj+QMAYJKot+jZheQPAIBJoj6Zzy7M+QMA4DFU/gAAmND2BwDAY7jVDwAAuAqVPwAAJtzqBwCAx7DaHwAAuAqVPwAAJm5f8EfyBwDAxO1z/rT9AQDwGCp/AABMWPAHAIDHRA2fbcOKUCikvLw8paamKiMjQ5MnT1ZdXd05v2sYhiZOnCifz6fXX3/d0nkSpvL/0+d/dTqENjv6l1SnQ7BFl+NnnA7BFsO/0s3pENrsJx9tcDoEW1zR7WtOh9Bmx5O7OB2CLZqPUPPFw6k5/+rqagWDQeXl5enzzz/Xj370I02YMEHvvPOOunSJ/f/g008/LZ+vdXEmTPIHAMDrKisrYz4vWrRIGRkZqq2tVUFBQcv2nTt3av78+dq2bZuysrIsn4fkDwCAiZ23+kUiEUUikZhtfr9ffr//gj8bDoclSenp6S3bPvvsM333u99VaWmpMjMzWxUT/R8AAEwMG0coFFIgEIgZoVDogjFEo1HNnj1b+fn5ys3Nbdk+Z84cjRo1Srfcckurr4/KHwCAdlRSUqLi4uKYbfFU/cFgULt379amTZtatq1cuVLr1q3Tjh072hQTyR8AABM72/7xtvi/qKioSKtXr9bGjRuVnZ3dsn3dunXav3+/unXrFvP9KVOmaPTo0dqwYUNcxyf5AwBg4tRqf8MwNGvWLFVUVGjDhg3q27dvzP5HH31UP/jBD2K2XX311VqwYIEmTZoU93lI/gAAJIhgMKilS5dqxYoVSk1NVX19vSQpEAioc+fOyszMPOciv5ycnLP+UDgfFvwBAGAStXFYUVZWpnA4rMLCQmVlZbWM5cuX23BV/0TlDwCAiSHn2v4X42eo/AEA8BgqfwAATKIuf7EPyR8AAJOoQ23/i4XkDwCAiVNz/hcLc/4AAHgMlT8AACZWb9G71JD8AQAwoe0PAABchcofAAAT2v4AAHiM25O/LW3/1jxaEAAAOMOW5O/3+/Xuu+/acSgAABxnyGfbSESW2v7FxcXn3N7c3Kx58+ape/fukqSf//znbY8MAACHRBMzZ9vGUvJ/+umnNWTIEHXr1i1mu2EYevfdd9WlSxf5fBf+jUUiEUUikZhtUSOqDj5uPgAAoL1ZSv5PPvmkXnzxRc2fP19jx45t2Z6cnKxFixZp0KBBcR0nFArpxz/+ccy23ql91SftG1bCAQCgXbj92f6WSu1HH31Uy5cv1wMPPKCHH35YTU1NrTppSUmJwuFwzMhJ7dOqYwEAYDfDxpGILPfZ8/LyVFtbq7/+9a8aPny4du/eHVer/4v8fr/S0tJiBi1/AECiiNo4ElGr7vPv2rWrFi9erGXLlmn8+PFqbm62Oy4AANBO2vSQn3//93/Xddddp9raWvXu3duumAAAcFTUYkf7UtPmJ/xlZ2crOzvbjlgAAEgIiTpXbxcm2gEA8Bie7Q8AgEmiLtSzC8kfAAATtz/hj7Y/AAAeQ+UPAICJ25/wR/IHAMCE1f4AAMBVqPwBADBx+4I/kj8AACbc6gcAgMcw5w8AAFyFyh8AABPm/AEA8Bi3z/nT9gcAwGOo/AEAMHF75U/yBwDAxHD5nD9tfwAAPCZhKv/rk3o6HUKb5Vx7xOkQbHHqQ6cjsEf3T7s5HUKb/SLj206HYIs5H7/tdAhtdlePK50OwRbJg1OcDuGSQNsfAACPcXvyp+0PAIDHUPkDAGDi9sf7kvwBADDhCX8AAHgMc/4AAMBVqPwBADBxe+VP8gcAwMTtC/5o+wMA4DFU/gAAmLh9tT+VPwAAJlEbhxWhUEh5eXlKTU1VRkaGJk+erLq6upb9J06c0KxZs9S/f3917txZOTk5evDBBxUOhy2dh+QPAECCqK6uVjAYVE1NjaqqqtTU1KQJEyaosbFRknTkyBEdOXJETz31lHbv3q1FixapsrJS99xzj6Xz0PYHAMDEqQV/lZWVMZ8XLVqkjIwM1dbWqqCgQLm5ufrd737Xsv/yyy/XE088oTvvvFOff/65OnaML62T/AEAMInamP4jkYgikUjMNr/fL7/ff8Gf/Uc7Pz09/bzfSUtLizvxS7T9AQBoV6FQSIFAIGaEQqEL/lw0GtXs2bOVn5+v3Nzcc37n+PHj+ulPf6p7773XUkxU/gAAmNj5kJ+SkhIVFxfHbIun6g8Gg9q9e7c2bdp0zv0NDQ26+eabNWjQID3++OOWYiL5AwBgYuecf7wt/i8qKirS6tWrtXHjRmVnZ5+1/+TJk7rxxhuVmpqqiooKJScnWzo+yR8AABOnHu9rGIZmzZqliooKbdiwQX379j3rOw0NDbrhhhvk9/u1cuVKderUyfJ5SP4AACSIYDCopUuXasWKFUpNTVV9fb0kKRAIqHPnzmpoaNCECRP02WefacmSJWpoaFBDQ4Mk6bLLLlNSUlJc5yH5AwBg4tQT/srKyiRJhYWFMdvLy8s1Y8YMbd++XX/6058kSf369Yv5zsGDB9WnT5+4ztOm5N/Y2KhXXnlF+/btU1ZWlm6//XZ17969LYcEAMBxdt7qZ4VhnP+8hYWFF/xOPCwl/0GDBmnTpk1KT0/XBx98oIKCAn388ce68sortX//fv30pz9VTU3NOecovuhc9zx+bjSroy++dgUAAGg9S/f5v/fee/r8888l/f3WhV69eunQoUPasmWLDh06pMGDB+uxxx674HHOdc9jdXhP664AAACbGTaORNTqh/xs3rxZjz/+uAKBgCSpa9eu+vGPf/yl9yN+UUlJicLhcMwYE7iqtaEAAGArp17sc7FYnvP3+f6+CuL06dPKysqK2fe1r31Nf/3rXy94jHPd80jLHwCAi8Ny8h83bpw6duyohoYG1dXVxTxy8NChQyz4AwBc8pxa8HexWEr+c+fOjfnctWvXmM+rVq3S6NGj2x4VAAAOcnfqb2PyN/vv//7vNgUDAADaHw/5AQDAJFEX6tmF5A8AgAlz/gAAeIy7U38b7vMHAACXJip/AABMmPMHAMBjDJc3/mn7AwDgMVT+AACY0PYHAMBj3H6rH21/AAA8hsofAAATd9f9JH8AAM5C2x8AALgKlT8AACas9gcAwGPc/pAfkj8AACZur/yZ8wcAwGMSpvLPaPY5HUKbdfxGutMh2OIrnT5xOgRbXP2XZKdDaLMGl/x5ftdlI5wOoc3eaj7udAi2mNWv0OkQLgm0/QEA8Bja/gAAwFWo/AEAMIkatP0BAPAUd6d+2v4AAHgOlT8AACZuf7Y/yR8AABO33+pH2x8AAI+h8gcAwMTt9/mT/AEAMGHOHwAAj2HOHwAAuAqVPwAAJsz5AwDgMYbLH+9L2x8AAI+h8gcAwITV/gAAeIzb5/xp+wMA4DFU/gAAmLj9Pn+SPwAAJm6f86ftDwCAx1hK/tu3b9fBgwdbPv/P//yP8vPz9fWvf13XXXedli1bFtdxIpGIGhoaYkaT0WwtcgAA2olhGLaNRGQp+d91113av3+/JOlXv/qV7rvvPg0fPlyPPfaY8vLyNHPmTL388ssXPE4oFFIgEIgZb5zc07orAADAZlEbRyKyNOf//vvv64orrpAkLVy4UM8884xmzpzZsj8vL09PPPGE7r777vMep6SkRMXFxTHblgy8z0ooAAC0G7cv+LNU+X/lK1/R8ePHJUkffvihRowYEbP/2muvjZkW+DJ+v19paWkxI9mXZCUUAABcJxQKKS8vT6mpqcrIyNDkyZNVV1cX853Tp08rGAyqe/fu6tq1q6ZMmaKjR49aOo+l5D9x4kSVlZVJksaMGaPXXnstZv8rr7yifv36WQoAAIBEE5Vh27CiurpawWBQNTU1qqqqUlNTkyZMmKDGxsaW78yZM0erVq3Sq6++qurqah05ckS33nqrpfNYavv/7Gc/U35+vsaMGaPhw4dr/vz52rBhgwYOHKi6ujrV1NSooqLCUgAAACQapxbqVVZWxnxetGiRMjIyVFtbq4KCAoXDYb300ktaunSpxo4dK0kqLy/XwIEDVVNTo29961txncdS5d+rVy/t2LFDI0eOVGVlpQzD0JYtW/Tmm28qOztb//u//6ubbrrJyiEBAHC1c93hFolE4vrZcDgsSUpPT5ck1dbWqqmpSePHj2/5zoABA5STk6PNmzfHHZPl+/y7deumefPmac+ePTp16pQikYj+8pe/6De/+Y2GDx9u9XAAACQcO9v+57rDLRQKXTiGaFSzZ89Wfn6+cnNzJUn19fVKSUlRt27dYr7bs2dP1dfXx319POEPAAATO1f7n+sON7/ff8GfCwaD2r17tzZt2mRbLP9A8gcAoB35/f64kv0XFRUVafXq1dq4caOys7NbtmdmZurMmTP65JNPYqr/o0ePKjMzM+7j83hfAABMooZh27DCMAwVFRWpoqJC69atU9++fWP2Dxs2TMnJyVq7dm3Ltrq6Oh0+fFgjR46M+zxU/gAAmDj1iJ9gMKilS5dqxYoVSk1NbZnHDwQC6ty5swKBgO655x4VFxcrPT1daWlpmjVrlkaOHBn3Sn+J5A8AQML4x7N0CgsLY7aXl5drxowZkqQFCxaoQ4cOmjJliiKRiG644QYtXLjQ0nlI/gAAmDj1St94ni/QqVMnlZaWqrS0tNXnIfkDAGDiVPK/WEj+AACYJOqreO3Can8AADyGyh8AABPa/gAAeIydT/hLRLT9AQDwGCp/AABM3L7gj+QPAICJ2+f8afsDAOAxVP4AAJi4ve3vMxLkCj++rdDpENps68b4X6eYyL6950mnQ7DFp/fd7XQIbZbcO83pEGzx6bYGp0Nos67D3fFv8eEfmpwOwRb933ujXY8/JHOUbcf6c/3bth3LLrT9AQDwGNr+AACYuP0+f5I/AAAm0cSYEW83JH8AAEzcXvkz5w8AgMdQ+QMAYELbHwAAj6HtDwAAXIXKHwAAE9r+AAB4DG1/AADgKlT+AACY0PYHAMBjaPsDAABXofIHAMDEMKJOh9CuSP4AAJhEXd72J/kDAGBiuHzBH3P+AAB4DJU/AAAmtP0BAPAY2v4AAMBVLCX/WbNm6Y9//GObTxqJRNTQ0BAzIs3uvq0CAHDpiBqGbSMRWUr+paWlKiws1JVXXqmf/exnqq+vb9VJQ6GQAoFAzFjw3uFWHQsAALsZNv6XiCy3/d98803ddNNNeuqpp5STk6NbbrlFq1evVjQaf+VeUlKicDgcM+YMyLEaCgAAaAXLyf/qq6/W008/rSNHjmjJkiWKRCKaPHmyvv71r+uxxx7Tvn37LngMv9+vtLS0mOFPYvkBACAxGIZh20hErc64ycnJmjp1qiorK3XgwAHNnDlTv/nNb9S/f3874wMA4KKLyrBtJCJbyu2cnBw9/vjjOnjwoCorK+04JAAAaCeW7vPv3bu3kpKSvnS/z+fT9ddf3+agAABwUqK26+1iKfkfPHiwveIAACBhJOotenbhCX8AAJi4vfJniT0AAB5D5Q8AgEmirtK3C8kfAAAT2v4AAMBVqPwBADBhtT8AAB6TqC/ksQttfwAAPIbkDwCASdQwbBtWbNy4UZMmTVKvXr3k8/n0+uuvx+z/9NNPVVRUpOzsbHXu3FmDBg3SCy+8YPn6SP4AAJg49Va/xsZGDRkyRKWlpefcX1xcrMrKSi1ZskTvvvuuZs+eraKiIq1cudLSeZjzBwAgQUycOFETJ0780v1vv/22pk+frsLCQknSvffeq1/84hfasmWLvvOd78R9Hip/AABMDBv/i0QiamhoiBmRSKRVcY0aNUorV67Uhx9+KMMwtH79eu3du1cTJkywdBySPwAAJna2/UOhkAKBQMwIhUKtiuu5557ToEGDlJ2drZSUFN14440qLS1VQUGBpePQ9gcAwMTOJ/yVlJSouLg4Zpvf72/VsZ577jnV1NRo5cqV6t27tzZu3KhgMKhevXpp/PjxcR+H5A8AQDvy+/2tTvZfdOrUKf3oRz9SRUWFbr75ZknS4MGDtXPnTj311FMkfwAA2iIRH/HT1NSkpqYmdegQO2OflJSkaDRq7WCGR5w+fdqYO3eucfr0aadDaTU3XINhuOM63HANhsF1JBI3XINhuOc6nHLy5Eljx44dxo4dOwxJxs9//nNjx44dxqFDhwzDMIwxY8YYV111lbF+/XrjwIEDRnl5udGpUydj4cKFls7jMwyXP8D4/9fQ0KBAIKBwOKy0tDSnw2kVN1yD5I7rcMM1SFxHInHDNUjuuQ6nbNiwQd/+9rfP2j59+nQtWrRI9fX1Kikp0ZtvvqkTJ06od+/euvfeezVnzhz5fL64z0PbHwCABFFYWHjexYaZmZkqLy9v83m41Q8AAI8h+QMA4DGeSf5+v19z58615XYLp7jhGiR3XIcbrkHiOhKJG65Bcs91uJ1nFvwBAIC/80zlDwAA/o7kDwCAx5D8AQDwGJI/AAAe44nkX1paqj59+qhTp0669tprtWXLFqdDsmTjxo2aNGmSevXqJZ/Pp9dff93pkCwLhULKy8tTamqqMjIyNHnyZNXV1TkdlmVlZWUaPHiw0tLSlJaWppEjR+qNN95wOqw2mTdvnnw+n2bPnu10KJY8/vjj8vl8MWPAgAFOh9UqH374oe688051795dnTt31tVXX61t27Y5HVbc+vTpc9a/hc/nUzAYdDo0fAnXJ//ly5eruLhYc+fO1fbt2zVkyBDdcMMNOnbsmNOhxa2xsVFDhgxRaWmp06G0WnV1tYLBoGpqalRVVaWmpiZNmDBBjY2NTodmSXZ2tubNm6fa2lpt27ZNY8eO1S233KI9e/Y4HVqrbN26Vb/4xS80ePBgp0NplauuukofffRRy9i0aZPTIVn28ccfKz8/X8nJyXrjjTf0zjvvaP78+frqV7/qdGhx27p1a8y/Q1VVlSTptttuczgyfCmb30mQcEaMGGEEg8GWz83NzUavXr2MUCjkYFStJ8moqKhwOow2O3bsmCHJqK6udjqUNvvqV79q/OpXv3I6DMtOnjxpXHHFFUZVVZUxZswY46GHHnI6JEvmzp1rDBkyxOkw2uyHP/yhcd111zkdhq0eeugh4/LLLzei0ajToeBLuLryP3PmjGpra2PecdyhQweNHz9emzdvdjAyhMNhSVJ6errDkbRec3Ozli1bpsbGRo0cOdLpcCwLBoO6+eabLb0DPNG8//776tWrl77xjW/ojjvu0OHDh50OybKVK1dq+PDhuu2225SRkaGhQ4fql7/8pdNhtdqZM2e0ZMkS3X333ZZeNIOLy9XJ//jx42publbPnj1jtvfs2VP19fUORYVoNKrZs2crPz9fubm5Todj2a5du9S1a1f5/X7df//9qqio0KBBg5wOy5Jly5Zp+/btCoVCTofSatdee60WLVqkyspKlZWV6eDBgxo9erROnjzpdGiWHDhwQGVlZbriiiu0Zs0aPfDAA3rwwQe1ePFip0Nrlddff12ffPKJZsyY4XQoOA/e6oeLLhgMavfu3Zfk/Kwk9e/fXzt37lQ4HNZrr72m6dOnq7q6+pL5A+CDDz7QQw89pKqqKnXq1MnpcFpt4sSJLf978ODBuvbaa9W7d2+98soruueeexyMzJpoNKrhw4frySeflCQNHTpUu3fv1gsvvKDp06c7HJ11L730kiZOnKhevXo5HQrOw9WVf48ePZSUlKSjR4/GbD969KgyMzMdisrbioqKtHr1aq1fv17Z2dlOh9MqKSkp6tevn4YNG6ZQKKQhQ4bomWeecTqsuNXW1urYsWO65ppr1LFjR3Xs2FHV1dV69tln1bFjRzU3NzsdYqt069ZNV155pfbt2+d0KJZkZWWd9YfjwIEDL8kpjEOHDumtt97SD37wA6dDwQW4OvmnpKRo2LBhWrt2bcu2aDSqtWvXXpJztJcywzBUVFSkiooKrVu3Tn379nU6JNtEo1FFIhGnw4jbuHHjtGvXLu3cubNlDB8+XHfccYd27typpKQkp0NslU8//VT79+9XVlaW06FYkp+ff9Ztr3v37lXv3r0diqj1ysvLlZGRoZtvvtnpUHABrm/7FxcXa/r06Ro+fLhGjBihp59+Wo2NjbrrrrucDi1un376aUw1c/DgQe3cuVPp6enKyclxMLL4BYNBLV26VCtWrFBqamrLmotAIKDOnTs7HF38SkpKNHHiROXk5OjkyZNaunSpNmzYoDVr1jgdWtxSU1PPWmvRpUsXde/e/ZJag/Hwww9r0qRJ6t27t44cOaK5c+cqKSlJt99+u9OhWTJnzhyNGjVKTz75pKZOnaotW7boxRdf1Isvvuh0aJZEo1GVl5dr+vTp6tjR9anl0uf07QYXw3PPPWfk5OQYKSkpxogRI4yamhqnQ7Jk/fr1hqSzxvTp050OLW7nil+SUV5e7nRoltx9991G7969jZSUFOOyyy4zxo0bZ7z55ptOh9Vml+KtftOmTTOysrKMlJQU42tf+5oxbdo0Y9++fU6H1SqrVq0ycnNzDb/fbwwYMMB48cUXnQ7JsjVr1hiSjLq6OqdDQRx4pS8AAB7j6jl/AABwNpI/AAAeQ/IHAMBjSP4AAHgMyR8AAI8h+QMA4DEkfwAAPIbkDwCAx5D8AQDwGJI/AAAeQ/IHAMBjSP4AAHjM/wdFbZdNYPoQawAAAABJRU5ErkJggg==",
+ "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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Jx44d05/+9CdlZGRo/Pjx6tu3ryZOnKgdO3ZYuj+SPwAAJkbUZ9sIhUIKBAIxIxQKtRlDNBrVwoULlZ+fr9zcXEnShx9+KElasmSJ5s+fr8rKSl1xxRWaMmWKPvjgg7jvj7Y/AACdqLi4WEVFRTHb/H5/m78XDAZVV1cXU9VHo9+sILzrrrt02223SZJGjRqlzZs364UXXojrjwqpHcn/vffeU01NjfLy8jRkyBC9//77euqppxSJRHTrrbdq8uTJbZ4jEomc0vJoNlqU7OtqNRwAAGxn55y/3++PK9l/W2FhoTZs2KDt27crOzu7dXtWVpYkadiwYTHHDx06VIcPH477/Jba/pWVlfre976n+++/X6NGjVJlZaUKCgq0f/9+HTp0SNOmTdOWLVvaPM/pWiAbT7xjJRQAADqNnXP+1q5rqLCwUBUVFdqyZYsGDhwYs3/AgAHq16/fKY//7du3T/3794/7OpaS/7/+67/qgQce0F//+leVlpbqxz/+sebPn6+qqipt3rxZDzzwgJYuXdrmeYqLixUOh2PG9NTLrYQCAIDrBINBvfzyyyovL1dqaqoaGhrU0NDQ+gy/z+fTAw88oKefflqvvPKK9u/fr5///Od6//33NW/evLivY6nt/8477+ill16SJM2ePVs/+clP9I//+I+t+2+55RaVlpa2eZ7TtUBo+QMAEoVTj/qVlJRIkiZNmhSzvbS0VHPnzpUkLVy4UCdPntSiRYv06aefauTIkaqqqtIll1wS93Usz/n7fN/8A+nSpYu6deumQCDQui81NbX1sQQAAM5XTr3e14hznuDBBx+Mec7fKktt/wEDBsQ8SrBz507l5OS0/nz48OHWxQgAACAxWar877nnHrW0tLT+/PfnDv9u48aNca32BwAgkbn9k76Wkv/dd9991v2PPfZYh4IBACARRPmqHwAAcBPe8AcAgIlTC/7OFZI/AAAmTj3qd66Q/AEAMLH6Zr7zDXP+AAB4DJU/AAAmtP0BAPAYHvUDAACuQuUPAIAJj/oBAOAxrPYHAACuQuUPAICJ2xf8kfwBADBx+5w/bX8AADyGyh8AABO3L/gj+QMAYMKc/znSPep0BB2XnvqF0yHYIinSzekQbHFn0gCnQ+iwwo+3Oh2CLZZn/IPTIXTY4SQX/EtK0gWDuzsdwnmBOX8AAOAqCVP5AwCQKGj7AwDgMS5f70fbHwAAr6HyBwDAhLY/AAAew2p/AADgKlT+AACYuOOtDmdG8gcAwMQQbX8AAOAiVP4AAJhEXf6gP8kfAACTqMvb/iR/AABMmPMHAACuQuUPAIAJj/oBAOAxtP0BAICrUPkDAGBC2x8AAI9xe/K3pe1vGC5/GwIAAC5iS/L3+/1677337DgVAACOM+SzbSQiS23/oqKi025vaWnR0qVL1bt3b0nSv/3bv3U8MgAAHBJNzJxtG0vJ/8knn9TIkSPVq1evmO2GYei9995Tjx495PO1/U8sEokoEonEbGs2WpTs62olHAAA0A6Wkv9jjz2m5557Tk888YQmT57cuj05OVllZWUaNmxYXOcJhUJ6+OGHY7b9qOdw3ZA6wko4AAB0Cre/29/SnP+DDz6oVatW6Z577tH999+v5ubmdl20uLhY4XA4ZszoeXm7zgUAgN0MG0cisrzgb+zYsaqtrdUnn3yiMWPGqK6uLq5W/7f5/X6lpaXFDFr+AIBEEbVxJKJ2Peffs2dPvfjii1q5cqWmTp2qlpYWu+MCAACdpEMv+fmnf/onXXXVVaqtrVX//v3tigkAAEdFLXa0zzcdfsNfdna2srOz7YgFAICEkKhz9Xbhwz4AAHgM7/YHAMAkURfq2YXkDwCAidvf8EfbHwAAj6HyBwDAxO1v+CP5AwBgwmp/AADgKlT+AACYsOAPAACPcerd/qFQSGPHjlVqaqoyMjI0c+ZM1dfXn/ZYwzA0ffp0+Xw+rVmzxtJ1SP4AAJg49VW/6upqBYNB1dTUqKqqSs3NzZo2bZqamppOOfbJJ5+0/GG9v6PtDwBAgqisrIz5uaysTBkZGaqtrVVBQUHr9j179uiJJ57Q7t27lZWVZfk6JH8AAEzsnPOPRCKKRCIx2/x+v/x+f5u/Gw6HJUnp6emt27744gv9+Mc/1vLly5WZmdmumGj7AwBgYuecfygUUiAQiBmhUKjtGKJRLVy4UPn5+crNzW3dvmjRIo0fP17XX399u++Pyh8AgE5UXFysoqKimG3xVP3BYFB1dXXasWNH67Z169Zpy5YtevvttzsUE8kfAAATOz/sE2+L/9sKCwu1YcMGbd++XdnZ2a3bt2zZogMHDqhXr14xx8+aNUsTJkzQtm3b4jo/yR8AABPDoef8DcPQggULVFFRoW3btmngwIEx+x988EHdcccdMduGDx+uZcuWacaMGXFfh+QPAECCCAaDKi8v19q1a5WamqqGhgZJUiAQUPfu3ZWZmXnaRX45OTmn/KFwNgmT/PelnP9vUs450cPpEGyxKeVTp0OwRWqXFKdD6LC+PXo5HYItFv71dadD6LCpFw53OgRbHN/Z4nQItkjt5PPb2fa3oqSkRJI0adKkmO2lpaWaO3eubddJmOQPAECicCr5G4b1Qrg9v8OjfgAAeAyVPwAAJuf/RPTZkfwBADBx+1f9SP4AAJg4Ned/rjDnDwCAx1D5AwBg4vbKn+QPAICJ2xf80fYHAMBjqPwBADBhtT8AAB7j9jl/2v4AAHgMlT8AACZuX/BH8gcAwCTq8vRP2x8AAI+h8gcAwMTtC/5I/gAAmLi76U/yBwDgFG6v/JnzBwDAY6j8AQAw4Q1/Z9HU1KTVq1dr//79ysrK0s0336zevXvbFRsAAI5w+6N+lpL/sGHDtGPHDqWnp+ujjz5SQUGB/va3v+myyy7TgQMH9Mgjj6impkYDBw4863kikYgikUjMtq+NFiX5ulq/AwAAYImlOf/3339fX3/9tSSpuLhY/fr106FDh7Rr1y4dOnRII0aM0EMPPdTmeUKhkAKBQMz4Y/id9t0BAAA2M2wciajdC/527typJUuWKBAISJJ69uyphx9+WDt27Gjzd4uLixUOh2NGfuDy9oYCAICtojaORGR5zt/n+2YVxMmTJ5WVlRWz76KLLtInn3zS5jn8fr/8fn9sILT8AQA4Jywn/ylTpigpKUmNjY2qr69Xbm5u675Dhw6x4A8AcN5jwd+3LF68OObnnj17xvy8fv16TZgwoeNRAQDgIHen/g4mf7Nf/vKXHQoGAAB0Pl7yAwCASaIu1LMLyR8AABPm/AEA8Bh3p34+7AMAgOdQ+QMAYMKcPwAAHmO4vPFP2x8AAI+h8gcAwIS2PwAAHuP2R/1o+wMA4DFU/gAAmLi77if5AwBwCtr+AADAVaj8AQAwYbU/AAAe4/aX/JD8AQAwcXvlz5w/AAAekzCVvxtWVqZf8KXTIdhiZLSX0yHgv73vDzgdgi3mp33P6RA6rCJy0OkQbNEnL83pEM4LtP0BAPAY2v4AAMBVqPwBADCJGrT9AQDwFHenftr+AAB4DpU/AAAmbngC7WxI/gAAmLj9UT/a/gAAeAyVPwAAJm5/zp/kDwCACXP+AAB4DHP+AADAVUj+AACYRG0cVoRCIY0dO1apqanKyMjQzJkzVV9f37r/008/1YIFCzR48GB1795dOTk5uvfeexUOhy1dh+QPAICJYRi2DSuqq6sVDAZVU1OjqqoqNTc3a9q0aWpqapIkHTlyREeOHNHjjz+uuro6lZWVqbKyUvPmzbN0Heb8AQBIEJWVlTE/l5WVKSMjQ7W1tSooKFBubq5+//vft+6/5JJL9Oijj+rWW2/V119/raSk+NI6yR8AABM7V/tHIhFFIpGYbX6/X36/v83f/Xs7Pz09/azHpKWlxZ34Jdr+AACcws45/1AopEAgEDNCoVDbMUSjWrhwofLz85Wbm3vaY44fP65HHnlEd955p6X7o/IHAKATFRcXq6ioKGZbPFV/MBhUXV2dduzYcdr9jY2Nuu666zRs2DAtWbLEUkwkfwAATOx8zj/eFv+3FRYWasOGDdq+fbuys7NP2X/ixAldc801Sk1NVUVFhZKTky2dn+QPAICJU2/4MwxDCxYsUEVFhbZt26aBAweeckxjY6Ouvvpq+f1+rVu3Tt26dbN8HZI/AAAJIhgMqry8XGvXrlVqaqoaGhokSYFAQN27d1djY6OmTZumL774Qi+//LIaGxvV2NgoSbrwwgvVtWvXuK5jacHfW2+9pYMHD7b+/Jvf/Eb5+fm6+OKLddVVV2nlypVxnScSibQG/PfxtdFiJRQAADqNU8/5l5SUKBwOa9KkScrKymodq1atkvRNHv7Tn/6kvXv3atCgQTHHfPTRR3Ffx1Lyv+2223TgwAFJ0q9//WvdddddGjNmjB566CGNHTtW8+fP1wsvvNDmeU638vGN8LtWQgEAoNM49Ya/M/0BMXfuXEnSpEmTznjMgAED4r6Opbb/Bx98oEsvvVSStGLFCj311FOaP39+6/6xY8fq0Ucf1e23337W85xu5eNjw+ef4WgAAM4tt3/Yx1Lyv+CCC3T8+HH1799ff/nLXzRu3LiY/VdeeWXMtMCZnG7lY5IvvnkKAADQMZba/tOnT1dJSYkkaeLEiXrllVdi9q9evVqDBg2yLzoAABwQlWHbSESWKv9f/OIXys/P18SJEzVmzBg98cQT2rZtm4YOHar6+nrV1NSooqKis2IFAOCcsLpQ73xjqfLv16+f3n77beXl5amyslKGYWjXrl169dVXlZ2drT/+8Y+69tprOytWAABgA8vP+ffq1UtLly7V0qVLOyMeAAAcl6jtervwkh8AAEzcvtqfr/oBAOAxVP4AAJhEXb7gj+QPAICJu1M/bX8AADyHyh8AABNW+wMA4DEkfwAAPIY3/AEAAFeh8gcAwIS2PwAAHsMb/gAAgKtQ+QMAYOL2BX8kfwAATNw+50/bHwAAj6HyBwDAxO1tf5+RIHf45f950ukQOqx53UanQ7BF8g0znA7BFsY7f3Y6hA77+p3/63QItkgaM8TpEDrM1yvd6RBsceyXf3Q6BFv0f+u1Tj3/yMzxtp3rzw1v2HYuu9D2BwDAY2j7AwBg4vbn/En+AACYRBNjRrzTkPwBADBxe+XPnD8AAB5D5Q8AgAltfwAAPIa2PwAAcBUqfwAATGj7AwDgMbT9AQCAq1D5AwBgQtsfAACPoe0PAABchcofAAATw4g6HUKnIvkDAGASdXnbn+QPAICJ4fIFf8z5AwDgMVT+AACY0PYHAMBjaPsDAABXsZT8FyxYoNdff73DF41EImpsbIwZkeavO3xeAADsEDUM20YispT8ly9frkmTJumyyy7TL37xCzU0NLTroqFQSIFAIGb8cvVr7ToXAAB2M2z8TyKy3PZ/9dVXde211+rxxx9XTk6Orr/+em3YsEHRaPwvRCguLlY4HI4ZD8yeajUUAADQDpaT//Dhw/Xkk0/qyJEjevnllxWJRDRz5kxdfPHFeuihh7R///42z+H3+5WWlhYz/MmsPQQAJAbDMGwbiajdC/6Sk5M1e/ZsVVZW6sMPP9T8+fP1n//5nxo8eLCd8QEAcM5FZdg2EpEtq/1zcnK0ZMkSHTx4UJWVlXacEgAAdBJLvfb+/fura9euZ9zv8/n0gx/8oMNBAQDgpERt19vFUvI/ePBgZ8UBAEDCSNRH9OzCKjsAAEzcXvnzhj8AADyGyh8AAJNEXaVvF5I/AAAmtP0BAICrkPwBADBx6sM+oVBIY8eOVWpqqjIyMjRz5kzV19fHHHPy5EkFg0H17t1bPXv21KxZs3T06FFL1yH5AwBg4tSHfaqrqxUMBlVTU6Oqqio1Nzdr2rRpampqaj1m0aJFWr9+vX73u9+purpaR44c0Q033GDpOsz5AwCQIMxvyS0rK1NGRoZqa2tVUFCgcDis559/XuXl5Zo8ebIkqbS0VEOHDlVNTY2+//3vx3Udkj8AACZ2vuQnEokoEonEbPP7/fL7/W3+bjgcliSlp6dLkmpra9Xc3KypU//nS7hDhgxRTk6Odu7cGXfyp+0PAICJnV/1C4VCCgQCMSMUCrUZQzQa1cKFC5Wfn6/c3FxJUkNDg1JSUtSrV6+YY/v27auGhoa474/KHwCATlRcXKyioqKYbfFU/cFgUHV1ddqxY4ftMZH8AQAwsbpQ72zibfF/W2FhoTZs2KDt27crOzu7dXtmZqa++uorffbZZzHV/9GjR5WZmRn3+Wn7AwBgYmfb3+p1CwsLVVFRoS1btmjgwIEx+0ePHq3k5GRt3ry5dVt9fb0OHz6svLy8uK9D5Q8AgIlTb/gLBoMqLy/X2rVrlZqa2jqPHwgE1L17dwUCAc2bN09FRUVKT09XWlqaFixYoLy8vLgX+0kkfwAAEkZJSYkkadKkSTHbS0tLNXfuXEnSsmXL1KVLF82aNUuRSERXX321VqxYYek6JH8AAEycerN/PB2Hbt26afny5Vq+fHmHLuQJJ0+eNBYvXmycPHnS6VDazQ33YBjuuA833INhcB+JxA33YBjuuQ+38xmGyz9d9N8aGxsVCAQUDoeVlpbmdDjt4oZ7kNxxH264B4n7SCRuuAfJPffhdqz2BwDAY0j+AAB4DMkfAACP8Uzy9/v9Wrx4seW3LCUSN9yD5I77cMM9SNxHInHDPUjuuQ+388yCPwAA8A3PVP4AAOAbJH8AADyG5A8AgMeQ/AEA8BhPJP/ly5drwIAB6tatm6688krt2rXL6ZAs2b59u2bMmKF+/frJ5/NpzZo1TodkWSgU0tixY5WamqqMjAzNnDlT9fX1TodlWUlJiUaMGKG0tDSlpaUpLy9PGzdudDqsDlm6dKl8Pp8WLlzodCiWLFmyRD6fL2YMGTLE6bDa5S9/+YtuvfVW9e7dW927d9fw4cO1e/dup8OK24ABA07538Ln8ykYDDodGs7A9cl/1apVKioq0uLFi/XWW29p5MiRuvrqq3Xs2DGnQ4tbU1OTRo4c2bGPODisurpawWBQNTU1qqqqUnNzs6ZNm6ampianQ7MkOztbS5cuVW1trXbv3q3Jkyfr+uuv1zvvvON0aO3y5ptv6j/+4z80YsQIp0Npl8svv1wff/xx69ixY4fTIVn2t7/9Tfn5+UpOTtbGjRv17rvv6oknntB3vvMdp0OL25tvvhnzv0NVVZUk6cYbb3Q4MpyRs58W6Hzjxo0zgsFg688tLS1Gv379jFAo5GBU7SfJqKiocDqMDjt27JghyaiurnY6lA77zne+Y/z61792OgzLTpw4YVx66aVGVVWVMXHiROO+++5zOiRLFi9ebIwcOdLpMDrsZz/7mXHVVVc5HYat7rvvPuOSSy4xotGo06HgDFxd+X/11Veqra3V1KlTW7d16dJFU6dO1c6dOx2MDOFwWJKUnp7ucCTt19LSopUrV6qpqUl5eXlOh2NZMBjUddddF/P/j/PNBx98oH79+um73/2ubrnlFh0+fNjpkCxbt26dxowZoxtvvFEZGRkaNWqUfvWrXzkdVrt99dVXevnll3X77bfL5/M5HQ7OwNXJ//jx42ppaVHfvn1jtvft21cNDQ0ORYVoNKqFCxcqPz9fubm5Todj2d69e9WzZ0/5/X7dfffdqqio0LBhw5wOy5KVK1fqrbfeUigUcjqUdrvyyitVVlamyspKlZSU6ODBg5owYYJOnDjhdGiWfPjhhyopKdGll16qTZs26Z577tG9996rF1980enQ2mXNmjX67LPPNHfuXKdDwVkkOR0AvCcYDKquru68nJ+VpMGDB2vPnj0Kh8N65ZVXNGfOHFVXV583fwB89NFHuu+++1RVVaVu3bo5HU67TZ8+vfW/jxgxQldeeaX69++v1atXa968eQ5GZk00GtWYMWP02GOPSZJGjRqluro6Pfvss5ozZ47D0Vn3/PPPa/r06erXr5/ToeAsXF359+nTR127dtXRo0djth89elSZmZkOReVthYWF2rBhg7Zu3ars7Gynw2mXlJQUDRo0SKNHj1YoFNLIkSP11FNPOR1W3Gpra3Xs2DFdccUVSkpKUlJSkqqrq/X0008rKSlJLS0tTofYLr169dJll12m/fv3Ox2KJVlZWaf84Th06NDzcgrj0KFDeu2113THHXc4HQra4Orkn5KSotGjR2vz5s2t26LRqDZv3nxeztGezwzDUGFhoSoqKrRlyxYNHDjQ6ZBsE41GFYlEnA4jblOmTNHevXu1Z8+e1jFmzBjdcsst2rNnj7p27ep0iO3y+eef68CBA8rKynI6FEvy8/NPeex137596t+/v0MRtV9paakyMjJ03XXXOR0K2uD6tn9RUZHmzJmjMWPGaNy4cXryySfV1NSk2267zenQ4vb555/HVDMHDx7Unj17lJ6erpycHAcji18wGFR5ebnWrl2r1NTU1jUXgUBA3bt3dzi6+BUXF2v69OnKycnRiRMnVF5erm3btmnTpk1Ohxa31NTUU9Za9OjRQ7179z6v1mDcf//9mjFjhvr3768jR45o8eLF6tq1q26++WanQ7Nk0aJFGj9+vB577DHNnj1bu3bt0nPPPafnnnvO6dAsiUajKi0t1Zw5c5SU5PrUcv5z+nGDc+Hf//3fjZycHCMlJcUYN26cUVNT43RIlmzdutWQdMqYM2eO06HF7XTxSzJKS0udDs2S22+/3ejfv7+RkpJiXHjhhcaUKVOMV1991emwOux8fNTvpptuMrKysoyUlBTjoosuMm666SZj//79TofVLuvXrzdyc3MNv99vDBkyxHjuueecDsmyTZs2GZKM+vp6p0NBHPikLwAAHuPqOX8AAHAqkj8AAB5D8gcAwGNI/gAAeAzJHwAAjyH5AwDgMSR/AAA8huQPAIDHkPwBAPAYkj8AAB5D8gcAwGNI/gAAeMz/BwwfjeDVuuqgAAAAAElFTkSuQmCC",
+ "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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SpYsaNmyo1q1b61e/+pWKi4srnXfq1KnyeDwhY9SoUbZioyUBAICJWy2JzMxMJScnq0+fPvrmm2/0X//1XxoxYoQ++ugjNWrUSCdOnNCJEyf0zDPP6Prrr9fRo0d133336cSJE3r99dcrnXvUqFFavXp1xWev12srNhIGAADMXFr0mJ6eHvJ5zZo1io2NVXZ2tgYPHqxu3brpf//3fyvOd+jQQb/5zW9055136ptvvlHdupf/te71ehUXF1ft2GhJAABwBQUCAZWUlISMQCBQpXu/bTU0bdq00mtiYmIqTRYkaefOnYqNjVWXLl00c+ZMnT59uupfQiQMAABYGEHnht/vl8/nCxl+vz9sDMFgUHPmzNGAAQPUrVu3S15z6tQpPfHEE5oxY0alc40aNUrr1q1TRkaGnnrqKWVmZiopKUnl5eVV/jvxGIYREc+OFA0d6nYINdZ4ZBu3Q3DENx8ddzsER9z4l1Nuh1BjbzW71u0QHHHxYpTbIdRYo8YX3Q7BEQkP/5vbITii4aSFV3T+0z8b4thcjf/0tqWi4PV6w64hmDlzpt566y3t3r1brVq1spwvKSnRT37yEzVt2lRbtmxRvXr1qhzTp59+qg4dOuidd97RsGHDqnQPFQYAAK4gr9ermJiYkBEuWZg1a5a2bt2qHTt2XDJZOHv2rEaNGqXo6GilpaXZShYkqX379mrevLny8vKqfA+LHgEAMDFcWvRoGIZmz56ttLQ07dy5U+3atbNcU1JSopEjR8rr9WrLli1q0KCB7Z9z/PhxnT59WvHx8VW+hwoDAABmLu3DkJycrPXr12vDhg2Kjo5WYWGhCgsLdf78eUn/ShZGjBih0tJSrVy5UiUlJRXXfHc9QmJiotLS0iRJ586d07x585SVlaUjR44oIyNDY8aMUceOHTVy5Mgqx0aFAQCACLF8+XJJ0lDTur7Vq1dr6tSpOnjwoPbt2ydJ6tixY8g1BQUFatu2rSQpNze34gmLqKgoffDBB1q7dq3OnDmjhIQEjRgxQk888YStvRhIGAAAMHGzJVGZoUOHhr3GPE/Dhg21bdu2GsdGwgAAgIlbCUMkI2EAAMCEhMGKRY8AACAsKgwAAJgZHrcjiDgkDAAAmNCSsKIlAQAAwqLCAACAiRGkJWFGwgAAgAktCStaEgAAICwqDAAAmBg8JWFBwgAAgAktCStaEgAAICwqDAAAmPCUhBUJAwAAJlV4IeQPDgkDAAAmVBisWMMAAADCsl1h+Pjjj5WVlaX+/fsrMTFR//jHP/T8888rEAjozjvv1K233hp2jkAgoEAgEHosGJS3DvkLAMB9VBisbP2GTk9P14033qi5c+fqpptuUnp6ugYPHqy8vDwdPXpUI0aM0Lvvvht2Hr/fL5/PFzKWHjtW7S8BAICTDMO5UVvYShgef/xxzZs3T6dPn9bq1av1y1/+UtOnT9f27duVkZGhefPmafHixWHnSU1NVXFxccj4VevW1f4SAADgyrKVMPz973/X1KlTJUkTJkzQ2bNn9Ytf/KLi/OTJk/XBBx+Encfr9SomJiZk0I4AAEQKI+hxbNQWttcweDz/+vJ16tRRgwYN5PP5Ks5FR0eruLjYuegAAHABW0Nb2frX+rZt2+rw4cMVn/fu3avW32klHDt2TPHx8c5FBwAAIoKtCsPMmTNVXl5e8blbt24h5996660qPSUBAEAk410SVrYShvvuu6/S84sWLapRMAAARIIgLQkLVhoCAICw2BoaAAATFj1akTAAAGBSmx6HdAoJAwAAJrVph0ansIYBAACERYUBAAATWhJWJAwAAJjwWKUVLQkAABAWFQYAAEx4rNKKhAEAABOekrCiJQEAAMIiYQAAwCRoeBwbdvj9fvXp00fR0dGKjY3V2LFjlZubG3LNhQsXlJycrGbNmqlx48YaP368ioqKKp3XMAw99thjio+PV8OGDTV8+PCQt09XBQkDAAAmhuFxbNiRmZmp5ORkZWVlafv27SorK9OIESNUWlpacc0DDzygN998U6+99poyMzN14sQJ/fznP6903qefflpLly7VihUrtG/fPjVq1EgjR47UhQsXqhwbaxgAAIgQ6enpIZ/XrFmj2NhYZWdna/DgwSouLtbKlSu1YcMG3XrrrZKk1atXq2vXrsrKytK//du/WeY0DENLlizRI488ojFjxkiS1q1bp5YtW2rz5s264447qhQbFQYAAEwMw7lRE8XFxZKkpk2bSpKys7NVVlam4cOHV1yTmJio1q1ba+/evZeco6CgQIWFhSH3+Hw+9evX77L3XAoVBgAATJzcuCkQCCgQCIQc83q98nq9lccQDGrOnDkaMGCAunXrJkkqLCxU/fr11aRJk5BrW7ZsqcLCwkvO8+3xli1bVvmeS4mYhMEIuh1BzRmnz7gdgiO+OfON2yE44t1WsW6HUGOdPv6b2yE44lDbnm6HUGP//Ooat0NwRELZRbdDuCo4uQ+D3+/XwoULQ47Nnz9fCxYsqPS+5ORkHTp0SLt373YslpqgJQEAwBWUmpqq4uLikJGamlrpPbNmzdLWrVu1Y8cOtWrVquJ4XFycLl68qDNnzoRcX1RUpLi4uEvO9e1x85MUld1zKSQMAACYOPlYpdfrVUxMTMi4XDvCMAzNmjVLaWlpevfdd9WuXbuQ87169VK9evWUkZFRcSw3N1fHjh1T//79Lzlnu3btFBcXF3JPSUmJ9u3bd9l7LiViWhIAAEQKtzZ6TE5O1oYNG/TGG28oOjq6Yo2Bz+dTw4YN5fP5NG3aNKWkpKhp06aKiYnR7Nmz1b9//5AnJBITE+X3+zVu3Dh5PB7NmTNHTz75pDp16qR27drp0UcfVUJCgsaOHVvl2EgYAACIEMuXL5ckDR06NOT46tWrNXXqVEnS7373O9WpU0fjx49XIBDQyJEj9eKLL4Zcn5ubW/GEhSQ9+OCDKi0t1YwZM3TmzBkNHDhQ6enpatCgQZVj8xhGZOyYXTh4qNsh1Fjjvj63Q3BEIPeM2yE4ouRYfbdDqLFOH3/kdgiOqA2LHs+WVr6i/WrRzZ/odgiOaHiX/4rOvyd+vGNz3Xzyfx2by01UGAAAMOFtlVYsegQAAGFRYQAAwKQWbA3kOBIGAABMDNGSMKMlAQAAwqLCAACASTAinh+MLCQMAACYBGlJWJAwAABgwhoGK9YwAACAsKgwAABgwmOVViQMAACY0JKwoiUBAADCosIAAIAJLQkrEgYAAExIGKwcaUlEyBuyAQDAFeJIwuD1evXxxx87MRUAAK4z5HFs1Ba2WhIpKSmXPF5eXq7FixerWbNmkqTnnnuu5pEBAOCSYO35Pe8YWwnDkiVL1LNnTzVp0iTkuGEY+vjjj9WoUSN5POH/lgOBgAKBQOixYFDeOjy0AQBAJLKVMCxatEgvvfSSnn32Wd16660Vx+vVq6c1a9bo+uuvr9I8fr9fCxcuDDn2/7Ruo7lt2toJBwCAK4J3SVjZ+lf6X//619q0aZNmzpypuXPnqqysrFo/NDU1VcXFxSFj9nWtqzUXAABOMxwctYXtHkCfPn2UnZ2tL7/8Ur1799ahQ4eq1Ib4Lq/Xq5iYmJBBOwIAECmCDo7aolr7MDRu3Fhr167Vxo0bNXz4cJWXlzsdFwAAiCA12rjpjjvu0MCBA5Wdna02bdo4FRMAAK4K2qyc/xDUeKfHVq1aqVWrVk7EAgBARKhNaw+cwsIBAAAQFu+SAADApDYtVnQKCQMAACbs9GhFSwIAAIRFhQEAABN2erQiYQAAwISnJKxoSQAAgLCoMAAAYMKiRysSBgAATHis0oqEAQAAE9YwWLGGAQAAhEWFAQAAE9YwWFFhAADAJOjgsGPXrl0aPXq0EhIS5PF4tHnz5pDzHo/nkuO3v/3tZedcsGCB5frExESbkZEwAAAQMUpLS9WzZ08tW7bskudPnjwZMlatWiWPx6Px48dXOu8NN9wQct/u3bttx0ZLAgAAE7eekkhKSlJSUtJlz8fFxYV8fuONN3TLLbeoffv2lc5bt25dy712UWEAAMDE8Dg3AoGASkpKQkYgEKhxjEVFRfrzn/+sadOmhb328OHDSkhIUPv27TV58mQdO3bM9s8jYQAA4Ary+/3y+Xwhw+/313jetWvXKjo6Wj//+c8rva5fv35as2aN0tPTtXz5chUUFGjQoEE6e/asrZ8XMS2JazrVczuEGqvT5lq3Q3BE3dOlbofgiDqfX/1PUh/uer3bITjintNlbodQY6//uHb8c6EW8W5HcFVwsiWRmpqqlJSUkGNer7fG865atUqTJ09WgwYNKr3uuy2OHj16qF+/fmrTpo1effXVKlUnvhUxCQMAAJHCyYTB6/U6kiB813vvvafc3Fxt2rTJ9r1NmjRR586dlZeXZ+s+WhIAAFxlVq5cqV69eqlnz5627z137pzy8/MVH2+v2kTCAACAieHgsOPcuXPKyclRTk6OJKmgoEA5OTkhixRLSkr02muv6d57773kHMOGDdMLL7xQ8Xnu3LnKzMzUkSNHtGfPHo0bN05RUVGaNGmSrdhoSQAAYOLWTo8HDhzQLbfcUvH527UPU6ZM0Zo1ayRJGzdulGEYl/2Fn5+fr1OnTlV8Pn78uCZNmqTTp0+rRYsWGjhwoLKystSiRQtbsZEwAABg4tY+DEOHDpVhVF6XmDFjhmbMmHHZ80eOHAn5vHHjRidCoyUBAADCo8IAAICJWxWGSEbCAACAydW/i4vzaEkAAICwqDAAAGDi1lMSkYyEAQAAE9YwWNGSAAAAYVFhAADAhEWPViQMAACYBEkZLGhJAACAsKgwAABgwqJHKxIGAABMaEhYkTAAAGBChcGKNQwAACAsKgwAAJiw06NVjRKG0tJSvfrqq8rLy1N8fLwmTZqkZs2aORUbAACu4LFKK1sJw/XXX6/du3eradOm+uyzzzR48GB99dVX6ty5s/Lz8/XEE08oKytL7dq1q3SeQCCgQCAQeqw8KG8UHRIAACKRrd/Q//jHP/TNN99IklJTU5WQkKCjR49q//79Onr0qHr06KGHH3447Dx+v18+ny9kPPe3gup9AwAAHGY4OGqLav8r/d69e7VgwQL5fD5JUuPGjbVw4ULt3r077L2pqakqLi4OGSk9K69KAADwfQk6OGoL22sYPJ5/rQS5cOGC4uPjQ85de+21+vLLL8PO4fV65fV6Q46V0I4AACBi2U4Yhg0bprp166qkpES5ubnq1q1bxbmjR4+y6BEAcNVj0aOVrYRh/vz5IZ8bN24c8vnNN9/UoEGDah4VAAAuIl2wqlHCYPbb3/62RsEAAIDIxMZNAACY1KbFik4hYQAAwIQ1DFYkDAAAmJAuWPEsIwAACIsKAwAAJqxhsCJhAADAxKApYUFLAgAAhEWFAQAAE1oSViQMAACY8FilFS0JAAAQFhUGAABMqC9YkTAAAGBCS8KKlgQAABFi165dGj16tBISEuTxeLR58+aQ81OnTpXH4wkZo0aNCjvvsmXL1LZtWzVo0ED9+vXT/v37bcdGwgAAgEnQwWFHaWmpevbsqWXLll32mlGjRunkyZMV45VXXql0zk2bNiklJUXz58/XwYMH1bNnT40cOVJffPGFrdhoSQAAYOLWxk1JSUlKSkqq9Bqv16u4uLgqz/ncc89p+vTpuvvuuyVJK1as0J///GetWrVKv/71r6s8DxUGAABMnKwwBAIBlZSUhIxAIFDt2Hbu3KnY2Fh16dJFM2fO1OnTpy977cWLF5Wdna3hw4dXHKtTp46GDx+uvXv32vq5JAwAAFxBfr9fPp8vZPj9/mrNNWrUKK1bt04ZGRl66qmnlJmZqaSkJJWXl1/y+lOnTqm8vFwtW7YMOd6yZUsVFhba+tkR05I4fcDtCGquZdeLbofgiNLcS/8P72pz4esGbodQY8e+inE7BEf8se2XbodQY332lrgdgiP+fh97GFaFky2J1NRUpaSkhBzzer3VmuuOO+6o+HP37t3Vo0cPdejQQTt37tSwYcNqFGc4EZMwAAAQKZxMq7xeb7UThHDat2+v5s2bKy8v75IJQ/PmzRUVFaWioqKQ40VFRbbWQUi0JAAAuGodP35cp0+fVnx8/CXP169fX7169VJGRkbFsWAwqIyMDPXv39/Wz6LCAACASdBw5ymJc+fOKS8vr+JzQUGBcnJy1LRpUzVt2lQLFy7U+PHjFRcXp/z8fD344IPq2LGjRo4cWXHPsGHDNG7cOM2aNUuSlJKSoilTpqh3797q27evlixZotLS0oqnJqqKhAEAABO39nk8cOCAbrnllorP3659mDJlipYvX64PPvhAa9eu1ZkzZ5SQkKARI0boiSeeCGl55Ofn69SpUxWfJ06cqC+//FKPPfaYCgsLdeONNyo9Pd2yEDIcEgYAACLE0KFDZVRS3di2bVvYOY4cOWI5NmvWrIqKQ3WRMAAAYMK7JKxIGAAAMHFrp8dIxlMSAAAgLCoMAACYsL2VFQkDAAAmrGGwImEAAMCENQxWrGEAAABhUWEAAMCENQxWJAwAAJhUtnnSDxUtCQAAEBYVBgAATHhKwoqEAQAAE9YwWNGSAAAAYVFhAADAhH0YrEgYAAAwYQ2DFS0JAAAQlq2E4eDBgyooKKj4/Mc//lEDBgzQddddp4EDB2rjxo1VmicQCKikpCRkBIIsMQEARAbDMBwbtYWthOHuu+9Wfn6+JOkPf/iD/uM//kO9e/fWww8/rD59+mj69OlatWpV2Hn8fr98Pl/IWP5FQdj7AAD4PgQdHLWFrTUMhw8fVqdOnSRJL774op5//nlNnz694nyfPn30m9/8Rvfcc0+l86SmpiolJSXk2IkB4+yEAgDAFcOiRytbCcM111yjU6dOqU2bNvr888/Vt2/fkPP9+vULaVlcjtfrldfrDTl2ug7LKQAAiFS2fksnJSVp+fLlkqQhQ4bo9ddfDzn/6quvqmPHjs5FBwCAC4IyHBu1ha0Kw1NPPaUBAwZoyJAh6t27t5599lnt3LlTXbt2VW5urrKyspSWlnalYgUA4HtRmxYrOsVWhSEhIUH/93//p/79+ys9PV2GYWj//v16++231apVK/31r3/VT3/60ysVKwAAcIntjZuaNGmixYsXa/HixVciHgAAXFebWglOYadHAABMeErCikcTAABAWFQYAAAwCbLo0YKEAQAAE9IFK1oSAAAgLCoMAACY8JSEFQkDAAAmJAxWJAwAAJiw06MVaxgAAEBYVBgAADChJWFFwgAAgAk7PVrRkgAAIELs2rVLo0ePVkJCgjwejzZv3lxxrqysTA899JC6d++uRo0aKSEhQXfddZdOnDhR6ZwLFiyQx+MJGYmJibZjI2EAAMDEMAzHhh2lpaXq2bOnli1bZjn39ddf6+DBg3r00Ud18OBB/elPf1Jubq5uu+22sPPecMMNOnnyZMXYvXu3rbgkWhIAAFi4tYYhKSlJSUlJlzzn8/m0ffv2kGMvvPCC+vbtq2PHjql169aXnbdu3bqKi4urUWxUGAAAuEoVFxfL4/GoSZMmlV53+PBhJSQkqH379po8ebKOHTtm+2dRYQAAwMTJfRgCgYACgUDIMa/XK6/XW6N5L1y4oIceekiTJk1STEzMZa/r16+f1qxZoy5duujkyZNauHChBg0apEOHDik6OrrKP89jRMjuFOd3rnI7hBoL/M8f3Q7BEQ1Tf+12CI4Ifmi/RxdpjJIzbofgCE/T5m6HUHMxP3I7Akc8NPOvbofgiKVHNl3R+XvG3ezYXOPuG6GFCxeGHJs/f74WLFhQ6X0ej0dpaWkaO3as5VxZWZnGjx+v48ePa+fOnZUmDGZnzpxRmzZt9Nxzz2natGlVvo8KAwAAV1BqaqpSUlJCjtWkulBWVqYJEybo6NGjevfdd20lC5LUpEkTde7cWXl5ebbuI2EAAMDEyX0YnGg/fOvbZOHw4cPasWOHmjVrZnuOc+fOKT8/X//+7/9u6z4WPQIAYBI0DMeGHefOnVNOTo5ycnIkSQUFBcrJydGxY8dUVlamX/ziFzpw4IBefvlllZeXq7CwUIWFhbp48WLFHMOGDdMLL7xQ8Xnu3LnKzMzUkSNHtGfPHo0bN05RUVGaNGmSrdioMAAAYOLWTo8HDhzQLbfcUvH521bGlClTtGDBAm3ZskWSdOONN4bct2PHDg0dOlSSlJ+fr1OnTlWcO378uCZNmqTTp0+rRYsWGjhwoLKystSiRQtbsZEwAAAQIYYOHVrpExpVeU7hyJEjIZ83btxY07AkkTAAAGBht5XwQ0DCAACACS+fsmLRIwAACIsKAwAAJrQkrEgYAAAwoSVhRUsCAACERYUBAAATWhJWJAwAAJjQkrCiJQEAAMKiwgAAgIlhBN0OIeKQMAAAYBKkJWFBwgAAgElV3tnwQ8MaBgAAEBYVBgAATGhJWJEwAABgQkvCipYEAAAIy1bCMHv2bL333ns1/qGBQEAlJSUhI3CxrMbzAgDghKBhODZqC1sJw7JlyzR06FB17txZTz31lAoLC6v1Q/1+v3w+X8j47Ya/VGsuAACcZjj4n9rCdkvi7bff1k9/+lM988wzat26tcaMGaOtW7cqGKz6JhepqakqLi4OGfN++VO7oQAAgO+J7YShe/fuWrJkiU6cOKH169crEAho7Nixuu666/Twww8rLy8v7Bxer1cxMTEhw1u/XrW+AAAATjMMw7FRW1R70WO9evU0YcIEpaen69NPP9X06dP18ssvq0uXLk7GBwDA9y4ow7FRWzjylETr1q21YMECFRQUKD093YkpAQBABLG1D0ObNm0UFRV12fMej0c/+clPahwUAABuqk2tBKfYShgKCgquVBwAAESM2vQ4pFPY6REAABMqDFbs9AgAAMKiwgAAgElterrBKSQMAACY0JKwoiUBAADCosIAAIAJT0lYkTAAAGBSm14a5RRaEgAAICwqDAAAmNCSsCJhAADAhKckrGhJAACAsKgwAABgwqJHKyoMAACYGIbh2LBj165dGj16tBISEuTxeLR582ZLXI899pji4+PVsGFDDR8+XIcPHw4777Jly9S2bVs1aNBA/fr10/79+23FJZEwAABg4VbCUFpaqp49e2rZsmWXPP/0009r6dKlWrFihfbt26dGjRpp5MiRunDhwmXn3LRpk1JSUjR//nwdPHhQPXv21MiRI/XFF1/Yio2EAQCACJGUlKQnn3xS48aNs5wzDENLlizRI488ojFjxqhHjx5at26dTpw4YalEfNdzzz2n6dOn6+6779b111+vFStW6JprrtGqVatsxUbCAACAieHgCAQCKikpCRmBQMB2TAUFBSosLNTw4cMrjvl8PvXr10979+695D0XL15UdnZ2yD116tTR8OHDL3vPZRk/EBcuXDDmz59vXLhwwe1Qqq02fAfDqB3fozZ8B8Pge0SS2vAdDKP2fA8nzZ8/35JHzJ8/P+x9koy0tLSKz3/9618NScaJEydCrrv99tuNCRMmXHKOzz//3JBk7NmzJ+T4vHnzjL59+9r6Hp7/L6har6SkRD6fT8XFxYqJiXE7nGqpDd9Bqh3fozZ8B4nvEUlqw3eQas/3cFIgELBUFLxer7xeb6X3eTwepaWlaezYsZKkPXv2aMCAATpx4oTi4+MrrpswYYI8Ho82bdpkmePEiRO69tprtWfPHvXv37/i+IMPPqjMzEzt27evyt+DxyoBALiCqpIcVEVcXJwkqaioKCRhKCoq0o033njJe5o3b66oqCgVFRWFHC8qKqqYr6pYwwAAwFWgXbt2iouLU0ZGRsWxkpIS7du3L6R68F3169dXr169Qu4JBoPKyMi47D2XQ4UBAIAIce7cOeXl5VV8LigoUE5Ojpo2barWrVtrzpw5evLJJ9WpUye1a9dOjz76qBISEiraFpI0bNgwjRs3TrNmzZIkpaSkaMqUKerdu7f69u2rJUuWqLS0VHfffbet2H4wCYPX69X8+fMdKQu5pTZ8B6l2fI/a8B0kvkckqQ3fQao938MtBw4c0C233FLxOSUlRZI0ZcoUrVmzRg8++KBKS0s1Y8YMnTlzRgMHDlR6eroaNGhQcU9+fr5OnTpV8XnixIn68ssv9dhjj6mwsFA33nij0tPT1bJlS1ux/WAWPQIAgOpjDQMAAAiLhAEAAIRFwgAAAMIiYQAAAGH9IBIGJ17r6aZwrzu9Gvj9fvXp00fR0dGKjY3V2LFjlZub63ZYti1fvlw9evRQTEyMYmJi1L9/f7311ltuh1Ujixcvlsfj0Zw5c9wOxZYFCxbI4/GEjMTERLfDqpbPP/9cd955p5o1a6aGDRuqe/fuOnDggNthVVnbtm0t/114PB4lJye7HRocVOsTBqde6+mmcK87vRpkZmYqOTlZWVlZ2r59u8rKyjRixAiVlpa6HZotrVq10uLFi5Wdna0DBw7o1ltv1ZgxY/T3v//d7dCq5f3339f//M//qEePHm6HUi033HCDTp48WTF2797tdki2ffXVVxowYIDq1aunt956Sx999JGeffZZ/ehHP3I7tCp7//33Q/572L59uyTp9ttvdzkyOMrWmyeuQn379jWSk5MrPpeXlxsJCQmG3+93Marqk+llJFerL774wpBkZGZmuh1Kjf3oRz8y/vCHP7gdhm1nz541OnXqZGzfvt0YMmSIcf/997sdki3z5883evbs6XYYNfbQQw8ZAwcOdDsMR91///1Ghw4djGAw6HYocFCtrjA4+lpPOKq4uFiS1LRpU5cjqb7y8nJt3LhRpaWltrdYjQTJycn62c9+FvLPx9Xm8OHDSkhIUPv27TV58mQdO3bM7ZBs27Jli3r37q3bb79dsbGxuummm/T73//e7bCq7eLFi1q/fr3uueceeTwet8OBg2p1wnDq1CmVl5dbdrNq2bKlCgsLXYoKwWBQc+bM0YABA9StWze3w7Htww8/VOPGjeX1enXfffcpLS1N119/vdth2bJx40YdPHhQfr/f7VCqrV+/flqzZo3S09O1fPlyFRQUaNCgQTp79qzbodny6aefavny5erUqZO2bdummTNn6le/+pXWrl3rdmjVsnnzZp05c0ZTp051OxQ47AezNTQiR3Jysg4dOnRV9pslqUuXLsrJyVFxcbFef/11TZkyRZmZmVdN0vDZZ5/p/vvv1/bt20O2k73aJCUlVfy5R48e6tevn9q0aaNXX31V06ZNczEye4LBoHr37q1FixZJkm666SYdOnRIK1as0JQpU1yOzr6VK1cqKSlJCQkJbocCh9XqCoOTr/WEM2bNmqWtW7dqx44datWqldvhVEv9+vXVsWNH9erVS36/Xz179tTzzz/vdlhVlp2drS+++EI//vGPVbduXdWtW1eZmZlaunSp6tatq/LycrdDrJYmTZqoc+fOIS/uuRrEx8dbks2uXbtele2Vo0eP6p133tG9997rdii4Amp1wuDkaz1RM4ZhaNasWUpLS9O7776rdu3auR2SY4LBoAKBgNthVNmwYcP04YcfKicnp2L07t1bkydPVk5OjqKiotwOsVrOnTun/Px8xcfHux2KLQMGDLA8YvzJJ5+oTZs2LkVUfatXr1ZsbKx+9rOfuR0KroBa35Jw6rWebgr3utOrQXJysjZs2KA33nhD0dHRFWtIfD6fGjZs6HJ0VZeamqqkpCS1bt1aZ8+e1YYNG7Rz505t27bN7dCqLDo62rJ2pFGjRmrWrNlVtaZk7ty5Gj16tNq0aaMTJ05o/vz5ioqK0qRJk9wOzZYHHnhAN998sxYtWqQJEyZo//79eumll/TSSy+5HZotwWBQq1ev1pQpU1S3bq3/1fLD5PZjGt+H//7v/zZat25t1K9f3+jbt6+RlZXldki27Nixw5BkGVOmTHE7tCq7VPySjNWrV7sdmi333HOP0aZNG6N+/fpGixYtjGHDhhlvv/2222HV2NX4WOXEiRON+Ph4o379+sa1115rTJw40cjLy3M7rGp58803jW7duhler9dITEw0XnrpJbdDsm3btm2GJCM3N9ftUHCF8HprAAAQVq1ewwAAAJxBwgAAAMIiYQAAAGGRMAAAgLBIGAAAQFgkDAAAICwSBgAAEBYJAwAACIuEAQAAhEXCAAAAwiJhAAAAYZEwAACAsP5fQ+Cwkm73CioAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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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",
+ "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",
+ " [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",
+ " [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]])"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ground_truth[240:275,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "ac35e1bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 2.09734881e-05, 1.30941775e-02, 1.83959462e-04, 3.06179025e-03],\n",
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+ " 1.37077363e-10, 1.03727289e-05, 2.30010544e-09, 3.51725384e-06,\n",
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+ " 1.64626672e-05, 1.35136979e-05, 3.20742693e-04, 3.91293503e-02,\n",
+ " 5.13291336e-04, 5.67608746e-04, 7.75005436e-04, 8.34570825e-02],\n",
+ " [2.43697898e-03, 9.60870832e-02, 3.86846781e-01, 2.01077177e-03,\n",
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+ " 2.18156278e-02, 1.51161730e-04, 2.05734029e-01, 5.47656091e-03,\n",
+ " 4.20755940e-03, 4.21694890e-02, 5.87108545e-04, 1.90183241e-02],\n",
+ " [8.44992828e-05, 1.13032404e-02, 3.18095654e-01, 1.22222467e-04,\n",
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+ " 5.97865619e-02, 1.31321114e-08, 5.58456838e-01, 7.86527060e-04,\n",
+ " 2.08381925e-06, 1.42805818e-02, 2.29267007e-05, 2.29989667e-03],\n",
+ " [1.19809301e-05, 5.14518376e-03, 2.69585252e-01, 1.21971070e-05,\n",
+ " 2.76181940e-03, 4.41201590e-03, 5.42267226e-05, 1.34239745e-04,\n",
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+ " 9.53314233e-11, 8.09929043e-05, 1.08291420e-06, 8.56136194e-06],\n",
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+ " 1.25727922e-01, 8.09234280e-08, 8.73315692e-01, 6.67478816e-05,\n",
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+ " [2.03575064e-06, 1.46479916e-03, 4.76197820e-05, 2.09547215e-06,\n",
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+ " 1.14609733e-01, 6.16342199e-10, 8.79018605e-01, 1.71905995e-04,\n",
+ " 1.33088201e-06, 2.92912264e-05, 2.89670315e-05, 9.12425341e-04],\n",
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+ " 6.96300413e-04, 2.96714925e-03, 1.27539039e-04, 4.54536546e-03,\n",
+ " 1.14995934e-01, 5.91794162e-07, 8.68174970e-01, 1.74761604e-04,\n",
+ " 1.37414918e-05, 1.94156775e-04, 2.00661190e-04, 5.14708413e-03],\n",
+ " [9.94997773e-09, 1.97875488e-04, 8.32087360e-04, 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, ?it/s]/gpfs3/well/win-fmrib-analysis/users/uap971/osl-dynamics/rotation/utils.py:244: RuntimeWarning: invalid value encountered in log\n",
+ " return 0.5 * np.log((1 + v)/(1 - v))\n",
+ "Compute twopair Fisher-z transformated correlation: 100%|â–ˆ| 8/8 [00:00<00:00, 22\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
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+ "