image-classification.ipynb

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   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
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   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
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   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
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   "source": [
    "# Basic classification: Classify images of clothing"
   ]
  },
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   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/classification\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/keras/classification.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "FbVhjPpzn6BM"
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   "source": [
    "This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It's okay if you don't understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.\n",
    "\n",
    "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
   ]
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     "text": [
      "2022-12-14 06:37:29.047458: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 06:37:29.047559: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 06:37:29.047570: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.11.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
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   "cell_type": "markdown",
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    "id": "yR0EdgrLCaWR"
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   "source": [
    "## Import the Fashion MNIST dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DLdCchMdCaWQ"
   },
   "source": [
    "This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:\n",
    "\n",
    "<table>\n",
    "  <tr><td>\n",
    "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
    "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
    "  </td></tr>\n",
    "  <tr><td align=\"center\">\n",
    "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
    "  </td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing you'll use here.\n",
    "\n",
    "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
    "\n",
    "Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and [load the Fashion MNIST data](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/fashion_mnist/load_data) directly from TensorFlow:"
   ]
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   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
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   "source": [
    "Loading the dataset returns four NumPy arrays:\n",
    "\n",
    "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n",
    "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n",
    "\n",
    "The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 9. These correspond to the *class* of clothing the image represents:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>Label</th>\n",
    "    <th>Class</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T-shirt/top</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>Trouser</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>Pullover</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>Dress</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>Coat</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>Sandal</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>Shirt</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>Sneaker</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>Bag</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>Ankle boot</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
   ]
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   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
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    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## Explore the data\n",
    "\n",
    "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_images.shape"
   ]
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   "source": [
    "Likewise, there are 60,000 labels in the training set:"
   ]
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    {
     "data": {
      "text/plain": [
       "60000"
      ]
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     "execution_count": 7,
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   "source": [
    "len(train_labels)"
   ]
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   "source": [
    "Each label is an integer between 0 and 9:"
   ]
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    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   "source": [
    "train_labels"
   ]
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   "source": [
    "There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:"
   ]
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    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 9,
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   "source": [
    "test_images.shape"
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   "source": [
    "And the test set contains 10,000 images labels:"
   ]
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    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
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   ],
   "source": [
    "len(test_labels)"
   ]
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   "source": [
    "## Preprocess the data\n",
    "\n",
    "The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
   ]
  },
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It's important that the *training set* and the *testing set* be preprocessed in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:37:31.039688Z",
     "iopub.status.busy": "2022-12-14T06:37:31.039049Z",
     "iopub.status.idle": "2022-12-14T06:37:31.212723Z",
     "shell.execute_reply": "2022-12-14T06:37:31.211984Z"
    },
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "To verify that the data is in the correct format and that you're ready to build and train the network, let's display the first 25 images from the *training set* and display the class name below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:37:31.216892Z",
     "iopub.status.busy": "2022-12-14T06:37:31.216617Z",
     "iopub.status.idle": "2022-12-14T06:37:31.906287Z",
     "shell.execute_reply": "2022-12-14T06:37:31.905372Z"
    },
    "id": "oZTImqg_CaW1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xp+Y2NW6bN2/uxdwkXVV8VRViU8m97rU6ZBMOlD2pFpFWj3PHiOoTOpe6/dR3wPKGXzwAAAAAJI6FBwAAAIDEsfAAAAAAkDgWHgAAAAASd9BkTLkJPKpCr0ouHzp0qBfLy8uLtevVq+f1URXC1fHdpO1FixZ5fSpVquTFtm3b5sXcBDhVvVqdl6rU+pvf/CbWnjp1qtfHTQpVr2lZ5iYndu3a1eujNhb4+OOPvZg7/tT7pxL43dfYTCfoulSCpIq5x1LnpcafSuxVFeFRMtS4dGOpJluGPlaNt1Srp2dlZXkxEslLr+rVq3sxdf1zqblNjZmQyuUquXzlypVeLGRTFJUIj7JFzUepPi6kQr2ax9Q4dWMrVqzY31Msc/jFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHEHTXK5mwykKosr7du392JukqZK4g5NXncTiVTCpFv12kwnHbvnoZKVVcJd06ZNvZhbdfrmm2/2+hx33HGxtqqAXlaoBDL39VRjRiXZ1qhRw4u540GNhdBk35CqqUUptHK1qrzuCk24S/pvKu/U61daN38I2XgBpUPIxhZmOtnW3YRFzafq+qS4c406ltpIpUGDBl7MTTg/GCpHH4yKMrncvSaGVDc309/b3M1bcnNz9/MMyx5+8QAAAACQOBYeAAAAABLHwgMAAABA4hLP8VD3ual7jdV95O5jVSGzA7nnNMSZZ57pxTIyMmLtatWqeX1CCw65972qewBVsbaQHBX1N6vXS70f06dPj7VVAa/yRN2jqcabq1WrVl4sMzPTi6WaYxRy72hR5kOo8wodyyFjRH3OQwqLYf+E5HOEFmsLUZTHCh0jql/o9QCpCX3NVb7f2rVrY2113Vy9enXQebjXzc2bN3t91q9f78VC5l31N6rCvkqq3zOQvJAcj5DvoaHHDs2zc+c2cjwAAAAAoAiw8AAAAACQOBYeAAAAABLHwgMAAABA4oo8EyqkUFpJJGB9+umnsfawYcO8Pp999pkXq169uherU6dOrK0KXqnEIvV3u8dXyUfq+Crh3H3O0EJIKnnYfeybb77p9enbt2/Q8csqN9FMjWWVIOkWmDTz3y+VuK4KUYYkrak+IUWPFFXAUiVuquOTJF56hMwPoUWvQhK7D6Q4YchmCSqm5i01flF0QpP33eRvM7Mjjzwy1m7WrJnXR8016j1dvnx5rK2Sxps3bx50LDcRvlGjRl6fH374wYuh9JozZ44Xc+cLNaeEbojhzlmhxQlVP/d74apVq4KOVZbxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiijzLO9UE0zVr1nixpUuXxtoqYcjtY6YTod3HqgRglVikErTd6qrZ2dleH5XEppKH3SQ5dV4q4a5Hjx5ebOPGjbH2+PHjvT4qOVBVnHaTnz///HOvT3kXUhFcvZ4qlmpib8h5pZoQF/qcodXuQxJPi7LKOvYu5L0OrbYbevyiEnrsVCujI3nq2tOqVatYOzT5u0aNGl7MvdatW7fO66M2hlFJ6Oo7hMu9TpuZrVixwovVr18/1g6t9I6iNWvWLC/WpEmTWFuNBfVdS3Gvf6Fzlrpuut/5li1b5vWZOHGiF1PfAcsKPgEAAAAAEsfCAwAAAEDiWHgAAAAASBwLDwAAAACJK/Lk8kmTJsXagwcP9vqsXLnSi6nkMDcJSyVq1axZ04upBHc3QU0lcasEIVWZ2k3qGTp0qNfn2GOP9WJuhVQzP5kuNzfX66NMnz7di+Xn58fabjKVmU6WVwlVmzZtSum8oJMV3XEaWuk51YTwVKljqyrrqt/OnTsTOSfsvwOpJB7CTUI/kMq97rHUOFJ/D+Mtee41VyVGL1682It99913Xqxly5ax9tq1a70+7sYtZmatW7f2Yu716fvvv/f61KpVy4upa3CIjIwML/bKK694sRtvvDHWJpG8ZHz00UdeLGRTltDNANx5LHRTDnV897FqvD/99NNejORyAAAAANgHFh4AAAAAEsfCAwAAAEDiDjjHY9euXbH7bwcNGhT7d3W/e8WK/tOq++hUPoJr27ZtXkzlZaiYa/369V5s4cKFXuy2224r9NjqnrxGjRp5MTfH45RTTvH6uIWXzMzmzp3rxdz7Y9W9+eq+aHXfofseuYWRDgapFrsLKaK5fft2LxZyL6mKhRaCC+mnzkvlQ6njh9xzTwHB4qHea3dcho6RkEJ9oe+r6hdyfHVear7OzMwMOg+ECclRGD16tBc74ogjvNjWrVtjbfVeqett48aNvdjs2bNjbTXnqhxHlRvZoEGDWFvlmah8kR9++MGLudflww47zOuD5KmCx+53GnW9OpBCgCHUXOd+LtT1VhUQLMv4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxB5xc/sorr8SSq93kMLdokJlf/MfMbOPGjV5MJXm5VIKQSjp0E81UwtqWLVu8mJt4ZmY2cODAWPutt97y+vTt29eLLViwwIu5r8VXX33l9Rk7dqwXU8lNblKSSrxXycOKm4ilHucWjlLv4cFIJYe5SWUqGTK0eFFIwTW1sYBK7HXHkeqjNoNQVBFQlIwdO3Z4MXd8FWXRv6Kkxpt6PjcpEyVDJWx37NjRi7njT11T1DVLCdnIImTuNPM3eFEFEVUifEhyPMnlJUMVPHY3CDiQeS3kuhnK/Vyo76HLli3zYuqzor57lEb84gEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJC4A04ur1evnlWvXr2g7SZxq4RjlQDTrFkzL+Y+ViVMbtiwwYvVrl3bizVv3rzQ83KTzPYWcxOD+/fv7/Xp0KGDF1MJT24CvXptatas6cVU8rB7XpUrV/b6pFoxWyVizZkzJ9ZWmwYcjEIqlyupJrupjQZCE8Ld44eegxp/Kiku5FgoeiFVedW4KYn3J2SsqvEWmhyPoqM2SGnUqJEXU4n/GRkZsbYao2ruDJlX1BhS17WQ5PWffp/ZQyX3qg1qVq5cWejxUbTWrl3rxdT7UL9+/VhbjQU1ZtSmL+48GfIdam8x9zxOP/10r89///tfL6Y2IurRo4cXK434xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxB5xcnp2dHUsac5NsmjZt6j1GJSGrZCA3qbpevXpeHxVTSWtuAo/qoxLi8vPzvZiblFmnTh2vz3fffefF3OQ6Mz+p3q2uubfzUn+3m2CnEjJVEl5IpcysrCyvz9SpUws9z4ORStoNkWpi74Ek2brPGZJIZ6aTQDdv3pzyeaBoqU0kXOp9DU2uTFLoZgZsZlH8VFVvNWbU9dUdk+p6oa5PalMZl0owVsdSc7N7roceeqjXZ+7cuUHHWr9+fay9Zs0ar4/a/Aap++abb4L6ueNBfe8Jnf/csavmW3WNDJnbcnJyvD5qrM2aNcuLkVwOAAAAAP8/Fh4AAAAAEsfCAwAAAEDiDjjHo2PHjpaZmVnQdovpvfDCC95jsrOzvVirVq28mFu8T+VbqHvr1L177n2i6h5UVSxQ9XPv01MFh1RRJXX/oHsfoHo+VUAwpDCjepyKqUKD7n2HqnBUgwYNYu2QQk9lSVEWUyvK++RDcjpC80xCCgiqcw+9fxolQ82L7nut3sOSKMrnji91b7TK8Zg/f74X69SpU9GdGDzq+qTmB3VNdHPAVO6Guhap8eBeS9X1UI1vVaD3hx9+iLW7dOni9fn000+9mLrGu6+Pyj0hx6NojRw50ovVrVvXi7lzSMi4MtPfO915Un0u1ON++l15D3ecqmKV6lxnzJjhxcoKfvEAAAAAkDgWHgAAAAASx8IDAAAAQOJYeAAAAABI3AEnl7vuuOOOWPvoo4/2+vz1r3/1Yip52S2SpxKjVRKbSnZzCwiGFBIy08mWbpJmSLEkM5187T42NLlT9XNfC5VwpwoaqYQqN8GpY8eOXp9f/epXsfaGDRvsqquu0idcBrmvcWiyuUqQTDXxPqSgkUo8U58BdSyX+hvVWFPPGZJcXpQJ+9i7pUuXFtontFikGjfuex36voaMSzXeVKKwSiBFslavXu3F1LVOFbidOXNmrK3mRFWoVh3fHQ+hG8+oDWSmT58ea5999tleH/XdQx3fTSZX3w1QtNQmE+q7j/udRl2vVDFolez9zjvvxNrnnHOO16datWpeTBXZVYWlQx737bffFvq40opfPAAAAAAkjoUHAAAAgMSx8AAAAACQOBYeAAAAABJ3wMnlu3fvjiUMuomCZ511lvcYFfv444+9mJuonpub6/VZv369F1PJiW4ikaqaGlrJt379+rG2Sqxs0qSJF1OJbW5i0YFUf3aTmkMT73v37u3F2rVrF2v36NEj5fNCWEJ4aNVwNxaaSB6ycYEay6FV16lcXnqoucad89R7rd7DkM0FQt97VYHcfWxo9eBmzZoFPSeKzsqVK72Ymh9Uku66detibTVmsrOzvZhK4q5Vq1asnZ6eHnReIVSyr/t8Zvrz455HXl6e16dt27YpnRc0ldg9btw4L+bOY2qeUUncSkhCuPo+qea/kMep+bxDhw6FHqu04hcPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxB1wcnmFChWCKiIX5pRTTvFin3/+eaGPmz17thdTCXBuctiSJUu8Ps2bN/diqgp1q1atCj0vlH2pVtlWCZJz586NtVUCmfocqZibbKn6qHNXMfc81KYLoahcXnp07drVi82ZMyfWdpN9zXQSo+ImaqrxnOp7rRJy1RgnSbf4bdq0yYupTUzcCt7K1q1bvZi63qrq3+41XlVKV+eqvhu4MVUJO3SzDnfMqwraKFpXXnmlF7vqqqu8mPt+qQ0Q1KYvSsh33rp163oxNee6Y37Dhg1eHxUbNGhQoedQWvGLBwAAAIDEsfAAAAAAkDgWHgAAAAASd8A5HiXt8MMPD4q52rdvn8TpAPI+TrcAmsqlWL16tRdTeRNuYawDyctw781Xz6eKYW7ZssWLqXujXaHFDnFg1H33l156aaw9duxYr8+qVau8mLpX3r3vPqQwlpkeX+4YbNGihddH5QCqvxHJcnPVzMwOPfRQL6byN1xqLlAF3FTekVvQ9pVXXvH6qNyQU089tdDzUOel5nQ1/lq2bBlrn3zyyV4fJG/69OlerGPHjoU+rkqVKkHHX7FiRaF9li1b5sXU58KdE1Ve0OjRo72YykkuK7jiAwAAAEgcCw8AAAAAiWPhAQAAACBxLDwAAAAAJK7MJ5cDSXELDoUWROvcubMXO/LII2PtmjVren1Ck8Td5MeMjAyvjzpXVfDKTexVid4qcVglW6qidS4SyYuHeq/dJN0zzzwz6Fhr1qzxYm7i5Pr1670+agw2bNiw0FiqRQz39pwoOk899ZQXU8UjVYL2RRddFGurzShUwuzixYu9mJvQ3qVLF/9kA51//vmF9rnwwgtTPj6KX4cOHbyYO1+MHz/e6zNr1iwv9vHHH3uxnj17FnoO1113nRdTSenu5+Kss84q9NhlHd8CAAAAACSOhQcAAACAxLHwAAAAAJC4lHM89twvt2HDhiI7GZRte8aCuve6qBXH+Es1x2Pbtm1ebPv27YX2STXHQ91jXZQ5Hqromzp/t/hXcc8NxTn+fvo8pXEOLMr8B/X3uQUxVZFB9XyqOJZbiM39rOxNacvxOBjGnyrKF5rj4c4taiyovyW0H8rfNThJas5ShXHVddkdk+np6V6fkO8BZiV/3SxKoeMv5YXHnhe+adOmqR4C5dTGjRstKysr8ecwY/zBVxzjb8/zmDEGEcf4Q0njGpysN998s8iO9dprrxXZsUqLwsZfWpTi0nj37t22dOlSq1GjBjuJwMx+XOVu3LjRsrOzE9+9iPEHV3GOPzPGIOIYfyhpXINRkkLHX8oLDwAAAAAIRXI5AAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxLDwAAAAAJO6gXnjcc889dvTRR+/134cMGWI1a9Y8oOe47LLLrF+/fgd0DJR/hY1FM7OTTjrJbrzxxmI5Hxx8GIMADlbMf8WnTC88Jk2aZIcccoidffbZJX0qJY4PRPFKS0vb5//uueeeIn/ON9980+6///599snNzbW0tDSbOnWq/Pd7773XfvWrX5nZj3/DW2+9VcRnieLCGERZd9lllxWM10qVKlmDBg2sd+/e9vzzz9vu3btL+vRQijH/lV0VS/oEDsRzzz1n119/vT333HO2dOlSy87OLulTwkEiLy+v4P8PHTrUBg8ebDk5OQWxjIyMIn/O2rVr7/Pft2/fXugx3n77bbvtttuK6pRQghiDKA/69OljL7zwgu3atcuWL19uo0aNskGDBtkbb7xhI0aMsIoV/a8pO3bssEqVKpXA2aK0YP4ru8rsLx75+fk2dOhQu+aaa+zss8+2IUOGxP593LhxlpaWZh999JF16dLFqlevbj169IgNTNf8+fOtZcuWdt1111kURbLP22+/bZ07d7aqVatay5Yt7d5777WdO3cWer733nuv1atXzzIzM+3qq6+ODdBt27bZDTfcYPXr17eqVatar169bMqUKbHHf/LJJ9a1a1erUqWKNWrUyG677baC573sssvsk08+sccff7xgtZ+bm1voOSF1DRs2LPhfVlaWpaWlxWJq0hs3bpx17drV0tPTrWbNmtazZ09buHBhrM9LL71kLVq0sKysLPvFL35hGzduLPg391etFi1a2P3332+XXnqpZWZm2lVXXWWHHnqomZl16tTJ0tLS7KSTTirov3jxYvv222+tT58+1qJFCzMz69+/v6WlpRW0zcyefvppa9WqlVWuXNnatm1rL730Uuwc09LS7Omnn7YzzzzTqlWrZi1btrQ33ngjxVcSqWIMMgbLgypVqljDhg2tcePG1rlzZ7vjjjvs7bfftvfff7/gur7n/T733HMtPT3dHnzwQTPb9/U4iiK75557rFmzZlalShXLzs62G264oeB5n3rqKTvssMOsatWq1qBBA7vggguK/W9H6pj/yvD8F5VRzz33XNSlS5coiqLonXfeiVq1ahXt3r274N/Hjh0bmVnUrVu3aNy4cdG3334bHX/88VGPHj0K+tx9993RUUcdFUVRFE2bNi1q2LBh9H//938F//7CCy9EWVlZBe1PP/00yszMjIYMGRLNnz8/+uCDD6IWLVpE99xzz17Pc+DAgVFGRkZ00UUXRTNnzoxGjhwZ1atXL7rjjjsK+txwww1RdnZ29N5770XffvttNHDgwKhWrVrR6tWroyiKoiVLlkTVq1ePrr322mjWrFnR8OHDo7p160Z33313FEVRtG7duqh79+7RlVdeGeXl5UV5eXnRzp07U35tsX/ccaLs2LEjysrKin7/+99H8+bNi7777rtoyJAh0cKFC6Mo+nEsZmRkRD/72c+iGTNmRJ9++mnUsGHD2Dg58cQTo0GDBhW0mzdvHmVmZkZ//etfo3nz5kXz5s2Lvvjii8jMojFjxkR5eXkFYyiKoujvf/97dPrpp0dRFEUrVqyIzCx64YUXory8vGjFihVRFEXRm2++GVWqVCl68skno5ycnOjhhx+ODjnkkOjjjz8uOI6ZRXXq1ImeeeaZKCcnJ7rzzjujQw45JPruu+8O9KVEihiDjMGyaODAgdF5550n/+2oo46KzjzzzCiKfny/69evHz3//PPR/Pnzo4ULFxZ6PX799dejzMzM6L333osWLlwYTZ48OfrXv/4VRVEUTZkyJTrkkEOiV155JcrNzY2+/vrr6PHHHy+WvxlFj/mvbM1/ZXbh0aNHj+ixxx6LoujHAVW3bt1o7NixBf++Z+ExZsyYgti7774bmVm0ZcuWKIr+38JjwoQJUa1ataK//vWvsedwB/Opp54a/eEPf4j1eemll6JGjRrt9TwHDhwY1a5dO9q0aVNB7Omnn44yMjKiXbt2Rfn5+VGlSpWil19+ueDft2/fHmVnZ0cPPfRQFEVRdMcdd0Rt27aNLayefPLJgmNEkf+BQPEJmfRWr14dmVk0btw4+e933313VL169WjDhg0FsZtvvjnq1q1bQVtNev369YsdZ8GCBZGZRd988433HL17947+/ve/F7TNLBo+fHisT48ePaIrr7wyFrvwwgujs846K/a4q6++OtanW7du0TXXXCP/NiSPMcgYLIv2tfC46KKLonbt2kVR9OP7feONN8b+vbDr8cMPPxy1adMm2r59u3fsYcOGRZmZmbGxjrKL+a9szX9l8larnJwc++KLL2zAgAFmZlaxYkW76KKL7LnnnvP6duzYseD/N2rUyMzMVqxYURBbtGiR9e7d2wYPHmy/+93v9vm806ZNs/vuu88yMjIK/nfllVdaXl6ebd68ea+PO+qoo6x69eoF7e7du1t+fr4tXrzY5s+fbzt27LCePXsW/HulSpWsa9euNmvWLDMzmzVrlnXv3t3S0tIK+vTs2dPy8/NtyZIl+zxnFL9FixbFxsgf/vAHq127tl122WV2xhlnWN++fe3xxx+P3aNq9uPPtjVq1ChoN2rUKDZWlS5dugSd04YNG+yTTz6xc889d5/9Zs2aFRuLZj+OtT1jcY/u3bt7bbcPSg5jEGVdFEWxa547zgq7Hl944YW2ZcsWa9mypV155ZU2fPjwgtuwevfubc2bN7eWLVvaJZdcYi+//PI+r+EoW5j/SrcyufB47rnnbOfOnZadnW0VK1a0ihUr2tNPP23Dhg2z9evXx/r+NAFtzyT2090y6tWrZ127drVXX33VNmzYsM/nzc/Pt3vvvdemTp1a8L8ZM2bY3LlzrWrVqkX4F6Isy87Ojo2Rq6++2szMXnjhBZs0aZL16NHDhg4dam3atLHPP/+84HFusmRaWlqhO7ukp6cHndP7779vRxxxhDVt2nQ//xqURYxBlHWzZs0quF/ezB9nhV2PmzZtajk5OfbUU09ZtWrV7Nprr7UTTjjBduzYYTVq1LCvv/7aXn31VWvUqJENHjzYjjrqKFu3bl0x/5VIAvNf6VbmFh47d+60F1980R5++OHYwJo2bZplZ2fbq6++ul/Hq1atmo0cOdKqVq1qZ5xxRiyRyNW5c2fLycmx1q1be/+rUGHvL+W0adNsy5YtBe3PP//cMjIyrGnTpgUJRBMmTCj49x07dtiUKVPsiCOOMDOzdu3a2aRJk2IJ7xMmTLAaNWpYkyZNzMyscuXKtmvXrv3625GMihUrxsbGT3fC6NSpk91+++02ceJEa9++vb3yyitF+tyVK1c2M/PGwttvv23nnXdeLFapUiWvX7t27WJj0ezHsbZnLO7x08l6T7tdu3YHdO4oOoxBlGUff/yxzZgxw84///y99gm5HlerVs369u1rTzzxhI0bN84mTZpkM2bMMLMfPyOnnXaaPfTQQzZ9+nTLzc21jz/+uFj+PiSL+a90K3Pb6Y4cOdLWrl1rV1xxhWVlZcX+7fzzz7fnnnuuYHUbKj093d59910788wz7cwzz7RRo0bJHREGDx5s55xzjjVr1swuuOACq1Chgk2bNs1mzpxpDzzwwF6Pv337drviiivszjvvtNzcXLv77rvtuuuuswoVKlh6erpdc801dvPNN1vt2rWtWbNm9tBDD9nmzZvtiiuuMDOza6+91h577DG7/vrr7brrrrOcnBy7++677aabbiqYYFu0aGGTJ0+23Nxcy8jIsNq1a+9zMYTitWDBAvvXv/5l5557rmVnZ1tOTo7NnTvXLr300iJ9nvr161u1atVs1KhR1qRJE6tataqlp6fb+++/b7///e9jfVu0aGEfffSR9ezZ06pUqWK1atWym2++2X7+859bp06d7LTTTrN33nnH3nzzTRszZkzssa+//rp16dLFevXqZS+//LJ98cUX8lZHlB6MQZRG27Zts2XLlsW20/3jH/9o55xzzj7HZmHX4yFDhtiuXbusW7duVr16dfvPf/5j1apVs+bNm9vIkSPt+++/txNOOMFq1apl7733nu3evdvatm1bjH85ihPzXylS0kkm++ucc86JJdn81OTJkyMzi6ZNm1aQXL527dqCf//mm28iM4sWLFgQRVF8V6soiqKNGzdGPXr0iE444YQoPz9fJiyNGjUq6tGjR1StWrUoMzMz6tq1a8FOGcqe5LnBgwdHderUiTIyMqIrr7wy2rp1a0GfLVu2RNdff31Ut27dqEqVKlHPnj2jL774InaccePGRccee2xUuXLlqGHDhtGtt94a7dixo+Dfc3JyouOOOy6qVq1a7G9E8kIS25YtWxb169cvatSoUVS5cuWoefPm0eDBgws2B3DHYhRF0aOPPho1b968oK0S2x599FHvuZ555pmoadOmUYUKFaITTzwxGjNmTNSkSROv34gRI6LWrVtHFStWjD3PU089FbVs2TKqVKlS1KZNm+jFF1+MPc7MoieffDLq3bt3VKVKlahFixbR0KFD9/n3I1mMQcZgWTRw4MDIzCIziypWrBjVq1cvOu2006Lnn3++YFxGkU7CjaJ9X4+HDx8edevWLcrMzIzS09Oj4447rmCzmfHjx0cnnnhiVKtWrahatWpRx44dGT9lGPNf2Zr/0qJoLwUrAJQLN9xwg+3cudOeeuqpIjleWlqaDR8+3Pr161ckx0P5xxgEcLBi/osrc7daAdg/7du393bAAIoTYxDAwYr5L46FB1DOXXXVVSV9CjjIMQYBHKyY/+K41QoAAABA4lL+xWP37t22dOlSq1GjRqzIDw5eURTZxo0bLTs7O/EdtRh/cBXn+DNjDCKO8YeSxjUYJSl0/KW88Fi6dCmFUCAtXry4oL5IUhh/2JviGH9mjEFojD+UNK7BKEmFjb+UFx57ysovXrzYMjMzUz1MAXXHV1GuoleuXBlrf/LJJ16ff//7317MrRViZt5e33sKxvyUqoD6xRdfeLFjjz021r777ru9PtWqVfNiIZJ+TV0bNmywpk2bFoyNJBX1+EPZV5zjz6x4xmDInbBF+Zn+7LPPvNhPq0fv0bhx45SOn5ub68W++eabWLt///4pHbuklcfxh7KFazBKUuj4S3nhsedil5mZWSYWHlu3bo21q1ev7vWpWNF/OSpVquTFqlSpss/23mLq+G4/9VqWlYVHSTxHUY0/lB/F9bN/cYzB4l54pKenezF1EUn171XHcufisv55Lk/jD2UT12CUpMLGH6WtAQAAACSOhQcAAACAxJVIHY9UbwFatWqVF3v88ce92JgxY7yYe6uVuqVg+/btXmzKlCle7M0339zneZrpW7TUfdGTJ0+OtXv06OH1qV27thc78cQTvdj1118fa9eqVavQ8wRQerlzZehONUuWLPFizz//fKz98MMPe302bNiwH2dXNNy/6ZJLLvH6/PnPf/ZigwYNSun5du/eXeg5AACSwWwLAAAAIHEsPAAAAAAkjoUHAAAAgMSVSI5HqPnz58fa55xzjtenYcOGXqxmzZpezM25OOSQQ7w+agvcLl26eLH8/PyUjqVySNz6Ijt37vT6bNu2zYt9+OGHXmzChAmx9q9//Wuvz89+9jMvBqDkpZp70KlTJy82d+5cL+bOI2pLcTWfuvlxZn7+mJpz8/LyvNiWLVu8mLtduHq+3//+917sD3/4gxc79dRTY+1XXnnF66NeU/I+Si+VE+q+X+q9Ct1Stri3rJ44caIXU7mdOTk5sXabNm0SPS+EK+4xk6pf/epXXuymm27yYp07d/Zi7vVCfadNFTMrAAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4kokuTw06eb222+PtRs1auT1UUXyVIK2+5wVK/p/ukoYchPJzfwkm9BE8k2bNnkxN+ldnVfVqlW9mEqGdJ/zySef9PqcfvrpXiwjI8OLAUiOmmtCkpm7d+/uxWbOnOnFGjRo4MXc+UHNw2reUnPSsmXLYm2VSO4mjZuZVa5c2Yu5yeRqvlMxNc+/+uqrsfbmzZu9Pm+99ZYXU6+9+x6VhmRRaAfy3hTl+zpu3LhYe8aMGV4ftfHDHXfc4cXc8ffBBx94fYoy4bc8SbVIdejj3Jh6XKrnsGPHDi+mClKrsXXBBRfE2nPmzPH6qO+0ak5Mcr7jFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDElZrK5So50U1gzMzM9PqoRByVDOkmGapE7127dnkxVZXcjanERFV9VyU6uo9VSUTqHFRCuJuAqf7GESNGeLFf/vKXXgxAckIT94YPHx5rf/75516fpk2bejG1+YQ7V4YkTe4t5s7FIdWl99bPnQPV3KnOQc2VzZo1i7VHjx7t9Xn//fe92Jlnnhn0nEhdqsn6qp+6JoZ48cUXvdhxxx0Xa48fP97r88QTT3ix7OxsLzZt2rRYW1UbV1WiH3vsMS929NFHezGEUWMm1Wrj6nuhS811avMLteGG+1g1r3366aderH///l7M3bzj8MMP9/qoTYcUdR5FhV88AAAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEldqksvXrl3rxdzkcpVQtm3bNi+mkrjdx6oKvSHVa838ZCCVkKQSi5SQCpgqWX7lypVerG7durG2+hvHjBnjxUguB5ITummF8rOf/SzWdj/jZmYbN270YjVr1vRibrKg2pgjdC5z+4VUXd+bkMeGzs3unKdeh7POOsuLqc1NGjZsGGur10HNzSh+s2bN8mLq/XIri5uZffnll7H2mjVrvD4DBw70YieeeKIXcxPH3WPvLeYmBZuZzZs3L9Zu3bq11wfhUt0sImSuVn1Ck7PduW3x4sVeHzVn1ahRw4u515qHH37Y69O4cWMvlmqV9VTxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEldqblCdPn26F3Pv0XRzPsx04RYVc4vrqeI/rVq18mItWrTwYtWrV4+1VVGY9PR0L6bu+XNzVGbMmOH1eeedd7yYes5169bF2vn5+V4fVVQQQHJC8znOO+88L+bmKKjCobm5uYU+ziysWKkSUkCrKKl8jtAicu7c787VZv61wEzf+/+LX/yi0OdDuFTvGVc5mxMnToy13XwcM7OsrCwvdvnll3uxRx99NNZW98DfdNNNXmzFihVezP0bVQG3r7/+2ot9+OGHXswdp+R4HBh3bjiQvLTly5fH2iovaPXq1V7sq6++KvRYKjepdu3aXkyN+fXr18faXbp08fqUBvziAQAAACBxLDwAAAAAJI6FBwAAAIDEsfAAAAAAkLhSk1zuJvKZmR1//PGx9ssvv+z1mTlzphe74447vJhK8gqhEtu2bNmyz7aZTuLeunWrF3OT0FUxvz/+8Y9e7Nhjj/VibvK9Sqz8/vvvvRiAkjdp0qRC+6iCqUpI4qRK9g1NAFYFp4pK6Hmpc3D/blUkUc3DU6ZM8WLuNSnJgloHA3eTgtBNBNQmKVWqVIm11fcAtWHAP//5Ty82atSoWPuMM87w+ij169cvtI9KQFeJwj/88IMXe/7552Ptnj17en3at29f6DngRyHjb/78+V7sxhtv9GLuRj6qmN+3337rxdSmRt99912sfdJJJ3l91IYH6lrgfi5CC1mnyn1NQzci4RcPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxJWa5PJbbrnFi7nJPyeffLLXp1OnTl5sw4YNXsxNLleJiZmZmV6sTp06XsytCqwqAIcmQ7qVJlWSnKpYqhLt3arG6tzd5CMUj5BkXDVmVLKW+7lQj1NJZRUrpvZxdyu+qnM4ECoB2D3XgyGxt1q1al5s+/btsXboe6jGmztPhbzuZmEJgyFVxPd2XiHHUtQYd6s9qwRMd0MPM7NXXnnFiz388MNB54EwIfOWoj4X7jj6+OOPvT6/+tWvvNg//vGPoOcsKqp6tfp+cswxx3ixypUrx9pqLLvH37hx4/6e4kFDfU9ztWrVyosNGTLEi6nvVkWlXr16XkxtiKE2FrjoootibZXMHvKdQvVTc7d7vQidu/nFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHGlJrlcVQv96KOPYu1hw4Z5fT744AMvNnDgQC/21FNPxdpuUreZ2bx587yYqprqJsWpJEeVuOkmi5n5ST0qIU5VxfzTn/7kxdzE8Vq1anl93nzzTS82ceJEL6aqqyJ1qSZHq4SukGOlmkjufk7MzB544AEvtnTp0pSOr4Qk/ZU306ZN82IrV670YllZWbG2SjJU84rq5yZeqyTD0CRxt9+BVBt3+6k+6hzUGHcfu3btWq+P2mAj1c8LwqU6B6rr3wknnLDP9t5s2bLFi7mfi9DzDBnLeXl5Xh91XVYb25x55pmFHmvhwoWxtvq+ggOjEsnd+UjNpale19QmSuq7rxpHn3zySax96623en1CE8BD+qW6uQG/eAAAAABIHAsPAAAAAIlj4QEAAAAgcaXmxtbbbrvNi7n33apiKO3atfNiI0aM8GL33Xdfoeeg7slT9wOH3N+s7hkOyQXZtGmT18ctWGhm1q1bNy/WsGHDWFvdK6iKEZLPUfxCczdSvfdcFUSbOnWqF3v99ddjbfd+ZzNd0GjAgAFe7NVXX92PM/x/3CJ5ZmYPPfRQrH3nnXemdOzSSs0FKo/Bpe7hVsWf1PhynzM0l0L1c+9pVucQeqyQe4lDH+eel5rT1bkuWbKk0HNA6ZHq+FPcfqH3wIdQeVtuoV+zsM+i+uy71wc1r+DAhFyrQ/M5Qgr7XnrppV4f9zq9t/Ny85RVTpMqyKl89913sfZvfvMbr0/jxo1jbZXbrPCLBwAAAIDEsfAAAAAAkDgWHgAAAAASx8IDAAAAQOJKTXJ5//79vZhbQPCrr77y+rhFdszMzj33XC+2YsWKWLtZs2ZeH1UERiXLuAk76nGKShSuXr16rK2SlFRRFrdwkJnZo48+WmifcePGebFOnToFxRAmJBkttEjV3LlzvZibaDZp0iSvjyqs2bJlSy/WpEmTWFsV68rNzfVi7733nhdL1WuvvebFJk+eXGTHL42+/vprL6aS7EOK66kCgiqB0N24IjQhUo1VN5k3pI+ZnitDCrKGzrFuP5VcqTZLUAm/7hhUG3qgZIQkgKs+6nMRMrZSLeSqNov597//7cXOOeccL/bLX/4y1lZj1P17Qj8nCJdq4UtFzYkuNRZUscB169Z5MbcQpfsd2sysadOmXkx9/3apYqzuJjYbN260//73v4Uei188AAAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEldqkstnzZrlxdzEa7cyt5nZcccd58UmTJjgxWbMmBFrq4Sh0EqnIYnCKhlNCanAqv5uN/HMzOzoo4+OtQ899FCvj0osatu2bWGnWa6o91m97m6yr0riVUKS0VRi2B133OHFhg4d6sXS09Nj7UaNGnl9unbt6sXURgmbN2+OtQ8//HCvzw8//ODF7rrrLi/mcjd0MNN/z0033eTFZs+eHWurjSWOOeaYQs+htFLzQ0h17tCE8JDnVMfaunVroedg5s9bBzIHutSxtm3b5sWysrK8mFvdWSWqq79bHf+xxx6LtV999VWvD1JPvC4t3PEdmqAdkuBep04dL6Y2bvnyyy+92K9//etYe/78+V6fHj16xNoklx+YVMdy6Hye6udCfW9Tmw6tWbMm1u7bt2/Q8Rs0aODF3Hny5JNP9vq43z3c7yZ7wy8eAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSs1yeUqccpNlFq8eLHXRyVeu0npZn6ijKoCqpKBVLXxkITw0GRLN7lXJT6qJF31N7qJlSopWCU1L1u2zIupKtdlUWjSlxKaTO5S1UKHDRsWa7sVP83Mateu7cWOPPJIL+aOyfXr13t9NmzY4MVU1V43GUwlOarP2Msvv+zF/vKXvxT6fB06dPBiKrHXTXJWFdXLMjX/KO5co+YHNU7VGE818TR0041Uueeq/h41b6k51t0QombNml4f9feo51SJ9vCVpUTyECFJ43szderUWPuoo47y+gwYMMCLjRw50ouNHj061nbHtpmfdKzmfYQr7irloaZNm+bFOnbs6MXy8vJi7ddee83ro8bI4MGDvZj7fbJ3796FnmcofvEAAAAAkDgWHgAAAAASx8IDAAAAQOJKTY6Huie5atWqsbbKt1D3frt5E2b+/XbqPl91D7Q6L/ex6l4+9TjVzz2Wuo9TnWvdunW9mMstJmOmC2otXbrUi5WXHA91z2aq9/A+8cQTXuzpp5/2YsuXL/di7r247du39/qo8a2O5VJ/Y2iOkTsm69Wr5/UJvW/YLWY1fPjwoMc98MADXuzJJ5+MtZs3b+71+c9//hNrq4JKpdUf/vAHL6byN9yYyodRn3NVuCzVgn5Jc+ddlW+hPrPqtXCLZKpcGnV9UDlzb731Vqxd1gvlQXPHX+j14c9//rMXcz+LV199tdfnpZde8mLq83rWWWfF2rm5uV4f97OSal4i9o87F6h5QH3XUmPLfayaZ6pUqeLF1HffVOf4Bx980Iu53zsvvPDClI6t8IsHAAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4kpNcrlKoHYTZVRydq1atbzYli1bvFhIcnlooqDbLzSRVyWPugmSKiFJnWuDBg28mJuMrxKZ1PHLUlJuYb7++utY+8MPP/T65OTkeDFVLMxNulevkypQ1qRJEy/mFvlTibGqEKDiJsKq9zR0cwM3GVf1UYUA3bFmZjZ58uRYu1GjRl6fTZs2ebHGjRt7sTZt2sTaKiH4mWeeibXVa1paff/9915MJRC6f5PafEIl3qvXq7Qml7tC5071eXTHs5qbQzcWadGiRaHHQtnnXidVEvc999zjxdS8W79+/VjbLRxrZnbYYYd5MXfcmvnXn4MxcdydC0K+J+6Ne20rygJ/Ic9nFjaHdOnSxYudfPLJXswtMBlKXUPU/OdeV0I2NArFLx4AAAAAEsfCAwAAAEDiWHgAAAAASBwLDwAAAACJKzXJ5YqbvKUScxo2bOjFVGJliNCEXPe8VMJTaMxNbFNJPopKRA1JxFLVsUOfszT65z//GUt+fvPNN2P/rjYaUO+zStxzE/7S09ODjpWfn+/F3HGkKiWrRHWVwOh+DlRivDovlXztjhH1eqnjqwS1rKysWFttbqA2g1CJw+55lOUNEH744Qcvpl5nlbznzmXqtVJzlPpMu/1CK3Gr91G9/yHUubrHD63cqzZjcD/HahMENZbUvLho0SIvVp6pMRNaxbu4ueeqxowao2renTVrVqx98803e33czS7MzBYvXuzFHn744Vg7dEOCqVOnejF3A4ru3bsHHaukhVT1Dp173FhpHY9KaPL6z372s1i7Y8eOXp8XXngh6Fju9Tzk+6uZ3vSlU6dOQc+ZCn7xAAAAAJA4Fh4AAAAAEsfCAwAAAEDiWHgAAAAASFypSS5PtSqsSlZVyTMulXSjEp5U0qGbwBOSFLU37vFVgp86L5Wc6iYnh1ZyVsnDZcUvfvELy8zMLGgfe+yxsX+fMGGC95iZM2d6sYULF3oxNwl17dq1Xh9VcTZkzKxYscLrs2rVKi8WkjiskijVeYVUeM3IyPBiKqleJeO7iX/qM6CSfUOSQFVy8dlnnx1rb9q0yR5//HGvX0kbP358UL+QJG6VXK5e0zVr1ngx9z0LTSQPmcuSruqt3n81Lt3Pi9roQV0f1GuoNucoz0ISd0OrRCc9HkI2ZVGJ5Gqjh0ceeSTWPuWUU7w+kydP9mKvv/56oecZSr1e7t+k/p7SyP1bQhPJUzV79mwv9vzzz3sxd9OAevXqBR1fzQPuPKO+Q6k55c477/RiK1eujLXdDXL2R0hCu+qj/sZWrVoVeiz3vQ2uIh/UCwAAAAAOAAsPAAAAAIlj4QEAAAAgcaUmx6MoqfvtQopUhRb9c4Xerxhyb526l3ndunVeTOV4HHbYYbG2Kkqk7s0PvS+vNIqiKHb+7du3j/17t27dgo6j8mEWLFgQa8+bN8/rk5ub68WWLl3qxdwxGTr+1JipU6dOrF2jRo1C+5jpAoVu0T/VR91bHHK/scrdCB1rbjE9dT+/+7nbsGFD0LGLm8rLUNRn3x0T6vVT84O6593NPQodbyHzovobQ99r91zVfBqa2+L2U3lZIa8NtKRzN5SQHIHQwnL33HOPF8vOzo61p0+f7vUZOnRo0PFTpT53bs6funaXBjt27IjlFLrvl/rb1OdN5T88++yzsbYqGK24124zs7fffjvWzsnJCTpWSD6wmotUgUmVF/Tee+8Veg7q+95PCyfvEVJAUM2J6nPdq1evQs+LHA8AAAAApRYLDwAAAACJY+EBAAAAIHEsPAAAAAAkrtRk1KkEWbf4U2hBJ5WI4yYdqmS0kOIrZmEFclQspLBhaPK3ei2aNWsWa3/55ZdeH5XAqpIty4qaNWvGCghu2rQp9u95eXneY0IToGrXrh1rn3TSSV4ftZFBSDKxes1Vgpd6n93nVMcKLSroHksVXHMLHJn5xRXV8dXroD4Dmzdv9mLufKCSEZs3bx5rq3MvDU488cSgfur9d+ekkOKUZvq1dz/76nHqHNR75sZUoqMab2redcevej7196hx775eoeeAsCRutZHB8uXLvZiad9X8GSLVhPa7777bi6nPj5tMPnz48JSezyzsGq/OQY1lVVC2NKpUqVLwBhr78vXXX3sxd2yFXiPr16/vxdyive+8847Xp2/fvoWe597OwzVgwAAv1qdPHy8WUqhPza+pWrZsmRdTm7f06NGjyJ7TxS8eAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSuR5HKV+KqSddxkt58mEe+LSigMqUyrziEkOTHVCr3qWCrBPTThs0WLFrG2Ond1fNWvrHKTpFTSVCh3k4LQRFWV5OxWRg99zdWYcZPpQpNlQxLa1SYPjRs39mIhmyekmlys+qn30a04XForl7/77rtB/dTGEm5MJfo3aNAg6FjuexY6P6j3LNVE9ZDxHDrfqWrB7rFCxtbeYgebkITZ7777zoupCs3qWu1uIlG9evX9OLt9++GHH7zYxIkTvZjaDGT8+PFFdh7ua5jqhjVmZosWLSqSc0rahAkTYvOze94XXHCB9xj12VUbEriysrK8WK1atbyYSsZ2ryGDBg3y+oQml7vOO+88L/btt996Mbd6eklYv369F0v1s0jlcgAAAAClFgsPAAAAAIlj4QEAAAAgcSw8AAAAACSuRJLLVSJVSGK3SnJVQpJoQ5O+QqqSqz7q+CoWklipEuNV5ejDDjss1g5NHg1NCDrYuAlqodVDVbIbDl6jRo0K6qc+524St/rcP/30017s4osv9mLufJCRkeH1UfODSlR3+4XMuXvjHkslAKuYSpJ0q8QvXLjQ61OzZs2g83KpCt0qsb+4RVEUm8NTrfQdUrk8yWrGB+LKK6/0YnPmzPFiI0eOTPQ8Ut14Rn3uZs+eXSTnlLTc3NzYtfHXv/517N/vuusu7zFq7lEbBLj9VIV0tbmBOpb7GqsNMW655RYv9r//+79e7NZbb421x44d6/U57bTTvFidOnW8WHFTSfxqU5kQ7vwQPOen9GwAAAAAsB9YeAAAAABIHAsPAAAAAIkrkRwPRd0b5t6D5xYM2xt1v7F7f5/KfwgpbqWOpYTcL2uW+j2h6v7mI488MtZW565i5HgAyXGLR5rpe2rdAmtmYXNN//79vdgNN9zgxV555ZVYW+WLrFmzxos1atTIi6m/yaWK8qk50L1vWxXgVMfq1q2bF3OLgn3yySdB5xBSQHDEiBFeTOUWFLe0tLSU8zrc4xRGXSvOOussL6busb/tttti7V/+8pf7cXZx9913X6yt8qhuvPFGL9ahQ4eUnzNJ6rvH2rVrS+BM9t/FF18cKxj5r3/9K/bvquik+tvUXNewYcNYW80N69at82J169b1Ym6emBrLf/nLX4Ji9erVi7VV/ue9997rxRT3O1lo/nGq1OuVat6be66h584vHgAAAAASx8IDAAAAQOJYeAAAAABIHAsPAAAAAIkr1cnlbvJP8+bNg47lFt0y85OBVHJnSIKhmV/oKzSJW3H/RpW0qYpnqSSrkAKL6m/cuXNnoY8DkBo1t6nE7lQT/JQ//elPQbEQav5xzz90Mw0VcwsU/jRRNQnqXNVmI1WrVo2133nnHa9PaUguHz9+vKWnpxe03ddTXetq167txX56jD3ca6n7muwtNm/ePC/28MMPx9qqwFr9+vW92AcffODFHn/88Vj7pJNO8vqkOt6LUmjSv/q+oL7HlAUtWrSItT///HOvT7NmzbzY9u3bvZhbtFO9TqoYofoeFfJeqOK/Ie+DmwRvFr6RQVFsDLGH+3erpHe1MVFIIVR1HVCf/RD84gEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJC4EkkuV8l9IdWzVZKcEpKg7VbLNTNbvXq1F3MTyc1SrzauuMlSKrFy06ZNXiwvL8+LuYk+6nVQieQqqQtA0Xjuuee82JtvvunF1Oe8uKvaKqEJxaWRm+hqZrZy5UovphL73WtGz549i+q0itSiRYtiSaS5ubmxf1+xYoX3GLW5gbomusm2anOSpk2berFf/epXXqxjx46x9pgxY7w+EydO9GIzZszwYr169Yq13cR1Mz/J3kxfE0tDErdKAj7jjDNK4EwO3O233x5rv/rqq16fxYsXezH1Pcr9zqe+H6n3TyVsu9991IYS6hxUQrv7+XnllVe8Poo6VlHO6SHfRVWSeEhyeeiGSSH4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxJZJcvmvXLi+mEsFSTeK+4IILvNiGDRtibbeS+d7OK6SauXpcaAK9m1ikktmzsrK8WJcuXQo9L5UsqP4edf4AioZKXF64cKEX69Gjhxdz561f/vKXRXZeikogDImFVt8N6aeSLVUspFp6nz59vD7PPvusF8vPz/diZ599dqx96623+idbClx88cVFUu1dba6yZMmSWHvNmjWF9jHT74075lUiuTvezczOOussL+Z+DlSCu1IaEskVlVz+yCOPxNp33XVXcZ3OAXErdquxMGrUKC82ePBgLzZlypRYW42PknD88cfH2ieffHIJnUlcSKK6+txlZ2cX+riirLDOLx4AAAAAEsfCAwAAAEDiWHgAAAAASFyJ5Hhs2bLFi4XcR7xu3bqg47sFbA5W6p489TqHvq4AikazZs28mCrk6RaqUvfTK6oYYXp6eqGPC82vKA1UbpqbI3f00UcX2sdM53hcd911qZ9cGVSnTp2gGIqeKnRZnsefyr1SMdecOXO82FdffeXFpk+f7sV++OGHWFvlK6nvTI0bN/Zi//jHP/Z5nmZhOb1FLSSH6ZZbbvFibdu2LfRxKg87VaXzigIAAACgXGHhAQAAACBxLDwAAAAAJI6FBwAAAIDElUhyee3atb1YmzZtvJhbFKhbt25Bxw8pNFiUxVBKK1VsbMGCBV7smGOOKY7TAfD/U3PUX/7yFy/mzpWNGjUKOn5pLZRWlELmcFUoVhVrU69XaU2qx8Hh/vvvL+lTKHXU90QVGzBgQHGczj6VxHfMkOc87bTTUjp2SDHtUMysAAAAABLHwgMAAABA4lh4AAAAAEhcyjkee+5R3rBhQ5GcyLZt27yYW1Br8+bNXh/1/OR4/Ei9pjt27PBioa9rYfY8JuT1P1BFPf5Q9hXn+Pvp86QyBtU5qsKq7mdTFbpTz79z504vpgrnlWWqgKB7H7Ka29RrrwqrukUYC3ufy9L4Q/nENRglKXT8pUUpjtAlS5Z4yd+AmdnixYutSZMmiT4H4w97Uxzjz4wxCI3xh5LGNRglqbDxl/LCY/fu3bZ06VKrUaPGQfHrAQoXRZFt3LjRsrOzE98RhvEHV3GOPzPGIOIYfyhpXINRkkLHX8oLDwAAAAAIRXI5AAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxLDz2oUWLFvbYY48VtNPS0uytt94qsfMB9ldubq6lpaXZ1KlTS/pUUMYw/6G8uOyyy6xfv37B/Zk3UdQYg/9PuV54XHbZZZaWlmZpaWlWuXJla926td133322c+fOkj41HARWrlxp11xzjTVr1syqVKliDRs2tDPOOMMmTJhQ0qeGgwDzH0ob5kSUNMZgyatY0ieQtD59+tgLL7xg27Zts/fee89+85vfWKVKlez2228v6VNLyfbt261y5colfRoIcP7559v27dvt3//+t7Vs2dKWL19uH330ka1evbqkT+2A7NixwypVqlTSp4EAzH8oTcrrnIiygzFY8sr1Lx5mVrCibd68uV1zzTV22mmn2YgRI+ykk06yG2+8Mda3X79+dtlllwUfe8aMGXbKKadYtWrVrE6dOnbVVVdZfn6+mZl98MEHVrVqVVu3bl3sMYMGDbJTTjmloP3ZZ5/Z8ccfb9WqVbOmTZvaDTfcYJs2bSr49xYtWtj9999vl156qWVmZtpVV121368Bit+6dets/Pjx9uc//9lOPvlka968uXXt2tVuv/12O/fcc83sx1tXnn32Wevfv79Vr17dDjvsMBsxYkTsODNnzrQzzzzTMjIyrEGDBnbJJZfYqlWrCv591KhR1qtXL6tZs6bVqVPHzjnnHJs/f/5ez2vXrl12+eWX2+GHH26LFi0yM7O3337bOnfubFWrVrWWLVvavffeG/uv4mlpafb000/bueeea+np6fbggw8W5UuFBDH/obQImRMfeeQR69Chg6Wnp1vTpk3t2muvLRhTZmZDhgyxmjVr2ujRo61du3aWkZFhffr0sby8vII+u3btsptuuqlgTrzlllssiqLYuezvvInygTFYOpT7hYerWrVqtn379gM+zqZNm+yMM86wWrVq2ZQpU+z111+3MWPG2HXXXWdmZqeeeqrVrFnThg0bVvCYXbt22dChQ+3iiy82M7P58+dbnz597Pzzz7fp06fb0KFD7bPPPis4xh5//etf7aijjrJvvvnG7rrrrgM+dyQvIyPDMjIy7K233rJt27bttd+9995rP//5z2369Ol21lln2cUXX2xr1qwxsx8nyVNOOcU6depkX375pY0aNcqWL19uP//5zwsev2nTJrvpppvsyy+/tI8++sgqVKhg/fv3t927d3vPtW3bNrvwwgtt6tSpNn78eGvWrJmNHz/eLr30Uhs0aJB999139s9//tOGDBniLS7uuece69+/v82YMcMuv/zyInqVUNyY/1BSQubEChUq2BNPPGHffvut/fvf/7aPP/7YbrnlllifzZs321//+ld76aWX7NNPP7VFixbZ73//+4J/f/jhh23IkCH2/PPP22effWZr1qyx4cOHx46xP/Mmyg/GYCkRlWMDBw6MzjvvvCiKomj37t3Rhx9+GFWpUiX6/e9/H5144onRoEGDYv3PO++8aODAgQXt5s2bR48++mhB28yi4cOHR1EURf/617+iWrVqRfn5+QX//u6770YVKlSIli1bFkVRFA0aNCg65ZRTCv599OjRUZUqVaK1a9dGURRFV1xxRXTVVVfFzmH8+PFRhQoVoi1bthScQ79+/Q7gVUBJeeONN6JatWpFVatWjXr06BHdfvvt0bRp0wr+3cyiO++8s6Cdn58fmVn0/vvvR1EURffff390+umnx465ePHiyMyinJwc+ZwrV66MzCyaMWNGFEVRtGDBgsjMovHjx0ennnpq1KtXr2jdunUF/U899dToD3/4Q+wYL730UtSoUaPYed54440pvgooKcx/KG0KmxNdr7/+elSnTp2C9gsvvBCZWTRv3ryC2JNPPhk1aNCgoN2oUaPooYceKmjv2LEjatKkScFnQdnbvPnNN9+k8FeiNGMMlrxy/4vHyJEjLSMjw6pWrWpnnnmmXXTRRXbPPfcc8HFnzZplRx11lKWnpxfEevbsabt377acnBwzM7v44ott3LhxtnTpUjMze/nll+3ss8+2mjVrmpnZtGnTbMiQIQWr8IyMDDvjjDNs9+7dtmDBgoLjdunS5YDPF8Xv/PPPt6VLl9qIESOsT58+Nm7cOOvcubMNGTKkoE/Hjh0L/n96erplZmbaihUrzOzH8TF27NjY+Dj88MPNzAp+kp07d64NGDDAWrZsaZmZmdaiRQszs4LbqPYYMGCAbdq0yT744APLysoqiE+bNs3uu+++2HNceeWVlpeXZ5s3by7oxxgsm5j/UJoUNieOGTPGTj31VGvcuLHVqFHDLrnkElu9enVsLqpevbq1atWqoN2oUaOCOXP9+vWWl5dn3bp1K/j3ihUremModN5E+cMYLHnlfuFx8skn29SpU23u3Lm2ZcsW+/e//23p6elWoUIF7567HTt2FOlzH3vssdaqVSt77bXXbMuWLTZ8+PCC2wzMzPLz8+3Xv/61TZ06teB/06ZNs7lz58YG9U8v7ihbqlatar1797a77rrLJk6caJdddpndfffdBf/uJmmnpaUV/NSan59vffv2jY2PPWP5hBNOMDOzvn372po1a+yZZ56xyZMn2+TJk83MvNtpzjrrLJs+fbpNmjQpFs/Pz7d77703dvwZM2bY3LlzrWrVqgX9GINlE/MfSpu9zYm5ubl2zjnnWMeOHW3YsGH21Vdf2ZNPPmlm8flMzZnuWC5M6LyJ8okxWLLK/a5W6enp1rp1ay9er149Lxlo5syZdvLJJwcdt127djZkyBDbtGlTwYVxwoQJVqFCBWvbtm1Bv4svvthefvlla9KkiVWoUMHOPvvsgn/r3Lmzfffdd/L8UD4dccQRwbUQOnfubMOGDbMWLVpYxYr+R3X16tWWk5NjzzzzjB1//PFm9mOyrnLNNddY+/bt7dxzz7V3333XTjzxxILnyMnJYQyWU8x/KO32zIlfffWV7d692x5++GGrUOHH/yb63//+d7+OlZWVZY0aNbLJkycX/MeZnTt32ldffWWdO3c2s/2bN3FwYAwWr3L/i8fenHLKKfbuu+/au+++a7Nnz7ZrrrnG24FlXy6++GKrWrWqDRw40GbOnGljx46166+/3i655BJr0KBBrN/XX39tDz74oF1wwQVWpUqVgn+79dZbbeLEiXbdddcV/FfJt99+20uuRNmzevVqO+WUU+w///mPTZ8+3RYsWGCvv/66PfTQQ3beeecFHeM3v/mNrVmzxgYMGGBTpkyx+fPn2+jRo+1//ud/bNeuXVarVi2rU6eO/etf/7J58+bZxx9/bDfddNNej3f99dfbAw88YOecc07BJDd48GB78cUX7d5777Vvv/3WZs2aZa+99prdeeedRfI6oHRi/kNxK2xObN26te3YscP+9re/2ffff28vvfSS/eMf/9jv5xk0aJD96U9/srfeestmz55t1157bWxs7++8ifKDMVg6lPtfPPbm8ssvt2nTptmll15qFStWtN/+9rfB/7XP7Md7/EaPHm2DBg2yY4891qpXr27nn3++PfLII7F+rVu3tq5du9oXX3wRqwJs9uP9/Z988on93//9nx1//PEWRZG1atXKLrrooqL4E1GCMjIyrFu3bvboo4/a/PnzbceOHda0aVO78sor7Y477gg6RnZ2tk2YMMFuvfVWO/30023btm3WvHlz69Onj1WoUMHS0tLstddesxtuuMHat29vbdu2tSeeeMJOOumkvR7zxhtvtN27d9tZZ51lo0aNsjPOOMNGjhxp9913n/35z3+2SpUq2eGHH27/+7//W0SvBEoj5j8Ut8LmxGrVqtkjjzxif/7zn+3222+3E044wf74xz/apZdeul/P87vf/c7y8vJs4MCBVqFCBbv88sutf//+tn79ejP7cdei/Z03UT4wBkuHtGh/b0wDAAAAgP100N5qBQAAAKD4sPAAAAAAkDgWHgAAAAASl3Jy+e7du23p0qVWo0YNS0tLK8pzQhkVRZFt3LjRsrOzC7aiSwrjD67iHH9mjEHEMf5Q0rgGoySFjr+UFx5Lly61pk2bpvpwlGOLFy+2Jk2aJPocjD/sTXGMPzPGIDTGH0oa12CUpMLGX8oLjxo1ahQ8QWZmZqqHOWCbNm3yYg888IAX21MVco8BAwZ4fa688sqiO7EUDR8+3Iu9+OKLXqx3795e7Nprr03knEJt2LDBmjZtWjA2klRaxl9pMHfuXC82ZswYL1arVi0v9tO6CmZm3bp18/pkZ2cfwNkVTm2sl8p/QSvO8WfGGEQc4w8ljWswSlLo+Et54bHni0FmZmaJDrpDDjnEi7lfpszMq/xcrVo1r09p+PBUr17di6mq1VWrVvVipeH8zVL70pjqc5T0+CsNMjIyvJgaH2rMu/3UhJH061tUC4+ieGwqz8MYxE8x/lDSuAajJBU2/kguBwAAAJA4Fh4AAAAAEpfyrVYl4eqrr/Zin3zyiRfbvXu3F2vQoEGsfdddd3l9nnjiCS+mkqcOO+ywWDsrK8vrs2bNGi82ceJEL7Z9+/ZYe8OGDV6fRo0aebGnn37ai73zzjux9jPPPOP1admypRdD6ZDqLUfXXHONF/viiy+82M6dO73Ytm3bCj3+//7v/3qxadOmebHNmzfH2ieccILX5+GHH/Zi6hawXbt2xdrqlkoAAFC28IsHAAAAgMSx8AAAAACQOBYeAAAAABJXqnM8Pv7441h7wYIFXp9OnTp5MZUn4eZ9HHXUUV6flStXerH58+d7Mbd2SJcuXbw+06dP92JqW9y6devG2urvWbFihRc79NBDvdi6deti7d/97ndeH1UnBKVDqjkey5Yt82KqZoebT2RmVrly5VjbHUNmZv/5z3+82NatW71YpUqVYu1vv/3W66M+Ayq3yj1XlQcCAADKFn7xAAAAAJA4Fh4AAAAAEsfCAwAAAEDiWHgAAAAASFypTi7/8MMPY+0WLVp4fVQBNDfJ1cxsx44dsbab1G2mE19Vwq9b3Ewl0apk2IyMDC9Wo0aNWPuHH37w+lSvXj3ovJo0aRJrqyT7zz77zIv16tXLi6H4qcKXFSr4/23ATbxetGiR1yc9Pd2LqQKC7kYJaoyqRHW10YObqK7G6G9/+1svpqi/GwAAlG1c3QEAAAAkjoUHAAAAgMSx8AAAAACQOBYeAAAAABJXqpPLly5dGmtnZmZ6fUKTy92EcPU4NznWTCfbqgrQrkMOOcSLqWTvzZs3x9oqkVydg0q+df9GVfWa5PLSQSVeq+Ry5eOPP4613QRxM3/TgtDjq7Gtjq8+P+4GDh07dgw6lqq83rBhw1g7NPEeAACUXly5AQAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEldqkstV8qibjJ2VleX1UbGtW7cW+nxuIqyZn5xtZpafn+/F3ArQKildHV/9je6xVB91rKpVq3oxl0ounzNnTqGPQ/LUe6PGkTJlypRY203ENjOrWbOmF8vJySn0PNTmBitXrgw6L3fzh/POO8/r88EHH3ixY445xou5f5NKxgcAAGULv3gAAAAASBwLDwAAAACJY+EBAAAAIHGlJsdjwYIFXszNd9iyZYvXRxUVrFWrlhdz8yQ2btzo9alY0X85VEE1935zlVOi7klXhQ3dHA/1OJUPoIqnqfvzXT/88EOhfZC80PdZGTt2bKF9VI5H7969vdj3339f6DmoHI+jjz7ai02dOjXWVp+d888/34s1b97ci7lUQU6Ubrm5uV5syZIlXowCpgBw8OAXDwAAAACJY+EBAAAAIHEsPAAAAAAkjoUHAAAAgMSVmuTyvLw8L1alSpVYWyVUqyRdlazqFgesUaNG0LFUAUE3SVydl0okV4UAq1WrFmurJFpVWK5Ro0ZebNOmTbG2Ovc6dep4MZU8XK9ePS+GoqOKVarNDRQ3IXzz5s1en88//9yL1a5d24u5Y14V5DzppJO8mEoSHjBgQKz9hz/8weujHEiiPUqH119/3YvdddddXqxPnz5ezN0IoX379kV2XgfiP//5T6zdpk0br0/Xrl2L63QAoFzgFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDElZrk8tWrV3sxN4F6/fr1Xp9PP/3Ui1188cVeLDs7O9ZWyezbtm3zYm7yt5lO9napRGH1OLdyuXpc/fr1vZhKHnaT3Nu1a+f12bBhgxebPXu2FyO5PFmhlbjHjx/vxVasWBFrq2Rc9Xlau3atF6tVq1asrTYaaNiwoRebN2+eF1PjDaXX7t27vZjaKOOHH37wYjfccEOhfVq2bOnFpk+f7sWuuuqqWHvixIn+yQZyN9R4/vnnvT6rVq3yYlu2bPFiGRkZsbZ7DcH+cTeROJANJJ544olYu3Pnzl6f0Oume63r2LGj16dx48b7e4oH7I9//GOsfeSRR3p9zj333OI6HaDI8IsHAAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4kpNcrlKat24cWOsPXbs2KDHffXVV17shBNOiLVVkqNbQddMJ4S7SZmqSvn27du9mJtIbma2devWWNutPm6mK7FXr17di02ePHmfxzYza9KkiRebNm2aFzv++OO9GIpOaGKlWz3ZzE/SVONKVahXGyW4Y1cdSz1OufDCC2Ptm266yevzyCOPeDH1WhRlIio0VTFeWbNmjRfLycmJtVu0aOH1CU3udedwNeZPPvlkLzZy5EgvNnz48FhbJY2ruW3gwIFerLRUUC8vdu3aFWurjVSUMWPGeLFf/OIXsbbaDMUdC2ZmU6dO9WLutfSpp57y+qiNEo499lgvdswxx8TaasON3NxcL/bRRx95sYULF8baaiyTXF56qflVjWV3bLVq1SroWGX5msgvHgAAAAASx8IDAAAAQOJYeAAAAABIXKnJ8fjf//1fL9a7d+9Ye926dV4ft5CQmS4a5RbJq1q1qtdH5XOoXA23yNaOHTu8PuqePHV89/5SN6/FzOyLL77wYq+//roXc++fV0W9/vGPf3ixKlWqeDEULff+5tACgh988IEXc/M31Pu8efNmL6bGaUgxTFWMULnkkktibfU3nnfeeV7s7bff9mJl+f7VJKmif+q1Cnn9Qsdghw4dvFjt2rVj7W+//dbr4xanNPPvgTfzx9f111/v9VG5aUcddZQX+93vfhdrqzwNtzDt3oTkUqn8voNNaCFKN6dj1qxZXh91XVuyZIkXe++992JtNdbUe9OsWbNCzysrK8vro2KLFy/2YlOmTIm1Ve6Jym35+c9/7sXcIsdz5szx+kBLOifi+++/j7Xvu+8+r4/Ke/vkk0+8WN++fWNtlRtZEtfDv//977H20Ucf7fXp1atXSsfmFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDElZrkcsUtnPfmm28GPU4lFI4fPz7WVsmKoQW1XCq5TsXcBGMzs8zMzFhbJfKqx7nJnWZmDzzwwD7PEyUnJDlMFbVUxaYOPfTQWHvbtm1eH7V5QtOmTb2YmyTXuHFjr49KFFXcz+uECRO8PhdffHHQsQ5GIUm6oe9F0v7yl7/E2qeeeqrXR20akJGR4cXchN8GDRp4fdxERzOzE088sdDzPBDuZ7a8J5Kr658bU31CNykYNWpUrP3oo496fa677jovpor3hSRaL1++3IupedjdiCM9Pd3roz6bqrCq20+Nd7fQqpn+XLvJ62vXrvX6uIn3anOasizkO1mqm2uozVbUJhkjRozwYm7ivzJjxgwvpgo+uu+r+13VrGgLOqsC29dee60Xc8+/X79+Xh+SywEAAACUWiw8AAAAACSOhQcAAACAxLHwAAAAAJC4UpNcrpKI3EQtleClEv5UpV03yUslH6njq2q1buXR0IRPdSz3PNxK5ma6QmoIlZSuhCYHInUhY0RVKVfj2600r5Lk1FjLz8/3Ym5ienZ2ttdn5cqVQee1aNGiWPuuu+7y+iiXXXaZFxsyZEjQY0uDKIpi81dIYqOa70LGyLJly7zYSy+95MXef/99L/bxxx8XevxQ3bp1i7VV5WV1DqpqszvvqqRdVdE6JLlczYHr16/3YuqzsWXLllh76dKlXp+fVsxWxyhLQsakukbm5OR4sbZt23qxe++9N9Z+/vnnvT6bNm3yYu5mGmZmv/rVr7xYqtatWxdrjx492uszdepUL+ZuzGHmJ6a3atXK66PmU5UI7ya9qznXTS5Xr1/S3PnPHUepJn/vT78Q7vXpjjvu8Pqo8a2q3btVydVmPzVq1PBiKlG9Zs2asfbw4cO9PpMnT/ZiderU8WLuGJk9e7bXx30dzMx69uzpxdyNbWbOnOn1SRW/eAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACSu1CSXqyQiN+k5NIlbVQt1Va5c2Ytt3brVi6lkSDdhMTRRXZ2/+5yqaqo61xDq+YoyWQuaSmh1x7KqNv7EE094saOPPtqLucmc27dv9/qoMaOS3Vx169b1YvPnz/di6m/MzMyMtVWCuFvd3Mxs3LhxXmzkyJGx9jnnnOP1Ka3cz/6BfOZuvPHGWPuLL77w+rivu5mucuxWp33qqadSPi/XP//5Ty/26quvejH1XrtJjKr68r///W8vpjbd6N27d6ztJt+amW3YsMGLhWwiopJ7DzvssIL/7yajlxYqaVyNSXW9cMebGleqqvwpp5zixd59991Y233fzXTSuNpYwBXy/u2Nm9x70UUXeX1UTCXbPvnkk7H2hx9+6PVRm4GojQvcef2nGxmUJmlpabHxlOp8p74zuRs2rFq1yuujkqXXrFnjxebOnRtrN23a1Otz1FFHeTG1sYB7/VNzqXq/TjvtNC/mUtduNY+p+c8dM+5GNGZm9erV82LuRgZmZmeddVasrTZAcDczCN3cgF88AAAAACSOhQcAAACAxLHwAAAAAJC4UpPjEULdV67u41T3UIbcr6sKQKliVm5ehjqWul9Rnat7X7C6v69NmzZeLETovb0oWiEFGR944AEvpu7ZdO8/NvPvTVUFvFTeh8ofCqH+npAcJvXZUbktVatW9WLvvfderK3uy//lL3/pn2wJKKp7nJUjjzwy1n755Ze9Pj/NM9ijdevWXswtTHXbbbd5fVSxrBBqDlT3S6t7od0xoYpZderUyYupQrFuYa+uXbsW+nx7487Xq1ev9vrUr1+/4P+XVAHB3bt3xz6P7vgLHY9PP/20F3PzMNzxaGZ20kkneTGV2+D2++yzz7w+7n3lZmHXP/U3hl7/QgreKSonz83VUN9ZVA6Tmt/cuV/lrroFX9Wxi5t7bQgtYqfyMtxinyoXQeUuqpwf930+4ogjvD6ffvqpF1OF+ho0aBBr/3Qe2EO9p02aNPFiLpUnoeZSt/ClmX+NV3OSeg1Vkc6srKxYW+UXujk3oeOPXzwAAAAAJI6FBwAAAIDEsfAAAAAAkDgWHgAAAAASV6aSy0P98MMPXsxNflTFAhWV6KMSKV2qGFNI0nto4UFVUMZNXFLJdShaoe+XSxXXU4nkKuHcLRinkmznzZvnxVTxLzcZVyWjhYx3RRVUU4mAqthYURa3S9r27dtjyfxu8p6bpGcWnsB65ZVXxtqqKJ9K7h08eLAXO+6442Lt0aNHF/p8ZnoMfv7557H2999/7/VRc2zHjh292LHHHhtrq00QVEK4Kkb55Zdfxtrq3FVSpiom5n621Zz702ToVAu9HqgKFSoEF9fdF5X46ib1q6RdtblB+/btvZj7+nXu3LnQPma6CJorZEOPvQn5LKrPyjPPPOPF+vTpE2vPmTPH66OKtFavXt2LufOG+hvd5HKV0Jy0oUOHxjYScTetuPzyy73HqCJ2qhCqm9itXjuVdL9y5cpCn1Mls6uCvWp8u9e26667zuujvn+p66s7t6kNCdS1W1mxYkWsrQouhm6s9PXXX8faquBnqvjFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHFlKrk8NCFz0qRJXsxNqFGVnVWisEpscxOLVB+VCKaSbd1ESpW8px7nJhGZ+cmB6u85kCS8g01IRdvQpM533nkn1lZJlCq5XL33bgKhqhaqKp2qMb9w4cJYWyW2qfNSf7e7UYLSsmVLL/bcc88V+rjSbMGCBbEERzcRVSUZqs+mqvTuJk6qhG23Irl6nJmf/HzVVVd5fVTCpdoUwz3W4Ycf7vVRCeFu8q2Z2ZQpU2Ltxo0be30Ut6qxmdnxxx8fa0+fPt3rc+qpp3ox9Xl05+K2bdt6fX76OSiKBO+SpCoThyS1Ll++3ItVrVrVi7mbAbhVvs3M5s+fX+jzKeq6mZeX58XUmHE3o1EbyqhzHTZsmBdr1qxZrF2rVi2vj9o8QSX8up8xVXHanXND5uCi1rt379imIe45qHE1c+bMlJ5LbU6irpELFizwYu55qflJHUvF3OukGn9qrKljufOGGgtqDlbJ+O7YUteB0O/R7vda9Tn/6quvYm01RpWyPVMCAAAAKBNYeAAAAABIHAsPAAAAAIlj4QEAAAAgcWUquTw0eU9VbXaTqlWSj0q+VYnjbqKPSugKTeJ2k0VVBVOVWJSTk+PF3EqwoUlE0Iry9XMrSbsVw810hVRVedkdM6qi6GeffebF2rRp48Xcz9TYsWO9Pmp8q0RoNU5dKjk6hErQLi3jOz09PZbE5yZaq9d99uzZXkwlC7oJfaoKsBo3Krly0KBBsXa/fv28PqqadMi8OHfuXK+Pqiw+Y8YML+ZuXqCSPtU5qPHmnofaGGH8+PFeTG3G4G4AoBKM69evX/D/1WeiOEyaNCmWVPrmm2/G/r1Ro0beY9Troq49bqK1+vyqv1tVQp41a1asrT7TqoL8qFGjvJibzKvmKJUkHrIphkr+VpsnqGO58/V3333n9VHjVsXcJGO1ocwVV1wRa6uNRpJWsWLF2Ln+4he/iP272y4O6vV03y81z6jEbjVOQ65j6jugOr4bKy3XtRDueHM3vtkbfvEAAAAAkDgWHgAAAAASx8IDAAAAQOJKdY6He4+tuqdSFW5ZuXKlF3PvSVb30al7+RT3fmOVB6LuOVXHd+8DVIXF1H2BKsfDVdYLWpU26r1Rr7FbEM3MbOrUqbF2vXr1gh6n7sU+9NBDY+3WrVt7fdS9vl9//bUXcwsM9erVy+vz+eefezF1z71b8Ep9xrKysrxYiNJ832u1atVi97m7BclUUT51L2zt2rW9mFvATY0blQd09NFHe7FFixbF2iqfQ+VgqCJobiGv7Oxsr4/KiVD3RrvF5tS91yqmPo/u66MKYqoxuGzZMi/mXlvU/P3TvAh1LSoO7dq1i+UHuePPbZuZrV692os1aNDAi7n5Ier9U2N51apVXsx9D1UeiHqNH3jgAS/m5sipwmWh74f7nOoc1OdCjSM3puatkFw4M7Mjjjgi1lbv46WXXprSsYtSZmZmbPy540GND/W5VDkR7veo0Mcp7nuh5hRV+FIdX809LjWOQr5jhhacVTH3b1KfgdDXyz2+uub/NMfNLDyHk2+mAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSvVyeUhiTgqcalOnTpezC2+o4p1qYRclditEpxcKqlH/T3usVQymjqWKpLoUonPpbkQW3EJTdRyX7/QZP1bb73Vi7lJa+o1V4ltKqHQLRioHte2bVsv5iYrmvkF6hYuXOj1ad++vRdTBfDcpDU32dxMJyqXdTVq1IjNJ+44UXONGm9qkwo3iVvNbSpJVxUuc59TFSRTxQjV/OMmMaq/R22MoAqxucn3quCdGm/q9XLPSyUAqyR+lRTZrFmzQs/hp5szhCScJqFmzZqxMXbRRReldBx1XXNfF1WUT40/9Vq412o1P6jkaDVXrlu3rtDnUxseqLnSHd8qUd19PvU4M//7gnptVHKvmiPcgo5NmjTx+rhjObSAW5Lcv0X9bSif1OdL4RcPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxJX55HJVtVclWIVUrVSVGUMqj4ZU493bsbZs2RJru8mkZroKekhinkqMV0l4oZUsywJ3zKhkRfXapVrl/S9/+YsXU5W+TzzxxFh74sSJXh/1PqjkWDepUf2NeXl5XkwlE7ueffZZL6b+HrcSu5mfRKjOS1XeLusqV64cG1Pue5aTkyMf43KrlJuZrV+/PtZ2K82bhVe6dan3R1VZD6lCrTbmUOegnjOk2rJKUFWfWXfcq+uDm7RrphPV3flaVYP/6TmkOoeUFmr+SU9P32fbzK9eDAD7UrZnSgAAAABlAgsPAAAAAIlj4QEAAAAgcSw8AAAAACSuVCeXh1DVZFVyuVvhUyVyhlZSdZN7Q5PL1fHdiqgqIVwdSz2nm4hat25dr09Iwn5Z5iaEqtdcUVV7Fy1aFGv/7W9/8/o8+uijXqx79+5ebNmyZbF2jx49vD5ff/21F1NJu24irNq0IDTRdcSIEbF23759vT7vvfde0LHc51RjTSXLK+5j1d9YWv3sZz+LtVWS9dy5c72YO0bM/A0Bvv/+e6+PSvhV84O7SUXIxgVmZoceeqgXcyvQq00xVLKyqkruHutAkrTdz7HaUEHNsWqzEff8Q8cuAGDv+MUDAAAAQOJYeAAAAABIHAsPAAAAAIkr8zkequCVul/Xvb/ZzYcwM6tTp44XU/f+u/ebq3u41b3SqviXm+Oh7pVWx1fn5d4jrnI8DjZvvPGGF/uf//kfL6beL3WfvEvd9/3tt996sWOOOSbWnj59utenVatWXmzmzJlezD1Xdc+6yhcYPny4F1M5HS411kKovIzs7Oygx7pjviwXuVQ5C23btg2KYf+440TllAAASg6/eAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACSuVCeXhxS7W7BggRdTybau/Px8L9ayZUsvphLVXSpR3S2KZaaL2bnnsWXLFq+PWzDOTCecq2JzrvJeQDAvLy/Wvvnmm70+7kYDZjrxP4RKvFZjZtKkSbH2cccd5/VRxeHUeblF0TZt2uT16d+/vxfr16+fFwsRWoTRTexVSdU1a9YMOlZ5H6cAAByM+MUDAAAAQOJYeAAAAABIHAsPAAAAAIlj4QEAAAAgcaU6uTyEqmhctWpVL+YmbauEbZWUvn37di/mJvOq6umHHnpo0LFcKllZ/Y07duzwYqpStEslpZcnI0aMiLXVe9OwYUMvphK03fdCVTJXr6dKqnaTpadMmeL1adKkiRfr0qWLF/v6669j7dzcXK/Pm2++6cUUNxFefS7S09ODjhUyvhs0aBB0LAAAUP7wiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiynxyuaqqrJKx3cTX+vXre31UUrBKtnWPpZ6vdu3aXmzz5s1ezE3cVRWbQ5LGzXRSvUv9jeXJpZdeGmv/97//9frMmjXLi6lK9u7rrhLJ1XuvXuNq1aoVeqz58+d7MbdKuZnZunXrYu2xY8d6fUKpKu4utelCyLF27tzp9QmtEO8m9oecJwAAKN3K97dQAAAAAKUCCw8AAAAAiWPhAQAAACBxZf7G6Tlz5ngx9x54M/8+9bVr13p9VEwVRVu9enWsvWHDBq/PvHnzvNjy5cu92NSpU2Pt7t27e31U/oHKBVH5LgcbN5fio48+8vosWbLEiw0ZMsSLvfvuu7G2W7jPLKxo3oFQRQvfe++9WPukk05K9BwOO+ywoH7u565ly5ZenyOPPDLoWCp3BgAAlG384gEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJC4Up1cHlLsrkuXLl5s1apVXswtGKgKA9arV8+LqSTXpUuX7rNtZnbMMcd4sW3btnmxhQsXxtqqWGD16tW9mJuUbmbWsGFDL+Yq7wUEQzRp0sSL3XnnnUExl9rc4Pvvv/di7sYFqsCkSsYOTexO0s033+zFjj32WC/mfsbU31inTp2g56RgIAAA5Q/fQgEAAAAkjoUHAAAAgMSx8AAAAACQuJRvpN5TwE4Vzysqu3btirVVvoUqsKZyKdx+u3fv9vps3rzZi6nn3LJlS6HPp44Vcl4qx0PlZajCde57oe6Td19Ts6Ir1rbn+VVxw6JWHOMvhCruuGnTJi/mjgeVY6SOlfTfF/IZU9Tf6J6/W7TTTOcrFZXiHH8/fZ6SHoMoHRh/KGkH4zUYpUfo+Et54bFx40YzM2vatGmqh0ARe/nll0v6FMzsx7GRlZWV+HOYMf7gK47xt+d5zBiDiGP8oaRxDUZJKmz8pUUpLo13795tS5cutRo1asj/So+DTxRFtnHjRsvOzk589yzGH1zFOf7MGIOIY/yhpHENRkkKHX8pLzwAAAAAIBTJ5QAAAAASx8IDAAAAQOJYeAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJA4Fh7/v3vuuceOPvroffY56aST7MYbbyyW80H5V9iYGzJkiNWsWfOAnuOyyy6zfv36HdAxgAO1v+MwNzfX0tLSbOrUqYmdEw4ejD+UFMaSr8wuPNLS0vb5v3vuuafIn/PNN9+0+++/f599Chtk9957r/3qV78ysx//hrfeequIzxLFZdKkSXbIIYfY2WefXdKnUuJYlJcNK1eutGuuucaaNWtmVapUsYYNG9oZZ5xhEyZMKOlTw0GA8YfixHgrnSqW9AmkKi8vr+D/Dx061AYPHmw5OTkFsYyMjCJ/ztq1a+/z37dv317oMd5++2277bbbiuqUUIKee+45u/766+25556zpUuXWnZ2dkmfErBP559/vm3fvt3+/e9/W8uWLW358uX20Ucf2erVq0v61HAQYPyhOJXX8bZjxw6rVKlSSZ9GysrsLx4NGzYs+F9WVpalpaXFYmrhMW7cOOvataulp6dbzZo1rWfPnrZw4cJYn5deeslatGhhWVlZ9otf/MI2btxY8G/uf9Vt0aKF3X///XbppZdaZmamXXXVVXbooYeamVmnTp0sLS3NTjrppIL+ixcvtm+//db69OljLVq0MDOz/v37W1paWkHbzOzpp5+2Vq1aWeXKla1t27b20ksvxc4xLS3Nnn76aTvzzDOtWrVq1rJlS3vjjTdSfCWRivz8fBs6dKhdc801dvbZZ9uQIUNi/z5u3DhLS0uzjz76yLp06WLVq1e3Hj16xBbHrvnz51vLli3tuuuusyiKZJ+3337bOnfubFWrVrWWLVvavffeazt37iz0fO+9916rV6+eZWZm2tVXXx1bJG/bts1uuOEGq1+/vlWtWtV69eplU6ZMiT3+k08+sa5du1qVKlWsUaNGdttttxU872WXXWaffPKJPf744wW/OObm5hZ6Tihe69ats/Hjx9uf//xnO/nkk6158+bWtWtXu/322+3cc881M7NHHnnEOnToYOnp6da0aVO79tprLT8/v+AYe27/Gz16tLVr184yMjKsT58+sf8QtGvXLrvpppusZs2aVqdOHbvlllu88Txq1Cjr1atXQZ9zzjnH5s+fXzwvBEoE4w/FKWS8paWl2bPPPmv9+/e36tWr22GHHWYjRoyIHWfmzJl25plnWkZGhjVo0MAuueQSW7VqVcG/7+9Y2rVrl11++eV2+OGH26JFi8ys8Ov6nu985557rqWnp9uDDz5YlC9V8YvKgRdeeCHKysraZ58dO3ZEWVlZ0e9///to3rx50XfffRcNGTIkWrhwYRRFUXT33XdHGRkZ0c9+9rNoxowZ0aeffho1bNgwuuOOOwqOceKJJ0aDBg0qaDdv3jzKzMyM/vrXv0bz5s2L5s2bF33xxReRmUVjxoyJ8vLyotWrVxf0//vf/x6dfvrpURRF0YoVKyIzi1544YUoLy8vWrFiRRRFUfTmm29GlSpVip588skoJycnevjhh6NDDjkk+vjjjwuOY2ZRnTp1omeeeSbKycmJ7rzzzuiQQw6JvvvuuwN9KRHoueeei7p06RJFURS98847UatWraLdu3cX/PvYsWMjM4u6desWjRs3Lvr222+j448/PurRo0dBn7vvvjs66qijoiiKomnTpkUNGzaM/u///q/g391x/emnn0aZmZnRkCFDovnz50cffPBB1KJFi+iee+7Z63kOHDgwysjIiC666KJo5syZ0ciRI6N69erFxvUNN9wQZWdnR++991707bffRgMHDoxq1apVMHaXLFkSVa9ePbr22mujWbNmRcOHD4/q1q0b3X333VEURdG6deui7t27R1deeWWUl5cX5eXlRTt37kz5tUUyduzYEWVkZEQ33nhjtHXrVtnn0UcfjT7++ONowYIF0UcffRS1bds2uuaaawr+/YUXXogqVaoUnXbaadGUKVOir776KmrXrl30y1/+sqDPn//856hWrVrRsGHDou+++y664ooroho1akTnnXdeQZ833ngjGjZsWDR37tzom2++ifr27Rt16NAh2rVrVxRFUbRgwYLIzKJvvvkmkdcCxY/xh+IUMt7MLGrSpEn0yiuvRHPnzo1uuOGGKCMjo+Dat3bt2qhevXrR7bffHs2aNSv6+uuvo969e0cnn3xywTH2Zyxt3bo16t+/f9SpU6eC73wh13Uzi+rXrx89//zz0fz58wu+t5ZVB83CY/Xq1ZGZRePGjZP/fvfdd0fVq1ePNmzYUBC7+eabo27duhW01cKjX79+sePsa8Lq3bt39Pe//72gbWbR8OHDY3169OgRXXnllbHYhRdeGJ111lmxx1199dWxPt26dYtN0EhWjx49osceeyyKoh8nuLp160Zjx44t+Pc9C48xY8YUxN59993IzKItW7ZEUfT/Fh4TJkyIatWqFf31r3+NPYc7rk899dToD3/4Q6zPSy+9FDVq1Giv5zlw4MCodu3a0aZNmwpiTz/9dJSRkRHt2rUrys/PjypVqhS9/PLLBf++ffv2KDs7O3rooYeiKIqiO+64I2rbtm1sYfXkk08WHCOK/M8GSqc33ngjqlWrVlS1atWoR48e0e233x5NmzZtr/1ff/31qE6dOgXtF154ITKzaN68eQWxJ598MmrQoEFBu1GjRgVjJ4p+/Hw0adIk9sXPtXLlysjMohkzZkRRxBe/8orxh+JU2Hgzs+jOO+8saOfn50dmFr3//vtRFEXR/fffX/Afi/dYvHhxZGZRTk6OfM69jaXx48dHp556atSrV69o3bp1Bf1DrutmFt14440pvgqlT5m91WpfFi1aZBkZGQX/+8Mf/mC1a9e2yy67zM444wzr27evPf7447GfZ81+vHWqRo0aBe1GjRrZihUr9vlcXbp0CTqnDRs22CeffFLwE9/ezJo1y3r27BmL9ezZ02bNmhWLde/e3Wu7fZCMnJwc++KLL2zAgAFmZlaxYkW76KKL7LnnnvP6duzYseD/N2rUyMwsNqYWLVpkvXv3tsGDB9vvfve7fT7vtGnT7L777ouN7SuvvNLy8vJs8+bNe33cUUcdZdWrVy9od+/e3fLz823x4sU2f/5827FjR2zMVapUybp27VownmbNmmXdu3e3tLS0gj49e/a0/Px8W7JkyT7PGaXL+eefb0uXLrURI0ZYnz59bNy4cda5c+eCWwXHjBljp556qjVu3Nhq1Khhl1xyia1evTo2vqpXr26tWrUqaP90nly/fr3l5eVZt27dCv69YsWK3jw5d+5cGzBggLVs2dIyMzMLbjXdc+sByifGH4pTYePNLH6NTk9Pt8zMzILxNG3aNBs7dmzsmnv44YebmRXcThU6lgYMGGCbNm2yDz74wLKysgriodf10O+aZUG5XHhkZ2fb1KlTC/539dVXm5nZCy+8YJMmTbIePXrY0KFDrU2bNvb5558XPM5N1klLS7Pdu3fv87nS09ODzun999+3I444wpo2bbqffw1Km+eee8527txp2dnZVrFiRatYsaI9/fTTNmzYMFu/fn2s70/H1J4v7j8dU/Xq1bOuXbvaq6++ahs2bNjn8+bn59u9994bG9szZsywuXPnWtWqVYvwL0R5VrVqVevdu7fdddddNnHiRLvsssvs7rvvttzcXDvnnHOsY8eONmzYMPvqq6/sySefNLP4xhlqnoz2kpO0N3379rU1a9bYM888Y5MnT7bJkyd7z4PyifGH4rS38bbHvr735efnW9++fWPX3KlTp9rcuXPthBNOMLPwsXTWWWfZ9OnTbdKkSbF46HU99LtmWVAuFx4VK1a01q1bF/zvp7tRderUyW6//XabOHGitW/f3l555ZUife7KlSub2Y8JRD/19ttv23nnnReLVapUyevXrl07b6u3CRMm2BFHHBGL/XTBtKfdrl27Azp3FG7nzp324osv2sMPPxybKKZNm2bZ2dn26quv7tfxqlWrZiNHjrSqVavaGWecEdvMwNW5c2fLycmJje09/6tQYe8f5WnTptmWLVsK2p9//rllZGRY06ZNCzYx+OmY27Fjh02ZMqVgzLVr184mTZoUu7hPmDDBatSoYU2aNDGzH8e9O5ZRNhxxxBG2adMm++qrr2z37t328MMP23HHHWdt2rSxpUuX7texsrKyrFGjRgUXX7MfPzNfffVVQXv16tWWk5Njd955p5166qnWrl07W7t2bZH9PShbGH8oTnvGW4jOnTvbt99+ay1atPCuuenp6fs1lq655hr705/+ZOeee6598sknsedI5bpelpXZ7XT314IFC+xf//qXnXvuuZadnW05OTk2d+5cu/TSS4v0eerXr2/VqlWzUaNGWZMmTaxq1aqWnp5u77//vv3+97+P9W3RooV99NFH1rNnT6tSpYrVqlXLbr75Zvv5z39unTp1stNOO83eeecde/PNN23MmDGxx77++uvWpUsX69Wrl7388sv2xRdfyFt9ULRGjhxpa9eutSuuuCL2c6nZjz/rPvfccwW/sIVKT0+3d999184880w788wzbdSoUXJXtsGDB9s555xjzZo1swsuuMAqVKhg06ZNs5kzZ9oDDzyw1+Nv377drrjiCrvzzjstNzfX7r77brvuuuusQoUKlp6ebtdcc43dfPPNVrt2bWvWrJk99NBDtnnzZrviiivMzOzaa6+1xx57zK6//nq77rrrLCcnx+6++2676aabCibGFi1a2OTJky03N9cyMjKsdu3a5XbSLKtWr15tF154oV1++eXWsWNHq1Gjhn355Zf20EMP2XnnnWetW7e2HTt22N/+9jfr27evTZgwwf7xj3/s9/MMGjTI/vSnP9lhhx1mhx9+uD3yyCO2bt26gn+vVauW1alTx/71r39Zo0aNbNGiRWwxfhBg/KE4FTbeQvzmN7+xZ555xgYMGGC33HKL1a5d2+bNm2evvfaaPfvss/s9lq6//nrbtWuXnXPOOfb+++9br169Ur6ul2klnGNSJEKSy5ctWxb169cvatSoUVS5cuWoefPm0eDBgwuSY3+6w9Aejz76aNS8efOCtkouf/TRR73neuaZZ6KmTZtGFSpUiE488cRozJgxUZMmTbx+I0aMiFq3bh1VrFgx9jxPPfVU1LJly6hSpUpRmzZtohdffDH2ODOLnnzyyah3795RlSpVohYtWkRDhw7d59+PonHOOefEEv1/avLkyZGZRdOmTStILl+7dm3Bv3/zzTeRmUULFiyIosgfcxs3box69OgRnXDCCVF+fr4c16NGjYp69OgRVatWLcrMzIy6du0a/etf/9rr+Q4cODA677zzosGDB0d16tSJMjIyoiuvvDK2y8eWLVui66+/Pqpbt25UpUqVqGfPntEXX3wRO864ceOiY489NqpcuXLUsGHD6NZbb4127NhR8O85OTnRcccdF1WrVi32N6L02Lp1a3TbbbdFnTt3jrKysqLq1atHbdu2je68885o8+bNURRF0SOPPBI1atQoqlatWnTGGWdEL774YmwcqzE5fPjw6KeXkh07dkSDBg2KMjMzo5o1a0Y33XRTdOmll8aSez/88MOoXbt2UZUqVaKOHTtG48aNi222QXJv+cP4Q3EKGW8mNvjJysqKXnjhhYL2nDlzov79+0c1a9aMqlWrFh1++OHRjTfeWLDZSipj6eGHH45q1KgRTZgwIYqiwq/r6jzLsrQo2s+bI7HfbrjhBtu5c6c99dRTRXK8tLQ0Gz58uPXr169IjgcAAAAk7aC51aoktW/f3tuFCgAAADiYsPAoBldddVVJnwIAAABQolh4lEHcHQcAAICyJuWFx+7du23p0qVWo0aNWGExHLyiKLKNGzdadnZ24jsaMf7gKs7xZ8YYRBzjDyWNazBKUuj4S3nhsXTpUorhQVq8eHFBfYekMP6wN8Ux/swYg9AYfyhpXINRkgobfykvPGrUqFHwBJmZmakeBuXIhg0brGnTpgVjI0llbfz9tICVmdlrr73m9flpocs9VD2PihXjH9vVq1d7fdR/gVITwYwZM2LtlStXen1WrVrlxd59910vVtKKc/yZlb0xGGLNmjVeTP1t7hgsLdzbUNVtqUn9l+CyNP72VGb+KfW6uP1CXztVAXzx4sWx9uzZs70+Xbp08WINGjQIes5ULVq0KNbOycnx+px22mleLNX/yh/62qfiYLwGp/p65ufnezE1JmfNmuXFjjzyyFi7SpUqXp9ly5Z5sfr163uxDh067PM8zfQ8Vhp/ZQodfylfPfb80ZmZmeXmoouiURwfiLI2/twFxJ4K9z+lJq+qVat6MfdLn3qceg+qVavmxdzzqFSpUqHPZ6a/jJYWxTUhl7UxGGLHjh1ejIXH/ikL468kFh7ul5Hq1asX2scs+bkm5LzUOZTGhcceB9M1ONXXU/VJT0/3Yuq66V7P1TVYHUv9h8SQ166sLDz2KOzcKC0MAAAAIHGl8z9bAeXMuHHjYu2ZM2d6fdR/JViwYIEXc38iVrdC1apVy4tlZWV5sZo1a8badevW9frk5uZ6MZRu6r+QjR49Otb+73//6/UZO3asF1u+fLkX27p1a6x99dVXe32++eYbL6b+66R7K8Phhx/u9Xn22We9WMeOHb2Y+xlSn6my9l8Pk6D+3lT/y/Gvf/1rL7Zt2zYv5v5XYTWuHn/8cS+mztX9Za5Tp05eny1btngx9Uvdd999F2urX11GjRrlxdatW+fFzj333Fj7/PPP9/qE/LK0t37whb5O7i10Gzdu9PrMmTPHi02fPt2LuddSdb1V48OdN838+ejoo4/2+pS3+YmRDQAAACBxLDwAAAAAJI6FBwAAAIDEkeMBFINNmzbF2oceeqjXR21lqvZJd+8Hbtu2rddH3WOt7iN2czzUlr7qWCrvo0WLFl4MRWvhwoVe7Oc//7kXc8ebmdn69etjbXVvtHr/1e4s7nm4OUxmOj9JcbdQVfde/+IXv/Bi6n7pq666Kta+7bbbvD7kfaS+29ftt9/uxdauXevFsrOzvZi705Wa29wxamaWl5fnxdzxcM0113h9unfv7sXU1rzuuao8N7Xbm9r9ys2bcrfqNTP77W9/68XU+4HUzZ8/34stWbIk1m7evLnXR401df1zx5G69h1yyCFerE6dOl7MzQX58ssvvT5qm+myjF88AAAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEkdyOVAM3MJEK1eu9Pq4hQHNdJKwG6tfv77XZ+fOnV5MJUi6ibwqyVEd69NPP/ViJJcn77LLLvNiKrlXFbRyk8RVMrFKqFbHcjdHUEUsTz31VC+WmZnpxTZs2BBrZ2RkeH1Ck7/fe++9WHvEiBFen4kTJwYdqzwLLVj3/fffx9qq8KlKElcJue5rrJ6vcePGQcdyk7Zff/11r49K/laJ4+6Y3LVrl9dHnauKuYnqM2bM8Pqo46tEZLef6gNNFe9zE8LdgpZmZk2aNPFiL730khcbPnx4rH3WWWd5fU477TQv1q5du0LPS23coophVqtWzYuVFfziAQAAACBxLDwAAAAAJI6FBwAAAIDEsfAAAAAAkDiSy4Fi4CbfqurMIdWmzfzq0ipZUSXjquO7CZ8qkVMll6uEZhS9Z555JtZevny510clzIYmyLrUuFGbEmzevDnWVomOaryp8RWSRKtiVatW9WL16tWLtd3EdTOzYcOGebHzzz/fi5VnFSuGXfo/+uijWFuNIXcsmOn3Rs0jLjUvNmrUyIu5m3O88847Xp+jjz7ai6kNPNzEXfU3VqpUyYupBH3386M+O+PHj/diJ510UqHHgn7N3Q0QzPT7PHXq1FhbbYqgNjeYN2+eF6tcuXKsvX37dq/P0qVLvZja2MLdKEFVXVdJ7wMGDAjqVxrxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiSC4vAqpq6j/+8Q8vduSRR3oxt7rveeedV3QnhlLDTRJXCZMqqfG7777zYm5it0rkVEKSFVW1X/U4dV4oek899VSsrd4LlUiuuAmyocmrqqp3yGNVArM6VzdRUz1OVRlWycpu8qlKSleViA+25PJQ7uc8dNMK9z0104nBLvV+qcRddzyoavchjzPzE8DVPKzmWLXxx9atW2Nt9dlR1d9VcnnoBgAHE5VI7iZnm+nrWOvWrWPt6dOne326du3qxRo2bOjF3OriasMAdawvvvjCi7lJ7qeccorXR30uJkyY4MXatGkTa3fq1MnrUxrwiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsdNhEXg888/92KqENKUKVO82N/+9rdYe9CgQV6fxx57LPWTc6j7cR944AEv5hYq++c//+n1UUWVoIukuYXMVL6Pui9a3SO8bt26WPuHH37w+qgCSpmZmV7MvRdWFaNr0KCBF8vLy/NiSJ66T17dy67GoPteq/v1Q4oMmvnjUj1OjV1137rbLyR3w0zfd+8WMlSPc+/PNtPFvrKzs73YwcYtZqbeP1Ukzy3KZ+a/X2q+U+NIjVN3jKjzUo9T98q7j1XHUp8nda7u363OwS1+iHDutc/MrH79+kH93Hnm9NNP9/qoa6QqTuk+VuWuqVwNNbbcsbxmzRqvT3p6uhdTnzv3unzYYYd5fVQ+VHHjFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDEkVz+EypBSCWjuVQhl6ysLC+mEs7dwkGPP/641+eSSy7xYsccc0yh56USrFShotWrV3uxzZs3x9oDBw70+px44omFnsPBSCWH1ahRI9auV6+e10clHarEYfe9UUm8KrmzZ8+eXsxNkFTjXSXxhhafQ7jLL7/ci7nvo/vem5ktXrzYi6kkSbcQliqmpsabGl8h4yaU+9jQgogqOXnZsmWx9qpVq7w+7mfRzOyTTz7xYgMGDAg6j/JCJau6iajuJhlm+n1QG164hdLUvKI2A1CbDbjUuFVUkniqY9ctFmjmz/3u32ymi+BBc+c/9T6rhG2VjO0eS11v1XvavHlzL+aOSVUssHHjxl7s22+/9WLuJj3qMxD6uXD7LVmyxOtz+OGHe7Hixi8eAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSO5/CdUEqXiJpAtWLDA66MSeFRim5sE2rp1a69Ply5dvNgFF1zgxZo1axZrP/LII16fQw891Iu5SadmfhJhnTp1vD7Q1q5d68XcBExV9VYld6rETTf59rvvvvP6qKrLixYt8mItWrSItd3Kz2Y6UZmq9UXv+uuv92IffPBBrK3Gg9pIQI2lTZs2xdoqKVMl2obMi6qPiqlNCdyxpBJDVSKyW4ndzGzmzJmxtnpt1Hl9+umnXuxgSy53qx6b+ZsZqHkrPz/fi6kNNtq2bRtrq00E1PhQ/dzzUMm3oePPpeY2NS9+/fXXXswdu+pzqDZ9geZuDqHeZzU3qCTx2rVrx9rq+5iaL9T79eyzz+7z2Gb+Rhd7487pasyouVp9Xt1jLV++3OtDcjkAAACAgwILDwAAAACJY+EBAAAAIHEsPAAAAAAkjuTyn1CJc8orr7wSa9esWdPro5KgVIKQWzVcJUy6SXlmZu+//74Xc5NH27Vr5/VR1YrXr1/vxdykQlUBs3379l4MOrFNJcy6VFKZStysW7durK2SKNWYVEl4ubm5sbbaRECN29BKwQjXqVMnL+Z+7s4//3yvj0rkbdmypRdzNxdQ84qaA9W4CakmrZI31fzmHkt9VlTFbJXQ2aRJk0L7/Pa3v/Vixx57rBc72Khk6ZDPuXutMNPjw51H1Hynxp+KhW4EE/K4kMrlqo+aF91EZLVxi5pj3XnYzN/442DkXkvVtXXjxo1eTF3/QjZKUN+P1Jz19ttvx9onnXSS10e9f+q7lvtZUd8dVdK7Si4/+uijY+3QBPfixi8eAAAAABLHwgMAAABA4lh4AAAAAEgcOR4pePDBB2PtrKwsr4+6J1ndE+oWNFL3MKqiR02bNvVi7v2rNWrU8PqoewXV/avu/beff/6516dPnz5eDPo+YlWAyqXu7VRjSxUMdNWqVcuLZWRkeLHDDjss1lZFBtWYVGMLyRs2bFhQv1/+8pdebOXKlbG2ysFQ+RzqXmi3qJuaQ9Tj1Fzm3uOs5kn1+VF5Z6NGjfJiCKOKjbnUPfBubqGZLjrqXlPU+6zmQDVm3H6p5m6Y+QUD1fOpfBT1Wnz//fextsqjUsefOnWqFyPHw8+JUNcwleOh+rnF9dT8p6jvTKeddlqsrb6PqceFFDtURWJD8+Xcx4Z+n0w1ZypV/OIBAAAAIHEsPAAAAAAkjoUHAAAAgMSx8AAAAACQuIM2uTw0wWbBggVezC3KoooEqaQelUzn9lPnpR7nJnea+cWeVPEsRR3fTQydNGlS0LGg38OQ4pSqj0oqU0UFXa1bt/Zi06ZN82JucrlKWFNFj0ITN1EyQuYMlbAdWvxSjXGXGiMqediNqWOr+S6kiKGizkHN/cWdcFnS5s+f78XcpGqVHKsKWLZp08aLufNb6PsX8n6pY4WMUTP/b1RjTSUrq35uTI0h9ffk5OQUep7lnSpE6W7SoxKv1Xc0NWe5RQVDP/OqiKa7uUrIvGYWNo+p7wEqUX3VqlVezH2s2vjBLVpt5hclThq/eAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACTuoEkud5NzVNVRlVh03333ebF69erF2qpKa2iyUUgCnEpIUpVU3YQn1UfFVOKSm0w3bty4wk4T/z81jtxkX5XErZJ93WrTe+vnUsmdEyZM8GJusqjaKCEvL8+LqTGJ0sNNygyl3leVJO7OIyrpU801bpVos9QT1VUCaYiQjR4ORkuXLvVi7mYDboKumU4KVtdXN4k2NHk/1bkm1fdZnbtKMK5Vq5YXc8e8uuarzUHUHHuwCak+r5Kl1Vyn3sMQapOCkGTvkGuymX7v3flPbQwyZ84cL7ZkyRIv5o4/NUe6myOZkVwOAAAAoBxi4QEAAAAgcSw8AAAAACSOhQcAAACAxJXL5HKVrBiS/PPOO+94sSFDhngxtyq0Sm5SCUIh1dJDH6cqyLoJVSrpTyXJKW7i3Lx587w+o0ePjrVV4hd+FFJpV72nqp9K8HQdccQRQeflVjFVY83dTMHs4KvqXNaoatLuHBiaSKkSZEOqTodusOEmkKpkYpWUnmoCKTQ1ZtQGAS51zVKbZ7hCKzuHbD6g+qhrndqkwJ13t23b5vVR813I5gbq9du4caMXU4n9Bxv1WrnjSPVR80CdOnW8mHutU++pmtfUOHXfe/X9Uo0/NY+FzKXqu5W6LmdlZcXa7uYQe4sVN37xAAAAAJA4Fh4AAAAAEsfCAwAAAEDiEs/xUPdehsZc6v5jdZ9eyP3nf/zjH73Y/fff78UOP/xwL+bek6fuSw0tQhTyd6t7ANV9te69iOp+VhULyT1R+QfTpk2LtUvDvYOlQcj9xuq1UkXY1Jh3C0Uqxx57rBcLuVdfjQ91z3PIPdwoOatWrfJibqFTVThU3beu5jd3LIXm/ITkNqncNFWk1S1yigOjxoM7l6k+aqypcRRyL7uixpZ7XmqMqmuW4j5Wzbnq2q3mQHcOV/O8OhYFWfXn3n1d1FhQuUkheTrqOh3yvUrF1Dmoz4D6Xuj+3WosqPNShQCXL18ea6tcl9LwPY1fPAAAAAAkjoUHAAAAgMSx8AAAAACQOBYeAAAAABKXeHJ5qsnfB2LEiBFe7JZbbom1c3JyvD5HHXWUF1OJZm6CnUpyVIl0KjHPTRoKfb1UErCblBSaFKySmdwEOFUgx02oUs93MAopgqUKIa1du7bQx5mFFQcMKTJo5o/v0ARQCggWD/f9D33dVUKkm2y7fv16r48aN+pYIZtihCZJusdSybcqFpI8HLJxxsEodK52E1FVIvnRRx/txdQ4chNfVaKtem9CkntVYbaQ4odmYYU11evVoEEDL+YmFKvXKzSp2T1/9TeWJ+q1cj/3ak4J3ejE/c6kxof6vqc2fXGFzLdmuti0+5xqrlNJ4ur7pHse6hwWL17sxYobv3gAAAAASBwLDwAAAACJY+EBAAAAIHEsPAAAAAAkLvHk8lCrV6/2YmPGjIm1p06d6vUZOXKkF5s5c6YXa9OmTaytKjurZCOVwOMmG4UkWu6Nm8imkswUVRXTTYBTCejq+Cp5yj0v9doU5etQnoSMo7p163p98vLyvJhKamzatGmh56Cqm6vkRPd9VklyahyFJNyh5IRUR1YVbNUYCalorZI+1edAzT/u+FJjUH0OVCIowqiNLBT3fQ1JjjULS+xWjwt9T1O91oRUplafHTXfbdq0yYu5ic5z5szx+qhkfPWcK1asiLUbN27s9SlP1JhxXxf1eqr5omHDhl7M/V6oNgUKreodMk7VmNm4caMXq1WrVqz95Zdfen2ysrK8mNrcwN3AQX1OVBJ/cWPmBgAAAJA4Fh4AAAAAEsfCAwAAAEDiWHgAAAAASFyRJ5ePGzcu1r7vvvu8PqpyoptIZWaWnZ0da+fn53t9VJL18ccf78XciqgqmUtVTQ1JEApNRsvMzPRibkKVSpRSVcNVP/f8VfVYlQSqYm7ylHrtu3fvHmtv3rzZ64MfrVy5MtYO2RzATI+t1q1bp3QOKpnOfU411lTCnToWil6qlcvVe+bG1BylksvVZ989DzWHKKoqtJvwq85dJQWvWbOm0OejSrm2bt06L6Zed/c6o+b45s2bezE1l7nvobqGhSaNu+Mt9H1WG2W41LHUPKwqr7dv3z7WVt911OdOfX5U8np5puYe93UJreqt+rljN/Qapt4H971Xc6TaiEG99+73tAULFnh9jjjiCC/WtWtXLzZq1KhYu0OHDl4f9RmbPXu2Fzv88MO9WFHhFw8AAAAAiWPhAQAAACBxLDwAAAAAJO6AczxWrFgRKzB1zTXXxP5d3dOmiqepmHtPqCqioo6v7v1V99S71D2noQXVQqiiM+55qftL1b2IqtiOW4BOnbu6L1Xdtxty7/8JJ5wQa6t7Gg9Gany4RdiWLFni9VH3v6v32S2GGUrdC+ve660KD6oxyb3zpZu6797NTatatarXR72vagy6/dR9w+q+dZWrofIBXOqzoWIIE5qX6F4b1Ht1xhlneLHp06d7MfcefnV9UtdI9T6756GOpcafOpb7nKGFNdVreNhhh8Xa//3vf70+Kh8gtEBheaYKlbrXUjX+evXq5cVCvmuF5qWpOcud/0LnIvU91L0Gu2Nob9R3Zvcar8aVmuOLu6ggv3gAAAAASBwLDwAAAACJY+EBAAAAIHEsPAAAAAAk7oCTy5999tlYwrKbcKUSwlXyluImCKkCfCoBSyX3uv1Ugo1KIlJJSm6itXq+kGJMZmbVq1ePtVXCmipCtGzZMi/WsGHDWLtRo0ZeH5VgrBKK3b9JFZxKNVkL4YmDKmm3du3aKT1nkyZNvNisWbNibZVwrBLnVLIlip47Z6j5SI0RtWGEO7+FFOzam5BCb2ouU3OlO75Un9Ck4xDq3A+2zRLUtU5xXyv1OLVhidqQwJ23DiS53L32qMeFFmR1qWukOr66LruJzqqIr3pt1AYyB9tmLSoR2n1d1PVJzWNqbIVQ39tCNjVSifHq++oPP/zgxdxzbdmyZdDj6tWr58XcjQvUeG/atKkXC9l8qSjxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiDji5/JBDDoklnLlJs27ytJlOBlJJPW5Cl0qoDk0UdJOSVEKcSiALSYALOXcznezmJg2phLKTTjrJi91///1ebPTo0bG2em1CE0rdJKXirmxZ3rjjSCX/qoRz9X7VqlUrpXOoX7++F5s9e3asrTYRULHGjRundA4oeurzqz777px0IAnbbr/QZM6QfiqBWX021KYYCBOy+YCZf91U17DQ5HL3+q3mMZWku2bNGi/mzmWqj0pWVmNm9erVsfaiRYu8PipJXFUgd797qO8sHTp08GIqaVq9FuWZmrPceUYlbKvE/JBNh9RcpK63ak4M2RBDHV8dyx1b6jO2cuVKL6YSx7t27Rprq8+5uzmSWfHPpfziAQAAACBxLDwAAAAAJI6FBwAAAIDEsfAAAAAAkLgDTi6/9dZbYwk/bhLMxx9/7D1GJVyp6pBuco5KIlIJcSrZ2+2nEn9ULKSauXqcm7CmHmdmdtNNN8XaN954o9cn1EsvvRRrq8rl6lxDkgNDKr5i70IS21QimEqmU0mTIVRFXvdYaryr9z608jGSp+a7kM95SBXxvXGPrxLc1SYfIQmXaj5SY14lkIagcrn+TKsE7fXr18fa6hoWkmRt5o/T0I1O1Lm63zPcTTLMzI477jgvpjbYcP9udQ4bN270Yuq1aNiw4T7bZmaHH364F5s7d64XO9iuuWo+ct8LlXhdt25dL/bll1+mdA5q7lHjwZ2P1JyiNgpSmwioz49LffdVmyC0bds21v7000+9PupvVBvIJIlfPAAAAAAkjoUHAAAAgMSx8AAAAACQuCK/UfuJJ56ItVUhoccee8yLvfjii17MLa63du1ar096eroXU8VQ3HvyVBEVda4hRf/Use68804vdscdd3ixojR9+vRYW90DqO6jVLkF9erVi7WXL1/u9XHvQT3Y7kndG3XPvXuPpronVBWMys7OLrLzatGihRdz3zN1D6pCjkfxUOPEVZS5DqG5Gu797So3RB0rZNyE3FNtpucthFH3lYfca67e58mTJ3sxdd/9kiVLYm31nqpzUGPGHSPq+dR98er47rFULtzMmTO9mCqA+OGHH8ba6vuDyqVR992ra+7BTn3XUtR1zB27aiyrsaa+M7kxdSyVw6Su8e48pvKdVa6n+r7qFiNUc6mixl+S+MUDAAAAQOJYeAAAAABIHAsPAAAAAIlj4QEAAAAgcUWeIeoWiFIJNjfffHNQzKWKEX799ddeTCWCLVy4MNZWBVNU4pJKurnuuuti7dtuu83rkypVYEsVKlL+9Kc/xdrVq1f3+qiEPpWE5yYpHXPMMYU+f6qF7coblfTlJqOpRHyViOi+DwdCFc9yE4dVIrE6V5Vwh5LhFnkzC0sSDy2iqpLQ1bzuCk3edM81NCFSfc4QZsWKFV6sdevWXsy9TqpCeqpIntpgw72WqkRbNa7U+HOPr65hao4KmctUIq/awEElMLvHV+eVk5PjxdTn4mAraqm418RmzZp5fVShvu+++86LdejQIdYO3VwjZOMMNW7V+FAbBrifC/V9Tx1ffV8I2bwjtHBnkvjFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHFFnlwemgidilNOOSUoVpYdyOs3cODAIjwTpEolvYYk46pkN7VBQMjjVGKiSnYLSWxTyZahFc5xYFKtXB4yJkLnGpVw7gpNTlTJve7nRX1WQhPhESZ0cwt3fli1apXXR801aqMWN9FazSEh493MT3o/9NBDgx4XMseqcVWvXj0vpj4/7t8YmvSuNrEJSRQuT9TGBYsXL461jz76aK+Pu3GQmVlubq4XO+qoo2JtNWep11yNB/c9zM7O9vqsXr260MeZ+eNPJcur7wFqgwj3s6j+npUrVwadV5L4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxB1f2ElBCatWqVWgflUCmkjRdIclvZmZ16tTxYm7ymUqYDE1UR8lQyeWpVvWuXLmyFwtJHFfVfdUYUWM1ZCypcakSOt3kYao/a+np6V5MJfe2aNEi1l6/fr3XRyWr5ufnezF3flOPU++zOlc3aVslxqvK6Ir7d6vHhc6LixYtirXVRgkqpq4PoQnz5UX79u29mPsaZGVleX1UEvd5553nxTZv3hxrq40GVDK26uduBqDmTfVZqVGjhhdz52917VbfA9RGD+6mET/72c+8PupzHrL5TVHiFw8AAAAAiWPhAQAAACBxLDwAAAAAJI4cD6CIqXuX3WI/devW9fps3brVi4Xc/x6a46Hu43TvjVb3Lat8AXUPN4peSI6Cei/UfcnuPcFLly71+qh7qNX4co+vcjzUffEqX8T9bKjnU/e7z5w504u5heVCcqsORkceeaQXUzlm06dPj7UffPBBr4+6L17dd+/OeSqXYu7cuV5sxIgRXszNPVFjbc6cOV5MjQd37J5++uleHzUm3bFm5v+N6j7/L7/80ovVrFnTi/Xs2dOLlWeqUK2Kub7++uug46sijS6VL6e4403lTahrsDq+uu671Byv5lI3x6h169ZeH5VnUtz4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxJJcDRaxDhw5erG/fvrG2SsatXbu2Fzv55JMLfT6VWKk0bNjQi7nJZyphsl69el5MJaei6KnEXVefPn282OjRo71Ybm5urK0KUKlER5XY6CZTuoWrzPS4VBsVuAntapy6BePMzFq2bOnFQpLJKSqoi7XdeuutXuyzzz6Ltc8991yvjyqeVpTuuuuuRI+fJJVcPmjQIC/Wq1cvLxby2T/YqOumShpXm7K4c1tIcV4zvSmLO4+p51Pvn9p4xr2+qgR0lWSvzj8kGV9tlBD6HaKo8IsHAAAAgMSx8AAAAACQOBYeAAAAABKX8k2EewpIbdiwochOBmXbnrGgipcVtdI8/tQ98e79pepeVXWfvLrf0/2bVSEhVbxI3b/vPqe6B1+dqyr+VdLvRXGOv58+T5J/t/u3hOYnqDHojqXNmzd7fdyCkmb6vXbHkho36lzVWHKPpZ5P3YOs/saQ90KNj6LI+yjr4y9kPKjnSjrHoyxTr5ea54tqPi3v12A1f6h5IGSeUddNJSTHQ73eKsdDXc/duUd9DkOP5ebeqdyTJHM8QsdfWpTiCF2yZIk1bdo0lYeinFu8eLE1adIk0edg/GFvimP8mTEGoTH+UNK4BqMkFTb+Ul547N6925YuXWo1atRglxCY2Y+r3I0bN1p2dnbiuyQw/uAqzvFnxhhEHOMPJY1rMEpS6PhLeeEBAAAAAKFILgcAAACQOBYeAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDEHdQLj3vuuceOPvrovf77kCFDrGbNmgf0HJdddpn169fvgI4BAEkrbD40MzvppJPsxhtvLJbzAVxpaWn21ltv7fXfx40bZ2lpabZu3bpiOycA+6dMLzwmTZpkhxxyiJ199tklfSolji8EpVNaWto+/3fPPfeU9CmijCqJsfXmm2/a/fffv88+ubm5lpaWZlOnTpX/fu+999qvfvUrMyv8iyQOLitXrrRrrrnGmjVrZlWqVLGGDRvaGWecYRMmTAh6fI8ePSwvL8+ysrL22Y//IIi9WbZsmV1//fXWsmVLq1KlijVt2tT69u1rH330UZE9R4sWLeyxxx4rsuOVNRVL+gQOxHPPPWfXX3+9Pffcc7Z06VLLzs4u6VMCYvLy8gr+/9ChQ23w4MGWk5NTEMvIyCj4/1EU2a5du6xixdL3sdy+fbtVrly5pE8DP7E/Y6uo1K5de5//vn379kKP8fbbb9ttt91WVKeEcuT888+37du327///W9r2bKlLV++3D766CNbvXp10OMrV65sDRs23Ou/79q1y9LS0orqdFHO5ObmWs+ePa1mzZr2l7/8xTp06GA7duyw0aNH229+8xubPXt2SZ9i+RCVURs3bowyMjKi2bNnRxdddFH04IMPxv597NixkZlFY8aMiY455pioWrVqUffu3aPZs2cX9Ln77rujo446qqA9b9686NBDD41+85vfRLt37/7/2rv3uCirdQ/gD8qdGfCGAoKogAYq5l2xIktSSy1zd6zTSd2ZtSsv1dZdlqm5u1+O1dZs6yk5WpaVYmXKR/OShBhkgiiIaCgYkHkX8QL6nD/8OMd3rYd4I16Ygd/38+nzaT2smXlnZs16Zznvsx5esmQJBwQEGO539erV3KNHD/by8uIOHTrwnDlzuKKiosrjHDduHN955508Z84cbtWqFdvtdn7kkUf4woULjj7nz5/nyZMnc2BgIHt5efHAgQM5PT3dcD9btmzhPn36sKenJwcFBfHTTz/teNxx48YxERn+Kygo+IOvKFhNHU9Xx+jatWu5Z8+e7OHhwZs3b652PEjjMikpia/9OGdmZvLNN9/MNpuN7XY79+zZkzMyMhx/T0lJ4RtuuIG9vb05NDSUJ0+ezGVlZY6/h4eH89y5c/mBBx5gu93O48aNq/XXA2qPNCYkmzdv5j59+rCvry8HBARwXFwcHzx4kJn/fz5cunQph4eHs7+/P48ZM4ZPnz7tuH18fDxPnTrV0ZbGiToXxcfHO/oXFhayp6cnnzp1isPDww39wsPDHf3ee+897tixI3t4eHCnTp146dKlhudBRPzee+/x0KFD2dvbmzt06MCff/55jV47cA4nTpxgIuItW7ZU2YeIePHixXzXXXexj48PR0ZG8pdffun4+9U59cSJE8z8/5+LL7/8kqOjo7lp06biGN28ebPFzw5cwbBhw7ht27aGc+FVV8fUoUOHeOTIkezn58d2u53vueceLi0tdfTbv38/jxw5klu3bs1+fn7cu3dv3rBhg+Pv8fHx2vhrbFz2GX/wwQfcu3dvZmb++uuvOSIigi9fvuz4+9UJqF+/frxlyxbes2cP33jjjRwXF+foc+3CIysri4OCgvi5555z/F09mW/dupX9/f05MTGRDxw4wOvXr+f27dvznDlzqjzOcePGsc1m4zFjxvDu3bt5zZo1HBgYyM8++6yjz5QpUzgkJITXrl3Le/bs4XHjxnHz5s352LFjzMx8+PBh9vX15ccee4xzc3M5KSmJW7VqxbNnz2Zm5pMnT/KAAQN44sSJXFJSwiUlJVxZWVnj1xasUdXCIzY2ltevX8/79+/nY8eOVTsezCw8unTpwv/1X//Fubm5vG/fPv7ss884MzOTma9MjH5+fjxv3jzet28fp6amco8ePXj8+PGO21/94vnmm2/y/v37ef/+/da9MPCnmVl4VFRUcEBAAE+bNo3379/POTk5nJiYyIcOHWLmK/OhzWbju+++m7Ozs3nr1q0cFBRkmKukhYc6TtLT0x3/6FNSUuIYt8zM8+fP59tuu42ZmY8cOcJExEuWLOGSkhI+cuQIMzOvWrWKPTw8eMGCBZyXl8dvvfUWN23alDdt2uS4HyLili1b8uLFizkvL49nzpzJTZs25ZycnD/7UkI9qaioYJvNxk888QSfP39e7ENEHBoaysuXL+f8/HyeMmUK22w2xxiTFh4eHh4cFxfHqampvHfvXj516hT/x3/8Bw8dOtRxvrz2HwKhcTp27Bi7ubnxyy+/XGWfS5cu8fXXX8833HAD//jjj7x9+3bu1auX4R9XMjMz+f333+fs7Gzet28fz5w5k729vR3z7LFjxzg0NJTnzp3rGH+NjcsuPOLi4vjtt99m5isTVqtWrQz/anHtLx5XffPNN0xEfO7cOWb+/4VHamoqN2/enN98803DY6gn81tvvVUblMuWLePg4OAqj3PcuHHcokULPnv2rCO2cOFCttlsfOnSJS4rK2MPDw/++OOPHX+/ePEih4SE8Ouvv87MzM8++yx37tzZsLBasGCB4z6Y9S8E4HyqWnisXr3aETMzHswsPOx2OycmJorHMWHCBH744YcNsZSUFG7SpInjsxEeHs533XVXjZ4n1D0zC49jx4797r8oz549m319fQ2/cEyfPp379evnaEsLD3WcFBQUMBHxzp07tcdISEjg+fPnO9pExElJSYY+cXFxPHHiREPsnnvu4dtvv91wu7/97W+GPv369eNHH31UfG7gGr744gtu3rw5e3t7c1xcHM+YMYOzsrIcfycinjlzpqNdVlbGRMTr1q1jZnnhQUSOf3S56uqVCABX/fDDD0xEvGrVqir7rF+/nps2bcqFhYWO2J49e5iItKtUrtWlSxf+17/+5WiHh4fzvHnzauW4XZFLJpfn5eVReno63XfffURE5O7uTmPGjKEPPvhA6xsbG+v4/+DgYCIiOnLkiCNWWFhICQkJNGvWLPr73//+u4+blZVFc+fOJZvN5vhv4sSJVFJSQuXl5VXernv37uTr6+toDxgwgMrKyqioqIgOHDhAFRUVNHDgQMffPTw8qG/fvpSbm0tERLm5uTRgwADDtakDBw6ksrIyOnz48O8eMzi/3r17O/7fzHgw46mnnqKHHnqIBg8eTK+++iodOHDA8besrCxKTEw0jOMhQ4bQ5cuXqaCgQDwucC2FhYWG9/fll1+mFi1a0Pjx42nIkCE0YsQIeueddwx5IkRXkh7tdrujHRwcbJgvJWbHyenTp+m7776jkSNH/m6/3Nxcw/gnujLfqeN/wIABWvuPfEbA+YwePZqKi4vpq6++oqFDh9KWLVuoZ8+elJiY6Ohz7Tndz8+P/P39f3eMenp6Gm4DIGHmavvk5uZSWFgYhYWFOWIxMTHUrFkzx9xTVlZG06ZNo+joaGrWrBnZbDbKzc2lwsJCy47d1bjkwuODDz6gyspKCgkJIXd3d3J3d6eFCxfSypUr6dSpU4a+Hh4ejv+/+sX98uXLjlhgYCD17duXPvnkEzp9+vTvPm5ZWRm98MILlJmZ6fgvOzub8vPzydvbuxafITQmfn5+f6h/kyZNtEmyoqLC0J4zZw7t2bOH7rjjDtq0aRPFxMRQUlISEV0Zx4888ohhHGdlZVF+fj5FRETU+LjAeYSEhBje37/97W9ERLRkyRJKS0ujuLg4WrFiBXXq1Im2b9/uuN218yXRlTnz2vlSYnacrFu3jmJiYgwnbQCVt7c3JSQk0PPPP0/btm2j8ePH0+zZsx1//6Nj1MfHBwnlUK2oqChyc3P70wnk06ZNo6SkJHr55ZcpJSWFMjMzqVu3bqY23mgsXG7hUVlZSUuXLqW33npL++IUEhJCn3zyyR+6Px8fH1qzZg15e3vTkCFD6MyZM1X27dmzJ+Xl5VFkZKT2X5MmVb+UWVlZdO7cOUd7+/btZLPZKCwsjCIiIsjT09OwXWBFRQVlZGRQTEwMERFFR0dTWlqa4ctmamoq2e12Cg0NJaIr/6pz6dKlP/TcwfmYGQ+BgYF05swZOnv2rKOPtHVpp06d6Mknn6T169fT3XffTUuWLCGiK+M4JydHHMfYuaphcHd3N7yv1+5G1aNHD5oxYwZt27aNunbtSsuXL6/Vx746htT56Msvv6Q777zTEPPw8ND6RUdHa9unpqamOsb/VdcumK62o6Oj/9Sxg/OJiYkxzHW1AedLULVo0YKGDBlCCxYsEMfbyZMnKTo6moqKiqioqMgRz8nJoZMnTzrmp9TUVBo/fjyNGjWKunXrRkFBQXTw4EHDfTX28edyC481a9bQiRMnaMKECdS1a1fDf6NHjxYvt6qOn58fffPNN+Tu7k7Dhg2jsrIysd+sWbNo6dKl9MILL9CePXsoNzeXPv30U5o5c+bv3v/FixdpwoQJlJOTQ2vXrqXZs2fTpEmTqEmTJuTn50ePPvooTZ8+nZKTkyknJ4cmTpxI5eXlNGHCBCIieuyxx6ioqIgmT55Me/fupS+//JJmz55NTz31lGPB0759e/rhhx/o4MGDdPTo0Wr/lRKck5nx0K9fP/L19aVnn32WDhw4QMuXLzdcinDu3DmaNGkSbdmyhQ4dOkSpqamUkZHh+FL29NNP07Zt22jSpEmUmZlJ+fn59OWXX9KkSZPq4ylDHSkoKKAZM2ZQWloaHTp0iNavX0/5+fm1/mW9devW5OPjQ8nJyfTrr7/SqVOnqLKyktatW6ddZtW+fXvauHEjlZaW0okTJ4iIaPr06ZSYmEgLFy6k/Px8+u///m9atWoVTZs2zXDbzz//nD788EPat28fzZ49m9LT0zGGXdixY8folltuoY8++oh27dpFBQUF9Pnnn9Prr7+uLVj/rPbt29OuXbsoLy+Pjh49qv1iDI3TggUL6NKlS9S3b19auXIl5efnU25uLr377rs0YMAAGjx4MHXr1o3uv/9++umnnyg9PZ3Gjh1L8fHxjktOo6KiaNWqVY5/EP/P//xP7ftY+/btaevWrfTLL7/Q0aNH6+Op1q96zjH5w4YPH25IMrzW1eSgrKwsLcmMmXnnzp2GrWbV7XTPnDnDcXFxfNNNN3FZWZmYsJmcnMxxcXHs4+PD/v7+3LdvX160aFGVx3s1iW3WrFncsmVLttlsPHHiRMOuHefOnePJkydzq1atarSdLjNzXl4e9+/fn318fLCdrpOqKrn82jHKbG48JCUlcWRkJPv4+PDw4cN50aJFjuTyCxcu8L333sthYWHs6enJISEhPGnSJEfiODNzeno6JyQksM1mYz8/P46NjTVsSd3Yk99cjZnk8tLSUr7rrrs4ODiYPT09OTw8nGfNmuXYoEKdD5mZ582bZ9jmVkoul8bJ4sWLOSwsjJs0acLx8fH87bffcmhoqNbvq6++4sjISHZ3d//D2+kuWLCAExIS2MvLi9u3b88rVqz43ecPzu38+fP8zDPPcM+ePTkgIIB9fX25c+fOPHPmTC4vL2dmeTOCgIAAXrJkCTNXvZ2u6siRI475j7CdLlyjuLiYH3/8cQ4PD2dPT09u27Ytjxw50jFGqttOt6CggAcNGsQ+Pj4cFhbG8+fP1+bNtLQ0jo2NZS8vr0a5na4bs4mMGgAAgBqaMmUKVVZW0nvvvVcr9+fm5kZJSUmoPg0A4GKcr0QyAAA0KF27dtV2oQIAgMYHCw8AALDUww8/XN+HAAAATgALDwAAcCm4QhgAwDXVeOFx+fJlKi4uJrvdjj2ygYiufBk4c+YMhYSE/O72wrUB4w9UdTn+iDAGwQjjD+obzsFQn8yOvxovPIqLi1EICkRFRUWO+iJWwfiDqtTF+CPCGAQZxh/UN5yDoT5VN/5qvPCw2+2OB/D396/p3fwu9ef0P7Oq3rp1q6GtFnQhIho7dmyN77+2LF68WIt17dpVizljoubp06cpLCzMMTasVBfjr6auLRZ5lY+PTz0cSe2orKzUYu7uzneVZl2OPyLnGYM1vezI7HxaXFysxZKTkw3tkydPan2k2gg33XSTFjMzl0nPUTr+2jxn/FGNdfyB88A52LzPPvtMi3333Xda7NixY1pMndukwtMtW7bUYv3799diU6dO/d3jdCVmx1+Nvz1cndD9/f1dYuHh5+dnaEtfBJ3hwyMdl3rsRM5xrFWpi5N9XYy/mvLw8NBiWHjUnbr6suksY9DqhYd0UlXH8/nz57U+TZs21WI1nctcYeFR14/pLOMPnE9jPweb4evrq8U8PT21mHQ+V0nnQ+l23t7eWswVX7vqVDf+XK5yOQAAAAAAuB4sPAAAAAAAwHLOe72ESSdOnNBio0ePrraf9DPYrl27tNilS5e0mJqtf/nyZa3P8ePH9YMVlJaWGtpHjhyp9vGI5J/s0tPTTT0mWEu6rOrixYuGtvq+ExG1bdtWi5m5jEbKKZEufZH6qdevtmjRQusTHh5e7TGA8zNz+cWaNWu02KJFi7SYOk4CAwO1PtK8KFUu37dvn6H94IMPan1qeumI2Uu0AMD5SXOK2d27mjdvbmifOnVK6xMQEKDFgoKCtNjZs2cNbekS0gMHDmix9evXa7Hnn3/e0JbO3RJXntvwiwcAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGA5p87xMHO92pNPPqnF9u7dq8WioqIMbWmrx4yMDC0mFchRt5ccNmyY1ictLU2LSdf+l5WVGdrS/sfSsebn52uxxMREQ3v8+PFaH6gfjzzyiKGt1kEgImrWrJkWk67j9PLyMrSlegnStbDS50kdy9LtpDoO4Dyk99XM+5+UlKT1Wbp0qRaTxpd6XbV6zTORvI99RESEFtu0aZOh3atXL61P9+7dtdifud4bAFyP2c/3/v37tZg6X0jzjFSPqE2bNtUeh5QLLOXhSvmSak25GTNmaH1eeeUVLWZm3nfW+dA5jwoAAAAAABoULDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLOXVyuUpKJszLy9NiUgLPb7/9ZmhLBa+kBCG1uBWRXixmy5Ytpm7n7l79yy0lA6nF54iIgoODtZiagITkcuexe/duQ1sqSiS5cOGCFispKTG01Q0KiOTPgL+/vxZTE+CkDRDAuUkbEJhJKpSKBaoFJYn08UZE1KFDB0NbKnr13XffaTGpSKa6ecG7776r9Vm4cKEW8/T01GKuklxZm5jZMAactYiYOk6l4zRbFE09V0vvc03v3+wxuHIBN2dU09ezoKBAi6lF+Yj0898vv/yi9amsrNRiUuFd9TtZeXm51kfamEi6f7Ww4bp167Q+UmHDZ555RouZKW7tDHNi/R8BAAAAAAA0eFh4AAAAAACA5bDwAAAAAAAAy2HhAQAAAAAAlnOp5PKnn35ai0nJt1LSoVrtWaoGLiXWSolFp0+fNrSl5F4pUUqK+fr6GtpSgruUuCkdv5r0vnLlSq3P6NGjtRhYr7S01NBu0aKF1kd9/4jkJHQ1Ka5jx45aH2ksS58LNZaamqr1AedW04TW6667Tot5eHhoMWnOUBMUpSq9gwYN0mLSRhknTpwwtNWNGIiITp06pcWkDUIaY3K5m5vb746B7OxsLSa9z9J5rHfv3n/u4K5hZpyaHcvS+a+ujwGJ5LXLzOv54IMParENGzZosVatWlUb+/XXX7U+0gZAUkK4uknGzz//rPWRPk/Sdzn1vG+z2bQ+ixYt0mLbt2/XYqtXrza0pfnPGRLOG/6sDAAAAAAA9Q4LDwAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHJOnVyuJsGkpaVpfcwmHarJ5RIp+VtK+FUThSVSAk9ISEi1jykls0v3JSUpqbddsGCB1gfJ5fVDTaqVkiPNbpTQpk2bau9LSoiTEsjUpGApwe/QoUNaTKqMDq4lNzdXix0/flyLRUZGarE9e/YY2lKiujSepQq/6lxmt9u1PuqGHkTmkssbQ3Xp8vJyQ1LsZ599Zvj7V199pd0mNjZWi0nzw9atWw3tdu3aaX1OnjypxaT3KyoqytD+7bfftD7SeypRH1M6v0vPR9q8RT2OZs2aaX2kc7CZ7xTSWJM2WJDma/XzI71earL1mTNnqj0mV7J582ZD+/vvv9f6qOOKSH6/1A0VpO920vlWeg/V13ngwIHV9iEiOnz4sBZTE9ql+U895xPJ8/c///lPQ1uq4O4MG27U/xEAAAAAAECDh4UHAAAAAABYDgsPAAAAAACwnFPneKjXoknX940dO1aLZWRkaDH1uk3pGkDp+k+poIxa/E0tgEVEFBwcbOq+zp49a2hL199J+RzSY6oFu9RrfaFuSO/XkSNHDG3pWmkpV6OiokKLqdemSsUCpeuPpYJGqpYtW2qx4uJiLYYcj7qh5ihIOQtmr9n94IMPDO3Q0FCtT5cuXbSYNFeq85t0PbN03bp6zTYRUUxMjKEtPR+1MBYR0d///nctpl6jLR17Q8vxWLdunaEQbWZmpuHvL774onablJQULZacnKzF1Byw66+/XutTUFCgxaQChWqOplTkTSrqdvToUS2mFt6VckP27t2rxaT5Tb2tVHBRmmOlXBB13lVzZIiIjh07psWk11XNm1K/KxAR5efnV9vHlS1btszQlr5DSTkzEvVzL50jpXOw1E/9riiNd+m+/vrXv2qxoqIiQ3vfvn1aHyk3rnnz5lpMyvtwRvjFAwAAAAAALIeFBwAAAAAAWA4LDwAAAAAAsBwWHgAAAAAAYDmnTi43Y+nSpVpMKpK3ceNGQ1tK5pKK90mJjmrCopSwJiUwSsm9aiKylDwlFWOaMWOGFnvqqae0GNQ9qQib+r5KiWFmiz+ZKVylJoUSyeNIPa6goCCtj1SkE+qGOo9IG2BIc9SmTZu02I4dOwxtKWFWmn+k+/f39ze0pTGibsJBRDRixIhq+0lFtqTY1KlTtdg777xjaEvH3tCKCgYHBxs2nFCTWn/88UftNunp6VosICCg2piULB0fH6/FfvnlFy2mnquHDh2q9Tl48KAWk5J0x4wZY2irm3cQyQm50tys9pMSdOPi4rSYdN5XE4OlTWCkz5j6eSLSCwZKGwKoycpmNhBxJermKtL8J809ERERWqymxRWlTTLUmHRc0pwibXig3pe0wYJU7FBKaFcT1Z0VfvEAAAAAAADLYeEBAAAAAACWw8IDAAAAAAAsh4UHAAAAAABYzqWSy6UqtFLy4MqVK7WYmrTWp08frY+UkHThwgUtpiYnSolF0rFKSY2qnJwcLSYlRamVW8F5SAmMaiKvVJFcIo0tldlkWamfelxSEptUORjqh5RoK9m2bZsWUystS5sUSEnBXbt21WJ5eXnV9pESXaWESLWCtVQJW62UTiRvlqB+9qQEd2luNvu6OqP8/HzD51h9D6WEU+n9OnDggBZTz5u7du3S+gwaNEiLlZaWarHIyEhDW6rgbbPZtFi7du20mEqtWE9EFBYWpsWk86v6ekmbzEjatGmjxb7++utq+0iv/f79+7VYRkaGoS19D1CP1eyxuwr13CN935OSsUNCQrSYOt9JSePSPCCdN9XzsjSnSGNS+iyq/ex2u9Znz549Wqxz585aTH3/1cr2RERRUVFarK7hFw8AAAAAALAcFh4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlnDq5XE3qkRKLpORbKUFITWCUEh+lJCIppiYSSYm8UrKRdKzq/Uu3QyJ5w6NWrCeSE28l6oYHUpKcNGaksax+VqT7unjxoqnjgtqnvmdmK2xLiddSTCUl90oJsoWFhYa2VCVaOlZpowK1urM0z0vHLo3LzMxMQ/uWW27R+jS05PLmzZsbPu9qFe+goCDtNlIiufS61PS+Vq9ercV69+5taEuJtt27d9dimzZt0mLqBgTdunXT+qjJ2URyBfItW7YY2uomDEREP/30kxaTxox6jpcqsasVyYnkpHD1OKT5W92kxOymJa7CTLVxaR6QNjdQvwNKyd9mNnMh0jdlkc6b0n1Jj6nGpLEgfV+Q5le1n7RZCJLLAQAAAACgUcDCAwAAAAAALIeFBwAAAAAAWM6pczzMXM9s9ppnqViWSro+Uiog6O3tbWibKTBj9jHd3Wv+ltT0enCoXdI1p+p169L7LF1fL13bqV77KxUSSk9P12L+/v5aTB0j0vX1rnz9u6tTr7uX3gvpmnEp56J9+/aGtnT9b4cOHbSYdK28Om5KSkq0PtJ199J1/S1btjS0peulpQJaUr5Bdna2oS3leDS0ebG8vNww96vv4Y033qjdJjk5WYtJ15ZHR0cb2tIcIhVwe+KJJ7SYmqsh5fts3LhRiw0cOFCLqc9JGsu33367FsvKytJiubm5hvZ9992n9Rk6dKgWk/I31ByV7du3a32kArOSmJgYQ/u6667T+qj5Vw0tH1QtVBoYGKj1kb6jSdTvR9LtpO+A0nyhnifN5kFKc5t6XNIcb7YgsEqdD4mIbr755mpvZzX84gEAAAAAAJbDwgMAAAAAACyHhQcAAAAAAFgOCw8AAAAAALCcUyeXm2E2UVAtziYVcpESf8wUdZMSi6TEHymhWE0ebWjJYY2RVJxSGm8qKalMGpPqRglS4S8puVgqjKWOb7NFOqFumEkg/Oqrr7SYlISpbkIgzUdSwqWa5EqkF5aTxryURCvNb+pmHVLRsLNnz2oxKdFZKhym+jMbeDijI0eOGF5DNVlfLapIJBdklM51p06dMrSl11dK2L711lurvX81cZiI6M0339Ri0phZtmyZoS0ll//1r3/VYlJi7ebNmw1tabMOKfH+iy++0GInT540tCMjI7U+0oYhxcXF1T6m9DlUPyvS59BVSPOF+nxCQkK0PtKcJZ2z1DlEeh+kOUXqp96/dN6Uzt0SNaHdzOY0RPJ3XzW2Y8cOU8dQ1/CLBwAAAAAAWA4LDwAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHIulWUnJVrWtAqtlECmJtIRyYltatKQlNwrJTdJycNqv4CAAP1gwaVIyWhqQqvZCuFSglqrVq0MbSnhTiJVZVVJibdSojrUDTPzm1S5XJort2zZYmhLYzA8PFyLqQmzRHqSsd1u1/pIVXOlDQ7U5yglmUrzop+fnxZTk1GlxFN1cwZXd/311xtei9WrVxv+LiU4BwcHa7HvvvtOi6mbCEgVyaXK5a+99poWU1/3N954Q+sjVaN/5513tJha9VzavCMtLU2LjRgxQotNmTLF0FY/J0RyUr1apZxI/17x9ddfa32Kioq0WNeuXbWYmmQsJfH379/f0JY2YXAVhYWFWkz9bmX2+550rlM3N5DOt2Y3nlDnTmm+lb4XmrkviXRcZjYekV5TZ4BfPAAAAAAAwHJYeAAAAAAAgOWw8AAAAAAAAMth4QEAAAAAAJZz6uRyNXmmponkEqk6rpRsJCXwqIlsUrVQKdlSShRWk9elRKMTJ05osebNm1d7rLX5eoF5UuVRM6TKwWbGnzQWfHx8anQMkpo+H/jzpDlJtXv3bi3Ws2dPLaYm9+7bt0/rIyXphoaGajF1bpESbW02m36wgrCwMEP78OHDWh9pwwbptVHnz/z8fK2PlMjrynx9fQ3J5evWrTP8vUuXLtpt7rvvPi127NixamPqe0VEtHz5ci0mVUY/dOiQoa0mRhMRRUREaLEHHnhAi61atcrQlhJ5pc9AQUGBFlM3IJDOt9K5VHq9evToUW0f6f6HDRumxZYsWWJoS58B9fxgJuHYWUmbFKjnROl9MLvpkBqTvmtJmwJJMTOvs3QM0nuoPkfpfC5tbqBWtpceU5rPnQF+8QAAAAAAAMth4QEAAAAAAJbDwgMAAAAAACzn1DkeZnIUpGs7pWv3PvjgA0Nbum5PKm4lXUen3r/0eFLBF6mojZrjIV3LN2PGDC32/vvvV3tcUD+ksSUVMlNJ413Kr1CvCZUKokn5RGaKWkpj1MyxQ92Qrg+Xcimk66XVgn5SXoZUwO3nn3/WYuo1zlKRyTZt2mgxqdihet29lDMnjd29e/dqMXUOzMjI0Po0tByP/fv3G3K61NwG6byQk5OjxW688UYtps4HqampWp/Y2Fgt5u/vr8Vyc3MN7Xbt2ml9PvroIy2Wl5enxdRCgNKY+f7777WYlEd3/fXXG9pSflxgYKAWk4oLf/PNN4Z2p06dtD5PPvmkFpPyrdQxL50f1HwoVy72Ks0N0nnMDOl9Vl8/s0Wepc9PTfNnpe+F6nFI48pMfo90XFJRbGeAb6oAAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLOXVyuRlmk3w2btxoaEtJRFKykURN9JGKtEhJulIivBq7thDUVTt27DB1XOAcpHGkvs9SYpiUxCYldqsFhqSEYDNJ6VUdh0oay1A/pPdVKtZ22223abEjR44Y2tLYkooFSptuqMnr+/fv1/pICZFHjx7VYuHh4Ya2mcJYREQxMTFaTC3YJm3W0dBEREQYzhvq6yfND507d9Ziy5Yt02LqaxwdHa31efHFF7XYgAEDtJj6Xqxdu1brIyUYFxUVaTE1mdzb21vr8/HHH2uxO++8s9rjKiws1PpIyfIlJSVabOTIkYa29BlLSkrSYv369dNivXr1MrRXr16t9VGT16Uke1chbZwhjV2VVHBPup06H5kttih9b1O/P5r9Hir1U+9fGjN9+/bVYsePH9di6vnh5MmTpo6rruEXDwAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHJYeAAAAAAAgOUaTXK5mgwp3U5KCpYqTapJSVLCp1RNWnpMM0lKUlKwGWaruoP11PdQep+l90ZKFmzbtq2hHRkZqfWRxqR0/2fPntUPVlHT6rFQ+1auXKnFpMrl0vuvvtc//PCD1mfdunXV3o5IT/idMWOG1mfFihVaTKoKrW6eIVXbHTx4sBY7c+aMFvvll18MbTVxvSGqqKgwbAChViCXkvw3b96sxX788UctFhISYmhLSdwdO3bUYlK1cZU0B95yyy1aTNo8QU1Cl8633bp102JSkq6ajC8l90qbIkjfF8LCwgzt/Px8rY+UXC4l1Y8aNcrQVhPXpduZmc+dlbSphPpeSGMmICBAi0mV5tUxKVUIl74zSUnoasxsdXOpn/odUHodoqKitJj0uVA/68567sa3UAAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHJYeAAAAAAAgOVcKrlcSvIxm1yuVhmVksalJDypkriaVGY2iUg6fvW+pIrqUhIREsedl5ScqI4Rafx5enpqMSk5TE34bN26tdZHSjKUNilQx5vUB5XLnUd5ebkWk5LLd+/ercWCg4MN7Z07d2p9pLlMSsJUk4yl6sHSWJISJ9U5UJonpcrobdq00WJqQrGU3NvQlJaWGt4jtcq2dF6QqtFLydjqfS1dulTro27cQkTUokULLaZuLJCamqr1kc5/UlVvtWK3NK4mT56sxdSNDIj0avc9evTQ+kjJ3wcPHtRimzZtMrSHDRum9enZs6cWkypMq+d4NXGdyHz1bVcgbVygzg3S+fC6667TYi1bttRi6kYtUlK6tGGAmWrj0mfMbEy9f+l8q87dREQZGRlazMx3U+n+a7qBUU3hmyoAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGC5RpPjoV7zbvb6PulafDPHJZGO1czxS9d1S0W2pOusoe5JOR7qGDGT70Mkv892u93QlnI8pOs4zXx+pGtopfEH9UN6L6RigVK+2t69ew1t6Xp6s3OZOi9KtzNbjMvMHCgVcJOueVfz4aQCnA2N3W435CIePnzY8PfS0lLtNr1799Ziau4YEdGBAweq7dO+fXstJuU/qEX+Bg0apPWRxrJ0Df/x48cNbSmnRMo9ke5fzQc4dOiQ1ke6fynHSM3VkPJYOnfurMVuv/12LbZv3z5DW/oM3HHHHYa2K493M/kIUh8pX8lMXob03c5s7qx0rjZDui/1OKT8TClnRSqaqRZVlfJmiouLtVhdF1rFLx4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLuVRy+Z/Rtm1bQ1tNwiGSk42kJCUzicJSkq6Z+zJb8EVNriNCcrmzkMaD+h5K40MiJQtKyZwqtfAXkZxwrBaHk5LrappIB7VPSgyMi4vTYlJBqOzsbENbmlfMzoEqacybTThXY1LSu3SsauE3Ir1AnJSoKcWkQrGuokmTJoakVXVzi7S0NO02UmFF6b1Rk6VHjRql9ZHmo23btmkxtUChVLBQ2phj8eLFWkwd361atdL6SHPn0KFDtZiaaP/aa69pffbs2aPFJk6cqMW6d+9uaL/yyitaH7WYMZH8fUTdJCAqKkrro26mII1tV2Em2Vs6F0nF9cx8/5IeT5pnpPmouuMkqnlRQakYpvTdLjo6WoutW7fO0FaLdhIRnThxQoshuRwAAAAAABocLDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLuVRyudmqulICkpqwIyVMmk0IN1Pt2Sz1WM1WE5YS1CIiImp8HGAt9X2WxoyUWCklwHXs2LHax5Mq7UrJllLVV3AeauV66T2U5ge14jSRXPm4tphNLpeoiZlSYryaREskJ2EOHjzY0F6/fr3WR3oNXTm5vHXr1mSz2RxtNaFUSkKV5ho1kZxIr6gdHx+v9dm5c6cWGzBggBZT563y8nJTxyUlr6tVyaX3VLqv3377TYvt3r3b0O7SpYvWR6ocLVVGLygoMLSlc7KUiCyNb/V7xrXvcVXHJVWzdhWenp5aTH0NpPdU3TiISN48Qb1/KdFb+r5npp90XNJ9SXObev/S91fpvqTPhZocLx27ek6pD/jFAwAAAAAALIeFBwAAAAAAWA4LDwAAAAAAsBwWHgAAAAAAYDmXSi43U0GXSK7QbKbauJT0ZTZBsqa3U/tJyUDScUmJleAcpAQyNZlcSgKUqtdKyeVmEmGlpHEpAVNNZJMqt0pjEuqGmgwrJVLm5eVpMWnzArXC7969e7U+AQEBpo5LnYvNJmWaiUnVl0tLS7WYdPyBgYGGtpT0mZOTo8XatGmjxVxFfn4++fr6Otqffvqp4e8hISHabex2uxaTqn8vX77c0JY2LZAqkKtJ1kR6Je7bbrtN6yMlqksV6qVEa5VUoXn//v1aTE3QlqqUS3OulHCemZlpaO/atUvr4+/vr8WkMa/O/VLC9Pbt2w1tV/5eIJ3r1Hnl4sWLWp+wsDAtpr4PRPqmC9L3KrPfMVXSsUukxHH1+4JUbVz6biCRnpPKyk1GzMI3CgAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHIuleNhlplrBaVr7cwW7zOTv2G2GKF6TZ7Za6W9vb2rPQaoH9deb32VOmak4lnSey9diy2NU5V6PT+RfE28em2xNLbNXr8KtU+9vr2oqEjrIxWUjIqK0mJJSUmGtpRnZLZ4lZnbmb2GWi1cJxVdk56P9NlQr4+W8q1qmrfnrOx2u2HOUXMnpJxHtWgekTwe+vXrV20faS6TCuKp78WOHTu0PmZz01TSWJAKAUrnZakYr0oqFnjw4EEtpn4O2rVrp/WRclak4nlqgTipYFznzp0NbSlXxFVI+VgqaZ4xO4+ZOY9J51ZpzKhziHQ7aa4zM/dIOR7S7cwcq/TamPk8WQ2/eAAAAAAAgOWw8AAAAAAAAMth4QEAAAAAAJbDwgMAAAAAACzXILNGpYQaNbFISrirafEYSU3vy2zio1SEqKb3Bdbr0KGDoS0V6pOKP0mJw2ZIheakwljqOJUS8LCRQf1RCwhKybdS4qs0ltSkRSnJ0OycYaZQlURKiFTva/z48Vqf4cOHa7GEhAQtJiXzqqRkUVd2+vRpw3NSiyhKc823336rxXr06KHF+vbta2hLRQZTUlK0mFSIUk1Clwr8jRo1SotJSeiFhYWGtrQBi9nCieqGDdL3B+k1lD6LaqE3NfmbSH5t1q1bp8VuvfVWQ1sqnqcmuLtyAUEpMV7dkMBscVEzRXYlNd1MSPq+Zza5XI1JG2JInxVpLlXHrrRpgfTdt67hFw8AAAAAALAcFh4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlGmRyuURNujl16pTWR0pQqymzlSbVCptSxU3puKQkP1VtJsuDeQUFBVpMrVbbokULrY9awZmIKC4urkbHICXZSmNLTTRTkyOJ5Aq9UDfURFTpfZWSBaWxpL63ZhMipfmndevWhnZxcbHWx2wVanUumzdvntbnueee02Ldu3fXYpGRkYa2lEQtzf2u7LrrriObzeZoqwm40oYR99xzjxaT5oecnBxDOzg4WOsjxaT3Zs2aNYa2mgRPJG+UIG2k0rVrV0O7ZcuWWh8pIVz6rKgbcUjPRzou6Xyujnk1cZ1I/+wQEUVHR2uxw4cPG9rSeWXMmDGGtitXLpe+M6mbAaibChDJ41ZKLr/2M0Ikz2vSeypRN8SQ7svs/KqSxsfp06e1mDRm1GRy6fHMVIi3Gn7xAAAAAAAAy2HhAQAAAAAAlsPCAwAAAAAALIeFBwAAAAAAWK7RJJdLyY8qX19fLVbT6t9mb6cm/0hJSlJyoHSsNT0GqF1StVq1cnlQUJDW5+eff9Zi119/fY2OITY2Vos1b95ci6nJy1Ki3pAhQ2p0DPDnqZWIpSRDqaqtlECtJqZLiZpSUro0JtQqysePH9f6qBsqVHWs6vwmVds1WwE6Ly/P0JYqnte0qrGz6tKliyEBu1u3bvV4NFUbO3ZsfR9CgyfND65MTS5Xk7qJiCIiIrTY+vXrtZg6J0oV6isrK7WYNP+p/sxGPmpCu3QM0neD+Ph4LabOpdJ9qXN3fcAvHgAAAAAAYDksPAAAAAAAwHJYeAAAAAAAgOVcKsfjz1xHpxYYKi0tNXU7qeCVGpMKskgxKVdD5ePjo8XMXGMoQQHB+iFdVy7FrCRd/7llyxYtZrZgEtQP9TrkHTt2aH2kYpShoaFabPny5dU+XlZWlhaT8uPU/I0uXbpofUaMGKHFpLlMvY5bui+1MGBV93X33Xcb2tKx9+rVS4sBQP3y8PDQYocOHTK0pRwPNX+SSM5LTElJMbSl71rS/UsxNX9WOo+aLUit9pPy86RCwlFRUVpMLZAp5d4dPXrU1HFZCb94AAAAAACA5bDwAAAAAAAAy2HhAQAAAAAAlsPCAwAAAAAALNdoksvbtm1raJ85c0brIxXlkxI31QJXZ8+e1fpIiVJSQT81sUgq4KUmDBGRoWAUOBepQI9UFK2m1PEgbVogxcwkkksJu1IRIqn4EtQ+dZOAt99+W+sjzTVvvPFGjR6ve/fupmJm9OjRo0a3M0saz+o8L83fCQkJlh0TANSMtAHLt99+a2hLid6tW7fWYo8++qipWEMzcuRIQ1s6n48ePbquDqdK+MUDAAAAAAAsh4UHAAAAAABYDgsPAAAAAACwXI1zPK7mK5w+fbrWDsZKFRUVhrZ07ZvZ69vVXBMzfYjM5XhI9yUdl/S6q9c8S9dDmi1qUxNXj0l6nrXNmcefs+Z4mOHKOR51Of6ufZy6HIPqPEYkjzdn/FzUBfW1qMvXpjGMP3BurnwOlvJu1flO+k4jPb6V33OcmTrfSa+XVBS7tt5Ds+PPjWs4Qg8fPkxhYWE1uSk0cEVFRWLl5NqE8QdVqYvxR4QxCDKMP6hvOAdDfapu/NV44XH58mUqLi4mu93+p3abgoaDmenMmTMUEhJi+b84YPyBqi7HHxHGIBhh/EF9wzkY6pPZ8VfjhQcAAAAAAIBZjfNCOAAAAAAAqFNYeAAAAAAAgOWw8AAAAAAAAMth4QEAAAAAAJbDwgMAAAAAACyHhQcAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGC5Rr3wGD9+PLm5uZGbmxt5eHhQmzZtKCEhgT788EO6fPlyfR8eNABXx1dV/82ZM6e+DxHAobS0lCZPnkwdO3YkLy8vCgsLoxEjRtDGjRtr7THat29Pb7/9dq3dHzQ8156b3dzcqGXLljR06FDatWtXfR8aNHCYA63XqBceRERDhw6lkpISOnjwIK1bt44GDRpEU6dOpeHDh1NlZaV4m4qKijo+SnBVJSUljv/efvtt8vf3N8SmTZvm6MvMVY65+nbx4sX6PgSw2MGDB6lXr160adMmeuONNyg7O5uSk5Np0KBB9Pjjj9f34UEjc/XcXFJSQhs3biR3d3caPnx4fR8WNGCYA+sIN2Ljxo3jO++8U4tv3LiRiYgXL17MzMxExO+99x6PGDGCfX19efbs2czMvHr1au7Rowd7eXlxhw4deM6cOVxRUcHMzJcvX+bZs2dzWFgYe3p6cnBwME+ePNnxGAsWLODIyEj28vLi1q1b8+jRoy1/vlC/lixZwgEBAY725s2bmYh47dq13LNnT/bw8ODNmzfz+fPnefLkyRwYGMheXl48cOBATk9Pr/J+mJmTkpL42o9zZmYm33zzzWyz2dhut3PPnj05IyPD8feUlBS+4YYb2Nvbm0NDQ3ny5MlcVlbm+Ht4eDjPnTuXH3jgAbbb7Txu3Lhafz3AuQwbNozbtm1rGAdXnThxgpmZDx06xCNHjmQ/Pz+22+18zz33cGlpqaPf/v37eeTIkdy6dWv28/Pj3r1784YNGxx/j4+PZyIy/Aegks7NKSkpTER85MgRZmb+xz/+wVFRUezj48MdOnTgmTNn8sWLFw23+ec//8mBgYFss9l4woQJ/PTTT3P37t3r6FmAq8EcWDca/S8ekltuuYW6d+9Oq1atcsTmzJlDo0aNouzsbHrwwQcpJSWFxo4dS1OnTqWcnBz697//TYmJifTSSy8REdHKlStp3rx59O9//5vy8/Np9erV1K1bNyIi+vHHH2nKlCk0d+5cysvLo+TkZLrpppvq5blC/XvmmWfo1VdfpdzcXIqNjaV//OMftHLlSvrf//1f+umnnygyMpKGDBlCx48fN32f999/P4WGhlJGRgbt2LGDnnnmGfLw8CAiogMHDtDQoUNp9OjRtGvXLlqxYgV9//33NGnSJMN9vPnmm9S9e3fauXMnPf/887X6nMG5HD9+nJKTk+nxxx8nPz8/7e/NmjWjy5cv05133knHjx+n7777jjZs2EA///wzjRkzxtGvrKyMbr/9dtq4cSPt3LmThg4dSiNGjKDCwkIiIlq1ahWFhobS3LlzHf+aDVCdsrIy+uijjygyMpJatmxJRER2u50SExMpJyeH3nnnHVq8eDHNmzfPcZuPP/6YXnrpJXrttddox44d1K5dO1q4cGF9PQVwcpgD61B9r3zqU1W/eDAzjxkzhqOjo5n5yi8eTzzxhOHvt956K7/88suG2LJlyzg4OJiZmd966y3u1KmT9i8wzMwrV65kf39/Pn36dC08C3AVVf3isXr1akesrKyMPTw8+OOPP3bELl68yCEhIfz666+L98Os/+Jht9s5MTFRPI4JEybwww8/bIilpKRwkyZN+Ny5c8x85RePu+66q0bPE1zPDz/8wETEq1atqrLP+vXruWnTplxYWOiI7dmzh4nI8IucqkuXLvyvf/3L0Q4PD+d58+bVynFDwzRu3Dhu2rQp+/n5sZ+fHxMRBwcH844dO6q8zRtvvMG9evVytPv168ePP/64oc/AgQPxiweIMAfWHfziUQVmJjc3N0e7d+/ehr9nZWXR3LlzyWazOf6bOHEilZSUUHl5Od1zzz107tw56tixI02cOJGSkpIc1+8nJCRQeHg4dezYkR544AH6+OOPqby8vE6fHziPa8fWgQMHqKKiggYOHOiIeXh4UN++fSk3N9f0fT711FP00EMP0eDBg+nVV1+lAwcOOP6WlZVFiYmJhrE7ZMgQunz5MhUUFIjHBQ0bM1fbJzc3l8LCwigsLMwRi4mJoWbNmjnGZllZGU2bNo2io6OpWbNmZLPZKDc31/GvfQBmDRo0iDIzMykzM5PS09NpyJAhNGzYMDp06BAREa1YsYIGDhxIQUFBZLPZaObMmYZxlpeXR3379jXcp9oGuApzYN3BwqMKubm51KFDB0db/emtrKyMXnjhBcfEmJmZSdnZ2ZSfn0/e3t4UFhZGeXl59N5775GPjw899thjdNNNN1FFRQXZ7Xb66aef6JNPPqHg4GCaNWsWde/enU6ePFnHzxKcgfSz7u9p0qSJNkmqGx7MmTOH9uzZQ3fccQdt2rSJYmJiKCkpiYiujN1HHnnEMHazsrIoPz+fIiIianxc4LqioqLIzc2N9u7d+6fuZ9q0aZSUlEQvv/wypaSkUGZmJnXr1g2bE8Af5ufnR5GRkRQZGUl9+vSh//mf/6GzZ8/S4sWLKS0tje6//366/fbbac2aNbRz50567rnnMM6gxjAH1h0sPASbNm2i7OxsGj16dJV9evbsSXl5eY6J8dr/mjS58rL6+PjQiBEj6N1336UtW7ZQWloaZWdnExGRu7s7DR48mF5//XXatWsXHTx4kDZt2lQnzw+cV0REBHl6elJqaqojVlFRQRkZGRQTE0NERIGBgXTmzBk6e/aso09mZqZ2X506daInn3yS1q9fT3fffTctWbKEiK6M3ZycHHHsenp6WvsEwSm1aNGChgwZQgsWLDCMq6tOnjxJ0dHRVFRUREVFRY54Tk4OnTx50jE2U1NTafz48TRq1Cjq1q0bBQUF0cGDBw335enpSZcuXbL0+UDD4+bmRk2aNKFz587Rtm3bKDw8nJ577jnq3bs3RUVFOX4Juapz586UkZFhiKltgKswB9Yd9/o+gPp24cIFKi0tpUuXLtGvv/5KycnJ9Morr9Dw4cNp7NixVd5u1qxZNHz4cGrXrh395S9/oSZNmlBWVhbt3r2bXnzxRUpMTKRLly5Rv379yNfXlz766CPy8fGh8PBwWrNmDf3888900003UfPmzWnt2rV0+fJl6ty5cx0+c3BGfn5+9Oijj9L06dOpRYsW1K5dO3r99depvLycJkyYQETkGFPPPvssTZkyhX744QdKTEx03Me5c+do+vTp9Je//IU6dOhAhw8fpoyMDMdC+umnn6b+/fvTpEmT6KGHHiI/Pz/KycmhDRs20Pz58+vjaYMTWLBgAQ0cOJD69u1Lc+fOpdjYWKqsrKQNGzbQwoULKScnh7p160b3338/vf3221RZWUmPPfYYxcfHOy7Li4qKolWrVtGIESPIzc2Nnn/+ea0mUvv27Wnr1q107733kpeXF7Vq1ao+ni44uavnZiKiEydO0Pz586msrIxGjBhBp0+fpsLCQvr000+pT58+9M033zh+0b1q8uTJNHHiROrduzfFxcXRihUraNeuXdSxY8f6eDrgAjAH1pH6TTGpX+PGjXNsZ+bu7s6BgYE8ePBg/vDDD/nSpUuOfkTESUlJ2u2Tk5M5Li6OfXx82N/fn/v27cuLFi1i5ivJvv369WN/f3/28/Pj/v3787fffsvMVxJ54+PjuXnz5uzj48OxsbG8YsWKOnnOUH+qSi6/uk3fVefOnePJkydzq1atxO10ma+Mr8jISPbx8eHhw4fzokWLHMnlFy5c4HvvvdexlXNISAhPmjTJkTjOzJyens4JCQlss9nYz8+PY2Nj+aWXXnL8vbEnvzVWxcXF/Pjjj3N4eDh7enpy27ZteeTIkbx582Zmrn4ryYKCAh40aBD7+PhwWFgYz58/n+Pj43nq1KmOPmlpaRwbG8teXl6NcitJqN6152YiYrvdzn369OEvvvjC0Wf69OncsmVLttlsPGbMGJ43b5626cbcuXO5VatWbLPZ+MEHH+QpU6Zw//796/jZgCvBHGg9N2YTGTUAAAAALiwhIYGCgoJo2bJl9X0oAI1Wo7/UCgAAABqW8vJyev/992nIkCHUtGlT+uSTT+jbb7+lDRs21PehATRq+MUDAAAAGpRz587RiBEjaOfOnXT+/Hnq3LkzzZw5k+6+++76PjSARg0LDwAAAAAAsBy20wUAAAAAAMth4QEAAAAAAJbDwgMAAAAAACyHhQcAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADL/R8qgnO1GDUKvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## Build the model\n",
    "\n",
    "Building the neural network requires configuring the layers of the model, then compiling the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### Set up the layers\n",
    "\n",
    "The basic building block of a neural network is the [*layer*](https://www.tensorflow.org/api_docs/python/tf/keras/layers). Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.\n",
    "\n",
    "Most of deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:37:31.910794Z",
     "iopub.status.busy": "2022-12-14T06:37:31.910145Z",
     "iopub.status.idle": "2022-12-14T06:37:35.441973Z",
     "shell.execute_reply": "2022-12-14T06:37:35.441176Z"
    },
    "id": "9ODch-OFCaW4"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
    "\n",
    "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.\n",
    "\n",
    "### Compile the model\n",
    "\n",
    "Before the model is ready for training, it needs a few more settings. These are added during the model's [*compile*](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile) step:\n",
    "\n",
    "* [*Loss function*](https://www.tensorflow.org/api_docs/python/tf/keras/losses) —This measures how accurate the model is during training. You want to minimize this function to \"steer\" the model in the right direction.\n",
    "* [*Optimizer*](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) —This is how the model is updated based on the data it sees and its loss function.\n",
    "* [*Metrics*](https://www.tensorflow.org/api_docs/python/tf/keras/metrics) —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:37:35.446051Z",
     "iopub.status.busy": "2022-12-14T06:37:35.445790Z",
     "iopub.status.idle": "2022-12-14T06:37:35.459162Z",
     "shell.execute_reply": "2022-12-14T06:37:35.458566Z"
    },
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## Train the model\n",
    "\n",
    "Training the neural network model requires the following steps:\n",
    "\n",
    "1. Feed the training data to the model. In this example, the training data is in the `train_images` and `train_labels` arrays.\n",
    "2. The model learns to associate images and labels.\n",
    "3. You ask the model to make predictions about a test set—in this example, the `test_images` array.\n",
    "4. Verify that the predictions match the labels from the `test_labels` array.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4P4zIV7E28Z"
   },
   "source": [
    "### Feed the model\n",
    "\n",
    "To start training,  call the [`model.fit`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit) method—so called because it \"fits\" the model to the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:37:35.462907Z",
     "iopub.status.busy": "2022-12-14T06:37:35.462237Z",
     "iopub.status.idle": "2022-12-14T06:38:15.225559Z",
     "shell.execute_reply": "2022-12-14T06:38:15.224722Z"
    },
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 39:10 - loss: 2.2333 - accuracy: 0.1250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 1.3738 - accuracy: 0.5286   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  48/1875 [..............................] - ETA: 3s - loss: 1.1290 - accuracy: 0.6042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  73/1875 [>.............................] - ETA: 3s - loss: 1.0034 - accuracy: 0.6511"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  98/1875 [>.............................] - ETA: 3s - loss: 0.9214 - accuracy: 0.6824"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 123/1875 [>.............................] - ETA: 3s - loss: 0.8622 - accuracy: 0.7045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 148/1875 [=>............................] - ETA: 3s - loss: 0.8246 - accuracy: 0.7194"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 173/1875 [=>............................] - ETA: 3s - loss: 0.7939 - accuracy: 0.7274"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 198/1875 [==>...........................] - ETA: 3s - loss: 0.7744 - accuracy: 0.7322"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 223/1875 [==>...........................] - ETA: 3s - loss: 0.7577 - accuracy: 0.7382"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 248/1875 [==>...........................] - ETA: 3s - loss: 0.7352 - accuracy: 0.7457"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 273/1875 [===>..........................] - ETA: 3s - loss: 0.7183 - accuracy: 0.7515"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 298/1875 [===>..........................] - ETA: 3s - loss: 0.7025 - accuracy: 0.7566"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 322/1875 [====>.........................] - ETA: 3s - loss: 0.6903 - accuracy: 0.7602"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 347/1875 [====>.........................] - ETA: 3s - loss: 0.6788 - accuracy: 0.7635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 372/1875 [====>.........................] - ETA: 3s - loss: 0.6650 - accuracy: 0.7685"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 398/1875 [=====>........................] - ETA: 3s - loss: 0.6563 - accuracy: 0.7714"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 424/1875 [=====>........................] - ETA: 2s - loss: 0.6500 - accuracy: 0.7738"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 450/1875 [======>.......................] - ETA: 2s - loss: 0.6392 - accuracy: 0.7771"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 475/1875 [======>.......................] - ETA: 2s - loss: 0.6289 - accuracy: 0.7809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 501/1875 [=======>......................] - ETA: 2s - loss: 0.6215 - accuracy: 0.7841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 527/1875 [=======>......................] - ETA: 2s - loss: 0.6179 - accuracy: 0.7852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 553/1875 [=======>......................] - ETA: 2s - loss: 0.6126 - accuracy: 0.7870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 579/1875 [========>.....................] - ETA: 2s - loss: 0.6071 - accuracy: 0.7889"
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 605/1875 [========>.....................] - ETA: 2s - loss: 0.6057 - accuracy: 0.7890"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 631/1875 [=========>....................] - ETA: 2s - loss: 0.6011 - accuracy: 0.7906"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 657/1875 [=========>....................] - ETA: 2s - loss: 0.5944 - accuracy: 0.7933"
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    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 683/1875 [=========>....................] - ETA: 2s - loss: 0.5930 - accuracy: 0.7943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 709/1875 [==========>...................] - ETA: 2s - loss: 0.5903 - accuracy: 0.7950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 735/1875 [==========>...................] - ETA: 2s - loss: 0.5855 - accuracy: 0.7965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 761/1875 [===========>..................] - ETA: 2s - loss: 0.5840 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 787/1875 [===========>..................] - ETA: 2s - loss: 0.5809 - accuracy: 0.7978"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 814/1875 [============>.................] - ETA: 2s - loss: 0.5772 - accuracy: 0.7990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 840/1875 [============>.................] - ETA: 2s - loss: 0.5725 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 865/1875 [============>.................] - ETA: 2s - loss: 0.5679 - accuracy: 0.8022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 891/1875 [=============>................] - ETA: 1s - loss: 0.5640 - accuracy: 0.8031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 916/1875 [=============>................] - ETA: 1s - loss: 0.5612 - accuracy: 0.8039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 942/1875 [==============>...............] - ETA: 1s - loss: 0.5596 - accuracy: 0.8041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 968/1875 [==============>...............] - ETA: 1s - loss: 0.5583 - accuracy: 0.8042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 994/1875 [==============>...............] - ETA: 1s - loss: 0.5555 - accuracy: 0.8050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1020/1875 [===============>..............] - ETA: 1s - loss: 0.5535 - accuracy: 0.8059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1046/1875 [===============>..............] - ETA: 1s - loss: 0.5526 - accuracy: 0.8064"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1072/1875 [================>.............] - ETA: 1s - loss: 0.5497 - accuracy: 0.8073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1098/1875 [================>.............] - ETA: 1s - loss: 0.5467 - accuracy: 0.8077"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1124/1875 [================>.............] - ETA: 1s - loss: 0.5458 - accuracy: 0.8079"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1150/1875 [=================>............] - ETA: 1s - loss: 0.5435 - accuracy: 0.8089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1176/1875 [=================>............] - ETA: 1s - loss: 0.5419 - accuracy: 0.8096"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1203/1875 [==================>...........] - ETA: 1s - loss: 0.5396 - accuracy: 0.8103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1229/1875 [==================>...........] - ETA: 1s - loss: 0.5376 - accuracy: 0.8109"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1255/1875 [===================>..........] - ETA: 1s - loss: 0.5351 - accuracy: 0.8118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1281/1875 [===================>..........] - ETA: 1s - loss: 0.5328 - accuracy: 0.8124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1306/1875 [===================>..........] - ETA: 1s - loss: 0.5309 - accuracy: 0.8131"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1875 [====================>.........] - ETA: 1s - loss: 0.5281 - accuracy: 0.8138"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1353/1875 [====================>.........] - ETA: 1s - loss: 0.5262 - accuracy: 0.8144"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1377/1875 [=====================>........] - ETA: 0s - loss: 0.5254 - accuracy: 0.8149"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1402/1875 [=====================>........] - ETA: 0s - loss: 0.5226 - accuracy: 0.8159"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1427/1875 [=====================>........] - ETA: 0s - loss: 0.5207 - accuracy: 0.8162"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1452/1875 [======================>.......] - ETA: 0s - loss: 0.5191 - accuracy: 0.8167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1478/1875 [======================>.......] - ETA: 0s - loss: 0.5175 - accuracy: 0.8173"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1504/1875 [=======================>......] - ETA: 0s - loss: 0.5159 - accuracy: 0.8180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1530/1875 [=======================>......] - ETA: 0s - loss: 0.5143 - accuracy: 0.8186"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1555/1875 [=======================>......] - ETA: 0s - loss: 0.5134 - accuracy: 0.8188"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1580/1875 [========================>.....] - ETA: 0s - loss: 0.5116 - accuracy: 0.8194"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1606/1875 [========================>.....] - ETA: 0s - loss: 0.5104 - accuracy: 0.8197"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1631/1875 [=========================>....] - ETA: 0s - loss: 0.5092 - accuracy: 0.8199"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1656/1875 [=========================>....] - ETA: 0s - loss: 0.5083 - accuracy: 0.8202"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1681/1875 [=========================>....] - ETA: 0s - loss: 0.5065 - accuracy: 0.8209"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1706/1875 [==========================>...] - ETA: 0s - loss: 0.5048 - accuracy: 0.8216"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1732/1875 [==========================>...] - ETA: 0s - loss: 0.5030 - accuracy: 0.8222"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1759/1875 [===========================>..] - ETA: 0s - loss: 0.5012 - accuracy: 0.8230"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1785/1875 [===========================>..] - ETA: 0s - loss: 0.5001 - accuracy: 0.8234"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1811/1875 [===========================>..] - ETA: 0s - loss: 0.4989 - accuracy: 0.8236"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1836/1875 [============================>.] - ETA: 0s - loss: 0.4979 - accuracy: 0.8240"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1862/1875 [============================>.] - ETA: 0s - loss: 0.4966 - accuracy: 0.8245"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4960 - accuracy: 0.8246\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 4s - loss: 0.4154 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  27/1875 [..............................] - ETA: 3s - loss: 0.3980 - accuracy: 0.8553"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  53/1875 [..............................] - ETA: 3s - loss: 0.3986 - accuracy: 0.8561"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  78/1875 [>.............................] - ETA: 3s - loss: 0.3936 - accuracy: 0.8586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 104/1875 [>.............................] - ETA: 3s - loss: 0.3838 - accuracy: 0.8612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 130/1875 [=>............................] - ETA: 3s - loss: 0.3901 - accuracy: 0.8579"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 157/1875 [=>............................] - ETA: 3s - loss: 0.3839 - accuracy: 0.8607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 182/1875 [=>............................] - ETA: 3s - loss: 0.3776 - accuracy: 0.8625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 207/1875 [==>...........................] - ETA: 3s - loss: 0.3760 - accuracy: 0.8617"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 232/1875 [==>...........................] - ETA: 3s - loss: 0.3791 - accuracy: 0.8607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 257/1875 [===>..........................] - ETA: 3s - loss: 0.3805 - accuracy: 0.8610"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 281/1875 [===>..........................] - ETA: 3s - loss: 0.3838 - accuracy: 0.8601"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 306/1875 [===>..........................] - ETA: 3s - loss: 0.3860 - accuracy: 0.8602"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 331/1875 [====>.........................] - ETA: 3s - loss: 0.3878 - accuracy: 0.8601"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 356/1875 [====>.........................] - ETA: 3s - loss: 0.3874 - accuracy: 0.8605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 382/1875 [=====>........................] - ETA: 2s - loss: 0.3874 - accuracy: 0.8605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 408/1875 [=====>........................] - ETA: 2s - loss: 0.3878 - accuracy: 0.8601"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 434/1875 [=====>........................] - ETA: 2s - loss: 0.3851 - accuracy: 0.8614"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 460/1875 [======>.......................] - ETA: 2s - loss: 0.3858 - accuracy: 0.8615"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 485/1875 [======>.......................] - ETA: 2s - loss: 0.3858 - accuracy: 0.8613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 511/1875 [=======>......................] - ETA: 2s - loss: 0.3848 - accuracy: 0.8620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 537/1875 [=======>......................] - ETA: 2s - loss: 0.3836 - accuracy: 0.8617"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 563/1875 [========>.....................] - ETA: 2s - loss: 0.3833 - accuracy: 0.8621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 588/1875 [========>.....................] - ETA: 2s - loss: 0.3812 - accuracy: 0.8630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 614/1875 [========>.....................] - ETA: 2s - loss: 0.3809 - accuracy: 0.8635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 639/1875 [=========>....................] - ETA: 2s - loss: 0.3799 - accuracy: 0.8636"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 665/1875 [=========>....................] - ETA: 2s - loss: 0.3801 - accuracy: 0.8634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 691/1875 [==========>...................] - ETA: 2s - loss: 0.3805 - accuracy: 0.8636"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 717/1875 [==========>...................] - ETA: 2s - loss: 0.3807 - accuracy: 0.8632"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 742/1875 [==========>...................] - ETA: 2s - loss: 0.3808 - accuracy: 0.8632"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 768/1875 [===========>..................] - ETA: 2s - loss: 0.3788 - accuracy: 0.8641"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 793/1875 [===========>..................] - ETA: 2s - loss: 0.3813 - accuracy: 0.8630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 819/1875 [============>.................] - ETA: 2s - loss: 0.3806 - accuracy: 0.8634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 845/1875 [============>.................] - ETA: 2s - loss: 0.3798 - accuracy: 0.8638"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 871/1875 [============>.................] - ETA: 2s - loss: 0.3806 - accuracy: 0.8633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 896/1875 [=============>................] - ETA: 1s - loss: 0.3803 - accuracy: 0.8635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 921/1875 [=============>................] - ETA: 1s - loss: 0.3799 - accuracy: 0.8641"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 946/1875 [==============>...............] - ETA: 1s - loss: 0.3796 - accuracy: 0.8640"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 971/1875 [==============>...............] - ETA: 1s - loss: 0.3796 - accuracy: 0.8642"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 996/1875 [==============>...............] - ETA: 1s - loss: 0.3796 - accuracy: 0.8644"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1022/1875 [===============>..............] - ETA: 1s - loss: 0.3790 - accuracy: 0.8645"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1047/1875 [===============>..............] - ETA: 1s - loss: 0.3784 - accuracy: 0.8650"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1073/1875 [================>.............] - ETA: 1s - loss: 0.3787 - accuracy: 0.8648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1098/1875 [================>.............] - ETA: 1s - loss: 0.3788 - accuracy: 0.8652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1124/1875 [================>.............] - ETA: 1s - loss: 0.3788 - accuracy: 0.8651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1149/1875 [=================>............] - ETA: 1s - loss: 0.3790 - accuracy: 0.8651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1174/1875 [=================>............] - ETA: 1s - loss: 0.3776 - accuracy: 0.8657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1199/1875 [==================>...........] - ETA: 1s - loss: 0.3773 - accuracy: 0.8658"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1225/1875 [==================>...........] - ETA: 1s - loss: 0.3768 - accuracy: 0.8657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1252/1875 [===================>..........] - ETA: 1s - loss: 0.3763 - accuracy: 0.8660"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1277/1875 [===================>..........] - ETA: 1s - loss: 0.3775 - accuracy: 0.8654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1302/1875 [===================>..........] - ETA: 1s - loss: 0.3770 - accuracy: 0.8655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1327/1875 [====================>.........] - ETA: 1s - loss: 0.3767 - accuracy: 0.8656"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1353/1875 [====================>.........] - ETA: 1s - loss: 0.3765 - accuracy: 0.8656"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1379/1875 [=====================>........] - ETA: 0s - loss: 0.3754 - accuracy: 0.8657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1406/1875 [=====================>........] - ETA: 0s - loss: 0.3755 - accuracy: 0.8656"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1432/1875 [=====================>........] - ETA: 0s - loss: 0.3757 - accuracy: 0.8657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1457/1875 [======================>.......] - ETA: 0s - loss: 0.3755 - accuracy: 0.8657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1482/1875 [======================>.......] - ETA: 0s - loss: 0.3746 - accuracy: 0.8660"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1507/1875 [=======================>......] - ETA: 0s - loss: 0.3747 - accuracy: 0.8661"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1533/1875 [=======================>......] - ETA: 0s - loss: 0.3748 - accuracy: 0.8658"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1558/1875 [=======================>......] - ETA: 0s - loss: 0.3745 - accuracy: 0.8658"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1583/1875 [========================>.....] - ETA: 0s - loss: 0.3741 - accuracy: 0.8661"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1609/1875 [========================>.....] - ETA: 0s - loss: 0.3746 - accuracy: 0.8662"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1633/1875 [=========================>....] - ETA: 0s - loss: 0.3740 - accuracy: 0.8664"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1658/1875 [=========================>....] - ETA: 0s - loss: 0.3735 - accuracy: 0.8665"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1682/1875 [=========================>....] - ETA: 0s - loss: 0.3736 - accuracy: 0.8664"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1707/1875 [==========================>...] - ETA: 0s - loss: 0.3738 - accuracy: 0.8663"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1732/1875 [==========================>...] - ETA: 0s - loss: 0.3734 - accuracy: 0.8664"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1757/1875 [===========================>..] - ETA: 0s - loss: 0.3729 - accuracy: 0.8664"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1781/1875 [===========================>..] - ETA: 0s - loss: 0.3727 - accuracy: 0.8663"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1806/1875 [===========================>..] - ETA: 0s - loss: 0.3730 - accuracy: 0.8663"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1832/1875 [============================>.] - ETA: 0s - loss: 0.3729 - accuracy: 0.8662"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1857/1875 [============================>.] - ETA: 0s - loss: 0.3725 - accuracy: 0.8662"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3721 - accuracy: 0.8663\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 4s - loss: 0.4014 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  26/1875 [..............................] - ETA: 3s - loss: 0.3821 - accuracy: 0.8690"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  51/1875 [..............................] - ETA: 3s - loss: 0.3550 - accuracy: 0.8756"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  76/1875 [>.............................] - ETA: 3s - loss: 0.3408 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 101/1875 [>.............................] - ETA: 3s - loss: 0.3387 - accuracy: 0.8809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 127/1875 [=>............................] - ETA: 3s - loss: 0.3273 - accuracy: 0.8841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 152/1875 [=>............................] - ETA: 3s - loss: 0.3266 - accuracy: 0.8847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 178/1875 [=>............................] - ETA: 3s - loss: 0.3308 - accuracy: 0.8834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 203/1875 [==>...........................] - ETA: 3s - loss: 0.3269 - accuracy: 0.8856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 229/1875 [==>...........................] - ETA: 3s - loss: 0.3277 - accuracy: 0.8856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 254/1875 [===>..........................] - ETA: 3s - loss: 0.3277 - accuracy: 0.8855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 280/1875 [===>..........................] - ETA: 3s - loss: 0.3312 - accuracy: 0.8842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 305/1875 [===>..........................] - ETA: 3s - loss: 0.3320 - accuracy: 0.8838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 331/1875 [====>.........................] - ETA: 3s - loss: 0.3321 - accuracy: 0.8832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 357/1875 [====>.........................] - ETA: 3s - loss: 0.3368 - accuracy: 0.8813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 382/1875 [=====>........................] - ETA: 2s - loss: 0.3352 - accuracy: 0.8807"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 406/1875 [=====>........................] - ETA: 2s - loss: 0.3352 - accuracy: 0.8806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 431/1875 [=====>........................] - ETA: 2s - loss: 0.3354 - accuracy: 0.8802"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 456/1875 [======>.......................] - ETA: 2s - loss: 0.3344 - accuracy: 0.8808"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 481/1875 [======>.......................] - ETA: 2s - loss: 0.3327 - accuracy: 0.8811"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 506/1875 [=======>......................] - ETA: 2s - loss: 0.3323 - accuracy: 0.8809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 531/1875 [=======>......................] - ETA: 2s - loss: 0.3358 - accuracy: 0.8794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 556/1875 [=======>......................] - ETA: 2s - loss: 0.3342 - accuracy: 0.8799"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 581/1875 [========>.....................] - ETA: 2s - loss: 0.3347 - accuracy: 0.8795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 606/1875 [========>.....................] - ETA: 2s - loss: 0.3346 - accuracy: 0.8798"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 631/1875 [=========>....................] - ETA: 2s - loss: 0.3346 - accuracy: 0.8800"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 656/1875 [=========>....................] - ETA: 2s - loss: 0.3339 - accuracy: 0.8800"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 682/1875 [=========>....................] - ETA: 2s - loss: 0.3343 - accuracy: 0.8799"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 708/1875 [==========>...................] - ETA: 2s - loss: 0.3339 - accuracy: 0.8796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 734/1875 [==========>...................] - ETA: 2s - loss: 0.3348 - accuracy: 0.8792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 760/1875 [===========>..................] - ETA: 2s - loss: 0.3337 - accuracy: 0.8795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 785/1875 [===========>..................] - ETA: 2s - loss: 0.3334 - accuracy: 0.8797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 809/1875 [===========>..................] - ETA: 2s - loss: 0.3333 - accuracy: 0.8794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 833/1875 [============>.................] - ETA: 2s - loss: 0.3331 - accuracy: 0.8797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 857/1875 [============>.................] - ETA: 2s - loss: 0.3344 - accuracy: 0.8794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 881/1875 [=============>................] - ETA: 2s - loss: 0.3336 - accuracy: 0.8797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 905/1875 [=============>................] - ETA: 1s - loss: 0.3340 - accuracy: 0.8797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 929/1875 [=============>................] - ETA: 1s - loss: 0.3335 - accuracy: 0.8795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 955/1875 [==============>...............] - ETA: 1s - loss: 0.3338 - accuracy: 0.8794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 980/1875 [==============>...............] - ETA: 1s - loss: 0.3347 - accuracy: 0.8794"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1005/1875 [===============>..............] - ETA: 1s - loss: 0.3360 - accuracy: 0.8790"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1030/1875 [===============>..............] - ETA: 1s - loss: 0.3359 - accuracy: 0.8792"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1055/1875 [===============>..............] - ETA: 1s - loss: 0.3355 - accuracy: 0.8792"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1080/1875 [================>.............] - ETA: 1s - loss: 0.3373 - accuracy: 0.8785"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1105/1875 [================>.............] - ETA: 1s - loss: 0.3371 - accuracy: 0.8787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1130/1875 [=================>............] - ETA: 1s - loss: 0.3366 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1154/1875 [=================>............] - ETA: 1s - loss: 0.3359 - accuracy: 0.8793"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1178/1875 [=================>............] - ETA: 1s - loss: 0.3353 - accuracy: 0.8794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1203/1875 [==================>...........] - ETA: 1s - loss: 0.3348 - accuracy: 0.8795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1228/1875 [==================>...........] - ETA: 1s - loss: 0.3353 - accuracy: 0.8794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1253/1875 [===================>..........] - ETA: 1s - loss: 0.3352 - accuracy: 0.8792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1278/1875 [===================>..........] - ETA: 1s - loss: 0.3351 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1304/1875 [===================>..........] - ETA: 1s - loss: 0.3351 - accuracy: 0.8789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1875 [====================>.........] - ETA: 1s - loss: 0.3351 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1355/1875 [====================>.........] - ETA: 1s - loss: 0.3347 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1380/1875 [=====================>........] - ETA: 1s - loss: 0.3349 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1405/1875 [=====================>........] - ETA: 0s - loss: 0.3354 - accuracy: 0.8786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1429/1875 [=====================>........] - ETA: 0s - loss: 0.3353 - accuracy: 0.8785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1454/1875 [======================>.......] - ETA: 0s - loss: 0.3353 - accuracy: 0.8782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1479/1875 [======================>.......] - ETA: 0s - loss: 0.3349 - accuracy: 0.8782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1504/1875 [=======================>......] - ETA: 0s - loss: 0.3350 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1529/1875 [=======================>......] - ETA: 0s - loss: 0.3344 - accuracy: 0.8787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1554/1875 [=======================>......] - ETA: 0s - loss: 0.3348 - accuracy: 0.8785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1579/1875 [========================>.....] - ETA: 0s - loss: 0.3344 - accuracy: 0.8786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1604/1875 [========================>.....] - ETA: 0s - loss: 0.3348 - accuracy: 0.8786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1629/1875 [=========================>....] - ETA: 0s - loss: 0.3351 - accuracy: 0.8785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1653/1875 [=========================>....] - ETA: 0s - loss: 0.3355 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1677/1875 [=========================>....] - ETA: 0s - loss: 0.3349 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1702/1875 [==========================>...] - ETA: 0s - loss: 0.3346 - accuracy: 0.8782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1727/1875 [==========================>...] - ETA: 0s - loss: 0.3352 - accuracy: 0.8779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1752/1875 [===========================>..] - ETA: 0s - loss: 0.3355 - accuracy: 0.8779"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1777/1875 [===========================>..] - ETA: 0s - loss: 0.3352 - accuracy: 0.8778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1801/1875 [===========================>..] - ETA: 0s - loss: 0.3353 - accuracy: 0.8777"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1826/1875 [============================>.] - ETA: 0s - loss: 0.3351 - accuracy: 0.8777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1851/1875 [============================>.] - ETA: 0s - loss: 0.3354 - accuracy: 0.8778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3355 - accuracy: 0.8779\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.3989 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  26/1875 [..............................] - ETA: 3s - loss: 0.2761 - accuracy: 0.9002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  51/1875 [..............................] - ETA: 3s - loss: 0.2910 - accuracy: 0.8909"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  75/1875 [>.............................] - ETA: 3s - loss: 0.2950 - accuracy: 0.8917"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  99/1875 [>.............................] - ETA: 3s - loss: 0.3038 - accuracy: 0.8873"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 124/1875 [>.............................] - ETA: 3s - loss: 0.3051 - accuracy: 0.8884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 149/1875 [=>............................] - ETA: 3s - loss: 0.3105 - accuracy: 0.8878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 174/1875 [=>............................] - ETA: 3s - loss: 0.3093 - accuracy: 0.8851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 199/1875 [==>...........................] - ETA: 3s - loss: 0.3045 - accuracy: 0.8860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 224/1875 [==>...........................] - ETA: 3s - loss: 0.3013 - accuracy: 0.8881"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 249/1875 [==>...........................] - ETA: 3s - loss: 0.3035 - accuracy: 0.8882"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 274/1875 [===>..........................] - ETA: 3s - loss: 0.3042 - accuracy: 0.8874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 299/1875 [===>..........................] - ETA: 3s - loss: 0.3058 - accuracy: 0.8864"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 324/1875 [====>.........................] - ETA: 3s - loss: 0.3061 - accuracy: 0.8864"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 349/1875 [====>.........................] - ETA: 3s - loss: 0.3040 - accuracy: 0.8875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 374/1875 [====>.........................] - ETA: 3s - loss: 0.3071 - accuracy: 0.8871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 399/1875 [=====>........................] - ETA: 3s - loss: 0.3067 - accuracy: 0.8872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 424/1875 [=====>........................] - ETA: 2s - loss: 0.3066 - accuracy: 0.8872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 449/1875 [======>.......................] - ETA: 2s - loss: 0.3072 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 474/1875 [======>.......................] - ETA: 2s - loss: 0.3101 - accuracy: 0.8856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 500/1875 [=======>......................] - ETA: 2s - loss: 0.3100 - accuracy: 0.8861"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 525/1875 [=======>......................] - ETA: 2s - loss: 0.3095 - accuracy: 0.8863"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 550/1875 [=======>......................] - ETA: 2s - loss: 0.3102 - accuracy: 0.8863"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 575/1875 [========>.....................] - ETA: 2s - loss: 0.3099 - accuracy: 0.8867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 600/1875 [========>.....................] - ETA: 2s - loss: 0.3085 - accuracy: 0.8874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 625/1875 [=========>....................] - ETA: 2s - loss: 0.3100 - accuracy: 0.8873"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 650/1875 [=========>....................] - ETA: 2s - loss: 0.3101 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 676/1875 [=========>....................] - ETA: 2s - loss: 0.3109 - accuracy: 0.8864"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 701/1875 [==========>...................] - ETA: 2s - loss: 0.3128 - accuracy: 0.8859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 727/1875 [==========>...................] - ETA: 2s - loss: 0.3137 - accuracy: 0.8855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 753/1875 [===========>..................] - ETA: 2s - loss: 0.3146 - accuracy: 0.8851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 778/1875 [===========>..................] - ETA: 2s - loss: 0.3139 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 804/1875 [===========>..................] - ETA: 2s - loss: 0.3143 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 830/1875 [============>.................] - ETA: 2s - loss: 0.3146 - accuracy: 0.8852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 856/1875 [============>.................] - ETA: 2s - loss: 0.3138 - accuracy: 0.8856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 881/1875 [=============>................] - ETA: 2s - loss: 0.3138 - accuracy: 0.8860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 906/1875 [=============>................] - ETA: 1s - loss: 0.3140 - accuracy: 0.8861"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 932/1875 [=============>................] - ETA: 1s - loss: 0.3145 - accuracy: 0.8862"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 958/1875 [==============>...............] - ETA: 1s - loss: 0.3135 - accuracy: 0.8866"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 983/1875 [==============>...............] - ETA: 1s - loss: 0.3140 - accuracy: 0.8863"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1008/1875 [===============>..............] - ETA: 1s - loss: 0.3126 - accuracy: 0.8867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1034/1875 [===============>..............] - ETA: 1s - loss: 0.3115 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1060/1875 [===============>..............] - ETA: 1s - loss: 0.3135 - accuracy: 0.8863"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1085/1875 [================>.............] - ETA: 1s - loss: 0.3130 - accuracy: 0.8868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1111/1875 [================>.............] - ETA: 1s - loss: 0.3128 - accuracy: 0.8866"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1137/1875 [=================>............] - ETA: 1s - loss: 0.3121 - accuracy: 0.8870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1162/1875 [=================>............] - ETA: 1s - loss: 0.3115 - accuracy: 0.8872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1187/1875 [=================>............] - ETA: 1s - loss: 0.3113 - accuracy: 0.8871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1211/1875 [==================>...........] - ETA: 1s - loss: 0.3107 - accuracy: 0.8872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1235/1875 [==================>...........] - ETA: 1s - loss: 0.3111 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1260/1875 [===================>..........] - ETA: 1s - loss: 0.3107 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1284/1875 [===================>..........] - ETA: 1s - loss: 0.3106 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1308/1875 [===================>..........] - ETA: 1s - loss: 0.3112 - accuracy: 0.8866"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1333/1875 [====================>.........] - ETA: 1s - loss: 0.3111 - accuracy: 0.8868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1358/1875 [====================>.........] - ETA: 1s - loss: 0.3114 - accuracy: 0.8868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1383/1875 [=====================>........] - ETA: 0s - loss: 0.3115 - accuracy: 0.8867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1408/1875 [=====================>........] - ETA: 0s - loss: 0.3112 - accuracy: 0.8867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1433/1875 [=====================>........] - ETA: 0s - loss: 0.3116 - accuracy: 0.8866"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1458/1875 [======================>.......] - ETA: 0s - loss: 0.3116 - accuracy: 0.8866"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1483/1875 [======================>.......] - ETA: 0s - loss: 0.3119 - accuracy: 0.8862"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1875 [=======================>......] - ETA: 0s - loss: 0.3117 - accuracy: 0.8866"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1875 [=======================>......] - ETA: 0s - loss: 0.3111 - accuracy: 0.8868"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1559/1875 [=======================>......] - ETA: 0s - loss: 0.3109 - accuracy: 0.8869"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1584/1875 [========================>.....] - ETA: 0s - loss: 0.3113 - accuracy: 0.8868"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1609/1875 [========================>.....] - ETA: 0s - loss: 0.3110 - accuracy: 0.8869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1635/1875 [=========================>....] - ETA: 0s - loss: 0.3100 - accuracy: 0.8871"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1660/1875 [=========================>....] - ETA: 0s - loss: 0.3102 - accuracy: 0.8868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1686/1875 [=========================>....] - ETA: 0s - loss: 0.3102 - accuracy: 0.8867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1713/1875 [==========================>...] - ETA: 0s - loss: 0.3106 - accuracy: 0.8867"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1740/1875 [==========================>...] - ETA: 0s - loss: 0.3101 - accuracy: 0.8868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1766/1875 [===========================>..] - ETA: 0s - loss: 0.3102 - accuracy: 0.8868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1792/1875 [===========================>..] - ETA: 0s - loss: 0.3097 - accuracy: 0.8871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1818/1875 [============================>.] - ETA: 0s - loss: 0.3097 - accuracy: 0.8872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1843/1875 [============================>.] - ETA: 0s - loss: 0.3091 - accuracy: 0.8873"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1867/1875 [============================>.] - ETA: 0s - loss: 0.3085 - accuracy: 0.8874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3085 - accuracy: 0.8874\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.3462 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  26/1875 [..............................] - ETA: 3s - loss: 0.3084 - accuracy: 0.8834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  50/1875 [..............................] - ETA: 3s - loss: 0.2792 - accuracy: 0.8931"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  75/1875 [>.............................] - ETA: 3s - loss: 0.2877 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  99/1875 [>.............................] - ETA: 3s - loss: 0.2945 - accuracy: 0.8883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 124/1875 [>.............................] - ETA: 3s - loss: 0.2982 - accuracy: 0.8846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 149/1875 [=>............................] - ETA: 3s - loss: 0.2956 - accuracy: 0.8880"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 174/1875 [=>............................] - ETA: 3s - loss: 0.2990 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 198/1875 [==>...........................] - ETA: 3s - loss: 0.2983 - accuracy: 0.8862"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 222/1875 [==>...........................] - ETA: 3s - loss: 0.2936 - accuracy: 0.8885"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 246/1875 [==>...........................] - ETA: 3s - loss: 0.2932 - accuracy: 0.8883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 270/1875 [===>..........................] - ETA: 3s - loss: 0.2954 - accuracy: 0.8877"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 295/1875 [===>..........................] - ETA: 3s - loss: 0.2934 - accuracy: 0.8894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 320/1875 [====>.........................] - ETA: 3s - loss: 0.2912 - accuracy: 0.8910"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 344/1875 [====>.........................] - ETA: 3s - loss: 0.2881 - accuracy: 0.8912"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 370/1875 [====>.........................] - ETA: 3s - loss: 0.2926 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 395/1875 [=====>........................] - ETA: 3s - loss: 0.2940 - accuracy: 0.8896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 421/1875 [=====>........................] - ETA: 3s - loss: 0.2959 - accuracy: 0.8878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 446/1875 [======>.......................] - ETA: 2s - loss: 0.2969 - accuracy: 0.8878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 471/1875 [======>.......................] - ETA: 2s - loss: 0.2981 - accuracy: 0.8873"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 496/1875 [======>.......................] - ETA: 2s - loss: 0.2994 - accuracy: 0.8870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 521/1875 [=======>......................] - ETA: 2s - loss: 0.2991 - accuracy: 0.8875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 545/1875 [=======>......................] - ETA: 2s - loss: 0.2973 - accuracy: 0.8886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 570/1875 [========>.....................] - ETA: 2s - loss: 0.2959 - accuracy: 0.8898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 595/1875 [========>.....................] - ETA: 2s - loss: 0.2965 - accuracy: 0.8895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 620/1875 [========>.....................] - ETA: 2s - loss: 0.2953 - accuracy: 0.8899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 645/1875 [=========>....................] - ETA: 2s - loss: 0.2954 - accuracy: 0.8900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 671/1875 [=========>....................] - ETA: 2s - loss: 0.2932 - accuracy: 0.8910"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 696/1875 [==========>...................] - ETA: 2s - loss: 0.2936 - accuracy: 0.8910"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 722/1875 [==========>...................] - ETA: 2s - loss: 0.2933 - accuracy: 0.8915"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 748/1875 [==========>...................] - ETA: 2s - loss: 0.2922 - accuracy: 0.8919"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 773/1875 [===========>..................] - ETA: 2s - loss: 0.2926 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 798/1875 [===========>..................] - ETA: 2s - loss: 0.2924 - accuracy: 0.8922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 823/1875 [============>.................] - ETA: 2s - loss: 0.2918 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 848/1875 [============>.................] - ETA: 2s - loss: 0.2911 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 873/1875 [============>.................] - ETA: 2s - loss: 0.2913 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 898/1875 [=============>................] - ETA: 2s - loss: 0.2921 - accuracy: 0.8917"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 923/1875 [=============>................] - ETA: 1s - loss: 0.2922 - accuracy: 0.8919"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 949/1875 [==============>...............] - ETA: 1s - loss: 0.2921 - accuracy: 0.8919"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 975/1875 [==============>...............] - ETA: 1s - loss: 0.2923 - accuracy: 0.8918"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1001/1875 [===============>..............] - ETA: 1s - loss: 0.2915 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1026/1875 [===============>..............] - ETA: 1s - loss: 0.2901 - accuracy: 0.8924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1051/1875 [===============>..............] - ETA: 1s - loss: 0.2899 - accuracy: 0.8925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1075/1875 [================>.............] - ETA: 1s - loss: 0.2898 - accuracy: 0.8925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1099/1875 [================>.............] - ETA: 1s - loss: 0.2907 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1124/1875 [================>.............] - ETA: 1s - loss: 0.2923 - accuracy: 0.8915"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1149/1875 [=================>............] - ETA: 1s - loss: 0.2920 - accuracy: 0.8919"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1174/1875 [=================>............] - ETA: 1s - loss: 0.2918 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1201/1875 [==================>...........] - ETA: 1s - loss: 0.2922 - accuracy: 0.8923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1226/1875 [==================>...........] - ETA: 1s - loss: 0.2930 - accuracy: 0.8921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1251/1875 [===================>..........] - ETA: 1s - loss: 0.2928 - accuracy: 0.8923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1276/1875 [===================>..........] - ETA: 1s - loss: 0.2921 - accuracy: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1301/1875 [===================>..........] - ETA: 1s - loss: 0.2919 - accuracy: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1326/1875 [====================>.........] - ETA: 1s - loss: 0.2913 - accuracy: 0.8929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1351/1875 [====================>.........] - ETA: 1s - loss: 0.2917 - accuracy: 0.8927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1376/1875 [=====================>........] - ETA: 1s - loss: 0.2918 - accuracy: 0.8926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1401/1875 [=====================>........] - ETA: 0s - loss: 0.2916 - accuracy: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1426/1875 [=====================>........] - ETA: 0s - loss: 0.2915 - accuracy: 0.8926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1451/1875 [======================>.......] - ETA: 0s - loss: 0.2918 - accuracy: 0.8927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1476/1875 [======================>.......] - ETA: 0s - loss: 0.2918 - accuracy: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1501/1875 [=======================>......] - ETA: 0s - loss: 0.2917 - accuracy: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1526/1875 [=======================>......] - ETA: 0s - loss: 0.2918 - accuracy: 0.8927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1551/1875 [=======================>......] - ETA: 0s - loss: 0.2918 - accuracy: 0.8926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1576/1875 [========================>.....] - ETA: 0s - loss: 0.2917 - accuracy: 0.8926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1601/1875 [========================>.....] - ETA: 0s - loss: 0.2918 - accuracy: 0.8925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1625/1875 [=========================>....] - ETA: 0s - loss: 0.2918 - accuracy: 0.8926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1651/1875 [=========================>....] - ETA: 0s - loss: 0.2922 - accuracy: 0.8924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1676/1875 [=========================>....] - ETA: 0s - loss: 0.2925 - accuracy: 0.8924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1701/1875 [==========================>...] - ETA: 0s - loss: 0.2926 - accuracy: 0.8923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1727/1875 [==========================>...] - ETA: 0s - loss: 0.2922 - accuracy: 0.8925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1752/1875 [===========================>..] - ETA: 0s - loss: 0.2919 - accuracy: 0.8926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1777/1875 [===========================>..] - ETA: 0s - loss: 0.2920 - accuracy: 0.8924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1803/1875 [===========================>..] - ETA: 0s - loss: 0.2919 - accuracy: 0.8924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1828/1875 [============================>.] - ETA: 0s - loss: 0.2925 - accuracy: 0.8923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1854/1875 [============================>.] - ETA: 0s - loss: 0.2930 - accuracy: 0.8920"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2936 - accuracy: 0.8918\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.2349 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  26/1875 [..............................] - ETA: 3s - loss: 0.2291 - accuracy: 0.9099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  51/1875 [..............................] - ETA: 3s - loss: 0.2579 - accuracy: 0.9044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  77/1875 [>.............................] - ETA: 3s - loss: 0.2569 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 104/1875 [>.............................] - ETA: 3s - loss: 0.2520 - accuracy: 0.9069"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 131/1875 [=>............................] - ETA: 3s - loss: 0.2568 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 156/1875 [=>............................] - ETA: 3s - loss: 0.2622 - accuracy: 0.9026"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 180/1875 [=>............................] - ETA: 3s - loss: 0.2633 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 204/1875 [==>...........................] - ETA: 3s - loss: 0.2653 - accuracy: 0.9027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 228/1875 [==>...........................] - ETA: 3s - loss: 0.2653 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 253/1875 [===>..........................] - ETA: 3s - loss: 0.2654 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 277/1875 [===>..........................] - ETA: 3s - loss: 0.2651 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 302/1875 [===>..........................] - ETA: 3s - loss: 0.2648 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 328/1875 [====>.........................] - ETA: 3s - loss: 0.2632 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 354/1875 [====>.........................] - ETA: 3s - loss: 0.2638 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 380/1875 [=====>........................] - ETA: 3s - loss: 0.2612 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 405/1875 [=====>........................] - ETA: 2s - loss: 0.2629 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 430/1875 [=====>........................] - ETA: 2s - loss: 0.2651 - accuracy: 0.9028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 456/1875 [======>.......................] - ETA: 2s - loss: 0.2684 - accuracy: 0.9013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 481/1875 [======>.......................] - ETA: 2s - loss: 0.2692 - accuracy: 0.9006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 506/1875 [=======>......................] - ETA: 2s - loss: 0.2690 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 532/1875 [=======>......................] - ETA: 2s - loss: 0.2712 - accuracy: 0.8999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 557/1875 [=======>......................] - ETA: 2s - loss: 0.2724 - accuracy: 0.8996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 583/1875 [========>.....................] - ETA: 2s - loss: 0.2728 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 609/1875 [========>.....................] - ETA: 2s - loss: 0.2741 - accuracy: 0.8986"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 635/1875 [=========>....................] - ETA: 2s - loss: 0.2735 - accuracy: 0.8989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 661/1875 [=========>....................] - ETA: 2s - loss: 0.2734 - accuracy: 0.8993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 686/1875 [=========>....................] - ETA: 2s - loss: 0.2748 - accuracy: 0.8986"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 712/1875 [==========>...................] - ETA: 2s - loss: 0.2757 - accuracy: 0.8985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 738/1875 [==========>...................] - ETA: 2s - loss: 0.2762 - accuracy: 0.8983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 764/1875 [===========>..................] - ETA: 2s - loss: 0.2758 - accuracy: 0.8982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 790/1875 [===========>..................] - ETA: 2s - loss: 0.2760 - accuracy: 0.8983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 815/1875 [============>.................] - ETA: 2s - loss: 0.2758 - accuracy: 0.8987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 840/1875 [============>.................] - ETA: 2s - loss: 0.2753 - accuracy: 0.8989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 865/1875 [============>.................] - ETA: 2s - loss: 0.2748 - accuracy: 0.8989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 891/1875 [=============>................] - ETA: 1s - loss: 0.2746 - accuracy: 0.8991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 916/1875 [=============>................] - ETA: 1s - loss: 0.2740 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 942/1875 [==============>...............] - ETA: 1s - loss: 0.2738 - accuracy: 0.8993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 968/1875 [==============>...............] - ETA: 1s - loss: 0.2747 - accuracy: 0.8989"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 993/1875 [==============>...............] - ETA: 1s - loss: 0.2746 - accuracy: 0.8992"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1018/1875 [===============>..............] - ETA: 1s - loss: 0.2744 - accuracy: 0.8992"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1043/1875 [===============>..............] - ETA: 1s - loss: 0.2729 - accuracy: 0.8996"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1068/1875 [================>.............] - ETA: 1s - loss: 0.2732 - accuracy: 0.8992"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1094/1875 [================>.............] - ETA: 1s - loss: 0.2741 - accuracy: 0.8989"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1120/1875 [================>.............] - ETA: 1s - loss: 0.2748 - accuracy: 0.8989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1146/1875 [=================>............] - ETA: 1s - loss: 0.2742 - accuracy: 0.8990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1172/1875 [=================>............] - ETA: 1s - loss: 0.2737 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1198/1875 [==================>...........] - ETA: 1s - loss: 0.2730 - accuracy: 0.8994"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1224/1875 [==================>...........] - ETA: 1s - loss: 0.2730 - accuracy: 0.8997"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1250/1875 [===================>..........] - ETA: 1s - loss: 0.2733 - accuracy: 0.8996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1276/1875 [===================>..........] - ETA: 1s - loss: 0.2741 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1302/1875 [===================>..........] - ETA: 1s - loss: 0.2743 - accuracy: 0.8991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1327/1875 [====================>.........] - ETA: 1s - loss: 0.2746 - accuracy: 0.8991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1352/1875 [====================>.........] - ETA: 1s - loss: 0.2744 - accuracy: 0.8991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1377/1875 [=====================>........] - ETA: 0s - loss: 0.2747 - accuracy: 0.8990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1402/1875 [=====================>........] - ETA: 0s - loss: 0.2744 - accuracy: 0.8990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1428/1875 [=====================>........] - ETA: 0s - loss: 0.2740 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1453/1875 [======================>.......] - ETA: 0s - loss: 0.2736 - accuracy: 0.8994"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1479/1875 [======================>.......] - ETA: 0s - loss: 0.2739 - accuracy: 0.8993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1505/1875 [=======================>......] - ETA: 0s - loss: 0.2742 - accuracy: 0.8991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1531/1875 [=======================>......] - ETA: 0s - loss: 0.2739 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1558/1875 [=======================>......] - ETA: 0s - loss: 0.2739 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1584/1875 [========================>.....] - ETA: 0s - loss: 0.2739 - accuracy: 0.8993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1610/1875 [========================>.....] - ETA: 0s - loss: 0.2741 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1636/1875 [=========================>....] - ETA: 0s - loss: 0.2739 - accuracy: 0.8992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1662/1875 [=========================>....] - ETA: 0s - loss: 0.2740 - accuracy: 0.8991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1687/1875 [=========================>....] - ETA: 0s - loss: 0.2743 - accuracy: 0.8989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1713/1875 [==========================>...] - ETA: 0s - loss: 0.2752 - accuracy: 0.8984"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1739/1875 [==========================>...] - ETA: 0s - loss: 0.2754 - accuracy: 0.8984"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1765/1875 [===========================>..] - ETA: 0s - loss: 0.2752 - accuracy: 0.8985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1790/1875 [===========================>..] - ETA: 0s - loss: 0.2756 - accuracy: 0.8982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1816/1875 [============================>.] - ETA: 0s - loss: 0.2757 - accuracy: 0.8982"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1841/1875 [============================>.] - ETA: 0s - loss: 0.2759 - accuracy: 0.8979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1866/1875 [============================>.] - ETA: 0s - loss: 0.2755 - accuracy: 0.8980"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2758 - accuracy: 0.8978\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 4s - loss: 0.4008 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  27/1875 [..............................] - ETA: 3s - loss: 0.2870 - accuracy: 0.8958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  53/1875 [..............................] - ETA: 3s - loss: 0.2640 - accuracy: 0.9009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  79/1875 [>.............................] - ETA: 3s - loss: 0.2602 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 105/1875 [>.............................] - ETA: 3s - loss: 0.2659 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 132/1875 [=>............................] - ETA: 3s - loss: 0.2675 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 158/1875 [=>............................] - ETA: 3s - loss: 0.2691 - accuracy: 0.9005"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 184/1875 [=>............................] - ETA: 3s - loss: 0.2639 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 210/1875 [==>...........................] - ETA: 3s - loss: 0.2576 - accuracy: 0.9049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 236/1875 [==>...........................] - ETA: 3s - loss: 0.2591 - accuracy: 0.9044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 263/1875 [===>..........................] - ETA: 3s - loss: 0.2614 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 289/1875 [===>..........................] - ETA: 3s - loss: 0.2583 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 315/1875 [====>.........................] - ETA: 3s - loss: 0.2583 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 341/1875 [====>.........................] - ETA: 2s - loss: 0.2614 - accuracy: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 367/1875 [====>.........................] - ETA: 2s - loss: 0.2606 - accuracy: 0.9049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 393/1875 [=====>........................] - ETA: 2s - loss: 0.2623 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 420/1875 [=====>........................] - ETA: 2s - loss: 0.2622 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 446/1875 [======>.......................] - ETA: 2s - loss: 0.2602 - accuracy: 0.9043"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 471/1875 [======>.......................] - ETA: 2s - loss: 0.2609 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 497/1875 [======>.......................] - ETA: 2s - loss: 0.2593 - accuracy: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 522/1875 [=======>......................] - ETA: 2s - loss: 0.2598 - accuracy: 0.9047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 547/1875 [=======>......................] - ETA: 2s - loss: 0.2606 - accuracy: 0.9043"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 572/1875 [========>.....................] - ETA: 2s - loss: 0.2603 - accuracy: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 598/1875 [========>.....................] - ETA: 2s - loss: 0.2608 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 624/1875 [========>.....................] - ETA: 2s - loss: 0.2607 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 650/1875 [=========>....................] - ETA: 2s - loss: 0.2612 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 675/1875 [=========>....................] - ETA: 2s - loss: 0.2613 - accuracy: 0.9035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 700/1875 [==========>...................] - ETA: 2s - loss: 0.2606 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 725/1875 [==========>...................] - ETA: 2s - loss: 0.2606 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 750/1875 [===========>..................] - ETA: 2s - loss: 0.2609 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 775/1875 [===========>..................] - ETA: 2s - loss: 0.2596 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 800/1875 [===========>..................] - ETA: 2s - loss: 0.2598 - accuracy: 0.9040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 825/1875 [============>.................] - ETA: 2s - loss: 0.2610 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 850/1875 [============>.................] - ETA: 2s - loss: 0.2607 - accuracy: 0.9035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 876/1875 [=============>................] - ETA: 1s - loss: 0.2601 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 901/1875 [=============>................] - ETA: 1s - loss: 0.2611 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 926/1875 [=============>................] - ETA: 1s - loss: 0.2609 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 951/1875 [==============>...............] - ETA: 1s - loss: 0.2607 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 975/1875 [==============>...............] - ETA: 1s - loss: 0.2612 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1000/1875 [===============>..............] - ETA: 1s - loss: 0.2619 - accuracy: 0.9028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1025/1875 [===============>..............] - ETA: 1s - loss: 0.2618 - accuracy: 0.9027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1050/1875 [===============>..............] - ETA: 1s - loss: 0.2623 - accuracy: 0.9026"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1076/1875 [================>.............] - ETA: 1s - loss: 0.2629 - accuracy: 0.9026"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1101/1875 [================>.............] - ETA: 1s - loss: 0.2619 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1127/1875 [=================>............] - ETA: 1s - loss: 0.2629 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1152/1875 [=================>............] - ETA: 1s - loss: 0.2630 - accuracy: 0.9028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1177/1875 [=================>............] - ETA: 1s - loss: 0.2623 - accuracy: 0.9030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1202/1875 [==================>...........] - ETA: 1s - loss: 0.2619 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1227/1875 [==================>...........] - ETA: 1s - loss: 0.2631 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1252/1875 [===================>..........] - ETA: 1s - loss: 0.2641 - accuracy: 0.9027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1277/1875 [===================>..........] - ETA: 1s - loss: 0.2651 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1303/1875 [===================>..........] - ETA: 1s - loss: 0.2646 - accuracy: 0.9022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1328/1875 [====================>.........] - ETA: 1s - loss: 0.2637 - accuracy: 0.9027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1353/1875 [====================>.........] - ETA: 1s - loss: 0.2638 - accuracy: 0.9025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1378/1875 [=====================>........] - ETA: 0s - loss: 0.2644 - accuracy: 0.9024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1403/1875 [=====================>........] - ETA: 0s - loss: 0.2643 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1429/1875 [=====================>........] - ETA: 0s - loss: 0.2641 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1455/1875 [======================>.......] - ETA: 0s - loss: 0.2637 - accuracy: 0.9024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1482/1875 [======================>.......] - ETA: 0s - loss: 0.2645 - accuracy: 0.9020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1875 [=======================>......] - ETA: 0s - loss: 0.2640 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1875 [=======================>......] - ETA: 0s - loss: 0.2642 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1560/1875 [=======================>......] - ETA: 0s - loss: 0.2642 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1587/1875 [========================>.....] - ETA: 0s - loss: 0.2642 - accuracy: 0.9022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1613/1875 [========================>.....] - ETA: 0s - loss: 0.2639 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1638/1875 [=========================>....] - ETA: 0s - loss: 0.2640 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1664/1875 [=========================>....] - ETA: 0s - loss: 0.2645 - accuracy: 0.9021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1690/1875 [==========================>...] - ETA: 0s - loss: 0.2648 - accuracy: 0.9019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1717/1875 [==========================>...] - ETA: 0s - loss: 0.2650 - accuracy: 0.9018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1743/1875 [==========================>...] - ETA: 0s - loss: 0.2654 - accuracy: 0.9017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1769/1875 [===========================>..] - ETA: 0s - loss: 0.2652 - accuracy: 0.9018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1794/1875 [===========================>..] - ETA: 0s - loss: 0.2648 - accuracy: 0.9018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1819/1875 [============================>.] - ETA: 0s - loss: 0.2649 - accuracy: 0.9017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1845/1875 [============================>.] - ETA: 0s - loss: 0.2649 - accuracy: 0.9016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1870/1875 [============================>.] - ETA: 0s - loss: 0.2652 - accuracy: 0.9016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2652 - accuracy: 0.9016\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.2014 - accuracy: 0.9375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  27/1875 [..............................] - ETA: 3s - loss: 0.2458 - accuracy: 0.9086"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  53/1875 [..............................] - ETA: 3s - loss: 0.2526 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  79/1875 [>.............................] - ETA: 3s - loss: 0.2591 - accuracy: 0.9047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 105/1875 [>.............................] - ETA: 3s - loss: 0.2573 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 131/1875 [=>............................] - ETA: 3s - loss: 0.2522 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 157/1875 [=>............................] - ETA: 3s - loss: 0.2576 - accuracy: 0.9035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 184/1875 [=>............................] - ETA: 3s - loss: 0.2562 - accuracy: 0.9049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 211/1875 [==>...........................] - ETA: 3s - loss: 0.2546 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 237/1875 [==>...........................] - ETA: 3s - loss: 0.2538 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 263/1875 [===>..........................] - ETA: 3s - loss: 0.2530 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 288/1875 [===>..........................] - ETA: 3s - loss: 0.2546 - accuracy: 0.9044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 314/1875 [====>.........................] - ETA: 3s - loss: 0.2542 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 339/1875 [====>.........................] - ETA: 3s - loss: 0.2557 - accuracy: 0.9027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 364/1875 [====>.........................] - ETA: 2s - loss: 0.2550 - accuracy: 0.9030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 389/1875 [=====>........................] - ETA: 2s - loss: 0.2531 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 415/1875 [=====>........................] - ETA: 2s - loss: 0.2532 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 441/1875 [======>.......................] - ETA: 2s - loss: 0.2531 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 465/1875 [======>.......................] - ETA: 2s - loss: 0.2516 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 491/1875 [======>.......................] - ETA: 2s - loss: 0.2523 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 516/1875 [=======>......................] - ETA: 2s - loss: 0.2505 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 541/1875 [=======>......................] - ETA: 2s - loss: 0.2506 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 566/1875 [========>.....................] - ETA: 2s - loss: 0.2506 - accuracy: 0.9044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 592/1875 [========>.....................] - ETA: 2s - loss: 0.2509 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 617/1875 [========>.....................] - ETA: 2s - loss: 0.2511 - accuracy: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 641/1875 [=========>....................] - ETA: 2s - loss: 0.2519 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 666/1875 [=========>....................] - ETA: 2s - loss: 0.2532 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 690/1875 [==========>...................] - ETA: 2s - loss: 0.2547 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 715/1875 [==========>...................] - ETA: 2s - loss: 0.2538 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 740/1875 [==========>...................] - ETA: 2s - loss: 0.2536 - accuracy: 0.9043"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 765/1875 [===========>..................] - ETA: 2s - loss: 0.2531 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 789/1875 [===========>..................] - ETA: 2s - loss: 0.2550 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 813/1875 [============>.................] - ETA: 2s - loss: 0.2544 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 838/1875 [============>.................] - ETA: 2s - loss: 0.2540 - accuracy: 0.9047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 863/1875 [============>.................] - ETA: 2s - loss: 0.2542 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 888/1875 [=============>................] - ETA: 1s - loss: 0.2533 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 913/1875 [=============>................] - ETA: 1s - loss: 0.2534 - accuracy: 0.9053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 938/1875 [==============>...............] - ETA: 1s - loss: 0.2536 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 964/1875 [==============>...............] - ETA: 1s - loss: 0.2537 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 989/1875 [==============>...............] - ETA: 1s - loss: 0.2536 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1015/1875 [===============>..............] - ETA: 1s - loss: 0.2540 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1041/1875 [===============>..............] - ETA: 1s - loss: 0.2545 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1066/1875 [================>.............] - ETA: 1s - loss: 0.2544 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1091/1875 [================>.............] - ETA: 1s - loss: 0.2549 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1116/1875 [================>.............] - ETA: 1s - loss: 0.2541 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1140/1875 [=================>............] - ETA: 1s - loss: 0.2544 - accuracy: 0.9050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1166/1875 [=================>............] - ETA: 1s - loss: 0.2537 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1191/1875 [==================>...........] - ETA: 1s - loss: 0.2540 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1216/1875 [==================>...........] - ETA: 1s - loss: 0.2541 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1241/1875 [==================>...........] - ETA: 1s - loss: 0.2539 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1266/1875 [===================>..........] - ETA: 1s - loss: 0.2542 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1291/1875 [===================>..........] - ETA: 1s - loss: 0.2534 - accuracy: 0.9053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1315/1875 [====================>.........] - ETA: 1s - loss: 0.2523 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1339/1875 [====================>.........] - ETA: 1s - loss: 0.2520 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1365/1875 [====================>.........] - ETA: 1s - loss: 0.2521 - accuracy: 0.9056"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1390/1875 [=====================>........] - ETA: 0s - loss: 0.2524 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1415/1875 [=====================>........] - ETA: 0s - loss: 0.2520 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1439/1875 [======================>.......] - ETA: 0s - loss: 0.2517 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1464/1875 [======================>.......] - ETA: 0s - loss: 0.2513 - accuracy: 0.9060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1489/1875 [======================>.......] - ETA: 0s - loss: 0.2510 - accuracy: 0.9061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1514/1875 [=======================>......] - ETA: 0s - loss: 0.2512 - accuracy: 0.9060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1538/1875 [=======================>......] - ETA: 0s - loss: 0.2519 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1562/1875 [=======================>......] - ETA: 0s - loss: 0.2522 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1587/1875 [========================>.....] - ETA: 0s - loss: 0.2524 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1612/1875 [========================>.....] - ETA: 0s - loss: 0.2522 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1637/1875 [=========================>....] - ETA: 0s - loss: 0.2522 - accuracy: 0.9057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1662/1875 [=========================>....] - ETA: 0s - loss: 0.2520 - accuracy: 0.9057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1686/1875 [=========================>....] - ETA: 0s - loss: 0.2531 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1712/1875 [==========================>...] - ETA: 0s - loss: 0.2529 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1738/1875 [==========================>...] - ETA: 0s - loss: 0.2530 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1764/1875 [===========================>..] - ETA: 0s - loss: 0.2528 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1789/1875 [===========================>..] - ETA: 0s - loss: 0.2534 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1814/1875 [============================>.] - ETA: 0s - loss: 0.2538 - accuracy: 0.9049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1838/1875 [============================>.] - ETA: 0s - loss: 0.2536 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1864/1875 [============================>.] - ETA: 0s - loss: 0.2535 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2536 - accuracy: 0.9049\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.3457 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  26/1875 [..............................] - ETA: 3s - loss: 0.2298 - accuracy: 0.9171"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  51/1875 [..............................] - ETA: 3s - loss: 0.2402 - accuracy: 0.9099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  77/1875 [>.............................] - ETA: 3s - loss: 0.2316 - accuracy: 0.9144"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 103/1875 [>.............................] - ETA: 3s - loss: 0.2355 - accuracy: 0.9099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 128/1875 [=>............................] - ETA: 3s - loss: 0.2292 - accuracy: 0.9138"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 154/1875 [=>............................] - ETA: 3s - loss: 0.2309 - accuracy: 0.9158"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 179/1875 [=>............................] - ETA: 3s - loss: 0.2300 - accuracy: 0.9157"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 205/1875 [==>...........................] - ETA: 3s - loss: 0.2321 - accuracy: 0.9151"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 229/1875 [==>...........................] - ETA: 3s - loss: 0.2304 - accuracy: 0.9155"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 254/1875 [===>..........................] - ETA: 3s - loss: 0.2312 - accuracy: 0.9146"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 279/1875 [===>..........................] - ETA: 3s - loss: 0.2379 - accuracy: 0.9119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 303/1875 [===>..........................] - ETA: 3s - loss: 0.2366 - accuracy: 0.9130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 328/1875 [====>.........................] - ETA: 3s - loss: 0.2378 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 353/1875 [====>.........................] - ETA: 3s - loss: 0.2428 - accuracy: 0.9113"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 377/1875 [=====>........................] - ETA: 3s - loss: 0.2415 - accuracy: 0.9115"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 401/1875 [=====>........................] - ETA: 3s - loss: 0.2451 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 426/1875 [=====>........................] - ETA: 2s - loss: 0.2468 - accuracy: 0.9081"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 452/1875 [======>.......................] - ETA: 2s - loss: 0.2437 - accuracy: 0.9089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 478/1875 [======>.......................] - ETA: 2s - loss: 0.2436 - accuracy: 0.9090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 504/1875 [=======>......................] - ETA: 2s - loss: 0.2425 - accuracy: 0.9097"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 530/1875 [=======>......................] - ETA: 2s - loss: 0.2426 - accuracy: 0.9098"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 555/1875 [=======>......................] - ETA: 2s - loss: 0.2430 - accuracy: 0.9092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 580/1875 [========>.....................] - ETA: 2s - loss: 0.2430 - accuracy: 0.9091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 605/1875 [========>.....................] - ETA: 2s - loss: 0.2419 - accuracy: 0.9095"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 630/1875 [=========>....................] - ETA: 2s - loss: 0.2415 - accuracy: 0.9097"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 655/1875 [=========>....................] - ETA: 2s - loss: 0.2419 - accuracy: 0.9094"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 681/1875 [=========>....................] - ETA: 2s - loss: 0.2422 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 706/1875 [==========>...................] - ETA: 2s - loss: 0.2422 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 730/1875 [==========>...................] - ETA: 2s - loss: 0.2417 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 754/1875 [===========>..................] - ETA: 2s - loss: 0.2423 - accuracy: 0.9089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 779/1875 [===========>..................] - ETA: 2s - loss: 0.2428 - accuracy: 0.9085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 804/1875 [===========>..................] - ETA: 2s - loss: 0.2429 - accuracy: 0.9081"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 829/1875 [============>.................] - ETA: 2s - loss: 0.2441 - accuracy: 0.9079"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 854/1875 [============>.................] - ETA: 2s - loss: 0.2456 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 879/1875 [=============>................] - ETA: 2s - loss: 0.2451 - accuracy: 0.9078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 904/1875 [=============>................] - ETA: 1s - loss: 0.2454 - accuracy: 0.9080"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 930/1875 [=============>................] - ETA: 1s - loss: 0.2455 - accuracy: 0.9077"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 955/1875 [==============>...............] - ETA: 1s - loss: 0.2447 - accuracy: 0.9082"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 980/1875 [==============>...............] - ETA: 1s - loss: 0.2439 - accuracy: 0.9086"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1005/1875 [===============>..............] - ETA: 1s - loss: 0.2442 - accuracy: 0.9085"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1030/1875 [===============>..............] - ETA: 1s - loss: 0.2458 - accuracy: 0.9079"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1055/1875 [===============>..............] - ETA: 1s - loss: 0.2459 - accuracy: 0.9077"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1081/1875 [================>.............] - ETA: 1s - loss: 0.2454 - accuracy: 0.9080"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1106/1875 [================>.............] - ETA: 1s - loss: 0.2459 - accuracy: 0.9078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1131/1875 [=================>............] - ETA: 1s - loss: 0.2447 - accuracy: 0.9083"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1156/1875 [=================>............] - ETA: 1s - loss: 0.2445 - accuracy: 0.9084"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1181/1875 [=================>............] - ETA: 1s - loss: 0.2443 - accuracy: 0.9084"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1205/1875 [==================>...........] - ETA: 1s - loss: 0.2437 - accuracy: 0.9086"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1230/1875 [==================>...........] - ETA: 1s - loss: 0.2436 - accuracy: 0.9088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1255/1875 [===================>..........] - ETA: 1s - loss: 0.2441 - accuracy: 0.9085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1280/1875 [===================>..........] - ETA: 1s - loss: 0.2445 - accuracy: 0.9084"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1305/1875 [===================>..........] - ETA: 1s - loss: 0.2443 - accuracy: 0.9085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1875 [====================>.........] - ETA: 1s - loss: 0.2444 - accuracy: 0.9084"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1356/1875 [====================>.........] - ETA: 1s - loss: 0.2443 - accuracy: 0.9086"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1382/1875 [=====================>........] - ETA: 1s - loss: 0.2446 - accuracy: 0.9084"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1407/1875 [=====================>........] - ETA: 0s - loss: 0.2446 - accuracy: 0.9085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1432/1875 [=====================>........] - ETA: 0s - loss: 0.2444 - accuracy: 0.9086"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1457/1875 [======================>.......] - ETA: 0s - loss: 0.2438 - accuracy: 0.9090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1483/1875 [======================>.......] - ETA: 0s - loss: 0.2436 - accuracy: 0.9091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1875 [=======================>......] - ETA: 0s - loss: 0.2435 - accuracy: 0.9091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1533/1875 [=======================>......] - ETA: 0s - loss: 0.2435 - accuracy: 0.9091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1558/1875 [=======================>......] - ETA: 0s - loss: 0.2434 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1583/1875 [========================>.....] - ETA: 0s - loss: 0.2432 - accuracy: 0.9094"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1608/1875 [========================>.....] - ETA: 0s - loss: 0.2431 - accuracy: 0.9094"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1633/1875 [=========================>....] - ETA: 0s - loss: 0.2431 - accuracy: 0.9091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1658/1875 [=========================>....] - ETA: 0s - loss: 0.2434 - accuracy: 0.9089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1683/1875 [=========================>....] - ETA: 0s - loss: 0.2438 - accuracy: 0.9086"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1708/1875 [==========================>...] - ETA: 0s - loss: 0.2439 - accuracy: 0.9085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1734/1875 [==========================>...] - ETA: 0s - loss: 0.2433 - accuracy: 0.9090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1759/1875 [===========================>..] - ETA: 0s - loss: 0.2437 - accuracy: 0.9089"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1783/1875 [===========================>..] - ETA: 0s - loss: 0.2441 - accuracy: 0.9088"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1808/1875 [===========================>..] - ETA: 0s - loss: 0.2442 - accuracy: 0.9087"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1833/1875 [============================>.] - ETA: 0s - loss: 0.2445 - accuracy: 0.9087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1858/1875 [============================>.] - ETA: 0s - loss: 0.2445 - accuracy: 0.9087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2445 - accuracy: 0.9086\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.5697 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  26/1875 [..............................] - ETA: 3s - loss: 0.2599 - accuracy: 0.9099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  51/1875 [..............................] - ETA: 3s - loss: 0.2400 - accuracy: 0.9124"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  77/1875 [>.............................] - ETA: 3s - loss: 0.2456 - accuracy: 0.9152"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 103/1875 [>.............................] - ETA: 3s - loss: 0.2402 - accuracy: 0.9132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 129/1875 [=>............................] - ETA: 3s - loss: 0.2493 - accuracy: 0.9087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 156/1875 [=>............................] - ETA: 3s - loss: 0.2459 - accuracy: 0.9113"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 181/1875 [=>............................] - ETA: 3s - loss: 0.2451 - accuracy: 0.9095"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 206/1875 [==>...........................] - ETA: 3s - loss: 0.2401 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 232/1875 [==>...........................] - ETA: 3s - loss: 0.2460 - accuracy: 0.9076"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 258/1875 [===>..........................] - ETA: 3s - loss: 0.2492 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 284/1875 [===>..........................] - ETA: 3s - loss: 0.2458 - accuracy: 0.9088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 309/1875 [===>..........................] - ETA: 3s - loss: 0.2463 - accuracy: 0.9085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 334/1875 [====>.........................] - ETA: 3s - loss: 0.2430 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 360/1875 [====>.........................] - ETA: 2s - loss: 0.2419 - accuracy: 0.9100"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 385/1875 [=====>........................] - ETA: 2s - loss: 0.2391 - accuracy: 0.9112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 410/1875 [=====>........................] - ETA: 2s - loss: 0.2376 - accuracy: 0.9122"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 436/1875 [=====>........................] - ETA: 2s - loss: 0.2392 - accuracy: 0.9111"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 462/1875 [======>.......................] - ETA: 2s - loss: 0.2370 - accuracy: 0.9117"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 488/1875 [======>.......................] - ETA: 2s - loss: 0.2360 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 513/1875 [=======>......................] - ETA: 2s - loss: 0.2365 - accuracy: 0.9119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 538/1875 [=======>......................] - ETA: 2s - loss: 0.2371 - accuracy: 0.9118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 563/1875 [========>.....................] - ETA: 2s - loss: 0.2357 - accuracy: 0.9122"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 588/1875 [========>.....................] - ETA: 2s - loss: 0.2356 - accuracy: 0.9121"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 613/1875 [========>.....................] - ETA: 2s - loss: 0.2355 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 638/1875 [=========>....................] - ETA: 2s - loss: 0.2352 - accuracy: 0.9131"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 662/1875 [=========>....................] - ETA: 2s - loss: 0.2334 - accuracy: 0.9137"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 687/1875 [=========>....................] - ETA: 2s - loss: 0.2342 - accuracy: 0.9137"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 712/1875 [==========>...................] - ETA: 2s - loss: 0.2332 - accuracy: 0.9141"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 737/1875 [==========>...................] - ETA: 2s - loss: 0.2346 - accuracy: 0.9138"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 763/1875 [===========>..................] - ETA: 2s - loss: 0.2350 - accuracy: 0.9140"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 788/1875 [===========>..................] - ETA: 2s - loss: 0.2359 - accuracy: 0.9133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 814/1875 [============>.................] - ETA: 2s - loss: 0.2353 - accuracy: 0.9135"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 840/1875 [============>.................] - ETA: 2s - loss: 0.2362 - accuracy: 0.9130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 866/1875 [============>.................] - ETA: 2s - loss: 0.2371 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 891/1875 [=============>................] - ETA: 1s - loss: 0.2375 - accuracy: 0.9129"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 915/1875 [=============>................] - ETA: 1s - loss: 0.2382 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 940/1875 [==============>...............] - ETA: 1s - loss: 0.2386 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 966/1875 [==============>...............] - ETA: 1s - loss: 0.2384 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 992/1875 [==============>...............] - ETA: 1s - loss: 0.2385 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1018/1875 [===============>..............] - ETA: 1s - loss: 0.2388 - accuracy: 0.9124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1043/1875 [===============>..............] - ETA: 1s - loss: 0.2392 - accuracy: 0.9123"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1068/1875 [================>.............] - ETA: 1s - loss: 0.2386 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1093/1875 [================>.............] - ETA: 1s - loss: 0.2388 - accuracy: 0.9124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1118/1875 [================>.............] - ETA: 1s - loss: 0.2381 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1143/1875 [=================>............] - ETA: 1s - loss: 0.2375 - accuracy: 0.9129"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1168/1875 [=================>............] - ETA: 1s - loss: 0.2372 - accuracy: 0.9130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1193/1875 [==================>...........] - ETA: 1s - loss: 0.2365 - accuracy: 0.9131"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1218/1875 [==================>...........] - ETA: 1s - loss: 0.2369 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1244/1875 [==================>...........] - ETA: 1s - loss: 0.2362 - accuracy: 0.9130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1270/1875 [===================>..........] - ETA: 1s - loss: 0.2370 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1296/1875 [===================>..........] - ETA: 1s - loss: 0.2366 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1321/1875 [====================>.........] - ETA: 1s - loss: 0.2372 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1345/1875 [====================>.........] - ETA: 1s - loss: 0.2372 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1370/1875 [====================>.........] - ETA: 1s - loss: 0.2369 - accuracy: 0.9128"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1396/1875 [=====================>........] - ETA: 0s - loss: 0.2365 - accuracy: 0.9129"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1421/1875 [=====================>........] - ETA: 0s - loss: 0.2365 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1447/1875 [======================>.......] - ETA: 0s - loss: 0.2364 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1473/1875 [======================>.......] - ETA: 0s - loss: 0.2364 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1499/1875 [======================>.......] - ETA: 0s - loss: 0.2365 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1525/1875 [=======================>......] - ETA: 0s - loss: 0.2363 - accuracy: 0.9128"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1550/1875 [=======================>......] - ETA: 0s - loss: 0.2369 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1575/1875 [========================>.....] - ETA: 0s - loss: 0.2370 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1600/1875 [========================>.....] - ETA: 0s - loss: 0.2376 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1624/1875 [========================>.....] - ETA: 0s - loss: 0.2367 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1648/1875 [=========================>....] - ETA: 0s - loss: 0.2370 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1674/1875 [=========================>....] - ETA: 0s - loss: 0.2374 - accuracy: 0.9124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1699/1875 [==========================>...] - ETA: 0s - loss: 0.2372 - accuracy: 0.9124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1724/1875 [==========================>...] - ETA: 0s - loss: 0.2365 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1749/1875 [==========================>...] - ETA: 0s - loss: 0.2366 - accuracy: 0.9126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1774/1875 [===========================>..] - ETA: 0s - loss: 0.2365 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1799/1875 [===========================>..] - ETA: 0s - loss: 0.2371 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1824/1875 [============================>.] - ETA: 0s - loss: 0.2368 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1849/1875 [============================>.] - ETA: 0s - loss: 0.2370 - accuracy: 0.9124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - ETA: 0s - loss: 0.2372 - accuracy: 0.9123"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2372 - accuracy: 0.9123\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff9a4a3e640>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "### Evaluate accuracy\n",
    "\n",
    "Next, compare how the model performs on the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:15.229593Z",
     "iopub.status.busy": "2022-12-14T06:38:15.228930Z",
     "iopub.status.idle": "2022-12-14T06:38:15.954035Z",
     "shell.execute_reply": "2022-12-14T06:38:15.953065Z"
    },
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.3446 - accuracy: 0.8803 - 597ms/epoch - 2ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test accuracy: 0.880299985408783\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy represents *overfitting*. Overfitting happens when a machine learning model performs worse on new, previously unseen inputs than it does on the training data. An overfitted model \"memorizes\" the noise and details in the training dataset to a point where it negatively impacts the performance of the model on the new data. For more information, see the following:\n",
    "*   [Demonstrate overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
    "*   [Strategies to prevent overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "### Make predictions\n",
    "\n",
    "With the model trained, you can use it to make predictions about some images.\n",
    "Attach a softmax layer to convert the model's linear outputs—[logits](https://developers.google.com/machine-learning/glossary#logits)—to probabilities, which should be easier to interpret."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:15.957507Z",
     "iopub.status.busy": "2022-12-14T06:38:15.957221Z",
     "iopub.status.idle": "2022-12-14T06:38:15.977916Z",
     "shell.execute_reply": "2022-12-14T06:38:15.977193Z"
    },
    "id": "DnfNA0CrQLSD"
   },
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:15.981531Z",
     "iopub.status.busy": "2022-12-14T06:38:15.980902Z",
     "iopub.status.idle": "2022-12-14T06:38:16.668476Z",
     "shell.execute_reply": "2022-12-14T06:38:16.667673Z"
    },
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/313 [..............................] - ETA: 23s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 46/313 [===>..........................] - ETA: 0s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/313 [=======>......................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/313 [============>.................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/313 [================>.............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/313 [====================>.........] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "273/313 [=========================>....] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.672834Z",
     "iopub.status.busy": "2022-12-14T06:38:16.672194Z",
     "iopub.status.idle": "2022-12-14T06:38:16.677628Z",
     "shell.execute_reply": "2022-12-14T06:38:16.676837Z"
    },
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.6023999e-09, 7.3096351e-10, 7.0384121e-09, 2.0413071e-15,\n",
       "       8.2098889e-10, 8.8997092e-04, 2.6417115e-09, 8.5922601e-03,\n",
       "       1.4499091e-09, 9.9051780e-01], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.681081Z",
     "iopub.status.busy": "2022-12-14T06:38:16.680587Z",
     "iopub.status.idle": "2022-12-14T06:38:16.684977Z",
     "shell.execute_reply": "2022-12-14T06:38:16.684359Z"
    },
    "id": "qsqenuPnCaXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "So, the model is most confident that this image is an ankle boot, or `class_names[9]`. Examining the test label shows that this classification is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.688575Z",
     "iopub.status.busy": "2022-12-14T06:38:16.688008Z",
     "iopub.status.idle": "2022-12-14T06:38:16.692055Z",
     "shell.execute_reply": "2022-12-14T06:38:16.691457Z"
    },
    "id": "Sd7Pgsu6CaXP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygh2yYC972ne"
   },
   "source": [
    "Graph this to look at the full set of 10 class predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.695227Z",
     "iopub.status.busy": "2022-12-14T06:38:16.694887Z",
     "iopub.status.idle": "2022-12-14T06:38:16.700925Z",
     "shell.execute_reply": "2022-12-14T06:38:16.700296Z"
    },
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  true_label, img = true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  true_label = true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zh9yABaME29S"
   },
   "source": [
    "### Verify predictions\n",
    "\n",
    "With the model trained, you can use it to make predictions about some images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "Let's look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.704132Z",
     "iopub.status.busy": "2022-12-14T06:38:16.703592Z",
     "iopub.status.idle": "2022-12-14T06:38:16.815086Z",
     "shell.execute_reply": "2022-12-14T06:38:16.814268Z"
    },
    "id": "HV5jw-5HwSmO"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.818639Z",
     "iopub.status.busy": "2022-12-14T06:38:16.817905Z",
     "iopub.status.idle": "2022-12-14T06:38:16.926188Z",
     "shell.execute_reply": "2022-12-14T06:38:16.925335Z"
    },
    "id": "Ko-uzOufSCSe"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "Let's plot several images with their predictions. Note that the model can be wrong even when very confident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:16.929710Z",
     "iopub.status.busy": "2022-12-14T06:38:16.929067Z",
     "iopub.status.idle": "2022-12-14T06:38:18.654980Z",
     "shell.execute_reply": "2022-12-14T06:38:18.654194Z"
    },
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R32zteKHCaXT"
   },
   "source": [
    "## Use the trained model\n",
    "\n",
    "Finally, use the trained model to make a prediction about a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:18.659701Z",
     "iopub.status.busy": "2022-12-14T06:38:18.659004Z",
     "iopub.status.idle": "2022-12-14T06:38:18.662948Z",
     "shell.execute_reply": "2022-12-14T06:38:18.662239Z"
    },
    "id": "yRJ7JU7JCaXT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. Accordingly, even though you're using a single image, you need to add it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:18.666296Z",
     "iopub.status.busy": "2022-12-14T06:38:18.665802Z",
     "iopub.status.idle": "2022-12-14T06:38:18.669591Z",
     "shell.execute_reply": "2022-12-14T06:38:18.668930Z"
    },
    "id": "lDFh5yF_CaXW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "Now predict the correct label for this image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:18.673179Z",
     "iopub.status.busy": "2022-12-14T06:38:18.672609Z",
     "iopub.status.idle": "2022-12-14T06:38:18.734510Z",
     "shell.execute_reply": "2022-12-14T06:38:18.733824Z"
    },
    "id": "o_rzNSdrCaXY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 22ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.8218006e-06 1.6241313e-12 9.9978024e-01 2.5675090e-10 1.4467262e-04\n",
      "  5.7958114e-14 6.5363645e-05 5.1339265e-20 3.2502893e-13 2.1984588e-13]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:18.737975Z",
     "iopub.status.busy": "2022-12-14T06:38:18.737365Z",
     "iopub.status.idle": "2022-12-14T06:38:18.826064Z",
     "shell.execute_reply": "2022-12-14T06:38:18.825175Z"
    },
    "id": "6Ai-cpLjO-3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`tf.keras.Model.predict` returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T06:38:18.829790Z",
     "iopub.status.busy": "2022-12-14T06:38:18.829156Z",
     "iopub.status.idle": "2022-12-14T06:38:18.834069Z",
     "shell.execute_reply": "2022-12-14T06:38:18.833291Z"
    },
    "id": "2tRmdq_8CaXb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFc2HbEVCaXd"
   },
   "source": [
    "And the model predicts a label as expected."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "classification.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}