diff --git a/ch17/ch17-optional-DCGAN.ipynb b/ch17/ch17-optional-DCGAN.ipynb
deleted file mode 100644
index ca0f3529..00000000
--- a/ch17/ch17-optional-DCGAN.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ch17-DCGAN.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"TDcU1S783G6q","colab_type":"code","outputId":"d1daae87-bf9f-450e-9091-96f0830034a9","executionInfo":{"status":"ok","timestamp":1566680661076,"user_tz":240,"elapsed":47887,"user":{"displayName":"Vahid Mirjalili","photoUrl":"","userId":"03695229825133505307"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["! pip install -q tensorflow-gpu==2.0.0-beta1"],"execution_count":1,"outputs":[{"output_type":"stream","text":["\u001b[K     |████████████████████████████████| 348.9MB 61kB/s \n","\u001b[K     |████████████████████████████████| 3.1MB 33.7MB/s \n","\u001b[K     |████████████████████████████████| 501kB 50.0MB/s \n","\u001b[?25h"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XnOWggQN3RbT","colab_type":"code","outputId":"0bc1af02-7ee7-4d48-d44a-9c20d222ad5a","executionInfo":{"status":"ok","timestamp":1566680740382,"user_tz":240,"elapsed":127152,"user":{"displayName":"Vahid Mirjalili","photoUrl":"","userId":"03695229825133505307"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VXasfQIW3XeR","colab_type":"code","outputId":"88dae23d-b0b8-4361-94dc-bc01a3ebd317","executionInfo":{"status":"ok","timestamp":1566680741639,"user_tz":240,"elapsed":4890,"user":{"displayName":"Vahid Mirjalili","photoUrl":"","userId":"03695229825133505307"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import tensorflow as tf\n","import tensorflow_datasets as tfds\n","import numpy as np\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","tf.__version__"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.0.0-beta1'"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"O9XN3qSZ6Kbr","colab_type":"code","outputId":"efa0b1af-0050-4664-c0f1-f375a5ab546a","executionInfo":{"status":"ok","timestamp":1566680742201,"user_tz":240,"elapsed":2926,"user":{"displayName":"Vahid Mirjalili","photoUrl":"","userId":"03695229825133505307"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["print(\"GPU Available: \", tf.test.is_gpu_available())\n","device_name = tf.test.gpu_device_name()\n","device_name"],"execution_count":4,"outputs":[{"output_type":"stream","text":["GPU Available:  True\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["'/device:GPU:0'"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"69fiIu-g3Z74","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"O9QIFmPZ3jjg","colab_type":"code","colab":{}},"source":["def make_dcgan_generator(z_size=20, output_size=(28, 28, 1),\n","                         n_filters=128, n_blocks=2):\n","    size_factor = 2**n_blocks\n","    hidden_size = (output_size[0]//size_factor, \n","                   output_size[1]//size_factor)\n","    \n","    model = tf.keras.Sequential([\n","        tf.keras.layers.Input(shape=(z_size,)),\n","        \n","        tf.keras.layers.Dense(\n","            units=n_filters*np.prod(hidden_size), \n","            use_bias=False),\n","        tf.keras.layers.BatchNormalization(),\n","        tf.keras.layers.LeakyReLU(),\n","        tf.keras.layers.Reshape(\n","            (hidden_size[0], hidden_size[1], n_filters)),\n","    \n","        tf.keras.layers.Conv2DTranspose(\n","            filters=n_filters, kernel_size=(5, 5), strides=(1, 1),\n","            padding='same', use_bias=False),\n","        tf.keras.layers.BatchNormalization(),\n","        tf.keras.layers.LeakyReLU()\n","    ])\n","        \n","    nf = n_filters\n","    for i in range(n_blocks):\n","        nf = nf // 2\n","        model.add(tf.keras.layers.Conv2DTranspose(\n","            filters=nf, kernel_size=(5, 5), strides=(2, 2),\n","            padding='same', use_bias=False))\n","        model.add(tf.keras.layers.BatchNormalization())\n","        model.add(tf.keras.layers.LeakyReLU())\n","                \n","        \n","    model.add(tf.keras.layers.Conv2DTranspose(\n","        filters=output_size[2], kernel_size=(5, 5), strides=(1, 1), \n","        padding='same', use_bias=False, activation='tanh'))\n","        \n","    return model\n","\n","def make_dcgan_discriminator(input_size=(28, 28, 1),\n","                             n_filters=64, n_blocks=2):\n","    model = tf.keras.Sequential([\n","        tf.keras.layers.Input(shape=input_size),\n","        tf.keras.layers.Conv2D(\n","            filters=n_filters, kernel_size=5, \n","            strides=(1, 1), padding='same'),\n","        tf.keras.layers.BatchNormalization(),\n","        tf.keras.layers.LeakyReLU()\n","    ])\n","    \n","    nf = n_filters\n","    for i in range(n_blocks):\n","        nf = nf*2\n","        model.add(tf.keras.layers.Conv2D(\n","            filters=nf, kernel_size=(5, 5), \n","            strides=(2, 2),padding='same'))\n","        model.add(tf.keras.layers.BatchNormalization())\n","        model.add(tf.keras.layers.LeakyReLU())\n","        model.add(tf.keras.layers.Dropout(0.3))\n","        \n","    model.add(tf.keras.layers.Conv2D(\n","            filters=1, kernel_size=(7, 7), padding='valid'))\n","    \n","    model.add(tf.keras.layers.Reshape((1,)))\n","    \n","    return model\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DX4INL735ARw","colab_type":"code","outputId":"dec87b5a-3c59-4cef-9ae6-0c1aaf3e3c0c","executionInfo":{"status":"ok","timestamp":1566680750136,"user_tz":240,"elapsed":1813,"user":{"displayName":"Vahid Mirjalili","photoUrl":"","userId":"03695229825133505307"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["gen_model = make_dcgan_generator()\n","gen_model.summary()\n","\n","disc_model = make_dcgan_discriminator()\n","disc_model.summary()"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense (Dense)                (None, 6272)              125440    \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 6272)              25088     \n","_________________________________________________________________\n","leaky_re_lu (LeakyReLU)      (None, 6272)              0         \n","_________________________________________________________________\n","reshape (Reshape)            (None, 7, 7, 128)         0         \n","_________________________________________________________________\n","conv2d_transpose (Conv2DTran (None, 7, 7, 128)         409600    \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 7, 7, 128)         512       \n","_________________________________________________________________\n","leaky_re_lu_1 (LeakyReLU)    (None, 7, 7, 128)         0         \n","_________________________________________________________________\n","conv2d_transpose_1 (Conv2DTr (None, 14, 14, 64)        204800    \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 14, 14, 64)        256       \n","_________________________________________________________________\n","leaky_re_lu_2 (LeakyReLU)    (None, 14, 14, 64)        0         \n","_________________________________________________________________\n","conv2d_transpose_2 (Conv2DTr (None, 28, 28, 32)        51200     \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 28, 28, 32)        128       \n","_________________________________________________________________\n","leaky_re_lu_3 (LeakyReLU)    (None, 28, 28, 32)        0         \n","_________________________________________________________________\n","conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         800       \n","=================================================================\n","Total params: 817,824\n","Trainable params: 804,832\n","Non-trainable params: 12,992\n","_________________________________________________________________\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 28, 28, 64)        1664      \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 28, 28, 64)        256       \n","_________________________________________________________________\n","leaky_re_lu_4 (LeakyReLU)    (None, 28, 28, 64)        0         \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 14, 14, 128)       204928    \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 14, 14, 128)       512       \n","_________________________________________________________________\n","leaky_re_lu_5 (LeakyReLU)    (None, 14, 14, 128)       0         \n","_________________________________________________________________\n","dropout (Dropout)            (None, 14, 14, 128)       0         \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 7, 7, 256)         819456    \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 7, 7, 256)         1024      \n","_________________________________________________________________\n","leaky_re_lu_6 (LeakyReLU)    (None, 7, 7, 256)         0         \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 7, 7, 256)         0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 1, 1, 1)           12545     \n","_________________________________________________________________\n","reshape_1 (Reshape)          (None, 1)                 0         \n","=================================================================\n","Total params: 1,040,385\n","Trainable params: 1,039,489\n","Non-trainable params: 896\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-iAGk1Ta6xmQ","colab_type":"code","colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css":{"data":"/* Copyright (c) Jupyter Development Team.
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definitions when they are used. */

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This file is copied from the JupyterLab project to define default styling for
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 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
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 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
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 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
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 * all copies or substantial portions of the Software.
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 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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The following CSS variables define the main, public API for styling JupyterLab.
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by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
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Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
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    cursor: pointer;

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                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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ogV2lkZ2V0IFRleHQgYW5kIFRleHRBcmVhIFN0eWluZyAqL1xuXG4ud2lkZ2V0LXRleHRhcmVhLCAud2lkZ2V0LXRleHQge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG59XG5cbi53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwidGV4dFwiXSwgLndpZGdldC10ZXh0IGlucHV0W3R5cGU9XCJudW1iZXJcIl17XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXRleHQgaW5wdXRbdHlwZT1cInRleHRcIl06ZGlzYWJsZWQsIC53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwibnVtYmVyXCJdOmRpc2FibGVkLCAud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhOmRpc2FibGVkIHtcbiAgICBvcGFjaXR5OiB2YXIoLS1qcC13aWRnZXRzLWRpc2FibGVkLW9wYWNpdHkpO1xufVxuXG4ud2lkZ2V0LXRleHQgaW5wdXRbdHlwZT1cInRleHRcIl0sIC53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwibnVtYmVyXCJdLCAud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhIHtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xuICAgIGJvcmRlcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLWNvbG9yKTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbiAgICBwYWRkaW5nOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpIGNhbGMoIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZykgKiAgMiApO1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgZmxleC1zaHJpbms6IDE7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xufVxuXG4ud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhIHtcbiAgICBoZWlnaHQ6IGluaGVyaXQ7XG4gICAgd2lkdGg6IGluaGVyaXQ7XG59XG5cbi53aWRnZXQtdGV4dCBpbnB1dDpmb2N1cywgLndpZGdldC10ZXh0YXJlYSB0ZXh0YXJlYTpmb2N1cyB7XG4gICAgYm9yZGVyLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWZvY3VzLWJvcmRlci1jb2xvcik7XG59XG5cbi8qIFdpZGdldCBTbGlkZXIgKi9cblxuLndpZGdldC1zbGlkZXIgLnVpLXNsaWRlciB7XG4gICAgLyogU2xpZGVyIFRyYWNrICovXG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWxheW91dC1jb2xvcjMpO1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWxheW91dC1jb2xvcjMpO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgcG9zaXRpb246IHJlbGF0aXZlO1xuICAgIGJvcmRlci1yYWRpdXM6IDBweDtcbn1cblxuLndpZGdldC1zbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLWhhbmRsZSB7XG4gICAgLyogU2xpZGVyIEhhbmRsZSAqL1xuICAgIG91dGxpbmU6IG5vbmUgIWltcG9ydGFudDsgLyogZm9jdXNlZCBzbGlkZXIgaGFuZGxlcyBhcmUgY29sb3JlZCAtIHNlZSBiZWxvdyAqL1xuICAgIHBvc2l0aW9uOiBhYnNvbHV0ZTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtYmFja2dyb3VuZC1jb2xvcik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1ib3JkZXItY29sb3IpO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgei1pbmRleDogMTtcbiAgICBiYWNrZ3JvdW5kLWltYWdlOiBub25lOyAvKiBPdmVycmlkZSBqcXVlcnktdWkgKi9cbn1cblxuLyogT3ZlcnJpZGUganF1ZXJ5LXVpICovXG4ud2lkZ2V0LXNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItaGFuZGxlOmhvdmVyLCAud2lkZ2V0LXNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItaGFuZGxlOmZvY3VzIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYWN0aXZlLWhhbmRsZS1jb2xvcik7XG59XG5cbi53aWRnZXQtc2xpZGVyIC51aS1zbGlkZXIgLnVpLXNsaWRlci1oYW5kbGU6YWN0aXZlIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICBib3JkZXItY29sb3I6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWFjdGl2ZS1oYW5kbGUtY29sb3IpO1xuICAgIHotaW5kZXg6IDI7XG4gICAgdHJhbnNmb3JtOiBzY2FsZSgxLjIpO1xufVxuXG4ud2lkZ2V0LXNsaWRlciAgLnVpLXNsaWRlciAudWktc2xpZGVyLXJhbmdlIHtcbiAgICAvKiBJbnRlcnZhbCBiZXR3ZWVuIHRoZSB0d28gc3BlY2lmaWVkIHZhbHVlIG9mIGEgZG91YmxlIHNsaWRlciAqL1xuICAgIHBvc2l0aW9uOiBhYnNvbHV0ZTtcbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICB6LWluZGV4OiAwO1xufVxuXG4vKiBTaGFwZXMgb2YgU2xpZGVyIEhhbmRsZXMgKi9cblxuLndpZGdldC1oc2xpZGVyIC51aS1zbGlkZXIgLnVpLXNsaWRlci1oYW5kbGUge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSk7XG4gICAgbWFyZ2luLXRvcDogY2FsYygodmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSkgLyAyIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgbWFyZ2luLWxlZnQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItaGFuZGxlLXNpemUpIC8gLTIgKyB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbiAgICBib3JkZXItcmFkaXVzOiA1MCU7XG4gICAgdG9wOiAwO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLWhhbmRsZSB7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKTtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKTtcbiAgICBtYXJnaW4tYm90dG9tOiBjYWxjKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSAvIC0yICsgdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgbWFyZ2luLWxlZnQ6IGNhbGMoKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgLSB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkpIC8gMiAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIGJvcmRlci1yYWRpdXM6IDUwJTtcbiAgICBsZWZ0OiAwO1xufVxuXG4ud2lkZ2V0LWhzbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLXJhbmdlIHtcbiAgICBoZWlnaHQ6IGNhbGMoIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgKiAyICk7XG4gICAgbWFyZ2luLXRvcDogY2FsYygodmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgKiAyICkgLyAyIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG59XG5cbi53aWRnZXQtdnNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItcmFuZ2Uge1xuICAgIHdpZHRoOiBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci10cmFjay10aGlja25lc3MpICogMiApO1xuICAgIG1hcmdpbi1sZWZ0OiBjYWxjKCh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci10cmFjay10aGlja25lc3MpIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAqIDIgKSAvIDIgLSB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbn1cblxuLyogSG9yaXpvbnRhbCBTbGlkZXIgKi9cblxuLndpZGdldC1oc2xpZGVyIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcblxuICAgIC8qIE92ZXJyaWRlIHRoZSBhbGlnbi1pdGVtcyBiYXNlbGluZS4gVGhpcyB3YXksIHRoZSBkZXNjcmlwdGlvbiBhbmQgcmVhZG91dFxuICAgIHN0aWxsIHNlZW0gdG8gYWxpZ24gdGhlaXIgYmFzZWxpbmUgcHJvcGVybHksIGFuZCB3ZSBkb24ndCBoYXZlIHRvIGhhdmVcbiAgICBhbGlnbi1zZWxmOiBzdHJldGNoIGluIHRoZSAuc2xpZGVyLWNvbnRhaW5lci4gKi9cbiAgICBhbGlnbi1pdGVtczogY2VudGVyO1xufVxuXG4ud2lkZ2V0cy1zbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIG92ZXJmbG93OiB2aXNpYmxlO1xufVxuXG4ud2lkZ2V0LWhzbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBtYXJnaW4tbGVmdDogY2FsYyh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkgLyAyIC0gMiAqIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIG1hcmdpbi1yaWdodDogY2FsYyh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkgLyAyIC0gMiAqIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIGZsZXg6IDEgMSB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aC1zaG9ydCk7XG59XG5cbi53aWRnZXQtaHNsaWRlciAudWktc2xpZGVyIHtcbiAgICAvKiBJbm5lciwgaW52aXNpYmxlIHNsaWRlIGRpdiAqL1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKTtcbiAgICBtYXJnaW4tdG9wOiBjYWxjKCh2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSkgLyAyKTtcbiAgICB3aWR0aDogMTAwJTtcbn1cblxuLyogVmVydGljYWwgU2xpZGVyICovXG5cbi53aWRnZXQtdmJveCAud2lkZ2V0LWxhYmVsIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG59XG5cbi53aWRnZXQtdnNsaWRlciB7XG4gICAgLyogVmVydGljYWwgU2xpZGVyICovXG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXZlcnRpY2FsLWhlaWdodCk7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXRpbnkpO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIGZsZXg6IDEgMSB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aC1zaG9ydCk7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xuICAgIG1hcmdpbi1ib3R0b206IGNhbGModmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItaGFuZGxlLXNpemUpIC8gMiAtIDIgKiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbiAgICBtYXJnaW4tdG9wOiBjYWxjKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSAvIDIgLSAyICogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgZGlzcGxheTogZmxleDtcbiAgICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnVpLXNsaWRlci12ZXJ0aWNhbCB7XG4gICAgLyogSW5uZXIsIGludmlzaWJsZSBzbGlkZSBkaXYgKi9cbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKTtcbiAgICBmbGV4LWdyb3c6IDE7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xufVxuXG4vKiBXaWRnZXQgUHJvZ3Jlc3MgU3R5bGluZyAqL1xuXG4ucHJvZ3Jlc3MtYmFyIHtcbiAgICAtd2Via2l0LXRyYW5zaXRpb246IG5vbmU7XG4gICAgLW1vei10cmFuc2l0aW9uOiBub25lO1xuICAgIC1tcy10cmFuc2l0aW9uOiBub25lO1xuICAgIC1vLXRyYW5zaXRpb246IG5vbmU7XG4gICAgdHJhbnNpdGlvbjogbm9uZTtcbn1cblxuLnByb2dyZXNzLWJhciB7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1icmFuZC1jb2xvcjEpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyLXN1Y2Nlc3Mge1xuICAgIGJhY2tncm91bmQtY29sb3I6IHZhcigtLWpwLXN1Y2Nlc3MtY29sb3IxKTtcbn1cblxuLnByb2dyZXNzLWJhci1pbmZvIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1pbmZvLWNvbG9yMSk7XG59XG5cbi5wcm9ncmVzcy1iYXItd2FybmluZyB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2Fybi1jb2xvcjEpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyLWRhbmdlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtZXJyb3ItY29sb3IxKTtcbn1cblxuLnByb2dyZXNzIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1sYXlvdXQtY29sb3IyKTtcbiAgICBib3JkZXI6IG5vbmU7XG4gICAgYm94LXNoYWRvdzogbm9uZTtcbn1cblxuLyogSG9yaXNvbnRhbCBQcm9ncmVzcyAqL1xuXG4ud2lkZ2V0LWhwcm9ncmVzcyB7XG4gICAgLyogUHJvZ3Jlc3MgQmFyICovXG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG4gICAgYWxpZ24taXRlbXM6IGNlbnRlcjtcblxufVxuXG4ud2lkZ2V0LWhwcm9ncmVzcyAucHJvZ3Jlc3Mge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBtYXJnaW4tdG9wOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpO1xuICAgIG1hcmdpbi1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZyk7XG4gICAgYWxpZ24tc2VsZjogc3RyZXRjaDtcbiAgICAvKiBPdmVycmlkZSBib290c3RyYXAgc3R5bGUgKi9cbiAgICBoZWlnaHQ6IGluaXRpYWw7XG59XG5cbi8qIFZlcnRpY2FsIFByb2dyZXNzICovXG5cbi53aWRnZXQtdnByb2dyZXNzIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtdmVydGljYWwtaGVpZ2h0KTtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgtdGlueSk7XG59XG5cbi53aWRnZXQtdnByb2dyZXNzIC5wcm9ncmVzcyB7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLXByb2dyZXNzLXRoaWNrbmVzcyk7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xuICAgIG1hcmdpbi1ib3R0b206IDA7XG59XG5cbi8qIFNlbGVjdCBXaWRnZXQgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWRyb3Bkb3duIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoKTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbn1cblxuLndpZGdldC1kcm9wZG93biA+IHNlbGVjdCB7XG4gICAgcGFkZGluZy1yaWdodDogMjBweDtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYm9yZGVyLXJhZGl1czogMDtcbiAgICBoZWlnaHQ6IGluaGVyaXQ7XG4gICAgZmxleDogMSAxIHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXNob3J0KTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgICBvdXRsaW5lOiBub25lICFpbXBvcnRhbnQ7XG4gICAgYm94LXNoYWRvdzogbm9uZTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbiAgICB2ZXJ0aWNhbC1hbGlnbjogdG9wO1xuICAgIHBhZGRpbmctbGVmdDogY2FsYyggdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSAqIDIpO1xuXHRhcHBlYXJhbmNlOiBub25lO1xuXHQtd2Via2l0LWFwcGVhcmFuY2U6IG5vbmU7XG5cdC1tb3otYXBwZWFyYW5jZTogbm9uZTtcbiAgICBiYWNrZ3JvdW5kLXJlcGVhdDogbm8tcmVwZWF0O1xuXHRiYWNrZ3JvdW5kLXNpemU6IDIwcHg7XG5cdGJhY2tncm91bmQtcG9zaXRpb246IHJpZ2h0IGNlbnRlcjtcbiAgICBiYWNrZ3JvdW5kLWltYWdlOiB2YXIoLS1qcC13aWRnZXRzLWRyb3Bkb3duLWFycm93KTtcbn1cbi53aWRnZXQtZHJvcGRvd24gPiBzZWxlY3Q6Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWRyb3Bkb3duID4gc2VsZWN0OmRpc2FibGVkIHtcbiAgICBvcGFjaXR5OiB2YXIoLS1qcC13aWRnZXRzLWRpc2FibGVkLW9wYWNpdHkpO1xufVxuXG4vKiBUbyBkaXNhYmxlIHRoZSBkb3R0ZWQgYm9yZGVyIGluIEZpcmVmb3ggYXJvdW5kIHNlbGVjdCBjb250cm9scy5cbiAgIFNlZSBodHRwOi8vc3RhY2tvdmVyZmxvdy5jb20vYS8xODg1MzAwMiAqL1xuLndpZGdldC1kcm9wZG93biA+IHNlbGVjdDotbW96LWZvY3VzcmluZyB7XG4gICAgY29sb3I6IHRyYW5zcGFyZW50O1xuICAgIHRleHQtc2hhZG93OiAwIDAgMCAjMDAwO1xufVxuXG4vKiBTZWxlY3QgYW5kIFNlbGVjdE11bHRpcGxlICovXG5cbi53aWRnZXQtc2VsZWN0IHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuXG4gICAgLyogQmVjYXVzZSBGaXJlZm94IGRlZmluZXMgdGhlIGJhc2VsaW5lIG9mIGEgc2VsZWN0IGFzIHRoZSBib3R0b20gb2YgdGhlXG4gICAgY29udHJvbCwgd2UgYWxpZ24gdGhlIGVudGlyZSBjb250cm9sIHRvIHRoZSB0b3AgYW5kIGFkZCBwYWRkaW5nIHRvIHRoZVxuICAgIHNlbGVjdCB0byBnZXQgYW4gYXBwcm94aW1hdGUgZmlyc3QgbGluZSBiYXNlbGluZSBhbGlnbm1lbnQuICovXG4gICAgYWxpZ24taXRlbXM6IGZsZXgtc3RhcnQ7XG59XG5cbi53aWRnZXQtc2VsZWN0ID4gc2VsZWN0IHtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgZmxleDogMSAxIHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXNob3J0KTtcbiAgICBvdXRsaW5lOiBub25lICFpbXBvcnRhbnQ7XG4gICAgb3ZlcmZsb3c6IGF1dG87XG4gICAgaGVpZ2h0OiBpbmhlcml0O1xuXG4gICAgLyogQmVjYXVzZSBGaXJlZm94IGRlZmluZXMgdGhlIGJhc2VsaW5lIG9mIGEgc2VsZWN0IGFzIHRoZSBib3R0b20gb2YgdGhlXG4gICAgY29udHJvbCwgd2UgYWxpZ24gdGhlIGVudGlyZSBjb250cm9sIHRvIHRoZSB0b3AgYW5kIGFkZCBwYWRkaW5nIHRvIHRoZVxuICAgIHNlbGVjdCB0byBnZXQgYW4gYXBwcm94aW1hdGUgZmlyc3QgbGluZSBiYXNlbGluZSBhbGlnbm1lbnQuICovXG4gICAgcGFkZGluZy10b3A6IDVweDtcbn1cblxuLndpZGdldC1zZWxlY3QgPiBzZWxlY3Q6Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lnZXQtc2VsZWN0ID4gc2VsZWN0ID4gb3B0aW9uIHtcbiAgICBwYWRkaW5nLWxlZnQ6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZyk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgLyogbGluZS1oZWlnaHQgZG9lc24ndCB3b3JrIG9uIHNvbWUgYnJvd3NlcnMgZm9yIHNlbGVjdCBvcHRpb25zICovXG4gICAgcGFkZGluZy10b3A6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KS12YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSkvMik7XG4gICAgcGFkZGluZy1ib3R0b206IGNhbGModmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KS12YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSkvMik7XG59XG5cblxuXG4vKiBUb2dnbGUgQnV0dG9ucyBTdHlsaW5nICovXG5cbi53aWRnZXQtdG9nZ2xlLWJ1dHRvbnMge1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXRvZ2dsZS1idXR0b25zIC53aWRnZXQtdG9nZ2xlLWJ1dHRvbiB7XG4gICAgbWFyZ2luLWxlZnQ6IHZhcigtLWpwLXdpZGdldHMtbWFyZ2luKTtcbiAgICBtYXJnaW4tcmlnaHQ6IHZhcigtLWpwLXdpZGdldHMtbWFyZ2luKTtcbn1cblxuLndpZGdldC10b2dnbGUtYnV0dG9ucyAuanVweXRlci1idXR0b246ZGlzYWJsZWQge1xuICAgIG9wYWNpdHk6IHZhcigtLWpwLXdpZGdldHMtZGlzYWJsZWQtb3BhY2l0eSk7XG59XG5cbi8qIFJhZGlvIEJ1dHRvbnMgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LXJhZGlvIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXJhZGlvLWJveCB7XG4gICAgZGlzcGxheTogZmxleDtcbiAgICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xuICAgIGFsaWduLWl0ZW1zOiBzdHJldGNoO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIG1hcmdpbi1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQtYWRqdXN0bWVudCk7XG59XG5cbi53aWRnZXQtcmFkaW8tYm94IGxhYmVsIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXJhZGlvLWl0ZW0taGVpZ2h0KTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbn1cblxuLndpZGdldC1yYWRpby1ib3ggaW5wdXQge1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1yYWRpby1pdGVtLWhlaWdodCk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQpO1xuICAgIG1hcmdpbjogMCBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogMiApIDAgMXB4O1xuICAgIGZsb2F0OiBsZWZ0O1xufVxuXG4vKiBDb2xvciBQaWNrZXIgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciA+IC53aWRnZXQtY29sb3JwaWNrZXItaW5wdXQge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBmbGV4LXNocmluazogMTtcbiAgICBtaW4td2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXRpbnkpO1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIGlucHV0W3R5cGU9XCJjb2xvclwiXSB7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIHBhZGRpbmc6IDAgMnB4OyAvKiBtYWtlIHRoZSBjb2xvciBzcXVhcmUgYWN0dWFsbHkgc3F1YXJlIG9uIENocm9tZSBvbiBPUyBYICovXG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItY29sb3IpO1xuICAgIGJvcmRlci1sZWZ0OiBub25lO1xuICAgIGZsZXgtZ3JvdzogMDtcbiAgICBmbGV4LXNocmluazogMDtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xuICAgIGFsaWduLXNlbGY6IHN0cmV0Y2g7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyLmNvbmNpc2UgaW5wdXRbdHlwZT1cImNvbG9yXCJdIHtcbiAgICBib3JkZXItbGVmdDogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLWNvbG9yKTtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciBpbnB1dFt0eXBlPVwiY29sb3JcIl06Zm9jdXMsIC53aWRnZXQtY29sb3JwaWNrZXIgaW5wdXRbdHlwZT1cInRleHRcIl06Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIGlucHV0W3R5cGU9XCJ0ZXh0XCJdIHtcbiAgICBmbGV4LWdyb3c6IDE7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogIDIgKTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgZmxleC1zaHJpbms6IDE7XG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciBpbnB1dFt0eXBlPVwidGV4dFwiXTpkaXNhYmxlZCB7XG4gICAgb3BhY2l0eTogdmFyKC0tanAtd2lkZ2V0cy1kaXNhYmxlZC1vcGFjaXR5KTtcbn1cblxuLyogRGF0ZSBQaWNrZXIgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWRhdGVwaWNrZXIge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl0ge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBmbGV4LXNocmluazogMTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogIDIgKTtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl06Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl06aW52YWxpZCB7XG4gICAgYm9yZGVyLWNvbG9yOiB2YXIoLS1qcC13YXJuLWNvbG9yMSk7XG59XG5cbi53aWRnZXQtZGF0ZXBpY2tlciBpbnB1dFt0eXBlPVwiZGF0ZVwiXTpkaXNhYmxlZCB7XG4gICAgb3BhY2l0eTogdmFyKC0tanAtd2lkZ2V0cy1kaXNhYmxlZC1vcGFjaXR5KTtcbn1cblxuLyogUGxheSBXaWRnZXQgKi9cblxuLndpZGdldC1wbGF5IHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgtc2hvcnQpO1xuICAgIGRpc3BsYXk6IGZsZXg7XG4gICAgYWxpZ24taXRlbXM6IHN0cmV0Y2g7XG59XG5cbi53aWRnZXQtcGxheSAuanVweXRlci1idXR0b24ge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBoZWlnaHQ6IGF1dG87XG59XG5cbi53aWRnZXQtcGxheSAuanVweXRlci1idXR0b246ZGlzYWJsZWQge1xuICAgIG9wYWNpdHk6IHZhcigtLWpwLXdpZGdldHMtZGlzYWJsZWQtb3BhY2l0eSk7XG59XG5cbi8qIFRhYiBXaWRnZXQgKi9cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiIHtcbiAgICBkaXNwbGF5OiBmbGV4O1xuICAgIGZsZXgtZGlyZWN0aW9uOiBjb2x1bW47XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciB7XG4gICAgLyogTmVjZXNzYXJ5IHNvIHRoYXQgYSB0YWIgY2FuIGJlIHNoaWZ0ZWQgZG93biB0byBvdmVybGF5IHRoZSBib3JkZXIgb2YgdGhlIGJveCBiZWxvdy4gKi9cbiAgICBvdmVyZmxvdy14OiB2aXNpYmxlO1xuICAgIG92ZXJmbG93LXk6IHZpc2libGU7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciA+IC5wLVRhYkJhci1jb250ZW50IHtcbiAgICAvKiBNYWtlIHN1cmUgdGhhdCB0aGUgdGFiIGdyb3dzIGZyb20gYm90dG9tIHVwICovXG4gICAgYWxpZ24taXRlbXM6IGZsZXgtZW5kO1xuICAgIG1pbi13aWR0aDogMDtcbiAgICBtaW4taGVpZ2h0OiAwO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAud2lkZ2V0LXRhYi1jb250ZW50cyB7XG4gICAgd2lkdGg6IDEwMCU7XG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgICBtYXJnaW46IDA7XG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtYm9yZGVyLWNvbG9yMSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIG92ZXJmbG93OiBhdXRvO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIge1xuICAgIGZvbnQ6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKSBIZWx2ZXRpY2EsIEFyaWFsLCBzYW5zLXNlcmlmO1xuICAgIG1pbi1oZWlnaHQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi1oZWlnaHQpICsgdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiIHtcbiAgICBmbGV4OiAwIDEgdmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi13aWR0aCk7XG4gICAgbWluLXdpZHRoOiAzNXB4O1xuICAgIG1pbi1oZWlnaHQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi1oZWlnaHQpICsgdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaG9yaXpvbnRhbC10YWItaGVpZ2h0KTtcbiAgICBtYXJnaW4tbGVmdDogY2FsYygtMSAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIHBhZGRpbmc6IDBweCAxMHB4O1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWxheW91dC1jb2xvcjIpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC11aS1mb250LWNvbG9yMik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1ib3R0b206IG5vbmU7XG4gICAgcG9zaXRpb246IHJlbGF0aXZlO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYi5wLW1vZC1jdXJyZW50IHtcbiAgICBjb2xvcjogdmFyKC0tanAtdWktZm9udC1jb2xvcjApO1xuICAgIC8qIFdlIHdhbnQgdGhlIGJhY2tncm91bmQgdG8gbWF0Y2ggdGhlIHRhYiBjb250ZW50IGJhY2tncm91bmQgKi9cbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC1sYXlvdXQtY29sb3IxKTtcbiAgICBtaW4taGVpZ2h0OiBjYWxjKHZhcigtLWpwLXdpZGdldHMtaG9yaXpvbnRhbC10YWItaGVpZ2h0KSArIDIgKiB2YXIoLS1qcC1ib3JkZXItd2lkdGgpKTtcbiAgICB0cmFuc2Zvcm06IHRyYW5zbGF0ZVkodmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgb3ZlcmZsb3c6IHZpc2libGU7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiLnAtbW9kLWN1cnJlbnQ6YmVmb3JlIHtcbiAgICBwb3NpdGlvbjogYWJzb2x1dGU7XG4gICAgdG9wOiBjYWxjKC0xICogdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgbGVmdDogY2FsYygtMSAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIGNvbnRlbnQ6ICcnO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi10b3AtYm9yZGVyKTtcbiAgICB3aWR0aDogY2FsYygxMDAlICsgMiAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWJyYW5kLWNvbG9yMSk7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiOmZpcnN0LWNoaWxkIHtcbiAgICBtYXJnaW4tbGVmdDogMDtcbn1cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLVRhYkJhci10YWI6aG92ZXI6bm90KC5wLW1vZC1jdXJyZW50KSB7XG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbn1cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLW1vZC1jbG9zYWJsZSA+IC5wLVRhYkJhci10YWJDbG9zZUljb24ge1xuICAgIG1hcmdpbi1sZWZ0OiA0cHg7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1tb2QtY2xvc2FibGUgPiAucC1UYWJCYXItdGFiQ2xvc2VJY29uOmJlZm9yZSB7XG4gICAgZm9udC1mYW1pbHk6IEZvbnRBd2Vzb21lO1xuICAgIGNvbnRlbnQ6ICdcXGYwMGQnOyAvKiBjbG9zZSAqL1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkljb24sXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkxhYmVsLFxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLVRhYkJhci10YWJDbG9zZUljb24ge1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWhvcml6b250YWwtdGFiLWhlaWdodCk7XG59XG5cbi8qIEFjY29yZGlvbiBXaWRnZXQgKi9cblxuLnAtQ29sbGFwc2Uge1xuICAgIGRpc3BsYXk6IGZsZXg7XG4gICAgZmxleC1kaXJlY3Rpb246IGNvbHVtbjtcbiAgICBhbGlnbi1pdGVtczogc3RyZXRjaDtcbn1cblxuLnAtQ29sbGFwc2UtaGVhZGVyIHtcbiAgICBwYWRkaW5nOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpO1xuICAgIGN1cnNvcjogcG9pbnRlcjtcbiAgICBjb2xvcjogdmFyKC0tanAtdWktZm9udC1jb2xvcjIpO1xuICAgIGJhY2tncm91bmQtY29sb3I6IHZhcigtLWpwLWxheW91dC1jb2xvcjIpO1xuICAgIGJvcmRlcjogdmFyKC0tanAtd2lkZ2V0cy1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIHBhZGRpbmc6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZykgKiAyIC8gMykgdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgZm9udC13ZWlnaHQ6IGJvbGQ7XG59XG5cbi5wLUNvbGxhcHNlLWhlYWRlcjpob3ZlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbn1cblxuLnAtQ29sbGFwc2Utb3BlbiA+IC5wLUNvbGxhcHNlLWhlYWRlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IwKTtcbiAgICBjdXJzb3I6IGRlZmF1bHQ7XG4gICAgYm9yZGVyLWJvdHRvbTogbm9uZTtcbn1cblxuLnAtQ29sbGFwc2UgLnAtQ29sbGFwc2UtaGVhZGVyOjpiZWZvcmUge1xuICAgIGNvbnRlbnQ6ICdcXGYwZGFcXDAwQTAnOyAgLyogY2FyZXQtcmlnaHQsIG5vbi1icmVha2luZyBzcGFjZSAqL1xuICAgIGRpc3BsYXk6IGlubGluZS1ibG9jaztcbiAgICBmb250OiBub3JtYWwgbm9ybWFsIG5vcm1hbCAxNHB4LzEgRm9udEF3ZXNvbWU7XG4gICAgZm9udC1zaXplOiBpbmhlcml0O1xuICAgIHRleHQtcmVuZGVyaW5nOiBhdXRvO1xuICAgIC13ZWJraXQtZm9udC1zbW9vdGhpbmc6IGFudGlhbGlhc2VkO1xuICAgIC1tb3otb3N4LWZvbnQtc21vb3RoaW5nOiBncmF5c2NhbGU7XG59XG5cbi5wLUNvbGxhcHNlLW9wZW4gPiAucC1Db2xsYXBzZS1oZWFkZXI6OmJlZm9yZSB7XG4gICAgY29udGVudDogJ1xcZjBkN1xcMDBBMCc7IC8qIGNhcmV0LWRvd24sIG5vbi1icmVha2luZyBzcGFjZSAqL1xufVxuXG4ucC1Db2xsYXBzZS1jb250ZW50cyB7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbiAgICBib3JkZXItbGVmdDogdmFyKC0tanAtd2lkZ2V0cy1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1yaWdodDogdmFyKC0tanAtd2lkZ2V0cy1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC1ib3JkZXItY29sb3IxKTtcbiAgICBvdmVyZmxvdzogYXV0bztcbn1cblxuLnAtQWNjb3JkaW9uIHtcbiAgICBkaXNwbGF5OiBmbGV4O1xuICAgIGZsZXgtZGlyZWN0aW9uOiBjb2x1bW47XG4gICAgYWxpZ24taXRlbXM6IHN0cmV0Y2g7XG59XG5cbi5wLUFjY29yZGlvbiAucC1Db2xsYXBzZSB7XG4gICAgbWFyZ2luLWJvdHRvbTogMDtcbn1cblxuLnAtQWNjb3JkaW9uIC5wLUNvbGxhcHNlICsgLnAtQ29sbGFwc2Uge1xuICAgIG1hcmdpbi10b3A6IDRweDtcbn1cblxuXG5cbi8qIEhUTUwgd2lkZ2V0ICovXG5cbi53aWRnZXQtaHRtbCwgLndpZGdldC1odG1sbWF0aCB7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG59XG5cbi53aWRnZXQtaHRtbCA+IC53aWRnZXQtaHRtbC1jb250ZW50LCAud2lkZ2V0LWh0bWxtYXRoID4gLndpZGdldC1odG1sLWNvbnRlbnQge1xuICAgIC8qIEZpbGwgb3V0IHRoZSBhcmVhIGluIHRoZSBIVE1MIHdpZGdldCAqL1xuICAgIGFsaWduLXNlbGY6IHN0cmV0Y2g7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIGZsZXgtc2hyaW5rOiAxO1xuICAgIC8qIE1ha2VzIHN1cmUgdGhlIGJhc2VsaW5lIGlzIHN0aWxsIGFsaWduZWQgd2l0aCBvdGhlciBlbGVtZW50cyAqL1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIC8qIE1ha2UgaXQgcG9zc2libGUgdG8gaGF2ZSBhYnNvbHV0ZWx5LXBvc2l0aW9uZWQgZWxlbWVudHMgaW4gdGhlIGh0bWwgKi9cbiAgICBwb3NpdGlvbjogcmVsYXRpdmU7XG59XG4iLCIvKiBUaGlzIGZpbGUgaGFzIGNvZGUgZGVyaXZlZCBmcm9tIFBob3NwaG9ySlMgQ1NTIGZpbGVzLCBhcyBub3RlZCBiZWxvdy4gVGhlIGxpY2Vuc2UgZm9yIHRoaXMgUGhvc3Bob3JKUyBjb2RlIGlzOlxuXG5Db3B5cmlnaHQgKGMpIDIwMTQtMjAxNywgUGhvc3Bob3JKUyBDb250cmlidXRvcnNcbkFsbCByaWdodHMgcmVzZXJ2ZWQuXG5cblJlZGlzdHJpYnV0aW9uIGFuZCB1c2UgaW4gc291cmNlIGFuZCBiaW5hcnkgZm9ybXMsIHdpdGggb3Igd2l0aG91dFxubW9kaWZpY2F0aW9uLCBhcmUgcGVybWl0dGVkIHByb3ZpZGVkIHRoYXQgdGhlIGZvbGxvd2luZyBjb25kaXRpb25zIGFyZSBtZXQ6XG5cbiogUmVkaXN0cmlidXRpb25zIG9mIHNvdXJjZSBjb2RlIG11c3QgcmV0YWluIHRoZSBhYm92ZSBjb3B5cmlnaHQgbm90aWNlLCB0aGlzXG4gIGxpc3Qgb2YgY29uZGl0aW9ucyBhbmQgdGhlIGZvbGxvd2luZyBkaXNjbGFpbWVyLlxuXG4qIFJlZGlzdHJpYnV0aW9ucyBpbiBiaW5hcnkgZm9ybSBtdXN0IHJlcHJvZHVjZSB0aGUgYWJvdmUgY29weXJpZ2h0IG5vdGljZSxcbiAgdGhpcyBsaXN0IG9mIGNvbmRpdGlvbnMgYW5kIHRoZSBmb2xsb3dpbmcgZGlzY2xhaW1lciBpbiB0aGUgZG9jdW1lbnRhdGlvblxuICBhbmQvb3Igb3RoZXIgbWF0ZXJpYWxzIHByb3ZpZGVkIHdpdGggdGhlIGRpc3RyaWJ1dGlvbi5cblxuKiBOZWl0aGVyIHRoZSBuYW1lIG9mIHRoZSBjb3B5cmlnaHQgaG9sZGVyIG5vciB0aGUgbmFtZXMgb2YgaXRzXG4gIGNvbnRyaWJ1dG9ycyBtYXkgYmUgdXNlZCB0byBlbmRvcnNlIG9yIHByb21vdGUgcHJvZHVjdHMgZGVyaXZlZCBmcm9tXG4gIHRoaXMgc29mdHdhcmUgd2l0aG91dCBzcGVjaWZpYyBwcmlvciB3cml0dGVuIHBlcm1pc3Npb24uXG5cblRISVMgU09GVFdBUkUgSVMgUFJPVklERUQgQlkgVEhFIENPUFlSSUdIVCBIT0xERVJTIEFORCBDT05UUklCVVRPUlMgXCJBUyBJU1wiXG5BTkQgQU5ZIEVYUFJFU1MgT1IgSU1QTElFRCBXQVJSQU5USUVTLCBJTkNMVURJTkcsIEJVVCBOT1QgTElNSVRFRCBUTywgVEhFXG5JTVBMSUVEIFdBUlJBTlRJRVMgT0YgTUVSQ0hBTlRBQklMSVRZIEFORCBGSVRORVNTIEZPUiBBIFBBUlRJQ1VMQVIgUFVSUE9TRSBBUkVcbkRJU0NMQUlNRUQuIElOIE5PIEVWRU5UIFNIQUxMIFRIRSBDT1BZUklHSFQgSE9MREVSIE9SIENPTlRSSUJVVE9SUyBCRSBMSUFCTEVcbkZPUiBBTlkgRElSRUNULCBJTkRJUkVDVCwgSU5DSURFTlRBTCwgU1BFQ0lBTCwgRVhFTVBMQVJZLCBPUiBDT05TRVFVRU5USUFMXG5EQU1BR0VTIChJTkNMVURJTkcsIEJVVCBOT1QgTElNSVRFRCBUTywgUFJPQ1VSRU1FTlQgT0YgU1VCU1RJVFVURSBHT09EUyBPUlxuU0VSVklDRVM7IExPU1MgT0YgVVNFLCBEQVRBLCBPUiBQUk9GSVRTOyBPUiBCVVNJTkVTUyBJTlRFUlJVUFRJT04pIEhPV0VWRVJcbkNBVVNFRCBBTkQgT04gQU5ZIFRIRU9SWSBPRiBMSUFCSUxJVFksIFdIRVRIRVIgSU4gQ09OVFJBQ1QsIFNUUklDVCBMSUFCSUxJVFksXG5PUiBUT1JUIChJTkNMVURJTkcgTkVHTElHRU5DRSBPUiBPVEhFUldJU0UpIEFSSVNJTkcgSU4gQU5ZIFdBWSBPVVQgT0YgVEhFIFVTRVxuT0YgVEhJUyBTT0ZUV0FSRSwgRVZFTiBJRiBBRFZJU0VEIE9GIFRIRSBQT1NTSUJJTElUWSBPRiBTVUNIIERBTUFHRS5cblxuKi9cblxuLypcbiAqIFRoZSBmb2xsb3dpbmcgc2VjdGlvbiBpcyBkZXJpdmVkIGZyb20gaHR0cHM6Ly9naXRodWIuY29tL3Bob3NwaG9yanMvcGhvc3Bob3IvYmxvYi8yM2I5ZDA3NWViYzViNzNhYjE0OGI2ZWJmYzIwYWY5N2Y4NTcxNGM0L3BhY2thZ2VzL3dpZGdldHMvc3R5bGUvdGFiYmFyLmNzcyBcbiAqIFdlJ3ZlIHNjb3BlZCB0aGUgcnVsZXMgc28gdGhhdCB0aGV5IGFyZSBjb25zaXN0ZW50IHdpdGggZXhhY3RseSBvdXIgY29kZS5cbiAqL1xuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIge1xuICBkaXNwbGF5OiBmbGV4O1xuICAtd2Via2l0LXVzZXItc2VsZWN0OiBub25lO1xuICAtbW96LXVzZXItc2VsZWN0OiBub25lO1xuICAtbXMtdXNlci1zZWxlY3Q6IG5vbmU7XG4gIHVzZXItc2VsZWN0OiBub25lO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhcltkYXRhLW9yaWVudGF0aW9uPSdob3Jpem9udGFsJ10ge1xuICBmbGV4LWRpcmVjdGlvbjogcm93O1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhcltkYXRhLW9yaWVudGF0aW9uPSd2ZXJ0aWNhbCddIHtcbiAgZmxleC1kaXJlY3Rpb246IGNvbHVtbjtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgPiAucC1UYWJCYXItY29udGVudCB7XG4gIG1hcmdpbjogMDtcbiAgcGFkZGluZzogMDtcbiAgZGlzcGxheTogZmxleDtcbiAgZmxleDogMSAxIGF1dG87XG4gIGxpc3Qtc3R5bGUtdHlwZTogbm9uZTtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXJbZGF0YS1vcmllbnRhdGlvbj0naG9yaXpvbnRhbCddID4gLnAtVGFiQmFyLWNvbnRlbnQge1xuICBmbGV4LWRpcmVjdGlvbjogcm93O1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhcltkYXRhLW9yaWVudGF0aW9uPSd2ZXJ0aWNhbCddID4gLnAtVGFiQmFyLWNvbnRlbnQge1xuICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiIHtcbiAgZGlzcGxheTogZmxleDtcbiAgZmxleC1kaXJlY3Rpb246IHJvdztcbiAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgb3ZlcmZsb3c6IGhpZGRlbjtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkljb24sXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkNsb3NlSWNvbiB7XG4gIGZsZXg6IDAgMCBhdXRvO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiTGFiZWwge1xuICBmbGV4OiAxIDEgYXV0bztcbiAgb3ZlcmZsb3c6IGhpZGRlbjtcbiAgd2hpdGUtc3BhY2U6IG5vd3JhcDtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYi5wLW1vZC1oaWRkZW4ge1xuICBkaXNwbGF5OiBub25lICFpbXBvcnRhbnQ7XG59XG5cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyLnAtbW9kLWRyYWdnaW5nIC5wLVRhYkJhci10YWIge1xuICBwb3NpdGlvbjogcmVsYXRpdmU7XG59XG5cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyLnAtbW9kLWRyYWdnaW5nW2RhdGEtb3JpZW50YXRpb249J2hvcml6b250YWwnXSAucC1UYWJCYXItdGFiIHtcbiAgbGVmdDogMDtcbiAgdHJhbnNpdGlvbjogbGVmdCAxNTBtcyBlYXNlO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhci5wLW1vZC1kcmFnZ2luZ1tkYXRhLW9yaWVudGF0aW9uPSd2ZXJ0aWNhbCddIC5wLVRhYkJhci10YWIge1xuICB0b3A6IDA7XG4gIHRyYW5zaXRpb246IHRvcCAxNTBtcyBlYXNlO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhci5wLW1vZC1kcmFnZ2luZyAucC1UYWJCYXItdGFiLnAtbW9kLWRyYWdnaW5nIHtcbiAgdHJhbnNpdGlvbjogbm9uZTtcbn1cblxuLyogRW5kIHRhYmJhci5jc3MgKi9cbiJdfQ== 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image\n","\n"],"execution_count":7,"outputs":[{"output_type":"stream","text":["\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/1.0.0...\u001b[0m\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"315dbd23d9184248830c58a6892a880d","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2a9fa2d95b3b4c41b2302baeaf15c39c","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3eb06c5b8b2f42f1b800c324d55226e4","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n","  InsecureRequestWarning)\n","/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n","  InsecureRequestWarning)\n","/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n","  InsecureRequestWarning)\n","/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n","  InsecureRequestWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["\n","\n","\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d78b876527fe469581f8ecc5a49a431a","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\r"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e01967a992434cc6bcd2d75515a15787","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=0, description='Shuffling...', max=10, style=ProgressStyle(description_width=…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["WARNING: Logging before flag parsing goes to stderr.\n","W0824 21:06:17.040940 140562200074112 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use eager execution and: \n","`tf.data.TFRecordDataset(path)`\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"67b0e11fc1d14926b9cdfb8866d8c2be","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"68faa81ad5464b0cbde661639277366d","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=0, description='Writing...', max=6000, 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bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ac6185075d1b493299bf9b3c837c2613","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=0, description='Writing...', max=10000, style=ProgressStyle(description_width…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\r\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XLQueWrb497K","colab_type":"code","outputId":"ad33b239-237e-4516-e997-00c9a8ef2062","executionInfo":{"status":"ok","timestamp":1566060346354,"user_tz":240,"elapsed":9205302,"user":{"displayName":"Vahid Mirjalili","photoUrl":"","userId":"03695229825133505307"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import time\n","num_epochs = 100\n","batch_size = 64\n","image_size = (28, 28)\n","z_size = 20\n","mode_z = 'uniform'\n","gen_hidden_layers = 1\n","gen_hidden_size = 100\n","disc_hidden_layers = 1\n","disc_hidden_size = 100\n","\n","tf.random.set_seed(1)\n","np.random.seed(1)\n","\n","\n","if mode_z == 'uniform':\n","    fixed_z = tf.random.uniform(\n","        shape=(batch_size, z_size),\n","        minval=-1, maxval=1)\n","elif mode_z == 'normal':\n","    fixed_z = tf.random.normal(\n","        shape=(batch_size, z_size))\n","\n","def create_samples(g_model, input_z):\n","    g_output = g_model(input_z, training=False)\n","    images = tf.reshape(g_output, (batch_size, *image_size))    \n","    return (images+1)/2.0\n","\n","## Set-up the dataset\n","mnist_trainset = mnist['train']\n","mnist_trainset = mnist_trainset.map(\n","    lambda ex: preprocess(ex, mode=mode_z))\n","\n","input_z, input_real = next(iter(mnist_trainset))\n","\n","mnist_trainset = mnist_trainset.shuffle(10000)\n","mnist_trainset = mnist_trainset.batch(\n","    batch_size, drop_remainder=True)\n","\n","## Set-up the model\n","with tf.device(device_name):\n","    gen_model = make_dcgan_generator()\n","    gen_model.build(input_shape=(None, z_size))\n","\n","    disc_model = make_dcgan_discriminator()\n","    disc_model.build(input_shape=(None, np.prod(image_size)))\n","\n","## Loss function and optimizers:\n","loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n","g_optimizer = tf.keras.optimizers.Adam()\n","d_optimizer = tf.keras.optimizers.Adam()\n","\n","avg_epoch_losses = []\n","avg_d_vals = []\n","epoch_samples = []\n","\n","start_time = time.time()\n","for epoch in range(1, num_epochs+1):\n","    losses = []\n","    for i,(input_z,input_real) in enumerate(mnist_trainset):\n","        \n","        ## Compute discriminator's real-loss and its gradients:\n","        with tf.GradientTape() as d_tape_real:\n","            d_logits_real = disc_model(input_real, training=True)\n","\n","            d_labels_real = tf.ones_like(d_logits_real)# * smoothing_factor\n","            \n","            d_loss_real = loss_fn(y_true=d_labels_real,\n","                                  y_pred=d_logits_real)\n","        d_grads_real = d_tape_real.gradient(\n","               d_loss_real, disc_model.trainable_variables)\n","        ## Optimization: Apply the gradients\n","        d_optimizer.apply_gradients(\n","            grads_and_vars=zip(d_grads_real,\n","                               disc_model.trainable_variables))\n","       \n","        \n","        ## Compute generator's loss and its gradients:\n","        with tf.GradientTape() as g_tape:\n","            g_output = gen_model(input_z)\n","            d_logits_fake = disc_model(g_output, training=True)\n","            labels_real = tf.ones_like(d_logits_fake)\n","            g_loss = loss_fn(y_true=labels_real,\n","                             y_pred=d_logits_fake)\n","            \n","        g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)\n","        g_optimizer.apply_gradients(\n","            grads_and_vars=zip(g_grads, gen_model.trainable_variables))\n","        \n","            \n","        ## Compute discriminator's fake-loss and its gradients:\n","        with tf.GradientTape() as d_tape_fake:\n","            d_logits_fake = disc_model(g_output.numpy(), training=True)\n","            d_labels_fake = tf.zeros_like(d_logits_fake)\n","\n","            d_loss_fake = loss_fn(y_true=d_labels_fake,\n","                                  y_pred=d_logits_fake)\n","\n","            d_grads_fake = d_tape_fake.gradient(\n","                d_loss_fake, disc_model.trainable_variables)\n","        ## Optimization: Apply the gradients\n","        d_optimizer.apply_gradients(\n","            grads_and_vars=zip(d_grads_fake, \n","                               disc_model.trainable_variables))\n","        \n","        d_loss = (d_loss_real + d_loss_fake)/2.0\n","        losses.append(\n","            (g_loss.numpy(), d_loss.numpy(), \n","             d_loss_real.numpy(), d_loss_fake.numpy()))\n","        \n","        \n","        d_probs_real = tf.reduce_mean(tf.sigmoid(d_logits_real))\n","        d_probs_fake = tf.reduce_mean(tf.sigmoid(d_logits_fake))\n","        avg_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))        \n","    avg_epoch_losses.append(np.mean(losses, axis=0))\n","    print('Epoch {:-3d} | ET {:.2f} min | Avg Losses >>'\n","          ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'\n","          .format(epoch, (time.time() - start_time)/60, \n","                  *list(avg_epoch_losses[-1])))\n","    epoch_samples.append(create_samples(\n","          gen_model, num_samples=8).numpy())\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch   1 | ET 1.62 min | Avg Losses >> G/D 11.5878/0.0551 [D-Real: 0.0120 D-Fake: 0.0982]\n","Epoch   2 | ET 3.16 min | Avg Losses >> G/D 12.7159/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   3 | ET 4.70 min | Avg Losses >> G/D 13.5042/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   4 | ET 6.26 min | Avg Losses >> G/D 13.9042/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   5 | ET 7.80 min | Avg Losses >> G/D 14.7967/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   6 | ET 9.34 min | Avg Losses >> G/D 15.1870/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   7 | ET 10.89 min | Avg Losses >> G/D 15.7337/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   8 | ET 12.43 min | Avg Losses >> G/D 16.3247/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch   9 | ET 13.97 min | Avg Losses >> G/D 16.6955/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  10 | ET 15.52 min | Avg Losses >> G/D 17.1411/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  11 | ET 17.06 min | Avg Losses >> G/D 17.4700/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  12 | ET 18.60 min | Avg Losses >> G/D 18.1582/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  13 | ET 20.14 min | Avg Losses >> G/D 18.4224/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  14 | ET 21.68 min | Avg Losses >> G/D 19.0358/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  15 | ET 23.22 min | Avg Losses >> G/D 19.5515/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  16 | ET 24.75 min | Avg Losses >> G/D 20.0417/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  17 | ET 26.29 min | Avg Losses >> G/D 20.4726/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  18 | ET 27.83 min | Avg Losses >> G/D 20.8334/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  19 | ET 29.36 min | Avg Losses >> G/D 21.4407/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  20 | ET 30.89 min | Avg Losses >> G/D 21.8508/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  21 | ET 32.44 min | Avg Losses >> G/D 22.2526/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  22 | ET 33.97 min | Avg Losses >> G/D 22.6377/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  23 | ET 35.52 min | Avg Losses >> G/D 23.0690/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  24 | ET 37.07 min | Avg Losses >> G/D 23.4874/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  25 | ET 38.61 min | Avg Losses >> G/D 23.7815/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  26 | ET 40.14 min | Avg Losses >> G/D 22.6020/0.1138 [D-Real: 0.1041 D-Fake: 0.1235]\n","Epoch  27 | ET 41.69 min | Avg Losses >> G/D 16.3304/0.0739 [D-Real: 0.0891 D-Fake: 0.0587]\n","Epoch  28 | ET 43.23 min | Avg Losses >> G/D 19.2976/0.0260 [D-Real: 0.0267 D-Fake: 0.0254]\n","Epoch  29 | ET 44.76 min | Avg Losses >> G/D 21.7710/0.0051 [D-Real: 0.0047 D-Fake: 0.0054]\n","Epoch  30 | ET 46.28 min | Avg Losses >> G/D 22.7175/0.0004 [D-Real: 0.0004 D-Fake: 0.0005]\n","Epoch  31 | ET 47.82 min | Avg Losses >> G/D 18.0605/0.1172 [D-Real: 0.1144 D-Fake: 0.1200]\n","Epoch  32 | ET 49.35 min | Avg Losses >> G/D 15.9308/0.0012 [D-Real: 0.0013 D-Fake: 0.0011]\n","Epoch  33 | ET 50.88 min | Avg Losses >> G/D 20.7703/0.0048 [D-Real: 0.0048 D-Fake: 0.0048]\n","Epoch  34 | ET 52.42 min | Avg Losses >> G/D 19.8592/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  35 | ET 53.95 min | Avg Losses >> G/D 21.4370/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  36 | ET 55.48 min | Avg Losses >> G/D 21.8310/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  37 | ET 57.02 min | Avg Losses >> G/D 22.2185/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  38 | ET 58.54 min | Avg Losses >> G/D 22.7537/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  39 | ET 60.07 min | Avg Losses >> G/D 23.4858/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  40 | ET 61.60 min | Avg Losses >> G/D 24.0924/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  41 | ET 63.14 min | Avg Losses >> G/D 23.8351/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  42 | ET 64.67 min | Avg Losses >> G/D 24.3796/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  43 | ET 66.20 min | Avg Losses >> G/D 25.0200/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  44 | ET 67.75 min | Avg Losses >> G/D 25.5366/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  45 | ET 69.27 min | Avg Losses >> G/D 25.2527/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  46 | ET 70.80 min | Avg Losses >> G/D 26.1628/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  47 | ET 72.34 min | Avg Losses >> G/D 26.7818/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  48 | ET 73.87 min | Avg Losses >> G/D 27.3121/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  49 | ET 75.40 min | Avg Losses >> G/D 26.8991/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  50 | ET 76.91 min | Avg Losses >> G/D 28.0603/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  51 | ET 78.44 min | Avg Losses >> G/D 28.1691/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  52 | ET 79.97 min | Avg Losses >> G/D 28.4989/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  53 | ET 81.47 min | Avg Losses >> G/D 28.2405/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  54 | ET 83.01 min | Avg Losses >> G/D 29.6009/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  55 | ET 84.52 min | Avg Losses >> G/D 30.1077/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  56 | ET 86.04 min | Avg Losses >> G/D 29.8691/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  57 | ET 87.56 min | Avg Losses >> G/D 30.6936/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  58 | ET 89.08 min | Avg Losses >> G/D 31.0307/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  59 | ET 90.61 min | Avg Losses >> G/D 30.7768/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  60 | ET 92.13 min | Avg Losses >> G/D 31.6255/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  61 | ET 93.66 min | Avg Losses >> G/D 32.1454/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  62 | ET 95.18 min | Avg Losses >> G/D 31.2347/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  63 | ET 96.70 min | Avg Losses >> G/D 33.5185/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  64 | ET 98.23 min | Avg Losses >> G/D 36.4600/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  65 | ET 99.75 min | Avg Losses >> G/D 35.6588/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  66 | ET 101.26 min | Avg Losses >> G/D 35.0426/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  67 | ET 102.79 min | Avg Losses >> G/D 34.5411/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  68 | ET 104.32 min | Avg Losses >> G/D 34.3160/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  69 | ET 105.84 min | Avg Losses >> G/D 33.7519/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  70 | ET 107.36 min | Avg Losses >> G/D 32.0705/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  71 | ET 108.90 min | Avg Losses >> G/D 32.8703/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  72 | ET 110.42 min | Avg Losses >> G/D 33.0637/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  73 | ET 111.94 min | Avg Losses >> G/D 33.3458/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  74 | ET 113.46 min | Avg Losses >> G/D 33.6650/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  75 | ET 114.99 min | Avg Losses >> G/D 33.7407/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  76 | ET 116.52 min | Avg Losses >> G/D 33.7356/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  77 | ET 118.04 min | Avg Losses >> G/D 33.8300/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  78 | ET 119.57 min | Avg Losses >> G/D 34.0158/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  79 | ET 121.09 min | Avg Losses >> G/D 34.1753/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  80 | ET 122.61 min | Avg Losses >> G/D 33.6558/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  81 | ET 124.14 min | Avg Losses >> G/D 33.8060/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  82 | ET 125.66 min | Avg Losses >> G/D 33.8519/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  83 | ET 127.18 min | Avg Losses >> G/D 33.8743/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  84 | ET 128.72 min | Avg Losses >> G/D 33.8756/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  85 | ET 130.25 min | Avg Losses >> G/D 33.8705/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  86 | ET 131.78 min | Avg Losses >> G/D 33.9098/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  87 | ET 133.32 min | Avg Losses >> G/D 33.8838/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  88 | ET 134.86 min | Avg Losses >> G/D 33.9247/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  89 | ET 136.41 min | Avg Losses >> G/D 33.9281/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  90 | ET 137.96 min | Avg Losses >> G/D 33.8812/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  91 | ET 139.50 min | Avg Losses >> G/D 33.7767/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  92 | ET 141.06 min | Avg Losses >> G/D 33.8204/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  93 | ET 142.60 min | Avg Losses >> G/D 33.8720/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  94 | ET 144.14 min | Avg Losses >> G/D 34.0033/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  95 | ET 145.69 min | Avg Losses >> G/D 34.0748/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  96 | ET 147.23 min | Avg Losses >> G/D 33.9154/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  97 | ET 148.78 min | Avg Losses >> G/D 34.0379/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  98 | ET 150.32 min | Avg Losses >> G/D 33.9534/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch  99 | ET 151.86 min | Avg Losses >> G/D 34.0685/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n","Epoch 100 | ET 153.40 min | Avg Losses >> G/D 34.1505/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nToA2xUQ6C3h","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}
\ No newline at end of file
diff --git a/ch17/ch17_optional_DCGAN.ipynb b/ch17/ch17_optional_DCGAN.ipynb
new file mode 100644
index 00000000..073ca046
--- /dev/null
+++ b/ch17/ch17_optional_DCGAN.ipynb
@@ -0,0 +1,1289 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Python Machine Learning 3rd Edition* by [Sebastian Raschka](https://sebastianraschka.com) & [Vahid Mirjalili](http://vahidmirjalili.com), Packt Publishing Ltd. 2019\n",
+    "\n",
+    "Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition\n",
+    "\n",
+    "Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 17: Generative Adversarial Networks (Optional, DCGAN)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 68
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 47887,
+     "status": "ok",
+     "timestamp": 1566680661076,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "TDcU1S783G6q",
+    "outputId": "d1daae87-bf9f-450e-9091-96f0830034a9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[K     |████████████████████████████████| 348.9MB 61kB/s \n",
+      "\u001b[K     |████████████████████████████████| 3.1MB 33.7MB/s \n",
+      "\u001b[K     |████████████████████████████████| 501kB 50.0MB/s \n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "# ! pip install -q tensorflow-gpu==2.0.0-beta1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 127152,
+     "status": "ok",
+     "timestamp": 1566680740382,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "XnOWggQN3RbT",
+    "outputId": "0bc1af02-7ee7-4d48-d44a-9c20d222ad5a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive/\n"
+     ]
+    }
+   ],
+   "source": [
+    "# from google.colab import drive\n",
+    "# drive.mount('/content/drive/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 4890,
+     "status": "ok",
+     "timestamp": 1566680741639,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "VXasfQIW3XeR",
+    "outputId": "88dae23d-b0b8-4361-94dc-bc01a3ebd317"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.0.0-beta1'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import tensorflow_datasets as tfds\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 2926,
+     "status": "ok",
+     "timestamp": 1566680742201,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "O9XN3qSZ6Kbr",
+    "outputId": "efa0b1af-0050-4664-c0f1-f375a5ab546a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU Available:  True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'/device:GPU:0'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(tf.__version__)\n",
+    "\n",
+    "print(\"GPU Available:\", tf.test.is_gpu_available())\n",
+    "\n",
+    "if tf.test.is_gpu_available():\n",
+    "    device_name = tf.test.gpu_device_name()\n",
+    "\n",
+    "else:\n",
+    "    device_name = 'cpu:0'\n",
+    "    \n",
+    "print(device_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "O9QIFmPZ3jjg"
+   },
+   "outputs": [],
+   "source": [
+    "def make_dcgan_generator(z_size=20, output_size=(28, 28, 1),\n",
+    "                         n_filters=128, n_blocks=2):\n",
+    "    size_factor = 2**n_blocks\n",
+    "    hidden_size = (output_size[0]//size_factor, \n",
+    "                   output_size[1]//size_factor)\n",
+    "    \n",
+    "    model = tf.keras.Sequential([\n",
+    "        tf.keras.layers.Input(shape=(z_size,)),\n",
+    "        \n",
+    "        tf.keras.layers.Dense(\n",
+    "            units=n_filters*np.prod(hidden_size), \n",
+    "            use_bias=False),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "        tf.keras.layers.LeakyReLU(),\n",
+    "        tf.keras.layers.Reshape(\n",
+    "            (hidden_size[0], hidden_size[1], n_filters)),\n",
+    "    \n",
+    "        tf.keras.layers.Conv2DTranspose(\n",
+    "            filters=n_filters, kernel_size=(5, 5), strides=(1, 1),\n",
+    "            padding='same', use_bias=False),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "        tf.keras.layers.LeakyReLU()\n",
+    "    ])\n",
+    "        \n",
+    "    nf = n_filters\n",
+    "    for i in range(n_blocks):\n",
+    "        nf = nf // 2\n",
+    "        model.add(tf.keras.layers.Conv2DTranspose(\n",
+    "            filters=nf, kernel_size=(5, 5), strides=(2, 2),\n",
+    "            padding='same', use_bias=False))\n",
+    "        model.add(tf.keras.layers.BatchNormalization())\n",
+    "        model.add(tf.keras.layers.LeakyReLU())\n",
+    "                \n",
+    "        \n",
+    "    model.add(tf.keras.layers.Conv2DTranspose(\n",
+    "        filters=output_size[2], kernel_size=(5, 5), strides=(1, 1), \n",
+    "        padding='same', use_bias=False, activation='tanh'))\n",
+    "        \n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "def make_dcgan_discriminator(input_size=(28, 28, 1),\n",
+    "                             n_filters=64, n_blocks=2):\n",
+    "    model = tf.keras.Sequential([\n",
+    "        tf.keras.layers.Input(shape=input_size),\n",
+    "        tf.keras.layers.Conv2D(\n",
+    "            filters=n_filters, kernel_size=5, \n",
+    "            strides=(1, 1), padding='same'),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "        tf.keras.layers.LeakyReLU()\n",
+    "    ])\n",
+    "    \n",
+    "    nf = n_filters\n",
+    "    for i in range(n_blocks):\n",
+    "        nf = nf*2\n",
+    "        model.add(tf.keras.layers.Conv2D(\n",
+    "            filters=nf, kernel_size=(5, 5), \n",
+    "            strides=(2, 2),padding='same'))\n",
+    "        model.add(tf.keras.layers.BatchNormalization())\n",
+    "        model.add(tf.keras.layers.LeakyReLU())\n",
+    "        model.add(tf.keras.layers.Dropout(0.3))\n",
+    "        \n",
+    "    model.add(tf.keras.layers.Conv2D(\n",
+    "            filters=1, kernel_size=(7, 7), padding='valid'))\n",
+    "    \n",
+    "    model.add(tf.keras.layers.Reshape((1,)))\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1813,
+     "status": "ok",
+     "timestamp": 1566680750136,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "DX4INL735ARw",
+    "outputId": "dec87b5a-3c59-4cef-9ae6-0c1aaf3e3c0c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense (Dense)                (None, 6272)              125440    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization (BatchNo (None, 6272)              25088     \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu (LeakyReLU)      (None, 6272)              0         \n",
+      "_________________________________________________________________\n",
+      "reshape (Reshape)            (None, 7, 7, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_transpose (Conv2DTran (None, 7, 7, 128)         409600    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_1 (Batch (None, 7, 7, 128)         512       \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu_1 (LeakyReLU)    (None, 7, 7, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_transpose_1 (Conv2DTr (None, 14, 14, 64)        204800    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_2 (Batch (None, 14, 14, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu_2 (LeakyReLU)    (None, 14, 14, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_transpose_2 (Conv2DTr (None, 28, 28, 32)        51200     \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_3 (Batch (None, 28, 28, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu_3 (LeakyReLU)    (None, 28, 28, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         800       \n",
+      "=================================================================\n",
+      "Total params: 817,824\n",
+      "Trainable params: 804,832\n",
+      "Non-trainable params: 12,992\n",
+      "_________________________________________________________________\n",
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d (Conv2D)              (None, 28, 28, 64)        1664      \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_4 (Batch (None, 28, 28, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu_4 (LeakyReLU)    (None, 28, 28, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_1 (Conv2D)            (None, 14, 14, 128)       204928    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_5 (Batch (None, 14, 14, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu_5 (LeakyReLU)    (None, 14, 14, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 14, 14, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 7, 7, 256)         819456    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_6 (Batch (None, 7, 7, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "leaky_re_lu_6 (LeakyReLU)    (None, 7, 7, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 7, 7, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_3 (Conv2D)            (None, 1, 1, 1)           12545     \n",
+      "_________________________________________________________________\n",
+      "reshape_1 (Reshape)          (None, 1)                 0         \n",
+      "=================================================================\n",
+      "Total params: 1,040,385\n",
+      "Trainable params: 1,039,489\n",
+      "Non-trainable params: 896\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "gen_model = make_dcgan_generator()\n",
+    "gen_model.summary()\n",
+    "\n",
+    "disc_model = make_dcgan_discriminator()\n",
+    "disc_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "resources": {
+      "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": {
+       "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

/*# 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+       "headers": [
+        [
+         "content-type",
+         "text/css"
+        ]
+       ],
+       "ok": true,
+       "status": 200,
+       "status_text": ""
+      }
+     }
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 35533,
+     "status": "ok",
+     "timestamp": 1566680786783,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "-iAGk1Ta6xmQ",
+    "outputId": "5d982155-11dd-47ca-b4a8-ca7fac6be686"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/1.0.0...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "315dbd23d9184248830c58a6892a880d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a9fa2d95b3b4c41b2302baeaf15c39c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3eb06c5b8b2f42f1b800c324d55226e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
+      "  InsecureRequestWarning)\n",
+      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
+      "  InsecureRequestWarning)\n",
+      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
+      "  InsecureRequestWarning)\n",
+      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
+      "  InsecureRequestWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d78b876527fe469581f8ecc5a49a431a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e01967a992434cc6bcd2d75515a15787",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=10, style=ProgressStyle(description_width=…"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W0824 21:06:17.040940 140562200074112 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use eager execution and: \n",
+      "`tf.data.TFRecordDataset(path)`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67b0e11fc1d14926b9cdfb8866d8c2be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
+      ]
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2885f1e840a0458b9b88493f646c420d",
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+    {
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "mnist_bldr = tfds.builder('mnist')\n",
+    "mnist_bldr.download_and_prepare()\n",
+    "mnist = mnist_bldr.as_dataset(shuffle_files=False)\n",
+    "\n",
+    "def preprocess(ex, mode='uniform'):\n",
+    "    image = ex['image']\n",
+    "    image = tf.image.convert_image_dtype(image, tf.float32)\n",
+    "\n",
+    "    image = image*2 - 1.0\n",
+    "    if mode == 'uniform':\n",
+    "        input_z = tf.random.uniform(shape=(z_size,),\n",
+    "                                    minval=-1.0, maxval=1.0)\n",
+    "    elif mode == 'normal':\n",
+    "        input_z = tf.random.normal(shape=(z_size,))\n",
+    "    return input_z, image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 9205302,
+     "status": "ok",
+     "timestamp": 1566060346354,
+     "user": {
+      "displayName": "Vahid Mirjalili",
+      "photoUrl": "",
+      "userId": "03695229825133505307"
+     },
+     "user_tz": 240
+    },
+    "id": "XLQueWrb497K",
+    "outputId": "ad33b239-237e-4516-e997-00c9a8ef2062"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch   1 | ET 1.62 min | Avg Losses >> G/D 11.5878/0.0551 [D-Real: 0.0120 D-Fake: 0.0982]\n",
+      "Epoch   2 | ET 3.16 min | Avg Losses >> G/D 12.7159/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   3 | ET 4.70 min | Avg Losses >> G/D 13.5042/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   4 | ET 6.26 min | Avg Losses >> G/D 13.9042/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   5 | ET 7.80 min | Avg Losses >> G/D 14.7967/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   6 | ET 9.34 min | Avg Losses >> G/D 15.1870/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   7 | ET 10.89 min | Avg Losses >> G/D 15.7337/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   8 | ET 12.43 min | Avg Losses >> G/D 16.3247/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch   9 | ET 13.97 min | Avg Losses >> G/D 16.6955/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  10 | ET 15.52 min | Avg Losses >> G/D 17.1411/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  11 | ET 17.06 min | Avg Losses >> G/D 17.4700/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  12 | ET 18.60 min | Avg Losses >> G/D 18.1582/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  13 | ET 20.14 min | Avg Losses >> G/D 18.4224/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  14 | ET 21.68 min | Avg Losses >> G/D 19.0358/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  15 | ET 23.22 min | Avg Losses >> G/D 19.5515/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  16 | ET 24.75 min | Avg Losses >> G/D 20.0417/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  17 | ET 26.29 min | Avg Losses >> G/D 20.4726/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  18 | ET 27.83 min | Avg Losses >> G/D 20.8334/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  19 | ET 29.36 min | Avg Losses >> G/D 21.4407/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  20 | ET 30.89 min | Avg Losses >> G/D 21.8508/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  21 | ET 32.44 min | Avg Losses >> G/D 22.2526/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  22 | ET 33.97 min | Avg Losses >> G/D 22.6377/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  23 | ET 35.52 min | Avg Losses >> G/D 23.0690/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  24 | ET 37.07 min | Avg Losses >> G/D 23.4874/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  25 | ET 38.61 min | Avg Losses >> G/D 23.7815/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  26 | ET 40.14 min | Avg Losses >> G/D 22.6020/0.1138 [D-Real: 0.1041 D-Fake: 0.1235]\n",
+      "Epoch  27 | ET 41.69 min | Avg Losses >> G/D 16.3304/0.0739 [D-Real: 0.0891 D-Fake: 0.0587]\n",
+      "Epoch  28 | ET 43.23 min | Avg Losses >> G/D 19.2976/0.0260 [D-Real: 0.0267 D-Fake: 0.0254]\n",
+      "Epoch  29 | ET 44.76 min | Avg Losses >> G/D 21.7710/0.0051 [D-Real: 0.0047 D-Fake: 0.0054]\n",
+      "Epoch  30 | ET 46.28 min | Avg Losses >> G/D 22.7175/0.0004 [D-Real: 0.0004 D-Fake: 0.0005]\n",
+      "Epoch  31 | ET 47.82 min | Avg Losses >> G/D 18.0605/0.1172 [D-Real: 0.1144 D-Fake: 0.1200]\n",
+      "Epoch  32 | ET 49.35 min | Avg Losses >> G/D 15.9308/0.0012 [D-Real: 0.0013 D-Fake: 0.0011]\n",
+      "Epoch  33 | ET 50.88 min | Avg Losses >> G/D 20.7703/0.0048 [D-Real: 0.0048 D-Fake: 0.0048]\n",
+      "Epoch  34 | ET 52.42 min | Avg Losses >> G/D 19.8592/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  35 | ET 53.95 min | Avg Losses >> G/D 21.4370/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  36 | ET 55.48 min | Avg Losses >> G/D 21.8310/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  37 | ET 57.02 min | Avg Losses >> G/D 22.2185/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  38 | ET 58.54 min | Avg Losses >> G/D 22.7537/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  39 | ET 60.07 min | Avg Losses >> G/D 23.4858/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  40 | ET 61.60 min | Avg Losses >> G/D 24.0924/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  41 | ET 63.14 min | Avg Losses >> G/D 23.8351/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  42 | ET 64.67 min | Avg Losses >> G/D 24.3796/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  43 | ET 66.20 min | Avg Losses >> G/D 25.0200/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  44 | ET 67.75 min | Avg Losses >> G/D 25.5366/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  45 | ET 69.27 min | Avg Losses >> G/D 25.2527/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  46 | ET 70.80 min | Avg Losses >> G/D 26.1628/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  47 | ET 72.34 min | Avg Losses >> G/D 26.7818/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  48 | ET 73.87 min | Avg Losses >> G/D 27.3121/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  49 | ET 75.40 min | Avg Losses >> G/D 26.8991/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  50 | ET 76.91 min | Avg Losses >> G/D 28.0603/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  51 | ET 78.44 min | Avg Losses >> G/D 28.1691/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  52 | ET 79.97 min | Avg Losses >> G/D 28.4989/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  53 | ET 81.47 min | Avg Losses >> G/D 28.2405/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  54 | ET 83.01 min | Avg Losses >> G/D 29.6009/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  55 | ET 84.52 min | Avg Losses >> G/D 30.1077/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  56 | ET 86.04 min | Avg Losses >> G/D 29.8691/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  57 | ET 87.56 min | Avg Losses >> G/D 30.6936/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  58 | ET 89.08 min | Avg Losses >> G/D 31.0307/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  59 | ET 90.61 min | Avg Losses >> G/D 30.7768/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  60 | ET 92.13 min | Avg Losses >> G/D 31.6255/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  61 | ET 93.66 min | Avg Losses >> G/D 32.1454/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  62 | ET 95.18 min | Avg Losses >> G/D 31.2347/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  63 | ET 96.70 min | Avg Losses >> G/D 33.5185/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  64 | ET 98.23 min | Avg Losses >> G/D 36.4600/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  65 | ET 99.75 min | Avg Losses >> G/D 35.6588/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  66 | ET 101.26 min | Avg Losses >> G/D 35.0426/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  67 | ET 102.79 min | Avg Losses >> G/D 34.5411/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  68 | ET 104.32 min | Avg Losses >> G/D 34.3160/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  69 | ET 105.84 min | Avg Losses >> G/D 33.7519/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  70 | ET 107.36 min | Avg Losses >> G/D 32.0705/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  71 | ET 108.90 min | Avg Losses >> G/D 32.8703/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  72 | ET 110.42 min | Avg Losses >> G/D 33.0637/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  73 | ET 111.94 min | Avg Losses >> G/D 33.3458/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  74 | ET 113.46 min | Avg Losses >> G/D 33.6650/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  75 | ET 114.99 min | Avg Losses >> G/D 33.7407/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  76 | ET 116.52 min | Avg Losses >> G/D 33.7356/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  77 | ET 118.04 min | Avg Losses >> G/D 33.8300/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  78 | ET 119.57 min | Avg Losses >> G/D 34.0158/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  79 | ET 121.09 min | Avg Losses >> G/D 34.1753/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  80 | ET 122.61 min | Avg Losses >> G/D 33.6558/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  81 | ET 124.14 min | Avg Losses >> G/D 33.8060/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  82 | ET 125.66 min | Avg Losses >> G/D 33.8519/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  83 | ET 127.18 min | Avg Losses >> G/D 33.8743/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  84 | ET 128.72 min | Avg Losses >> G/D 33.8756/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  85 | ET 130.25 min | Avg Losses >> G/D 33.8705/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  86 | ET 131.78 min | Avg Losses >> G/D 33.9098/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  87 | ET 133.32 min | Avg Losses >> G/D 33.8838/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  88 | ET 134.86 min | Avg Losses >> G/D 33.9247/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  89 | ET 136.41 min | Avg Losses >> G/D 33.9281/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  90 | ET 137.96 min | Avg Losses >> G/D 33.8812/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  91 | ET 139.50 min | Avg Losses >> G/D 33.7767/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  92 | ET 141.06 min | Avg Losses >> G/D 33.8204/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  93 | ET 142.60 min | Avg Losses >> G/D 33.8720/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  94 | ET 144.14 min | Avg Losses >> G/D 34.0033/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  95 | ET 145.69 min | Avg Losses >> G/D 34.0748/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  96 | ET 147.23 min | Avg Losses >> G/D 33.9154/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  97 | ET 148.78 min | Avg Losses >> G/D 34.0379/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  98 | ET 150.32 min | Avg Losses >> G/D 33.9534/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch  99 | ET 151.86 min | Avg Losses >> G/D 34.0685/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
+      "Epoch 100 | ET 153.40 min | Avg Losses >> G/D 34.1505/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "\n",
+    "num_epochs = 100\n",
+    "batch_size = 64\n",
+    "image_size = (28, 28)\n",
+    "z_size = 20\n",
+    "mode_z = 'uniform'\n",
+    "gen_hidden_layers = 1\n",
+    "gen_hidden_size = 100\n",
+    "disc_hidden_layers = 1\n",
+    "disc_hidden_size = 100\n",
+    "\n",
+    "tf.random.set_seed(1)\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "\n",
+    "if mode_z == 'uniform':\n",
+    "    fixed_z = tf.random.uniform(\n",
+    "        shape=(batch_size, z_size),\n",
+    "        minval=-1, maxval=1)\n",
+    "elif mode_z == 'normal':\n",
+    "    fixed_z = tf.random.normal(\n",
+    "        shape=(batch_size, z_size))\n",
+    "\n",
+    "def create_samples(g_model, input_z):\n",
+    "    g_output = g_model(input_z, training=False)\n",
+    "    images = tf.reshape(g_output, (batch_size, *image_size))    \n",
+    "    return (images+1)/2.0\n",
+    "\n",
+    "## Set-up the dataset\n",
+    "mnist_trainset = mnist['train']\n",
+    "mnist_trainset = mnist_trainset.map(\n",
+    "    lambda ex: preprocess(ex, mode=mode_z))\n",
+    "\n",
+    "input_z, input_real = next(iter(mnist_trainset))\n",
+    "\n",
+    "mnist_trainset = mnist_trainset.shuffle(10000)\n",
+    "mnist_trainset = mnist_trainset.batch(\n",
+    "    batch_size, drop_remainder=True)\n",
+    "\n",
+    "## Set-up the model\n",
+    "with tf.device(device_name):\n",
+    "    gen_model = make_dcgan_generator()\n",
+    "    gen_model.build(input_shape=(None, z_size))\n",
+    "\n",
+    "    disc_model = make_dcgan_discriminator()\n",
+    "    disc_model.build(input_shape=(None, np.prod(image_size)))\n",
+    "\n",
+    "## Loss function and optimizers:\n",
+    "loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+    "g_optimizer = tf.keras.optimizers.Adam()\n",
+    "d_optimizer = tf.keras.optimizers.Adam()\n",
+    "\n",
+    "avg_epoch_losses = []\n",
+    "avg_d_vals = []\n",
+    "epoch_samples = []\n",
+    "\n",
+    "start_time = time.time()\n",
+    "for epoch in range(1, num_epochs+1):\n",
+    "    losses = []\n",
+    "    for i,(input_z,input_real) in enumerate(mnist_trainset):\n",
+    "        \n",
+    "        ## Compute discriminator's real-loss and its gradients:\n",
+    "        with tf.GradientTape() as d_tape_real:\n",
+    "            d_logits_real = disc_model(input_real, training=True)\n",
+    "\n",
+    "            d_labels_real = tf.ones_like(d_logits_real)# * smoothing_factor\n",
+    "            \n",
+    "            d_loss_real = loss_fn(y_true=d_labels_real,\n",
+    "                                  y_pred=d_logits_real)\n",
+    "        d_grads_real = d_tape_real.gradient(\n",
+    "               d_loss_real, disc_model.trainable_variables)\n",
+    "        ## Optimization: Apply the gradients\n",
+    "        d_optimizer.apply_gradients(\n",
+    "            grads_and_vars=zip(d_grads_real,\n",
+    "                               disc_model.trainable_variables))\n",
+    "       \n",
+    "        \n",
+    "        ## Compute generator's loss and its gradients:\n",
+    "        with tf.GradientTape() as g_tape:\n",
+    "            g_output = gen_model(input_z)\n",
+    "            d_logits_fake = disc_model(g_output, training=True)\n",
+    "            labels_real = tf.ones_like(d_logits_fake)\n",
+    "            g_loss = loss_fn(y_true=labels_real,\n",
+    "                             y_pred=d_logits_fake)\n",
+    "            \n",
+    "        g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)\n",
+    "        g_optimizer.apply_gradients(\n",
+    "            grads_and_vars=zip(g_grads, gen_model.trainable_variables))\n",
+    "        \n",
+    "            \n",
+    "        ## Compute discriminator's fake-loss and its gradients:\n",
+    "        with tf.GradientTape() as d_tape_fake:\n",
+    "            d_logits_fake = disc_model(g_output.numpy(), training=True)\n",
+    "            d_labels_fake = tf.zeros_like(d_logits_fake)\n",
+    "\n",
+    "            d_loss_fake = loss_fn(y_true=d_labels_fake,\n",
+    "                                  y_pred=d_logits_fake)\n",
+    "\n",
+    "            d_grads_fake = d_tape_fake.gradient(\n",
+    "                d_loss_fake, disc_model.trainable_variables)\n",
+    "        ## Optimization: Apply the gradients\n",
+    "        d_optimizer.apply_gradients(\n",
+    "            grads_and_vars=zip(d_grads_fake, \n",
+    "                               disc_model.trainable_variables))\n",
+    "        \n",
+    "        d_loss = (d_loss_real + d_loss_fake)/2.0\n",
+    "        losses.append(\n",
+    "            (g_loss.numpy(), d_loss.numpy(), \n",
+    "             d_loss_real.numpy(), d_loss_fake.numpy()))\n",
+    "        \n",
+    "        \n",
+    "        d_probs_real = tf.reduce_mean(tf.sigmoid(d_logits_real))\n",
+    "        d_probs_fake = tf.reduce_mean(tf.sigmoid(d_logits_fake))\n",
+    "        avg_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))        \n",
+    "    avg_epoch_losses.append(np.mean(losses, axis=0))\n",
+    "    print('Epoch {:03d} | ET {:.2f} min | Avg Losses >>'\n",
+    "          ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'\n",
+    "          .format(epoch, (time.time() - start_time)/60, \n",
+    "                  *list(avg_epoch_losses[-1])))\n",
+    "    epoch_samples.append(create_samples(\n",
+    "          gen_model, num_samples=8).numpy())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nToA2xUQ6C3h"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "ch17-DCGAN.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/ch17/ch17_part1.ipynb b/ch17/ch17_part1.ipynb
index 87c8de06..8c77a639 100644
--- a/ch17/ch17_part1.ipynb
+++ b/ch17/ch17_part1.ipynb
@@ -15,8 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Chapter 17: Generative Adversarial Networks (part 1/2)\n",
-    "====="
+    "# Chapter 17: Generative Adversarial Networks (Part 1/2)"
    ]
   },
   {
@@ -28,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -38,11 +37,11 @@
       "Sebastian Raschka & Vahid Mirjalili \n",
       "last updated: 2019-11-03 \n",
       "\n",
-      "numpy 1.17.3\n",
-      "scipy 1.3.1\n",
-      "matplotlib 3.1.1\n",
+      "numpy 1.17.2\n",
+      "scipy 1.2.1\n",
+      "matplotlib 3.1.0\n",
       "tensorflow 2.0.0\n",
-      "tensorflow_datasets 1.2.0\n"
+      "tensorflow_datasets 1.3.0\n"
      ]
     }
    ],
@@ -72,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -82,7 +81,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {
       "image/png": {
        "width": 500
@@ -104,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -114,7 +113,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {
       "image/png": {
        "width": 700
@@ -136,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -146,7 +145,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {
       "image/png": {
        "width": 700
@@ -168,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +177,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 6,
      "metadata": {
       "image/png": {
        "width": 800
@@ -207,17 +206,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 7,
      "metadata": {
       "image/png": {
        "width": 700
@@ -232,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -242,7 +241,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {
       "image/png": {
        "width": 600
@@ -257,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -267,7 +266,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {
       "image/png": {
        "width": 600
@@ -282,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -292,20 +291,7 @@
     "id": "8ytj8tX3vxJ0",
     "outputId": "3b024f4f-ed79-45ff-8a26-ee92cb274a46"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[K     |████████████████████████████████| 380.8MB 47kB/s \n",
-      "\u001b[K     |████████████████████████████████| 3.8MB 39.2MB/s \n",
-      "\u001b[K     |████████████████████████████████| 450kB 54.2MB/s \n",
-      "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.0.0 which is incompatible.\u001b[0m\n",
-      "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 2.0.1 which is incompatible.\u001b[0m\n",
-      "\u001b[?25h"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Uncomment the following line if running this notebook on Google Colab\n",
     "#! pip install -q tensorflow-gpu==2.0.0"
@@ -313,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -335,18 +321,23 @@
     }
    ],
    "source": [
-    "#import tensorflow as tf\n",
-    "#print(tf.__version__)\n",
+    "import tensorflow as tf\n",
+    "print(tf.__version__)\n",
+    "\n",
+    "print(\"GPU Available:\", tf.test.is_gpu_available())\n",
     "\n",
-    "#print(\"GPU Available:\", tf.test.is_gpu_available())\n",
+    "if tf.test.is_gpu_available():\n",
+    "    device_name = tf.test.gpu_device_name()\n",
     "\n",
-    "#device_name = tf.test.gpu_device_name()\n",
-    "#print(device_name)"
+    "else:\n",
+    "    device_name = 'cpu:0'\n",
+    "    \n",
+    "print(device_name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 12,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -356,19 +347,7 @@
     "id": "ehoy80rqwNnl",
     "outputId": "c88a25d8-cc23-40f1-b032-19fe4cb5097a"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
-      "\n",
-      "Enter your authorization code:\n",
-      "··········\n",
-      "Mounted at /content/drive/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#from google.colab import drive\n",
     "#drive.mount('/content/drive/')"
@@ -383,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +372,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {
       "image/png": {
        "width": 600
@@ -408,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 14,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -420,12 +399,12 @@
     "import tensorflow_datasets as tfds\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n"
+    "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 15,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -467,12 +446,12 @@
     "            units=num_output_units, \n",
     "            activation=None)\n",
     "        )\n",
-    "    return model\n"
+    "    return model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 16,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -507,7 +486,7 @@
    "source": [
     "image_size = (28, 28)\n",
     "z_size = 20\n",
-    "mode_z = 'uniform' # 'uniform' vs. 'normal'\n",
+    "mode_z = 'uniform'  # 'uniform' vs. 'normal'\n",
     "gen_hidden_layers = 1\n",
     "gen_hidden_size = 100\n",
     "disc_hidden_layers = 1\n",
@@ -521,12 +500,12 @@
     "    num_output_units=np.prod(image_size))\n",
     "\n",
     "gen_model.build(input_shape=(None, z_size))\n",
-    "gen_model.summary()\n"
+    "gen_model.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 17,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -566,7 +545,7 @@
     "    num_hidden_units=disc_hidden_size)\n",
     "\n",
     "disc_model.build(input_shape=(None, np.prod(image_size)))\n",
-    "disc_model.summary()\n"
+    "disc_model.summary()"
    ]
   },
   {
@@ -578,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 18,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -593,544 +572,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/1.0.0...\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "874e9679b3b046938522e71383387441",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
-      ]
-     },
-     "metadata": {
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-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82a090ff17324a1db4d5ff4c8cbe8327",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {
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-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e6208b807f4d4f13ad53d64af9e16dfa",
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-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n",
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n",
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n",
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use eager execution and: \n",
-      "`tf.data.TFRecordDataset(path)`\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use eager execution and: \n",
-      "`tf.data.TFRecordDataset(path)`\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
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-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
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-    },
-    {
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-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "83370a16f0784669ac8093e09764b616",
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
       "Before preprocessing:  \n",
       "dtype:  <dtype: 'uint8'>  Min: 0 Max: 255\n",
       "After preprocessing:  \n",
@@ -1182,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 19,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1197,7 +638,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input-z -- shape:    (32, 20)\n",
+      "input-z -- shape: (32, 20)\n",
       "input-real -- shape: (32, 784)\n",
       "Output of G -- shape: (32, 784)\n",
       "Disc. (real) -- shape: (32, 1)\n",
@@ -1208,7 +649,7 @@
    "source": [
     "mnist_trainset = mnist_trainset.batch(32, drop_remainder=True)\n",
     "input_z, input_real = next(iter(mnist_trainset))\n",
-    "print('input-z -- shape:   ', input_z.shape)\n",
+    "print('input-z -- shape:', input_z.shape)\n",
     "print('input-real -- shape:', input_real.shape)\n",
     "\n",
     "g_output = gen_model(input_z)\n",
@@ -1217,7 +658,7 @@
     "d_logits_real = disc_model(input_real)\n",
     "d_logits_fake = disc_model(g_output)\n",
     "print('Disc. (real) -- shape:', d_logits_real.shape)\n",
-    "print('Disc. (fake) -- shape:', d_logits_fake.shape)\n"
+    "print('Disc. (fake) -- shape:', d_logits_fake.shape)"
    ]
   },
   {
@@ -1229,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 20,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1279,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 21,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1294,111 +735,119 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch   1 | ET 0.88 min | Avg Losses >> G/D 2.9594/0.2843 [D-Real: 0.0306 D-Fake: 0.2537]\n",
-      "Epoch   2 | ET 1.77 min | Avg Losses >> G/D 5.2096/0.3193 [D-Real: 0.1002 D-Fake: 0.2191]\n",
-      "Epoch   3 | ET 2.65 min | Avg Losses >> G/D 3.3892/0.6756 [D-Real: 0.3020 D-Fake: 0.3736]\n",
-      "Epoch   4 | ET 3.52 min | Avg Losses >> G/D 1.8860/0.9579 [D-Real: 0.4869 D-Fake: 0.4709]\n",
-      "Epoch   5 | ET 4.40 min | Avg Losses >> G/D 1.9762/0.8601 [D-Real: 0.4551 D-Fake: 0.4050]\n",
-      "Epoch   6 | ET 5.28 min | Avg Losses >> G/D 1.8806/0.8778 [D-Real: 0.4712 D-Fake: 0.4066]\n",
-      "Epoch   7 | ET 6.15 min | Avg Losses >> G/D 1.5819/0.9651 [D-Real: 0.5262 D-Fake: 0.4388]\n",
-      "Epoch   8 | ET 7.03 min | Avg Losses >> G/D 1.5399/0.9792 [D-Real: 0.5409 D-Fake: 0.4383]\n",
-      "Epoch   9 | ET 7.93 min | Avg Losses >> G/D 1.5019/1.0253 [D-Real: 0.5492 D-Fake: 0.4761]\n",
-      "Epoch  10 | ET 8.81 min | Avg Losses >> G/D 1.3863/1.0697 [D-Real: 0.5762 D-Fake: 0.4935]\n",
-      "Epoch  11 | ET 9.70 min | Avg Losses >> G/D 1.3803/1.0716 [D-Real: 0.5765 D-Fake: 0.4951]\n",
-      "Epoch  12 | ET 10.58 min | Avg Losses >> G/D 1.2668/1.1083 [D-Real: 0.5854 D-Fake: 0.5229]\n",
-      "Epoch  13 | ET 11.46 min | Avg Losses >> G/D 1.2761/1.1257 [D-Real: 0.5892 D-Fake: 0.5365]\n",
-      "Epoch  14 | ET 12.33 min | Avg Losses >> G/D 1.2746/1.1479 [D-Real: 0.5969 D-Fake: 0.5510]\n",
-      "Epoch  15 | ET 13.22 min | Avg Losses >> G/D 1.1949/1.1807 [D-Real: 0.6099 D-Fake: 0.5708]\n",
-      "Epoch  16 | ET 14.10 min | Avg Losses >> G/D 1.1192/1.1956 [D-Real: 0.6200 D-Fake: 0.5756]\n",
-      "Epoch  17 | ET 14.99 min | Avg Losses >> G/D 1.2309/1.1850 [D-Real: 0.6067 D-Fake: 0.5783]\n",
-      "Epoch  18 | ET 15.87 min | Avg Losses >> G/D 1.1278/1.1909 [D-Real: 0.6187 D-Fake: 0.5722]\n",
-      "Epoch  19 | ET 16.76 min | Avg Losses >> G/D 1.1365/1.1987 [D-Real: 0.6168 D-Fake: 0.5819]\n",
-      "Epoch  20 | ET 17.65 min | Avg Losses >> G/D 1.1734/1.1800 [D-Real: 0.6070 D-Fake: 0.5729]\n",
-      "Epoch  21 | ET 18.54 min | Avg Losses >> G/D 1.0919/1.2120 [D-Real: 0.6220 D-Fake: 0.5900]\n",
-      "Epoch  22 | ET 19.44 min | Avg Losses >> G/D 1.1372/1.1761 [D-Real: 0.6065 D-Fake: 0.5696]\n",
-      "Epoch  23 | ET 20.32 min | Avg Losses >> G/D 1.1275/1.1725 [D-Real: 0.6035 D-Fake: 0.5690]\n",
-      "Epoch  24 | ET 21.22 min | Avg Losses >> G/D 1.0793/1.1977 [D-Real: 0.6114 D-Fake: 0.5863]\n",
-      "Epoch  25 | ET 22.11 min | Avg Losses >> G/D 1.1034/1.1953 [D-Real: 0.6102 D-Fake: 0.5851]\n",
-      "Epoch  26 | ET 23.00 min | Avg Losses >> G/D 1.0347/1.2466 [D-Real: 0.6328 D-Fake: 0.6138]\n",
-      "Epoch  27 | ET 23.91 min | Avg Losses >> G/D 1.0255/1.2596 [D-Real: 0.6350 D-Fake: 0.6245]\n",
-      "Epoch  28 | ET 24.80 min | Avg Losses >> G/D 1.1072/1.2307 [D-Real: 0.6203 D-Fake: 0.6104]\n",
-      "Epoch  29 | ET 25.70 min | Avg Losses >> G/D 0.9715/1.2571 [D-Real: 0.6397 D-Fake: 0.6174]\n",
-      "Epoch  30 | ET 26.60 min | Avg Losses >> G/D 0.9633/1.2690 [D-Real: 0.6419 D-Fake: 0.6272]\n",
-      "Epoch  31 | ET 27.50 min | Avg Losses >> G/D 1.0649/1.2372 [D-Real: 0.6254 D-Fake: 0.6118]\n",
-      "Epoch  32 | ET 28.39 min | Avg Losses >> G/D 1.0405/1.2371 [D-Real: 0.6293 D-Fake: 0.6078]\n",
-      "Epoch  33 | ET 29.29 min | Avg Losses >> G/D 1.0068/1.2492 [D-Real: 0.6340 D-Fake: 0.6152]\n",
-      "Epoch  34 | ET 30.17 min | Avg Losses >> G/D 1.0046/1.2469 [D-Real: 0.6307 D-Fake: 0.6162]\n",
-      "Epoch  35 | ET 31.06 min | Avg Losses >> G/D 1.0378/1.2389 [D-Real: 0.6255 D-Fake: 0.6133]\n",
-      "Epoch  36 | ET 31.94 min | Avg Losses >> G/D 1.0345/1.2360 [D-Real: 0.6263 D-Fake: 0.6097]\n",
-      "Epoch  37 | ET 32.83 min | Avg Losses >> G/D 0.9831/1.2645 [D-Real: 0.6402 D-Fake: 0.6243]\n",
-      "Epoch  38 | ET 33.72 min | Avg Losses >> G/D 1.0033/1.2622 [D-Real: 0.6361 D-Fake: 0.6261]\n",
-      "Epoch  39 | ET 34.61 min | Avg Losses >> G/D 0.9782/1.2765 [D-Real: 0.6426 D-Fake: 0.6339]\n",
-      "Epoch  40 | ET 35.50 min | Avg Losses >> G/D 0.9908/1.2612 [D-Real: 0.6369 D-Fake: 0.6243]\n",
-      "Epoch  41 | ET 36.38 min | Avg Losses >> G/D 0.9704/1.2702 [D-Real: 0.6401 D-Fake: 0.6302]\n",
-      "Epoch  42 | ET 37.27 min | Avg Losses >> G/D 1.0068/1.2753 [D-Real: 0.6416 D-Fake: 0.6337]\n",
-      "Epoch  43 | ET 38.16 min | Avg Losses >> G/D 0.9727/1.2746 [D-Real: 0.6431 D-Fake: 0.6315]\n",
-      "Epoch  44 | ET 39.06 min | Avg Losses >> G/D 0.9575/1.2734 [D-Real: 0.6408 D-Fake: 0.6326]\n",
-      "Epoch  45 | ET 39.95 min | Avg Losses >> G/D 0.9671/1.2898 [D-Real: 0.6481 D-Fake: 0.6417]\n",
-      "Epoch  46 | ET 40.84 min | Avg Losses >> G/D 0.9479/1.2947 [D-Real: 0.6500 D-Fake: 0.6447]\n",
-      "Epoch  47 | ET 41.72 min | Avg Losses >> G/D 0.9699/1.2964 [D-Real: 0.6508 D-Fake: 0.6455]\n",
-      "Epoch  48 | ET 42.61 min | Avg Losses >> G/D 0.9511/1.2938 [D-Real: 0.6523 D-Fake: 0.6416]\n",
-      "Epoch  49 | ET 43.48 min | Avg Losses >> G/D 0.9733/1.2775 [D-Real: 0.6428 D-Fake: 0.6347]\n",
-      "Epoch  50 | ET 44.36 min | Avg Losses >> G/D 0.9831/1.2877 [D-Real: 0.6466 D-Fake: 0.6412]\n",
-      "Epoch  51 | ET 45.24 min | Avg Losses >> G/D 0.9500/1.2959 [D-Real: 0.6512 D-Fake: 0.6447]\n",
-      "Epoch  52 | ET 46.11 min | Avg Losses >> G/D 0.9210/1.3000 [D-Real: 0.6550 D-Fake: 0.6450]\n",
-      "Epoch  53 | ET 46.99 min | Avg Losses >> G/D 0.9236/1.3071 [D-Real: 0.6539 D-Fake: 0.6531]\n",
-      "Epoch  54 | ET 47.86 min | Avg Losses >> G/D 0.9528/1.3060 [D-Real: 0.6533 D-Fake: 0.6527]\n",
-      "Epoch  55 | ET 48.75 min | Avg Losses >> G/D 0.9319/1.3040 [D-Real: 0.6556 D-Fake: 0.6484]\n",
-      "Epoch  56 | ET 49.63 min | Avg Losses >> G/D 0.9739/1.2916 [D-Real: 0.6487 D-Fake: 0.6429]\n",
-      "Epoch  57 | ET 50.52 min | Avg Losses >> G/D 0.9298/1.3057 [D-Real: 0.6584 D-Fake: 0.6473]\n",
-      "Epoch  58 | ET 51.39 min | Avg Losses >> G/D 0.9265/1.3041 [D-Real: 0.6548 D-Fake: 0.6493]\n",
-      "Epoch  59 | ET 52.26 min | Avg Losses >> G/D 0.9911/1.2862 [D-Real: 0.6468 D-Fake: 0.6393]\n",
-      "Epoch  60 | ET 53.14 min | Avg Losses >> G/D 0.9762/1.2940 [D-Real: 0.6491 D-Fake: 0.6450]\n",
-      "Epoch  61 | ET 54.02 min | Avg Losses >> G/D 0.9179/1.3109 [D-Real: 0.6572 D-Fake: 0.6537]\n",
-      "Epoch  62 | ET 54.90 min | Avg Losses >> G/D 0.8989/1.3089 [D-Real: 0.6562 D-Fake: 0.6528]\n",
-      "Epoch  63 | ET 55.78 min | Avg Losses >> G/D 0.9401/1.3036 [D-Real: 0.6535 D-Fake: 0.6500]\n",
-      "Epoch  64 | ET 56.66 min | Avg Losses >> G/D 0.9147/1.3114 [D-Real: 0.6578 D-Fake: 0.6536]\n",
-      "Epoch  65 | ET 57.53 min | Avg Losses >> G/D 0.9435/1.2985 [D-Real: 0.6513 D-Fake: 0.6472]\n",
-      "Epoch  66 | ET 58.40 min | Avg Losses >> G/D 0.9098/1.3060 [D-Real: 0.6550 D-Fake: 0.6510]\n",
-      "Epoch  67 | ET 59.28 min | Avg Losses >> G/D 0.8940/1.3126 [D-Real: 0.6597 D-Fake: 0.6529]\n",
-      "Epoch  68 | ET 60.18 min | Avg Losses >> G/D 0.9312/1.3029 [D-Real: 0.6546 D-Fake: 0.6484]\n",
-      "Epoch  69 | ET 61.05 min | Avg Losses >> G/D 0.9417/1.3003 [D-Real: 0.6519 D-Fake: 0.6484]\n",
-      "Epoch  70 | ET 61.93 min | Avg Losses >> G/D 0.9001/1.3132 [D-Real: 0.6594 D-Fake: 0.6538]\n",
-      "Epoch  71 | ET 62.80 min | Avg Losses >> G/D 0.9156/1.3111 [D-Real: 0.6584 D-Fake: 0.6527]\n",
-      "Epoch  72 | ET 63.68 min | Avg Losses >> G/D 0.9449/1.3060 [D-Real: 0.6547 D-Fake: 0.6513]\n",
-      "Epoch  73 | ET 64.55 min | Avg Losses >> G/D 0.9091/1.3022 [D-Real: 0.6537 D-Fake: 0.6485]\n",
-      "Epoch  74 | ET 65.44 min | Avg Losses >> G/D 0.8866/1.3179 [D-Real: 0.6629 D-Fake: 0.6550]\n",
-      "Epoch  75 | ET 66.31 min | Avg Losses >> G/D 0.9664/1.2913 [D-Real: 0.6485 D-Fake: 0.6427]\n",
-      "Epoch  76 | ET 67.19 min | Avg Losses >> G/D 0.9493/1.3029 [D-Real: 0.6560 D-Fake: 0.6469]\n",
-      "Epoch  77 | ET 68.06 min | Avg Losses >> G/D 0.9123/1.3062 [D-Real: 0.6554 D-Fake: 0.6508]\n",
-      "Epoch  78 | ET 68.92 min | Avg Losses >> G/D 0.9136/1.3196 [D-Real: 0.6617 D-Fake: 0.6579]\n",
-      "Epoch  79 | ET 69.80 min | Avg Losses >> G/D 0.9175/1.3117 [D-Real: 0.6577 D-Fake: 0.6540]\n",
-      "Epoch  80 | ET 70.68 min | Avg Losses >> G/D 0.9420/1.3076 [D-Real: 0.6547 D-Fake: 0.6529]\n",
-      "Epoch  81 | ET 71.55 min | Avg Losses >> G/D 0.9363/1.3001 [D-Real: 0.6516 D-Fake: 0.6485]\n",
-      "Epoch  82 | ET 72.43 min | Avg Losses >> G/D 0.9087/1.3111 [D-Real: 0.6590 D-Fake: 0.6521]\n",
-      "Epoch  83 | ET 73.30 min | Avg Losses >> G/D 0.9177/1.3179 [D-Real: 0.6598 D-Fake: 0.6581]\n",
-      "Epoch  84 | ET 74.18 min | Avg Losses >> G/D 0.9163/1.3150 [D-Real: 0.6598 D-Fake: 0.6552]\n",
-      "Epoch  85 | ET 75.08 min | Avg Losses >> G/D 0.9207/1.3149 [D-Real: 0.6568 D-Fake: 0.6581]\n",
-      "Epoch  86 | ET 75.96 min | Avg Losses >> G/D 0.9189/1.3168 [D-Real: 0.6608 D-Fake: 0.6561]\n",
-      "Epoch  87 | ET 76.85 min | Avg Losses >> G/D 0.9022/1.3211 [D-Real: 0.6623 D-Fake: 0.6587]\n",
-      "Epoch  88 | ET 77.73 min | Avg Losses >> G/D 0.9088/1.3058 [D-Real: 0.6546 D-Fake: 0.6512]\n",
-      "Epoch  89 | ET 78.63 min | Avg Losses >> G/D 0.9098/1.3084 [D-Real: 0.6576 D-Fake: 0.6508]\n",
-      "Epoch  90 | ET 79.52 min | Avg Losses >> G/D 0.9320/1.3070 [D-Real: 0.6543 D-Fake: 0.6527]\n",
-      "Epoch  91 | ET 80.41 min | Avg Losses >> G/D 0.9126/1.3155 [D-Real: 0.6603 D-Fake: 0.6552]\n",
-      "Epoch  92 | ET 81.30 min | Avg Losses >> G/D 0.9324/1.3080 [D-Real: 0.6578 D-Fake: 0.6502]\n",
-      "Epoch  93 | ET 82.17 min | Avg Losses >> G/D 0.8925/1.3137 [D-Real: 0.6605 D-Fake: 0.6532]\n",
-      "Epoch  94 | ET 83.04 min | Avg Losses >> G/D 0.9029/1.3153 [D-Real: 0.6581 D-Fake: 0.6571]\n",
-      "Epoch  95 | ET 83.91 min | Avg Losses >> G/D 0.9381/1.3141 [D-Real: 0.6588 D-Fake: 0.6553]\n",
-      "Epoch  96 | ET 84.76 min | Avg Losses >> G/D 0.9071/1.3126 [D-Real: 0.6580 D-Fake: 0.6546]\n",
-      "Epoch  97 | ET 85.65 min | Avg Losses >> G/D 0.8900/1.3217 [D-Real: 0.6657 D-Fake: 0.6560]\n",
-      "Epoch  98 | ET 86.52 min | Avg Losses >> G/D 0.9522/1.3152 [D-Real: 0.6574 D-Fake: 0.6578]\n",
-      "Epoch  99 | ET 87.39 min | Avg Losses >> G/D 0.8981/1.3073 [D-Real: 0.6575 D-Fake: 0.6499]\n",
-      "Epoch 100 | ET 88.25 min | Avg Losses >> G/D 0.8909/1.3262 [D-Real: 0.6655 D-Fake: 0.6607]\n"
+      "Epoch 001 | ET 0.57 min | Avg Losses >> G/D 2.9623/0.2842 [D-Real: 0.0306 D-Fake: 0.2536]\n",
+      "Epoch 002 | ET 1.14 min | Avg Losses >> G/D 5.2725/0.3102 [D-Real: 0.0964 D-Fake: 0.2138]\n",
+      "Epoch 003 | ET 1.69 min | Avg Losses >> G/D 3.3026/0.6801 [D-Real: 0.3028 D-Fake: 0.3773]\n",
+      "Epoch 004 | ET 2.25 min | Avg Losses >> G/D 1.9424/0.9151 [D-Real: 0.4656 D-Fake: 0.4495]\n",
+      "Epoch 005 | ET 2.82 min | Avg Losses >> G/D 2.0693/0.8153 [D-Real: 0.4439 D-Fake: 0.3713]\n",
+      "Epoch 006 | ET 3.39 min | Avg Losses >> G/D 1.7809/0.9067 [D-Real: 0.4936 D-Fake: 0.4131]\n",
+      "Epoch 007 | ET 3.95 min | Avg Losses >> G/D 1.5657/0.9922 [D-Real: 0.5379 D-Fake: 0.4543]\n",
+      "Epoch 008 | ET 4.53 min | Avg Losses >> G/D 1.5014/1.0448 [D-Real: 0.5500 D-Fake: 0.4948]\n",
+      "Epoch 009 | ET 5.09 min | Avg Losses >> G/D 1.4315/1.0260 [D-Real: 0.5630 D-Fake: 0.4631]\n",
+      "Epoch 010 | ET 5.66 min | Avg Losses >> G/D 1.4287/1.0447 [D-Real: 0.5596 D-Fake: 0.4851]\n",
+      "Epoch 011 | ET 6.23 min | Avg Losses >> G/D 1.3619/1.0489 [D-Real: 0.5665 D-Fake: 0.4824]\n",
+      "Epoch 012 | ET 6.81 min | Avg Losses >> G/D 1.2719/1.1121 [D-Real: 0.5904 D-Fake: 0.5218]\n",
+      "Epoch 013 | ET 7.36 min | Avg Losses >> G/D 1.3408/1.0927 [D-Real: 0.5773 D-Fake: 0.5154]\n",
+      "Epoch 014 | ET 7.93 min | Avg Losses >> G/D 1.2135/1.1812 [D-Real: 0.6079 D-Fake: 0.5732]\n",
+      "Epoch 015 | ET 8.49 min | Avg Losses >> G/D 1.1733/1.1606 [D-Real: 0.6084 D-Fake: 0.5522]\n",
+      "Epoch 016 | ET 9.06 min | Avg Losses >> G/D 1.2356/1.1599 [D-Real: 0.5962 D-Fake: 0.5637]\n",
+      "Epoch 017 | ET 9.63 min | Avg Losses >> G/D 1.1220/1.1934 [D-Real: 0.6149 D-Fake: 0.5784]\n",
+      "Epoch 018 | ET 10.19 min | Avg Losses >> G/D 1.1029/1.1966 [D-Real: 0.6175 D-Fake: 0.5791]\n",
+      "Epoch 019 | ET 10.75 min | Avg Losses >> G/D 1.1922/1.1914 [D-Real: 0.6085 D-Fake: 0.5829]\n",
+      "Epoch 020 | ET 11.32 min | Avg Losses >> G/D 1.0598/1.2376 [D-Real: 0.6312 D-Fake: 0.6063]\n",
+      "Epoch 021 | ET 11.90 min | Avg Losses >> G/D 1.0602/1.2534 [D-Real: 0.6338 D-Fake: 0.6196]\n",
+      "Epoch 022 | ET 12.47 min | Avg Losses >> G/D 1.1103/1.2108 [D-Real: 0.6203 D-Fake: 0.5905]\n",
+      "Epoch 023 | ET 13.03 min | Avg Losses >> G/D 1.0860/1.2048 [D-Real: 0.6197 D-Fake: 0.5850]\n",
+      "Epoch 024 | ET 13.59 min | Avg Losses >> G/D 1.0497/1.2167 [D-Real: 0.6236 D-Fake: 0.5931]\n",
+      "Epoch 025 | ET 14.16 min | Avg Losses >> G/D 1.0683/1.2279 [D-Real: 0.6223 D-Fake: 0.6056]\n",
+      "Epoch 026 | ET 14.73 min | Avg Losses >> G/D 1.0891/1.2173 [D-Real: 0.6175 D-Fake: 0.5997]\n",
+      "Epoch 027 | ET 15.28 min | Avg Losses >> G/D 1.0197/1.2427 [D-Real: 0.6333 D-Fake: 0.6093]\n",
+      "Epoch 028 | ET 15.85 min | Avg Losses >> G/D 1.0286/1.2659 [D-Real: 0.6391 D-Fake: 0.6268]\n",
+      "Epoch 029 | ET 16.42 min | Avg Losses >> G/D 1.1130/1.2365 [D-Real: 0.6223 D-Fake: 0.6142]\n",
+      "Epoch 030 | ET 16.99 min | Avg Losses >> G/D 1.0242/1.2239 [D-Real: 0.6249 D-Fake: 0.5990]\n",
+      "Epoch 031 | ET 17.55 min | Avg Losses >> G/D 0.9946/1.2412 [D-Real: 0.6308 D-Fake: 0.6104]\n",
+      "Epoch 032 | ET 18.10 min | Avg Losses >> G/D 1.0321/1.2538 [D-Real: 0.6317 D-Fake: 0.6221]\n",
+      "Epoch 033 | ET 18.67 min | Avg Losses >> G/D 1.0685/1.2362 [D-Real: 0.6260 D-Fake: 0.6101]\n",
+      "Epoch 034 | ET 19.22 min | Avg Losses >> G/D 1.0251/1.2544 [D-Real: 0.6325 D-Fake: 0.6219]\n",
+      "Epoch 035 | ET 19.79 min | Avg Losses >> G/D 0.9702/1.2580 [D-Real: 0.6382 D-Fake: 0.6198]\n",
+      "Epoch 036 | ET 20.35 min | Avg Losses >> G/D 0.9914/1.2649 [D-Real: 0.6390 D-Fake: 0.6260]\n",
+      "Epoch 037 | ET 20.92 min | Avg Losses >> G/D 1.0055/1.2693 [D-Real: 0.6386 D-Fake: 0.6306]\n",
+      "Epoch 038 | ET 21.51 min | Avg Losses >> G/D 1.0040/1.2812 [D-Real: 0.6419 D-Fake: 0.6392]\n",
+      "Epoch 039 | ET 22.06 min | Avg Losses >> G/D 0.9891/1.2725 [D-Real: 0.6401 D-Fake: 0.6324]\n",
+      "Epoch 040 | ET 22.63 min | Avg Losses >> G/D 0.9616/1.2864 [D-Real: 0.6491 D-Fake: 0.6373]\n",
+      "Epoch 041 | ET 23.18 min | Avg Losses >> G/D 0.9690/1.2788 [D-Real: 0.6436 D-Fake: 0.6351]\n",
+      "Epoch 042 | ET 23.74 min | Avg Losses >> G/D 0.9848/1.2739 [D-Real: 0.6417 D-Fake: 0.6322]\n",
+      "Epoch 043 | ET 24.29 min | Avg Losses >> G/D 1.0114/1.2738 [D-Real: 0.6397 D-Fake: 0.6341]\n",
+      "Epoch 044 | ET 24.86 min | Avg Losses >> G/D 0.9466/1.2930 [D-Real: 0.6511 D-Fake: 0.6419]\n",
+      "Epoch 045 | ET 25.41 min | Avg Losses >> G/D 0.9361/1.3058 [D-Real: 0.6562 D-Fake: 0.6496]\n",
+      "Epoch 046 | ET 25.97 min | Avg Losses >> G/D 0.9592/1.2875 [D-Real: 0.6483 D-Fake: 0.6392]\n",
+      "Epoch 047 | ET 26.53 min | Avg Losses >> G/D 1.0068/1.2743 [D-Real: 0.6407 D-Fake: 0.6336]\n",
+      "Epoch 048 | ET 27.10 min | Avg Losses >> G/D 0.9689/1.2823 [D-Real: 0.6458 D-Fake: 0.6365]\n",
+      "Epoch 049 | ET 27.65 min | Avg Losses >> G/D 0.9443/1.2899 [D-Real: 0.6491 D-Fake: 0.6408]\n",
+      "Epoch 050 | ET 28.23 min | Avg Losses >> G/D 0.9471/1.2870 [D-Real: 0.6480 D-Fake: 0.6390]\n",
+      "Epoch 051 | ET 28.79 min | Avg Losses >> G/D 0.9411/1.2829 [D-Real: 0.6481 D-Fake: 0.6348]\n",
+      "Epoch 052 | ET 29.35 min | Avg Losses >> G/D 0.9696/1.2895 [D-Real: 0.6476 D-Fake: 0.6419]\n",
+      "Epoch 053 | ET 29.93 min | Avg Losses >> G/D 0.9756/1.2875 [D-Real: 0.6478 D-Fake: 0.6397]\n",
+      "Epoch 054 | ET 30.48 min | Avg Losses >> G/D 0.9076/1.3094 [D-Real: 0.6579 D-Fake: 0.6515]\n",
+      "Epoch 055 | ET 31.04 min | Avg Losses >> G/D 0.9347/1.2970 [D-Real: 0.6513 D-Fake: 0.6457]\n",
+      "Epoch 056 | ET 31.61 min | Avg Losses >> G/D 0.9921/1.2795 [D-Real: 0.6445 D-Fake: 0.6349]\n",
+      "Epoch 057 | ET 32.17 min | Avg Losses >> G/D 0.9234/1.2859 [D-Real: 0.6476 D-Fake: 0.6383]\n",
+      "Epoch 058 | ET 32.74 min | Avg Losses >> G/D 0.9218/1.3023 [D-Real: 0.6545 D-Fake: 0.6477]\n",
+      "Epoch 059 | ET 33.30 min | Avg Losses >> G/D 0.9878/1.2904 [D-Real: 0.6468 D-Fake: 0.6435]\n",
+      "Epoch 060 | ET 33.86 min | Avg Losses >> G/D 0.9035/1.3061 [D-Real: 0.6565 D-Fake: 0.6496]\n",
+      "Epoch 061 | ET 34.42 min | Avg Losses >> G/D 0.9180/1.3007 [D-Real: 0.6528 D-Fake: 0.6479]\n",
+      "Epoch 062 | ET 34.99 min | Avg Losses >> G/D 0.9528/1.2827 [D-Real: 0.6456 D-Fake: 0.6371]\n",
+      "Epoch 063 | ET 35.56 min | Avg Losses >> G/D 0.9221/1.3042 [D-Real: 0.6588 D-Fake: 0.6454]\n",
+      "Epoch 064 | ET 36.11 min | Avg Losses >> G/D 0.9197/1.3041 [D-Real: 0.6546 D-Fake: 0.6495]\n",
+      "Epoch 065 | ET 36.66 min | Avg Losses >> G/D 0.9269/1.3043 [D-Real: 0.6581 D-Fake: 0.6461]\n",
+      "Epoch 066 | ET 37.22 min | Avg Losses >> G/D 0.9599/1.2967 [D-Real: 0.6494 D-Fake: 0.6473]\n",
+      "Epoch 067 | ET 37.79 min | Avg Losses >> G/D 0.9217/1.3013 [D-Real: 0.6546 D-Fake: 0.6467]\n",
+      "Epoch 068 | ET 38.35 min | Avg Losses >> G/D 0.8900/1.3229 [D-Real: 0.6626 D-Fake: 0.6603]\n",
+      "Epoch 069 | ET 38.92 min | Avg Losses >> G/D 0.9653/1.2918 [D-Real: 0.6462 D-Fake: 0.6456]\n",
+      "Epoch 070 | ET 39.49 min | Avg Losses >> G/D 0.9369/1.3004 [D-Real: 0.6540 D-Fake: 0.6464]\n",
+      "Epoch 071 | ET 40.06 min | Avg Losses >> G/D 0.8978/1.3103 [D-Real: 0.6573 D-Fake: 0.6530]\n",
+      "Epoch 072 | ET 40.64 min | Avg Losses >> G/D 0.9030/1.3175 [D-Real: 0.6623 D-Fake: 0.6552]\n",
+      "Epoch 073 | ET 41.20 min | Avg Losses >> G/D 0.9831/1.3025 [D-Real: 0.6523 D-Fake: 0.6502]\n",
+      "Epoch 074 | ET 41.77 min | Avg Losses >> G/D 0.9057/1.3062 [D-Real: 0.6588 D-Fake: 0.6474]\n",
+      "Epoch 075 | ET 42.35 min | Avg Losses >> G/D 0.9089/1.3080 [D-Real: 0.6573 D-Fake: 0.6507]\n",
+      "Epoch 076 | ET 42.92 min | Avg Losses >> G/D 0.9358/1.2964 [D-Real: 0.6512 D-Fake: 0.6451]\n",
+      "Epoch 077 | ET 43.48 min | Avg Losses >> G/D 0.8992/1.3077 [D-Real: 0.6570 D-Fake: 0.6507]\n",
+      "Epoch 078 | ET 44.05 min | Avg Losses >> G/D 0.9329/1.3075 [D-Real: 0.6575 D-Fake: 0.6500]\n",
+      "Epoch 079 | ET 44.60 min | Avg Losses >> G/D 0.9428/1.3039 [D-Real: 0.6537 D-Fake: 0.6502]\n",
+      "Epoch 080 | ET 45.17 min | Avg Losses >> G/D 0.8922/1.3155 [D-Real: 0.6601 D-Fake: 0.6554]\n",
+      "Epoch 081 | ET 45.72 min | Avg Losses >> G/D 0.9155/1.3162 [D-Real: 0.6594 D-Fake: 0.6568]\n",
+      "Epoch 082 | ET 46.27 min | Avg Losses >> G/D 0.9309/1.3090 [D-Real: 0.6557 D-Fake: 0.6533]\n",
+      "Epoch 083 | ET 46.83 min | Avg Losses >> G/D 0.9252/1.3144 [D-Real: 0.6592 D-Fake: 0.6552]\n",
+      "Epoch 084 | ET 47.38 min | Avg Losses >> G/D 0.9327/1.3094 [D-Real: 0.6570 D-Fake: 0.6525]\n",
+      "Epoch 085 | ET 47.93 min | Avg Losses >> G/D 0.8829/1.3179 [D-Real: 0.6623 D-Fake: 0.6556]\n",
+      "Epoch 086 | ET 48.50 min | Avg Losses >> G/D 0.9281/1.3037 [D-Real: 0.6533 D-Fake: 0.6504]\n",
+      "Epoch 087 | ET 49.06 min | Avg Losses >> G/D 0.9422/1.3056 [D-Real: 0.6543 D-Fake: 0.6514]\n",
+      "Epoch 088 | ET 49.63 min | Avg Losses >> G/D 0.9061/1.3163 [D-Real: 0.6606 D-Fake: 0.6557]\n",
+      "Epoch 089 | ET 50.19 min | Avg Losses >> G/D 0.9138/1.3179 [D-Real: 0.6608 D-Fake: 0.6571]\n",
+      "Epoch 090 | ET 50.75 min | Avg Losses >> G/D 0.9168/1.3117 [D-Real: 0.6577 D-Fake: 0.6540]\n",
+      "Epoch 091 | ET 51.32 min | Avg Losses >> G/D 0.9136/1.3244 [D-Real: 0.6627 D-Fake: 0.6617]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 092 | ET 51.89 min | Avg Losses >> G/D 0.9016/1.3206 [D-Real: 0.6622 D-Fake: 0.6584]\n",
+      "Epoch 093 | ET 52.46 min | Avg Losses >> G/D 0.9014/1.3204 [D-Real: 0.6644 D-Fake: 0.6560]\n",
+      "Epoch 094 | ET 53.01 min | Avg Losses >> G/D 0.9268/1.3179 [D-Real: 0.6574 D-Fake: 0.6605]\n",
+      "Epoch 095 | ET 53.55 min | Avg Losses >> G/D 0.9311/1.3149 [D-Real: 0.6601 D-Fake: 0.6548]\n",
+      "Epoch 096 | ET 54.11 min | Avg Losses >> G/D 0.8736/1.3190 [D-Real: 0.6642 D-Fake: 0.6548]\n",
+      "Epoch 097 | ET 54.67 min | Avg Losses >> G/D 0.9130/1.3203 [D-Real: 0.6623 D-Fake: 0.6579]\n",
+      "Epoch 098 | ET 55.24 min | Avg Losses >> G/D 0.9456/1.3104 [D-Real: 0.6562 D-Fake: 0.6542]\n",
+      "Epoch 099 | ET 55.80 min | Avg Losses >> G/D 0.9030/1.3119 [D-Real: 0.6595 D-Fake: 0.6523]\n",
+      "Epoch 100 | ET 56.35 min | Avg Losses >> G/D 0.9064/1.3140 [D-Real: 0.6596 D-Fake: 0.6544]\n"
      ]
     }
    ],
    "source": [
     "import time\n",
+    "\n",
+    "\n",
     "num_epochs = 100\n",
     "batch_size = 64\n",
     "image_size = (28, 28)\n",
@@ -1421,6 +870,7 @@
     "    fixed_z = tf.random.normal(\n",
     "        shape=(batch_size, z_size))\n",
     "\n",
+    "\n",
     "def create_samples(g_model, input_z):\n",
     "    g_output = g_model(input_z, training=False)\n",
     "    images = tf.reshape(g_output, (batch_size, *image_size))    \n",
@@ -1467,7 +917,7 @@
     "            g_output = gen_model(input_z)\n",
     "            d_logits_fake = disc_model(g_output, training=True)\n",
     "            labels_real = tf.ones_like(d_logits_fake)\n",
-    "            g_loss = loss_fn(y_true=labels_real,y_pred=d_logits_fake)\n",
+    "            g_loss = loss_fn(y_true=labels_real, y_pred=d_logits_fake)\n",
     "            \n",
     "        g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)\n",
     "        g_optimizer.apply_gradients(\n",
@@ -1507,18 +957,18 @@
     "    all_losses.append(epoch_losses)\n",
     "    all_d_vals.append(epoch_d_vals)\n",
     "    print(\n",
-    "        'Epoch {:-3d} | ET {:.2f} min | Avg Losses >>'\n",
+    "        'Epoch {:03d} | ET {:.2f} min | Avg Losses >>'\n",
     "        ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'\n",
     "        .format(\n",
     "            epoch, (time.time() - start_time)/60, \n",
     "            *list(np.mean(all_losses[-1], axis=0))))\n",
     "    epoch_samples.append(\n",
-    "        create_samples(gen_model, fixed_z).numpy())\n"
+    "        create_samples(gen_model, fixed_z).numpy())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 22,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -1538,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 23,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1551,13 +1001,13 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Nnz4dgOTkZMLDwzl//jxeXl788MMPPP300/z444/UqVPH4Jg1a9YQExNjiXCN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\nYG0rbDcfqG4apzpKj+opPaqn9LRUPa11z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\n",
       "text/plain": [
        "<Figure size 1152x432 with 4 Axes>"
       ]
      },
      "metadata": {
-      "tags": []
+      "needs_background": "light"
      },
      "output_type": "display_data"
     }
@@ -1615,13 +1065,13 @@
     "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
     "\n",
     "\n",
-    "plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-gan-learning-curve.pdf')\n",
-    "plt.show()\n"
+    "# plt.savefig('images/ch17-gan-learning-curve.pdf')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1634,14 +1084,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Ls3lM6y69PMr0I/mVV15ZUnRTbxf9+/cvZclOt/DYliWrUiylf+qpp0KOlJk7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\nO5TYtX8nnXRSic8888yQY9n9zTffHHLNtZO6Xj9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fW+S7YPTo0eWI0o/RWKHgtAaboPrx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\nf3503ciRIwvvz3b+YcOGRbqTT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u3du3c7PinDoktGyd/tnj17hu12KBKVnpkI4AF43kzx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\nhAMqQgghhJAU+X+DSuztZOPPZgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 720x1008 with 30 Axes>"
       ]
      },
-     "metadata": {
-      "tags": []
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1664,8 +1112,18 @@
     "        image = epoch_samples[e-1][j]\n",
     "        ax.imshow(image, cmap='gray_r')\n",
     "    \n",
-    "plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-vanila-gan-samples.pdf')\n",
-    "plt.show()\n"
+    "#plt.savefig('images/ch17-vanila-gan-samples.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "\n",
+    "----"
    ]
   },
   {
@@ -1683,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1691,20 +1149,13 @@
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook ch17_part1.ipynb to script\n",
-      "[NbConvertApp] Writing 13396 bytes to ch17_part1.py\n"
+      "[NbConvertApp] Writing 13559 bytes to ch17_part1.py\n"
      ]
     }
    ],
    "source": [
     "! python ../.convert_notebook_to_script.py --input ch17_part1.ipynb --output ch17_part1.py"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1729,9 +1180,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/ch17/ch17_part1.py b/ch17/ch17_part1.py
index 6e454c28..3e452ce5 100644
--- a/ch17/ch17_part1.py
+++ b/ch17/ch17_part1.py
@@ -2,7 +2,7 @@
 
 
 import tensorflow as tf
-from google.colab import drive
+#from google.colab import drive
 import tensorflow_datasets as tfds
 import numpy as np
 import matplotlib.pyplot as plt
@@ -15,8 +15,7 @@
 # 
 # Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)
 
-# Chapter 17: Generative Adversarial Networks (part 1/2)
-# =====
+# # Chapter 17: Generative Adversarial Networks (Part 1/2)
 
 # Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
 
@@ -83,13 +82,18 @@
 
 print("GPU Available:", tf.test.is_gpu_available())
 
-device_name = tf.test.gpu_device_name()
+if tf.test.is_gpu_available():
+    device_name = tf.test.gpu_device_name()
+
+else:
+    device_name = 'cpu:0'
+    
 print(device_name)
 
 
 
 
-drive.mount('/content/drive/')
+#drive.mount('/content/drive/')
 
 
 # ## Implementing the generator and the discriminator networks 
@@ -145,7 +149,7 @@ def make_discriminator_network(
 
 image_size = (28, 28)
 z_size = 20
-mode_z = 'uniform' # 'uniform' vs. 'normal'
+mode_z = 'uniform'  # 'uniform' vs. 'normal'
 gen_hidden_layers = 1
 gen_hidden_size = 100
 disc_hidden_layers = 1
@@ -213,7 +217,7 @@ def preprocess(ex, mode='uniform'):
 
 mnist_trainset = mnist_trainset.batch(32, drop_remainder=True)
 input_z, input_real = next(iter(mnist_trainset))
-print('input-z -- shape:   ', input_z.shape)
+print('input-z -- shape:', input_z.shape)
 print('input-real -- shape:', input_real.shape)
 
 g_output = gen_model(input_z)
@@ -250,6 +254,8 @@ def preprocess(ex, mode='uniform'):
 
 
 
+
+
 num_epochs = 100
 batch_size = 64
 image_size = (28, 28)
@@ -272,6 +278,7 @@ def preprocess(ex, mode='uniform'):
     fixed_z = tf.random.normal(
         shape=(batch_size, z_size))
 
+
 def create_samples(g_model, input_z):
     g_output = g_model(input_z, training=False)
     images = tf.reshape(g_output, (batch_size, *image_size))    
@@ -318,7 +325,7 @@ def create_samples(g_model, input_z):
             g_output = gen_model(input_z)
             d_logits_fake = disc_model(g_output, training=True)
             labels_real = tf.ones_like(d_logits_fake)
-            g_loss = loss_fn(y_true=labels_real,y_pred=d_logits_fake)
+            g_loss = loss_fn(y_true=labels_real, y_pred=d_logits_fake)
             
         g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)
         g_optimizer.apply_gradients(
@@ -358,7 +365,7 @@ def create_samples(g_model, input_z):
     all_losses.append(epoch_losses)
     all_d_vals.append(epoch_d_vals)
     print(
-        'Epoch {:-3d} | ET {:.2f} min | Avg Losses >>'
+        'Epoch {:03d} | ET {:.2f} min | Avg Losses >>'
         ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'
         .format(
             epoch, (time.time() - start_time)/60, 
@@ -432,7 +439,7 @@ def create_samples(g_model, input_z):
 ax2.tick_params(axis='both', which='major', labelsize=15)
 
 
-plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-gan-learning-curve.pdf')
+# plt.savefig('images/ch17-gan-learning-curve.pdf')
 plt.show()
 
 
@@ -456,10 +463,17 @@ def create_samples(g_model, input_z):
         image = epoch_samples[e-1][j]
         ax.imshow(image, cmap='gray_r')
     
-plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-vanila-gan-samples.pdf')
+#plt.savefig('images/ch17-vanila-gan-samples.pdf')
 plt.show()
 
 
+# 
+# ----
+
+# 
+# 
+# Readers may ignore the next cell.
+# 
 
 
 
diff --git a/ch17/ch17_part2.ipynb b/ch17/ch17_part2.ipynb
index 90078e00..7e17f1b5 100644
--- a/ch17/ch17_part2.ipynb
+++ b/ch17/ch17_part2.ipynb
@@ -15,8 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Chapter 17: Generative Adversarial Networks (part 2/2)\n",
-    "====="
+    "# Chapter 17: Generative Adversarial Networks (Part 2/2)"
    ]
   },
   {
@@ -38,11 +37,11 @@
       "Sebastian Raschka & Vahid Mirjalili \n",
       "last updated: 2019-11-03 \n",
       "\n",
-      "numpy 1.17.3\n",
-      "scipy 1.3.1\n",
-      "matplotlib 3.1.1\n",
+      "numpy 1.17.2\n",
+      "scipy 1.2.1\n",
+      "matplotlib 3.1.0\n",
       "tensorflow 2.0.0\n",
-      "tensorflow_datasets 1.2.0\n"
+      "tensorflow_datasets 1.3.0\n"
      ]
     }
    ],
@@ -223,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 8,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -233,25 +232,14 @@
     "id": "TDcU1S783G6q",
     "outputId": "d1daae87-bf9f-450e-9091-96f0830034a9"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[K     |████████████████████████████████| 348.9MB 61kB/s \n",
-      "\u001b[K     |████████████████████████████████| 3.1MB 33.7MB/s \n",
-      "\u001b[K     |████████████████████████████████| 501kB 50.0MB/s \n",
-      "\u001b[?25h"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#! pip install -q tensorflow-gpu==2.0.0-beta1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 9,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -261,19 +249,7 @@
     "id": "XnOWggQN3RbT",
     "outputId": "0bc1af02-7ee7-4d48-d44a-9c20d222ad5a"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
-      "\n",
-      "Enter your authorization code:\n",
-      "··········\n",
-      "Mounted at /content/drive/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#from google.colab import drive\n",
     "#drive.mount('/content/drive/')"
@@ -281,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -296,32 +272,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "GPU Available:  True\n"
+      "2.0.0\n",
+      "GPU Available: True\n",
+      "/device:GPU:0\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'/device:GPU:0'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#import tensorflow as tf\n",
-    "#print(\"GPU Available: \", tf.test.is_gpu_available())\n",
-    "#device_name = tf.test.gpu_device_name()\n",
-    "#device_name"
+    "import tensorflow as tf\n",
+    "print(tf.__version__)\n",
+    "\n",
+    "print(\"GPU Available:\", tf.test.is_gpu_available())\n",
+    "\n",
+    "if tf.test.is_gpu_available():\n",
+    "    device_name = tf.test.gpu_device_name()\n",
+    "\n",
+    "else:\n",
+    "    device_name = 'cpu:0'\n",
+    "    \n",
+    "print(device_name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -331,20 +305,7 @@
     "id": "VXasfQIW3XeR",
     "outputId": "88dae23d-b0b8-4361-94dc-bc01a3ebd317"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2.0.0-beta1'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "import tensorflow_datasets as tfds\n",
@@ -355,18 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "69fiIu-g3Z74"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 12,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -440,12 +390,12 @@
     "    \n",
     "    model.add(tf.keras.layers.Reshape((1,)))\n",
     "    \n",
-    "    return model\n"
+    "    return model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 13,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -550,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -560,7 +510,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 14,
      "metadata": {
       "image/png": {
        "width": 700
@@ -575,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -585,7 +535,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {
       "image/png": {
        "width": 800
@@ -621,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 16,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -645,545 +595,8 @@
     "id": "-iAGk1Ta6xmQ",
     "outputId": "5d982155-11dd-47ca-b4a8-ca7fac6be686"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/1.0.0...\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "315dbd23d9184248830c58a6892a880d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2a9fa2d95b3b4c41b2302baeaf15c39c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3eb06c5b8b2f42f1b800c324d55226e4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n",
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n",
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n",
-      "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
-      "  InsecureRequestWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d78b876527fe469581f8ecc5a49a431a",
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-       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e01967a992434cc6bcd2d75515a15787",
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-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=10, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W0824 21:06:17.040940 140562200074112 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use eager execution and: \n",
-      "`tf.data.TFRecordDataset(path)`\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-    {
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-     "text": [
-      "\r",
-      "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "\n",
     "mnist_bldr = tfds.builder('mnist')\n",
     "mnist_bldr.download_and_prepare()\n",
     "mnist = mnist_bldr.as_dataset(shuffle_files=False)\n",
@@ -1194,17 +607,16 @@
     "\n",
     "    image = image*2 - 1.0\n",
     "    if mode == 'uniform':\n",
-    "      input_z = tf.random.uniform(shape=(z_size,),\n",
-    "                                  minval=-1.0, maxval=1.0)\n",
+    "        input_z = tf.random.uniform(shape=(z_size,),\n",
+    "                                    minval=-1.0, maxval=1.0)\n",
     "    elif mode == 'normal':\n",
-    "      input_z = tf.random.normal(shape=(z_size,))\n",
-    "    return input_z, image\n",
-    "\n"
+    "        input_z = tf.random.normal(shape=(z_size,))\n",
+    "    return input_z, image"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 18,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1219,111 +631,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch   1 | ET 1.62 min | Avg Losses >> G/D 11.5878/0.0551 [D-Real: 0.0120 D-Fake: 0.0982]\n",
-      "Epoch   2 | ET 3.16 min | Avg Losses >> G/D 12.7159/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   3 | ET 4.70 min | Avg Losses >> G/D 13.5042/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   4 | ET 6.26 min | Avg Losses >> G/D 13.9042/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   5 | ET 7.80 min | Avg Losses >> G/D 14.7967/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   6 | ET 9.34 min | Avg Losses >> G/D 15.1870/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   7 | ET 10.89 min | Avg Losses >> G/D 15.7337/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   8 | ET 12.43 min | Avg Losses >> G/D 16.3247/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch   9 | ET 13.97 min | Avg Losses >> G/D 16.6955/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  10 | ET 15.52 min | Avg Losses >> G/D 17.1411/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  11 | ET 17.06 min | Avg Losses >> G/D 17.4700/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  12 | ET 18.60 min | Avg Losses >> G/D 18.1582/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  13 | ET 20.14 min | Avg Losses >> G/D 18.4224/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  14 | ET 21.68 min | Avg Losses >> G/D 19.0358/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  15 | ET 23.22 min | Avg Losses >> G/D 19.5515/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  16 | ET 24.75 min | Avg Losses >> G/D 20.0417/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  17 | ET 26.29 min | Avg Losses >> G/D 20.4726/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  18 | ET 27.83 min | Avg Losses >> G/D 20.8334/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  19 | ET 29.36 min | Avg Losses >> G/D 21.4407/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  20 | ET 30.89 min | Avg Losses >> G/D 21.8508/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  21 | ET 32.44 min | Avg Losses >> G/D 22.2526/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  22 | ET 33.97 min | Avg Losses >> G/D 22.6377/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  23 | ET 35.52 min | Avg Losses >> G/D 23.0690/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  24 | ET 37.07 min | Avg Losses >> G/D 23.4874/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  25 | ET 38.61 min | Avg Losses >> G/D 23.7815/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  26 | ET 40.14 min | Avg Losses >> G/D 22.6020/0.1138 [D-Real: 0.1041 D-Fake: 0.1235]\n",
-      "Epoch  27 | ET 41.69 min | Avg Losses >> G/D 16.3304/0.0739 [D-Real: 0.0891 D-Fake: 0.0587]\n",
-      "Epoch  28 | ET 43.23 min | Avg Losses >> G/D 19.2976/0.0260 [D-Real: 0.0267 D-Fake: 0.0254]\n",
-      "Epoch  29 | ET 44.76 min | Avg Losses >> G/D 21.7710/0.0051 [D-Real: 0.0047 D-Fake: 0.0054]\n",
-      "Epoch  30 | ET 46.28 min | Avg Losses >> G/D 22.7175/0.0004 [D-Real: 0.0004 D-Fake: 0.0005]\n",
-      "Epoch  31 | ET 47.82 min | Avg Losses >> G/D 18.0605/0.1172 [D-Real: 0.1144 D-Fake: 0.1200]\n",
-      "Epoch  32 | ET 49.35 min | Avg Losses >> G/D 15.9308/0.0012 [D-Real: 0.0013 D-Fake: 0.0011]\n",
-      "Epoch  33 | ET 50.88 min | Avg Losses >> G/D 20.7703/0.0048 [D-Real: 0.0048 D-Fake: 0.0048]\n",
-      "Epoch  34 | ET 52.42 min | Avg Losses >> G/D 19.8592/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  35 | ET 53.95 min | Avg Losses >> G/D 21.4370/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  36 | ET 55.48 min | Avg Losses >> G/D 21.8310/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  37 | ET 57.02 min | Avg Losses >> G/D 22.2185/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  38 | ET 58.54 min | Avg Losses >> G/D 22.7537/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  39 | ET 60.07 min | Avg Losses >> G/D 23.4858/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  40 | ET 61.60 min | Avg Losses >> G/D 24.0924/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  41 | ET 63.14 min | Avg Losses >> G/D 23.8351/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  42 | ET 64.67 min | Avg Losses >> G/D 24.3796/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  43 | ET 66.20 min | Avg Losses >> G/D 25.0200/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  44 | ET 67.75 min | Avg Losses >> G/D 25.5366/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  45 | ET 69.27 min | Avg Losses >> G/D 25.2527/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  46 | ET 70.80 min | Avg Losses >> G/D 26.1628/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  47 | ET 72.34 min | Avg Losses >> G/D 26.7818/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  48 | ET 73.87 min | Avg Losses >> G/D 27.3121/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  49 | ET 75.40 min | Avg Losses >> G/D 26.8991/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  50 | ET 76.91 min | Avg Losses >> G/D 28.0603/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  51 | ET 78.44 min | Avg Losses >> G/D 28.1691/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  52 | ET 79.97 min | Avg Losses >> G/D 28.4989/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  53 | ET 81.47 min | Avg Losses >> G/D 28.2405/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  54 | ET 83.01 min | Avg Losses >> G/D 29.6009/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  55 | ET 84.52 min | Avg Losses >> G/D 30.1077/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  56 | ET 86.04 min | Avg Losses >> G/D 29.8691/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  57 | ET 87.56 min | Avg Losses >> G/D 30.6936/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  58 | ET 89.08 min | Avg Losses >> G/D 31.0307/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  59 | ET 90.61 min | Avg Losses >> G/D 30.7768/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  60 | ET 92.13 min | Avg Losses >> G/D 31.6255/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  61 | ET 93.66 min | Avg Losses >> G/D 32.1454/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  62 | ET 95.18 min | Avg Losses >> G/D 31.2347/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  63 | ET 96.70 min | Avg Losses >> G/D 33.5185/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  64 | ET 98.23 min | Avg Losses >> G/D 36.4600/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  65 | ET 99.75 min | Avg Losses >> G/D 35.6588/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  66 | ET 101.26 min | Avg Losses >> G/D 35.0426/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  67 | ET 102.79 min | Avg Losses >> G/D 34.5411/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  68 | ET 104.32 min | Avg Losses >> G/D 34.3160/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  69 | ET 105.84 min | Avg Losses >> G/D 33.7519/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  70 | ET 107.36 min | Avg Losses >> G/D 32.0705/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  71 | ET 108.90 min | Avg Losses >> G/D 32.8703/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  72 | ET 110.42 min | Avg Losses >> G/D 33.0637/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  73 | ET 111.94 min | Avg Losses >> G/D 33.3458/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  74 | ET 113.46 min | Avg Losses >> G/D 33.6650/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  75 | ET 114.99 min | Avg Losses >> G/D 33.7407/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  76 | ET 116.52 min | Avg Losses >> G/D 33.7356/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  77 | ET 118.04 min | Avg Losses >> G/D 33.8300/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  78 | ET 119.57 min | Avg Losses >> G/D 34.0158/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  79 | ET 121.09 min | Avg Losses >> G/D 34.1753/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  80 | ET 122.61 min | Avg Losses >> G/D 33.6558/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  81 | ET 124.14 min | Avg Losses >> G/D 33.8060/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  82 | ET 125.66 min | Avg Losses >> G/D 33.8519/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  83 | ET 127.18 min | Avg Losses >> G/D 33.8743/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  84 | ET 128.72 min | Avg Losses >> G/D 33.8756/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  85 | ET 130.25 min | Avg Losses >> G/D 33.8705/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  86 | ET 131.78 min | Avg Losses >> G/D 33.9098/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  87 | ET 133.32 min | Avg Losses >> G/D 33.8838/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  88 | ET 134.86 min | Avg Losses >> G/D 33.9247/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  89 | ET 136.41 min | Avg Losses >> G/D 33.9281/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  90 | ET 137.96 min | Avg Losses >> G/D 33.8812/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  91 | ET 139.50 min | Avg Losses >> G/D 33.7767/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  92 | ET 141.06 min | Avg Losses >> G/D 33.8204/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  93 | ET 142.60 min | Avg Losses >> G/D 33.8720/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  94 | ET 144.14 min | Avg Losses >> G/D 34.0033/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  95 | ET 145.69 min | Avg Losses >> G/D 34.0748/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  96 | ET 147.23 min | Avg Losses >> G/D 33.9154/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  97 | ET 148.78 min | Avg Losses >> G/D 34.0379/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  98 | ET 150.32 min | Avg Losses >> G/D 33.9534/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch  99 | ET 151.86 min | Avg Losses >> G/D 34.0685/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n",
-      "Epoch 100 | ET 153.40 min | Avg Losses >> G/D 34.1505/0.0000 [D-Real: 0.0000 D-Fake: 0.0000]\n"
+      "Epoch 001 | ET 1.78 min | Avg Losses >> G/D 9.1237/0.1301 [D-Real: 0.1152 D-Fake: 0.1450]\n"
+     ]
+    },
+    {
+     "ename": "InvalidArgumentError",
+     "evalue": "In[0] is not a matrix. Instead it has shape [] [Op:MatMul]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-eddf76b7f4b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    121\u001b[0m                   *list(avg_epoch_losses[-1])))\n\u001b[1;32m    122\u001b[0m     epoch_samples.append(create_samples(\n\u001b[0;32m--> 123\u001b[0;31m           gen_model, 8).numpy())\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-18-eddf76b7f4b6>\u001b[0m in \u001b[0;36mcreate_samples\u001b[0;34m(g_model, input_z)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_z\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mg_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_z\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m     \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mimage_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    889\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    890\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    254\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_graph_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSequential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m  \u001b[0;31m# handle the corner case where self.layers is empty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    706\u001b[0m     return self._run_internal_graph(\n\u001b[1;32m    707\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m         convert_kwargs_to_constants=base_layer_utils.call_context().saving)\n\u001b[0m\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py\u001b[0m in \u001b[0;36m_run_internal_graph\u001b[0;34m(self, inputs, training, mask, convert_kwargs_to_constants)\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m           \u001b[0;31m# Compute outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 860\u001b[0;31m           \u001b[0moutput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomputed_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m           \u001b[0;31m# Update tensor_dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    889\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    890\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/core.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   1054\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse_tensor_dense_matmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_math_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmat_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m       \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmat_mul\u001b[0;34m(a, b, transpose_a, transpose_b, name)\u001b[0m\n\u001b[1;32m   6124\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6125\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6126\u001b[0;31m       \u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6127\u001b[0m   \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6128\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mtranspose_a\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
+      "\u001b[0;31mInvalidArgumentError\u001b[0m: In[0] is not a matrix. Instead it has shape [] [Op:MatMul]"
      ]
     }
    ],
    "source": [
     "import time\n",
+    "\n",
+    "\n",
     "num_epochs = 100\n",
     "batch_size = 64\n",
     "image_size = (28, 28)\n",
@@ -1377,7 +712,7 @@
     "\n",
     "avg_epoch_losses = []\n",
     "avg_d_vals = []\n",
-    "epoch_samples = []\n",
+    "epoch_samples = {}\n",
     "\n",
     "start_time = time.time()\n",
     "for epoch in range(1, num_epochs+1):\n",
@@ -1438,12 +773,12 @@
     "        d_probs_fake = tf.reduce_mean(tf.sigmoid(d_logits_fake))\n",
     "        avg_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))        \n",
     "    avg_epoch_losses.append(np.mean(losses, axis=0))\n",
-    "    print('Epoch {:-3d} | ET {:.2f} min | Avg Losses >>'\n",
+    "    print('Epoch {:03d} | ET {:.2f} min | Avg Losses >>'\n",
     "          ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'\n",
     "          .format(epoch, (time.time() - start_time)/60, \n",
     "                  *list(avg_epoch_losses[-1])))\n",
     "    epoch_samples.append(create_samples(\n",
-    "          gen_model, num_samples=8).numpy())\n"
+    "          gen_model, num_samples=8).numpy())"
    ]
   },
   {
@@ -1501,8 +836,8 @@
     "ax.tick_params(axis='both', which='major', labelsize=15)\n",
     "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
     "\n",
-    "#plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-wdcgan-learning-curve.pdf')\n",
-    "plt.show()\n"
+    "#plt.savefig('images/ch17-wdcgan-learning-curve.pdf')\n",
+    "plt.show()"
    ]
   },
   {
@@ -1528,8 +863,15 @@
     "        image = epoch_samples[e-1][j]\n",
     "        ax.imshow(image, cmap='gray_r')\n",
     "    \n",
-    "#plt.savefig('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-wdcgan-samples.pdf')\n",
-    "plt.show()\n"
+    "#plt.savefig('images/ch17-wdcgan-samples.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mode collapse"
    ]
   },
   {
@@ -1537,38 +879,28 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "Image(filename='images/17_16.png', width=600)"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mode collapse"
+    "<br>\n",
+    "<br>\n",
+    "\n",
+    "----"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 12,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "Image(filename='images/17_16.png', width=600)"
+    "\n",
+    "\n",
+    "Readers may ignore the next cell.\n",
+    "\n"
    ]
   },
   {
@@ -1576,7 +908,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "! python ../.convert_notebook_to_script.py --input ch17_part2.ipynb --output ch17_part2.py"
+   ]
   }
  ],
  "metadata": {
@@ -1601,9 +935,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }