diff --git a/ch17/ch17_optional_DCGAN.ipynb b/ch17/ch17_optional_DCGAN.ipynb
deleted file mode 100644
index ed270577..00000000
--- a/ch17/ch17_optional_DCGAN.ipynb
+++ /dev/null
@@ -1,1010 +0,0 @@
-{
- "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 for Synthesizing New Data (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": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sebastian Raschka & Vahid Mirjalili \n",
-      "last updated: 2019-11-06 \n",
-      "\n",
-      "numpy 1.17.2\n",
-      "scipy 1.2.1\n",
-      "matplotlib 3.1.0\n",
-      "tensorflow 2.0.0\n",
-      "tensorflow_datasets 1.3.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext watermark\n",
-    "%watermark -a \"Sebastian Raschka & Vahid Mirjalili\" -u -d -p numpy,scipy,matplotlib,tensorflow,tensorflow_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "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": [],
-   "source": [
-    "## For running on Google-Colab\n",
-    "# ! pip install -q tensorflow-gpu==2.0.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "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": [],
-   "source": [
-    "# from google.colab import drive\n",
-    "# drive.mount('/content/drive/')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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": [],
-   "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": 6,
-   "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": [
-      "2.0.0\n",
-      "GPU Available: True\n",
-      "/device:GPU:0\n"
-     ]
-    }
-   ],
-   "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": "markdown",
-   "metadata": {},
-   "source": [
-    " * **Defining the generator and discriminator networks**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {
-      "image/png": {
-       "width": 700
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/17_12.png', width=700)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_dcgan_generator(\n",
-    "        z_size=100, \n",
-    "        output_size=(28, 28, 1),\n",
-    "        n_filters=64):\n",
-    "    \n",
-    "    hidden_size = (7, 7)\n",
-    "    \n",
-    "    model = tf.keras.Sequential()\n",
-    "    \n",
-    "    # 100 ==> 784 ==> 7x7x64\n",
-    "    model.add(tf.keras.layers.Dense(\n",
-    "        units=n_filters*np.prod(hidden_size), use_bias=False)\n",
-    "    )\n",
-    "    model.add(tf.keras.layers.BatchNormalization())\n",
-    "    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))\n",
-    "\n",
-    "    model.add(tf.keras.layers.Reshape(\n",
-    "        target_shape=(hidden_size[0], hidden_size[1], n_filters))\n",
-    "    )\n",
-    "\n",
-    "    # 7x7x64 ==> 14*14*32\n",
-    "    model.add(tf.keras.layers.Conv2DTranspose(\n",
-    "        filters=n_filters//2, kernel_size=(3, 3), strides=(2, 2),\n",
-    "        padding='same', use_bias=False, activation=None)\n",
-    "    )\n",
-    "    model.add(tf.keras.layers.BatchNormalization())\n",
-    "    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))\n",
-    "    model.add(tf.keras.layers.Dropout(0.5))\n",
-    "    \n",
-    "    # 14x14x32 ==> 28x28x16\n",
-    "    model.add(tf.keras.layers.Conv2DTranspose(\n",
-    "        filters=n_filters//4, kernel_size=(3, 3), strides=(2, 2),\n",
-    "        padding='same', use_bias=False, activation=None)\n",
-    "    )\n",
-    "    model.add(tf.keras.layers.BatchNormalization())\n",
-    "    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))\n",
-    "    model.add(tf.keras.layers.Dropout(0.5))\n",
-    "    \n",
-    "    # 28x28x16 ==> 28x28x8\n",
-    "    model.add(tf.keras.layers.Conv2DTranspose(\n",
-    "        filters=n_filters//8, kernel_size=(3, 3), strides=(1, 1),\n",
-    "        padding='same', use_bias=False, activation=None)\n",
-    "    )\n",
-    "    model.add(tf.keras.layers.BatchNormalization())\n",
-    "    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))\n",
-    "    model.add(tf.keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    # 28x28x8 ==> 28x28x1\n",
-    "    model.add(tf.keras.layers.Conv2DTranspose(\n",
-    "        filters=1, kernel_size=(3, 3), strides=(1, 1),\n",
-    "        padding='same', use_bias=False, activation='tanh')\n",
-    "    )\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense (Dense)                multiple                  62720     \n",
-      "_________________________________________________________________\n",
-      "batch_normalization (BatchNo multiple                  12544     \n",
-      "_________________________________________________________________\n",
-      "leaky_re_lu (LeakyReLU)      multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "reshape (Reshape)            multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_transpose (Conv2DTran multiple                  18432     \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch multiple                  128       \n",
-      "_________________________________________________________________\n",
-      "leaky_re_lu_1 (LeakyReLU)    multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_transpose_1 (Conv2DTr multiple                  4608      \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch multiple                  64        \n",
-      "_________________________________________________________________\n",
-      "leaky_re_lu_2 (LeakyReLU)    multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_transpose_2 (Conv2DTr multiple                  1152      \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch multiple                  32        \n",
-      "_________________________________________________________________\n",
-      "leaky_re_lu_3 (LeakyReLU)    multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_transpose_3 (Conv2DTr multiple                  72        \n",
-      "=================================================================\n",
-      "Total params: 99,752\n",
-      "Trainable params: 93,368\n",
-      "Non-trainable params: 6,384\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "gen_model = make_dcgan_generator()\n",
-    "gen_model.build(input_shape=(None, 20))\n",
-    "gen_model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {
-      "image/png": {
-       "width": 700
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/17_13.png', width=700)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_dcgan_discriminator(\n",
-    "        input_size=(28, 28, 1),\n",
-    "        n_filters=64):\n",
-    "    \n",
-    "    hidden_size = (7, 7)\n",
-    "    \n",
-    "    model = tf.keras.Sequential()\n",
-    "    \n",
-    "    model.add(tf.keras.layers.Reshape(\n",
-    "        target_shape=(input_size[0], input_size[1], input_size[2]))\n",
-    "    )\n",
-    "\n",
-    "    # 7x7x64 ==> 14*14*32\n",
-    "    model.add(tf.keras.layers.Conv2D(\n",
-    "        filters=n_filters//8, kernel_size=(3, 3), strides=(2, 2),\n",
-    "        padding='same', use_bias=False, activation=None)\n",
-    "    )\n",
-    "    model.add(tf.keras.layers.BatchNormalization())\n",
-    "    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))\n",
-    "    model.add(tf.keras.layers.Dropout(0.5))\n",
-    "    \n",
-    "    # 14x14x32 ==> 28x28x16\n",
-    "    model.add(tf.keras.layers.Conv2D(\n",
-    "        filters=n_filters//2, kernel_size=(3, 3), strides=(2, 2),\n",
-    "        padding='same', use_bias=False, activation=None)\n",
-    "    )\n",
-    "    model.add(tf.keras.layers.BatchNormalization())\n",
-    "    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))\n",
-    "    model.add(tf.keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    model.add(tf.keras.layers.Reshape(\n",
-    "        target_shape=(np.prod([input_size[0]//4, input_size[1]//4, n_filters//2]),))\n",
-    "    )\n",
-    "    \n",
-    "    model.add(tf.keras.layers.Dense(\n",
-    "        units=1, use_bias=False)\n",
-    "    )\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "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_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "reshape_1 (Reshape)          multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "conv2d (Conv2D)              multiple                  72        \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch multiple                  32        \n",
-      "_________________________________________________________________\n",
-      "leaky_re_lu_4 (LeakyReLU)    multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            multiple                  2304      \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch multiple                  128       \n",
-      "_________________________________________________________________\n",
-      "leaky_re_lu_5 (LeakyReLU)    multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "reshape_2 (Reshape)          multiple                  0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              multiple                  1568      \n",
-      "=================================================================\n",
-      "Total params: 4,104\n",
-      "Trainable params: 4,024\n",
-      "Non-trainable params: 80\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "disc_model = make_dcgan_discriminator()\n",
-    "disc_model.build(input_shape=(None, 28, 28, 1))\n",
-    "disc_model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* **Loading and preprocessing the data**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "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": [],
-   "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",
-    "    \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\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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",
-    "\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",
-    "mnist_trainset = mnist_trainset.shuffle(10000)\n",
-    "\n",
-    "#mnist_trainset = mnist_trainset.batch(\n",
-    "#    batch_size, drop_remainder=True)\n",
-    "\n",
-    "mnist_trainset = mnist_trainset.batch(\n",
-    "    batch_size, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* **Final Training**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 001 | ET 1.17 min | Avg Losses >> G/D 1.4423/1.2245 [D-Real: 0.6096 D-Fake: 0.6149]\n",
-      "Epoch 002 | ET 2.30 min | Avg Losses >> G/D 1.5769/0.9223 [D-Real: 0.4605 D-Fake: 0.4618]\n",
-      "Epoch 003 | ET 3.44 min | Avg Losses >> G/D 2.2352/0.5379 [D-Real: 0.2684 D-Fake: 0.2696]\n",
-      "Epoch 004 | ET 4.58 min | Avg Losses >> G/D 1.4573/1.0198 [D-Real: 0.5179 D-Fake: 0.5020]\n",
-      "Epoch 005 | ET 5.73 min | Avg Losses >> G/D 1.2001/1.0968 [D-Real: 0.5515 D-Fake: 0.5453]\n",
-      "Epoch 006 | ET 6.86 min | Avg Losses >> G/D 1.0188/1.2351 [D-Real: 0.6220 D-Fake: 0.6131]\n",
-      "Epoch 007 | ET 8.01 min | Avg Losses >> G/D 0.9194/1.3020 [D-Real: 0.6565 D-Fake: 0.6454]\n",
-      "Epoch 008 | ET 9.15 min | Avg Losses >> G/D 0.8732/1.3115 [D-Real: 0.6674 D-Fake: 0.6441]\n",
-      "Epoch 009 | ET 10.30 min | Avg Losses >> G/D 0.8083/1.3442 [D-Real: 0.6789 D-Fake: 0.6653]\n",
-      "Epoch 010 | ET 11.45 min | Avg Losses >> G/D 0.7985/1.3474 [D-Real: 0.6790 D-Fake: 0.6684]\n",
-      "Epoch 011 | ET 12.60 min | Avg Losses >> G/D 0.7744/1.3548 [D-Real: 0.6802 D-Fake: 0.6746]\n",
-      "Epoch 012 | ET 13.73 min | Avg Losses >> G/D 0.7608/1.3628 [D-Real: 0.6901 D-Fake: 0.6727]\n",
-      "Epoch 013 | ET 14.88 min | Avg Losses >> G/D 0.7643/1.3544 [D-Real: 0.6801 D-Fake: 0.6743]\n",
-      "Epoch 014 | ET 16.03 min | Avg Losses >> G/D 0.7413/1.3629 [D-Real: 0.6801 D-Fake: 0.6828]\n",
-      "Epoch 015 | ET 17.16 min | Avg Losses >> G/D 0.7381/1.3694 [D-Real: 0.6876 D-Fake: 0.6818]\n",
-      "Epoch 016 | ET 18.30 min | Avg Losses >> G/D 0.7428/1.3662 [D-Real: 0.6866 D-Fake: 0.6796]\n",
-      "Epoch 017 | ET 19.44 min | Avg Losses >> G/D 0.7345/1.3709 [D-Real: 0.6867 D-Fake: 0.6841]\n",
-      "Epoch 018 | ET 20.59 min | Avg Losses >> G/D 0.7265/1.3703 [D-Real: 0.6817 D-Fake: 0.6887]\n",
-      "Epoch 019 | ET 21.74 min | Avg Losses >> G/D 0.7352/1.3672 [D-Real: 0.6819 D-Fake: 0.6853]\n",
-      "Epoch 020 | ET 22.89 min | Avg Losses >> G/D 0.7174/1.3754 [D-Real: 0.6867 D-Fake: 0.6886]\n",
-      "Epoch 021 | ET 24.03 min | Avg Losses >> G/D 0.7264/1.3731 [D-Real: 0.6866 D-Fake: 0.6866]\n",
-      "Epoch 022 | ET 25.17 min | Avg Losses >> G/D 0.7262/1.3714 [D-Real: 0.6843 D-Fake: 0.6871]\n",
-      "Epoch 023 | ET 26.30 min | Avg Losses >> G/D 0.7128/1.3777 [D-Real: 0.6884 D-Fake: 0.6893]\n",
-      "Epoch 024 | ET 27.44 min | Avg Losses >> G/D 0.7185/1.3762 [D-Real: 0.6900 D-Fake: 0.6862]\n",
-      "Epoch 025 | ET 28.59 min | Avg Losses >> G/D 0.7170/1.3745 [D-Real: 0.6844 D-Fake: 0.6901]\n",
-      "Epoch 026 | ET 29.73 min | Avg Losses >> G/D 0.7170/1.3788 [D-Real: 0.6945 D-Fake: 0.6843]\n",
-      "Epoch 027 | ET 30.88 min | Avg Losses >> G/D 0.7077/1.3786 [D-Real: 0.6868 D-Fake: 0.6918]\n",
-      "Epoch 028 | ET 32.02 min | Avg Losses >> G/D 0.7135/1.3797 [D-Real: 0.6939 D-Fake: 0.6858]\n",
-      "Epoch 029 | ET 33.16 min | Avg Losses >> G/D 0.7091/1.3790 [D-Real: 0.6901 D-Fake: 0.6889]\n",
-      "Epoch 030 | ET 34.30 min | Avg Losses >> G/D 0.7133/1.3763 [D-Real: 0.6873 D-Fake: 0.6891]\n",
-      "Epoch 031 | ET 35.45 min | Avg Losses >> G/D 0.7035/1.3791 [D-Real: 0.6849 D-Fake: 0.6943]\n",
-      "Epoch 032 | ET 36.60 min | Avg Losses >> G/D 0.7056/1.3810 [D-Real: 0.6884 D-Fake: 0.6926]\n",
-      "Epoch 033 | ET 37.74 min | Avg Losses >> G/D 0.7030/1.3815 [D-Real: 0.6911 D-Fake: 0.6904]\n",
-      "Epoch 034 | ET 38.89 min | Avg Losses >> G/D 0.7008/1.3812 [D-Real: 0.6886 D-Fake: 0.6926]\n",
-      "Epoch 035 | ET 40.03 min | Avg Losses >> G/D 0.7035/1.3825 [D-Real: 0.6928 D-Fake: 0.6897]\n",
-      "Epoch 036 | ET 41.17 min | Avg Losses >> G/D 0.6979/1.3822 [D-Real: 0.6864 D-Fake: 0.6959]\n",
-      "Epoch 037 | ET 42.32 min | Avg Losses >> G/D 0.7029/1.3806 [D-Real: 0.6884 D-Fake: 0.6922]\n",
-      "Epoch 038 | ET 43.46 min | Avg Losses >> G/D 0.7034/1.3834 [D-Real: 0.6934 D-Fake: 0.6900]\n",
-      "Epoch 039 | ET 44.60 min | Avg Losses >> G/D 0.7004/1.3830 [D-Real: 0.6913 D-Fake: 0.6917]\n",
-      "Epoch 040 | ET 45.75 min | Avg Losses >> G/D 0.7083/1.3784 [D-Real: 0.6892 D-Fake: 0.6891]\n",
-      "Epoch 041 | ET 46.89 min | Avg Losses >> G/D 0.7068/1.3823 [D-Real: 0.6929 D-Fake: 0.6894]\n",
-      "Epoch 042 | ET 48.03 min | Avg Losses >> G/D 0.6999/1.3820 [D-Real: 0.6892 D-Fake: 0.6928]\n",
-      "Epoch 043 | ET 49.18 min | Avg Losses >> G/D 0.6978/1.3831 [D-Real: 0.6893 D-Fake: 0.6938]\n",
-      "Epoch 044 | ET 50.32 min | Avg Losses >> G/D 0.7251/1.3680 [D-Real: 0.6807 D-Fake: 0.6873]\n",
-      "Epoch 045 | ET 51.47 min | Avg Losses >> G/D 0.6947/1.3837 [D-Real: 0.6872 D-Fake: 0.6965]\n",
-      "Epoch 046 | ET 52.62 min | Avg Losses >> G/D 0.6997/1.3836 [D-Real: 0.6900 D-Fake: 0.6936]\n",
-      "Epoch 047 | ET 53.77 min | Avg Losses >> G/D 0.7068/1.3840 [D-Real: 0.6982 D-Fake: 0.6859]\n",
-      "Epoch 048 | ET 54.91 min | Avg Losses >> G/D 0.6997/1.3836 [D-Real: 0.6899 D-Fake: 0.6937]\n",
-      "Epoch 049 | ET 56.05 min | Avg Losses >> G/D 0.6967/1.3852 [D-Real: 0.6917 D-Fake: 0.6935]\n",
-      "Epoch 050 | ET 57.20 min | Avg Losses >> G/D 0.6986/1.3824 [D-Real: 0.6872 D-Fake: 0.6952]\n",
-      "Epoch 051 | ET 58.34 min | Avg Losses >> G/D 0.7016/1.3818 [D-Real: 0.6899 D-Fake: 0.6919]\n",
-      "Epoch 052 | ET 59.49 min | Avg Losses >> G/D 0.6989/1.3854 [D-Real: 0.6935 D-Fake: 0.6919]\n",
-      "Epoch 053 | ET 60.64 min | Avg Losses >> G/D 0.7013/1.3839 [D-Real: 0.6940 D-Fake: 0.6899]\n",
-      "Epoch 054 | ET 61.78 min | Avg Losses >> G/D 0.7049/1.3853 [D-Real: 0.7007 D-Fake: 0.6846]\n",
-      "Epoch 055 | ET 62.92 min | Avg Losses >> G/D 0.7259/1.3617 [D-Real: 0.6717 D-Fake: 0.6899]\n",
-      "Epoch 056 | ET 64.07 min | Avg Losses >> G/D 0.7057/1.3851 [D-Real: 0.6998 D-Fake: 0.6853]\n",
-      "Epoch 057 | ET 65.20 min | Avg Losses >> G/D 0.7005/1.3853 [D-Real: 0.6961 D-Fake: 0.6892]\n",
-      "Epoch 058 | ET 66.34 min | Avg Losses >> G/D 0.6972/1.3847 [D-Real: 0.6921 D-Fake: 0.6925]\n",
-      "Epoch 059 | ET 67.49 min | Avg Losses >> G/D 0.7129/1.3753 [D-Real: 0.6868 D-Fake: 0.6884]\n",
-      "Epoch 060 | ET 68.64 min | Avg Losses >> G/D 0.7019/1.3858 [D-Real: 0.6976 D-Fake: 0.6882]\n",
-      "Epoch 061 | ET 69.79 min | Avg Losses >> G/D 0.6951/1.3855 [D-Real: 0.6917 D-Fake: 0.6938]\n",
-      "Epoch 062 | ET 70.95 min | Avg Losses >> G/D 0.6983/1.3851 [D-Real: 0.6944 D-Fake: 0.6907]\n",
-      "Epoch 063 | ET 72.10 min | Avg Losses >> G/D 0.6969/1.3857 [D-Real: 0.6935 D-Fake: 0.6922]\n",
-      "Epoch 064 | ET 73.25 min | Avg Losses >> G/D 0.6981/1.3853 [D-Real: 0.6948 D-Fake: 0.6905]\n",
-      "Epoch 065 | ET 74.40 min | Avg Losses >> G/D 0.7005/1.3845 [D-Real: 0.6952 D-Fake: 0.6892]\n",
-      "Epoch 066 | ET 75.56 min | Avg Losses >> G/D 0.7050/1.3830 [D-Real: 0.6959 D-Fake: 0.6871]\n",
-      "Epoch 067 | ET 76.71 min | Avg Losses >> G/D 0.6947/1.3843 [D-Real: 0.6887 D-Fake: 0.6957]\n",
-      "Epoch 068 | ET 77.86 min | Avg Losses >> G/D 0.7003/1.3844 [D-Real: 0.6939 D-Fake: 0.6905]\n",
-      "Epoch 069 | ET 79.01 min | Avg Losses >> G/D 0.6959/1.3851 [D-Real: 0.6917 D-Fake: 0.6933]\n",
-      "Epoch 070 | ET 80.17 min | Avg Losses >> G/D 0.6996/1.3841 [D-Real: 0.6937 D-Fake: 0.6904]\n",
-      "Epoch 071 | ET 81.32 min | Avg Losses >> G/D 0.6971/1.3853 [D-Real: 0.6937 D-Fake: 0.6916]\n",
-      "Epoch 072 | ET 82.47 min | Avg Losses >> G/D 0.7062/1.3841 [D-Real: 0.6992 D-Fake: 0.6849]\n",
-      "Epoch 073 | ET 83.62 min | Avg Losses >> G/D 0.6978/1.3837 [D-Real: 0.6896 D-Fake: 0.6941]\n",
-      "Epoch 074 | ET 84.78 min | Avg Losses >> G/D 0.6973/1.3848 [D-Real: 0.6931 D-Fake: 0.6917]\n",
-      "Epoch 075 | ET 85.94 min | Avg Losses >> G/D 0.6995/1.3847 [D-Real: 0.6945 D-Fake: 0.6902]\n",
-      "Epoch 076 | ET 87.09 min | Avg Losses >> G/D 0.6966/1.3849 [D-Real: 0.6921 D-Fake: 0.6928]\n",
-      "Epoch 077 | ET 88.24 min | Avg Losses >> G/D 0.7004/1.3837 [D-Real: 0.6934 D-Fake: 0.6903]\n",
-      "Epoch 078 | ET 89.39 min | Avg Losses >> G/D 0.6973/1.3851 [D-Real: 0.6924 D-Fake: 0.6927]\n",
-      "Epoch 079 | ET 90.55 min | Avg Losses >> G/D 0.6972/1.3856 [D-Real: 0.6942 D-Fake: 0.6914]\n",
-      "Epoch 080 | ET 91.71 min | Avg Losses >> G/D 0.6942/1.3855 [D-Real: 0.6917 D-Fake: 0.6938]\n",
-      "Epoch 081 | ET 92.86 min | Avg Losses >> G/D 0.6936/1.3848 [D-Real: 0.6889 D-Fake: 0.6959]\n",
-      "Epoch 082 | ET 94.01 min | Avg Losses >> G/D 0.6967/1.3839 [D-Real: 0.6905 D-Fake: 0.6934]\n",
-      "Epoch 083 | ET 95.16 min | Avg Losses >> G/D 0.6999/1.3849 [D-Real: 0.6934 D-Fake: 0.6915]\n",
-      "Epoch 084 | ET 96.32 min | Avg Losses >> G/D 0.6937/1.3835 [D-Real: 0.6871 D-Fake: 0.6964]\n",
-      "Epoch 085 | ET 97.47 min | Avg Losses >> G/D 0.6967/1.3847 [D-Real: 0.6908 D-Fake: 0.6939]\n",
-      "Epoch 086 | ET 98.63 min | Avg Losses >> G/D 0.6954/1.3849 [D-Real: 0.6909 D-Fake: 0.6940]\n",
-      "Epoch 087 | ET 99.78 min | Avg Losses >> G/D 0.7017/1.3851 [D-Real: 0.6972 D-Fake: 0.6879]\n",
-      "Epoch 088 | ET 100.93 min | Avg Losses >> G/D 0.7004/1.3854 [D-Real: 0.6964 D-Fake: 0.6890]\n",
-      "Epoch 089 | ET 102.08 min | Avg Losses >> G/D 0.6973/1.3856 [D-Real: 0.6941 D-Fake: 0.6916]\n",
-      "Epoch 090 | ET 103.21 min | Avg Losses >> G/D 0.6968/1.3852 [D-Real: 0.6932 D-Fake: 0.6920]\n",
-      "Epoch 091 | ET 104.37 min | Avg Losses >> G/D 0.6957/1.3843 [D-Real: 0.6903 D-Fake: 0.6940]\n",
-      "Epoch 092 | ET 105.52 min | Avg Losses >> G/D 0.6986/1.3842 [D-Real: 0.6926 D-Fake: 0.6916]\n",
-      "Epoch 093 | ET 106.68 min | Avg Losses >> G/D 0.7032/1.3860 [D-Real: 0.6996 D-Fake: 0.6863]\n",
-      "Epoch 094 | ET 107.83 min | Avg Losses >> G/D 0.6995/1.3856 [D-Real: 0.6973 D-Fake: 0.6884]\n",
-      "Epoch 095 | ET 108.98 min | Avg Losses >> G/D 0.6969/1.3854 [D-Real: 0.6939 D-Fake: 0.6915]\n",
-      "Epoch 096 | ET 110.14 min | Avg Losses >> G/D 0.6992/1.3852 [D-Real: 0.6958 D-Fake: 0.6893]\n",
-      "Epoch 097 | ET 111.29 min | Avg Losses >> G/D 0.7088/1.3778 [D-Real: 0.6881 D-Fake: 0.6898]\n",
-      "Epoch 098 | ET 112.44 min | Avg Losses >> G/D 0.7067/1.3890 [D-Real: 0.7023 D-Fake: 0.6868]\n",
-      "Epoch 099 | ET 113.59 min | Avg Losses >> G/D 0.6960/1.3856 [D-Real: 0.6938 D-Fake: 0.6918]\n",
-      "Epoch 100 | ET 114.74 min | Avg Losses >> G/D 0.6959/1.3857 [D-Real: 0.6939 D-Fake: 0.6918]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "\n",
-    "# Delete the previously instantiated\n",
-    "# objects that we have defined\n",
-    "# for printing the model summaries\n",
-    "del gen_model \n",
-    "del disc_model\n",
-    "\n",
-    "## Set-up the model\n",
-    "with tf.device(device_name):\n",
-    "    gen_model = make_dcgan_generator()\n",
-    "    disc_model = make_dcgan_discriminator()\n",
-    "\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",
-    "all_losses = []\n",
-    "all_d_vals = []\n",
-    "epoch_samples = []\n",
-    "\n",
-    "start_time = time.time()\n",
-    "for epoch in range(1, num_epochs+1):\n",
-    "    epoch_losses, epoch_d_vals = [], []\n",
-    "    for i,(input_z,input_real) in enumerate(mnist_trainset): #.take(4)\n",
-    "        \n",
-    "        ## Compute generator's loss\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",
-    "            \n",
-    "            g_loss = loss_fn(y_true=tf.ones_like(d_logits_fake),\n",
-    "                             y_pred=d_logits_fake)\n",
-    "            \n",
-    "        ## > Compute the gradients of g_loss\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 loss\n",
-    "        with tf.GradientTape() as d_tape:\n",
-    "            d_logits_real = disc_model(input_real, training=True)\n",
-    "\n",
-    "            d_logits_fake = disc_model(g_output, training=True)\n",
-    "\n",
-    "            d_loss_real = loss_fn(y_true=tf.ones_like(d_logits_real),\n",
-    "                                  y_pred=d_logits_real)\n",
-    "            \n",
-    "            d_loss_fake = loss_fn(y_true=tf.zeros_like(d_logits_fake),\n",
-    "                                  y_pred=d_logits_fake)\n",
-    "\n",
-    "            d_loss = d_loss_real + d_loss_fake\n",
-    "\n",
-    "        ##  > Compute the gradients of d_loss\n",
-    "        d_grads = d_tape.gradient(d_loss, disc_model.trainable_variables)\n",
-    "        d_optimizer.apply_gradients(\n",
-    "            grads_and_vars=zip(d_grads, disc_model.trainable_variables))\n",
-    "                           \n",
-    "        epoch_losses.append(\n",
-    "            (g_loss.numpy(), d_loss.numpy(), \n",
-    "             d_loss_real.numpy(), d_loss_fake.numpy()))\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",
-    "        epoch_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))        \n",
-    "    all_losses.append(epoch_losses)\n",
-    "    all_d_vals.append(epoch_d_vals)\n",
-    "    print(\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())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "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": [],
-   "source": [
-    "#import pickle\n",
-    "#pickle.dump({'all_losses':all_losses, \n",
-    "#             'all_d_vals':all_d_vals,\n",
-    "#             'samples':epoch_samples}, \n",
-    "#             open('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-dcgan-learning.pkl', 'wb'))\n",
-    "\n",
-    "#gen_model.save('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-dcgangan_gen.h5')\n",
-    "#disc_model.save('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-dcgan_disc.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import itertools\n",
-    "\n",
-    "\n",
-    "fig = plt.figure(figsize=(16, 6))\n",
-    "\n",
-    "## Plotting the losses\n",
-    "ax = fig.add_subplot(1, 2, 1)\n",
-    "g_losses = [item[0] for item in itertools.chain(*all_losses)]\n",
-    "d_losses = [item[1]/2.0 for item in itertools.chain(*all_losses)]\n",
-    "plt.plot(g_losses, label='Generator loss', alpha=0.95)\n",
-    "plt.plot(d_losses, label='Discriminator loss', alpha=0.95)\n",
-    "plt.legend(fontsize=20)\n",
-    "ax.set_xlabel('Iteration', size=15)\n",
-    "ax.set_ylabel('Loss', size=15)\n",
-    "\n",
-    "epochs = np.arange(1, 101)\n",
-    "epoch2iter = lambda e: e*len(all_losses[-1])\n",
-    "epoch_ticks = [1, 20, 40, 60, 80, 100]\n",
-    "newpos = [epoch2iter(e) for e in epoch_ticks]\n",
-    "ax2 = ax.twiny()\n",
-    "ax2.set_xticks(newpos)\n",
-    "ax2.set_xticklabels(epoch_ticks)\n",
-    "ax2.xaxis.set_ticks_position('bottom')\n",
-    "ax2.xaxis.set_label_position('bottom')\n",
-    "ax2.spines['bottom'].set_position(('outward', 60))\n",
-    "ax2.set_xlabel('Epoch', size=15)\n",
-    "ax2.set_xlim(ax.get_xlim())\n",
-    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
-    "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
-    "\n",
-    "## Plotting the outputs of the discriminator\n",
-    "ax = fig.add_subplot(1, 2, 2)\n",
-    "d_vals_real = [item[0] for item in itertools.chain(*all_d_vals)]\n",
-    "d_vals_fake = [item[1] for item in itertools.chain(*all_d_vals)]\n",
-    "plt.plot(d_vals_real, alpha=0.75, label=r'Real: $D(\\mathbf{x})$')\n",
-    "plt.plot(d_vals_fake, alpha=0.75, label=r'Fake: $D(G(\\mathbf{z}))$')\n",
-    "plt.legend(fontsize=20)\n",
-    "ax.set_xlabel('Iteration', size=15)\n",
-    "ax.set_ylabel('Discriminator output', size=15)\n",
-    "\n",
-    "ax2 = ax.twiny()\n",
-    "ax2.set_xticks(newpos)\n",
-    "ax2.set_xticklabels(epoch_ticks)\n",
-    "ax2.xaxis.set_ticks_position('bottom')\n",
-    "ax2.xaxis.set_label_position('bottom')\n",
-    "ax2.spines['bottom'].set_position(('outward', 60))\n",
-    "ax2.set_xlabel('Epoch', size=15)\n",
-    "ax2.set_xlim(ax.get_xlim())\n",
-    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
-    "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
-    "\n",
-    "\n",
-    "#plt.savefig('images/ch17-dcgan-learning-curve.pdf')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x1008 with 30 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "selected_epochs = [1, 2, 4, 10, 50, 100]\n",
-    "fig = plt.figure(figsize=(10, 14))\n",
-    "for i,e in enumerate(selected_epochs):\n",
-    "    for j in range(5):\n",
-    "        ax = fig.add_subplot(6, 5, i*5+j+1)\n",
-    "        ax.set_xticks([])\n",
-    "        ax.set_yticks([])\n",
-    "        if j == 0:\n",
-    "            ax.text(\n",
-    "                -0.06, 0.5, 'Epoch {}'.format(e),\n",
-    "                rotation=90, size=18, color='red',\n",
-    "                horizontalalignment='right',\n",
-    "                verticalalignment='center', \n",
-    "                transform=ax.transAxes)\n",
-    "        \n",
-    "        image = epoch_samples[e-1][j]\n",
-    "        ax.imshow(image, cmap='gray_r')\n",
-    "    \n",
-    "#plt.savefig('images/ch17-dcgan-samples.pdf')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[NbConvertApp] Converting notebook ch17_optional_DCGAN.ipynb to script\n",
-      "[NbConvertApp] Writing 15401 bytes to ch17_optional_DCGAN.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "! python ../.convert_notebook_to_script.py --input ch17_optional_DCGAN.ipynb --output ch17_optional_DCGAN.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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": "3.7.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/ch17/ch17_optional_DCGAN.py b/ch17/ch17_optional_DCGAN.py
deleted file mode 100644
index 2152b3e5..00000000
--- a/ch17/ch17_optional_DCGAN.py
+++ /dev/null
@@ -1,517 +0,0 @@
-# coding: utf-8
-
-
-# from google.colab import drive
-import tensorflow as tf
-import tensorflow_datasets as tfds
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-import itertools
-
-# *Python Machine Learning 3rd Edition* by [Sebastian Raschka](https://sebastianraschka.com) & [Vahid Mirjalili](http://vahidmirjalili.com), Packt Publishing Ltd. 2019
-# 
-# Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
-# 
-# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)
-
-# # Chapter 17 - Generative Adversarial Networks for Synthesizing New Data (Optional, DCGAN)
-
-# 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).
-
-
-
-
-
-
-
-
-
-
-
-## For running on Google-Colab
-# ! pip install -q tensorflow-gpu==2.0.0
-
-
-
-
-# drive.mount('/content/drive/')
-
-
-
-
-
-
-
-
-print(tf.__version__)
-
-print("GPU Available:", tf.test.is_gpu_available())
-
-if tf.test.is_gpu_available():
-    device_name = tf.test.gpu_device_name()
-
-else:
-    device_name = 'cpu:0'
-    
-print(device_name)
-
-
-#  * **Defining the generator and discriminator networks**
-
-
-
-
-
-# def make_dcgan_generator(
-#         z_size=20, 
-#         output_size=(28, 28, 1),
-#         n_filters=64, 
-#         n_blocks=2):
-#     size_factor = 2**n_blocks
-#     hidden_size = (
-#         output_size[0]//size_factor, 
-#         output_size[1]//size_factor
-#     )
-#     
-#     model = tf.keras.Sequential([
-#         tf.keras.layers.Input(shape=(z_size,)),
-#         
-#         tf.keras.layers.Dense(
-#             units=n_filters*np.prod(hidden_size), 
-#             use_bias=False),
-#         
-#         tf.keras.layers.BatchNormalization(),
-#         tf.keras.layers.LeakyReLU(),
-#         tf.keras.layers.Reshape(
-#                 (hidden_size[0], hidden_size[1], n_filters)),
-#     
-#         tf.keras.layers.Conv2DTranspose(
-#             filters=n_filters, kernel_size=(3, 3), strides=(1, 1),
-#             padding='same', use_bias=False),
-#         tf.keras.layers.BatchNormalization(),
-#         tf.keras.layers.LeakyReLU()
-#     ])
-#         
-#     nf = n_filters
-#     for i in range(n_blocks):
-#         nf = nf // 2
-#         model.add(
-#             tf.keras.layers.Conv2DTranspose(
-#                 filters=nf, kernel_size=(3, 3), strides=(2, 2),
-#                 padding='same', use_bias=False))
-#         
-#         model.add(tf.keras.layers.BatchNormalization())
-#         
-#         model.add(tf.keras.layers.LeakyReLU())
-#                 
-#     model.add(
-#         tf.keras.layers.Conv2DTranspose(
-#             filters=output_size[2], kernel_size=(5, 5), 
-#             strides=(1, 1), padding='same', use_bias=True, 
-#             activation='tanh'))
-#         
-#     return model
-
-
-
-def make_dcgan_generator(
-        z_size=100, 
-        output_size=(28, 28, 1),
-        n_filters=64):
-    
-    hidden_size = (7, 7)
-    
-    model = tf.keras.Sequential()
-    
-    # 100 ==> 784 ==> 7x7x64
-    model.add(tf.keras.layers.Dense(
-        units=n_filters*np.prod(hidden_size), use_bias=False)
-    )
-    model.add(tf.keras.layers.BatchNormalization())
-    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))
-
-    model.add(tf.keras.layers.Reshape(
-        target_shape=(hidden_size[0], hidden_size[1], n_filters))
-    )
-
-    # 7x7x64 ==> 14*14*32
-    model.add(tf.keras.layers.Conv2DTranspose(
-        filters=n_filters//2, kernel_size=(3, 3), strides=(2, 2),
-        padding='same', use_bias=False, activation=None)
-    )
-    model.add(tf.keras.layers.BatchNormalization())
-    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))
-    model.add(tf.keras.layers.Dropout(0.5))
-    
-    # 14x14x32 ==> 28x28x16
-    model.add(tf.keras.layers.Conv2DTranspose(
-        filters=n_filters//4, kernel_size=(3, 3), strides=(2, 2),
-        padding='same', use_bias=False, activation=None)
-    )
-    model.add(tf.keras.layers.BatchNormalization())
-    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))
-    model.add(tf.keras.layers.Dropout(0.5))
-    
-    # 28x28x16 ==> 28x28x8
-    model.add(tf.keras.layers.Conv2DTranspose(
-        filters=n_filters//8, kernel_size=(3, 3), strides=(1, 1),
-        padding='same', use_bias=False, activation=None)
-    )
-    model.add(tf.keras.layers.BatchNormalization())
-    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))
-    model.add(tf.keras.layers.Dropout(0.5))
-
-    # 28x28x8 ==> 28x28x1
-    model.add(tf.keras.layers.Conv2DTranspose(
-        filters=1, kernel_size=(3, 3), strides=(1, 1),
-        padding='same', use_bias=False, activation='tanh')
-    )
-
-    return model
-
-
-
-
-gen_model = make_dcgan_generator()
-gen_model.build(input_shape=(None, 20))
-gen_model.summary()
-
-
-
-
-
-
-# 
-# 
-# def make_dcgan_discriminator(
-#         input_size=(28, 28, 1),
-#         n_filters=16, 
-#         n_blocks=2):
-#     
-#     model = tf.keras.Sequential()
-#     model.add(tf.keras.layers.Input(shape=input_size))
-#     #    [tf.keras.layers.Input(shape=input_size),
-#     #    tf.keras.layers.Conv2D(
-#     #        filters=n_filters, kernel_size=5, 
-#     #        strides=(2, 2), padding='same', use_bias=False),
-#     #    tf.keras.layers.BatchNormalization(),
-#     ##    tf.keras.layers.LeakyReLU(),
-#     #    model.add(tf.keras.layers.Dropout(0.5)
-#     #])
-#     
-#     nf = n_filters
-#     for i in range(n_blocks):
-#         model.add(
-#             tf.keras.layers.Conv2D(
-#                 filters=nf, kernel_size=(3, 3), 
-#                 strides=(2, 2),padding='same', use_bias=False))
-#         model.add(tf.keras.layers.BatchNormalization())
-#         model.add(tf.keras.layers.LeakyReLU())
-#         model.add(tf.keras.layers.Dropout(0.5))
-#         nf = nf*2
-#         
-#     model.add(tf.keras.layers.Conv2D(
-#               filters=1, kernel_size=(7, 7), padding='valid',
-#               use_bias=True, activation=None))
-#     
-#     model.add(tf.keras.layers.Reshape((1,)))
-#     
-#     return model
-
-
-
-def make_dcgan_discriminator(
-        input_size=(28, 28, 1),
-        n_filters=64):
-    
-    hidden_size = (7, 7)
-    
-    model = tf.keras.Sequential()
-    
-    model.add(tf.keras.layers.Reshape(
-        target_shape=(input_size[0], input_size[1], input_size[2]))
-    )
-
-    # 7x7x64 ==> 14*14*32
-    model.add(tf.keras.layers.Conv2D(
-        filters=n_filters//8, kernel_size=(3, 3), strides=(2, 2),
-        padding='same', use_bias=False, activation=None)
-    )
-    model.add(tf.keras.layers.BatchNormalization())
-    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))
-    model.add(tf.keras.layers.Dropout(0.5))
-    
-    # 14x14x32 ==> 28x28x16
-    model.add(tf.keras.layers.Conv2D(
-        filters=n_filters//2, kernel_size=(3, 3), strides=(2, 2),
-        padding='same', use_bias=False, activation=None)
-    )
-    model.add(tf.keras.layers.BatchNormalization())
-    model.add(tf.keras.layers.LeakyReLU(alpha=0.0001))
-    model.add(tf.keras.layers.Dropout(0.5))
-
-    model.add(tf.keras.layers.Reshape(
-        target_shape=(np.prod([input_size[0]//4, input_size[1]//4, n_filters//2]),))
-    )
-    
-    model.add(tf.keras.layers.Dense(
-        units=1, use_bias=False)
-    )
-
-    return model
-
-
-
-
-disc_model = make_dcgan_discriminator()
-disc_model.build(input_shape=(None, 28, 28, 1))
-disc_model.summary()
-
-
-# * **Loading and preprocessing the data**
-
-
-
-mnist_bldr = tfds.builder('mnist')
-mnist_bldr.download_and_prepare()
-mnist = mnist_bldr.as_dataset(shuffle_files=False)
-
-def preprocess(ex, mode='uniform'):
-    image = ex['image']
-    image = tf.image.convert_image_dtype(image, tf.float32)
-
-    image = image*2 - 1.0
-    
-    if mode == 'uniform':
-        input_z = tf.random.uniform(shape=(z_size,),
-                                    minval=-1.0, maxval=1.0)
-    elif mode == 'normal':
-        input_z = tf.random.normal(shape=(z_size,))
-    return input_z, image
-
-
-
-
-num_epochs = 100
-batch_size = 64
-image_size = (28, 28)
-z_size = 20
-mode_z = 'uniform'
-#gen_hidden_layers = 1
-#gen_hidden_size = 100
-#disc_hidden_layers = 1
-#disc_hidden_size = 100
-
-tf.random.set_seed(1)
-np.random.seed(1)
-
-
-if mode_z == 'uniform':
-    fixed_z = tf.random.uniform(
-        shape=(batch_size, z_size),
-        minval=-1, maxval=1)
-elif mode_z == 'normal':
-    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))    
-    return (images+1)/2.0
-
-## Set-up the dataset
-mnist_trainset = mnist['train']
-mnist_trainset = mnist_trainset.map(
-    lambda ex: preprocess(ex, mode=mode_z))
-
-mnist_trainset = mnist_trainset.shuffle(10000)
-
-#mnist_trainset = mnist_trainset.batch(
-#    batch_size, drop_remainder=True)
-
-mnist_trainset = mnist_trainset.batch(
-    batch_size, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)
-
-
-# * **Final Training**
-
-
-
-
-
-# Delete the previously instantiated
-# objects that we have defined
-# for printing the model summaries
-del gen_model 
-del disc_model
-
-## Set-up the model
-with tf.device(device_name):
-    gen_model = make_dcgan_generator()
-    disc_model = make_dcgan_discriminator()
-
-
-## Loss function and optimizers:
-loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)
-g_optimizer = tf.keras.optimizers.Adam()
-d_optimizer = tf.keras.optimizers.Adam()
-
-all_losses = []
-all_d_vals = []
-epoch_samples = []
-
-start_time = time.time()
-for epoch in range(1, num_epochs+1):
-    epoch_losses, epoch_d_vals = [], []
-    for i,(input_z,input_real) in enumerate(mnist_trainset): #.take(4)
-        
-        ## Compute generator's loss
-        with tf.GradientTape() as g_tape:
-            g_output = gen_model(input_z)
-            d_logits_fake = disc_model(g_output, training=True)
-            
-            g_loss = loss_fn(y_true=tf.ones_like(d_logits_fake),
-                             y_pred=d_logits_fake)
-            
-        ## > Compute the gradients of g_loss
-        g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)
-        g_optimizer.apply_gradients(
-            grads_and_vars=zip(g_grads, gen_model.trainable_variables))
-
-        
-        ## Compute discriminator's loss
-        with tf.GradientTape() as d_tape:
-            d_logits_real = disc_model(input_real, training=True)
-
-            d_logits_fake = disc_model(g_output, training=True)
-
-            d_loss_real = loss_fn(y_true=tf.ones_like(d_logits_real),
-                                  y_pred=d_logits_real)
-            
-            d_loss_fake = loss_fn(y_true=tf.zeros_like(d_logits_fake),
-                                  y_pred=d_logits_fake)
-
-            d_loss = d_loss_real + d_loss_fake
-
-        ##  > Compute the gradients of d_loss
-        d_grads = d_tape.gradient(d_loss, disc_model.trainable_variables)
-        d_optimizer.apply_gradients(
-            grads_and_vars=zip(d_grads, disc_model.trainable_variables))
-                           
-        epoch_losses.append(
-            (g_loss.numpy(), d_loss.numpy(), 
-             d_loss_real.numpy(), d_loss_fake.numpy()))
-        
-        d_probs_real = tf.reduce_mean(tf.sigmoid(d_logits_real))
-        d_probs_fake = tf.reduce_mean(tf.sigmoid(d_logits_fake))
-        epoch_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))        
-    all_losses.append(epoch_losses)
-    all_d_vals.append(epoch_d_vals)
-    print(
-        'Epoch {:03d} | ET {:.2f} min | Avg Losses >>'
-        ' G/D {:.4f}/{:.4f} [D-Real: {:.4f} D-Fake: {:.4f}]'
-        .format(
-            epoch, (time.time() - start_time)/60, 
-            *list(np.mean(all_losses[-1], axis=0))))
-    epoch_samples.append(
-        create_samples(gen_model, fixed_z).numpy())
-
-
-
-
-#import pickle
-#pickle.dump({'all_losses':all_losses, 
-#             'all_d_vals':all_d_vals,
-#             'samples':epoch_samples}, 
-#             open('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-dcgan-learning.pkl', 'wb'))
-
-#gen_model.save('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-dcgangan_gen.h5')
-#disc_model.save('/content/drive/My Drive/Colab Notebooks/PyML-3rd-edition/ch17-dcgan_disc.h5')
-
-
-
-
-
-
-fig = plt.figure(figsize=(16, 6))
-
-## Plotting the losses
-ax = fig.add_subplot(1, 2, 1)
-g_losses = [item[0] for item in itertools.chain(*all_losses)]
-d_losses = [item[1]/2.0 for item in itertools.chain(*all_losses)]
-plt.plot(g_losses, label='Generator loss', alpha=0.95)
-plt.plot(d_losses, label='Discriminator loss', alpha=0.95)
-plt.legend(fontsize=20)
-ax.set_xlabel('Iteration', size=15)
-ax.set_ylabel('Loss', size=15)
-
-epochs = np.arange(1, 101)
-epoch2iter = lambda e: e*len(all_losses[-1])
-epoch_ticks = [1, 20, 40, 60, 80, 100]
-newpos = [epoch2iter(e) for e in epoch_ticks]
-ax2 = ax.twiny()
-ax2.set_xticks(newpos)
-ax2.set_xticklabels(epoch_ticks)
-ax2.xaxis.set_ticks_position('bottom')
-ax2.xaxis.set_label_position('bottom')
-ax2.spines['bottom'].set_position(('outward', 60))
-ax2.set_xlabel('Epoch', size=15)
-ax2.set_xlim(ax.get_xlim())
-ax.tick_params(axis='both', which='major', labelsize=15)
-ax2.tick_params(axis='both', which='major', labelsize=15)
-
-## Plotting the outputs of the discriminator
-ax = fig.add_subplot(1, 2, 2)
-d_vals_real = [item[0] for item in itertools.chain(*all_d_vals)]
-d_vals_fake = [item[1] for item in itertools.chain(*all_d_vals)]
-plt.plot(d_vals_real, alpha=0.75, label=r'Real: $D(\mathbf{x})$')
-plt.plot(d_vals_fake, alpha=0.75, label=r'Fake: $D(G(\mathbf{z}))$')
-plt.legend(fontsize=20)
-ax.set_xlabel('Iteration', size=15)
-ax.set_ylabel('Discriminator output', size=15)
-
-ax2 = ax.twiny()
-ax2.set_xticks(newpos)
-ax2.set_xticklabels(epoch_ticks)
-ax2.xaxis.set_ticks_position('bottom')
-ax2.xaxis.set_label_position('bottom')
-ax2.spines['bottom'].set_position(('outward', 60))
-ax2.set_xlabel('Epoch', size=15)
-ax2.set_xlim(ax.get_xlim())
-ax.tick_params(axis='both', which='major', labelsize=15)
-ax2.tick_params(axis='both', which='major', labelsize=15)
-
-
-#plt.savefig('images/ch17-dcgan-learning-curve.pdf')
-plt.show()
-
-
-
-
-selected_epochs = [1, 2, 4, 10, 50, 100]
-fig = plt.figure(figsize=(10, 14))
-for i,e in enumerate(selected_epochs):
-    for j in range(5):
-        ax = fig.add_subplot(6, 5, i*5+j+1)
-        ax.set_xticks([])
-        ax.set_yticks([])
-        if j == 0:
-            ax.text(
-                -0.06, 0.5, 'Epoch {}'.format(e),
-                rotation=90, size=18, color='red',
-                horizontalalignment='right',
-                verticalalignment='center', 
-                transform=ax.transAxes)
-        
-        image = epoch_samples[e-1][j]
-        ax.imshow(image, cmap='gray_r')
-    
-#plt.savefig('images/ch17-dcgan-samples.pdf')
-plt.show()
-
-
-
-
-