01-DCGANS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright Pierian Data</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>\n",
    "\n",
    "# GANs - Generative Adverserial Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ad27c62f48>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape and Rescale Images for DCGAN\n",
    "\n",
    "Generator will use tanh activation function for the last layer, so we want to reshape X_train to be within -1 to 1 limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1, 28, 28, 1) * 2. - 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering out the Data for Faster Training on Smaller Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_zeros = X_train[y_train==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5923, 28, 28, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_zeros.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense,Reshape,Dropout,LeakyReLU,Flatten,BatchNormalization,Conv2D,Conv2DTranspose\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "codings_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Sequential()\n",
    "generator.add(Dense(7 * 7 * 128, input_shape=[codings_size]))\n",
    "generator.add(Reshape([7, 7, 128]))\n",
    "generator.add(BatchNormalization())\n",
    "generator.add(Conv2DTranspose(64, kernel_size=5, strides=2, padding=\"same\",\n",
    "                                 activation=\"relu\"))\n",
    "generator.add(BatchNormalization())\n",
    "generator.add(Conv2DTranspose(1, kernel_size=5, strides=2, padding=\"same\",\n",
    "                                 activation=\"tanh\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = Sequential()\n",
    "discriminator.add(Conv2D(64, kernel_size=5, strides=2, padding=\"same\",\n",
    "                        activation=LeakyReLU(0.3),\n",
    "                        input_shape=[28, 28, 1]))\n",
    "discriminator.add(Dropout(0.5))\n",
    "discriminator.add(Conv2D(128, kernel_size=5, strides=2, padding=\"same\",\n",
    "                        activation=LeakyReLU(0.3)))\n",
    "discriminator.add(Dropout(0.5))\n",
    "discriminator.add(Flatten())\n",
    "discriminator.add(Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "GAN = Sequential([generator, discriminator])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
    "discriminator.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "GAN.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.engine.sequential.Sequential at 0x1ad27ce4488>,\n",
       " <tensorflow.python.keras.engine.sequential.Sequential at 0x1ad544bebc8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GAN.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "sequential (Sequential)      (None, 28, 28, 1)         840705    \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 1)                 212865    \n",
      "=================================================================\n",
      "Total params: 1,053,570\n",
      "Trainable params: 840,321\n",
      "Non-trainable params: 213,249\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "GAN.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 6272)              633472    \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 7, 7, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose (Conv2DTran (None, 14, 14, 64)        204864    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 14, 14, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 28, 28, 1)         1601      \n",
      "=================================================================\n",
      "Total params: 840,705\n",
      "Trainable params: 840,321\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "GAN.layers[0].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 14, 14, 64)        1664      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 7, 7, 128)         204928    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 6273      \n",
      "=================================================================\n",
      "WARNING:tensorflow:Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "Total params: 425,730\n",
      "Trainable params: 212,865\n",
      "Non-trainable params: 212,865\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "GAN.layers[1].summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up Training Batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/46444018/meaning-of-buffer-size-in-dataset-map-dataset-prefetch-and-dataset-shuffle\n",
    "\n",
    "The buffer_size in Dataset.shuffle() can affect the randomness of your dataset, and hence the order in which elements are produced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# my_data = X_train\n",
    "my_data = only_zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices(my_data).shuffle(buffer_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensorflow.python.data.ops.dataset_ops.ShuffleDataset"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.batch(batch_size, drop_remainder=True).prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE: The generator never actually sees any real images. It learns by viewing the gradients going back through the discriminator. The better the discrimnator gets through training, the more information the discriminator contains in its gradients, which means the generator can being to make progress in learning how to generate fake images, in our case, fake zeros.**\n",
    "\n",
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently on Epoch 1\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 2\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 3\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 4\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 5\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 6\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 7\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 8\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 9\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 10\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 11\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 12\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 13\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 14\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 15\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 16\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 17\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 18\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 19\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "Currently on Epoch 20\n",
      "\tCurrently on batch number 20 of 185\n",
      "\tCurrently on batch number 40 of 185\n",
      "\tCurrently on batch number 60 of 185\n",
      "\tCurrently on batch number 80 of 185\n",
      "\tCurrently on batch number 100 of 185\n",
      "\tCurrently on batch number 120 of 185\n",
      "\tCurrently on batch number 140 of 185\n",
      "\tCurrently on batch number 160 of 185\n",
      "\tCurrently on batch number 180 of 185\n",
      "TRAINING COMPLETE\n"
     ]
    }
   ],
   "source": [
    "# Grab the seprate components\n",
    "generator, discriminator = GAN.layers\n",
    "\n",
    "# For every epcoh\n",
    "for epoch in range(epochs):\n",
    "    print(f\"Currently on Epoch {epoch+1}\")\n",
    "    i = 0\n",
    "    # For every batch in the dataset\n",
    "    for X_batch in dataset:\n",
    "        i=i+1\n",
    "        if i%20 == 0:\n",
    "            print(f\"\\tCurrently on batch number {i} of {len(my_data)//batch_size}\")\n",
    "        #####################################\n",
    "        ## TRAINING THE DISCRIMINATOR ######\n",
    "        ###################################\n",
    "        \n",
    "        # Create Noise\n",
    "        noise = tf.random.normal(shape=[batch_size, codings_size])\n",
    "        \n",
    "        # Generate numbers based just on noise input\n",
    "        gen_images = generator(noise)\n",
    "        \n",
    "        # Concatenate Generated Images against the Real Ones\n",
    "        # TO use tf.concat, the data types must match!\n",
    "        X_fake_vs_real = tf.concat([gen_images, tf.dtypes.cast(X_batch,tf.float32)], axis=0)\n",
    "        \n",
    "        # Targets set to zero for fake images and 1 for real images\n",
    "        y1 = tf.constant([[0.]] * batch_size + [[1.]] * batch_size)\n",
    "        \n",
    "        # This gets rid of a Keras warning\n",
    "        discriminator.trainable = True\n",
    "        \n",
    "        # Train the discriminator on this batch\n",
    "        discriminator.train_on_batch(X_fake_vs_real, y1)\n",
    "        \n",
    "        \n",
    "        #####################################\n",
    "        ## TRAINING THE GENERATOR     ######\n",
    "        ###################################\n",
    "        \n",
    "        # Create some noise\n",
    "        noise = tf.random.normal(shape=[batch_size, codings_size])\n",
    "        \n",
    "        # We want discriminator to belive that fake images are real\n",
    "        y2 = tf.constant([[1.]] * batch_size)\n",
    "        \n",
    "        # Avois a warning\n",
    "        discriminator.trainable = False\n",
    "        \n",
    "        GAN.train_on_batch(noise, y2)\n",
    "        \n",
    "print(\"TRAINING COMPLETE\")            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most likely your generator will only learn to create one type of noisey zero\n",
    "# Regardless of what noise is passed in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.quora.com/What-does-it-mean-if-all-produced-images-of-a-GAN-look-the-same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = tf.random.normal(shape=[10, codings_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([10, 100])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ad7853fe08>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = generator(noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_image = images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# single_image.numpy().reshape(28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAQ+klEQVR4nO3dfZBV9XkH8O93X3hzfQERShGrIonSVondgK3V0bw4ynSKNuaFmWaw4wTThmlsMzWMnUz4pxPH1GTM1NghAYOdSMrEWMlAqgyxUttKWZAiuk0hlCKwAypVQF725T79Yw+dFfc853LPvefc3ef7mWF29z733PNw93733Ht/9/x+NDOIyOjXUnYDIlIMhV0kCIVdJAiFXSQIhV0kiLYidzaG42x8S0dq3axSYDdn77yBt03m2jnhb68RlRSZ97u3ad77PM/vxN+319vJynH02qlhr5Ar7CRvB/AogFYA3zezh7zrj2/pwA0dv59at5Mn87STi1UaFxi2tmbt3N++zf81VXr7zrWlqvcNZjz5y9o+zx+irLBm9Mb22h/eWb8z6+v3byDPgSvH/+vlk+tSazU/jSfZCuAxAHcAmA1gIcnZtd6eiDRWntfscwHsNrM9ZtYL4EcAFtSnLRGptzxhnw7gjSE/708uex+Si0l2kezqtVM5dicieeQJ+3AvqD7wAs3MlptZp5l1juG4HLsTkTzyhH0/gBlDfr4UwMF87YhIo+QJ+xYAs0heQXIMgM8BWFuftkSk3moemzCzfpJLADyHwaG3lWb2mrtNpYLKeyfSr1AZ8HfqDcWUOUSUwfL8vwDYQMb2jRxnt4x9N1LW/yujN+utffjL8j6eMndQ++/M+nqdm03vK9c4u5mtB7A+z22ISDH0cVmRIBR2kSAUdpEgFHaRIBR2kSAUdpEgCj2fHUD+8clab7eZz/lu5t5Gsjz3a5mfL2gQHdlFglDYRYJQ2EWCUNhFglDYRYJQ2EWCKH7ozTt1MM9wR+YpiaNvKKUQWTO8ljlsmGf22awZfbNml+3PmF22CenILhKEwi4ShMIuEoTCLhKEwi4ShMIuEoTCLhJEsePspDt+mblkc45x07Ba/PHi1qtnuvU35k92620ZC+9O3JU+7fHb14xxt+0737/t6S/6O2/bvju1Zr3pfVVTH4l0ZBcJQmEXCUJhFwlCYRcJQmEXCUJhFwlCYRcJovjz2T1Z56R7NB3zsFo/fKVbn7d6p1v/6WS/nrl/53fal3OOgWuvu8etX/nApNRa/959/o2PwsdTrrCT3AvgGIABAP1m1lmPpkSk/upxZL/VzN6qw+2ISAPpNbtIEHnDbgCeJ7mV5OLhrkByMckukl19dirn7kSkVnmfxt9oZgdJTgGwgeR/mtmmoVcws+UAlgPABS0Xj753PURGiFxHdjM7mHw9DOAZAHPr0ZSI1F/NYSd5Hsnzz3wP4DYA+cZpRKRh8jyNnwrgGQ7O3d0G4Ckz+8fMrVqcub77dU56vVUm+OeMf+bCLrfeygn1bOd92umfaz+QMUfBfbP/2a0/99aM9OIoHEfPUnPYzWwPgOvq2IuINJCG3kSCUNhFglDYRYJQ2EWCUNhFgij2FFczWJ+z1G2e4ZBmXlq40Zzpoq3dH946Yc11lvO5WNDhf6zj+Wm/lV48dqzO3TQ/HdlFglDYRYJQ2EWCUNhFglDYRYJQ2EWCUNhFghi5g6xnG83j6BlaZ12RWrvnyWfdba9q96dzPm19bv3Z9/wlnb+799bU2r5dU91tN/zeI2797cpYt370O+mPiY6v/aa7LV7pdsvW73xepEnpyC4ShMIuEoTCLhKEwi4ShMIuEoTCLhKEwi4SBK3A8ekLOMnmtd6WfoVKxhK+znnbyJh2eESPw2ecq390ffqyzJuuXeNum7Vs8hNHZ7r1n143za1bX2960ft9Aqj8jj8Wvnr1Y259AttTa4+9c4277bqvfsytj123xa2XZbNtxFE7MuwDRkd2kSAUdpEgFHaRIBR2kSAUdpEgFHaRIBR2kSBG1vns3lj6SB5Hz9B/6/VufdO1y2u+7Qd6bnLru+Y64+QAYBl1T8bnKtp37HHrJzJ+55Nb05erXnyRP+f8zw/Mc+sjcXHxzCM7yZUkD5PcOeSySSQ3kNyVfJ3Y2DZFJK9qnsb/AMDtZ122FMBGM5sFYGPys4g0scywm9kmAEfOungBgFXJ96sA3FnnvkSkzmp9g26qmfUAQPJ1StoVSS4m2UWyqw+na9ydiOTV8HfjzWy5mXWaWWc7/AkCRaRxag37IZLTACD5erh+LYlII9Qa9rUAFiXfLwLgz1csIqXLHGcnuRrALQAmk9wP4OsAHgKwhuS9APYB+HTVe8w67zyijPPVv//Eo269BRNSa1t7/bHsX97UvOvaV06ecuvjMu43f1v/of/O7Avc+gXba951aTLDbmYLU0ofr3MvItJA+risSBAKu0gQCrtIEAq7SBAKu0gQxZ7iSoCt6dMHZy6DO0pPY/3vp65161e0v+LWB5zhzM/+/I/dbT90qsutl6l1ir8c9MUt42u+7Tb401hPenGfWx95CzbryC4ShsIuEoTCLhKEwi4ShMIuEoTCLhKEwi4SRLHj7AZYZXSOlXve/sJvu/Xum/2lh7P+Jh+39Om+Pvy4f5poU/82BjKW8M6hlf59eqzzUrc+/sDBerZTCB3ZRYJQ2EWCUNhFglDYRYJQ2EWCUNhFglDYRYIo/nz2lvTpf0fqLNNt03/Vrf/Lsu+49Va259r/Awc/llqzbd25brtMp6+e7tazxso9Pf3H3fqEddvceqmfT/Cm0HYa05FdJAiFXSQIhV0kCIVdJAiFXSQIhV0kCIVdJIjiz2dv4DnKDeWMbU55+pi76dic4+j/O3DCrb/+jfR55ydUNufad5kOfXScW+8z/7HUzvS54b9x+FZ3W6s08Yc+alw/IfPITnIlycMkdw65bBnJAyS3J//m17R3ESlMNU/jfwDg9mEu/7aZzUn+ra9vWyJSb5lhN7NNAI4U0IuINFCeN+iWkNyRPM2fmHYlkotJdpHs6kP6XGki0li1hv1xADMBzAHQA+CRtCua2XIz6zSzznaMrXF3IpJXTWE3s0NmNmBmFQDfAzC3vm2JSL3VFHaS04b8eBeAnWnXFZHmkDnOTnI1gFsATCa5H8DXAdxCcg4Gz57dC+C+qvfonYOcMW5apmOfnZdae+Kyv8112+9WTrr1P7zhbrc+4cDIHEvnWP9l3T8seditt7PDrXvr1m9a9VF326mVf3XrI1Fm2M1s4TAXr2hALyLSQPq4rEgQCrtIEAq7SBAKu0gQCrtIEMWe4jqCfXHZjxt229f/05+49asOvNKwfZfp5Cevc+uXtfnDX1mnuPYMpA9pTul6z912NNKRXSQIhV0kCIVdJAiFXSQIhV0kCIVdJAiFXSQILdmcaL3oQrd+d8e/O9Ux7rbHK6fc+tVLD7v1frfa3FovuSS19t2/eTRja/9+fcsZRweAu7/2F6m1iS+/nLHv0UdHdpEgFHaRIBR2kSAUdpEgFHaRIBR2kSAUdpEgtGTzGdOmuOWxrP2u8pYOBoDey9PHogGgZf+BmvfdaC0TJvhXWJO+XPWkFv+xcLziLxd200tL3PpVT21NrVmNyx6PZDqyiwShsIsEobCLBKGwiwShsIsEobCLBKGwiwSheeMTPOGfc37c0sd8L+R4d9s2+OPsX1z5E7e+4q473PpA9+70YsYkARzjnzP+5j3Xu/U1D37TrV/alr4s8y/6/Iffom/+uVuftWKbW6/09br1aDKP7CRnkHyBZDfJ10h+Obl8EskNJHclXyc2vl0RqVU1T+P7AXzFzK4BcAOAL5GcDWApgI1mNgvAxuRnEWlSmWE3sx4z25Z8fwxAN4DpABYAWJVcbRWAOxvVpIjkd05v0JG8HMBHAGwGMNXMeoDBPwgAhv1wOcnFJLtIdvXB/6yziDRO1WEn2QHgaQD3m9nRarczs+Vm1mlmne1If7NGRBqrqrCTbMdg0H9oZmfeOj5EclpSnwbAnyJVREqVOfRGkgBWAOg2s28NKa0FsAjAQ8nXZxvSYUHsXf/Jyop3fj21tvii191tO1rGufVPdfj7/tSGv3fr71bSp1TOGt66qMUfnprZ9m9uvQJ/2PFdZxrtBRvud7e95rketz5wWi8Lz0U14+w3Avg8gFdJbk8uexCDIV9D8l4A+wB8ujEtikg9ZIbdzF4CkLayw8fr246INIo+LisShMIuEoTCLhKEwi4ShMIuEgSLnFL3Ak6yea23pV+hUuI000xfShoAWj80M7XW8wl/GuofP/CwW5/Z3uHWG2kg4xTYCvzHxwsn/c8Q/NWf/VFqbfzP/FNUM6cdDzgddJbNthFH7ciwD2Yd2UWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCKH6cveUT6VcYoeOmLeP8sWaO82foWbrtRbd+s3/zrtPW59a7e/1x9j9Y96du/bJ1/vZjf7bFreeS8dkI1wh9rGXROLuIKOwiUSjsIkEo7CJBKOwiQSjsIkEo7CJBaJx9BGi78nK3bi3p482Vif658rbVn/M+a8ln/c6ai8bZRURhF4lCYRcJQmEXCUJhFwlCYRcJQmEXCaKa9dlnAHgSwK8AqABYbmaPklwG4AsA3kyu+qCZrW9Uo5H179lbdgsyClSzPns/gK+Y2TaS5wPYSnJDUvu2mf1149oTkXqpZn32HgA9yffHSHYDmN7oxkSkvs7pNTvJywF8BMDm5KIlJHeQXElyYso2i0l2kezqw+lczYpI7aoOO8kOAE8DuN/MjgJ4HMBMAHMweOR/ZLjtzGy5mXWaWWc7/LnYRKRxqgo7yXYMBv2HZvYTADCzQ2Y2YGYVAN8DMLdxbYpIXplhJ0kAKwB0m9m3hlw+bcjV7gKws/7tiUi9VPNu/I0APg/gVZLbk8seBLCQ5BwABmAvgPsa0qGI1EU178a/BGC482M1pi4ygugTdCJBKOwiQSjsIkEo7CJBKOwiQSjsIkFUM85eX3T+vthAcX2IBKMju0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQhS7ZTPJNAP8z5KLJAN4qrIFz06y9NWtfgHqrVT17+zUzu2S4QqFh/8DOyS4z6yytAUez9tasfQHqrVZF9aan8SJBKOwiQZQd9uUl79/TrL01a1+AeqtVIb2V+ppdRIpT9pFdRAqisIsEUUrYSd5O8hckd5NcWkYPaUjuJfkqye0ku0ruZSXJwyR3DrlsEskNJHclX4ddY6+k3paRPJDcd9tJzi+ptxkkXyDZTfI1kl9OLi/1vnP6KuR+K/w1O8lWAP8F4JMA9gPYAmChmb1eaCMpSO4F0GlmpX8Ag+TNAI4DeNLMfiO57GEAR8zsoeQP5UQz+2qT9LYMwPGyl/FOViuaNnSZcQB3ArgHJd53Tl+fQQH3WxlH9rkAdpvZHjPrBfAjAAtK6KPpmdkmAEfOungBgFXJ96sw+GApXEpvTcHMesxsW/L9MQBnlhkv9b5z+ipEGWGfDuCNIT/vR3Ot924Anie5leTispsZxlQz6wEGHzwAppTcz9kyl/Eu0lnLjDfNfVfL8ud5lRH24ZaSaqbxvxvN7HoAdwD4UvJ0VapT1TLeRRlmmfGmUOvy53mVEfb9AGYM+flSAAdL6GNYZnYw+XoYwDNovqWoD51ZQTf5erjkfv5fMy3jPdwy42iC+67M5c/LCPsWALNIXkFyDIDPAVhbQh8fQPK85I0TkDwPwG1ovqWo1wJYlHy/CMCzJfbyPs2yjHfaMuMo+b4rfflzMyv8H4D5GHxH/pcA/rKMHlL6uhLAfyT/Xiu7NwCrMfi0rg+Dz4juBXAxgI0AdiVfJzVRb38H4FUAOzAYrGkl9fa7GHxpuAPA9uTf/LLvO6evQu43fVxWJAh9gk4kCIVdJAiFXSQIhV0kCIVdJAiFXSQIhV0kiP8D/rhbEf0rumIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAARHUlEQVR4nO3dfZBV9XkH8O93X9iVXUBWxezwUk1KGpUmqFukajO2sUTJtJimaaWNsR3qOqPOSLVtrJlWMtM/GFubMdNoBxMitkbNjFrplPGlTDoEnVBWBAXxlWJEtqxCjCiw7O59+sdeOyvsec7lnnvuuevz/cwwu3ufe855OHu/e+69v3vOj2YGEfn4ayq6ARGpD4VdJAiFXSQIhV0kCIVdJIiWem5sEtutnR3Jd9DIQONhSl2/svpj8i/liH2Ao3Zk3DtkCjvJywDcCaAZwPfMbKV3/3Z2YGHb5Yl1GxzM0k6+nB2c+kfKW7aS5bNI23ba4s3Nbt1GRvwVTNQ/4Fl/Z1mWb/L3ufc7+enQ48mr9TtyNkg2A/gugMsBnA1gKcmzq12fiOQry2v2BQBeM7NdZnYUwIMAltSmLRGptSxhnwngzTE/7ynf9hEke0n2kewbsiMZNiciWWQJ+3gvSo57IWJmq8ysx8x6WtmeYXMikkWWsO8BMHvMz7MA7M3WjojkJUvYNwOYS/JMkpMAXAlgbW3aEpFaq3rozcyGSd4A4AmMDr2tNrMdKQvBjh6tdpMTV5HDTxm3bcPDNWpkgsn6O8uyfMkfzjQrVbXdTOPsZrYOwLos6xCR+tDHZUWCUNhFglDYRYJQ2EWCUNhFglDYRYKo6/nsACbuKY8Tte+8FXn6rpwQHdlFglDYRYJQ2EWCUNhFglDYRYJQ2EWCqP/Qm0woLbOOu9LYR/xi4Sy3PuWVXyTWbOcud1kbCng6dI50ZBcJQmEXCUJhFwlCYRcJQmEXCUJhFwlCYRcJQuPswbHFfwjc/cxDbn1OS6dbH7ShxNpf9l/oLvv6Ff4Y//BbKXOS6PTaj9CRXSQIhV0kCIVdJAiFXSQIhV0kCIVdJAiFXSQIjbMH98Y3F7j1OS19mdbfxtbE2tdOecZd9k+vutGtz/nOu269dOhQcnEij8FX2XumsJPcDeAggBEAw2bWk2V9IpKfWhzZf9PM3qnBekQkR3rNLhJE1rAbgCdJPkuyd7w7kOwl2UeybwiDGTcnItXK+jT+IjPbS3IGgKdIvmRmG8bewcxWAVgFAFPZNYHfFRGZ2DId2c1sb/nrAIBHAfhv7YpIYaoOO8kOklM+/B7AIgDba9WYiNRWlqfxpwN4lKNT9rYA+KGZPV6TrqRmmjo63PrW3jtT1pA8Tl6JQ6Xka7//za7fc5e1lEdn08nT3Hrp8BFn5SP+yhuZN02280K56rCb2S4An6t2eRGpLw29iQShsIsEobCLBKGwiwShsIsEoVNcPwZauj+RWLtmw8ZM6x5KGaJ6Z+SwW//8xhsSa3949rPusv9zTpdb//nFc9z61IcHEmtmzvAVADDlOFiaeEN3OrKLBKGwiwShsIsEobCLBKGwiwShsIsEobCLBKFx9gngnWt/3a0/87ffSax5l3IGgKePNLv1m2673q2f8uTrbr1r0UmJtcnzkk9/BYD/vPAut7705K+79eaNMxJrpf0H3GVt3i+79aYP/Eusjbz0mlvPdCnrKpfVkV0kCIVdJAiFXSQIhV0kCIVdJAiFXSQIhV0kCFodp66dyi67oOnS5Duk9NLU3p5YKw2mTC3VyFP0epcGBvDom5vc+uSmSYm1ESu5y170jevc+rT7/W2n7deW2bMSa997+kF32e6WTrc+MPKBW7/wwb9IrJ35mH8eftO3/LlK3x9K3ucAcPIy5zLWAIbf2uvWq7XJ1uM9OzDuA0pHdpEgFHaRIBR2kSAUdpEgFHaRIBR2kSAUdpEgJtT57O5YeiOPo6d4ZfV5bn1y03NVr/vczX/s1rv/9adVr7sSI/3/m1jram7LtO4Zzf501CNTkq/tzhH/8XLGlP1u/bOde9z6fwyf5daLkHpkJ7ma5ADJ7WNu6yL5FMlXy1+n59umiGRVydP4ewFcdsxttwBYb2ZzAawv/ywiDSw17Ga2AcCx1/BZAmBN+fs1AK6ocV8iUmPVvkF3upn1A0D5a+LFvkj2kuwj2TeElM+vi0hucn833sxWmVmPmfW0ItsbMiJSvWrDvo9kNwCUvyZPlykiDaHasK8FcHX5+6sBPFabdkQkL6nj7CQfAHAJgFNJ7gFwG4CVAH5EchmAnwH4asVbLOB62YVLOV992flPZ1r9oA0l1mb+0Rvusv7Z7vlqgX/N+qwumb8zsfbmQ3PdZUvmHwd/a/LLbn1dx/luvQipYTezpQmlL9S4FxHJkT4uKxKEwi4ShMIuEoTCLhKEwi4SxIQ6xXWiKl08360v77o7ZQ3Jl9AGgDv2z0ve9mH/ksl546TkSy6X4A+lZh2Y+69tn0msnb3b/xzYpzuST80FgClN/qDl27/R7dan79rt1vOgI7tIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEBpnr4Phyf6I8cHSsFvvTPmT/PzBmYk1trzvLmtDR/2VZ2RDyf+3V1K2fc6kk9z6oZK//CcfSh4Lt1b/oX9ay0G33p5y2nLzlSnXc1njl/OgI7tIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEPUfZ/fGJyfqpaJTDJ7s7+YpTX59xPxzp+dN2ZtY+8mIP61x3rxx/Ju/8mfuslPv9M8pf/+aLrfe8tKW5L4mT3aXnd/2pluf1uRfY2D/tsQZ0QAAU/G6W8+DjuwiQSjsIkEo7CJBKOwiQSjsIkEo7CJBKOwiQdR/nP1jOpbuOXCOf+7zSUy+tjoANNP/m9w7/dnE2tMdl7nLlg76523nyba86NYPLvLPZy8dfi1lA8mPNaaMs5/Z6n+2oQn+73TavP1uvYjPm6Qe2UmuJjlAcvuY21aQfIvk1vK/xbl0JyI1U8nT+HsBjHd4+LaZzS//W1fbtkSk1lLDbmYbAByoQy8ikqMsb9DdQPL58tP86Ul3ItlLso9k3xAGM2xORLKoNux3A/gUgPkA+gHckXRHM1tlZj1m1tOKtio3JyJZVRV2M9tnZiNmVgJwD4AFtW1LRGqtqrCTHDsf7ZcBbE+6r4g0htRxdpIPALgEwKkk9wC4DcAlJOcDMAC7AVybY48Njy3+btyx7LtuPW0cPc0pTcnj0Uy5vnmhUsaTS4cOZVq993vZefscd9lpzj6txIrPrHXr/9Q2P7FWOnIk07aTpIbdzJaOc/P3c+hFRHKkj8uKBKGwiwShsIsEobCLBKGwiwShKZtrwEZG3HrWobU0JSQPYZUO5zOM0wjY6p8afPDfZyXWdsy7O2Xt/rrTLu/9bsolvJtOnpZYK+1L+Vh5lafA6sguEoTCLhKEwi4ShMIuEoTCLhKEwi4ShMIuEoSmbK6FlL4Hbcitt7E10+Zv7l+YWPOmTG50aacODzx8pltff84PEmuTm/xLSaeNoz9+2F/+h4sudOvD+/a4dZeboeSSjuwiQSjsIkEo7CJBKOwiQSjsIkEo7CJBKOwiQWjK5jr43a8sc+tPPHJfpvX/5N5fS6zNwDOZ1p2ntHH0l+86163vOM+/RHcb2xNrh0r+5w/+7u0et76l93Nu3d54wa1nUuXlwXVkFwlCYRcJQmEXCUJhFwlCYRcJQmEXCUJhFwmi/uPsTc3JtZJ//fWJis+97NZ3HvWnJj5rkn/u9Lu/mny+/Ax3yezY1ubW9/z5+Ym1ddfd7i47p6UvZev+td2HLPnx9KWdX3WXbf+dfW7djuQ4jp4mr+vGk5xN8sckd5LcQfLG8u1dJJ8i+Wr56/SqOhCRuqjkafwwgJvN7CwACwFcT/JsALcAWG9mcwGsL/8sIg0qNexm1m9mW8rfHwSwE8BMAEsArCnfbQ2AK/JqUkSyO6E36EieAeBcAJsAnG5m/cDoHwQkvDwk2Uuyj2TfEFLmsBKR3FQcdpKdAB4GsNzM3qt0OTNbZWY9ZtbTCv/NHBHJT0VhJ9mK0aDfb2aPlG/eR7K7XO8GMJBPiyJSC7SUt/FJEqOvyQ+Y2fIxt/89gP1mtpLkLQC6zOyvvHVNZZdd0Lwo+Q4f06G3NAu3+Zea/tZpO6pe938P+uu+ZtvX3foj597j1k9tdoZSAXQy+dlc3lNZ//7rlybWDn3JP8V15L2Kn7w2lE22Hu/ZgXHPga1knP0iAFcBeIHk1vJttwJYCeBHJJcB+BkAf+BSRAqVGnYz2wgg6Wz5L9S2HRHJiz4uKxKEwi4ShMIuEoTCLhKEwi4SRP1PcZXjTG/5ILd1L2jzp4PetuCBlDV01q6ZY6RNi7y/dNitL/y3m9z6p296LrE2kaeyrpaO7CJBKOwiQSjsIkEo7CJBKOwiQSjsIkEo7CJB1H+cPeg56557/3mxW1/+13fVqZPae2Uo+TMEvdctT6wBQPsTyePkAPArLdvceingWLpHR3aRIBR2kSAUdpEgFHaRIBR2kSAUdpEgFHaRIFKvG19LU9llF1AXpD1RX9zuX8P80s4XE2ufndTuLvvzEX+66M4mfxaf8zd/za3PunZ/Ym1kn+YVqTXvuvE6sosEobCLBKGwiwShsIsEobCLBKGwiwShsIsEUcn87LMB3AfgEwBKAFaZ2Z0kVwC4BsDb5bveambrvHVpnF0kX1nnZx8GcLOZbSE5BcCzJJ8q175tZv9Qq0ZFJD+VzM/eD6C//P1BkjsBzMy7MRGprRN6zU7yDADnAthUvukGks+TXE1yesIyvST7SPYNYTBTsyJSvYrDTrITwMMAlpvZewDuBvApAPMxeuS/Y7zlzGyVmfWYWU8r/M9Zi0h+Kgo7yVaMBv1+M3sEAMxsn5mNmFkJwD0AFuTXpohklRp2kgTwfQA7zewfx9zePeZuXwawvfbtiUitVPJu/EUArgLwAsmt5dtuBbCU5HwABmA3gGtz6VBEaqKSd+M3Ahhv3M4dUxeRxqJP0IkEobCLBKGwiwShsIsEobCLBKGwiwRR/ymbRSQbjnsG6yjnjHUd2UWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCqOuUzSTfBvDGmJtOBfBO3Ro4MY3aW6P2Bai3atWyt18ys9PGK9Q17MdtnOwzs57CGnA0am+N2heg3qpVr970NF4kCIVdJIiiw76q4O17GrW3Ru0LUG/Vqktvhb5mF5H6KfrILiJ1orCLBFFI2EleRvJlkq+RvKWIHpKQ3E3yBZJbSfYV3MtqkgMkt4+5rYvkUyRfLX8dd469gnpbQfKt8r7bSnJxQb3NJvljkjtJ7iB5Y/n2Qved01dd9lvdX7OTbAbwCoDfBrAHwGYAS83sxbo2koDkbgA9Zlb4BzBIfh7A+wDuM7N55dtuB3DAzFaW/1BON7NvNEhvKwC8X/Q03uXZirrHTjMO4AoAf4IC953T1x+gDvutiCP7AgCvmdkuMzsK4EEASwroo+GZ2QYAB465eQmANeXv12D0wVJ3Cb01BDPrN7Mt5e8PAvhwmvFC953TV10UEfaZAN4c8/MeNNZ87wbgSZLPkuwtuplxnG5m/cDogwfAjIL7OVbqNN71dMw04w2z76qZ/jyrIsI+3gW0Gmn87yIzOw/A5QCuLz9dlcpUNI13vYwzzXhDqHb686yKCPseALPH/DwLwN4C+hiXme0tfx0A8CgabyrqfR/OoFv+OlBwP/+vkabxHm+acTTAvity+vMiwr4ZwFySZ5KcBOBKAGsL6OM4JDvKb5yAZAeARWi8qajXAri6/P3VAB4rsJePaJRpvJOmGUfB+67w6c/NrO7/ACzG6DvyrwP4ZhE9JPT1SQDbyv92FN0bgAcw+rRuCKPPiJYBOAXAegCvlr92NVBv/wLgBQDPYzRY3QX1djFGXxo+D2Br+d/ioved01dd9ps+LisShD5BJxKEwi4ShMIuEoTCLhKEwi4ShMIuEoTCLhLE/wGCVUGKA6IQbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAARYklEQVR4nO3dfYxc1XkG8OeZ9XoXr23wR2wsYzBQDKGkccjG0LhNHeVDgFKZVCKKm1LSopo/ggRRpAbRRkGqVLmlNAIpQXESFCdKSQgfCX9QArIiWaaKxQL+jKE2xhjjxWtjhNd2vLsz8/aPvVQL7H3vMHfu3Fm/z09a7e5958w9vp5n78ycuefQzCAiZ75K2R0QkfZQ2EWCUNhFglDYRYJQ2EWCmNbOnU1nj/Wir527PDOQfl0jKpI4jZMYtZFJHzC5wk7yGgD3AugC8EMzW+fdvhd9uKry2fQbdPKD1gtcVr+zwpq162ndbt1qNadYz7XvTEX+n+U8bqDzxDXruHTyY9GxxTam1pp+Gk+yC8B3AVwL4HIAa0he3uz9iUix8rxmXwFgr5ntM7NRAD8HsLo13RKRVssT9sUAXpvw+8Fk27uQXEtygOTAGEZy7E5E8sgT9sleUL3vhY6ZrTezfjPr70ZPjt2JSB55wn4QwJIJv58H4FC+7ohIUfKE/VkAl5C8kOR0AF8G8HhruiUirdb00JuZVUneCuA3GB96e8DMdjXQsNldlitPv3P+m606Vuj9d6y8/y5zhiQDyjXObmZPAHiiRX0RkQLp47IiQSjsIkEo7CJBKOwiQSjsIkEo7CJBKOwiQSjsIkEo7CJBKOwiQSjsIkEo7CJBKOwiQbR1KumwMmZJrcyY4Tfv6nLr9VOnUmtWrbptJQ6d2UWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCaP84e57VUKeorFVYK+ec7dZPX7rIrffufzO1Vj+SXgMAGx1161lsLGMc31stNe/qt94qrQBYSW8f8fMHOrOLBKGwiwShsIsEobCLBKGwiwShsIsEobCLBKHr2Vsh63r12TPdeu3cOW797Yunu/XXV6WPw89+2R+jr/qX0uPtZf5Y+Pm/8ZdFPuuFV1NrNnzCbVuZPcuto7fHr1fSz2XVffv9tmegXGEnuR/AMIAagKqZ9beiUyLSeq04s3/azI624H5EpEB6zS4SRN6wG4CnSD5Hcu1kNyC5luQAyYExjOTcnYg0K+/T+JVmdojkAgBPk3zRzDZNvIGZrQewHgBmc+6ZeaWLyBSQ68xuZoeS70MAHgOwohWdEpHWazrsJPtIznrnZwCfB7CzVR0TkdbK8zR+IYDHOD7GPA3Af5nZk5mtvGuQzR+zLVUlfe5277ppAKgf98eTuetlt3722X/s1sdmpI83H73SuZ4cwKJLh9z62vO3uPV/m3mdW7/k7fRx/u5Dx9y2w8szPiNwln+umn48/fHUe+Cg2/ZMvN696bCb2T4AH21hX0SkQBp6EwlCYRcJQmEXCUJhFwlCYRcJov2XuNY7c3iN0/xDUTl7dmqt/vZxt62N+dM125hbRvembW69a9knUmu9Q/7f867L/A81/uqN5W4dNX/Ycdq+wdRafeFcty39UUMMfdzf97IfvpVaq9U683FYJJ3ZRYJQ2EWCUNhFglDYRYJQ2EWCUNhFglDYRYLQVNIJyxh3rR1LH7MteqnprMstz92Uvizz0bv9vh35H/8y0kXP+FOJXbZ5u1uve31/07/Eta96kVu/dFfG5xeOD7v1aHRmFwlCYRcJQmEXCUJhFwlCYRcJQmEXCUJhFwlC4+zvKHisPJeMJaH3/O281NqTV9zttv3ckdvc+lnf96dcrp4+7dZdGf8u2+/vGxlzENROnHTuvIP/vwuiM7tIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEO0fZ/fGVgOOfTbi2N9d7dZfvPG7qbURm+62Ped3fr36xmG3nkvW/3fdnzjeRv3r2eXdMs/sJB8gOURy54Rtc0k+TXJP8n1Osd0UkbwaeRr/YwDXvGfbHQA2mtklADYmv4tIB8sMu5ltAvDe+YNWA9iQ/LwBwPUt7peItFizb9AtNLNBAEi+L0i7Icm1JAdIDozBn89MRIpT+LvxZrbezPrNrL8bPUXvTkRSNBv2wyQXAUDyfah1XRKRIjQb9scB3JT8fBOAX7emOyJSlMxxdpIPAlgFYD7JgwC+DWAdgIdI3gzgAIAbGt6jxtLfp9LX59Y3/8t9br2L3am1YzV/LPrcR/a69TJXMWffDP8GC+e75WnH3k6t1d501gEAAMsY48+Yy78TZYbdzNaklD7T4r6ISIH0cVmRIBR2kSAUdpEgFHaRIBR2kSDaf4lrpSu9Vi9zoKc4XfPmuvWHtz3p1nvoX4bqWbX5Vrd+8Zs7mr7v3LzHAoD6RYvd+uAnZ7n14x+ZnVqbt8VfDnrBoy+59VrGctOdSGd2kSAUdpEgFHaRIBR2kSAUdpEgFHaRIBR2kSC0ZHMLMGPp4Ie2PeHWZ1R6c+3/9sH+1Nof3fR7t60V/dkGZ+pwdvvHrfLWCbf+h6v9c9W9/b9MrT310Y+4bV/ZuMitQ+PsItKpFHaRIBR2kSAUdpEgFHaRIBR2kSAUdpEg2jvOToCV9HFXM2c55ywlTlH91zv3u/WZOcfRv3l4uVvf3e+MlVvnTnmcteQyR8bcenXEf/j2Mr39M4cudNue+8YBtz4V6cwuEoTCLhKEwi4ShMIuEoTCLhKEwi4ShMIuEkR7x9kNsHqO8XA6f5us4Ouyr/6T1NJXZj2f0dj/mzpY9a/b3v4pf3502HDG/kvkff4hY954Oz3i1meec8qtbzt9fmrt1I45/r5H/aWsp6LMMzvJB0gOkdw5YdtdJF8nuTX5uq7YbopIXo08jf8xgGsm2f4dM1uefPlTsYhI6TLDbmabAEy9OXhE5F3yvEF3K8ntydP81BdAJNeSHCA5MAb/NZiIFKfZsN8P4GIAywEMArgn7YZmtt7M+s2svxs9Te5ORPJqKuxmdtjMamZWB/ADACta2y0RabWmwk5y4jy7XwSwM+22ItIZMsfZST4IYBWA+SQPAvg2gFUklwMwAPsB3NLIzlipoNI3I7WeNa7qyZz/3Jm/HAAqM9L7BQDf+8X3UmtdnOnvO8Mnn/y6W182/Gyu+y+VM5aeNW/8qRVL3fqq87a69ct7X0+tzd9Wd9tatXPnAWhWZtjNbM0km39UQF9EpED6uKxIEAq7SBAKu0gQCrtIEAq7SBDtX7K55gyROdNMA4CNFTcccuyv0i9hBYALu58pbN8X/Kqwu84v4zLUzGWXZ89OL85xagBe/YL/ePjqzFfduqf7lD/0dibSmV0kCIVdJAiFXSQIhV0kCIVdJAiFXSQIhV0kiPYv2TwtfZf1rEtcsy5jzeGtywu7awzVTrr13kF/SmTLGOuGpY8Zc1q33/Tjl7n1ap/f/sTi6W593nPp0xf+YYk/zr7wAn/qw4umD7n1+17/bGptxu9edtsWPDF5KXRmFwlCYRcJQmEXCUJhFwlCYRcJQmEXCUJhFwmirePsVq+jfsoZUy5wHN1dOhjARQ8fd+uDX0lfVnlBlz8N9WjGvr/1yE/d+sujC9x6X2U0tTavy18Oeqj2klvv5Zhbv/2/b3Tr57yYPg7/1jJ/DP+q+elTQTfiub0XpNY+jFdy3fdUpDO7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBDtvZ7dAKv7Y85l4S7/+uY/33xrau3hP/2+23ZZt3/N98Ie/5is7D3q1vMYMf++XxjxzwfLl+9z66euSP+337XkMbftlT1vuPU9Y2e79bP29qTW6sf9zx+ciTLP7CSXkPwtyd0kd5G8Ldk+l+TTJPck3+cU310RaVYjT+OrAL5hZh8GcDWAr5G8HMAdADaa2SUANia/i0iHygy7mQ2a2fPJz8MAdgNYDGA1gA3JzTYAuL6oTopIfh/oDTqSSwF8DMAWAAvNbBAY/4MAYNIPcJNcS3KA5MAYMuaYE5HCNBx2kjMBPALgdjPzrxqZwMzWm1m/mfV3I/0NExEpVkNhJ9mN8aD/zMweTTYfJrkoqS8C4E/1KSKlomVcfkmSGH9NfszMbp+w/W4Ab5rZOpJ3AJhrZv/o3ddszrWrKunT+2ZdhlqmSl9fau3ol/zlnu/51v1ufWWPv3xwF4v7OMSJ+mm3ftr8y47HMv7PZjjTYPfSH/kdy9j3uqOfcOsvfOH81Fr1kD+sV+jl1gXaYhtx3I5NutZ1I+PsKwHcCGAHya3JtjsBrAPwEMmbARwAcEMrOisixcgMu5ltBjDpXwoAn2ltd0SkKPq4rEgQCrtIEAq7SBAKu0gQCrtIEJnj7K00lcfZXUwbrBjXNX++W9/9r0vd+tZr73Prdee4PT86y217pOovm/wXZ73m1mdV/AGdinM+GbGq2/aWV//SrZ+8wZ+KuvrG4fTiVH2sZfDG2XVmFwlCYRcJQmEXCUJhFwlCYRcJQmEXCUJhFwlC4+xTQKW3160f+PqVqbULfjnotn3lbxa59X9e8wu3vrrPX1b50RPnpdbW7bzGbbv07w+49drxhidMCkPj7CKisItEobCLBKGwiwShsIsEobCLBKGwiwShcfYzXca19lnHvOtDH/Kbnzzp735a+vXuteFht60eDx+cxtlFRGEXiUJhFwlCYRcJQmEXCUJhFwlCYRcJInMVV5JLAPwEwLkA6gDWm9m9JO8C8A8AjiQ3vdPMniiqo9KknGPVtSNHsm8kU0Ij67NXAXzDzJ4nOQvAcySfTmrfMbP/KK57ItIqjazPPghgMPl5mORuAIuL7piItNYHes1OcimAjwHYkmy6leR2kg+QnJPSZi3JAZIDYxjJ1VkRaV7DYSc5E8AjAG43s+MA7gdwMYDlGD/z3zNZOzNbb2b9ZtbfjZ4WdFlEmtFQ2El2YzzoPzOzRwHAzA6bWc3M6gB+AGBFcd0Ukbwyw06SAH4EYLeZ/eeE7ROnJf0igJ2t756ItEoj78avBHAjgB0ktybb7gSwhuRyAAZgP4BbGtojnb8vVsto61yuqcshRVyNvBu/GcBkKdOYusgUok/QiQShsIsEobCLBKGwiwShsIsEobCLBNHIOHvrkGBXV3q9kjHtsdPWxqp+W6tn1DVO33FyToMt76Yzu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQbV2ymeQRAK9O2DQfwNG2deCD6dS+dWq/APWtWa3s2wVmNuk6220N+/t2Tg6YWX9pHXB0at86tV+A+tasdvVNT+NFglDYRYIoO+zrS96/p1P71qn9AtS3ZrWlb6W+ZheR9in7zC4ibaKwiwRRSthJXkPyJZJ7Sd5RRh/SkNxPcgfJrSQHSu7LAySHSO6csG0uyadJ7km+T7rGXkl9u4vk68mx20ryupL6toTkb0nuJrmL5G3J9lKPndOvthy3tr9mJ9kF4H8BfA7AQQDPAlhjZr9va0dSkNwPoN/MSv8ABslPATgB4CdmdkWy7d8BHDOzdckfyjlm9s0O6dtdAE6UvYx3slrRoonLjAO4HsBXUeKxc/r1JbThuJVxZl8BYK+Z7TOzUQA/B7C6hH50PDPbBODYezavBrAh+XkDxh8sbZfSt45gZoNm9nzy8zCAd5YZL/XYOf1qizLCvhjAaxN+P4jOWu/dADxF8jmSa8vuzCQWmtkgMP7gAbCg5P68V+Yy3u30nmXGO+bYNbP8eV5lhH2yicU6afxvpZldCeBaAF9Lnq5KYxpaxrtdJllmvCM0u/x5XmWE/SCAJRN+Pw/AoRL6MSkzO5R8HwLwGDpvKerD76ygm3wfKrk//6+TlvGebJlxdMCxK3P58zLC/iyAS0heSHI6gC8DeLyEfrwPyb7kjROQ7APweXTeUtSPA7gp+fkmAL8usS/v0inLeKctM46Sj13py5+bWdu/AFyH8XfkXwbwT2X0IaVfFwHYlnztKrtvAB7E+NO6MYw/I7oZwDwAGwHsSb7P7aC+/RTADgDbMR6sRSX17c8w/tJwO4Ctydd1ZR87p19tOW76uKxIEPoEnUgQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQ/we/hHQHctgzaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for image in images:\n",
    "    plt.imshow(image.numpy().reshape(28,28))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Review our conclusion video to learn about what is state-of-the-art in this field! We need very powerful GPUs to achieve realistic results!**"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}