pixel-cnn/Pixel-CNN-for-MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/kartikgill/The-GAN-Book/blob/main/Skill-01/Pixel-CNN-for-MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fKGmrMCaG7lu"
   },
   "source": [
    "# Importing useful libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:45:32.978725Z",
     "start_time": "2021-02-14T05:45:31.328613Z"
    },
    "collapsed": true,
    "id": "_U4JaBWo6DZq"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:45:55.505911Z",
     "start_time": "2021-02-14T05:45:51.469171Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "exqCpOqqG7lz",
    "outputId": "48cc38b1-bea5-4b10-ff50-29c4a6ee8289"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "print (tensorflow.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JO6Wa7J26DZw"
   },
   "source": [
    "# Load and show the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:45:59.032910Z",
     "start_time": "2021-02-14T05:45:57.084465Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "vyJ2HUyB6DZw",
    "outputId": "43de4369-33e9-4d0d-c4ac-90fe9d268324"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shapes: X=(60000, 28, 28), y=(60000,)\n",
      "Testing data shapes: X=(10000, 28, 28), y=(10000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "(trainX, trainy), (testX, testy) = mnist.load_data()\n",
    "\n",
    "print('Training data shapes: X=%s, y=%s' % (trainX.shape, trainy.shape))\n",
    "print('Testing data shapes: X=%s, y=%s' % (testX.shape, testy.shape))\n",
    "\n",
    "\n",
    "for k in range(9):\n",
    "    plt.figure(figsize=(9,6))\n",
    "    for j in range(9):\n",
    "        i = np.random.randint(0, 10000)\n",
    "        plt.subplot(990 + 1 + j)\n",
    "        plt.imshow(trainX[i], cmap='gray_r')\n",
    "        plt.axis('off')\n",
    "        #plt.title(trainy[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IYUGwxhn6DZx"
   },
   "source": [
    "# Prepare data for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:45:59.454734Z",
     "start_time": "2021-02-14T05:45:59.034083Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9GPNh9S_6DZx",
    "outputId": "9d14ac64-123a-4548-b792-bf6cb4832089"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "trainX = np.where(trainX < (0.33 * 256), 0, 1)\n",
    "train_data = trainX.astype(np.float32)\n",
    "\n",
    "testX = np.where(testX < (0.33 * 256), 0, 1)\n",
    "test_data = testX.astype(np.float32)\n",
    "\n",
    "train_data = np.reshape(train_data, (60000, 28, 28, 1))\n",
    "test_data = np.reshape(test_data, (10000, 28, 28, 1))\n",
    "\n",
    "print (train_data.shape, test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hE5soQF66DZy"
   },
   "source": [
    "# Define Masked CNN layers for Pixel CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CpbPG3sFG7l1"
   },
   "source": [
    "### Following code is inspired from : https://keras.io/examples/generative/pixelcnn/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:45:59.998956Z",
     "start_time": "2021-02-14T05:45:59.973846Z"
    },
    "collapsed": true,
    "id": "ydMsCq9hG7l2"
   },
   "outputs": [],
   "source": [
    "# The first layer is the PixelCNN layer. This layer simply\n",
    "# builds on the 2D convolutional layer, but includes masking.\n",
    "\n",
    "import tensorflow\n",
    "\n",
    "class PixelConvLayer(tensorflow.keras.layers.Layer):\n",
    "    def __init__(self, mask_type, **kwargs):\n",
    "        super(PixelConvLayer, self).__init__()\n",
    "        self.mask_type = mask_type\n",
    "        self.conv = tensorflow.keras.layers.Conv2D(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        # Build the conv2d layer to initialize kernel variables\n",
    "        self.conv.build(input_shape)\n",
    "        # Use the initialized kernel to create the mask\n",
    "        kernel_shape = self.conv.kernel.get_shape()\n",
    "        self.mask = np.zeros(shape=kernel_shape)\n",
    "        self.mask[: kernel_shape[0] // 2, ...] = 1.0\n",
    "        self.mask[kernel_shape[0] // 2, : kernel_shape[1] // 2, ...] = 1.0\n",
    "        if self.mask_type == \"B\":\n",
    "            self.mask[kernel_shape[0] // 2, kernel_shape[1] // 2, ...] = 1.0\n",
    "\n",
    "    def call(self, inputs):\n",
    "        self.conv.kernel.assign(self.conv.kernel * self.mask)\n",
    "        return self.conv(inputs)\n",
    "\n",
    "\n",
    "# Next, we build our residual block layer.\n",
    "# This is just a normal residual block, but based on the PixelConvLayer.\n",
    "class ResidualBlock(tensorflow.keras.layers.Layer):\n",
    "    def __init__(self, filters, **kwargs):\n",
    "        super(ResidualBlock, self).__init__(**kwargs)\n",
    "        self.conv1 = tensorflow.keras.layers.Conv2D(\n",
    "            filters=filters, kernel_size=1, activation=\"relu\"\n",
    "        )\n",
    "        self.pixel_conv = PixelConvLayer(\n",
    "            mask_type=\"B\",\n",
    "            filters=filters // 2,\n",
    "            kernel_size=3,\n",
    "            activation=\"relu\",\n",
    "            padding=\"same\",\n",
    "        )\n",
    "        self.conv2 = tensorflow.keras.layers.Conv2D(\n",
    "            filters=filters, kernel_size=1, activation=\"relu\"\n",
    "        )\n",
    "\n",
    "    def call(self, inputs):\n",
    "        x = self.conv1(inputs)\n",
    "        x = self.pixel_conv(x)\n",
    "        x = self.conv2(x)\n",
    "        return tensorflow.keras.layers.add([inputs, x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wcklm1YBG7l2"
   },
   "source": [
    "# Define Pixel-CNN    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:46:01.390690Z",
     "start_time": "2021-02-14T05:46:01.110612Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "voksaARVG7l2",
    "outputId": "7429b5f0-c1d4-4af2-a0d6-74ad9acb19b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "pixel_conv_layer (PixelConvL (None, 28, 28, 128)       6400      \n",
      "_________________________________________________________________\n",
      "residual_block (ResidualBloc (None, 28, 28, 128)       98624     \n",
      "_________________________________________________________________\n",
      "residual_block_1 (ResidualBl (None, 28, 28, 128)       98624     \n",
      "_________________________________________________________________\n",
      "residual_block_2 (ResidualBl (None, 28, 28, 128)       98624     \n",
      "_________________________________________________________________\n",
      "residual_block_3 (ResidualBl (None, 28, 28, 128)       98624     \n",
      "_________________________________________________________________\n",
      "residual_block_4 (ResidualBl (None, 28, 28, 128)       98624     \n",
      "_________________________________________________________________\n",
      "pixel_conv_layer_6 (PixelCon (None, 28, 28, 128)       16512     \n",
      "_________________________________________________________________\n",
      "pixel_conv_layer_7 (PixelCon (None, 28, 28, 128)       16512     \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 28, 28, 1)         129       \n",
      "=================================================================\n",
      "Total params: 532,673\n",
      "Trainable params: 532,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = tensorflow.keras.Input(shape=(28,28,1))\n",
    "x = PixelConvLayer(\n",
    "    mask_type=\"A\", filters=128, kernel_size=7, activation=\"relu\", padding=\"same\"\n",
    ")(inputs)\n",
    "\n",
    "for _ in range(5):\n",
    "    x = ResidualBlock(filters=128)(x)\n",
    "\n",
    "for _ in range(2):\n",
    "    x = PixelConvLayer(\n",
    "        mask_type=\"B\",\n",
    "        filters=128,\n",
    "        kernel_size=1,\n",
    "        strides=1,\n",
    "        activation=\"relu\",\n",
    "        padding=\"valid\",\n",
    "    )(x)\n",
    "\n",
    "out = tensorflow.keras.layers.Conv2D(\n",
    "    filters=1, kernel_size=1, strides=1, activation=\"sigmoid\", padding=\"valid\"\n",
    ")(x)\n",
    "\n",
    "pixel_cnn = tensorflow.keras.Model(inputs, out)\n",
    "pixel_cnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jzk1pL2OG7l3"
   },
   "source": [
    "# Compiling PixelCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:46:03.386754Z",
     "start_time": "2021-02-14T05:46:03.360332Z"
    },
    "collapsed": true,
    "id": "7CzYmq3fG7l3"
   },
   "outputs": [],
   "source": [
    "adam = tensorflow.keras.optimizers.Adam(learning_rate=0.0005)\n",
    "pixel_cnn.compile(optimizer=adam, loss=\"binary_crossentropy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "upSo92nBG7l3"
   },
   "source": [
    "# Training Pixel-CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-14T05:46:17.995822Z",
     "start_time": "2021-02-14T05:46:05.535616Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UBxzQ9zB6DZ2",
    "outputId": "40f8bddf-6929-40a6-b15f-37e8b9d984a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "469/469 [==============================] - 57s 116ms/step - loss: 0.1748 - val_loss: 0.0923\n",
      "Epoch 2/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0918 - val_loss: 0.0892\n",
      "Epoch 3/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0894 - val_loss: 0.0882\n",
      "Epoch 4/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0881 - val_loss: 0.0873\n",
      "Epoch 5/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0873 - val_loss: 0.0878\n",
      "Epoch 6/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0866 - val_loss: 0.0869\n",
      "Epoch 7/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0863 - val_loss: 0.0860\n",
      "Epoch 8/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0861 - val_loss: 0.0854\n",
      "Epoch 9/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0856 - val_loss: 0.0852\n",
      "Epoch 10/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0852 - val_loss: 0.0849\n",
      "Epoch 11/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0848 - val_loss: 0.0852\n",
      "Epoch 12/50\n",
      "469/469 [==============================] - 53s 114ms/step - loss: 0.0847 - val_loss: 0.0849\n",
      "Epoch 13/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0845 - val_loss: 0.0847\n",
      "Epoch 14/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0843 - val_loss: 0.0843\n",
      "Epoch 15/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0840 - val_loss: 0.0843\n",
      "Epoch 16/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0841 - val_loss: 0.0842\n",
      "Epoch 17/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0838 - val_loss: 0.0839\n",
      "Epoch 18/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0838 - val_loss: 0.0843\n",
      "Epoch 19/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0836 - val_loss: 0.0837\n",
      "Epoch 20/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0835 - val_loss: 0.0837\n",
      "Epoch 21/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0832 - val_loss: 0.0840\n",
      "Epoch 22/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0833 - val_loss: 0.0838\n",
      "Epoch 23/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0831 - val_loss: 0.0835\n",
      "Epoch 24/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0830 - val_loss: 0.0834\n",
      "Epoch 25/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0830 - val_loss: 0.0834\n",
      "Epoch 26/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0828 - val_loss: 0.0834\n",
      "Epoch 27/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0827 - val_loss: 0.0832\n",
      "Epoch 28/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0826 - val_loss: 0.0837\n",
      "Epoch 29/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0825 - val_loss: 0.0832\n",
      "Epoch 30/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0825 - val_loss: 0.0832\n",
      "Epoch 31/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0824 - val_loss: 0.0830\n",
      "Epoch 32/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0823 - val_loss: 0.0836\n",
      "Epoch 33/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0824 - val_loss: 0.0831\n",
      "Epoch 34/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0822 - val_loss: 0.0831\n",
      "Epoch 35/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0823 - val_loss: 0.0830\n",
      "Epoch 36/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0820 - val_loss: 0.0829\n",
      "Epoch 37/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0820 - val_loss: 0.0829\n",
      "Epoch 38/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0819 - val_loss: 0.0829\n",
      "Epoch 39/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0818 - val_loss: 0.0829\n",
      "Epoch 40/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0817 - val_loss: 0.0831\n",
      "Epoch 41/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0819 - val_loss: 0.0830\n",
      "Epoch 42/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0816 - val_loss: 0.0829\n",
      "Epoch 43/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0817 - val_loss: 0.0832\n",
      "Epoch 44/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0815 - val_loss: 0.0830\n",
      "Epoch 45/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0815 - val_loss: 0.0829\n",
      "Epoch 46/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0815 - val_loss: 0.0827\n",
      "Epoch 47/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0813 - val_loss: 0.0829\n",
      "Epoch 48/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0812 - val_loss: 0.0829\n",
      "Epoch 49/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0813 - val_loss: 0.0830\n",
      "Epoch 50/50\n",
      "469/469 [==============================] - 53s 113ms/step - loss: 0.0812 - val_loss: 0.0828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8e00fea5c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixel_cnn.fit(\n",
    "    x=train_data, y=train_data, batch_size=128, epochs=50, validation_data=(test_data, test_data), verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Neh5e4PDG7l3"
   },
   "source": [
    "# Display Results 81 images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-02-14T05:48:55.091Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 471,
     "referenced_widgets": [
      "a55377f692c44831a6d1253400c19eb1",
      "e6642d8c52054560814a097d918956f0",
      "bdf97b63c6494dcbb6e0ff8bba180dcd",
      "6eb2bdbb96e948569ba7d53697b58a2f",
      "d2bc99800f0940c9a354b476dc772e0c",
      "973e85ac796b4008ba470bd60b8585d8",
      "c53f302b04f040c5ba548320b6652b12",
      "4c4fcb85a4b84fcb9ed6031e289d0de9"
     ]
    },
    "id": "ohIPV6Qj6DZ2",
    "outputId": "c3bb137d-d9f9-49de-ff4c-e2399d03e381"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a55377f692c44831a6d1253400c19eb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=28.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nTzBnzhzOSXScNVStQ848Ojoa+/btw5AhQywu06tXL4eNoriPvn37Ys+ePXybQXEyN2/eZMYrf/DgAb7//nucOHGCKffksa5todPpkJSUxJooZufOnRg7diyPVnWc2bNno6SkBAaDAampqVi0aFGnJwwSKh2ZqCosLAwA4Ovri7Fjx8LHx8dVZnUKiUSCwsJCzJ49G0ajEVVVVTbX2b9/P/r27evQ/hxy5sHBwQgODrZYnpOTY3VUI4pw8PX1FezkDBTncefOHdy5c4dvM9xCY2MjTp48yfxPT09HfHw8evbsyaNVHefEiRMwGAxISUlBcnIy+vfvz7dJgiI8PFxwQ0i3RiQSIS4uDt999x02bNhg8wnC1q1bMXPmTIefHjn9xdmRI0fw448/4sqVK4iMjMSsWbOcvQsKhWKDRYsW4aeffrL5Ec7ixYs97tFzR1m+fLlHvQIjhGDPnj1oaWlBYmIiUlJSBO20KNZZvXo1qqurUVdXh9OnT6OyspIp8/HxQWJiIgBgzZo1nfoewmnOvLi4GCaTCevWrcOdO3fQr18/JCcnIzU11Vm7oFAodrJjxw7U1tbi1KlTrDsCPz8/1gdDu3fv7hIfVHUlWlpasHTpUgBARkaG3ZEXpaWl7WZeowiDjIwMAMDGjRtRXFzM5AcFBWH37t1O2UennLnJZGKuMqZOncqaq3bhwoVd6mMNrlmNevbs6bEfjgUEBLSbGY7Czf3791ltu1u3bh4R2nbgwAHMnj0barWayRsxYgQOHjzIo1WuRa/Xs8JEIyIiPDp64e7du6wPw8xIpVLm3WpFRQUIIViwYAHKy8uhUCiYmccowmL9+vWum+DHkcB4o9FI6uvrSWVlpcWg/rS0NFJfX0/q6+s7MsqUzcB4FySrmEwmotPprGp8/vy5I/p41/jbb791ZjAYexFEPTqCuf0OGjSIVe/jx49njV3vyRo7gEfoKykpYdWVJ404acZoNBJfX1+rg6b07t2baZ9isZg1SuP27dsFr9GFvLQaRYQQq76eK/Po0aOYOXOm3RcMSqUSGo3G7uX/H3cFaFs9AFqt1uaX+dHR0Y5OcyoIjS7GYzVKJBJWaFNrIiMjUV5ebv7rsRo7gDs0dlpfaWkpa/rP5uZme+/MBVeHcrkcBoOhQxsvLCxEXFycpWLBaXQBL61GW86cQqFQKBSKwKFfvlAoFAqF4uFQZ06hUCgUiodDnTmFQqFQKB4OdeYUCoVCoXg41JlTKBQKheLhUGdOoVAoFIqH838BoaqXDm2jxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "# Create an empty array of pixels.\n",
    "batch = 81\n",
    "pixels = np.zeros(shape=(batch,) + (pixel_cnn.input_shape)[1:])\n",
    "batch, rows, cols, channels = pixels.shape\n",
    "\n",
    "# Iterate over the pixels because generation has to be done sequentially pixel by pixel.\n",
    "for row in tqdm_notebook(range(rows)):\n",
    "    for col in range(cols):\n",
    "        for channel in range(channels):\n",
    "            # Feed the whole array and retrieving the pixel value probabilities for the next\n",
    "            # pixel.\n",
    "            probs = pixel_cnn.predict(pixels)[:, row, col, channel]\n",
    "            # Use the probabilities to pick pixel values and append the values to the image\n",
    "            # frame.\n",
    "            pixels[:, row, col, channel] = tensorflow.math.ceil(\n",
    "              probs - tensorflow.random.uniform(probs.shape)\n",
    "            )\n",
    "\n",
    "counter = 0\n",
    "for i in range(9):\n",
    "    plt.figure(figsize=(9,6))\n",
    "    for j in range(9):\n",
    "        plt.subplot(990 + 1 + j)\n",
    "        plt.imshow(pixels[counter,:,:,0], cmap='gray_r')\n",
    "        counter += 1\n",
    "        plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "_fw5FTn6G7l4"
   },
   "outputs": [],
   "source": []
  }
 ],
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