PixelCNNMNIST.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alimoradi296/DeepLearning/blob/main/PixelCNNMNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zk6uPI7n3CRg"
      },
      "source": [
        "#IMPORT LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4VZleoL53Gol"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.activations import relu\n",
        "from tensorflow.keras import Sequential\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_KZZC0O5LHj"
      },
      "source": [
        "#LOAD MNIST DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655,
          "referenced_widgets": [
            "b7fedf0e82434bbbb1218cb77a54f6f2",
            "9d0b41f84b9042dd8d98fc8ace4bb6ce",
            "fb82534f2cfc4492970addd8a1402e63",
            "38766bde34b14cbeadf757e6b1ce6a7b",
            "521a81bfd758480ab08232667f7f5cfa",
            "799686fb81ff40259f87df460bb77b97",
            "f0c4ec8443d1406cad0e24384af50f3d",
            "d1b7438231c44747acbc29a65a04c5c7",
            "68fa075880cc40148c09383161b96d6d",
            "625fea74da55458abb76cb629329ae8f",
            "521968e672d149838a4236f3922dcbb4"
          ]
        },
        "id": "DnK6Lx2233Ty",
        "outputId": "32831452-55b1-443f-9940-c5600348edf0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 11.06 MiB (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to ~/tensorflow_datasets/mnist/3.0.1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...:   0%|          | 0/5 [00:00<?, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7fedf0e82434bbbb1218cb77a54f6f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset mnist downloaded and prepared to ~/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\n",
            "WARNING: For consistency with `tfds.load`, the `tfds.show_examples` signature has been modified from (info, ds) to (ds, info).\n",
            "The old signature is deprecated and will be removed. Please change your call to `tfds.show_examples(ds, info)`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "(ds_train,ds_test),ds_info=tfds.load('mnist',\n",
        "                                     split=['test','test'],\n",
        "                                     shuffle_files=True,\n",
        "                                     as_supervised=True,\n",
        "                                     with_info=True)\n",
        "fig=tfds.show_examples(ds_info,ds_train)\n",
        "\n",
        "#change image type to binary 0 or 1\n",
        "def binarize(image,label):\n",
        "  image=tf.cast(image,tf.float32)\n",
        "  print(image)\n",
        "  image=tf.math.round(image/255.)\n",
        "  print(image)\n",
        "  tf.cast(image,tf.int32)\n",
        "  return image,tf.cast(image,tf.int32)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "10aMvYg26Wj2"
      },
      "outputs": [],
      "source": [
        "import pprint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6or2WOFF6qMI",
        "outputId": "bf489099-9e5a-4f65-c406-9244ff289107"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tfds.core.DatasetInfo(\n",
            "    name='mnist',\n",
            "    full_name='mnist/3.0.1',\n",
            "    description=\"\"\"\n",
            "    The MNIST database of handwritten digits.\n",
            "    \"\"\",\n",
            "    homepage='http://yann.lecun.com/exdb/mnist/',\n",
            "    data_path='~/tensorflow_datasets/mnist/3.0.1',\n",
            "    file_format=tfrecord,\n",
            "    download_size=11.06 MiB,\n",
            "    dataset_size=21.00 MiB,\n",
            "    features=FeaturesDict({\n",
            "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
            "    }),\n",
            "    supervised_keys=('image', 'label'),\n",
            "    disable_shuffling=False,\n",
            "    splits={\n",
            "        'test': <SplitInfo num_examples=10000, num_shards=1>,\n",
            "        'train': <SplitInfo num_examples=60000, num_shards=1>,\n",
            "    },\n",
            "    citation=\"\"\"@article{lecun2010mnist,\n",
            "      title={MNIST handwritten digit database},\n",
            "      author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n",
            "      journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},\n",
            "      volume={2},\n",
            "      year={2010}\n",
            "    }\"\"\",\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(ds_info)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGCb9xzh5cKv",
        "outputId": "247af636-7cec-4ae4-c6a5-bf3198e356f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<PrefetchDataset element_spec=(TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>\n",
            "Tensor(\"Cast:0\", shape=(28, 28, 1), dtype=float32)\n",
            "Tensor(\"Round:0\", shape=(28, 28, 1), dtype=float32)\n",
            "<MapDataset element_spec=(TensorSpec(shape=(28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(28, 28, 1), dtype=tf.int32, name=None))>\n",
            "<CacheDataset element_spec=(TensorSpec(shape=(28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(28, 28, 1), dtype=tf.int32, name=None))>\n",
            "<ShuffleDataset element_spec=(TensorSpec(shape=(28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(28, 28, 1), dtype=tf.int32, name=None))>\n",
            "<BatchDataset element_spec=(TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None, 28, 28, 1), dtype=tf.int32, name=None))>\n",
            "Tensor(\"Cast:0\", shape=(28, 28, 1), dtype=float32)\n",
            "Tensor(\"Round:0\", shape=(28, 28, 1), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(ds_train)\n",
        "ds_train=ds_train.map(binarize)\n",
        "print(ds_train)\n",
        "ds_train=ds_train.cache() # put data set into memory\n",
        "print(ds_train)\n",
        "ds_train=ds_train.shuffle(ds_info.splits['train'].num_examples)\n",
        "print(ds_train)\n",
        "ds_train=ds_train.batch(64)\n",
        "print(ds_train)\n",
        "ds_test=ds_test.map(binarize).batch(64).cache().prefetch(64)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fdrqJKQ7JGj"
      },
      "source": [
        "#CREATE CUSTOM LAYERS AND PIXELCNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7e0Qc_57AZD",
        "outputId": "05195574-09ac-4c80-dd6f-cbbf0e7bd258"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"PixelCnn\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " masked_conv2d (MaskedConv2D  (None, 28, 28, 128)      6400      \n",
            " )                                                               \n",
            "                                                                 \n",
            " residual_block (ResidualBlo  (None, 28, 28, 128)      53504     \n",
            " ck)                                                             \n",
            "                                                                 \n",
            " residual_block_1 (ResidualB  (None, 28, 28, 128)      53504     \n",
            " lock)                                                           \n",
            "                                                                 \n",
            " residual_block_2 (ResidualB  (None, 28, 28, 128)      53504     \n",
            " lock)                                                           \n",
            "                                                                 \n",
            " residual_block_3 (ResidualB  (None, 28, 28, 128)      53504     \n",
            " lock)                                                           \n",
            "                                                                 \n",
            " residual_block_4 (ResidualB  (None, 28, 28, 128)      53504     \n",
            " lock)                                                           \n",
            "                                                                 \n",
            " residual_block_5 (ResidualB  (None, 28, 28, 128)      53504     \n",
            " lock)                                                           \n",
            "                                                                 \n",
            " residual_block_6 (ResidualB  (None, 28, 28, 128)      53504     \n",
            " lock)                                                           \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 28, 28, 64)        8256      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 28, 28, 1)         65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 389,249\n",
            "Trainable params: 389,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "class MaskedConv2D(layers.Layer):\n",
        "    def __init__(self, mask_type, kernel=5, filters=1):\n",
        "        super(MaskedConv2D, self).__init__()\n",
        "        self.kernel = kernel\n",
        "        self.filters = filters\n",
        "        self.mask_type = mask_type\n",
        "        \n",
        "    def build(self, input_shape):\n",
        "\n",
        "        self.w = self.add_weight(shape=[self.kernel,\n",
        "                                        self.kernel,\n",
        "                                        input_shape[-1],\n",
        "                                        self.filters],\n",
        "                                initializer='glorot_normal',\n",
        "                                trainable=True)\n",
        "        \n",
        "        self.b = self.add_weight(shape=(self.filters,),\n",
        "                                initializer='zeros',\n",
        "                                trainable=True)\n",
        "        # Create Mask \n",
        "        mask = np.ones(self.kernel**2, dtype=np.float32)\n",
        "        center = len(mask)//2\n",
        "        mask[center+1:] = 0\n",
        "        if self.mask_type == 'A':\n",
        "            mask[center] = 0\n",
        "            \n",
        "        mask = mask.reshape((self.kernel, self.kernel, 1, 1))\n",
        "        \n",
        "        self.mask = tf.constant(mask, dtype='float32')\n",
        "        \n",
        "    def call(self, inputs):\n",
        "        # mask the convolution\n",
        "        masked_w = tf.math.multiply(self.w, self.mask)\n",
        "\n",
        "        # perform conv2d using low level API\n",
        "        output = tf.nn.conv2d(inputs, masked_w, 1, \"SAME\") + self.b\n",
        "        \n",
        "        return tf.nn.relu(output)\n",
        "    \n",
        "class ResidualBlock(layers.Layer):\n",
        "    def __init__(self, h=32):\n",
        "        super(ResidualBlock, self).__init__()\n",
        "\n",
        "        self.forward = Sequential([MaskedConv2D('B', kernel=1, filters=h),\n",
        "                                   MaskedConv2D('B', kernel=3, filters=h),\n",
        "                                   MaskedConv2D('B', kernel=1, filters=2*h)])         \n",
        "    def call(self, inputs):\n",
        "        x = self.forward(inputs)\n",
        "        return x + inputs\n",
        "    \n",
        "def SimplePixelCnn(hidden_features=64, \n",
        "                   output_features=64, \n",
        "                   resblocks_num=7):\n",
        "\n",
        "    inputs  = layers.Input(shape=[28,28,1])\n",
        "    x = inputs\n",
        "    \n",
        "    x = MaskedConv2D('A',kernel=7, filters=2*hidden_features)(x)\n",
        "\n",
        "    for _ in range(resblocks_num):\n",
        "        x = ResidualBlock(hidden_features)(x)\n",
        "        \n",
        "    x = layers.Conv2D(output_features, (1,1), padding='same', activation='relu')(x)    \n",
        "    x = layers.Conv2D(1, (1,1), padding='same', activation='sigmoid')(x)\n",
        "            \n",
        "    return tf.keras.Model(inputs\n",
        "\n",
        "=inputs, outputs=x, name='PixelCnn')\n",
        "\n",
        "pixel_cnn = SimplePixelCnn()\n",
        "pixel_cnn.summary()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aX1yn2k7xdb"
      },
      "source": [
        "#TRAIN\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rh96FEJq7r4g",
        "outputId": "ea07a15a-da6c-4397-d86a-364effdd3979"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "157/157 [==============================] - 27s 92ms/step - loss: 0.1513 - binary_crossentropy: 0.1511 - val_loss: 0.1114 - val_binary_crossentropy: 0.1114\n",
            "Epoch 2/50\n",
            "157/157 [==============================] - 14s 89ms/step - loss: 0.1029 - binary_crossentropy: 0.1029 - val_loss: 0.1147 - val_binary_crossentropy: 0.1147\n",
            "Epoch 3/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0979 - binary_crossentropy: 0.0979 - val_loss: 0.0947 - val_binary_crossentropy: 0.0947\n",
            "Epoch 4/50\n",
            "157/157 [==============================] - 14s 90ms/step - loss: 0.0942 - binary_crossentropy: 0.0942 - val_loss: 0.0916 - val_binary_crossentropy: 0.0916\n",
            "Epoch 5/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0920 - binary_crossentropy: 0.0920 - val_loss: 0.0924 - val_binary_crossentropy: 0.0924\n",
            "Epoch 6/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0907 - binary_crossentropy: 0.0906 - val_loss: 0.0898 - val_binary_crossentropy: 0.0898\n",
            "Epoch 7/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0896 - binary_crossentropy: 0.0896 - val_loss: 0.0891 - val_binary_crossentropy: 0.0891\n",
            "Epoch 8/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0887 - binary_crossentropy: 0.0887 - val_loss: 0.0898 - val_binary_crossentropy: 0.0898\n",
            "Epoch 9/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0880 - binary_crossentropy: 0.0880 - val_loss: 0.0921 - val_binary_crossentropy: 0.0921\n",
            "Epoch 10/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0874 - binary_crossentropy: 0.0874 - val_loss: 0.0884 - val_binary_crossentropy: 0.0884\n",
            "Epoch 11/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0868 - binary_crossentropy: 0.0868 - val_loss: 0.0868 - val_binary_crossentropy: 0.0868\n",
            "Epoch 12/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0862 - binary_crossentropy: 0.0862 - val_loss: 0.0867 - val_binary_crossentropy: 0.0867\n",
            "Epoch 13/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0859 - binary_crossentropy: 0.0859 - val_loss: 0.0855 - val_binary_crossentropy: 0.0855\n",
            "Epoch 14/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0855 - binary_crossentropy: 0.0855 - val_loss: 0.0855 - val_binary_crossentropy: 0.0855\n",
            "Epoch 15/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0851 - binary_crossentropy: 0.0852 - val_loss: 0.0853 - val_binary_crossentropy: 0.0853\n",
            "Epoch 16/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0848 - binary_crossentropy: 0.0848 - val_loss: 0.0851 - val_binary_crossentropy: 0.0851\n",
            "Epoch 17/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0844 - binary_crossentropy: 0.0844 - val_loss: 0.0846 - val_binary_crossentropy: 0.0846\n",
            "Epoch 18/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0841 - binary_crossentropy: 0.0841 - val_loss: 0.0848 - val_binary_crossentropy: 0.0848\n",
            "Epoch 19/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0838 - binary_crossentropy: 0.0837 - val_loss: 0.0843 - val_binary_crossentropy: 0.0843\n",
            "Epoch 20/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0836 - binary_crossentropy: 0.0836 - val_loss: 0.0848 - val_binary_crossentropy: 0.0848\n",
            "Epoch 21/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0833 - binary_crossentropy: 0.0832 - val_loss: 0.0830 - val_binary_crossentropy: 0.0830\n",
            "Epoch 22/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0830 - binary_crossentropy: 0.0829 - val_loss: 0.0831 - val_binary_crossentropy: 0.0831\n",
            "Epoch 23/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0827 - binary_crossentropy: 0.0827 - val_loss: 0.0832 - val_binary_crossentropy: 0.0832\n",
            "Epoch 24/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0824 - binary_crossentropy: 0.0824 - val_loss: 0.0830 - val_binary_crossentropy: 0.0830\n",
            "Epoch 25/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0822 - binary_crossentropy: 0.0822 - val_loss: 0.0829 - val_binary_crossentropy: 0.0829\n",
            "Epoch 26/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0818 - binary_crossentropy: 0.0818 - val_loss: 0.0817 - val_binary_crossentropy: 0.0817\n",
            "Epoch 27/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0816 - binary_crossentropy: 0.0816 - val_loss: 0.0826 - val_binary_crossentropy: 0.0826\n",
            "Epoch 28/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0813 - binary_crossentropy: 0.0813 - val_loss: 0.0821 - val_binary_crossentropy: 0.0821\n",
            "Epoch 29/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0811 - binary_crossentropy: 0.0811 - val_loss: 0.0818 - val_binary_crossentropy: 0.0818\n",
            "Epoch 30/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0808 - binary_crossentropy: 0.0808 - val_loss: 0.0818 - val_binary_crossentropy: 0.0818\n",
            "Epoch 31/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0806 - binary_crossentropy: 0.0806 - val_loss: 0.0812 - val_binary_crossentropy: 0.0812\n",
            "Epoch 32/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0803 - binary_crossentropy: 0.0803 - val_loss: 0.0817 - val_binary_crossentropy: 0.0817\n",
            "Epoch 33/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0800 - binary_crossentropy: 0.0800 - val_loss: 0.0804 - val_binary_crossentropy: 0.0804\n",
            "Epoch 34/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0798 - binary_crossentropy: 0.0798 - val_loss: 0.0799 - val_binary_crossentropy: 0.0799\n",
            "Epoch 35/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0796 - binary_crossentropy: 0.0795 - val_loss: 0.0795 - val_binary_crossentropy: 0.0795\n",
            "Epoch 36/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0793 - binary_crossentropy: 0.0793 - val_loss: 0.0799 - val_binary_crossentropy: 0.0799\n",
            "Epoch 37/50\n",
            "157/157 [==============================] - 16s 99ms/step - loss: 0.0790 - binary_crossentropy: 0.0790 - val_loss: 0.0798 - val_binary_crossentropy: 0.0798\n",
            "Epoch 38/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0788 - binary_crossentropy: 0.0788 - val_loss: 0.0794 - val_binary_crossentropy: 0.0794\n",
            "Epoch 39/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0785 - binary_crossentropy: 0.0785 - val_loss: 0.0785 - val_binary_crossentropy: 0.0785\n",
            "Epoch 40/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0783 - binary_crossentropy: 0.0783 - val_loss: 0.0786 - val_binary_crossentropy: 0.0786\n",
            "Epoch 41/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0781 - binary_crossentropy: 0.0781 - val_loss: 0.0796 - val_binary_crossentropy: 0.0796\n",
            "Epoch 42/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0778 - binary_crossentropy: 0.0778 - val_loss: 0.0781 - val_binary_crossentropy: 0.0781\n",
            "Epoch 43/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0776 - binary_crossentropy: 0.0776 - val_loss: 0.0793 - val_binary_crossentropy: 0.0793\n",
            "Epoch 44/50\n",
            "157/157 [==============================] - 14s 92ms/step - loss: 0.0773 - binary_crossentropy: 0.0773 - val_loss: 0.0780 - val_binary_crossentropy: 0.0780\n",
            "Epoch 45/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0771 - binary_crossentropy: 0.0771 - val_loss: 0.0804 - val_binary_crossentropy: 0.0804\n",
            "Epoch 46/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0769 - binary_crossentropy: 0.0768 - val_loss: 0.0772 - val_binary_crossentropy: 0.0772\n",
            "Epoch 47/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0767 - binary_crossentropy: 0.0767 - val_loss: 0.0765 - val_binary_crossentropy: 0.0765\n",
            "Epoch 48/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0764 - binary_crossentropy: 0.0764 - val_loss: 0.0763 - val_binary_crossentropy: 0.0763\n",
            "Epoch 49/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0762 - binary_crossentropy: 0.0762 - val_loss: 0.0767 - val_binary_crossentropy: 0.0767\n",
            "Epoch 50/50\n",
            "157/157 [==============================] - 14s 91ms/step - loss: 0.0760 - binary_crossentropy: 0.0760 - val_loss: 0.0765 - val_binary_crossentropy: 0.0765\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f49400a1400>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "pixel_cnn.compile(\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "    optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.001),\n",
        "    metrics=[ tf.keras.losses.BinaryCrossentropy()]\n",
        ")\n",
        "\n",
        "pixel_cnn.fit(ds_train,epochs=50,validation_data=ds_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "qN0SNOzv8R3j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee29b7d8-2c20-418a-fc53-1a581cc09b4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(16, 28, 28, 1)\n"
          ]
        }
      ],
      "source": [
        "grid_row = 4\n",
        "grid_col = 4\n",
        "batch = grid_row * grid_col\n",
        "h = w = 28\n",
        "images = np.ones((batch,h,w,1), dtype=np.float32)\n",
        "print(images.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "for row in range(h):\n",
        "\n",
        "    for col in range(w):\n",
        "\n",
        "        prob = pixel_cnn.predict(images)[:,row,col,0]\n",
        "\n",
        "        pixel_samples = tf.random.categorical(tf.math.log(np.stack([1-prob, prob],1)), 1)\n",
        "\n",
        "        images[:,row,col,0] = tf.reshape(pixel_samples,[batch])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKnADivoi1F_",
        "outputId": "4ff9ec1b-b237-4695-87de-07fccf606453"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 275ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
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            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display\n",
        "f, axarr = plt.subplots(grid_row, grid_col, figsize=(grid_col*1.1,grid_row))\n",
        "\n",
        "i = 0\n",
        "for row in range(grid_row):\n",
        "    for col in range(grid_col):\n",
        "        axarr[row,col].imshow(images[i,:,:,0], cmap='gray')\n",
        "        axarr[row,col].axis('off')\n",
        "        i += 1\n",
        "f.tight_layout(0.1, h_pad=0.2, w_pad=0.1)        \n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "jKDTXEtHhyps",
        "outputId": "d7448dee-9f43-4910-f6e9-89fc031dd1ac"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-b54490f8bb25>:10: MatplotlibDeprecationWarning: The 'renderer' parameter of tight_layout() is deprecated since Matplotlib 3.2 and will be removed two minor releases later.  If any parameter follows 'renderer', they should be pass as keyword, not positionally.\n",
            "  f.tight_layout(0.1, h_pad=0.2, w_pad=0.1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 316.8x288 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "koMQVczijRbF"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO26jymVQ2p6V7JOBz1scYg",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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