mnist_gan.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mnist_gan.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPhRU2YJ1r/KtE8OcOA5tJs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/novoforce/Exploring-Tensorflow/blob/main/mnist_gan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsaDcN5_b7db"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import time"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDUy9yaycuob",
        "outputId": "7440a629-747e-484d-ad7d-ebbc5cfac80a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#create tuples for getting the train and test images\n",
        "(train_images,train_labels),(test_images,test_labels)= tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TB0L3c_ddPfq",
        "outputId": "f802594f-8599-4a82-e656-46ccb6986a66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "#plotting an example from the train images\n",
        "print(\"The label of the below image is: \",train_labels[0])\n",
        "plt.imshow(train_images[0])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The label of the below image is:  5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdc065e62b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LJX1U0jpJmyT9l6SeLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajrF9g4-dbFX",
        "outputId": "90a4c9b3-3f95-4fcd-a32f-7fcabd3c0bc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(train_images[0],'and the shape of the train_image is: ',train_images[0].shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
            "  175  26 166 255 247 127   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
            "  225 172 253 242 195  64   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
            "   93  82  82  56  39   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
            "   25   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
            "  150  27   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
            "  253 187   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249\n",
            "  253 249  64   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
            "  253 207   2   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
            "  250 182   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
            "   78   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]] and the shape of the train_image is:  (28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D99RF-vod4Rw"
      },
      "source": [
        "train_images= train_images.reshape(train_images.shape[0],28,28,1) #(60000,28,28,1)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCBbfyUz3dHV",
        "outputId": "0fc16aee-ed72-4c01-e220-f90433e67dc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_images= (train_images-127.5)/127.5 #normalization\n",
        "\n",
        "train_images[0]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[-1.        ],\n",
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              "\n",
              "       [[-1.        ],\n",
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              "        [-1.        ],\n",
              "        [-1.        ],\n",
              "        [-1.        ]],\n",
              "\n",
              "       [[-1.        ],\n",
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              "        [-1.        ],\n",
              "        [-1.        ],\n",
              "        [-1.        ]],\n",
              "\n",
              "       [[-1.        ],\n",
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              "        [-1.        ]],\n",
              "\n",
              "       [[-1.        ],\n",
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              "        [-1.        ],\n",
              "        [-1.        ]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdZUxG2w35fC"
      },
      "source": [
        "BUFFER_SIZE= train_images.shape[0]\n",
        "BATCH_SIZE= 100\n",
        "train_dataset= tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVgIeiVN507N"
      },
      "source": [
        "# Discriminator\n",
        "This is same as a categorical model. It will basically a binary classifier as its job is to find whether the image is real or fake"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSKq4cq15T2G"
      },
      "source": [
        "def make_discriminator_model():\n",
        "  model= tf.keras.Sequential()\n",
        "  model.add(tf.keras.layers.Conv2D(7,(3,3),padding='same',input_shape=(28,28,1)))\n",
        "  model.add(tf.keras.layers.Flatten())\n",
        "  model.add(tf.keras.layers.LeakyReLU())\n",
        "  model.add(tf.keras.layers.Dense(50,activation='relu'))\n",
        "  model.add(tf.keras.layers.Dense(1))\n",
        "  model.summary()\n",
        "  return model"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7Eckz5I7NET",
        "outputId": "0dd48fe0-93e9-4a2c-8032-45389ef9f3b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "discriminator= make_discriminator_model()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 28, 28, 7)         70        \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 5488)              0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 5488)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 50)                274450    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 274,571\n",
            "Trainable params: 274,571\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPRGjqE17QuF",
        "outputId": "bb155856-2a05-4f90-aef6-f4ded57a8585",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#pass some data and check\n",
        "discriminator(np.random.rand(1,28,28,1).astype('float32'))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[-0.06293635]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPCjjAeF7rk1"
      },
      "source": [
        "discriminator_optimizer= tf.optimizers.Adam(1e-3)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tfPHnoO77PH"
      },
      "source": [
        "def get_diriminator_loss(real_predictions,fake_predictions):\n",
        "  real_predictions= tf.sigmoid(real_predictions) #so as to between 0 and 1\n",
        "  fake_predictions= tf.sigmoid(fake_predictions) #so as to between 0 and 1\n",
        "  real_loss= tf.losses.binary_crossentropy(tf.ones_like(real_predictions),real_predictions)\n",
        "  fake_loss= tf.losses.binary_crossentropy(tf.zeros_like(fake_predictions),fake_predictions)\n",
        "  return real_loss + fake_loss"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yiBB1TMp-Tr0"
      },
      "source": [
        "# Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GBwVvTR9fi3"
      },
      "source": [
        "def make_generator_model():\n",
        "  model= tf.keras.Sequential()\n",
        "  model.add(tf.keras.layers.Dense(7*7*256,input_shape=(100,))) #since we need to upsample to 28*28 so we are taking 7*7 and will upsampling.\n",
        "  model.add(tf.keras.layers.BatchNormalization())\n",
        "  model.add(tf.keras.layers.Reshape((7,7,256))) #7*7\n",
        "  model.add(tf.keras.layers.Conv2DTranspose(128,(3,3),padding='same'))#7*7\n",
        "  model.add(tf.keras.layers.BatchNormalization())\n",
        "  model.add(tf.keras.layers.Conv2DTranspose(64,(3,3),strides=(2,2),padding='same'))#14*14\n",
        "  model.add(tf.keras.layers.BatchNormalization())\n",
        "  model.add(tf.keras.layers.Conv2DTranspose(1,(3,3),strides=(2,2),padding='same'))#28*28\n",
        "  model.summary()\n",
        "  return model"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-1EcqccCNFY",
        "outputId": "1db2f6aa-42c4-412e-bdba-90d1fb390638",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "generator= make_generator_model()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_2 (Dense)              (None, 12544)             1266944   \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 12544)             50176     \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose (Conv2DTran (None, 7, 7, 128)         295040    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 14, 14, 64)        73792     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTr (None, 28, 28, 1)         577       \n",
            "=================================================================\n",
            "Total params: 1,687,297\n",
            "Trainable params: 1,661,825\n",
            "Non-trainable params: 25,472\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aucQG7K7CQir"
      },
      "source": [
        "def get_generator_loss(fake_predictions):\n",
        "  fake_predictions= tf.sigmoid(fake_predictions)\n",
        "  fake_loss= tf.losses.binary_crossentropy(tf.ones_like(fake_predictions),fake_predictions)\n",
        "  return fake_loss"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utNhQZVjRctY"
      },
      "source": [
        "generator_optimizer= tf.optimizers.Adam(1e-4)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PCi9YbCcRmhE"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZwJUix-Rkhp"
      },
      "source": [
        "def train(dataset,epochs):\n",
        "  for _ in range(epochs):\n",
        "    for images in dataset:\n",
        "      images= tf.cast(images,tf.dtypes.float32)\n",
        "      train_step(images)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEd-LpYoSAd6"
      },
      "source": [
        "def train_step(images):\n",
        "  fake_image_noise= np.random.randn(BATCH_SIZE,100).astype(\"float32\")\n",
        "  with tf.GradientTape() as gen_tape, tf.GradientTape() as desc_tape:\n",
        "    generated_images= generator(fake_image_noise)\n",
        "    real_output= discriminator(images)\n",
        "    fake_output= discriminator(generated_images)\n",
        "\n",
        "    gen_loss= get_generator_loss(fake_output)\n",
        "    disc_loss= get_diriminator_loss(real_output,fake_output)\n",
        "\n",
        "    gradients_of_generator= gen_tape.gradient(gen_loss,generator.trainable_variables)\n",
        "    gradients_of_discriminator= desc_tape.gradient(disc_loss,discriminator.trainable_variables)\n",
        "\n",
        "    generator_optimizer.apply_gradients(zip(gradients_of_generator,generator.trainable_variables))\n",
        "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator,discriminator.trainable_variables))\n",
        "\n",
        "    print(\"generator_loss: \",np.mean(gen_loss))\n",
        "    print(\"discriminator_loss: \",np.mean(disc_loss))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdgu9gXVYRft",
        "outputId": "528566a8-a9d9-4565-9640-7d57b000e2ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train(train_dataset,20)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "discriminator_loss:  0.008115516\n",
            "generator_loss:  13.198923\n",
            "discriminator_loss:  0.032068163\n",
            "generator_loss:  13.069942\n",
            "discriminator_loss:  0.061262526\n",
            "generator_loss:  13.952153\n",
            "discriminator_loss:  0.0020752803\n",
            "generator_loss:  12.775043\n",
            "discriminator_loss:  0.006118924\n",
            "generator_loss:  14.018236\n",
            "discriminator_loss:  0.002125761\n",
            "generator_loss:  13.33625\n",
            "discriminator_loss:  0.029123805\n",
            "generator_loss:  13.547131\n",
            "discriminator_loss:  0.016882466\n",
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            "discriminator_loss:  0.0018665249\n",
            "generator_loss:  13.229434\n",
            "discriminator_loss:  0.008861162\n",
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            "discriminator_loss:  0.055984605\n",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JNvShznZKtP"
      },
      "source": [
        ""
      ],
      "execution_count": 28,
      "outputs": []
    }
  ]
}