GAN_tf_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GAN-tf-mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMW4+FubQz6rovdW6f004Ch",
      "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/dvircohen0/GAN/blob/main/GAN_tf_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDZsjtNJ-EIs"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "X_train = X_train.reshape(X_train.shape[0], 28*28)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28*28)\n",
        "X_train = X_train.astype(np.float32)\n",
        "X_test = X_test.astype(np.float32)\n",
        "X_train /= 255\n",
        "X_test /= 255\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNjwJIrDJtaC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f571765e-24d3-485d-85d6-ecd7f127ee5e"
      },
      "source": [
        "from tqdm import tqdm\n",
        "from keras.layers import Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers.core import Reshape, Dense, Dropout \n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "np.random.seed(1000)\n",
        "randomDim = 100\n",
        "\n",
        "generator = Sequential()\n",
        "generator.add(Dense(256, input_dim=randomDim))\n",
        "generator.add(LeakyReLU(0.2))\n",
        "generator.add(Dense(512))\n",
        "generator.add(LeakyReLU(0.2))\n",
        "generator.add(Dense(1024))\n",
        "generator.add(LeakyReLU(0.2))\n",
        "generator.add(Dense(784, activation='tanh'))\n",
        "adam = Adam(lr=0.0002, beta_1=0.5)\n",
        "generator.compile(loss='binary_crossentropy', optimizer=adam)\n",
        "\n",
        "discriminator = Sequential()\n",
        "discriminator.add(Dense(1024, input_dim=784))\n",
        "discriminator.add(LeakyReLU(0.2))\n",
        "discriminator.add(Dropout(0.3))\n",
        "discriminator.add(Dense(512))\n",
        "discriminator.add(LeakyReLU(0.2))\n",
        "discriminator.add(Dropout(0.3))\n",
        "discriminator.add(Dense(256))\n",
        "discriminator.add(LeakyReLU(0.2))\n",
        "discriminator.add(Dropout(0.3))\n",
        "discriminator.add(Dense(1, activation='sigmoid'))\n",
        "discriminator.compile(loss='binary_crossentropy', optimizer=adam)\n",
        "\n",
        "discriminator.trainable = False\n",
        "ganInput = Input(shape=(randomDim,))\n",
        "x = generator(ganInput)\n",
        "ganOutput = discriminator(x)\n",
        "gan = Model(inputs=ganInput, outputs=ganOutput)\n",
        "gan.compile(loss='binary_crossentropy', optimizer=adam)\n",
        "\n",
        "def train(epochs, batchSize):\n",
        "    batchCount = int(X_train.shape[0] / batchSize)\n",
        "    \n",
        "    for e in range(1, epochs+1):\n",
        "        print ('-'*15, 'Epoch %d' % e, '-'*15)\n",
        "        for _ in tqdm(range(batchCount)):\n",
        "            noise = np.random.normal(0, 1, size=[batchSize, randomDim])\n",
        "            imageBatch = X_train[np.random.randint(0, X_train.shape[0], size=batchSize)]\n",
        "            generatedImages = generator.predict(noise)\n",
        "            X = np.concatenate([imageBatch, generatedImages])\n",
        "            yDis = np.zeros(2*batchSize)\n",
        "            yDis[:batchSize] = 1\n",
        "            discriminator.trainable = True\n",
        "            dloss = discriminator.train_on_batch(X, yDis)\n",
        "            noise = np.random.normal(0, 1, size=[batchSize, randomDim])\n",
        "            yGen = np.ones(batchSize)\n",
        "            discriminator.trainable = False\n",
        "            gloss = gan.train_on_batch(noise, yGen)\n",
        "train(100, 128)\n",
        " "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 1 ---------------\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 2 ---------------\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 3 ---------------\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 4 ---------------\n"
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        {
          "output_type": "stream",
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            "--------------- Epoch 5 ---------------\n"
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        {
          "output_type": "stream",
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            "--------------- Epoch 6 ---------------\n"
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        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 7 ---------------\n"
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          "output_type": "stream",
          "text": [
            "--------------- Epoch 8 ---------------\n"
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        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 9 ---------------\n"
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        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 10 ---------------\n"
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            "--------------- Epoch 11 ---------------\n"
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            "--------------- Epoch 12 ---------------\n"
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            "--------------- Epoch 13 ---------------\n"
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            "--------------- Epoch 14 ---------------\n"
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            "--------------- Epoch 15 ---------------\n"
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            "--------------- Epoch 17 ---------------\n"
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            "--------------- Epoch 18 ---------------\n"
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            "--------------- Epoch 19 ---------------\n"
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            "--------------- Epoch 21 ---------------\n"
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            "--------------- Epoch 22 ---------------\n"
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            "--------------- Epoch 23 ---------------\n"
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            "--------------- Epoch 24 ---------------\n"
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            "--------------- Epoch 25 ---------------\n"
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            "--------------- Epoch 26 ---------------\n"
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            "--------------- Epoch 27 ---------------\n"
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          "output_type": "stream",
          "text": [
            "--------------- Epoch 28 ---------------\n"
          ],
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          "text": [
            "--------------- Epoch 29 ---------------\n"
          ],
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          "text": [
            "--------------- Epoch 30 ---------------\n"
          ],
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        },
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            "--------------- Epoch 31 ---------------\n"
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          "text": [
            "--------------- Epoch 32 ---------------\n"
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            "--------------- Epoch 33 ---------------\n"
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            "--------------- Epoch 34 ---------------\n"
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            "--------------- Epoch 35 ---------------\n"
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            "--------------- Epoch 36 ---------------\n"
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            "--------------- Epoch 37 ---------------\n"
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            "--------------- Epoch 38 ---------------\n"
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            "--------------- Epoch 39 ---------------\n"
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            "--------------- Epoch 40 ---------------\n"
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            "--------------- Epoch 41 ---------------\n"
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            "--------------- Epoch 42 ---------------\n"
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          "output_type": "stream",
          "text": [
            "100%|██████████| 468/468 [00:22<00:00, 20.88it/s]\n",
            "  1%|          | 3/468 [00:00<00:22, 21.10it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 96 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 468/468 [00:22<00:00, 20.76it/s]\n",
            "  1%|          | 3/468 [00:00<00:21, 21.53it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 97 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 468/468 [00:22<00:00, 20.82it/s]\n",
            "  0%|          | 2/468 [00:00<00:23, 19.67it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 98 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 468/468 [00:22<00:00, 20.73it/s]\n",
            "  1%|          | 3/468 [00:00<00:22, 20.59it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 99 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 468/468 [00:22<00:00, 20.76it/s]\n",
            "  1%|          | 3/468 [00:00<00:21, 21.28it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 100 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 468/468 [00:22<00:00, 20.79it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf48JfSzD7DI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "01efd6a4-4f9f-4b1c-8c90-f0033ffd33eb"
      },
      "source": [
        "examples=5\n",
        "dim=(1,5)\n",
        "figsize=(10, 10)\n",
        "noise = np.random.normal(0, 1, size=[examples, randomDim])\n",
        "generatedImages = generator.predict(noise)\n",
        "generatedImages = generatedImages.reshape(examples, 28, 28)\n",
        "\n",
        "plt.figure(figsize=figsize)\n",
        "for i in range(generatedImages.shape[0]):\n",
        "    plt.subplot(dim[0], dim[1], i+1)\n",
        "    plt.imshow(generatedImages[i], interpolation='nearest', cmap='gray_r')\n",
        "    plt.axis('off')\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}