EDL_9_1_GAN.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_9_1_GAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install livelossplot --quiet"
      ],
      "metadata": {
        "id": "d_92PLgJvmJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kiUINXkSfWc2"
      },
      "outputs": [],
      "source": [
        "#@title The Imports\n",
        "from tensorflow.keras.datasets import mnist as data\n",
        "#from tensorflow.keras.datasets import fashion_mnist as data\n",
        "\n",
        "#from tensorflow.keras.utils import np_utils\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Input, Dense, Activation, Flatten, Reshape, Dropout, ZeroPadding2D\n",
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, UpSampling2D, Convolution2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import LeakyReLU\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from livelossplot import PlotLosses\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "import random\n",
        "from tqdm.notebook import tqdm as tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load and Normalize Training/Test Data\n",
        "(train_images, train_labels), (test_images, test_labels) = data.load_data()\n",
        "\n",
        "# Scale Dataset -1 to +1\n",
        "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype(\"float32\") / 127.5 - 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQlqVSsZfj7d",
        "outputId": "0813643e-0aac-42bf-82dc-abc4832ce10e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Extract Class of Images\n",
        "def extract(images, labels, class_):\n",
        "  idx = labels == class_\n",
        "  print(idx)\n",
        "  imgs = images[idx]\n",
        "  print(imgs.shape)\n",
        "  return imgs\n",
        "\n",
        "train_images = extract(train_images, train_labels, 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYBy0zWFHv1V",
        "outputId": "6a20fbcb-0cb2-4877-ebca-11afe4dc11e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ True False False ...  True False False]\n",
            "(5421, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Internal Hyperparameters and Optimizers Setup\n",
        "latent_dim = 100\n",
        "\n",
        "g_optimizer = Adam(0.0002, 0.5)\n",
        "d_optimizer = RMSprop(.00001)"
      ],
      "metadata": {
        "id": "u6H0Kjtofwrj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Determine Base Convolution Size\n",
        "cs = int(train_images.shape[1] / 4)\n",
        "print(train_images.shape)\n",
        "channels = train_images.shape[3]\n",
        "img_shape = (train_images.shape[1], train_images.shape[2], channels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLrt46129kOK",
        "outputId": "74b2b0ac-3a35-44b9-d186-82b2a6b36694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5421, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Building the Generator (g)\n",
        "def build_generator():\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Dense(128 * cs * cs, activation=\"relu\", input_dim=latent_dim))\n",
        "  model.add(Reshape((cs, cs, 128)))\n",
        "  model.add(UpSampling2D())\n",
        "  model.add(Conv2D(128, kernel_size=3, padding=\"same\"))\n",
        "  model.add(BatchNormalization(momentum=0.8))\n",
        "  model.add(Activation(\"relu\"))\n",
        "  model.add(UpSampling2D())\n",
        "  model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n",
        "  model.add(BatchNormalization(momentum=0.8))\n",
        "  model.add(Activation(\"relu\"))\n",
        "  model.add(Conv2D(channels, kernel_size=3, padding=\"same\"))\n",
        "  model.add(Activation(\"tanh\"))\n",
        "\n",
        "  model.summary(line_length = 100)\n",
        "\n",
        "  noise = Input(shape=(latent_dim,))\n",
        "  img = model(noise)\n",
        "  return Model(noise, img)\n",
        "g = build_generator()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YH7fCvZWho-r",
        "outputId": "e86070ee-de7f-44bc-e2a4-486312ba280e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "____________________________________________________________________________________________________\n",
            " Layer (type)                                Output Shape                            Param #        \n",
            "====================================================================================================\n",
            " dense (Dense)                               (None, 6272)                            633472         \n",
            "                                                                                                    \n",
            " reshape (Reshape)                           (None, 7, 7, 128)                       0              \n",
            "                                                                                                    \n",
            " up_sampling2d (UpSampling2D)                (None, 14, 14, 128)                     0              \n",
            "                                                                                                    \n",
            " conv2d (Conv2D)                             (None, 14, 14, 128)                     147584         \n",
            "                                                                                                    \n",
            " batch_normalization (BatchNormalization)    (None, 14, 14, 128)                     512            \n",
            "                                                                                                    \n",
            " activation (Activation)                     (None, 14, 14, 128)                     0              \n",
            "                                                                                                    \n",
            " up_sampling2d_1 (UpSampling2D)              (None, 28, 28, 128)                     0              \n",
            "                                                                                                    \n",
            " conv2d_1 (Conv2D)                           (None, 28, 28, 64)                      73792          \n",
            "                                                                                                    \n",
            " batch_normalization_1 (BatchNormalization)  (None, 28, 28, 64)                      256            \n",
            "                                                                                                    \n",
            " activation_1 (Activation)                   (None, 28, 28, 64)                      0              \n",
            "                                                                                                    \n",
            " conv2d_2 (Conv2D)                           (None, 28, 28, 1)                       577            \n",
            "                                                                                                    \n",
            " activation_2 (Activation)                   (None, 28, 28, 1)                       0              \n",
            "                                                                                                    \n",
            "====================================================================================================\n",
            "Total params: 856,193\n",
            "Trainable params: 855,809\n",
            "Non-trainable params: 384\n",
            "____________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Building the Discriminator (d)\n",
        "def build_discriminator():\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=img_shape, padding=\"same\"))\n",
        "  model.add(LeakyReLU(alpha=0.2))\n",
        "  model.add(Dropout(0.25))\n",
        "  model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n",
        "  model.add(ZeroPadding2D(padding=((0,1),(0,1))))\n",
        "  model.add(BatchNormalization(momentum=0.8))\n",
        "  model.add(LeakyReLU(alpha=0.2))\n",
        "  model.add(Dropout(0.25))\n",
        "  model.add(Conv2D(128, kernel_size=3, strides=2, padding=\"same\"))\n",
        "  model.add(BatchNormalization(momentum=0.8))\n",
        "  model.add(LeakyReLU(alpha=0.2))\n",
        "  model.add(Dropout(0.25))\n",
        "  model.add(Conv2D(256, kernel_size=3, strides=1, padding=\"same\"))\n",
        "  model.add(BatchNormalization(momentum=0.8))\n",
        "  model.add(LeakyReLU(alpha=0.2))\n",
        "  model.add(Dropout(0.25))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "  model.summary()\n",
        "\n",
        "  img = Input(shape=img_shape)\n",
        "  validity = model(img)\n",
        "\n",
        "  return Model(img, validity)\n",
        "\n",
        "d = build_discriminator()\n",
        "d.compile(loss='binary_crossentropy',\n",
        "            optimizer=d_optimizer,\n",
        "            metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLLMvHLPiVJ_",
        "outputId": "490bfca8-57cc-4098-bddf-562f561a1803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 14, 14, 32)        320       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 14, 14, 32)        0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 14, 14, 32)        0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 7, 7, 64)          18496     \n",
            "                                                                 \n",
            " zero_padding2d (ZeroPadding  (None, 8, 8, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 8, 8, 64)         256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 8, 8, 64)          0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 8, 8, 64)          0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 4, 4, 128)        512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 4, 4, 128)         0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 4, 4, 128)         0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 4, 4, 256)         295168    \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 4, 4, 256)        1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 4, 4, 256)         0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 4, 4, 256)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 4097      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 393,729\n",
            "Trainable params: 392,833\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Building the GAN\n",
        "z = Input(shape=(latent_dim,))\n",
        "img = g(z)\n",
        "\n",
        "# For the combined model we will only train the generator\n",
        "d.trainable = False\n",
        "\n",
        "# The discriminator takes generated images as input and determines validity\n",
        "valid = d(img)\n",
        "\n",
        "# The combined model  (stacked generator and discriminator)\n",
        "# Trains the generator to fool the discriminator\n",
        "gan = Model(z, valid)\n",
        "gan.compile(loss='binary_crossentropy', optimizer=g_optimizer)"
      ],
      "metadata": {
        "id": "eZIb_Y1Yikws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plot Helper Functions    \n",
        "def plot_generated(n_ex=10, dim=(1, 10), figsize=(12, 2)):\n",
        "    noise = np.random.normal(0, 1, size=(n_ex, latent_dim))\n",
        "    generated_images = g.predict(noise)\n",
        "    generated_images = generated_images.reshape(n_ex, img_shape[0], img_shape[1])\n",
        "\n",
        "    plt.figure(figsize=figsize)\n",
        "    for i in range(generated_images.shape[0]):\n",
        "        plt.subplot(dim[0], dim[1], i+1)\n",
        "        plt.imshow((1-generated_images[i])*255, interpolation='nearest', cmap='gray_r')\n",
        "        plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "e3lCo1msizjr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Training the GAN\n",
        "EPOCHS = 10 #@param {type:\"slider\", min:5, max:100, step:1}\n",
        "BATCH_SIZE = 256 #@param {type:\"slider\", min:64, max:256, step:2}\n",
        "\n",
        "groups = { \"Discriminator\" : {\"Real\", \"Fake\"}, \"Generator\":{\"Gen\"}}\n",
        "plotlosses = PlotLosses(groups=groups)\n",
        "\n",
        "batches = int(train_images.shape[0] / BATCH_SIZE)\n",
        "\n",
        "# Adversarial ground truths\n",
        "valid = np.ones((BATCH_SIZE, 1))\n",
        "fake = np.zeros((BATCH_SIZE, 1))\n",
        "\n",
        "for e in range(EPOCHS):\n",
        "  for i in tqdm(range(batches)):\n",
        "    # ---------------------\n",
        "    #  Train Discriminator\n",
        "    # ---------------------\n",
        "    # Select a random half of images\n",
        "    idx = np.random.randint(0, train_images.shape[0], BATCH_SIZE)\n",
        "    imgs = train_images[idx]  \n",
        "\n",
        "    # Sample noise and generate a batch of new images\n",
        "    noise = np.random.normal(0, 1, (BATCH_SIZE, latent_dim))\n",
        "    gen_imgs = g.predict(noise)\n",
        "\n",
        "    # Train the discriminator (real classified as ones and generated as zeros)\n",
        "    d_loss_real = d.train_on_batch(imgs, valid)\n",
        "    d_loss_fake = d.train_on_batch(gen_imgs, fake)\n",
        "    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)  \n",
        "\n",
        "    # ---------------------\n",
        "    #  Train Generator\n",
        "    # ---------------------\n",
        "    # Train the generator (wants discriminator to mistake images as real)\n",
        "    g_loss = gan.train_on_batch(noise, valid)  \n",
        "\n",
        "  loss = dict(Real = d_loss[0], Fake = d_loss[1], Gen = g_loss)  \n",
        "  plotlosses.update(loss)\n",
        "  plotlosses.send()\n",
        "  plot_generated()\n"
      ],
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            "41611fe22dd84c13900bfa621fc180ee",
            "cffa0e95be9544ce838d0890fc125253",
            "7a2c50dc76dc446ea466adb149ae9be8",
            "2c4cefe716564b0d8efe1d1d8e77beec",
            "1a8f19285a46496f880862935831b9a8",
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            "b288b49b86ad4fe4913c06211cb9525f",
            "bc9d31a8a3104cb794c97d6fe73c7664",
            "5c937dda3c9b4a78aa13164f92794179",
            "89e03e1ab3594b8e8f41e3184502b2d4",
            "631501fba8a74034bed449031935b594",
            "c88f33b5fd9643178d1d73c6ab2bd8f5",
            "8014ddb6dc36453f94608731ddaaf3c1",
            "a4a2aa6926b84369861448e3460b8a78",
            "309bf15884ab451697f2582b6622a50a",
            "4eee4ed1a0ad4a7b824d61710f4cf388",
            "fcbe8296774e41d68de28d5a49ea9f1f",
            "f09bcb6169f54657a181b28e1546d28b",
            "d573a3250e2f46a38f585b873ea5f357",
            "917677f47dae43138de01466c64c2244",
            "03020c58266e46b49ebe07f8d5f7bcf9",
            "56935e5909874da4af90961274c920ae",
            "8465b3c17d6d47b3b7db1519e60ef528",
            "3410c9d7f7074bad882498ae4f415bc6",
            "84235e631a3e456f8d16a71c534c02df",
            "dc1ad8f126b94f2fa70f4d08ebea61be",
            "e90510656218422b970eec2485ee2edf",
            "5581e057ece44be98acf728ecc56805d",
            "5a1df3d2dfb14dd08dd2e5c2c2d99e42",
            "14c858c1510d49f7893dbb4f59875778",
            "5b123411766646fe8452b838ae97f325",
            "c292e02838db4622b9f49540172e3f07",
            "9a09ef70663145719b86c9ec1174bb59",
            "883318e205024e16bb80d41f03f3c18d",
            "9e59361ea6e54b83a9edd7d76182f789",
            "0f7fb68ff9b44f4b8c1a7dab9d75579d",
            "0029cbb8346d46baa00c096c66a03454",
            "ff69a0c7a02a49ecb559672ec6784f53",
            "8618ce4f6fe5446fa33ff6cc0b8fc615",
            "d9dd7410045d486babcf59c5460dded5",
            "b482321599584c6c9aa8cdff5ae15bdc",
            "6e586f1f3a704657aa35b601dd8a2a63",
            "9bd3cd22f19f44748e4d53e1daf1f13e",
            "2397ecc7a3264653a31fbc20804838ba"
          ]
        },
        "id": "EhCSzeX5jQI1",
        "outputId": "51dd114e-0132-4d6b-bbd0-2da27267a6aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Discriminator\n",
            "\tReal             \t (min:    0.763, max:    1.032, cur:    0.763)\n",
            "\tFake             \t (min:    0.410, max:    0.549, cur:    0.549)\n",
            "Generator\n",
            "\tGen              \t (min:    0.335, max:    0.716, cur:    0.716)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}