Untitled6.ipynb

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        "<a href=\"https://colab.research.google.com/github/luke1961/ConsoleHelloWorld/blob/master/Untitled6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "'''GAN model builder and util functions\r\n",
        "\r\n",
        "[1] Radford, Alec, Luke Metz, and Soumith Chintala.\r\n",
        "\"Unsupervised representation learning with deep convolutional\r\n",
        "generative adversarial networks.\" arXiv preprint arXiv:1511.06434 (2015).\r\n",
        "\r\n",
        "'''\r\n",
        "\r\n",
        "\r\n",
        "from tensorflow.keras.layers import Activation, Dense, Input\r\n",
        "from tensorflow.keras.layers import Conv2D, Flatten\r\n",
        "from tensorflow.keras.layers import Reshape, Conv2DTranspose\r\n",
        "from tensorflow.keras.layers import LeakyReLU\r\n",
        "from tensorflow.keras.layers import BatchNormalization\r\n",
        "from tensorflow.keras.layers import concatenate\r\n",
        "from tensorflow.keras.models import Model\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "import math\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import os\r\n",
        "\r\n",
        "def generator(inputs,\r\n",
        "              image_size,\r\n",
        "              activation='sigmoid',\r\n",
        "              labels=None,\r\n",
        "              codes=None):\r\n",
        "    \"\"\"Build a Generator Model\r\n",
        "\r\n",
        "    Stack of BN-ReLU-Conv2DTranpose to generate fake images.\r\n",
        "    Output activation is sigmoid instead of tanh in [1].\r\n",
        "    Sigmoid converges easily.\r\n",
        "\r\n",
        "    Arguments:\r\n",
        "        inputs (Layer): Input layer of the generator (the z-vector)\r\n",
        "        image_size (int): Target size of one side \r\n",
        "            (assuming square image)\r\n",
        "        activation (string): Name of output activation layer\r\n",
        "        labels (tensor): Input labels\r\n",
        "        codes (list): 2-dim disentangled codes for InfoGAN\r\n",
        "\r\n",
        "    Returns:\r\n",
        "        Model: Generator Model\r\n",
        "    \"\"\"\r\n",
        "    image_resize = image_size // 4\r\n",
        "    # network parameters\r\n",
        "    kernel_size = 5\r\n",
        "    layer_filters = [128, 64, 32, 1]\r\n",
        "\r\n",
        "    if labels is not None:\r\n",
        "        if codes is None:\r\n",
        "            # ACGAN labels\r\n",
        "            # concatenate z noise vector and one-hot labels\r\n",
        "            inputs = [inputs, labels]\r\n",
        "        else:\r\n",
        "            # infoGAN codes\r\n",
        "            # concatenate z noise vector, \r\n",
        "            # one-hot labels and codes 1 & 2\r\n",
        "            inputs = [inputs, labels] + codes\r\n",
        "        x = concatenate(inputs, axis=1)\r\n",
        "    elif codes is not None:\r\n",
        "        # generator 0 of StackedGAN\r\n",
        "        inputs = [inputs, codes]\r\n",
        "        x = concatenate(inputs, axis=1)\r\n",
        "    else:\r\n",
        "        # default input is just 100-dim noise (z-code)\r\n",
        "        x = inputs\r\n",
        "\r\n",
        "    x = Dense(image_resize * image_resize * layer_filters[0])(x)\r\n",
        "    x = Reshape((image_resize, image_resize, layer_filters[0]))(x)\r\n",
        "\r\n",
        "    for filters in layer_filters:\r\n",
        "        # first two convolution layers use strides = 2\r\n",
        "        # the last two use strides = 1\r\n",
        "        if filters > layer_filters[-2]:\r\n",
        "            strides = 2\r\n",
        "        else:\r\n",
        "            strides = 1\r\n",
        "        x = BatchNormalization()(x)\r\n",
        "        x = Activation('relu')(x)\r\n",
        "        x = Conv2DTranspose(filters=filters,\r\n",
        "                            kernel_size=kernel_size,\r\n",
        "                            strides=strides,\r\n",
        "                            padding='same')(x)\r\n",
        "\r\n",
        "    if activation is not None:\r\n",
        "        x = Activation(activation)(x)\r\n",
        "\r\n",
        "    # generator output is the synthesized image x\r\n",
        "    return Model(inputs, x, name='generator')\r\n",
        "\r\n",
        "\r\n",
        "def discriminator(inputs,\r\n",
        "                  activation='sigmoid',\r\n",
        "                  num_labels=None,\r\n",
        "                  num_codes=None):\r\n",
        "    \"\"\"Build a Discriminator Model\r\n",
        "\r\n",
        "    Stack of LeakyReLU-Conv2D to discriminate real from fake\r\n",
        "    The network does not converge with BN so it is not used here\r\n",
        "    unlike in [1]\r\n",
        "\r\n",
        "    Arguments:\r\n",
        "        inputs (Layer): Input layer of the discriminator (the image)\r\n",
        "        activation (string): Name of output activation layer\r\n",
        "        num_labels (int): Dimension of one-hot labels for ACGAN & InfoGAN\r\n",
        "        num_codes (int): num_codes-dim Q network as output \r\n",
        "                    if StackedGAN or 2 Q networks if InfoGAN\r\n",
        "                    \r\n",
        "\r\n",
        "    Returns:\r\n",
        "        Model: Discriminator Model\r\n",
        "    \"\"\"\r\n",
        "    kernel_size = 5\r\n",
        "    layer_filters = [32, 64, 128, 256]\r\n",
        "\r\n",
        "    x = inputs\r\n",
        "    for filters in layer_filters:\r\n",
        "        # first 3 convolution layers use strides = 2\r\n",
        "        # last one uses strides = 1\r\n",
        "        if filters == layer_filters[-1]:\r\n",
        "            strides = 1\r\n",
        "        else:\r\n",
        "            strides = 2\r\n",
        "        x = LeakyReLU(alpha=0.2)(x)\r\n",
        "        x = Conv2D(filters=filters,\r\n",
        "                   kernel_size=kernel_size,\r\n",
        "                   strides=strides,\r\n",
        "                   padding='same')(x)\r\n",
        "\r\n",
        "    x = Flatten()(x)\r\n",
        "    # default output is probability that the image is real\r\n",
        "    outputs = Dense(1)(x)\r\n",
        "    if activation is not None:\r\n",
        "        print(activation)\r\n",
        "        outputs = Activation(activation)(outputs)\r\n",
        "\r\n",
        "    if num_labels:\r\n",
        "        # ACGAN and InfoGAN have 2nd output\r\n",
        "        # 2nd output is 10-dim one-hot vector of label\r\n",
        "        layer = Dense(layer_filters[-2])(x)\r\n",
        "        labels = Dense(num_labels)(layer)\r\n",
        "        labels = Activation('softmax', name='label')(labels)\r\n",
        "        if num_codes is None:\r\n",
        "            outputs = [outputs, labels]\r\n",
        "        else:\r\n",
        "            # InfoGAN have 3rd and 4th outputs\r\n",
        "            # 3rd output is 1-dim continous Q of 1st c given x\r\n",
        "            code1 = Dense(1)(layer)\r\n",
        "            code1 = Activation('sigmoid', name='code1')(code1)\r\n",
        "\r\n",
        "            # 4th output is 1-dim continuous Q of 2nd c given x\r\n",
        "            code2 = Dense(1)(layer)\r\n",
        "            code2 = Activation('sigmoid', name='code2')(code2)\r\n",
        "\r\n",
        "            outputs = [outputs, labels, code1, code2]\r\n",
        "    elif num_codes is not None:\r\n",
        "        # StackedGAN Q0 output\r\n",
        "        # z0_recon is reconstruction of z0 normal distribution\r\n",
        "        z0_recon =  Dense(num_codes)(x)\r\n",
        "        z0_recon = Activation('tanh', name='z0')(z0_recon)\r\n",
        "        outputs = [outputs, z0_recon]\r\n",
        "\r\n",
        "    return Model(inputs, outputs, name='discriminator')\r\n",
        "\r\n",
        "\r\n",
        "def train(models, x_train, params):\r\n",
        "    \"\"\"Train the Discriminator and Adversarial Networks\r\n",
        "\r\n",
        "    Alternately train Discriminator and Adversarial networks by batch.\r\n",
        "    Discriminator is trained first with properly real and fake images.\r\n",
        "    Adversarial is trained next with fake images pretending to be real\r\n",
        "    Generate sample images per save_interval.\r\n",
        "\r\n",
        "    # Arguments\r\n",
        "        models (list): Generator, Discriminator, Adversarial models\r\n",
        "        x_train (tensor): Train images\r\n",
        "        params (list) : Networks parameters\r\n",
        "\r\n",
        "    \"\"\"\r\n",
        "    # the GAN models\r\n",
        "    generator, discriminator, adversarial = models\r\n",
        "    # network parameters\r\n",
        "    batch_size, latent_size, train_steps, model_name = params\r\n",
        "    # the generator image is saved every 500 steps\r\n",
        "    save_interval = 500\r\n",
        "    # noise vector to see how the generator output \r\n",
        "    # evolves during training\r\n",
        "    noise_input = np.random.uniform(-1.0, 1.0, size=[16, latent_size])\r\n",
        "    # number of elements in train dataset\r\n",
        "    train_size = x_train.shape[0]\r\n",
        "    for i in range(train_steps):\r\n",
        "        # train the discriminator for 1 batch\r\n",
        "        # 1 batch of real (label=1.0) and fake images (label=0.0)\r\n",
        "        # randomly pick real images from dataset\r\n",
        "        rand_indexes = np.random.randint(0,\r\n",
        "                                         train_size, \r\n",
        "                                         size=batch_size)\r\n",
        "        real_images = x_train[rand_indexes]\r\n",
        "        # generate fake images from noise using generator \r\n",
        "        # generate noise using uniform distribution\r\n",
        "        noise = np.random.uniform(-1.0,\r\n",
        "                                  1.0, \r\n",
        "                                  size=[batch_size, latent_size])\r\n",
        "        # generate fake images\r\n",
        "        fake_images = generator.predict(noise)\r\n",
        "        # real + fake images = 1 batch of train data\r\n",
        "        x = np.concatenate((real_images, fake_images))\r\n",
        "        # label real and fake images\r\n",
        "        # real images label is 1.0\r\n",
        "        y = np.ones([2 * batch_size, 1])\r\n",
        "        # fake images label is 0.0\r\n",
        "        y[batch_size:, :] = 0.0\r\n",
        "        # train discriminator network, log the loss and accuracy\r\n",
        "        loss, acc = discriminator.train_on_batch(x, y)\r\n",
        "        log = \"%d: [discriminator loss: %f, acc: %f]\" % (i, loss, acc)\r\n",
        "\r\n",
        "        # train the adversarial network for 1 batch\r\n",
        "        # 1 batch of fake images with label=1.0\r\n",
        "        # since the discriminator weights are frozen \r\n",
        "        # in adversarial network only the generator is trained\r\n",
        "        # generate noise using uniform distribution\r\n",
        "        noise = np.random.uniform(-1.0,\r\n",
        "                                  1.0, \r\n",
        "                                  size=[batch_size, latent_size])\r\n",
        "        # label fake images as real or 1.0\r\n",
        "        y = np.ones([batch_size, 1])\r\n",
        "        # train the adversarial network \r\n",
        "        # note that unlike in discriminator training, \r\n",
        "        # we do not save the fake images in a variable\r\n",
        "        # the fake images go to the discriminator input\r\n",
        "        # of the adversarial for classification\r\n",
        "        # log the loss and accuracy\r\n",
        "        loss, acc = adversarial.train_on_batch(noise, y)\r\n",
        "        log = \"%s [adversarial loss: %f, acc: %f]\" % (log, loss, acc)\r\n",
        "        print(log)\r\n",
        "        if (i + 1) % save_interval == 0:\r\n",
        "            # plot generator images on a periodic basis\r\n",
        "            plot_images(generator,\r\n",
        "                        noise_input=noise_input,\r\n",
        "                        show=False,\r\n",
        "                        step=(i + 1),\r\n",
        "                        model_name=model_name)\r\n",
        "   \r\n",
        "    # save the model after training the generator\r\n",
        "    # the trained generator can be reloaded \r\n",
        "    # for future MNIST digit generation\r\n",
        "    generator.save(model_name + \".h5\")\r\n",
        "\r\n",
        "\r\n",
        "def plot_images(generator,\r\n",
        "                noise_input,\r\n",
        "                noise_label=None,\r\n",
        "                noise_codes=None,\r\n",
        "                show=False,\r\n",
        "                step=0,\r\n",
        "                model_name=\"gan\"):\r\n",
        "    \"\"\"Generate fake images and plot them\r\n",
        "\r\n",
        "    For visualization purposes, generate fake images\r\n",
        "    then plot them in a square grid\r\n",
        "\r\n",
        "    # Arguments\r\n",
        "        generator (Model): The Generator Model for \r\n",
        "            fake images generation\r\n",
        "        noise_input (ndarray): Array of z-vectors\r\n",
        "        show (bool): Whether to show plot or not\r\n",
        "        step (int): Appended to filename of the save images\r\n",
        "        model_name (string): Model name\r\n",
        "\r\n",
        "    \"\"\"\r\n",
        "    os.makedirs(model_name, exist_ok=True)\r\n",
        "    filename = os.path.join(model_name, \"%05d.png\" % step)\r\n",
        "    rows = int(math.sqrt(noise_input.shape[0]))\r\n",
        "    if noise_label is not None:\r\n",
        "        noise_input = [noise_input, noise_label]\r\n",
        "        if noise_codes is not None:\r\n",
        "            noise_input += noise_codes\r\n",
        "\r\n",
        "    images = generator.predict(noise_input)\r\n",
        "    plt.figure(figsize=(2.2, 2.2))\r\n",
        "    num_images = images.shape[0]\r\n",
        "    image_size = images.shape[1]\r\n",
        "    for i in range(num_images):\r\n",
        "        plt.subplot(rows, rows, i + 1)\r\n",
        "        image = np.reshape(images[i], [image_size, image_size])\r\n",
        "        plt.imshow(image, cmap='gray')\r\n",
        "        plt.axis('off')\r\n",
        "    plt.savefig(filename)\r\n",
        "    if show:\r\n",
        "        plt.show()\r\n",
        "    else:\r\n",
        "        plt.close('all')\r\n",
        "\r\n",
        "\r\n",
        "def test_generator(generator):\r\n",
        "    noise_input = np.random.uniform(-1.0, 1.0, size=[16, 100])\r\n",
        "    plot_images(generator,\r\n",
        "                noise_input=noise_input,\r\n",
        "                show=True,\r\n",
        "                model_name=\"test_outputs\")\r\n",
        "\r\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "e8f10b6c-36af-4f4b-d2e7-3b939c369ecb"
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      "source": [
        "'''Trains LSGAN on MNIST using Keras\r\n",
        "\r\n",
        "LSGAN is similar to DCGAN except for the MSE loss used by the \r\n",
        "Discriminator and Adversarial networks.\r\n",
        "  \r\n",
        "[1] Radford, Alec, Luke Metz, and Soumith Chintala.\r\n",
        "\"Unsupervised representation learning with deep convolutional\r\n",
        "generative adversarial networks.\" arXiv preprint arXiv:1511.06434 (2015).\r\n",
        "\r\n",
        "[2] Mao, Xudong, et al. \"Least squares generative adversarial networks.\" \r\n",
        "2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.\r\n",
        "'''\r\n",
        "\r\n",
        "from __future__ import absolute_import\r\n",
        "from __future__ import division\r\n",
        "from __future__ import print_function\r\n",
        "\r\n",
        "from tensorflow.keras.layers import Input\r\n",
        "from tensorflow.keras.optimizers import RMSprop\r\n",
        "from tensorflow.keras.models import Model\r\n",
        "from tensorflow.keras.datasets import mnist\r\n",
        "from tensorflow.keras.models import load_model\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "import argparse\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "def build_and_train_models():\r\n",
        "    \"\"\"Load the dataset, build LSGAN discriminator,\r\n",
        "    generator, and adversarial models.\r\n",
        "    Call the LSGAN train routine.\r\n",
        "    \"\"\"\r\n",
        "    # load MNIST dataset\r\n",
        "    (x_train, _), (_, _) = mnist.load_data()\r\n",
        "\r\n",
        "    # reshape data for CNN as (28, 28, 1) and normalize\r\n",
        "    image_size = x_train.shape[1]\r\n",
        "    x_train = np.reshape(x_train, \r\n",
        "                         [-1, image_size, image_size, 1])\r\n",
        "    x_train = x_train.astype('float32') / 255\r\n",
        "\r\n",
        "    model_name = \"lsgan_mnist\"\r\n",
        "    # network parameters\r\n",
        "    # the latent or z vector is 100-dim\r\n",
        "    latent_size = 100\r\n",
        "    input_shape = (image_size, image_size, 1)\r\n",
        "    batch_size = 64\r\n",
        "    lr = 2e-4\r\n",
        "    decay = 6e-8\r\n",
        "    train_steps = 20000\r\n",
        "\r\n",
        "    # build discriminator model\r\n",
        "    inputs = Input(shape=input_shape, name='discriminator_input')\r\n",
        "    discriminator1 = discriminator(inputs, activation=None)\r\n",
        "    # [1] uses Adam, but discriminator easily \r\n",
        "    # converges with RMSprop\r\n",
        "    optimizer = RMSprop(lr=lr, decay=decay)\r\n",
        "    # LSGAN uses MSE loss [2]\r\n",
        "    discriminator1.compile(loss='mse',\r\n",
        "                          optimizer=optimizer,\r\n",
        "                          metrics=['accuracy'])\r\n",
        "    discriminator1.summary()\r\n",
        "\r\n",
        "    # build generator model\r\n",
        "    input_shape = (latent_size, )\r\n",
        "    inputs = Input(shape=input_shape, name='z_input')\r\n",
        "    generator1 = generator(inputs, image_size)\r\n",
        "    generator1.summary()\r\n",
        "\r\n",
        "    # build adversarial model = generator + discriminator\r\n",
        "    optimizer = RMSprop(lr=lr*0.5, decay=decay*0.5)\r\n",
        "    # freeze the weights of discriminator \r\n",
        "    # during adversarial training\r\n",
        "    discriminator.trainable = False\r\n",
        "    adversarial = Model(inputs,\r\n",
        "                        discriminator1(generator1(inputs)),\r\n",
        "                        name=model_name)\r\n",
        "    # LSGAN uses MSE loss [2]\r\n",
        "    adversarial.compile(loss='mse',\r\n",
        "                        optimizer=optimizer,\r\n",
        "                        metrics=['accuracy'])\r\n",
        "    adversarial.summary()\r\n",
        "\r\n",
        "    # train discriminator and adversarial networks\r\n",
        "    models = (generator1, discriminator1, adversarial)\r\n",
        "    params = (batch_size, latent_size, train_steps, model_name)\r\n",
        "    train(models, x_train, params)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "build_and_train_models()\r\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "15000: [discriminator loss: 0.025003, acc: 1.000000] [adversarial loss: 0.071427, acc: 0.953125]\n",
            "15001: [discriminator loss: 0.024459, acc: 1.000000] [adversarial loss: 0.338724, acc: 0.281250]\n",
            "15002: [discriminator loss: 0.047092, acc: 0.976562] [adversarial loss: 0.412842, acc: 0.109375]\n",
            "15003: [discriminator loss: 0.042525, acc: 0.992188] [adversarial loss: 0.626279, acc: 0.000000]\n",
            "15004: [discriminator loss: 0.055546, acc: 0.984375] [adversarial loss: 0.657846, acc: 0.015625]\n",
            "15005: [discriminator loss: 0.039287, acc: 0.992188] [adversarial loss: 0.477738, acc: 0.031250]\n",
            "15006: [discriminator loss: 0.067886, acc: 0.968750] [adversarial loss: 0.368170, acc: 0.265625]\n",
            "15007: [discriminator loss: 0.090168, acc: 0.945312] [adversarial loss: 1.034584, acc: 0.000000]\n",
            "15008: [discriminator loss: 0.132976, acc: 0.820312] [adversarial loss: 0.361455, acc: 0.078125]\n",
            "15009: [discriminator loss: 0.078287, acc: 0.968750] [adversarial loss: 0.916483, acc: 0.000000]\n",
            "15010: [discriminator loss: 0.043662, acc: 0.976562] [adversarial loss: 0.327688, acc: 0.218750]\n",
            "15011: [discriminator loss: 0.116189, acc: 0.937500] [adversarial loss: 0.997768, acc: 0.000000]\n",
            "15012: [discriminator loss: 0.092194, acc: 0.882812] [adversarial loss: 0.455255, acc: 0.000000]\n",
            "15013: [discriminator loss: 0.115116, acc: 0.976562] [adversarial loss: 0.962724, acc: 0.000000]\n",
            "15014: [discriminator loss: 0.085325, acc: 0.921875] [adversarial loss: 0.576019, acc: 0.000000]\n",
            "15015: [discriminator loss: 0.084195, acc: 0.992188] [adversarial loss: 0.929353, acc: 0.000000]\n",
            "15016: [discriminator loss: 0.051414, acc: 0.984375] [adversarial loss: 0.643242, acc: 0.000000]\n",
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            "19999: [discriminator loss: 0.045013, acc: 0.992188] [adversarial loss: 0.076560, acc: 1.000000]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}