Deep Learning Projects/Generative Adversarial Networks/Wasserstein_GAN.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Activation, Dense, Input, Conv2D, Flatten, Reshape, Conv2DTranspose, LeakyReLU, BatchNormalization\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.datasets import mnist\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import os"
      ],
      "metadata": {
        "id": "zf9qUjcGHzWJ"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gen_block(x, filters = 128, kernel_size = 5, strides = 2, padding = \"same\"):\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "    x = Conv2DTranspose(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding)(x)\n",
        "    return x\n",
        "\n",
        "def build_generator(inputs, image_size):\n",
        "  image_resize = image_size // 4\n",
        "  x = Dense(image_resize * image_resize * 128)(inputs)\n",
        "  x = Reshape((image_resize, image_resize, 128))(x)\n",
        "  x = gen_block(x = x, filters = 128, kernel_size = 5, strides = 2, padding = \"same\")\n",
        "  x = gen_block(x = x, filters = 64, kernel_size = 5, strides = 2, padding = \"same\")\n",
        "  x = gen_block(x = x, filters = 32, kernel_size = 5, strides = 1, padding = \"same\")\n",
        "  x = gen_block(x = x, filters = 1, kernel_size = 5, strides = 1, padding = \"same\")\n",
        "  x = Activation('sigmoid')(x)\n",
        "  generator = Model(inputs, x, name = \"generator\")\n",
        "  return generator\n",
        "\n",
        "\n",
        "def disc_block(x, filters = 64, kernel_size = 5, strides = 2, padding = \"same\"):\n",
        "  x = BatchNormalization()(x)\n",
        "  x = LeakyReLU(alpha = 0.2)(x)\n",
        "  x = Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding)(x)\n",
        "  return x\n",
        "\n",
        "def build_discriminator(inputs):\n",
        "   x = inputs\n",
        "   x = LeakyReLU(alpha = 0.2)(x)\n",
        "   x = Conv2D(filters = 32, kernel_size = 5, strides = 2, padding = \"same\")(x)\n",
        "   x = disc_block(x = x, filters = 64, kernel_size = 5, strides = 2, padding = \"same\")\n",
        "   x = disc_block(x = x, filters = 128, kernel_size = 5, strides = 2, padding = \"same\")\n",
        "   x = disc_block(x = x, filters = 256, kernel_size = 5, strides = 1, padding = \"same\")\n",
        "   x = Flatten()(x)\n",
        "   x = Dense(1)(x)\n",
        "   x = Activation(\"linear\")(x)\n",
        "   discriminator = Model(inputs, x, name = 'discriminator')\n",
        "   return discriminator"
      ],
      "metadata": {
        "id": "m0SNfmStCf7a"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, _), (_ , _) = mnist.load_data()\n",
        "image_size = x_train.shape[1]\n",
        "x_train = np.reshape(x_train, [-1, image_size, image_size, 1])\n",
        "x_train = x_train.astype('float32') / 255\n",
        "\n",
        "model_name = \"wagan_mnist\"\n",
        "n_critic = 5\n",
        "clip_value = 0.01\n",
        "latent_size = 100 # latent vector of 100-dim\n",
        "batch_size = 64\n",
        "lr = 5e-5\n",
        "train_steps = 3000 # one can train for more number of steps to get better quality\n",
        "input_shape = (image_size, image_size, 1)\n",
        "\n",
        "# loss function for the WGAN\n",
        "def wassertian_loss(y_label, y_pred):\n",
        "  return -K.mean(y_label * y_pred)\n",
        "\n",
        "# discriminator model\n",
        "inputs = Input(shape = input_shape, name = \"discriminator_input\")\n",
        "discriminator = build_discriminator(inputs)\n",
        "discriminator.compile(loss = wassertian_loss,\n",
        "                      optimizer = RMSprop(learning_rate = lr),\n",
        "                      metrics = ['accuracy'])\n",
        "discriminator.summary()\n",
        "\n",
        "# generator model\n",
        "input_shape = (latent_size,)\n",
        "inputs = Input(shape = input_shape, name = 'generator_input')\n",
        "generator = build_generator(inputs, image_size)\n",
        "generator.summary()\n",
        "\n",
        "# adversarial model\n",
        "discriminator.trainable = False\n",
        "adversarial = Model(inputs, discriminator(generator(inputs)), name = model_name)\n",
        "adversarial.compile(loss = wassertian_loss,\n",
        "                    optimizer = RMSprop(learning_rate = lr),\n",
        "                    metrics = ['accuracy'])\n",
        "adversarial.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25neDnZcHXjc",
        "outputId": "21b548bd-6292-4482-933c-63eac3933108"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"discriminator\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " discriminator_input (Input  [(None, 28, 28, 1)]       0         \n",
            " Layer)                                                          \n",
            "                                                                 \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 28, 28, 1)         0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 14, 14, 32)        832       \n",
            "                                                                 \n",
            " batch_normalization_7 (Bat  (None, 14, 14, 32)        128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 14, 14, 32)        0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 7, 7, 64)          51264     \n",
            "                                                                 \n",
            " batch_normalization_8 (Bat  (None, 7, 7, 64)          256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 7, 7, 64)          0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 4, 4, 128)         204928    \n",
            "                                                                 \n",
            " batch_normalization_9 (Bat  (None, 4, 4, 128)         512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " leaky_re_lu_7 (LeakyReLU)   (None, 4, 4, 128)         0         \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 4, 4, 256)         819456    \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 4097      \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 1)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1081473 (4.13 MB)\n",
            "Trainable params: 1081025 (4.12 MB)\n",
            "Non-trainable params: 448 (1.75 KB)\n",
            "_________________________________________________________________\n",
            "Model: \"generator\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " generator_input (InputLaye  [(None, 100)]             0         \n",
            " r)                                                              \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 6272)              633472    \n",
            "                                                                 \n",
            " reshape_1 (Reshape)         (None, 7, 7, 128)         0         \n",
            "                                                                 \n",
            " batch_normalization_10 (Ba  (None, 7, 7, 128)         512       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_7 (Activation)   (None, 7, 7, 128)         0         \n",
            "                                                                 \n",
            " conv2d_transpose_4 (Conv2D  (None, 14, 14, 128)       409728    \n",
            " Transpose)                                                      \n",
            "                                                                 \n",
            " batch_normalization_11 (Ba  (None, 14, 14, 128)       512       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_8 (Activation)   (None, 14, 14, 128)       0         \n",
            "                                                                 \n",
            " conv2d_transpose_5 (Conv2D  (None, 28, 28, 64)        204864    \n",
            " Transpose)                                                      \n",
            "                                                                 \n",
            " batch_normalization_12 (Ba  (None, 28, 28, 64)        256       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_9 (Activation)   (None, 28, 28, 64)        0         \n",
            "                                                                 \n",
            " conv2d_transpose_6 (Conv2D  (None, 28, 28, 32)        51232     \n",
            " Transpose)                                                      \n",
            "                                                                 \n",
            " batch_normalization_13 (Ba  (None, 28, 28, 32)        128       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_10 (Activation)  (None, 28, 28, 32)        0         \n",
            "                                                                 \n",
            " conv2d_transpose_7 (Conv2D  (None, 28, 28, 1)         801       \n",
            " Transpose)                                                      \n",
            "                                                                 \n",
            " activation_11 (Activation)  (None, 28, 28, 1)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1301505 (4.96 MB)\n",
            "Trainable params: 1300801 (4.96 MB)\n",
            "Non-trainable params: 704 (2.75 KB)\n",
            "_________________________________________________________________\n",
            "Model: \"wagan_mnist\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " generator_input (InputLaye  [(None, 100)]             0         \n",
            " r)                                                              \n",
            "                                                                 \n",
            " generator (Functional)      (None, 28, 28, 1)         1301505   \n",
            "                                                                 \n",
            " discriminator (Functional)  (None, 1)                 1081473   \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2382978 (9.09 MB)\n",
            "Trainable params: 1300801 (4.96 MB)\n",
            "Non-trainable params: 1082177 (4.13 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_images(generator,\n",
        "                noise_input,\n",
        "                noise_label=None,\n",
        "                noise_codes=None,\n",
        "                show=False,\n",
        "                step=0,\n",
        "                model_name=\"gan\"):\n",
        "    os.makedirs(model_name, exist_ok=True)\n",
        "    filename = os.path.join(model_name, \"%05d.png\" % step)\n",
        "    rows = int(math.sqrt(noise_input.shape[0]))\n",
        "    if noise_label is not None:\n",
        "        noise_input = [noise_input, noise_label]\n",
        "        if noise_codes is not None:\n",
        "            noise_input += noise_codes\n",
        "\n",
        "    images = generator.predict(noise_input)\n",
        "    plt.figure(figsize=(2.2, 2.2))\n",
        "    num_images = images.shape[0]\n",
        "    image_size = images.shape[1]\n",
        "    for i in range(num_images):\n",
        "        plt.subplot(rows, rows, i + 1)\n",
        "        image = np.reshape(images[i], [image_size, image_size])\n",
        "        plt.imshow(image, cmap='gray')\n",
        "        plt.axis('off')\n",
        "    plt.savefig(filename)\n",
        "    if show:\n",
        "        plt.show()\n",
        "    else:\n",
        "        plt.close('all')"
      ],
      "metadata": {
        "id": "7NRRNH8YDa2W"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(models, x_train, params):\n",
        "\n",
        "  generator, discriminator, adversarial = models\n",
        "  batch_size, latent_size, n_critic, clip_value, train_steps, model_name = params\n",
        "  save_interval = 500\n",
        "\n",
        "  noise_input = np.random.uniform(-1.0, 1.0, size = [16, latent_size])\n",
        "  train_size = x_train.shape[0]\n",
        "  real_labels = np.ones((batch_size, 1))\n",
        "\n",
        "  for i in range(train_steps):\n",
        "\n",
        "    loss = 0\n",
        "    acc = 0\n",
        "    for _ in range(n_critic):\n",
        "\n",
        "      rand_indexes = np.random.randint(0, train_size, size = batch_size)\n",
        "      real_images = x_train[rand_indexes]\n",
        "      noise = np.random.uniform(-1.0, 1.0, size = [batch_size, latent_size])\n",
        "      fake_images = generator.predict(noise)\n",
        "      real_loss, real_acc = discriminator.train_on_batch(real_images, real_labels)\n",
        "      fake_loss, fake_acc = discriminator.train_on_batch(fake_images, -real_labels)\n",
        "\n",
        "      # average\n",
        "      loss += 0.5 * (real_loss + fake_loss)\n",
        "      acc  += 0.5 * (real_acc + fake_acc)\n",
        "\n",
        "      # clip weights\n",
        "      for layer in discriminator.layers:\n",
        "        weights = layer.get_weights()\n",
        "        weights = [np.clip(weight, -clip_value, clip_value) for weight in weights]\n",
        "        layer.set_weights(weights)\n",
        "\n",
        "    loss /= n_critic\n",
        "    acc /= n_critic\n",
        "    log = f\"{i}: [Discriminator Loss: {loss}, acc: {acc}]\"\n",
        "    print(log)\n",
        "\n",
        "    noise = np.random.uniform(-1.0, 1.0, size = [batch_size, latent_size])\n",
        "    loss, acc = adversarial.train_on_batch(noise, real_labels)\n",
        "    log = f\"{i}: [Adversarial Loss: {loss}, acc: {acc}]\"\n",
        "    print(log)\n",
        "\n",
        "    if (i + 1) % save_interval == 0:\n",
        "      plot_images(generator,\n",
        "                  noise_input=noise_input,\n",
        "                  show=False,\n",
        "                  step=(i + 1),\n",
        "                  model_name=model_name)\n",
        "\n",
        "    generator.save(model_name + \".keras\")"
      ],
      "metadata": {
        "id": "mRsfbm3YDluc"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = (generator, discriminator, adversarial)\n",
        "params = (batch_size, latent_size, n_critic, clip_value, train_steps, model_name)\n",
        "train(models, x_train, params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWgjhpduIIwV",
        "outputId": "cc3da2fe-67c8-4b57-84f3-1cca7de0eae3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2286: [Discriminator Loss: -0.6408732652664184, acc: 0.0328125]\n",
            "2286: [Adversarial Loss: -0.8805802464485168, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2287: [Discriminator Loss: -0.6365715071558953, acc: 0.028125]\n",
            "2287: [Adversarial Loss: -0.8811934590339661, acc: 1.0]\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2288: [Discriminator Loss: -0.625835494697094, acc: 0.03125]\n",
            "2288: [Adversarial Loss: -0.8842266798019409, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2289: [Discriminator Loss: -0.6473916083574295, acc: 0.0359375]\n",
            "2289: [Adversarial Loss: -0.8821587562561035, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2290: [Discriminator Loss: -0.6203341871500015, acc: 0.03125]\n",
            "2290: [Adversarial Loss: -0.882789671421051, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2291: [Discriminator Loss: -0.6335417419672013, acc: 0.0328125]\n",
            "2291: [Adversarial Loss: -0.884346067905426, acc: 1.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2292: [Discriminator Loss: -0.613951712846756, acc: 0.0296875]\n",
            "2292: [Adversarial Loss: -0.8836376667022705, acc: 1.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2293: [Discriminator Loss: -0.6170762091875076, acc: 0.0296875]\n",
            "2293: [Adversarial Loss: -0.8829010128974915, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2294: [Discriminator Loss: -0.6279604092240334, acc: 0.0234375]\n",
            "2294: [Adversarial Loss: -0.8834280967712402, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 8ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2295: [Discriminator Loss: -0.6235668420791626, acc: 0.0234375]\n",
            "2295: [Adversarial Loss: -0.8847955465316772, acc: 1.0]\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 8ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2296: [Discriminator Loss: -0.6200098782777786, acc: 0.028125]\n",
            "2296: [Adversarial Loss: -0.8862097859382629, acc: 1.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2297: [Discriminator Loss: -0.6325232714414597, acc: 0.0296875]\n",
            "2297: [Adversarial Loss: -0.8851766586303711, acc: 1.0]\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2298: [Discriminator Loss: -0.6285212486982346, acc: 0.04375]\n",
            "2298: [Adversarial Loss: -0.8877362012863159, acc: 1.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2299: [Discriminator Loss: -0.6346653997898102, acc: 0.028125]\n",
            "2299: [Adversarial Loss: -0.8865265846252441, acc: 1.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2300: [Discriminator Loss: -0.6180512428283691, acc: 0.0203125]\n",
            "2300: [Adversarial Loss: -0.8860092759132385, acc: 1.0]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2301: [Discriminator Loss: -0.6312598794698715, acc: 0.034375]\n",
            "2301: [Adversarial Loss: -0.8850786685943604, acc: 1.0]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2302: [Discriminator Loss: -0.625003308057785, acc: 0.025]\n",
            "2302: [Adversarial Loss: -0.8848126530647278, acc: 1.0]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
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            "2/2 [==============================] - 0s 5ms/step\n",
            "2303: [Discriminator Loss: -0.6299555420875549, acc: 0.0109375]\n",
            "2303: [Adversarial Loss: -0.8828144073486328, acc: 1.0]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2304: [Discriminator Loss: -0.6126562088727951, acc: 0.0265625]\n",
            "2304: [Adversarial Loss: -0.8842970132827759, acc: 1.0]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2305: [Discriminator Loss: -0.6206053510308266, acc: 0.021875]\n",
            "2305: [Adversarial Loss: -0.886542558670044, acc: 1.0]\n",
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            "2/2 [==============================] - 0s 7ms/step\n",
            "2306: [Discriminator Loss: -0.6166688591241837, acc: 0.01875]\n",
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            "2307: [Discriminator Loss: -0.628776341676712, acc: 0.0265625]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2308: [Discriminator Loss: -0.6332614332437515, acc: 0.0234375]\n",
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            "2309: [Discriminator Loss: -0.6336185783147812, acc: 0.021875]\n",
            "2309: [Adversarial Loss: -0.885239839553833, acc: 1.0]\n",
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            "2310: [Discriminator Loss: -0.6368166327476501, acc: 0.0265625]\n",
            "2310: [Adversarial Loss: -0.8857948184013367, acc: 1.0]\n",
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            "2311: [Discriminator Loss: -0.6390443414449691, acc: 0.0296875]\n",
            "2311: [Adversarial Loss: -0.8823747038841248, acc: 1.0]\n",
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            "2312: [Discriminator Loss: -0.6224312722682953, acc: 0.0203125]\n",
            "2312: [Adversarial Loss: -0.8824403285980225, acc: 1.0]\n",
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            "2313: [Discriminator Loss: -0.6266423419117928, acc: 0.0234375]\n",
            "2313: [Adversarial Loss: -0.8821611404418945, acc: 1.0]\n",
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            "2314: [Discriminator Loss: -0.6248867928981781, acc: 0.025]\n",
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            "2315: [Discriminator Loss: -0.6298581689596177, acc: 0.0203125]\n",
            "2315: [Adversarial Loss: -0.8844496607780457, acc: 1.0]\n",
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            "2316: [Discriminator Loss: -0.6296456068754196, acc: 0.0328125]\n",
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            "2317: [Discriminator Loss: -0.6396173298358917, acc: 0.025]\n",
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            "2318: [Discriminator Loss: -0.6355397582054139, acc: 0.025]\n",
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            "2321: [Discriminator Loss: -0.6509560912847518, acc: 0.034375]\n",
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            "2399: [Discriminator Loss: -0.5835697397589683, acc: 0.0203125]\n",
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            "2497: [Adversarial Loss: -0.843746542930603, acc: 1.0]\n",
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            "2498: [Discriminator Loss: -0.4884432375431061, acc: 0.0171875]\n",
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            "2500: [Discriminator Loss: -0.49351072013378144, acc: 0.040625]\n",
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            "2548: [Discriminator Loss: -0.4914025247097015, acc: 0.025]\n",
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            "2596: [Discriminator Loss: -0.33486585645005107, acc: 0.4984375]\n",
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            "2611: [Discriminator Loss: -0.17761517465114593, acc: 0.496875]\n",
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            "2/2 [==============================] - 0s 5ms/step\n",
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            "2694: [Discriminator Loss: -0.32510837726294994, acc: 0.434375]\n",
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            "2/2 [==============================] - 0s 5ms/step\n",
            "2695: [Discriminator Loss: -0.34449920654296873, acc: 0.3921875]\n",
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            "2698: [Discriminator Loss: -0.37611372470855714, acc: 0.2265625]\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
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            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
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            "2744: [Discriminator Loss: -0.33170839995145796, acc: 0.15]\n",
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            "2/2 [==============================] - 0s 5ms/step\n",
            "2989: [Discriminator Loss: -0.07635287940502167, acc: 0.0]\n",
            "2989: [Adversarial Loss: -0.14397571980953217, acc: 0.0]\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2990: [Discriminator Loss: -0.07569640055298806, acc: 0.0]\n",
            "2990: [Adversarial Loss: -0.13752219080924988, acc: 0.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2991: [Discriminator Loss: -0.08154270425438881, acc: 0.0]\n",
            "2991: [Adversarial Loss: -0.13500168919563293, acc: 0.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2992: [Discriminator Loss: -0.08118298724293709, acc: 0.0]\n",
            "2992: [Adversarial Loss: -0.1295330971479416, acc: 0.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2993: [Discriminator Loss: -0.0772174872457981, acc: 0.0]\n",
            "2993: [Adversarial Loss: -0.137607142329216, acc: 0.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2994: [Discriminator Loss: -0.07054354250431061, acc: 0.0]\n",
            "2994: [Adversarial Loss: -0.14130952954292297, acc: 0.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2995: [Discriminator Loss: -0.06028987169265747, acc: 0.0]\n",
            "2995: [Adversarial Loss: -0.14769291877746582, acc: 0.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 14ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 8ms/step\n",
            "2996: [Discriminator Loss: -0.058403848111629485, acc: 0.0]\n",
            "2996: [Adversarial Loss: -0.15242791175842285, acc: 0.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 12ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2997: [Discriminator Loss: -0.05007091611623764, acc: 0.0]\n",
            "2997: [Adversarial Loss: -0.15792451798915863, acc: 0.0]\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2998: [Discriminator Loss: -0.04747993052005768, acc: 0.0]\n",
            "2998: [Adversarial Loss: -0.16001388430595398, acc: 0.0]\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2999: [Discriminator Loss: -0.04318501204252243, acc: 0.0]\n",
            "2999: [Adversarial Loss: -0.17116373777389526, acc: 0.0]\n",
            "1/1 [==============================] - 0s 16ms/step\n"
          ]
        }
      ]
    }
  ]
}