dcgan_fashion_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2_dcgan_fashion_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohanrajmit/DL-Training/blob/master/dcgan_fashion_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxWgWoUYNNYe"
      },
      "source": [
        "# Get Started: DCGAN for Fashion-MNIST\n",
        "\n",
        "\n",
        "This Colab notebook is a DCGAN implementation with TensorFlow 2 / Keras, trained with Fashion-MNIST to generates gray-scale 28x28x1 images. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcrOk6pURp50"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJaCNlDDRz6d"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywl3XeMzZ_87"
      },
      "source": [
        "Its always a good practice to check the current version of TensorFlow..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4i5sjvhDXJdW",
        "outputId": "0782a8e6-4000-4225-f029-4c883f68c4a9"
      },
      "source": [
        "print(tf.version.VERSION)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mu11hf0_rqRq"
      },
      "source": [
        "## Prepare Fashion-MNIST data\n",
        "The first step is to get data ready for training.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fswXPGriDWZa"
      },
      "source": [
        "### Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CLbL4NKDQOf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21f2d1a3-5acf-4a16-b948-00819f700cb0"
      },
      "source": [
        "# load the Fashion MNIST dataset and it's OK to use just the training data\n",
        "(train_images, train_labels), (_, _) = tf.keras.datasets.fashion_mnist.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZL-abbaGOpaY"
      },
      "source": [
        "There are 60,000 training gray scale images in Fashion-MNIST."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebRZA80bHTOj",
        "outputId": "3cbaf856-5043-4a9c-fc92-63efcded0d19"
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgswpgqfCAYQ",
        "outputId": "49990276-65e1-422e-f40c-e208c26e0ce7"
      },
      "source": [
        "type(train_images)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFE71AWEDZBp"
      },
      "source": [
        "### Visualization\n",
        "\n",
        "I always like to visualize the training data to get an idea of what the images look like. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wYkCxiECGSxR",
        "outputId": "58c9b9e2-aab4-4481-95d6-a9b22ab91805"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0], cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eo818wWbDY7S"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIcdRCYcDThc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7de4f370-a6e9-42db-adc1-94317dbd47ca"
      },
      "source": [
        "# Reshape data and also convert the data type to 'float32'\n",
        "print(train_images.shape)\n",
        "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n",
        "print(train_images.shape)\n",
        "\n",
        "# Normalize the images to [-1, 1] which is the range of the tanh activation\n",
        "train_images = (train_images - 127.5) / 127.5  "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(60000, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BY-sz-IFbWr7"
      },
      "source": [
        "## The generator model\n",
        "\n",
        "Define the generator model architecture with the Keras `Sequential` API.\n",
        "\n",
        "Use `ReLU` except for the last layer which has `tanh` as the activation.\n",
        "\n",
        "The job of the generator is to make images, to do so in DCGAN, we feed random noise as input to the generator and upsample till the desired image size becomes 28x28x1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2mrQ1VCMlX2"
      },
      "source": [
        "# latent dimension of the random noise\n",
        "LATENT_DIM = 100 \n",
        "# weight initializer for G per DCGAN paper\n",
        "WEIGHT_INIT = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02) \n",
        "# number of channels, 1 for gray scale and 3 for color images\n",
        "CHANNELS = 1"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVjZj3kUqutJ"
      },
      "source": [
        "def build_generator():\n",
        "    # create a Keras Sequential model \n",
        "    model = Sequential(name=\"generator\")\n",
        "\n",
        "    # prepare for reshape: FC => BN => RN layers, note: input shape defined in the 1st Dense layer  \n",
        "    model.add(layers.Dense(7 * 7 * 256, input_dim=LATENT_DIM))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.ReLU())\n",
        "\n",
        "    # 1D => 3D: reshape the output of the previous layer \n",
        "    model.add(layers.Reshape((7, 7, 256)))\n",
        "\n",
        "    # upsample to 14x14: apply a transposed CONV => BN => RELU\n",
        "    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(2, 2),padding=\"same\", kernel_initializer=WEIGHT_INIT))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add((layers.ReLU()))\n",
        "\n",
        "    # upsample to 28x28: apply a transposed CONV => BN => RELU\n",
        "    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2),padding=\"same\", kernel_initializer=WEIGHT_INIT))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add((layers.ReLU()))\n",
        "\n",
        "    # final layer: Conv2D with tanh activation\n",
        "    model.add(layers.Conv2D(CHANNELS, (5, 5), padding=\"same\", activation=\"tanh\"))\n",
        "\n",
        "    # return the generator model\n",
        "    return model"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEQz2iA2-XUq"
      },
      "source": [
        "# build the generator model\n",
        "generator = build_generator() "
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wEDbYmNL-t99",
        "outputId": "6184e718-b0e1-49da-efd7-0ca0fcc8a0a5"
      },
      "source": [
        "generator.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"generator\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_1 (Dense)             (None, 12544)             1266944   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 12544)            50176     \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_3 (ReLU)              (None, 12544)             0         \n",
            "                                                                 \n",
            " reshape_1 (Reshape)         (None, 7, 7, 256)         0         \n",
            "                                                                 \n",
            " conv2d_transpose_2 (Conv2DT  (None, 14, 14, 128)      819328    \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 14, 14, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_4 (ReLU)              (None, 14, 14, 128)       0         \n",
            "                                                                 \n",
            " conv2d_transpose_3 (Conv2DT  (None, 28, 28, 64)       204864    \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 28, 28, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_5 (ReLU)              (None, 28, 28, 64)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 28, 28, 1)         1601      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,343,681\n",
            "Trainable params: 2,318,209\n",
            "Non-trainable params: 25,472\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qAUg0qNbXpS"
      },
      "source": [
        "## The discriminator model\n",
        "\n",
        "Define the discriminator model architecture with the Keras `Sequential` API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9OaVagUqgOW"
      },
      "source": [
        "def build_discriminator(width, height, depth, alpha=0.2):\n",
        "    # create a Keras Sequential model\n",
        "    model = Sequential(name=\"discriminator\")\n",
        "    input_shape = (height, width, depth)\n",
        "\n",
        "    # first set of CONV => BN => leaky ReLU layers\n",
        "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding=\"same\", \n",
        "        input_shape=input_shape))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU(alpha=alpha))\n",
        "\n",
        "    # second set of CONV => BN => leacy ReLU layers\n",
        "    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding=\"same\"))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU(alpha=alpha))\n",
        "\n",
        "    # flatten and apply dropout\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dropout(0.3))\n",
        "\n",
        "    # sigmoid in the last layer outputs a single value for binary classification\n",
        "    model.add(layers.Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "    # return the discriminator model\n",
        "    return model"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "db7E0vd9-5S2"
      },
      "source": [
        "# build the discriminator model\n",
        "discriminator = build_discriminator(28, 28, 1) "
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "synlf4iD_S9Z",
        "outputId": "9b891cae-e4ba-45a3-bcac-d15996825a08"
      },
      "source": [
        "discriminator.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"discriminator\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 14, 14, 64)        1664      \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 14, 14, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 14, 14, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 7, 7, 128)         204928    \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 7, 7, 128)        512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 7, 7, 128)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 6273      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 213,633\n",
            "Trainable params: 213,249\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urgSpXaPQlKk"
      },
      "source": [
        "## Define `DCGAN` class: override `train_step`\n",
        "Subclass `keras.Model` and override `train_step` to implement the DCGAN architecture. This is where the magic happens and the heart of the DCGAN implementation. \n",
        "\n",
        "**The discriminator loss** includes both the loss from the real images and the loss from the fake images.  \n",
        "**The generator loss** is calculated with thefake images.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uc4nZaU999iq"
      },
      "source": [
        "class DCGAN(keras.Model):\n",
        "    def __init__(self, discriminator, generator, latent_dim):\n",
        "        super().__init__()\n",
        "        self.discriminator = discriminator\n",
        "        self.generator = generator\n",
        "        self.latent_dim = latent_dim\n",
        "        self.d_loss_metric = keras.metrics.Mean(name=\"d_loss\")\n",
        "        self.g_loss_metric = keras.metrics.Mean(name=\"g_loss\")\n",
        "\n",
        "    def compile(self, d_optimizer, g_optimizer, loss_fn):\n",
        "        super(DCGAN, self).compile()\n",
        "        self.d_optimizer = d_optimizer\n",
        "        self.g_optimizer = g_optimizer\n",
        "        self.loss_fn = loss_fn\n",
        "\n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [self.d_loss_metric, self.g_loss_metric]\n",
        "\n",
        "    def train_step(self, real_images):\n",
        "\n",
        "        batch_size = tf.shape(real_images)[0]\n",
        "        noise = tf.random.normal(shape=(batch_size, self.latent_dim))\n",
        "\n",
        "        # Step 1. Train the discriminator with both real images (label as 1) and fake images (classified as label as 0) \n",
        "        with tf.GradientTape() as tape:\n",
        "            # Compute discriminator loss on real images\n",
        "            pred_real = self.discriminator(real_images, training=True)\n",
        "            d_loss_real = self.loss_fn(tf.ones((batch_size, 1)), pred_real)\n",
        "\n",
        "            # Compute discriminator loss on fake images\n",
        "            fake_images = self.generator(noise)\n",
        "            pred_fake = self.discriminator(fake_images, training=True)\n",
        "            d_loss_fake = self.loss_fn(tf.zeros((batch_size, 1)), pred_fake)\n",
        "\n",
        "            # total discriminator loss\n",
        "            d_loss = (d_loss_real + d_loss_fake)/2\n",
        "        # Compute discriminator gradients\n",
        "        grads = tape.gradient(d_loss, self.discriminator.trainable_variables)\n",
        "        # Update discriminator weights\n",
        "        self.d_optimizer.apply_gradients(zip(grads, self.discriminator.trainable_variables))\n",
        "\n",
        "        # Step 2. Train the generator (do not update weights of the discriminator)\n",
        "        # G wants D to think the fake images are real (label as 1)\n",
        "        misleading_labels = tf.ones((batch_size, 1)) \n",
        "\n",
        "        with tf.GradientTape() as tape:\n",
        "            fake_images = self.generator(noise, training=True)\n",
        "            pred_fake = self.discriminator(fake_images, training=True)\n",
        "            g_loss = self.loss_fn(misleading_labels, pred_fake)\n",
        "        # Compute generator gradients\n",
        "        grads = tape.gradient(g_loss, self.generator.trainable_variables)\n",
        "        # Update generator weights\n",
        "        self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_variables))\n",
        "\n",
        "        self.d_loss_metric.update_state(d_loss)\n",
        "        self.g_loss_metric.update_state(g_loss)\n",
        "\n",
        "        return {\"d_loss\": self.d_loss_metric.result(), \"g_loss\": self.g_loss_metric.result()}"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QtlnNc4lQw53"
      },
      "source": [
        "## Use Keras `Callback` to monitor training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ncj2hR0HSV8l"
      },
      "source": [
        "The nice thing about overriding `train_step` of `keras.Model` is that we can subclass `Callback` to monitor our DCGAN training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bRdNGQbpSIby"
      },
      "source": [
        "I use the seed noise to create images (with the generator model) throughout the training to visually inspect the progress of the generator. You can also save the generator model at the end of the training which could potentially be used for inference later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGhG9Trm-HpP"
      },
      "source": [
        "class GANMonitor(keras.callbacks.Callback):\n",
        "    def __init__(self, num_img=3, latent_dim=100):\n",
        "        self.num_img = num_img\n",
        "        self.latent_dim = latent_dim\n",
        "\n",
        "        # Create random noise seed for visualization during training\n",
        "        self.seed = tf.random.normal([16, latent_dim])\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        # random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))\n",
        "        # generated_images = self.model.generator(random_latent_vectors)\n",
        "        generated_images = self.model.generator(self.seed)\n",
        "        generated_images = (generated_images * 127.5) + 127.5\n",
        "        generated_images.numpy()\n",
        "\n",
        "        fig = plt.figure(figsize=(4, 4))\n",
        "        for i in range(self.num_img):\n",
        "            plt.subplot(4, 4, i+1)\n",
        "            img = keras.utils.array_to_img(generated_images[i]) \n",
        "            plt.imshow(img, cmap='gray')\n",
        "            plt.axis('off')\n",
        "        plt.savefig('epoch_{:03d}.png'.format(epoch)) \n",
        "        plt.show()\n",
        "\n",
        "    def on_train_end(self, logs=None):\n",
        "        self.model.generator.save('generator.h5')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OubidSS_Lrl"
      },
      "source": [
        "## Train the DCGAN model\n",
        "\n",
        "Compile the `dcgan` model, with the `Adam` optimizer, learning rate of 0.0002, and the **Binary Cross Entropy** loss functionas recommended by the paper. In future GAN tutorials, you will learn other loss functions for other GAN variants."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzjTO5ZqRLVY"
      },
      "source": [
        "Putting together the `dcgan` model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxaV8juyRIOs"
      },
      "source": [
        "dcgan = DCGAN(discriminator=discriminator, generator=generator, latent_dim=LATENT_DIM)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltkSRpe4Kxrb"
      },
      "source": [
        "Compile the `dcgan` model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pdFGNUs_K-e"
      },
      "source": [
        "LR = 0.0002 # learning rate\n",
        "\n",
        "dcgan.compile(\n",
        "    d_optimizer=keras.optimizers.Adam(learning_rate=LR, beta_1 = 0.5),\n",
        "    g_optimizer=keras.optimizers.Adam(learning_rate=LR, beta_1 = 0.5),\n",
        "    loss_fn=keras.losses.BinaryCrossentropy(),\n",
        ")"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WhnM4ptN2rp"
      },
      "source": [
        "Now we simply call `model.fit()` to traing the `dcgan` model!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GbTrrP_QD4Xf",
        "outputId": "436b81eb-d2f5-4b91-e3d3-3c60ffc09b9d"
      },
      "source": [
        "NUM_EPOCHS = 50 # number of epochs\n",
        "dcgan.fit(train_images, epochs=NUM_EPOCHS, callbacks=[GANMonitor(num_img=16, latent_dim=LATENT_DIM)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "   4/1875 [..............................] - ETA: 42s - d_loss: 0.7051 - g_loss: 0.9264    "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0095s vs `on_train_batch_end` time: 0.0097s). Check your callbacks.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1873/1875 [============================>.] - ETA: 0s - d_loss: 0.2057 - g_loss: 0.8922"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1875/1875 [==============================] - 49s 19ms/step - d_loss: 0.2060 - g_loss: 0.8926\n",
            "Epoch 2/50\n",
            "1875/1875 [==============================] - ETA: 0s - d_loss: 0.6176 - g_loss: 0.9911"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1875/1875 [==============================] - 37s 20ms/step - d_loss: 0.6176 - g_loss: 0.9911\n",
            "Epoch 3/50\n",
            "1873/1875 [============================>.] - ETA: 0s - d_loss: 0.6428 - g_loss: 0.9061"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1875/1875 [==============================] - 37s 20ms/step - d_loss: 0.6427 - g_loss: 0.9061\n",
            "Epoch 4/50\n",
            "1873/1875 [============================>.] - ETA: 0s - d_loss: 0.6565 - g_loss: 0.8701"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1875/1875 [==============================] - 36s 19ms/step - d_loss: 0.6565 - g_loss: 0.8701\n",
            "Epoch 5/50\n",
            "1874/1875 [============================>.] - ETA: 0s - d_loss: 0.6632 - g_loss: 0.8543"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1875/1875 [==============================] - 36s 19ms/step - d_loss: 0.6632 - g_loss: 0.8544\n",
            "Epoch 6/50\n",
            "1261/1875 [===================>..........] - ETA: 11s - d_loss: 0.6629 - g_loss: 0.8588"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sp0C8wIwXeAN"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}