FashionGAN-Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64f04b6a-fe54-41ce-92fe-ae94fc587387",
   "metadata": {
    "id": "64f04b6a-fe54-41ce-92fe-ae94fc587387"
   },
   "source": [
    "# 1. Import Dependencies and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3c355f3-39f9-4f73-ac4e-5ad62198a580",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install tensorflow tensorflow-gpu matplotlib tensorflow-datasets ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6112401-2397-423d-b8c2-113a9d323ab0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b84be907-35e2-43db-a645-b6b164302aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bringing in tensorflow\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus: \n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0f2aa32-064b-448c-bb27-f19a48c40115",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Brining in tensorflow datasets for fashion mnist \n",
    "import tensorflow_datasets as tfds\n",
    "# Bringing in matplotlib for viz stuff\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c933f988-d1ee-4d4d-8028-368a158c27e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the tensorflow datasets api to bring in the data source\n",
    "ds = tfds.load('fashion_mnist', split='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c361db0d-8e7b-43e1-97f9-5e3f7cb01ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.as_numpy_iterator().next()['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1635e4-4beb-493d-92c1-b106c806ca70",
   "metadata": {
    "id": "ea1635e4-4beb-493d-92c1-b106c806ca70"
   },
   "source": [
    "# 2. Viz Data and Build Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b0c62caf-e406-4d12-af31-6f4848155844",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do some data transformation\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3215c900-6e85-4b39-b300-ea18faf30e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup connection aka iterator\n",
    "dataiterator = ds.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1d6e079-46da-43ca-80d2-b3c864d90360",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Getting data out of the pipeline\n",
    "dataiterator.next()['image']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "deb5fca0-fd8a-4557-9c72-1a60c289a2e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup the subplot formatting \n",
    "fig, ax = plt.subplots(ncols=4, figsize=(20,20))\n",
    "# Loop four times and get images \n",
    "for idx in range(4): \n",
    "    # Grab an image and label\n",
    "    sample = dataiterator.next()\n",
    "    # Plot the image using a specific subplot \n",
    "    ax[idx].imshow(np.squeeze(sample['image']))\n",
    "    # Appending the image label as the plot title \n",
    "    ax[idx].title.set_text(sample['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "66c9d901-6a5c-42fd-ad06-cc03f7829728",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale and return images only \n",
    "def scale_images(data): \n",
    "    image = data['image']\n",
    "    return image / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dfc9b6b1-e06e-421c-9c5c-bfc3b3e3be77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload the dataset \n",
    "ds = tfds.load('fashion_mnist', split='train')\n",
    "# Running the dataset through the scale_images preprocessing step\n",
    "ds = ds.map(scale_images) \n",
    "# Cache the dataset for that batch \n",
    "ds = ds.cache()\n",
    "# Shuffle it up \n",
    "ds = ds.shuffle(60000)\n",
    "# Batch into 128 images per sample\n",
    "ds = ds.batch(128)\n",
    "# Reduces the likelihood of bottlenecking \n",
    "ds = ds.prefetch(64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fbb52952-faa1-445f-8931-2f0f37224bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 28, 28, 1)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.as_numpy_iterator().next().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5b08df-7b20-41f4-a8ff-112dface1cb0",
   "metadata": {
    "id": "9a5b08df-7b20-41f4-a8ff-112dface1cb0"
   },
   "source": [
    "# 3. Build Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f66add-a3db-467f-96c3-f87b9f880159",
   "metadata": {
    "id": "38f66add-a3db-467f-96c3-f87b9f880159"
   },
   "source": [
    "### 3.1 Import Modelling Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bb72da39-377f-4264-b525-c87f49fb0356",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bring in the sequential api for the generator and discriminator\n",
    "from tensorflow.keras.models import Sequential\n",
    "# Bring in the layers for the neural network\n",
    "from tensorflow.keras.layers import Conv2D, Dense, Flatten, Reshape, LeakyReLU, Dropout, UpSampling2D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c40405df-1439-4661-8785-d76698df8152",
   "metadata": {
    "id": "c40405df-1439-4661-8785-d76698df8152"
   },
   "source": [
    "### 3.2 Build Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5d29d43a-e02a-4031-a0ec-de8aa810c118",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_generator(): \n",
    "    model = Sequential()\n",
    "    \n",
    "    # Takes in random values and reshapes it to 7x7x128\n",
    "    # Beginnings of a generated image\n",
    "    model.add(Dense(7*7*128, input_dim=128))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Reshape((7,7,128)))\n",
    "    \n",
    "    # Upsampling block 1 \n",
    "    model.add(UpSampling2D())\n",
    "    model.add(Conv2D(128, 5, padding='same'))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    \n",
    "    # Upsampling block 2 \n",
    "    model.add(UpSampling2D())\n",
    "    model.add(Conv2D(128, 5, padding='same'))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    \n",
    "    # Convolutional block 1\n",
    "    model.add(Conv2D(128, 4, padding='same'))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    \n",
    "    # Convolutional block 2\n",
    "    model.add(Conv2D(128, 4, padding='same'))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    \n",
    "    # Conv layer to get to one channel\n",
    "    model.add(Conv2D(1, 4, padding='same', activation='sigmoid'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "741b0d58-1b9f-4260-8405-dc400c73f843",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = build_generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "259ab9c1-6d6c-49a0-b0c4-f45b7c68f588",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_10 (Dense)            (None, 6272)              809088    \n",
      "                                                                 \n",
      " leaky_re_lu_26 (LeakyReLU)  (None, 6272)              0         \n",
      "                                                                 \n",
      " reshape_9 (Reshape)         (None, 7, 7, 128)         0         \n",
      "                                                                 \n",
      " up_sampling2d_12 (UpSamplin  (None, 14, 14, 128)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_19 (Conv2D)          (None, 14, 14, 128)       409728    \n",
      "                                                                 \n",
      " leaky_re_lu_27 (LeakyReLU)  (None, 14, 14, 128)       0         \n",
      "                                                                 \n",
      " up_sampling2d_13 (UpSamplin  (None, 28, 28, 128)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_20 (Conv2D)          (None, 28, 28, 128)       409728    \n",
      "                                                                 \n",
      " leaky_re_lu_28 (LeakyReLU)  (None, 28, 28, 128)       0         \n",
      "                                                                 \n",
      " conv2d_21 (Conv2D)          (None, 28, 28, 128)       262272    \n",
      "                                                                 \n",
      " leaky_re_lu_29 (LeakyReLU)  (None, 28, 28, 128)       0         \n",
      "                                                                 \n",
      " conv2d_22 (Conv2D)          (None, 28, 28, 128)       262272    \n",
      "                                                                 \n",
      " leaky_re_lu_30 (LeakyReLU)  (None, 28, 28, 128)       0         \n",
      "                                                                 \n",
      " conv2d_23 (Conv2D)          (None, 28, 28, 1)         2049      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,155,137\n",
      "Trainable params: 2,155,137\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "10ba4d1c-6a15-4097-bf63-5fe6ddb404b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = generator.predict(np.random.randn(4,128,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "9b4e0cb6-d741-4d43-b845-2a8f2615765b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate new fashion\n",
    "img = generator.predict(np.random.randn(4,128,1))\n",
    "# Setup the subplot formatting \n",
    "fig, ax = plt.subplots(ncols=4, figsize=(20,20))\n",
    "# Loop four times and get images \n",
    "for idx, img in enumerate(img): \n",
    "    # Plot the image using a specific subplot \n",
    "    ax[idx].imshow(np.squeeze(img))\n",
    "    # Appending the image label as the plot title \n",
    "    ax[idx].title.set_text(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2415abbf-24ed-4bac-8fb8-12c65017ec22",
   "metadata": {
    "id": "2415abbf-24ed-4bac-8fb8-12c65017ec22"
   },
   "source": [
    "### 3.3 Build Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b4e70bcb-cfd5-42bb-aed0-79f19bb38d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_discriminator(): \n",
    "    model = Sequential()\n",
    "    \n",
    "    # First Conv Block\n",
    "    model.add(Conv2D(32, 5, input_shape = (28,28,1)))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Dropout(0.4))\n",
    "    \n",
    "    # Second Conv Block\n",
    "    model.add(Conv2D(64, 5))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Dropout(0.4))\n",
    "    \n",
    "    # Third Conv Block\n",
    "    model.add(Conv2D(128, 5))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Dropout(0.4))\n",
    "    \n",
    "    # Fourth Conv Block\n",
    "    model.add(Conv2D(256, 5))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Dropout(0.4))\n",
    "    \n",
    "    # Flatten then pass to dense layer\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    return model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "7173eb57-250b-4d21-9b37-de842c4552ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = build_discriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "ed6fecbc-f214-4f50-865c-91887b2430e7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_15\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_32 (Conv2D)          (None, 24, 24, 32)        832       \n",
      "                                                                 \n",
      " leaky_re_lu_39 (LeakyReLU)  (None, 24, 24, 32)        0         \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 24, 24, 32)        0         \n",
      "                                                                 \n",
      " conv2d_33 (Conv2D)          (None, 20, 20, 64)        51264     \n",
      "                                                                 \n",
      " leaky_re_lu_40 (LeakyReLU)  (None, 20, 20, 64)        0         \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 20, 20, 64)        0         \n",
      "                                                                 \n",
      " conv2d_34 (Conv2D)          (None, 16, 16, 128)       204928    \n",
      "                                                                 \n",
      " leaky_re_lu_41 (LeakyReLU)  (None, 16, 16, 128)       0         \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 16, 16, 128)       0         \n",
      "                                                                 \n",
      " conv2d_35 (Conv2D)          (None, 12, 12, 256)       819456    \n",
      "                                                                 \n",
      " leaky_re_lu_42 (LeakyReLU)  (None, 12, 12, 256)       0         \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 12, 12, 256)       0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 36864)             0         \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 36864)             0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 36865     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,113,345\n",
      "Trainable params: 1,113,345\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "19e32424-f9c5-499c-a13f-b450bc525bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = img[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "9ce3acc9-02c8-468f-915a-0efd52da0bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8cd15246-b40c-4c7a-912d-b88a1c5c463b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.50057805],\n",
       "       [0.5006448 ],\n",
       "       [0.50065   ],\n",
       "       [0.5005127 ]], dtype=float32)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discriminator.predict(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b343b0-38d3-4281-bedb-72099a18097e",
   "metadata": {
    "id": "39b343b0-38d3-4281-bedb-72099a18097e"
   },
   "source": [
    "# 4. Construct Training Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "884abab3-2f74-442d-856f-e104ef1ac8ef",
   "metadata": {
    "id": "884abab3-2f74-442d-856f-e104ef1ac8ef"
   },
   "source": [
    "### 4.1 Setup Losses and Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "0bb1d23a-ea68-451a-bb38-e7795dc24311",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adam is going to be the optimizer for both\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "# Binary cross entropy is going to be the loss for both \n",
    "from tensorflow.keras.losses import BinaryCrossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "198b2d4e-d6b9-4b6c-a98c-65cd1b81da26",
   "metadata": {},
   "outputs": [],
   "source": [
    "g_opt = Adam(learning_rate=0.0001) \n",
    "d_opt = Adam(learning_rate=0.00001) \n",
    "g_loss = BinaryCrossentropy()\n",
    "d_loss = BinaryCrossentropy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f170b0e-f731-4cbd-8068-24896f462c08",
   "metadata": {
    "id": "9f170b0e-f731-4cbd-8068-24896f462c08"
   },
   "source": [
    "### 4.2 Build Subclassed Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "9e2f5654-ed22-462d-be32-6c43d8b99b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the base model class to subclass our training step \n",
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "40a0af46-0243-4396-94d6-c1316d984de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FashionGAN(Model): \n",
    "    def __init__(self, generator, discriminator, *args, **kwargs):\n",
    "        # Pass through args and kwargs to base class \n",
    "        super().__init__(*args, **kwargs)\n",
    "        \n",
    "        # Create attributes for gen and disc\n",
    "        self.generator = generator \n",
    "        self.discriminator = discriminator \n",
    "        \n",
    "    def compile(self, g_opt, d_opt, g_loss, d_loss, *args, **kwargs): \n",
    "        # Compile with base class\n",
    "        super().compile(*args, **kwargs)\n",
    "        \n",
    "        # Create attributes for losses and optimizers\n",
    "        self.g_opt = g_opt\n",
    "        self.d_opt = d_opt\n",
    "        self.g_loss = g_loss\n",
    "        self.d_loss = d_loss \n",
    "\n",
    "    def train_step(self, batch):\n",
    "        # Get the data \n",
    "        real_images = batch\n",
    "        fake_images = self.generator(tf.random.normal((128, 128, 1)), training=False)\n",
    "        \n",
    "        # Train the discriminator\n",
    "        with tf.GradientTape() as d_tape: \n",
    "            # Pass the real and fake images to the discriminator model\n",
    "            yhat_real = self.discriminator(real_images, training=True) \n",
    "            yhat_fake = self.discriminator(fake_images, training=True)\n",
    "            yhat_realfake = tf.concat([yhat_real, yhat_fake], axis=0)\n",
    "            \n",
    "            # Create labels for real and fakes images\n",
    "            y_realfake = tf.concat([tf.zeros_like(yhat_real), tf.ones_like(yhat_fake)], axis=0)\n",
    "            \n",
    "            # Add some noise to the TRUE outputs\n",
    "            noise_real = 0.15*tf.random.uniform(tf.shape(yhat_real))\n",
    "            noise_fake = -0.15*tf.random.uniform(tf.shape(yhat_fake))\n",
    "            y_realfake += tf.concat([noise_real, noise_fake], axis=0)\n",
    "            \n",
    "            # Calculate loss - BINARYCROSS \n",
    "            total_d_loss = self.d_loss(y_realfake, yhat_realfake)\n",
    "            \n",
    "        # Apply backpropagation - nn learn \n",
    "        dgrad = d_tape.gradient(total_d_loss, self.discriminator.trainable_variables) \n",
    "        self.d_opt.apply_gradients(zip(dgrad, self.discriminator.trainable_variables))\n",
    "        \n",
    "        # Train the generator \n",
    "        with tf.GradientTape() as g_tape: \n",
    "            # Generate some new images\n",
    "            gen_images = self.generator(tf.random.normal((128,128,1)), training=True)\n",
    "                                        \n",
    "            # Create the predicted labels\n",
    "            predicted_labels = self.discriminator(gen_images, training=False)\n",
    "                                        \n",
    "            # Calculate loss - trick to training to fake out the discriminator\n",
    "            total_g_loss = self.g_loss(tf.zeros_like(predicted_labels), predicted_labels) \n",
    "            \n",
    "        # Apply backprop\n",
    "        ggrad = g_tape.gradient(total_g_loss, self.generator.trainable_variables)\n",
    "        self.g_opt.apply_gradients(zip(ggrad, self.generator.trainable_variables))\n",
    "        \n",
    "        return {\"d_loss\":total_d_loss, \"g_loss\":total_g_loss}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "24d248c3-f4c1-4478-a699-a5811a7b1fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create instance of subclassed model\n",
    "fashgan = FashionGAN(generator, discriminator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "e1cf7e02-ee1a-4901-bdf0-9aa2301f8cfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "fashgan.compile(g_opt, d_opt, g_loss, d_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06d0adb-38d0-4558-b824-7416cf880082",
   "metadata": {
    "id": "e06d0adb-38d0-4558-b824-7416cf880082"
   },
   "source": [
    "### 4.3 Build Callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "548f6918-366c-4799-9dac-1acedaab40c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tensorflow.keras.preprocessing.image import array_to_img\n",
    "from tensorflow.keras.callbacks import Callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "d3e2bb77-2d7d-40d0-809f-526b8fd34170",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelMonitor(Callback):\n",
    "    def __init__(self, num_img=3, latent_dim=128):\n",
    "        self.num_img = num_img\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        random_latent_vectors = tf.random.uniform((self.num_img, self.latent_dim,1))\n",
    "        generated_images = self.model.generator(random_latent_vectors)\n",
    "        generated_images *= 255\n",
    "        generated_images.numpy()\n",
    "        for i in range(self.num_img):\n",
    "            img = array_to_img(generated_images[i])\n",
    "            img.save(os.path.join('images', f'generated_img_{epoch}_{i}.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e2f159-25e7-4e35-95ef-f0fd18ac5897",
   "metadata": {
    "id": "16e2f159-25e7-4e35-95ef-f0fd18ac5897"
   },
   "source": [
    "### 4.3 Train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "a779dceb-aba6-4bf3-af49-0d32a76dd2f7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "469/469 [==============================] - 43s 88ms/step - d_loss: 0.5911 - g_loss: 0.6832\n",
      "Epoch 2/20\n",
      "469/469 [==============================] - 41s 88ms/step - d_loss: 0.4158 - g_loss: 3.2742\n",
      "Epoch 3/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2878 - g_loss: 6.5225\n",
      "Epoch 4/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2780 - g_loss: 6.5009\n",
      "Epoch 5/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2755 - g_loss: 6.2294\n",
      "Epoch 6/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2732 - g_loss: 5.9429\n",
      "Epoch 7/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2728 - g_loss: 5.6715\n",
      "Epoch 8/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2708 - g_loss: 5.4097\n",
      "Epoch 9/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2704 - g_loss: 5.1352\n",
      "Epoch 10/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2701 - g_loss: 4.8858\n",
      "Epoch 11/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2694 - g_loss: 4.6344\n",
      "Epoch 12/20\n",
      "469/469 [==============================] - 40s 85ms/step - d_loss: 0.2691 - g_loss: 4.3753\n",
      "Epoch 13/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2694 - g_loss: 4.1332\n",
      "Epoch 14/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2687 - g_loss: 3.9182\n",
      "Epoch 15/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2679 - g_loss: 3.7484\n",
      "Epoch 16/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2681 - g_loss: 3.5493\n",
      "Epoch 17/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2683 - g_loss: 3.3835\n",
      "Epoch 18/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2675 - g_loss: 3.2472\n",
      "Epoch 19/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.2679 - g_loss: 3.1084\n",
      "Epoch 20/20\n",
      "469/469 [==============================] - 40s 86ms/step - d_loss: 0.3128 - g_loss: 2.6171\n"
     ]
    }
   ],
   "source": [
    "# Recommend 2000 epochs\n",
    "hist = fashgan.fit(ds, epochs=20, callbacks=[ModelMonitor()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c665a1-a4cc-41ac-a08a-2e14ba64e88d",
   "metadata": {
    "id": "39c665a1-a4cc-41ac-a08a-2e14ba64e88d"
   },
   "source": [
    "### 4.4 Review Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "54381e8c-93ee-4022-9df6-24c4356720fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.suptitle('Loss')\n",
    "plt.plot(hist.history['d_loss'], label='d_loss')\n",
    "plt.plot(hist.history['g_loss'], label='g_loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d319a982-7ae5-4754-adcf-b490f17a79d6",
   "metadata": {
    "id": "d319a982-7ae5-4754-adcf-b490f17a79d6"
   },
   "source": [
    "# 5. Test Out the Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206ba81f-978a-4c31-9c3d-6ebe5a5bfc29",
   "metadata": {
    "id": "206ba81f-978a-4c31-9c3d-6ebe5a5bfc29"
   },
   "source": [
    "### 5.1 Generate Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "c46f3d6a-8aa5-40d2-a5ac-67a0606a82f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.load_weights(os.path.join('archive', 'generatormodel.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "14cde11f-cb26-4ebf-ad04-2c64a54f871e",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = generator.predict(tf.random.normal((16, 128, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "f745982f-c4d7-451f-91a7-f7c4341cb7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=4, nrows=4, figsize=(10,10))\n",
    "for r in range(4): \n",
    "    for c in range(4): \n",
    "        ax[r][c].imshow(imgs[(r+1)*(c+1)-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5137cffa-784d-4076-beef-0a067b86d3aa",
   "metadata": {
    "id": "5137cffa-784d-4076-beef-0a067b86d3aa"
   },
   "source": [
    "### 5.2 Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7011d68-ef71-4377-91e2-e26a02fab382",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.save('generator.h5')\n",
    "discriminator.save('discriminator.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d14c2bd3-a344-4ac1-b2ee-6c90420368e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "background_execution": "on",
   "collapsed_sections": [
    "206ba81f-978a-4c31-9c3d-6ebe5a5bfc29"
   ],
   "name": "FashionGAN.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "fashgan",
   "language": "python",
   "name": "fashgan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}