WGAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-beta1\n",
      "GPU Available:  True\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import (\n",
    "    Conv2D,\n",
    "    LeakyReLU,\n",
    "    Dropout,\n",
    "    BatchNormalization,\n",
    "    Flatten,\n",
    "    Dense,\n",
    "    Activation,\n",
    "    Input,\n",
    "    Reshape,\n",
    "    Conv2DTranspose\n",
    ")\n",
    "print(tf.__version__)\n",
    "print(\"GPU Available: \", tf.test.is_gpu_available())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "def critic():\n",
    "        dropout_prob = .4\n",
    "\n",
    "        inputs = Input(shape=(128, 128, 3))\n",
    "\n",
    "        # Input size = 128x128x3\n",
    "        x = Conv2D(filters=128, kernel_size=5, padding='same', input_shape=(128, 128, 3))(inputs)\n",
    "        x = LeakyReLU(0.2)(x) # size = 64x64\n",
    "        # Output size = 64x64x128\n",
    "\n",
    "        # Input size = 64x64x128\n",
    "        x = Conv2D(filters=256, kernel_size=5, padding='same', strides=(2, 2))(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x) # size = 32x32\n",
    "        # Output size = 32x32x256\n",
    "\n",
    "        # Input size = 32x32x256\n",
    "        x = Conv2D(filters=512, kernel_size=5, strides=(2, 2), padding='same')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x) # size = 16x16\n",
    "        # Output size = 16x16x512\n",
    "\n",
    "        # Input size = 16x16x512\n",
    "        x = Conv2D(filters=1024, kernel_size=5, strides=(2, 2), padding='same')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x) # size = 8x8\n",
    "        # Output size = 8x8x1024\n",
    "\n",
    "        # Input size = 8x8x1024\n",
    "        x = Flatten()(x)\n",
    "        out = Dense(1)(x)\n",
    "\n",
    "        net = Model(inputs=inputs, outputs=out)\n",
    "\n",
    "        return net\n",
    "    \n",
    "    def generator():\n",
    "        dropout_prob = .4\n",
    "\n",
    "        # Input size = 100\n",
    "        inputs = Input(shape=(100,))\n",
    "        x = Dense(8*8*1024, input_shape=(100,))(inputs)\n",
    "        x = Reshape(target_shape=(8, 8, 1024))(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x)\n",
    "        x = Dropout(dropout_prob)(x)\n",
    "        # Output size = 8x8x1024\n",
    "\n",
    "        # Input size = 8x8x1024\n",
    "        x = Conv2DTranspose(filters=512, kernel_size=5, strides=(2, 2), padding='same')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x)\n",
    "        x = Dropout(dropout_prob)(x)\n",
    "        # Output size = 16x16x512\n",
    "\n",
    "        # Input size = 16x16x512\n",
    "        x = Conv2DTranspose(filters=256, kernel_size=5, strides=(2, 2), padding='same')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x)\n",
    "        x = Dropout(dropout_prob)(x)\n",
    "        # Output size = 32x32x256\n",
    "\n",
    "        # Input size = 32x32x256\n",
    "        x = Conv2DTranspose(filters=128, kernel_size=5, strides=(2, 2), padding='same')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x)\n",
    "        x = Dropout(dropout_prob)(x)\n",
    "        # Output size = 64x64x128\n",
    "\n",
    "        # Input size = 64x64x128\n",
    "        x = Conv2DTranspose(filters=3, kernel_size=5, strides=(2, 2), padding='same')(x)\n",
    "        out = Activation('tanh')(x)\n",
    "        # Output size = 128x128x3\n",
    "\n",
    "        net = Model(inputs=inputs, outputs=out)\n",
    "\n",
    "        return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, _), (_,_) = tf.keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0722 18:10:29.768954  7532 deprecation.py:323] From <ipython-input-3-52f28e52ec3f>:16: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation.\n"
     ]
    }
   ],
   "source": [
    "def preprocess_fn(image):\n",
    "    x = tf.reshape(tf.cast(image, tf.float32), (32,32,3))\n",
    "#     x /= 255\n",
    "    x = 2*x/255 - 1 # convert image to [-1, 1] range\n",
    "    x = tf.image.random_flip_left_right(x)\n",
    "    x = tf.image.random_hue(x, 0.08)\n",
    "    x = tf.image.random_saturation(x, 0.6, 1.6)\n",
    "    x = tf.image.random_brightness(x, 0.05)\n",
    "    x = tf.image.random_contrast(x, 0.7, 1.3)\n",
    "    return x\n",
    "\n",
    "real_ds = tf.data.Dataset.from_tensor_slices(x_train)\n",
    "real_ds = real_ds.shuffle(60000)\n",
    "real_ds = real_ds.repeat()\n",
    "real_ds = real_ds.apply(tf.data.experimental.map_and_batch(\n",
    "        preprocess_fn, 64, num_parallel_batches=6, drop_remainder=True))\n",
    "real_ds = real_ds.prefetch(tf.data.experimental.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from WGAN import WGAN\n",
    "wgan = WGAN(real_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 100\n",
      "Step: 101\n",
      "Step: 102\n",
      "Step: 103\n",
      "Step: 104\n",
      "Step: 105\n",
      "Step: 106\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-943da714a60f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mwgan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mC_train_on_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[0mwgan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mwgan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mG_train_on_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Repos\\WGAN\\WGAN.py\u001b[0m in \u001b[0;36mclip_weights\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     66\u001b[0m             \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m             \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 68\u001b[1;33m             \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     69\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Applications\\Anaconda\\envs\\tf2\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36mset_weights\u001b[1;34m(self, weights)\u001b[0m\n\u001b[0;32m   1135\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_updates\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mupdates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1137\u001b[1;33m   \u001b[1;32mdef\u001b[0m \u001b[0mset_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1138\u001b[0m     \"\"\"Sets the weights of the layer, from Numpy arrays.\n\u001b[0;32m   1139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from IPython.display import clear_output, Image\n",
    "\n",
    "for step in range(0, 15001):\n",
    "    for i in range(0,5):\n",
    "        wgan.C_train_on_batch()\n",
    "        wgan.clip_weights()\n",
    "    wgan.G_train_on_batch()\n",
    "        \n",
    "    if step % 50 == 0:\n",
    "        clear_output(wait=True)\n",
    "    print(f\"Step: {step}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24eca478160>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "noise = np.random.uniform(-1.0, 1.0, size=(5,100,))\n",
    "fake_batch = wgan.G(noise)\n",
    "print(fake_batch.shape)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.axis('off')\n",
    "fake_batch = (fake_batch + 1) / 2\n",
    "plt.imshow((fake_batch[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf2",
   "language": "python",
   "name": "tf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}