Initial copy-up of DCGAN code

DCGAN_train.ipynb

@@ -0,0 +1,408 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d3774bd-5295-42ac-b0e6-4f3d3a82901a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import gdown\n",
+    "from zipfile import ZipFile\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4f7cd728-3373-4fb7-b595-f594b7b14525",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.makedirs(\"celeba_gan\")\n",
+    "\n",
+    "url = \"https://drive.google.com/uc?id=1O7m1010EJjLE5QxLZiM9Fpjs7Oj6e684\"\n",
+    "output = \"celeba_gan/data.zip\"\n",
+    "gdown.download(url, output, quiet=True)\n",
+    "\n",
+    "with ZipFile(\"celeba_gan/data.zip\", \"r\") as zipobj:\n",
+    "    zipobj.extractall(\"celeba_gan\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c74b2281-2fae-4be9-8463-0f9bba9d0c45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 202599 files belonging to 1 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = keras.preprocessing.image_dataset_from_directory(\n",
+    "    \"celeba_gan\", label_mode=None, image_size=(64, 64), batch_size=32\n",
+    ")\n",
+    "dataset = dataset.map(lambda x: x / 255.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c9e9b947-45b0-456c-ba7e-914d43045f18",
+   "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": [
+    "for x in dataset:\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow((x.numpy() * 255).astype(\"int32\")[0])\n",
+    "    break\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2dea3fa4-1ac8-4889-8b52-8ec3e2ac7c9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"discriminator\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv2d (Conv2D)             (None, 32, 32, 64)        3136      \n",
+      "                                                                 \n",
+      " leaky_re_lu (LeakyReLU)     (None, 32, 32, 64)        0         \n",
+      "                                                                 \n",
+      " conv2d_1 (Conv2D)           (None, 16, 16, 128)       131200    \n",
+      "                                                                 \n",
+      " leaky_re_lu_1 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
+      "                                                                 \n",
+      " conv2d_2 (Conv2D)           (None, 8, 8, 128)         262272    \n",
+      "                                                                 \n",
+      " leaky_re_lu_2 (LeakyReLU)   (None, 8, 8, 128)         0         \n",
+      "                                                                 \n",
+      " flatten (Flatten)           (None, 8192)              0         \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 8192)              0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 1)                 8193      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 404,801\n",
+      "Trainable params: 404,801\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "discriminator = keras.Sequential(\n",
+    "    [\n",
+    "        keras.Input(shape=(64, 64, 3)),\n",
+    "        layers.Conv2D(64, kernel_size=4, strides=2, padding=\"same\"),\n",
+    "        layers.LeakyReLU(alpha=0.2),\n",
+    "        layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
+    "        layers.LeakyReLU(alpha=0.2),\n",
+    "        layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
+    "        layers.LeakyReLU(alpha=0.2),\n",
+    "        layers.Flatten(),\n",
+    "        layers.Dropout(0.2),\n",
+    "        layers.Dense(1, activation=\"sigmoid\"),\n",
+    "    ],\n",
+    "    name=\"discriminator\",\n",
+    ")\n",
+    "discriminator.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2a2507b1-9ad7-48f3-8f90-1052ac67886b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"generator\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_1 (Dense)             (None, 8192)              1056768   \n",
+      "                                                                 \n",
+      " reshape (Reshape)           (None, 8, 8, 128)         0         \n",
+      "                                                                 \n",
+      " conv2d_transpose (Conv2DTra  (None, 16, 16, 128)      262272    \n",
+      " nspose)                                                         \n",
+      "                                                                 \n",
+      " leaky_re_lu_3 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
+      "                                                                 \n",
+      " conv2d_transpose_1 (Conv2DT  (None, 32, 32, 256)      524544    \n",
+      " ranspose)                                                       \n",
+      "                                                                 \n",
+      " leaky_re_lu_4 (LeakyReLU)   (None, 32, 32, 256)       0         \n",
+      "                                                                 \n",
+      " conv2d_transpose_2 (Conv2DT  (None, 64, 64, 512)      2097664   \n",
+      " ranspose)                                                       \n",
+      "                                                                 \n",
+      " leaky_re_lu_5 (LeakyReLU)   (None, 64, 64, 512)       0         \n",
+      "                                                                 \n",
+      " conv2d_3 (Conv2D)           (None, 64, 64, 3)         38403     \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 3,979,651\n",
+      "Trainable params: 3,979,651\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "latent_dim = 128\n",
+    "\n",
+    "generator = keras.Sequential(\n",
+    "    [\n",
+    "        keras.Input(shape=(latent_dim,)),\n",
+    "        layers.Dense(8 * 8 * 128),\n",
+    "        layers.Reshape((8, 8, 128)),\n",
+    "        layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\"),\n",
+    "        layers.LeakyReLU(alpha=0.2),\n",
+    "        layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding=\"same\"),\n",
+    "        layers.LeakyReLU(alpha=0.2),\n",
+    "        layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding=\"same\"),\n",
+    "        layers.LeakyReLU(alpha=0.2),\n",
+    "        layers.Conv2D(3, kernel_size=5, padding=\"same\", activation=\"sigmoid\"),\n",
+    "    ],\n",
+    "    name=\"generator\",\n",
+    ")\n",
+    "generator.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88691fae-b91b-40ad-9ce3-765777608598",
+   "metadata": {},
+   "source": [
+    "# Override train_step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0cd186bd-94f4-4f3b-9937-5062bb568415",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GAN(keras.Model):\n",
+    "    def __init__(self, discriminator, generator, latent_dim):\n",
+    "        super(GAN, self).__init__()\n",
+    "        self.discriminator = discriminator\n",
+    "        self.generator = generator\n",
+    "        self.latent_dim = latent_dim\n",
+    "\n",
+    "    def compile(self, d_optimizer, g_optimizer, loss_fn):\n",
+    "        super(GAN, self).compile()\n",
+    "        self.d_optimizer = d_optimizer\n",
+    "        self.g_optimizer = g_optimizer\n",
+    "        self.loss_fn = loss_fn\n",
+    "        self.d_loss_metric = keras.metrics.Mean(name=\"d_loss\")\n",
+    "        self.g_loss_metric = keras.metrics.Mean(name=\"g_loss\")\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",
+    "        # Sample random points in the latent space\n",
+    "        batch_size = tf.shape(real_images)[0]\n",
+    "        random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n",
+    "\n",
+    "        # Decode them to fake images\n",
+    "        generated_images = self.generator(random_latent_vectors)\n",
+    "\n",
+    "        # Combine them with real images\n",
+    "        combined_images = tf.concat([generated_images, real_images], axis=0)\n",
+    "\n",
+    "        # Assemble labels discriminating real from fake images\n",
+    "        labels = tf.concat(\n",
+    "            [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0\n",
+    "        )\n",
+    "        # Add random noise to the labels - important trick!\n",
+    "        labels += 0.05 * tf.random.uniform(tf.shape(labels))\n",
+    "\n",
+    "        # Train the discriminator\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            predictions = self.discriminator(combined_images)\n",
+    "            d_loss = self.loss_fn(labels, predictions)\n",
+    "        grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\n",
+    "        self.d_optimizer.apply_gradients(\n",
+    "            zip(grads, self.discriminator.trainable_weights)\n",
+    "        )\n",
+    "\n",
+    "        # Sample random points in the latent space\n",
+    "        random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n",
+    "\n",
+    "        # Assemble labels that say \"all real images\"\n",
+    "        misleading_labels = tf.zeros((batch_size, 1))\n",
+    "\n",
+    "        # Train the generator (note that we should *not* update the weights\n",
+    "        # of the discriminator)!\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            predictions = self.discriminator(self.generator(random_latent_vectors))\n",
+    "            g_loss = self.loss_fn(misleading_labels, predictions)\n",
+    "        grads = tape.gradient(g_loss, self.generator.trainable_weights)\n",
+    "        self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))\n",
+    "\n",
+    "        # Update metrics\n",
+    "        self.d_loss_metric.update_state(d_loss)\n",
+    "        self.g_loss_metric.update_state(g_loss)\n",
+    "        return {\n",
+    "            \"d_loss\": self.d_loss_metric.result(),\n",
+    "            \"g_loss\": self.g_loss_metric.result(),\n",
+    "        }\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ccd520d-d223-4447-92c8-24299d7b1f5e",
+   "metadata": {},
+   "source": [
+    "## Create a callback that periodically saves generated images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "621b2abf-e343-47b8-82dd-5103a738f249",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GANMonitor(keras.callbacks.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.normal(shape=(self.num_img, self.latent_dim))\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 = keras.preprocessing.image.array_to_img(generated_images[i])\n",
+    "            img.save(\"generated_img_%03d_%d.png\" % (epoch, i))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0588f900-8567-4d3d-87e0-5ae559d85c36",
+   "metadata": {},
+   "source": [
+    "## Train the end-to-end model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1c771d14-b327-40ab-8458-4eaf73c16a28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   5/6332 [..............................] - ETA: 16:15:50 - d_loss: 0.6776 - g_loss: 0.7854"
+     ]
+    },
+    {
+     "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~\\AppData\\Local\\Temp/ipykernel_15592/2002100634.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m )\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m gan.fit(\n\u001b[0m\u001b[0;32m     11\u001b[0m     \u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mGANMonitor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_img\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlatent_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlatent_dim\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     12\u001b[0m )\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1382\u001b[0m                 _r=1):\n\u001b[0;32m   1383\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1384\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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   1385\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1386\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\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~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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    151\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    913\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    914\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\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--> 915\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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    916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    945\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    946\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 947\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    948\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    949\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2954\u001b[0m       (graph_function,\n\u001b[0;32m   2955\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2956\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   2957\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   2958\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1851\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1852\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1853\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1854\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1855\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    497\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    498\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 499\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    500\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\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    501\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\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---> 54\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     55\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 1  # In practice, use ~100 epochs\n",
+    "\n",
+    "gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)\n",
+    "gan.compile(\n",
+    "    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
+    "    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
+    "    loss_fn=keras.losses.BinaryCrossentropy(),\n",
+    ")\n",
+    "\n",
+    "gan.fit(\n",
+    "    dataset, epochs=epochs, callbacks=[GANMonitor(num_img=10, latent_dim=latent_dim)]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce3c558b-a39a-48f5-b109-d077057b3dcf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}