src/mnist/06_LSGAN_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSGANs - Least Squares Generative Adversarial Networks\n",
    "\n",
    "Brief introduction to Least Squares Generative Adversarial Networks or LSGANs. This notebook is organized as follows:\n",
    "\n",
    "1. **Research Paper**\n",
    "2. **Background**\n",
    "3. **Definition**\n",
    "4. **Training WGAN with MNIST dataset, Keras and TensorFlow**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Research Paper\n",
    "\n",
    "* [Least Squares Generative Adversarial Networks](https://arxiv.org/pdf/1611.04076.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Background\n",
    "\n",
    "**Generative adversarial nets** consists of two models: a generative model $G$ that captures the data distribution, and a discriminative model $D$ that estimates the probability that a sample came from the training data rather than $G$.\n",
    "\n",
    "The generator distribution $p_g$ over data data $x$, the generator builds a mapping function from a prior noise distribution $p_z(z)$ to data space as $G(z;\\theta_g)$.\n",
    "\n",
    "The discriminator, $D(x;\\theta_d)$, outputs a single scalar representing the probability that $x$ came form training data rather than $p_g$.\n",
    "\n",
    "The **value function** $V(G,D)$:\n",
    "\n",
    "$$ \\underset{G}{min} \\: \\underset{D}{max} \\; V_{GAN}(D,G) = \\mathbb{E}_{x\\sim p_{data}(x)}[log D(x)] + \\mathbb{E}_{z\\sim p_{z}(z)}[log(1 - D(G(z)))]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Definition\n",
    "\n",
    "Least Squares Generative Adversarial Networks (LSGANs) adopt the **least squares loss function** for the discriminator. \n",
    "\n",
    "The least squares loss function is able to move the fake samples toward the decision boundary, because the least squares loss function penalizes samples that lie in a long way on the correct side of the decision boundary. \n",
    "\n",
    "Another benefit of LSGANs is the improved stability of learning process.\n",
    "\n",
    "### Network Design\n",
    "\n",
    "<img src=\"../../img/network_design_gan.png\" width=\"600\"> \n",
    "\n",
    "\n",
    "### Cost Funcion\n",
    "\n",
    "$$ \n",
    "\\begin{aligned}\n",
    "    \\underset{D}{min} \\; V_{LSGAN}(D,G) =& \\frac{1}{2} \\mathbb{E}_{x\\sim p_{data}(x)}[(D(x)-b)^2] + \\frac{1}{2} \\mathbb{E}_{z\\sim p_{z}(z)}[(D(G(z))-a)^2] \\\\\n",
    "    \\underset{G}{min} \\; V_{LSGAN}(D,G) =& \\frac{1}{2} \\mathbb{E}_{z\\sim p_{z}(z)}[(D(G(z))-c)^2]\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "where $a$ and $b$ are the labels for fake data and real data, respectively, and $c$ denotes the value that G wants D to believe for fake data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Training LSGANs with MNIST dataset, Keras and TensorFlow\n",
    "\n",
    "* Data\n",
    "    * Rescale the MNIST images to be between -1 and 1.\n",
    "\n",
    "* Generator\n",
    "    * **Simple fully connected neural network**, **LeakyReLU activation** and **BatchNormalization**.\n",
    "    * The input to the generator is called 'latent sample' (100 values) which is a series of randomly generated numbers, and produces 784 (=28x28) data points which represent a digit image. We use the **normal distribution**.\n",
    "        The last activation is **tanh**.\n",
    "\n",
    "* Discriminator\n",
    "    * **Simple fully connected neural network** and **LeakyReLU activation**.\n",
    "    * The last activation is **sigmoid**.\n",
    "\n",
    "*  Loss\n",
    "    * loss='mse'\n",
    "\n",
    "* Optimizer\n",
    "    * Adam(lr=0.0002, beta_1=0.5)\n",
    "\n",
    "* batch_size = 64\n",
    "* epochs = 100\n",
    "\n",
    "\n",
    "### 1. Load data\n",
    "\n",
    "#### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:01.518979Z",
     "start_time": "2018-08-09T20:02:01.162370Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:43:37.962270: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-02 11:43:37.995477: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:03.108652Z",
     "start_time": "2018-08-09T20:02:02.907072Z"
    }
   },
   "outputs": [],
   "source": [
    "# load dataset\n",
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore visual data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:03.611045Z",
     "start_time": "2018-08-09T20:02:03.111061Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WIDTH_SIZE = 4\n",
    "HEIGHT_SIZE = 3\n",
    "\n",
    "fig = plt.figure(figsize=(WIDTH_SIZE, HEIGHT_SIZE))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    x_y = X_train[y_train == i]\n",
    "    plt.imshow(x_y[0], cmap='gray', interpolation='none')\n",
    "    plt.title(\"Class %d\" % (i))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    \n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reshaping and normalizing the inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:03.616255Z",
     "start_time": "2018-08-09T20:02:03.612986Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape (60000, 28, 28)\n",
      "X_train reshape: (60000, 784)\n"
     ]
    }
   ],
   "source": [
    "print('X_train.shape', X_train.shape)\n",
    "\n",
    "# reshaping the inputs\n",
    "X_train = X_train.reshape(60000, 28*28)\n",
    "# normalizing the inputs (-1, 1)\n",
    "X_train = (X_train.astype('float32') / 255 - 0.5) * 2\n",
    "\n",
    "print('X_train reshape:', X_train.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Define model\n",
    "\n",
    "#### Generator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:06.592374Z",
     "start_time": "2018-08-09T20:02:05.526273Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:43:39.113284: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.128455: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.128563: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.129114: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-02 11:43:39.130023: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.130123: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.130181: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.175684: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.175778: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.175835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-11-02 11:43:39.175891: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14226 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "# latent space dimension\n",
    "latent_dim = 100\n",
    "\n",
    "# imagem dimension 28x28x1\n",
    "img_dim = 784\n",
    "\n",
    "seed_value = 42\n",
    "init = tf.keras.initializers.RandomNormal(\n",
    "    mean=0.0, stddev=0.02, seed=seed_value\n",
    ")\n",
    "\n",
    "# Generator network\n",
    "generator = tf.keras.models.Sequential()\n",
    "\n",
    "# Input layer and hidden layer 1\n",
    "generator.add(\n",
    "    tf.keras.layers.Dense(\n",
    "        128, input_shape=(latent_dim,), kernel_initializer=init\n",
    "    )\n",
    ")\n",
    "generator.add(tf.keras.layers.LeakyReLU(alpha=0.2))\n",
    "generator.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
    "\n",
    "# Hidden layer 2\n",
    "generator.add(tf.keras.layers.Dense(256))\n",
    "generator.add(tf.keras.layers.LeakyReLU(alpha=0.2))\n",
    "generator.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
    "\n",
    "# Hidden layer 3\n",
    "generator.add(tf.keras.layers.Dense(512))\n",
    "generator.add(tf.keras.layers.LeakyReLU(alpha=0.2))\n",
    "generator.add(tf.keras.layers.BatchNormalization(momentum=0.8))\n",
    "\n",
    "# Output layer\n",
    "generator.add(tf.keras.layers.Dense(img_dim, activation=\"tanh\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generator model visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:06.601583Z",
     "start_time": "2018-08-09T20:02:06.594445Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               12928     \n",
      "                                                                 \n",
      " leaky_re_lu (LeakyReLU)     (None, 128)               0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 128)              512       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               33024     \n",
      "                                                                 \n",
      " leaky_re_lu_1 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 256)              1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 512)               131584    \n",
      "                                                                 \n",
      " leaky_re_lu_2 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 512)              2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 784)               402192    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 583,312\n",
      "Trainable params: 581,520\n",
      "Non-trainable params: 1,792\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# prints a summary representation of your model\n",
    "generator.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:07.436658Z",
     "start_time": "2018-08-09T20:02:07.134694Z"
    }
   },
   "outputs": [],
   "source": [
    "# Discriminator network\n",
    "discriminator = tf.keras.models.Sequential()\n",
    "\n",
    "# Hidden layer 1\n",
    "discriminator.add(\n",
    "    tf.keras.layers.Dense(128, input_shape=(img_dim,), kernel_initializer=init)\n",
    ")\n",
    "discriminator.add(tf.keras.layers.LeakyReLU(alpha=0.2))\n",
    "\n",
    "# Hidden layer 2\n",
    "discriminator.add(tf.keras.layers.Dense(256))\n",
    "discriminator.add(tf.keras.layers.LeakyReLU(alpha=0.2))\n",
    "\n",
    "# Hidden layer 3\n",
    "discriminator.add(tf.keras.layers.Dense(512))\n",
    "discriminator.add(tf.keras.layers.LeakyReLU(alpha=0.2))\n",
    "\n",
    "# Output layer\n",
    "discriminator.add(tf.keras.layers.Dense(1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discriminator model visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:07.443796Z",
     "start_time": "2018-08-09T20:02:07.438685Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 128)               100480    \n",
      "                                                                 \n",
      " leaky_re_lu_3 (LeakyReLU)   (None, 128)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 256)               33024     \n",
      "                                                                 \n",
      " leaky_re_lu_4 (LeakyReLU)   (None, 256)               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 512)               131584    \n",
      "                                                                 \n",
      " leaky_re_lu_5 (LeakyReLU)   (None, 512)               0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 513       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 265,601\n",
      "Trainable params: 265,601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# prints a summary representation of your model\n",
    "discriminator.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Compile model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:07.842045Z",
     "start_time": "2018-08-09T20:02:07.804575Z"
    }
   },
   "outputs": [],
   "source": [
    "# Optimizer\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)\n",
    "\n",
    "discriminator.compile(\n",
    "    optimizer=optimizer, loss=\"mse\", metrics=[\"binary_accuracy\"]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combined network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:08.602791Z",
     "start_time": "2018-08-09T20:02:07.844004Z"
    }
   },
   "outputs": [],
   "source": [
    "discriminator.trainable = False\n",
    "\n",
    "z = tf.keras.layers.Input(shape=(latent_dim,))\n",
    "img = generator(z)\n",
    "decision = discriminator(img)\n",
    "d_g = tf.keras.models.Model(inputs=z, outputs=decision)\n",
    "\n",
    "# Optimize w.r.t. MSE loss instead of crossentropy\n",
    "d_g.compile(optimizer=optimizer, loss=\"mse\", metrics=[\"binary_accuracy\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-09T20:02:08.610389Z",
     "start_time": "2018-08-09T20:02:08.604800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 100)]             0         \n",
      "                                                                 \n",
      " sequential (Sequential)     (None, 784)               583312    \n",
      "                                                                 \n",
      " sequential_1 (Sequential)   (None, 1)                 265601    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 848,913\n",
      "Trainable params: 581,520\n",
      "Non-trainable params: 267,393\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# prints a summary representation of your model\n",
    "d_g.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Fit model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T06:01:56.549495Z",
     "start_time": "2018-08-09T20:02:08.612634Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:43:39.524770: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 1/100, d_loss=0.141, g_loss=0.877                                                                                                                        \n",
      "1/1 [==============================] - 0s 43ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 2/100, d_loss=0.185, g_loss=0.370                                                                                                                        \n",
      "epoch = 3/100, d_loss=0.220, g_loss=0.500                                                                                                                        \n",
      "epoch = 4/100, d_loss=0.160, g_loss=0.300                                                                                                                        \n",
      "epoch = 5/100, d_loss=0.208, g_loss=0.420                                                                                                                        \n",
      "epoch = 6/100, d_loss=0.205, g_loss=0.310                                                                                                                        \n",
      "epoch = 7/100, d_loss=0.198, g_loss=0.306                                                                                                                        \n",
      "epoch = 8/100, d_loss=0.186, g_loss=0.367                                                                                                                        \n",
      "epoch = 9/100, d_loss=0.190, g_loss=0.305                                                                                                                        \n",
      "epoch = 10/100, d_loss=0.191, g_loss=0.365                                                                                                                        \n",
      "epoch = 11/100, batch = 72/1875, d_loss=0.166, g_loss=0.428                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:46:52.294132: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 11/100, batch = 1425/1875, d_loss=0.119, g_loss=0.448                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:47:06.291714: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 11/100, d_loss=0.157, g_loss=0.430                                                                                                                        \n",
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 12/100, d_loss=0.218, g_loss=0.394                                                                                                                        \n",
      "epoch = 13/100, d_loss=0.195, g_loss=0.449                                                                                                                        \n",
      "epoch = 14/100, batch = 1566/1875, d_loss=0.222, g_loss=0.315                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:48:06.292366: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 14/100, d_loss=0.178, g_loss=0.416                                                                                                                        \n",
      "epoch = 15/100, d_loss=0.207, g_loss=0.463                                                                                                                        \n",
      "epoch = 16/100, d_loss=0.171, g_loss=0.473                                                                                                                        \n",
      "epoch = 17/100, d_loss=0.165, g_loss=0.412                                                                                                                        \n",
      "epoch = 18/100, d_loss=0.205, g_loss=0.401                                                                                                                        \n",
      "epoch = 19/100, d_loss=0.180, g_loss=0.487                                                                                                                        \n",
      "epoch = 20/100, d_loss=0.176, g_loss=0.477                                                                                                                        \n",
      "epoch = 21/100, batch = 1547/1875, d_loss=0.227, g_loss=0.370                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:50:22.312750: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 21/100, d_loss=0.182, g_loss=0.440                                                                                                                        \n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 22/100, d_loss=0.149, g_loss=0.436                                                                                                                        \n",
      "epoch = 23/100, batch = 1603/1875, d_loss=0.209, g_loss=0.399                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:51:02.304420: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 23/100, d_loss=0.154, g_loss=0.458                                                                                                                        \n",
      "epoch = 24/100, d_loss=0.179, g_loss=0.443                                                                                                                        \n",
      "epoch = 25/100, d_loss=0.121, g_loss=0.433                                                                                                                        \n",
      "epoch = 26/100, d_loss=0.139, g_loss=0.373                                                                                                                        \n",
      "epoch = 27/100, d_loss=0.217, g_loss=0.522                                                                                                                        \n",
      "epoch = 28/100, d_loss=0.139, g_loss=0.529                                                                                                                        \n",
      "epoch = 29/100, d_loss=0.158, g_loss=0.419                                                                                                                        \n",
      "epoch = 30/100, d_loss=0.209, g_loss=0.501                                                                                                                        \n",
      "epoch = 31/100, batch = 1032/1875, d_loss=0.230, g_loss=0.370                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:53:32.321158: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 31/100, d_loss=0.164, g_loss=0.515                                                                                                                        \n",
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 32/100, d_loss=0.183, g_loss=0.496                                                                                                                        \n",
      "epoch = 33/100, batch = 1667/1875, d_loss=0.252, g_loss=0.419                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:54:18.317459: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 33/100, d_loss=0.140, g_loss=0.415                                                                                                                        \n",
      "epoch = 34/100, d_loss=0.157, g_loss=0.452                                                                                                                        \n",
      "epoch = 35/100, d_loss=0.178, g_loss=0.450                                                                                                                        \n",
      "epoch = 36/100, d_loss=0.189, g_loss=0.412                                                                                                                        \n",
      "epoch = 37/100, d_loss=0.201, g_loss=0.473                                                                                                                        \n",
      "epoch = 38/100, d_loss=0.130, g_loss=0.462                                                                                                                        \n",
      "epoch = 39/100, d_loss=0.170, g_loss=0.471                                                                                                                        \n",
      "epoch = 40/100, d_loss=0.159, g_loss=0.558                                                                                                                        \n",
      "epoch = 41/100, d_loss=0.137, g_loss=0.609                                                                                                                        \n",
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 42/100, d_loss=0.144, g_loss=0.526                                                                                                                        \n",
      "epoch = 43/100, batch = 1129/1875, d_loss=0.202, g_loss=0.492                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 11:57:29.337691: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 43/100, d_loss=0.135, g_loss=0.557                                                                                                                        \n",
      "epoch = 44/100, d_loss=0.146, g_loss=0.410                                                                                                                        \n",
      "epoch = 45/100, d_loss=0.166, g_loss=0.498                                                                                                                        \n",
      "epoch = 46/100, d_loss=0.142, g_loss=0.501                                                                                                                        \n",
      "epoch = 47/100, d_loss=0.151, g_loss=0.470                                                                                                                        \n",
      "epoch = 48/100, d_loss=0.135, g_loss=0.520                                                                                                                        \n",
      "epoch = 49/100, d_loss=0.157, g_loss=0.569                                                                                                                        \n",
      "epoch = 50/100, d_loss=0.170, g_loss=0.505                                                                                                                        \n",
      "epoch = 51/100, d_loss=0.192, g_loss=0.518                                                                                                                        \n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 52/100, d_loss=0.143, g_loss=0.485                                                                                                                        \n",
      "epoch = 53/100, batch = 1685/1875, d_loss=0.244, g_loss=0.360                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:00:51.356026: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 53/100, d_loss=0.174, g_loss=0.604                                                                                                                        \n",
      "epoch = 54/100, batch = 381/1875, d_loss=0.167, g_loss=0.404                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:00:57.344737: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 54/100, d_loss=0.147, g_loss=0.496                                                                                                                        \n",
      "epoch = 55/100, d_loss=0.159, g_loss=0.456                                                                                                                        \n",
      "epoch = 56/100, d_loss=0.194, g_loss=0.587                                                                                                                        \n",
      "epoch = 57/100, d_loss=0.105, g_loss=0.500                                                                                                                        \n",
      "epoch = 58/100, d_loss=0.131, g_loss=0.556                                                                                                                        \n",
      "epoch = 59/100, d_loss=0.126, g_loss=0.517                                                                                                                        \n",
      "epoch = 60/100, d_loss=0.128, g_loss=0.468                                                                                                                        \n",
      "epoch = 61/100, d_loss=0.186, g_loss=0.599                                                                                                                        \n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 62/100, batch = 693/1875, d_loss=0.192, g_loss=0.432                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:03:37.356460: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 62/100, d_loss=0.153, g_loss=0.441                                                                                                                        \n",
      "epoch = 63/100, d_loss=0.145, g_loss=0.592                                                                                                                        \n",
      "epoch = 64/100, d_loss=0.158, g_loss=0.505                                                                                                                        \n",
      "epoch = 65/100, d_loss=0.129, g_loss=0.599                                                                                                                        \n",
      "epoch = 66/100, d_loss=0.187, g_loss=0.537                                                                                                                        \n",
      "epoch = 67/100, batch = 1629/1875, d_loss=0.195, g_loss=0.529                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:05:25.369024: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 67/100, d_loss=0.151, g_loss=0.459                                                                                                                        \n",
      "epoch = 68/100, batch = 1655/1875, d_loss=0.176, g_loss=0.419                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:05:45.369832: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 68/100, d_loss=0.121, g_loss=0.450                                                                                                                        \n",
      "epoch = 69/100, batch = 529/1875, d_loss=0.171, g_loss=0.558                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:05:53.367913: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 69/100, batch = 1489/1875, d_loss=0.178, g_loss=0.534                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:06:03.367811: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 69/100, d_loss=0.184, g_loss=0.525                                                                                                                        \n",
      "epoch = 70/100, d_loss=0.135, g_loss=0.487                                                                                                                        \n",
      "epoch = 71/100, batch = 602/1875, d_loss=0.194, g_loss=0.627                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:06:33.373256: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 71/100, d_loss=0.141, g_loss=0.547                                                                                                                        \n",
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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7/Xbv3t33/p999pnJF1xwgcn77ruvyWeeeabJqX2PjfnRj35k8oIFC3xvX2hxWPNM6c+nfZLqgw8+MPmII44I9Xw6U1Kff/To0Q2XdQ/wfEjCmmdb2D16mVMHAACAvKOoAwAAcEAsR5rkmo4sCcJIk/To+ID99tuv4fIDDzxgruvZs6fJpaWloZ5LT5nryJPmze3/z+gpeR29oM9/+umn+z5/6s+qYxD0ufOxbVgU1dXVmZy6tZrned748eN976+vkaeeesrkAQMGmNyqVSvfx9OvcxcuXOh7e4S3Y8cOk7VtQrftUmG38Qr7davS18SWLVtMLsRXroAfztQBAAA4gKIOAADAARR1AAAADmCkSSO0X2r27Nkmb9y40eSuXbvm+pAyEpV/9q49dVu3bm24nLplmOd53tq1a7P63EVFRSa/9NJLJp999tm+99c+y5/97Gcmjxs3zuRDDjnkBx/r008/NXnIkCG+z90UUVlzP9pb2KdPH5P19xTUExdEexdrampM7tatm8k6tibq4rDmAwcONHnZsmUmZ7rGYemWeg8++KDJ1113XT4PJ7Q4rHnUhB1p8txzz5k8YcKEbB5OaIw0AQAASACKOgAAAAdQ1AEAADiAOXWNWLRokck6ZyxolhIad8ABB5icuu1S7969zXXat1hSUuL72Non8fLLL5t81FFHmfzb3/7W5G3btpl88sknm6zzqHQmWt++fU2eOHFiw+WLLrrIXDdlyhQPe5o6darJLVq0MPnzzz83uV+/fibrbMJjjz3W5Msuu8zkyy+/3OS49dDF0YEHHmhyvnvo1JVXXmnyk08+WZgDQd5oP5r21s6bN8/k888/3+RC99QF4UwdAACAAyjqAAAAHEBRBwAA4ADm1DWic+fOJs+dO9dk7a+67bbbcnxEmXFxlpH2WxW6H0p7g1L3mvW7zvP23M9SezibolBrrn1tYWZC6ZqOHj3a5GnTppl8//33m7xixQqTly9fbnKXLl1MXr9+fZOPtTFt2rRpuKzzz/IhDu9z/R3/+Mc/NvmNN94w+ZtvvjFZ9/tV2hur7y3X9lmOw5pHXdj+7UJjTh0AAEACUNQBAAA4gKIOAADAAfTUNWLYsGEmv/LKKyaXlZWZrDPWqqqqTM60VydT9F0kj4tr3rKlHatZV1dXoCNpXGo/YSHe8y6seSY9mZ63537CrvXQKRfWHOHQUwcAAJAAFHUAAAAOoKgDAABwAHu/NmLNmjUmn3vuuSan7lmK7EhaL4wrMu2BCiNqPXSq0L2zLsj0d8jnBpKOM3UAAAAOoKgDAABwAEUdAACAA+ipa4TuBbdw4cICHUly0AsTT/SRAUB0cKYOAADAARR1AAAADkjr69ekf8US95+/Kccf95856Vjz5GHNk4c1T56g9UvrTF1tbW1WDgaF0ZT1Y83jjTVPHtY8eVjz5Alav2b1aZTtu3fv9iorK73i4uI9ho0iuurr673a2lqvrKxsj+G+QVjzeGLNk4c1Tx7WPHnSXfO0ijoAAABEG/9QAgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA6gqAMAAHAARR0AAIADKOoAAAAc0DKdG+3evdurrKz0iouLvWbNmuX6mJAl9fX1Xm1trVdWVuY1bx6ufmfN44k1Tx7WPHlY8+RJd83TKuoqKyu98vLyrB0c8quiosLr3bt3qPuw5vHGmicPa548rHnyBK15WiV+cXFx1g4I+deU9WPN4401Tx7WPHlY8+QJWr+0ijpO0cZbU9aPNY831jx5WPPkYc2TJ2j9+IcSAAAADkirpw5AOKn/N1VfX1/AIwEAJAVn6gAAABxAUQcAAOAAijoAAAAH0FOHRNLhjdr3lmkfHH10AIB840wdAACAAyjqAAAAHEBRBwAA4AB66jzPKy0tNXnt2rUFOhI0VcuW9qVcV1dnsk7h3r17t8ktWrQwWXvi9PZAUu3atctkfe9EiR6bfg7o5wQQd5ypAwAAcABFHQAAgAMo6gAAABxAT50X3EP37bffmty6dWuTZ86cafKll16alePC/9G+x65du5o8ffp0k6+99lqTv/jiC5M3bdpkclFRkcn333+/yVdffbXJ7dq18z9gZJ3+zocMGWLyokWLTNb+KWYHZkeUe+iU9v8pfY2sWbPG5IceesjkO++8MzsHBuQIZ+oAAAAcQFEHAADgAIo6AAAAB9BTl4bi4mKTdd/QysrKfB5OImnfo+aePXua/Nlnn5m8cuVKkwcNGuT7fNdff73JS5cuNVnXvG/fvianzrXL9Sys1L4gl/rG9Hes70Pt7dKeu8WLF5usfZKzZ882eevWrSa79LtE47SXtlevXiZrr66+hrZt25aT40L+6N9znUmqM1D19ql27NiRvQNrIs7UAQAAOICiDgAAwAEUdQAAAA6gp64ROrvI7zt0z/O8tm3b5vJw0IirrrrK5OHDh5u8c+dOk/v06WNyp06dTK6trTVZ++CuueYak+fOnWvy+vXrTe7cuXPD5TZt2pjrtm/f7mVC5yA+8cQTGT1eLulMxzA9JyeccILJf//7303u0KGD7/0PPfRQk5955pm0n9vz9jx2fU0h/oI+2/VvAT10hferX/3K5D/84Q8mX3TRRSbfddddJu+7774m/+Mf/zC5vLzcZH0N1NTUNFzu2LGjuU7nGN54441evnGmDgAAwAEUdQAAAA7g69dGhB1Bcd1115n861//OpuHg0ZUVFT4Xq//DF2/ItdxFXPmzDFZx2PoV5xDhw41+fzzzzc5dXuiv/zlL+a6sWPH/tBhpyXKX7eqMF+36siSv/3tbybrV+a5pseuxzNq1Kg8Hg1y4cADDzRZv2pD7un7TL8S16xrdO+994Z6vq+++irU7VXqV676d2Tq1Kkm62gs/ep39erVGR1LYzhTBwAA4ACKOgAAAAdQ1AEAADiAnrpGBP0z90xvj8w9+OCDvtefeOKJJuvWL2rMmDEma8/cT3/6U5O1l2LdunVpP3arVq1MTtKojNR+mEceecRcd84555icOhYmCkaOHGny/vvvb/Inn3zScDno9YZoCOp/zkXPU9JMmDDBZB0tlOs+xk2bNpmsI6a03zrM3/OgY+/atavJ1dXVaT92U1GNAAAAOICiDgAAwAEUdQAAAA6gp87zvC5duoS6/ddff22yblekW04hPO1VCOpR0h43nSkW1rJly0Ld/p///Gfat9XXT7du3UI9V5yl9hMOGjTIXJfr3prnn3/e5PPOOy+jx1u+fLnJqdsHjRgxwly3ZMmSjJ4L2aF9mqeddprv7ceNG5fDo0mGY445xuSw7/OqqiqTv/vuO5MPPvhgkzdu3Bjq8R966CGTL7/8cpN15mmqY4891uT58+eHeu5c4EwdAACAAyjqAAAAHEBRBwAA4AB66jzPu/3220PdvlevXiZrPxcyF3bOV4sWLUzO95qE6aUoKSnJ4ZFEW+o+j5MmTTLXrVy5MqPHfvnll00eP368yToH7/PPPze5b9++Jl944YWhnj91T8hFixaZ6/z6cpA/c+fO9b1e1zxsby32dNVVV5m8Zs0ak++55x6Ty8rKTF67dq3Juleszv3UOXT6t0B78oJ6/LZt22Zyal9mmL2t84UzdQAAAA6gqAMAAHAARR0AAIADmtWn0XxUU1PjderUKR/HUxA6V07nzgXJ9XytTFVXV5t+n3QUes2DXpabN282ubi4OJeHE9quXbsaLutegtovqP2A2RCHNc+07/HZZ581WffnDft85eXlJq9atcpkfZ+Hed+3a9fOZO3ryYY4rHmh6Wugrq7OZF2XqH2uqKiueY8ePRou677Yugb6Pgp6nxYVFZk8bNgwk99///10D7NRX3zxhcn9+/fP6PGyLWjNOVMHAADgAIo6AAAAB1DUAQAAOCCRw5OeeeYZk4N66EaPHm3yvHnzsn5MCCdsH0m+pc5E++Mf/2iumzBhQr4PJ5L69etn8pdfful7e+21mTJlislB+wVrz1xFRYVv7t69u8m6p6R+jvjNtdNZV3qsqT1InrfnfpdomqD5gDqHTvcRRdNs2LCh4bK+b9u3b2+yznrbuXOn72Pr+1Dn0oX1xBNPmKzzM+OGM3UAAAAOoKgDAABwAEUdAACAAxI5p073eAzq5UmdOeZ58dvHMaqzjFItXrzY5EMOOcT39lGfDZjaJ6J7Eyr9WXSvw9LS0tDPH9U1T53Jp70zYddUZ4y1bt3aZP1o03mA+r4OS+djffTRR2nf9+KLLzZ5+vTpJmv/XzqiuuaFVFNTY7L2T+tesCeeeGLOjymborrmqe81fZ/ttddeJl9zzTUm616x+r4aPnx4Rsemvba6R/ScOXNMzsVMyUwwpw4AACABKOoAAAAcQFEHAADggLw1h2kfmvbD5JJ+/xzUQ6e0VwfZp/1Jav78+fk5kCYaPHiwyX59dNo7pnOWmtJDFxedO3duuBy2hy7T/X4z7aFTZ5xxRpPv+9RTT2XxSPD/3XHHHSbra6SystLkM888M+fHlER+rfqPPvqoyePGjTNZe22POOIIk7Un7oQTTjBZ58jq54z2a8+YMcNknSuq/Yf6ORQ1nKkDAABwAEUdAACAAyjqAAAAHJCIOXU6d2bMmDG+t9++fbvJbdu2zfox5VNUZxmlWrVqlcl77723yfoybd48Wv8/oj2B/fv3b7g8a9Ysc53uKbp+/fqsH09U13zs2LENl19//fVQ99UZY1u2bMnKMTWV7mEZptdGP2O0r7Ipcxijuua5pK+J2tpak/VzQ/cbXr16dW4OLE/isObaT697JOt7QftNtWdOe+q031r7mbVHT/+e62tG/7ZovfDmm296hcScOgAAgASgqAMAAHAARR0AAIAD4rWJaRNNnjzZ5LB7vSL3gn7nRUVFeTqS7wXtEzp16lSTf/Ob35ic2vehPSBVVVUmR60/MJdee+21Jt+3a9euJhe6py6TXkjdv3LJkiUZHk0yaT+W9tBpP5XOqUPu6ZxXne94zz33mHzSSSeZ/O6774Z6Pl1zpXu5vvTSSyYfffTRJmsPYKF76oIk568JAACAwyjqAAAAHEBRBwAA4AAne+rmzp1r8ujRo0Pd/9///nc2Dwdp2GeffXyv15lg2heRbffdd5/JM2fONLlHjx6+90/tkzv33HPNdS+++GKGRxdf48ePb7g8e/bsUPdds2ZNtg8nFN2jMsz8yjfeeMPkjz76yOSmzKVLIv09/ec///G9/U033WTygAEDTF6xYkV2Dgw/aNu2bSZPnDjR5E2bNpmsPci5dtZZZ5msc/VKSkryeTgZ40wdAACAAyjqAAAAHEBRBwAA4AAne+qOP/54k9PY3tb4n//5n2weDtKga6S9MzoTLNMeJO2b0L63gQMHmqxzxBYsWGCyzkZKnYX46quvNvUwY2/dunUm66y5KNNZcnfffbfJdXV1JutrKtVll11m8tKlSzM8umTS+ZG9e/f2vf2jjz5qsu7zidzTz/YNGzYU6EgaV1FRYbLuDzxo0CCTU/ul9WcLW2vkAmfqAAAAHEBRBwAA4ACKOgAAAAc42VMXNMNs7dq1Jrdq1crk999/P+vHBH86B+6CCy7wvb32M73++usmn3rqqSbrPqG6n98dd9xhsvZVfPDBByZr78Rxxx1n8sKFC3/wtknSvXt3k6P0u9AeuGnTppmsvTT7779/2o+tn0H6+iwtLU37sZJMe2eD9vXUNQ3aU1ofP0qvT1fo3ta5nkMX9HydO3c2WT/rlb4m8j1HLyzO1AEAADiAog4AAMABFHUAAAAOiGVPnfZBPP300ya3adPG9/7az6L7AdbU1GRwdEhX6swpneMV1FO3Y8cOk9955x2TdR5anz59TNa5d9rTp/Oshg4darL2TAX13OF7qT1P2hcZRPeQ1P1Tv/76a5N1T8eioiKTb775ZpOvv/56kzOZhdiuXbsm3zfJysrKTF68eLHJ+r6bN2+eyStXrjS5f//+vs/H+7RpgnoRU/vUtQ9y+/btJuu+3tqzpln7JrWHTv82XHrppSY//vjjnh/9WQ444ACTU/8WLFu2zPexCoEzdQAAAA6gqAMAAHBAs/o0zj/X1NR4nTp1ysfxNImekg/6+lXp6VvXTslXV1d7HTt2DHWfQq/5mjVrTNbtgPSrOx1LU11d7Xt7/XrW7+uDxvKsWbNMPu2000wu9D97j8Oa5/p9pl/DtG7dOqfPl/q1Utu2bXP6XI2Jw5oH0baHDh06+N5e38cbN27M+jFFWaHWXN9L+l5LpeOifvGLX5i8efNmk3v27Gmyvgb0s/yBBx4wefLkySaHbaPQLSEPPvjgUPfPtaA150wdAACAAyjqAAAAHEBRBwAA4IBYjjRR+k+cg+j3/6710MVFap+ajjLQESTa0/bhhx/6Xq9janTEhPZxXH311SZ369bN5KqqKpN1BEuhe+jiSEeUZHsMSLZ76HTUUZR60eKqpKTEZL/eLM/zvNWrV5uctB66qAjafi11PNDbb79trttrr71Mvvjii03WHvetW7earD11YXvog0Sthy4sztQBAAA4gKIOAADAARR1AAAADnCip077qfQ79xEjRpisc2j0O3z6o/IjdfsY7ZXxu63ned4LL7xg8hVXXGHynDlzTF63bp3JFRUVJpeXl5v8zTffmKyvEb0e4em2Xe+9957JxxxzTD4Px6usrDS5S5cuJuusRGRuxYoVJuvvXPsYDzrooJwfE4IF9dTNnTu34fJRRx1lrtNtu4L89a9/Nfmkk04yWf/eB/XY67HrFpFxrwc4UwcAAOAAijoAAAAHUNQBAAA4wImeOp0z16JFiwIdCfLlzjvv9M1qyJAhJi9fvjzU8+Wyr2LBggUmDx8+PGfPFWUjR440edKkSSY/9thjJq9atcrkXr16max7Puq8q/bt2zfhKJFNpaWlJofZUxTRldorqTM/g8yfP99k7aFT2gu7adMmk/VzRfcFV3GfW8uZOgAAAAdQ1AEAADiAog4AAMABzerT+AK5pqaGfQ5jrLq62uvYsWOo+7Dm8caaJw9rnjwurLnu+ax7Qus8y379+pmssw11X3CdcTpx4kSTP/nkE5O1py9qgtacM3UAAAAOoKgDAABwAEUdAACAA5yYUwcAAOJHe+iUzpcMO2NUzZgxI6P7Rx1n6gAAABxAUQcAAOAAijoAAAAH0FMH4Ae1atXKZJ35BLiseXN73iOXe0AD2cCZOgAAAAdQ1AEAADggra9f09hJDBHWlPVjzeMtW2vO6yA+eJ9nX9R/P6x58gStX1pn6mpra7NyMCiMpqwfax5v2Vrzuro68x+ii/d59tXX15v/ooY1T56g9WtWn8Yrdffu3V5lZaVXXFzsNWvWLGsHh9yqr6/3amtrvbKysj0afoOw5vHEmicPa548rHnypLvmaRV1AAAAiDb+oQQAAIADKOoAAAAcQFEHAADgAIo6AAAAB1DUAQAAOICiDgAAwAEUdQAAAA74XwUbutWGqa2jAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 72/100, batch = 1372/1875, d_loss=0.163, g_loss=0.536                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:07:01.371850: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 72/100, d_loss=0.142, g_loss=0.612                                                                                                                        \n",
      "epoch = 73/100, d_loss=0.181, g_loss=0.657                                                                                                                        \n",
      "epoch = 74/100, batch = 1802/1875, d_loss=0.129, g_loss=0.569                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:07:45.377509: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 74/100, d_loss=0.130, g_loss=0.475                                                                                                                        \n",
      "epoch = 75/100, batch = 687/1875, d_loss=0.175, g_loss=0.553                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:07:53.381431: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 75/100, d_loss=0.147, g_loss=0.572                                                                                                                        \n",
      "epoch = 76/100, d_loss=0.147, g_loss=0.592                                                                                                                        \n",
      "epoch = 77/100, d_loss=0.156, g_loss=0.600                                                                                                                        \n",
      "epoch = 78/100, d_loss=0.129, g_loss=0.591                                                                                                                        \n",
      "epoch = 79/100, d_loss=0.162, g_loss=0.624                                                                                                                        \n",
      "epoch = 80/100, d_loss=0.124, g_loss=0.400                                                                                                                        \n",
      "epoch = 81/100, d_loss=0.190, g_loss=0.497                                                                                                                        \n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 82/100, d_loss=0.161, g_loss=0.464                                                                                                                        \n",
      "epoch = 83/100, d_loss=0.126, g_loss=0.693                                                                                                                        \n",
      "epoch = 84/100, d_loss=0.182, g_loss=0.651                                                                                                                        \n",
      "epoch = 85/100, d_loss=0.152, g_loss=0.550                                                                                                                        \n",
      "epoch = 86/100, d_loss=0.142, g_loss=0.387                                                                                                                        \n",
      "epoch = 87/100, batch = 313/1875, d_loss=0.144, g_loss=0.474                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:11:46.397509: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 87/100, batch = 509/1875, d_loss=0.175, g_loss=0.488                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:11:48.396613: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 87/100, d_loss=0.200, g_loss=0.549                                                                                                                        \n",
      "epoch = 88/100, d_loss=0.165, g_loss=0.577                                                                                                                        \n",
      "epoch = 89/100, d_loss=0.177, g_loss=0.638                                                                                                                        \n",
      "epoch = 90/100, d_loss=0.176, g_loss=0.683                                                                                                                        \n",
      "epoch = 91/100, d_loss=0.147, g_loss=0.356                                                                                                                        \n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 92/100, batch = 1191/1875, d_loss=0.151, g_loss=0.548                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:13:34.398364: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 92/100, d_loss=0.143, g_loss=0.559                                                                                                                        \n",
      "epoch = 93/100, d_loss=0.164, g_loss=0.466                                                                                                                        \n",
      "epoch = 94/100, d_loss=0.199, g_loss=0.587                                                                                                                                                                                                                             \n",
      "epoch = 95/100, batch = 1445/1875, d_loss=0.212, g_loss=0.546                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:14:36.406565: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 95/100, d_loss=0.228, g_loss=0.566                                                                                                                        \n",
      "epoch = 96/100, d_loss=0.143, g_loss=0.500                                                                                                                        \n",
      "epoch = 97/100, batch = 549/1875, d_loss=0.153, g_loss=0.623                                                                                                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-02 12:15:06.408588: W tensorflow/core/data/root_dataset.cc:266] Optimization loop failed: CANCELLED: Operation was cancelled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch = 97/100, d_loss=0.162, g_loss=0.587                                                                                                                        \n",
      "epoch = 98/100, d_loss=0.129, g_loss=0.649                                                                                                                        \n",
      "epoch = 99/100, d_loss=0.183, g_loss=0.600                                                                                                                        \n",
      "epoch = 100/100, d_loss=0.182, g_loss=0.697                                                                                                                        \n",
      "epoch = 101/100, d_loss=0.135, g_loss=0.561                                                                                                                        \n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 100\n",
    "batch_size = 32\n",
    "\n",
    "a = tf.zeros(shape=(batch_size // 2, 1))\n",
    "b = tf.ones(shape=(batch_size // 2, 1))\n",
    "c = tf.ones(shape=(batch_size, 1))\n",
    "\n",
    "d_loss = []\n",
    "d_g_loss = []\n",
    "\n",
    "for e in range(epochs + 1):\n",
    "    for i in range(len(X_train) // batch_size):\n",
    "\n",
    "        # Train Discriminator weights\n",
    "        discriminator.trainable = True\n",
    "\n",
    "        # Real samples\n",
    "        X_real = X_train[i * batch_size // 2 : (i + 1) * batch_size // 2]\n",
    "\n",
    "        # Fake Samples\n",
    "        z = tf.random.normal(\n",
    "            shape=(batch_size // 2, latent_dim), mean=0, stddev=1\n",
    "        )\n",
    "        X_fake = generator.predict_on_batch(z)\n",
    "\n",
    "        # Discriminator loss\n",
    "        d_loss_batch = discriminator.train_on_batch(\n",
    "            x=tf.concat((X_fake, X_real), axis=0),\n",
    "            y=tf.concat((a, b), axis=0),\n",
    "        )\n",
    "\n",
    "        # Train Generator weights\n",
    "        discriminator.trainable = False\n",
    "\n",
    "        z = tf.random.normal(shape=(batch_size, latent_dim), mean=0, stddev=1)\n",
    "        d_g_loss_batch = d_g.train_on_batch(x=z, y=c)\n",
    "\n",
    "        print(\n",
    "            \"epoch = %d/%d, batch = %d/%d, d_loss=%.3f, g_loss=%.3f\"\n",
    "            % (\n",
    "                e + 1,\n",
    "                epochs,\n",
    "                i,\n",
    "                len(X_train) // batch_size,\n",
    "                d_loss_batch[0],\n",
    "                d_g_loss_batch[0],\n",
    "            ),\n",
    "            100 * \" \",\n",
    "            end=\"\\r\",\n",
    "        )\n",
    "\n",
    "    d_loss.append(d_loss_batch[0])\n",
    "    d_g_loss.append(d_g_loss_batch[0])\n",
    "    print(\n",
    "        \"epoch = %d/%d, d_loss=%.3f, g_loss=%.3f\"\n",
    "        % (e + 1, epochs, d_loss[-1], d_g_loss[-1]),\n",
    "        100 * \" \",\n",
    "    )\n",
    "\n",
    "    if e % 10 == 0:\n",
    "        samples = 10\n",
    "\n",
    "        z = tf.random.normal(shape=(samples, latent_dim), mean=0, stddev=1)\n",
    "        x_fake = generator.predict(z)\n",
    "\n",
    "        for k in range(samples):\n",
    "            plt.subplot(2, 5, k + 1)\n",
    "            plt.imshow(x_fake[k].reshape(28, 28), cmap=\"gray\")\n",
    "            plt.xticks([])\n",
    "            plt.yticks([])\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T06:01:56.728686Z",
     "start_time": "2018-08-10T06:01:56.551734Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the metrics\n",
    "plt.plot(d_loss)\n",
    "plt.plot(d_g_loss)\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Discriminator', 'Adversarial'], loc='center right')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}