Generative_Adversarial_Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "## Generative Adversarial Networks (GANs) on Fashion MNIST\n",
    "\n",
    "Generative Adversarial Networks (GANs) have revolutionized the field of artificial intelligence by enabling the generation of realistic synthetic data. In this notebook, we will explore the application of GANs to the Fashion MNIST dataset, which consists of grayscale images of various clothing items such as shirts, pants, shoes, and more.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "The main objectives of this notebook are:\n",
    "\n",
    "1. **Understand GAN Architecture**: which consist of a generator and a discriminator. The generator aims to create realistic images, while the discriminator attempts to distinguish between real and generated images.\n",
    "2. **Implement a GAN**: building and training the generator and discriminator networks.\n",
    "3. **Train the GAN**: To train the GAN on the Fashion MNIST dataset. We will monitor the training process, including the losses of both the generator and discriminator, and adjust hyperparameters as necessary.\n",
    "4. **Generate Fashion Images**: We will visualize and evaluate the quality of these synthetic images.\n",
    "5. **Analyze Results**: To analyze the results and understand the strengths and limitations of our GAN model. We will discuss potential improvements and future directions for research.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "The Fashion MNIST dataset is a large collection of 28x28 grayscale images, each labeled with one of ten categories representing different types of clothing and accessories. Each image is a low-resolution representation of a fashion item, making it a suitable challenge for a quick implementation of a generative model.\n",
    "\n",
    "## Model\n",
    "The model is built using tensoflow notably with LeakyReLU, Dropout,  BatchNormalization and Convolution layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install required libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ihWWlBF5RA-S"
   },
   "outputs": [],
   "source": [
    "# !pip install numpy\n",
    "# !pip install scipy\n",
    "# !pip install sklearn\n",
    "# !pip install tensorflow-[gpu]\n",
    "# !pip install tensorflow\n",
    "# !pip install keras\n",
    "# !pip install matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "t2AVQGlbOKl0"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.layers import Dense, Input, LeakyReLU, Dropout,  BatchNormalization, Reshape, Flatten, Conv2D\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys,os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "# We need to make sure we are using a GPU and not CPU\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2CmtZAl987y"
   },
   "source": [
    "### Download datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "we0iv6X9MjV5",
    "outputId": "c1cd0ae4-fe55-4489-bafe-16d69171276b"
   },
   "outputs": [],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "x_train, x_test  = x_train / 255.0 * 2 -1, x_test / 255.0 * 2 -1,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y is the labels, as of the first version we will not use the labels in the training process but we will refactor the models to iclude a text input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZsIvSsvy7E0V",
    "outputId": "7852fbfd-9d6a-4bce-ec47-dd87cd9ebd0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fashion mnist dataset consists of 28x28 pixel images, Pretty low res\n",
    "\n",
    "note we need to have datasets in the from of (28,28,1) for tensorflow to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IX1EnM5uNNHn",
    "outputId": "625e85e5-258a-437a-f02f-6510813cebd7",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets visualize some of the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "id": "uY1xYuPo4vuS",
    "outputId": "bd4965ef-bfe6-49b3-fc93-e2085b1763b2"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(10):\n",
    "    plt.subplot(5, 5, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_train[i], cmap=\"gray\")\n",
    "    plt.xlabel(class_names[y_test[i]])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "sAGNOEyCNO9W"
   },
   "outputs": [],
   "source": [
    "N, H, W = x_train.shape\n",
    "D = H * W\n",
    "\n",
    "x_train = x_train.reshape(-1,D)\n",
    "x_test = x_test.reshape(-1,D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "px0vRvRBNcli"
   },
   "outputs": [],
   "source": [
    "latent_dim = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "bcoZ-PthNgg9"
   },
   "outputs": [],
   "source": [
    "# New Generator / Discriminator models\n",
    "\n",
    "def build_generator(latent_dim):\n",
    "  i = Input(shape=(latent_dim,))\n",
    "  x = Dense(256, activation=LeakyReLU(alpha=0.2))(i)\n",
    "  x = BatchNormalization(momentum=0.8)(x)\n",
    "  x = Dense(256, activation=LeakyReLU(alpha=0.2))(x)\n",
    "  x = BatchNormalization(momentum=0.8)(x)\n",
    "  x = Dense(256, activation=LeakyReLU(alpha=0.2))(x)\n",
    "  x = BatchNormalization(momentum=0.8)(x)\n",
    "  x = Dense(D, activation='tanh')(x)\n",
    "  model = Model(i,x)\n",
    "  return model\n",
    "\n",
    "def build_discriminator(img_size):\n",
    "    i = Input(shape=(img_size,))\n",
    "    x = Reshape((28, 28, 1))(i)\n",
    "    x = Conv2D(64, kernel_size=4, strides=2, padding='same', activation=LeakyReLU(alpha=0.2))(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    x = Conv2D(128, kernel_size=4, strides=2, padding='same', activation=LeakyReLU(alpha=0.2))(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    x = Conv2D(256, kernel_size=4, strides=2, padding='same', activation=LeakyReLU(alpha=0.2))(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(1, activation='sigmoid')(x)\n",
    "    model = Model(i, x)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "c0R6mt4EOMtk"
   },
   "outputs": [],
   "source": [
    "# Old Generator / Discriminator models\n",
    "# I tried to work without convolution layers here at first and didnt quite like its performance\n",
    "# I also decided to test out the sequential api, functional is much better IMO\n",
    "\n",
    "# def build_generator(latent_dim):\n",
    "#     model = Sequential([\n",
    "#         Dense(256, input_shape=(latent_dim,)),\n",
    "#         LeakyReLU(alpha=0.2),\n",
    "#         BatchNormalization(),\n",
    "#         Dense(512),\n",
    "#         LeakyReLU(alpha=0.2),\n",
    "#         BatchNormalization(),\n",
    "#         Dense(1024),\n",
    "#         LeakyReLU(alpha=0.2),\n",
    "#         BatchNormalization(),\n",
    "#         Dense(D, activation='tanh')\n",
    "#     ])\n",
    "#     return model\n",
    "\n",
    "# def build_discriminator(img_size):\n",
    "#     model = Sequential([\n",
    "#         Dense(1024, input_shape=(img_size,)),\n",
    "#         LeakyReLU(alpha=0.2),\n",
    "#         Dropout(0.3),\n",
    "#         Dense(512),\n",
    "#         LeakyReLU(alpha=0.2),\n",
    "#         Dropout(0.3),\n",
    "#         Dense(256),\n",
    "#         LeakyReLU(alpha=0.2),\n",
    "#         Dropout(0.3),\n",
    "#         Dense(1, activation='sigmoid')\n",
    "#     ])\n",
    "#     return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to comile the generator and discrimitator and then combine the two into a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "CWJCf3ChOvxR"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-02 12:40:48.841857: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:48.860926: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:48.861566: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:48.862544: 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:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-07-02 12:40:48.864467: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:48.865230: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:48.866016: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:49.637268: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:49.637935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:49.637951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2024-07-02 12:40:49.638357: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-07-02 12:40:49.638397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 819 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1060 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    }
   ],
   "source": [
    "# Discriminator - Model to tell if an image is real of AI\n",
    "discriminator = build_discriminator(D)\n",
    "discriminator.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer=Adam(0.0002,0.5),\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Generator - Model the creates the Images\n",
    "generator = build_generator(latent_dim)\n",
    "z = Input(shape=(latent_dim,))\n",
    "img = generator(z)\n",
    "discriminator.trainable = False\n",
    "\n",
    "fake_pred = discriminator(img)\n",
    "\n",
    "combined_model = Model(z, fake_pred)\n",
    "\n",
    "combined_model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "HGPltYo97otc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: /home/kip/miniconda3/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n"
     ]
    }
   ],
   "source": [
    "# Clear the gan_images directory\n",
    "!rm -rf gan_images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "preparation for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "0j0roudKQDqj"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "epochs = 10000\n",
    "sample_period = 200\n",
    "\n",
    "ones = np.ones(batch_size)\n",
    "zeros = np.zeros(batch_size)\n",
    "\n",
    "d_losses = []\n",
    "g_losses = []\n",
    "\n",
    "if not os.path.exists('gan_images'):\n",
    "  os.makedirs('gan_images')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering we can run upto 50,000 epochs, we want to be able to see how the model is performing as it is in the trainin process.\n",
    "\n",
    "One great way to do this is to save sample images during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Ncm78ZyWQemr"
   },
   "outputs": [],
   "source": [
    "def sample_images(epoch):\n",
    "  rows, cols = 5, 5\n",
    "  noise = np.random.randn(rows * cols, latent_dim)\n",
    "  imgs = generator.predict(noise)\n",
    "\n",
    "  imgs = 0.5 * imgs + 0.5\n",
    "\n",
    "  fig, axs = plt.subplots(rows, cols)\n",
    "  idx = 0\n",
    "\n",
    "  for i in range(rows):\n",
    "    for j in range(cols):\n",
    "      axs[i,j].imshow(imgs[idx].reshape(H,W),cmap='gray')\n",
    "      axs[i,j].axis('off')\n",
    "      idx += 1\n",
    "    fig.savefig(\"gan_images/%d.png\"%epoch)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are other ways to do do this, I found this to be the implementation that is easiest to understand\n",
    "\n",
    "Basically creating the training loops is the most complex part of building a GAN, this is really a condensed and consice version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NahcbPGDWka6",
    "outputId": "b0adbf18-34e2-4b74-c0a6-97fde1221afe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 418ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-02 12:40:59.512945: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100\n",
      "2024-07-02 12:40:59.843031: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-07-02 12:40:59.878934: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-07-02 12:40:59.879010: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2024-07-02 12:40:59.916827: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-07-02 12:40:59.916925: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] INTERNAL: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1/10000, d_loss:  0.71,     d_acc: 0.11, g_loss: 0.67\n",
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     "text": [
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 15ms/step\n",
      "epoch: 9901/10000, d_loss:  0.08,     d_acc: 0.95, g_loss: 0.82\n",
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     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "  # Select random real Images\n",
    "  idx = np.random.randint(0, x_train.shape[0], batch_size)\n",
    "  real_imgs = x_train[idx]\n",
    "\n",
    "  # Genreate fake images\n",
    "  noise = np.random.randn(batch_size, latent_dim)\n",
    "  fake_imgs = generator.predict(noise)\n",
    "\n",
    "  # model.train_on_batch is like one step in model.fit we train only on the images\n",
    "  # we the test the discriminator on real images and fake images\n",
    "  d_loss_real, d_acc_real = discriminator.train_on_batch(real_imgs, ones)\n",
    "  d_loss_fake, d_acc_fake = discriminator.train_on_batch(fake_imgs, zeros)\n",
    "  \n",
    "  # Average the losses and accuracy \n",
    "  d_loss = 0.5 * (d_loss_real + d_loss_fake)\n",
    "  d_acc = 0.5 * (d_acc_real + d_acc_fake)\n",
    "\n",
    "  # train the generator\n",
    "  noise = np.random.rand(batch_size, latent_dim)\n",
    "  g_loss = combined_model.train_on_batch(noise, ones)\n",
    "  \n",
    "  # Keep the losses, accuracy in an array so we can visualize later\n",
    "  d_losses.append(d_loss)\n",
    "  g_losses.append(g_loss)\n",
    "    \n",
    "  # We want to save the images after every 100 training epochs\n",
    "  if epoch % 100 == 0:\n",
    "    print(f\"epoch: {epoch+1}/{epochs}, d_loss: {d_loss: .2f}, \\\n",
    "    d_acc: {d_acc:.2f}, g_loss: {g_loss:.2f}\")\n",
    "\n",
    "  if epoch % sample_period == 0:\n",
    "    sample_images(epoch)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this model works well, we do not want to see the losses diverge, the should be close.\n",
    "\n",
    "We want the discriminator loss to increase alittle as this would mean it is having difficulty differentiating the real from fake images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "u6_CtD3VYxNO",
    "outputId": "bc2b14f0-696d-4e4e-a091-4d66c8b04ed0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac37142fd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(g_losses, label='g_losses')\n",
    "plt.plot(d_losses, label='d_losses')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model still has lots of space to improve, I only ran it for 10,000 epochs (25 minutes). In future I will run it for 30,000-50,000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qqbwazD5g1wy",
    "outputId": "fad679ca-e102-4986-d473-956a6efe35eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: /home/kip/miniconda3/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n",
      "0.png\t  2000.png  3400.png  4600.png\t600.png   7200.png  8400.png  9800.png\r\n",
      "1000.png  2200.png  3600.png  4800.png\t6000.png  7400.png  8600.png\r\n",
      "1200.png  2400.png  3800.png  5000.png\t6200.png  7600.png  8800.png\r\n",
      "1400.png  2600.png  400.png   5200.png\t6400.png  7800.png  9000.png\r\n",
      "1600.png  2800.png  4000.png  5400.png\t6600.png  800.png   9200.png\r\n",
      "1800.png  3000.png  4200.png  5600.png\t6800.png  8000.png  9400.png\r\n",
      "200.png   3200.png  4400.png  5800.png\t7000.png  8200.png  9600.png\r\n"
     ]
    }
   ],
   "source": [
    "!ls gan_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets run a predictions with the generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "OzmybN3s_wKx",
    "outputId": "5ae887e6-56e4-4271-90a9-d02fae1e9345"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 24ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows, cols = 5, 4\n",
    "noise = np.random.randn(rows * cols, latent_dim)\n",
    "imgs = generator.predict(noise)\n",
    "\n",
    "imgs = 0.5 * imgs + 0.5\n",
    "\n",
    "fig, axs = plt.subplots(rows, cols)\n",
    "idx = 0\n",
    "\n",
    "for i in range(rows):\n",
    "    for j in range(cols):\n",
    "        axs[i,j].imshow(imgs[idx].reshape(H,W),cmap='gray')\n",
    "        axs[i,j].axis('off')\n",
    "        idx += 1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Works\n",
    "\n",
    "1. Train on 50,000 epochs\n",
    "2. Add text prompts"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}