Fashion Image Generation with GANs Exploring AI-Driven Fashion Synthesis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5kGzqWo4kgL"
      },
      "source": [
        "# Fashion Image Generation using GANs:\n",
        "\n",
        "In this project, we implement a Generative Adversarial Network (GAN) model to generate fashion images from a custom dataset sourced from Polyvore. The primary objective is to explore how GANs can be utilized for generating realistic images that can assist in fashion recommendations. The dataset can be accessed [here](https://github.com/AemikaChow/AiDLab-fAshIon-Data/blob/main/Datasets/cleaned-maryland.md).\n",
        "\n",
        "## Authors\n",
        "- Sajjad Saed\n",
        "- Kamand Kalashi\n",
        "\n",
        "\n",
        "## Project Overview\n",
        "\n",
        "The project consists of several critical steps, including dataset preparation, model architecture design, training, and visualization. Below is a summary of the steps involved:\n",
        "\n",
        "### 1. Dataset Preparation\n",
        "- The dataset consists of fashion images organized into folders based on their respective class labels.\n",
        "- Each image is read, resized to 128x128 pixels, and converted to grayscale.\n",
        "- The images are normalized and split into training and test sets to ensure proper model training and evaluation.\n",
        "\n",
        "### 2. GAN Architecture\n",
        "The GAN consists of two main components: the Generator and the Discriminator.\n",
        "\n",
        "#### Generator\n",
        "- **Input Layer**: Receives a noise vector of dimension 100.\n",
        "- **Layers**:\n",
        "  - Dense layer reshaping the input to a feature map of size (8, 8, 512).\n",
        "  - Several Conv2DTranspose layers to upsample the image to (128, 128).\n",
        "  - Final layer with a tanh activation to produce an image.\n",
        "\n",
        "#### Discriminator\n",
        "- **Input Layer**: Takes in images of size (128, 128, 1).\n",
        "- **Layers**:\n",
        "  - Several Conv2D layers to downsample the image.\n",
        "  - Flattening layer followed by a Dense layer with sigmoid activation to classify images as real or fake.\n",
        "\n",
        "### 3. Model Compilation\n",
        "- The Discriminator is compiled with:\n",
        "  - **Optimizer**: Adam with a learning rate adjusted for decay.\n",
        "  - **Loss Function**: Binary cross-entropy.\n",
        "  \n",
        "- The GAN model combines the Generator and Discriminator, with the Discriminator frozen during Generator training.\n",
        "\n",
        "### 4. Model Training\n",
        "The GAN is trained over 150 epochs with a batch size of 32. The training involves alternating between training the Discriminator and Generator while adjusting learning rates based on epoch progression. \n",
        "\n",
        "### 5. Results Visualization\n",
        "- Plots of Generator and Discriminator loss and accuracy are generated per epoch.\n",
        "- Sample images generated by the Generator are visualized to assess the quality of the generated images.\n",
        "\n",
        "## Requirements\n",
        "To run this project, ensure you have the following libraries installed:\n",
        "- NumPy\n",
        "- OpenCV\n",
        "- TensorFlow\n",
        "- Keras\n",
        "- Matplotlib\n",
        "- Seaborn\n",
        "\n",
        "## Conclusions\n",
        "This project demonstrates how GANs can effectively generate fashion images that mimic real images from the dataset. The results showcase the potential of GANs in the fashion industry, especially in areas like virtual try-ons and fashion recommendations. Future work could explore improving image quality through advanced GAN architectures or integrating the GAN with a recommendation engine to enhance user experience in fashion selection.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Importing Required Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hMzYwpHZokcq"
      },
      "outputs": [],
      "source": [
        "# Importing required libraries\n",
        "# Feature extraction and feature selection\n",
        "import numpy as np\n",
        "\n",
        "# imqage processing and visualization\n",
        "import cv2\n",
        "import tqdm as tqdm\n",
        "from PIL import Image\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import argparse\n",
        "import math\n",
        "import random\n",
        "import seaborn as sns\n",
        "\n",
        "# Data training and create cnn model\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.models import Model, Sequential, load_model\n",
        "from keras.layers import Conv2D, MaxPool2D, Dropout, Dense, MaxPooling2D, GlobalAveragePooling2D, Flatten, Input, Multiply, Reshape, Embedding, Conv2DTranspose, BatchNormalization, LeakyReLU, Concatenate\n",
        "from keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam, SGD, RMSprop\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from keras.callbacks import EarlyStopping,ReduceLROnPlateau, TensorBoard, ModelCheckpoint\n",
        "import itertools\n",
        "import ipywidgets as widgets\n",
        "\n",
        "# keras libraries\n",
        "from keras.layers import Activation\n",
        "# from keras.layers.core import Activation\n",
        "# from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.datasets import fashion_mnist\n",
        "# from keras.datasets.fashion_mnist import load_data\n",
        "from tensorflow.keras.datasets import mnist\n",
        "# from keras.utils.vis_utils import plot_model\n",
        "from keras.utils import plot_model\n",
        "# from keras.layers.advanced_activations import LeakyReLU\n",
        "from numpy import expand_dims, zeros, asarray, ones\n",
        "from numpy.random import randn, randint\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPvIaGVlqLeB",
        "outputId": "293b2c2c-fd79-49e1-bc97-380cd0b4f070"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Mount Google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dOo3HmadBYIZ"
      },
      "outputs": [],
      "source": [
        "dataset_path = '/content/drive/MyDrive/Polyvore/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "I4ig5elLC_-E"
      },
      "outputs": [],
      "source": [
        "# return dataset class names and count\n",
        "def get_class_names(data_path):\n",
        "    class_names = [f for f in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, f))]\n",
        "    return class_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_yUtn_Sc4ZK",
        "outputId": "e26665a8-b8b9-43f4-ddb6-beea3bba140d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset classes count: 8  and names are: ['bag', 'dress', 'eyewear', 'hats', 'neckwear', 'pants', 'shoes', 'watches']\n"
          ]
        }
      ],
      "source": [
        "# get dataset class names and count\n",
        "class_names = get_class_names(dataset_path)\n",
        "print(\"Dataset classes count:\", len(class_names),\" and names are:\", class_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "8feiAXERX-IB"
      },
      "outputs": [],
      "source": [
        "# Constants\n",
        "img_shape = (128, 128, 1)  # Adjust the image size as per our dataset\n",
        "num_classes = len(class_names)\n",
        "batch_size = 32\n",
        "epochs = 150\n",
        "z_dim = 100  # Dimension of the noise vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGPwinX1xwjQ",
        "outputId": "11b17eb4-c5dc-4817-e1e1-8a3a2a9ab6e6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 786/786 [00:19<00:00, 39.78it/s] \n",
            "100%|██████████| 800/800 [00:14<00:00, 53.42it/s] \n",
            "100%|██████████| 780/780 [00:16<00:00, 46.66it/s] \n",
            "100%|██████████| 782/782 [00:13<00:00, 55.96it/s] \n",
            "100%|██████████| 791/791 [00:15<00:00, 51.68it/s] \n",
            "100%|██████████| 793/793 [00:15<00:00, 51.25it/s] \n",
            "100%|██████████| 789/789 [00:16<00:00, 48.06it/s] \n",
            "100%|██████████| 786/786 [00:14<00:00, 53.28it/s] \n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[[786, 'bag'],\n",
              " [800, 'dress'],\n",
              " [780, 'eyewear'],\n",
              " [782, 'hats'],\n",
              " [791, 'neckwear'],\n",
              " [793, 'pants'],\n",
              " [789, 'shoes'],\n",
              " [786, 'watches']]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# to resizing all data to same size (128*128)\n",
        "image_size = 128\n",
        "# Array to store all images with its label\n",
        "all_data = []\n",
        "image_count = []\n",
        "count = 0\n",
        "\n",
        "# For each index in the list of classes (class_names), by using method join with arguments dataset_path and i,\n",
        "# reads all images of these classes and after resizing to 12*128, store the image with its label in all_data array\n",
        "for i in class_names:\n",
        "  path=os.path.join(dataset_path,i)\n",
        "  class_label = class_names.index(i)\n",
        "  for image in tqdm.tqdm(os.listdir(path)):\n",
        "    try:\n",
        "      image_array=cv2.imread(os.path.join(path,image),cv2.IMREAD_GRAYSCALE)\n",
        "      new_image_array = cv2.resize(image_array, (image_size, image_size))\n",
        "      all_data.append([new_image_array, class_label])\n",
        "      # plt.imshow(new_image_array)\n",
        "      # plt.show()\n",
        "      # break\n",
        "      if os.path.isfile(os.path.join(path,image)):\n",
        "        count += 1\n",
        "    except Exception as e:\n",
        "      pass\n",
        "  image_count.append([count, class_names[class_label]])\n",
        "  count = 0\n",
        "image_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBMeCERnfQg5",
        "outputId": "04341f1a-f115-4f0e-f447-46dce7730796"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6307\n"
          ]
        }
      ],
      "source": [
        "print(len(all_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kp27lLuOyVA1",
        "outputId": "9d71d0ce-923e-4f11-e33d-5b798fc282b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(6307, 128, 128)\n",
            "(6307, 128, 128, 1)\n"
          ]
        }
      ],
      "source": [
        "# split images and labels to X and Y arays\n",
        "# images\n",
        "X = []\n",
        "# labels\n",
        "Y = []\n",
        "for i, j in all_data:\n",
        "  X.append(i)\n",
        "  Y.append(j)\n",
        "# reshape images to 128*128 and channel 1 (black & white )\n",
        "X = np.array(X).reshape(-1, image_size, image_size)\n",
        "print(X.shape)\n",
        "# to normalize\n",
        "X = X/255\n",
        "X = X.reshape(-1, 128, 128, 1)\n",
        "print(X.shape)\n",
        "\n",
        "# Converts class vector (Y) to binary class matrix --> one hot encoding\n",
        "y_categorical = to_categorical(Y,num_classes = 8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UTJIyqqJ1_oD"
      },
      "outputs": [],
      "source": [
        "# split the all_data into 80% train data and 20% test data by random state 42\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_categorical, test_size = 0.2, random_state = 42 , shuffle = True)\n",
        "# split 15% of the train data for validation\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.15, random_state = 42, shuffle = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTeT8155DqIM",
        "outputId": "8c3485a0-22a4-4973-cdcc-c82d8fe3985b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "all data: 6307\n",
            "total train data: 4288 and shape: (4288, 128, 128, 1)\n",
            "total validation data: 757 and shape: (757, 128, 128, 1)\n",
            "total test data: 1262 and shape: (1262, 128, 128, 1)\n"
          ]
        }
      ],
      "source": [
        "print('all data:', len(all_data))\n",
        "print('total train data:', len(X_train) , 'and shape:', X_train.shape)\n",
        "print('total validation data:', len(X_val), 'and shape:', X_val.shape)\n",
        "print('total test data:', len(X_test), 'and shape:', X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R94sYhyWVxSA",
        "outputId": "6d352070-e40f-4e64-8ff3-d37f937a64d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4288, 128, 128, 1)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ctu2vWMJ64H6"
      },
      "source": [
        "# Load and Preprocess the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CVW2CxkZbU6",
        "outputId": "aa09bdde-4bcd-47e7-c644-9e6546683650"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 6307 images belonging to 8 classes.\n"
          ]
        }
      ],
      "source": [
        "# Load and preprocess the dataset\n",
        "data_generator = ImageDataGenerator(rescale=1./255)\n",
        "train_data = data_generator.flow_from_directory(dataset_path,\n",
        "                                                target_size=(128, 128),\n",
        "                                                batch_size=batch_size,\n",
        "                                                color_mode='grayscale',  # Load images in grayscale\n",
        "                                                class_mode=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UJ7xZKw6v0L"
      },
      "source": [
        "# Define the Generator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "4rTzDtKeZECD"
      },
      "outputs": [],
      "source": [
        "def create_generator(z_dim=100):\n",
        "    model = Sequential([\n",
        "        # Starting layer to reshape input noise\n",
        "        Dense(512 * 8 * 8, input_dim=z_dim),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "        Reshape((8, 8, 512)),\n",
        "\n",
        "        # Upsample to 16x16\n",
        "        Conv2DTranspose(256, kernel_size=4, strides=2, padding='same'),\n",
        "        BatchNormalization(),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "\n",
        "        # Upsample to 32x32\n",
        "        Conv2DTranspose(128, kernel_size=4, strides=2, padding='same'),\n",
        "        BatchNormalization(),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "\n",
        "        # Upsample to 64x64\n",
        "        Conv2DTranspose(64, kernel_size=4, strides=2, padding='same'),\n",
        "        BatchNormalization(),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "\n",
        "        # Upsample to 128x128\n",
        "        Conv2DTranspose(1, kernel_size=4, strides=2, padding='same', activation='tanh'),\n",
        "    ])\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZXl2skR6_Xx"
      },
      "source": [
        "# Define the Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "LzztABt5ZP3l"
      },
      "outputs": [],
      "source": [
        "def create_discriminator(img_shape=(128, 128, 1)):\n",
        "    model = Sequential([\n",
        "        Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=img_shape),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "        Dropout(0.3),\n",
        "\n",
        "        Conv2D(128, kernel_size=4, strides=2, padding='same'),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "        Dropout(0.3),\n",
        "\n",
        "        Conv2D(256, kernel_size=4, strides=2, padding='same'),\n",
        "        LeakyReLU(alpha=0.2),\n",
        "        Dropout(0.3),\n",
        "\n",
        "        Flatten(),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbOPBPPq4RTj",
        "outputId": "5c2cc050-c487-45ad-ccbb-91bc27e76669"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n",
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        }
      ],
      "source": [
        "# Adjust these values based on your observations\n",
        "initial_gen_lr,gen_lr = 0.000287, 0.000287\n",
        "initial_disc_lr,disc_lr = 0.000196, 0.000196\n",
        "decay_factor = 0.5\n",
        "epoch_to_decay = 10  # Decay every 10 epochs\n",
        "\n",
        "# Optimizer for generator and discriminator\n",
        "# optimizer = Adam(lr=initial_lr, beta_1=0.5)\n",
        "optimizer_gen = Adam(lr=initial_gen_lr, beta_1=0.5)\n",
        "optimizer_disc = Adam(lr=initial_disc_lr, beta_1=0.5)\n",
        "\n",
        "\n",
        "\n",
        "# Adjust learning rate based on epoch\n",
        "def adjust_learning_rate(optimizer_gen, optimizer_disc, epoch):\n",
        "    gen_lr = initial_gen_lr * (decay_factor ** (epoch // epoch_to_decay))\n",
        "    disc_lr = initial_disc_lr * (decay_factor ** (epoch // epoch_to_decay))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL67z_Uu7JT2"
      },
      "source": [
        "# Build the GAN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_lo0cv1r9MQ",
        "outputId": "365a10a5-d67e-4699-c8b8-53626dbec87f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n",
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        }
      ],
      "source": [
        "# Build and compile the discriminator\n",
        "discriminator = create_discriminator(img_shape)\n",
        "discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=disc_lr, beta_1=0.5), metrics=['accuracy'])\n",
        "\n",
        "# Build the generator\n",
        "generator = create_generator(z_dim)\n",
        "\n",
        "# Build and compile the GAN\n",
        "discriminator.trainable = False  # Freeze discriminator during generator training\n",
        "\n",
        "gan_input = Input(shape=(z_dim,))\n",
        "gan_output = discriminator(generator(gan_input))\n",
        "gan = Model(gan_input, gan_output)\n",
        "\n",
        "gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=gen_lr, beta_1=0.5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSyMIENe7RSd"
      },
      "source": [
        "# Training the GAN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXciYXxit5wP",
        "outputId": "ee30851e-546c-42b9-f622-7b04c827d0ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "Epoch: 125/150, discriminator_loss: 0.20175727732112136, generator_loss: 18.17778968811035, discriminator_ACC: 1.0\n",
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            "Epoch: 127/150, discriminator_loss: 0.731475698825534, generator_loss: 5.5691704750061035, discriminator_ACC: 0.5\n",
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            "Epoch: 128/150, discriminator_loss: 0.5027136281132698, generator_loss: 2.6471708380881864e-08, discriminator_ACC: 0.5\n",
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            "Epoch: 129/150, discriminator_loss: 2.844847468135413, generator_loss: 0.013701972551643848, discriminator_ACC: 0.5\n",
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            "Epoch: 130/150, discriminator_loss: 2.892919182777405, generator_loss: 13.609491348266602, discriminator_ACC: 0.0\n",
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            "Epoch: 131/150, discriminator_loss: 19.360982656478882, generator_loss: 4.449562072753906, discriminator_ACC: 0.0\n",
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            "Epoch: 132/150, discriminator_loss: 4.6333116163586965, generator_loss: 0.000593016273342073, discriminator_ACC: 0.5\n",
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            "Epoch: 133/150, discriminator_loss: 5.845275819301605, generator_loss: 13.66524887084961, discriminator_ACC: 0.0\n",
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            "Epoch: 134/150, discriminator_loss: 0.00010261616262141615, generator_loss: 0.14596006274223328, discriminator_ACC: 1.0\n",
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            "Epoch: 135/150, discriminator_loss: 0.000135275806314894, generator_loss: 11.871053695678711, discriminator_ACC: 1.0\n",
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            "Epoch: 136/150, discriminator_loss: 19.041761674670852, generator_loss: 0.0021788463927805424, discriminator_ACC: 0.5\n",
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            "Epoch: 137/150, discriminator_loss: 0.5290530596382723, generator_loss: 5.143976211547852, discriminator_ACC: 0.5\n",
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            "Epoch: 138/150, discriminator_loss: 0.003039680537312961, generator_loss: 3.286787986755371, discriminator_ACC: 1.0\n",
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            "Epoch: 139/150, discriminator_loss: 9.098889527842402, generator_loss: 4.633883953094482, discriminator_ACC: 0.5\n",
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            "Epoch: 140/150, discriminator_loss: 10.15740191936493, generator_loss: 0.5140781998634338, discriminator_ACC: 0.0\n",
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            "Epoch: 141/150, discriminator_loss: 8.023578306540912, generator_loss: 4.2111897468566895, discriminator_ACC: 0.5\n",
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            "Epoch: 142/150, discriminator_loss: 9.462184515295576, generator_loss: 9.804340152186342e-06, discriminator_ACC: 0.5\n",
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            "Epoch: 143/150, discriminator_loss: 2.052032381296158, generator_loss: 8.4669189453125, discriminator_ACC: 0.0\n",
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            "Epoch: 144/150, discriminator_loss: 14.496612787246704, generator_loss: 45.00936508178711, discriminator_ACC: 0.0\n",
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            "Epoch: 145/150, discriminator_loss: 0.0002515271506808858, generator_loss: 4.904009923839547e-12, discriminator_ACC: 1.0\n",
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            "Epoch: 146/150, discriminator_loss: 4.6744644343853, generator_loss: 0.0011320611229166389, discriminator_ACC: 0.5\n",
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            "Epoch: 147/150, discriminator_loss: 15.21053695697647, generator_loss: 22.23379898071289, discriminator_ACC: 0.5\n",
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            "Epoch: 148/150, discriminator_loss: 2.9242651415988803, generator_loss: 32.31007385253906, discriminator_ACC: 0.5\n",
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            "Epoch: 149/150, discriminator_loss: 7.067197918891907, generator_loss: 11.806411743164062, discriminator_ACC: 0.0\n",
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          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 150/150, discriminator_loss: 0.013677723839035136, generator_loss: 36.81424331665039, discriminator_ACC: 1.0\n"
          ]
        }
      ],
      "source": [
        "# Train the GAN\n",
        "def train_gan(generator, discriminator, gan, train_generator, epochs, z_dim):\n",
        "    # Initialize lists to store generator and discriminator losses and accuracies\n",
        "    discriminator_losses, generator_losses, discriminator_accuracies = [], [], []\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        # adjusting learning rate before starting an epoch\n",
        "        adjust_learning_rate(optimizer_gen,optimizer_disc, epoch)\n",
        "        for _ in range(train_generator.samples // train_generator.batch_size):\n",
        "            # Sample random noise to generate fake images\n",
        "            noise = np.random.normal(0, 1, (train_generator.batch_size, z_dim))\n",
        "            fake_images = generator.predict(noise)\n",
        "\n",
        "            # Get a batch of real images\n",
        "            real_images = next(train_generator)[0] if train_generator.class_mode is None else next(train_generator)[0][0]\n",
        "\n",
        "            if len(real_images.shape) == 3:  # Missing batch dimension\n",
        "              real_images = np.expand_dims(real_images, axis=0)  # Add batch dimension\n",
        "\n",
        "            actual_batch_size = real_images.shape[0]\n",
        "\n",
        "            # Labels for real and fake images\n",
        "            real_y = np.ones((actual_batch_size, 1))\n",
        "            fake_y = np.zeros((actual_batch_size, 1))\n",
        "\n",
        "            # Train the discriminator (real classified as ones and generated (fake) as zeros)\n",
        "            d_loss_real, d_acc_real = discriminator.train_on_batch(real_images, real_y)\n",
        "            # d_loss_fake, d_acc_fake = discriminator.train_on_batch(fake_images, fake_y)\n",
        "            d_loss_fake, d_acc_fake = discriminator.train_on_batch(fake_images[:actual_batch_size], fake_y)\n",
        "            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "            d_acc = 0.5 * np.add(d_acc_real, d_acc_fake)\n",
        "\n",
        "            # Train the generator (wants discriminator to mistake images as real)\n",
        "            noise = np.random.normal(0, 1, (actual_batch_size, z_dim))\n",
        "            g_loss = gan.train_on_batch(noise, real_y)\n",
        "\n",
        "        # Save losses and accuracies so you can plot them later\n",
        "        discriminator_losses.append(d_loss)\n",
        "        generator_losses.append(g_loss)\n",
        "        discriminator_accuracies.append(d_acc)\n",
        "\n",
        "        # Optionally, print the progress\n",
        "        # print('Epoch:%d, discriminator_loss=%.3f, generator_loss=%.3f, discriminator_ACC=%.3f' %(i+1, discriminator_losses[i]/epoch, generator_losses[i]/epoch, discriminator_accuracies[i]/epoch))\n",
        "        print(f\"Epoch: {epoch+1}/{epochs}, discriminator_loss: {d_loss}, generator_loss: {g_loss}, discriminator_ACC: {d_acc}\")\n",
        "\n",
        "    generator.save('generator.h5')\n",
        "    discriminator.save('discriminator.h5')\n",
        "    gan.save('gan.h5')\n",
        "\n",
        "    return discriminator_losses, generator_losses, discriminator_accuracies\n",
        "\n",
        "# Begin training\n",
        "d_losses, g_losses, d_accuracies = train_gan(generator, discriminator, gan, train_data, epochs, z_dim)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dT3KPhynBL8N"
      },
      "source": [
        "# Plotting the generator and discriminator's loss and accuracy per epoch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "B5pOZNYkBBn8",
        "outputId": "e483f50e-09b6-4cf8-d0e1-37007015fdfe"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "# Plot discriminator's loss\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.plot(range(1, epochs+1), d_losses, label='Discriminator Loss', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Discriminator Loss per Epoch')\n",
        "plt.legend()\n",
        "\n",
        "# Plot generator's loss\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot(range(1, epochs+1), g_losses, label='Generator Loss', color='orange', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Generator Loss per Epoch')\n",
        "plt.legend()\n",
        "\n",
        "# Plot discriminator's accuracy\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.plot(range(1, epochs+1), d_accuracies, label='Discriminator Accuracy', color='green', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Discriminator Accuracy per Epoch')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7FepxyLNe-L"
      },
      "source": [
        "# Visualize Generated Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "Bu-xLrYY6feG",
        "outputId": "86262bf9-27cb-4377-f1bd-313157aa16f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 1s 604ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x100 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize Generated Images\n",
        "def plot_generated_images(generator, examples=10, dim=(1, 10), figsize=(10, 1)):\n",
        "    noise = np.random.normal(0, 1, size=[examples, 100])\n",
        "    generated_images = generator.predict(noise)\n",
        "    generated_images = generated_images.reshape(examples, 128, 128, 1)\n",
        "    plt.figure(figsize=figsize)\n",
        "    for i in range(generated_images.shape[0]):\n",
        "        plt.subplot(dim[0], dim[1], i+1)\n",
        "        plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')\n",
        "        plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "plot_generated_images(generator)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}