Generative_AI(Autoencoder,_Variational_Autoencoder,_GANs).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMDz85VRD4r1nxDuy44wdRt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ali3brt/20-Newsgroups-Dataset/blob/main/Generative_AI(Autoencoder%2C_Variational_Autoencoder%2C_GANs).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8_LK9HBjTMYV",
        "outputId": "5f033aa6-fbc6-462a-f300-4cfc39762e25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 11ms/step - loss: 0.0319 - val_loss: 0.0127\n",
            "Epoch 2/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - loss: 0.0116 - val_loss: 0.0101\n",
            "Epoch 3/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - loss: 0.0097 - val_loss: 0.0087\n",
            "Epoch 4/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - loss: 0.0083 - val_loss: 0.0084\n",
            "Epoch 5/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - loss: 0.0077 - val_loss: 0.0076\n",
            "Epoch 6/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - loss: 0.0073 - val_loss: 0.0073\n",
            "Epoch 7/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - loss: 0.0070 - val_loss: 0.0072\n",
            "Epoch 8/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - loss: 0.0068 - val_loss: 0.0069\n",
            "Epoch 9/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - loss: 0.0068 - val_loss: 0.0072\n",
            "Epoch 10/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - loss: 0.0067 - val_loss: 0.0069\n",
            "Epoch 1/10, VAE Loss: 608.6793212890625\n",
            "Epoch 2/10, VAE Loss: 605.7876586914062\n",
            "Epoch 3/10, VAE Loss: 599.097900390625\n",
            "Epoch 4/10, VAE Loss: 597.9661254882812\n",
            "Epoch 5/10, VAE Loss: 600.0632934570312\n",
            "Epoch 6/10, VAE Loss: 599.4857177734375\n",
            "Epoch 7/10, VAE Loss: 598.4924926757812\n",
            "Epoch 8/10, VAE Loss: 597.7554931640625\n",
            "Epoch 9/10, VAE Loss: 597.55224609375\n",
            "Epoch 10/10, VAE Loss: 597.4507446289062\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 761ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input 0 of layer \"functional_34\" is incompatible with the layer: expected shape=(None, 128), found shape=(10, 32, 32, 3)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-69ea78a6f923>\u001b[0m in \u001b[0;36m<cell line: 125>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;31m# Visualizing AE and VAE reconstructions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0mplot_reconstruction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautoencoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0mplot_reconstruction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-15-69ea78a6f923>\u001b[0m in \u001b[0;36mplot_reconstruction\u001b[0;34m(model, data, num_images)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;31m# Visualization of reconstruction results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_reconstruction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_images\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m     \u001b[0mreconstructed_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_images\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/layers/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    243\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mspec_dim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mspec_dim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m                         raise ValueError(\n\u001b[0m\u001b[1;32m    246\u001b[0m                             \u001b[0;34mf'Input {input_index} of layer \"{layer_name}\" is '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m                             \u001b[0;34m\"incompatible with the layer: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input 0 of layer \"functional_34\" is incompatible with the layer: expected shape=(None, 128), found shape=(10, 32, 32, 3)"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models, datasets\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Dense, Flatten, Reshape, Conv2D, Conv2DTranspose\n",
        "from tensorflow.keras.losses import MeanSquaredError\n",
        "\n",
        "# Load CFAR100 dataset\n",
        "(train_images, _), (test_images, _) = datasets.cifar100.load_data()\n",
        "\n",
        "# Normalize pixel values to [0, 1]\n",
        "train_images = train_images.astype(\"float32\") / 255.0\n",
        "test_images = test_images.astype(\"float32\") / 255.0\n",
        "\n",
        "# Reshape images\n",
        "train_images = np.reshape(train_images, (train_images.shape[0], 32, 32, 3))\n",
        "test_images = np.reshape(test_images, (test_images.shape[0], 32, 32, 3))\n",
        "\n",
        "# Create Autoencoder Model (AE)\n",
        "def build_autoencoder():\n",
        "    # Encoder\n",
        "    encoder_input = Input(shape=(32, 32, 3))\n",
        "    x = Conv2D(32, (3, 3), activation='relu', strides=2, padding='same')(encoder_input)\n",
        "    x = Conv2D(64, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "    x = Conv2D(128, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "    x = Flatten()(x)\n",
        "    encoded = Dense(128, activation='relu')(x)\n",
        "\n",
        "    # Decoder\n",
        "    x = Dense(4*4*128, activation='relu')(encoded)\n",
        "    x = Reshape((4, 4, 128))(x)\n",
        "    x = Conv2DTranspose(128, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "    x = Conv2DTranspose(64, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "    x = Conv2DTranspose(32, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "    decoder_output = Conv2DTranspose(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
        "\n",
        "    autoencoder = Model(encoder_input, decoder_output)\n",
        "    autoencoder.compile(optimizer='adam', loss=MeanSquaredError())\n",
        "\n",
        "    return autoencoder\n",
        "\n",
        "# Create Variational Autoencoder Model (VAE)\n",
        "class VAE(Model):\n",
        "    def __init__(self, **kwargs):\n",
        "        super(VAE, self).__init__(**kwargs)\n",
        "        self.encoder_input = Input(shape=(32, 32, 3))\n",
        "\n",
        "        # Encoder layers\n",
        "        x = Conv2D(32, (3, 3), activation='relu', strides=2, padding='same')(self.encoder_input)\n",
        "        x = Conv2D(64, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "        x = Conv2D(128, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "        x = Flatten()(x)\n",
        "\n",
        "        # Latent variables\n",
        "        self.z_mean = Dense(128, name=\"z_mean\")(x)\n",
        "        self.z_log_var = Dense(128, name=\"z_log_var\")(x)\n",
        "\n",
        "        self.encoder = Model(self.encoder_input, [self.z_mean, self.z_log_var])\n",
        "\n",
        "        # Decoder\n",
        "        latent_inputs = Input(shape=(128,))\n",
        "        x = Dense(4*4*128, activation='relu')(latent_inputs)\n",
        "        x = Reshape((4, 4, 128))(x)\n",
        "        x = Conv2DTranspose(128, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "        x = Conv2DTranspose(64, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "        x = Conv2DTranspose(32, (3, 3), activation='relu', strides=2, padding='same')(x)\n",
        "        decoder_output = Conv2DTranspose(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
        "\n",
        "        self.decoder = Model(latent_inputs, decoder_output)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        z_mean, z_log_var = self.encoder(inputs)\n",
        "        z = self.reparameterize(z_mean, z_log_var)\n",
        "        reconstructed = self.decoder(z)\n",
        "        return reconstructed, z_mean, z_log_var\n",
        "\n",
        "    def reparameterize(self, z_mean, z_log_var):\n",
        "        epsilon = tf.random.normal(shape=tf.shape(z_mean))\n",
        "        return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n",
        "\n",
        "# Loss for Variational Autoencoder\n",
        "def vae_loss(x, x_reconstructed, z_mean, z_log_var):\n",
        "    reconstruction_loss = tf.reduce_mean(tf.reduce_sum(tf.keras.losses.binary_crossentropy(x, x_reconstructed), axis=(1, 2)))\n",
        "    kl_loss = -0.5 * tf.reduce_mean(tf.reduce_sum(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), axis=1))\n",
        "    return reconstruction_loss + kl_loss\n",
        "\n",
        "# Instantiate and compile both models\n",
        "autoencoder = build_autoencoder()\n",
        "vae = VAE()\n",
        "\n",
        "vae.compile(optimizer='adam', loss=lambda x, x_reconstructed: vae_loss(x, x_reconstructed, vae.z_mean, vae.z_log_var))\n",
        "\n",
        "# Training the models\n",
        "epochs = 10\n",
        "batch_size = 64\n",
        "\n",
        "# Train Autoencoder\n",
        "autoencoder.fit(train_images, train_images, epochs=epochs, batch_size=batch_size, validation_data=(test_images, test_images))\n",
        "\n",
        "# Train Variational Autoencoder\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).batch(batch_size)\n",
        "for epoch in range(epochs):\n",
        "    for x_batch in train_dataset:\n",
        "        with tf.GradientTape() as tape:\n",
        "            reconstructed, z_mean, z_log_var = vae(x_batch)\n",
        "            loss = vae_loss(x_batch, reconstructed, z_mean, z_log_var)\n",
        "        grads = tape.gradient(loss, vae.trainable_variables)\n",
        "        vae.optimizer.apply_gradients(zip(grads, vae.trainable_variables))\n",
        "    print(f\"Epoch {epoch + 1}/{epochs}, VAE Loss: {loss.numpy()}\")\n",
        "\n",
        "# Visualization of reconstruction results\n",
        "def plot_reconstruction(model, data, num_images=10):\n",
        "    reconstructed_images = model.predict(data[:num_images])\n",
        "    fig, axs = plt.subplots(2, num_images, figsize=(15, 5))\n",
        "    for i in range(num_images):\n",
        "        axs[0, i].imshow(data[i])\n",
        "        axs[0, i].axis('off')\n",
        "        axs[1, i].imshow(reconstructed_images[i])\n",
        "        axs[1, i].axis('off')\n",
        "    plt.show()\n",
        "\n",
        "# Visualizing AE and VAE reconstructions\n",
        "plot_reconstruction(autoencoder, test_images)\n",
        "plot_reconstruction(vae.decoder, test_images)\n"
      ]
    }
  ]
}