Image_Generation_with_DCGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Image Generation with DCGAN.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNhYzQmwk8z6X9U5bKsQfuE",
      "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/Pareek-Yash/ImageGeneration_with_DCGAN/blob/main/Image_Generation_with_DCGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Tpw8dGDwC7Rx"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import gdown\n",
        "from zipfile import ZipFile"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6P7tIwjQH6OL"
      },
      "source": [
        "\n",
        "\n",
        "#Initializing the Parameters\n",
        "\n",
        "###Initalizing Parameters at one place helps in tweaking all parameters from one place for optimizing the Network.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "jXzVV5ZeIrV0"
      },
      "outputs": [],
      "source": [
        "alpha = 0.2\n",
        "latent_dim = 128\n",
        "lr = 0.001\n",
        "batch_size = 32\n",
        "codings_size = 128\n",
        "num_epochs = 50"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = tf.keras.datasets.fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FkT0id34E_w",
        "outputId": "f415d716-fa82-4114-e069-30371ce45663"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs(\"celeba_gan\")\n",
        "\n",
        "with ZipFile(\"/content/celeba_gan.zip\", \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"celeba_gan\")"
      ],
      "metadata": {
        "id": "bqkk57frBDpY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "P_mmYtIcBaux"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(rescale=1/255.)\n",
        "test_datagen = ImageDataGenerator(rescale=1/255.)"
      ],
      "metadata": {
        "id": "U3RX3X1Xh2-W"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for dirpath, dirnames, filenames in os.walk(\"celeba_gan\"):\n",
        "  print(f\"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SErbqynBiM6w",
        "outputId": "1cc0a1df-456f-45e5-825f-bdd0432cc513"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 directories and 0 images in 'celeba_gan'.\n",
            "There are 0 directories and 2000 images in 'celeba_gan/celeba_gan'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize our images\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import random\n",
        "\n",
        "def view_random_image(target_folder):\n",
        "\n",
        "  # Get a random image path\n",
        "  random_image = random.sample(os.listdir(target_folder), 1)\n",
        "  print(random_image)\n",
        "\n",
        "  # Read in the image and plot it using matplotlib\n",
        "  img = mpimg.imread(target_folder + \"/\" + random_image[0])\n",
        "  plt.imshow(img)\n",
        "  plt.axis(\"off\");\n",
        "\n",
        "  print(f\"Image shape: {img.shape}\") # show the shape of the image\n",
        "\n",
        "  return img"
      ],
      "metadata": {
        "id": "aqSEPVPijTav"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = view_random_image(target_folder=\"/content/celeba_gan/celeba_gan\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QswuUgymjhO6",
        "outputId": "9704899a-0cfb-4b63-b277-392e13ad912b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['001169.jpg']\n",
            "Image shape: (218, 178, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "U2YnbHM8Bozg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "metadata": {
        "id": "sgJ_RqPplOr4"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir ='/content/celeba_gan/'\n",
        "train_data = datagen.flow_from_directory(\n",
        "  directory=data_dir, target_size=(64, 64), batch_size=32, seed=42\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WqE-HCfMM2vr",
        "outputId": "2cce783a-f2a7-4e5b-de04-5e66a030b7e1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2000 images belonging to 1 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tcLD4ykDxz6",
        "outputId": "33424dcd-3fde-4fc7-a03b-6e3941171d59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2000 files belonging to 1 classes.\n"
          ]
        }
      ],
      "source": [
        "dataset = keras.preprocessing.image_dataset_from_directory(\n",
        "    \"celeba_gan\", label_mode=None, image_size=(64, 64), batch_size=32\n",
        ")\n",
        "dataset = dataset.map(lambda x: x / 255.0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30TcSVXnETAC"
      },
      "source": [
        "\n",
        "\n",
        "#Creating the Discriminator\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BWK_pEsEdMR",
        "outputId": "ac032f28-c808-4107-9bbf-6a655dec0fc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"discriminator\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 64)        4864      \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 16, 16, 128)       131200    \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 8, 8, 256)         524544    \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 8, 8, 256)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 16384)             0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 16384)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 16385     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 676,993\n",
            "Trainable params: 676,993\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "discriminator = keras.Sequential(\n",
        "    [\n",
        "     keras.Input(shape=(64,64,3)),\n",
        "     layers.Conv2D(64, kernel_size=5, strides=2, padding='same'),\n",
        "     layers.LeakyReLU(alpha=alpha),\n",
        "     layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
        "     layers.LeakyReLU(alpha=0.2),\n",
        "     layers.Conv2D(256, kernel_size=4, strides=2, padding=\"same\"),\n",
        "     layers.LeakyReLU(alpha=0.2),\n",
        "\n",
        "     layers.Flatten(),\n",
        "     layers.Dropout(0.2),\n",
        "     layers.Dense(1, activation=\"sigmoid\"),\n",
        "    ],\n",
        "    name = \"discriminator\"\n",
        ")\n",
        "discriminator.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hN1VIGYGKHkM",
        "outputId": "7a98fa4b-7b1f-4aa3-ff10-8eda659867e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"generator\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_1 (Dense)             (None, 8192)              1056768   \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 8, 8, 128)         0         \n",
            "                                                                 \n",
            " conv2d_transpose (Conv2DTra  (None, 16, 16, 128)      262272    \n",
            " nspose)                                                         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 16, 16, 128)      512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_transpose_1 (Conv2DT  (None, 32, 32, 256)      524544    \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 32, 32, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 32, 32, 256)       0         \n",
            "                                                                 \n",
            " conv2d_transpose_2 (Conv2DT  (None, 64, 64, 512)      2097664   \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 64, 64, 512)      2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 64, 64, 512)       0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 64, 64, 3)         38403     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,983,235\n",
            "Trainable params: 3,981,443\n",
            "Non-trainable params: 1,792\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.python.keras.layers.normalization.batch_normalization import BatchNormalization\n",
        "generator = keras.Sequential(\n",
        "    [\n",
        "      keras.Input(shape=(latent_dim,)),\n",
        "      layers.Dense(8*8*128),\n",
        "      layers.Reshape((8, 8, 128)),\n",
        "      layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\"),\n",
        "      layers.BatchNormalization(),\n",
        "      layers.LeakyReLU(alpha=0.2),\n",
        "      layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding=\"same\"),\n",
        "      layers.BatchNormalization(),\n",
        "      layers.LeakyReLU(alpha=0.2),\n",
        "      layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding=\"same\"),\n",
        "      layers.BatchNormalization(),\n",
        "      layers.LeakyReLU(alpha=0.2),\n",
        "      layers.Conv2D(3, kernel_size=5, padding=\"same\", activation=\"tanh\"),\n",
        "    ],\n",
        "    name=\"generator\",\n",
        ")\n",
        "generator.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CHpSDs-CBuVY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8uEVFYC9HRVl"
      },
      "outputs": [],
      "source": [
        "def plot_multiple_images(images, n_cols=None):\n",
        "    n_cols = n_cols or len(images)\n",
        "    n_rows = (len(images) - 1) // n_cols + 1\n",
        "    if images.shape[-1] == 1:\n",
        "        images = np.squeeze(images, axis=-1)\n",
        "    plt.figure(figsize=(n_cols, n_rows))\n",
        "    for index, image in enumerate(images):\n",
        "        plt.subplot(n_rows, n_cols, index + 1)\n",
        "        plt.imshow(image, cmap=\"binary\")\n",
        "        plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class save_imgs(keras.callbacks.Callback):\n",
        "    def __init__(self, num_img=3, codings_size=128):\n",
        "        self.num_img = num_img\n",
        "        self.codings_size = codings_size\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        noise = tf.random.normal(shape=(self.num_img, self.codings_size))\n",
        "        generated_images = self.model.generator(noise)\n",
        "        generated_images *= 255\n",
        "        generated_images.numpy()\n",
        "        for i in range(self.num_img):\n",
        "            img = keras.preprocessing.image.array_to_img(generated_images[i])\n",
        "            img.save(\"generated_img_%03d_%d.png\" % (epoch, i))"
      ],
      "metadata": {
        "id": "obP4Wy1G2w4S"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GAN(keras.Model):\n",
        "    def __init__(self, discriminator, generator, codings_size, batch_size):\n",
        "        super(GAN, self).__init__()\n",
        "        self.discriminator = discriminator\n",
        "        self.generator = generator\n",
        "        self.codings_size = codings_size\n",
        "        self.batch_size = batch_size\n",
        "\n",
        "    def compile(self, d_opt, g_opt, loss_fn):\n",
        "        '''\n",
        "        To customize the fit method you should override the training step function of the Model class.\n",
        "        This is the function that is called by fit() for every batch of data.\n",
        "        This will allow us using the fit() method normally but with our own custom modification\n",
        "        https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit\n",
        "        '''\n",
        "        super(GAN, self).compile()\n",
        "        self.d_optimizer = d_opt\n",
        "        self.g_optimizer = g_opt\n",
        "        self.loss_fn = loss_fn\n",
        "        self.d_loss_metric = keras.metrics.Mean(name=\"d_loss\")\n",
        "        self.g_loss_metric = keras.metrics.Mean(name=\"g_loss\")\n",
        "\n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [self.d_loss_metric, self.g_loss_metric]\n",
        "\n",
        "    def train_step(self, real_images):\n",
        "        # Sample random points in the latent space\n",
        "        batch_size = tf.shape(real_images)[0]\n",
        "        noise = tf.random.normal(shape=[batch_size, self.codings_size])\n",
        "        \n",
        "        # Create fake images\n",
        "        generated_images = self.generator(noise)\n",
        "\n",
        "        X_fake_and_real = tf.concat([generated_images, real_images], axis=0)\n",
        "\n",
        "        # Assemble labels discriminating real from fake images\n",
        "        y1 = tf.concat(\n",
        "            [tf.zeros((batch_size, 1)), tf.ones((batch_size, 1))], axis=0\n",
        "        )\n",
        "        # Add random noise to the labels - important trick!\n",
        "        y1 += 0.05 * tf.random.uniform(tf.shape(y1))\n",
        "\n",
        "        # Train the discriminator\n",
        "        with tf.GradientTape() as tape:\n",
        "            predictions = self.discriminator(X_fake_and_real)\n",
        "            d_loss = self.loss_fn(y1, predictions)\n",
        "        grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\n",
        "        self.d_optimizer.apply_gradients(\n",
        "            zip(grads, self.discriminator.trainable_weights)\n",
        "        )\n",
        "\n",
        "        # Phase 2\n",
        "        noise = tf.random.normal(shape=[batch_size, codings_size])\n",
        "\n",
        "        # Assemble labels that say \"all real images\"\n",
        "        y2 = tf.zeros((batch_size, 1))\n",
        "\n",
        "        # Train the generator with discriminator **OFF**\n",
        "        discriminator.trainable = False\n",
        "        with tf.GradientTape() as tape:\n",
        "            predictions = self.discriminator(self.generator(noise))\n",
        "            g_loss = self.loss_fn(y2, predictions)\n",
        "        grads = tape.gradient(g_loss, self.generator.trainable_weights)\n",
        "        self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))\n",
        "\n",
        "        # Update metrics\n",
        "        self.d_loss_metric.update_state(d_loss)\n",
        "        self.g_loss_metric.update_state(g_loss)\n",
        "        return {\n",
        "            \"d_loss\": self.d_loss_metric.result(),\n",
        "            \"g_loss\": self.g_loss_metric.result(),\n",
        "        }"
      ],
      "metadata": {
        "id": "8WPdcuqFw-y1"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gan = GAN(discriminator=discriminator, generator=generator, codings_size=codings_size, batch_size=batch_size)\n",
        "gan.compile(\n",
        "    d_opt=keras.optimizers.Adam(learning_rate=0.0001),\n",
        "    g_opt=keras.optimizers.Adam(learning_rate=0.0001),\n",
        "    loss_fn=keras.losses.BinaryCrossentropy(),\n",
        ")\n",
        "\n",
        "gan.fit(\n",
        "    dataset, epochs=1, callbacks=[save_imgs(num_img=10, codings_size=codings_size)]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmz-6hlaCCeQ",
        "outputId": "1d520761-1ed3-41a3-d20b-1c61f76e1c6a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1640s 26s/step - d_loss: 0.6346 - g_loss: 0.5687\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb33861e050>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.image as mpig\n",
        "import random\n",
        "\n",
        "def view_gen_image(target_folder):\n",
        "\n",
        "  random_image = random.sample(os.listdir(target_folder), 1)\n",
        "\n",
        "  img = mpig.imread(target_folder + \"/\" + random_image[0])\n",
        "  plt.imshow(img)\n",
        "  plt.axis(\"off\")\n",
        "\n",
        "  print(f\"Image shape: {img.shape}\")"
      ],
      "metadata": {
        "id": "4cuvEn_cKBqD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "view_gen_images(/content)"
      ],
      "metadata": {
        "id": "uK7pf47TKkoj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}