DCGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DCGAN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM9w1kHU0MG8jzpyvtytUeA",
      "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/jaydeepthik/keras-GAN/blob/master/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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MxZoZ811XQ-5",
        "outputId": "d01a1156-57ba-40cc-f172-71cce60a9248"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.7.0'"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install imageio\n",
        "! pip install git+https://github.com/tensorflow/docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZxY_aSCXaqn",
        "outputId": "7d94b2e2-8da5-4325-f779-88d853a665c8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (2.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio) (1.19.5)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio) (7.1.2)\n",
            "Collecting git+https://github.com/tensorflow/docs\n",
            "  Cloning https://github.com/tensorflow/docs to /tmp/pip-req-build-81zin630\n",
            "  Running command git clone -q https://github.com/tensorflow/docs /tmp/pip-req-build-81zin630\n",
            "Requirement already satisfied: astor in /usr/local/lib/python3.7/dist-packages (from tensorflow-docs==0.0.0.dev0) (0.8.1)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from tensorflow-docs==0.0.0.dev0) (1.0.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-docs==0.0.0.dev0) (2.11.3)\n",
            "Requirement already satisfied: protobuf>=3.14 in /usr/local/lib/python3.7/dist-packages (from tensorflow-docs==0.0.0.dev0) (3.17.3)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from tensorflow-docs==0.0.0.dev0) (3.13)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.14->tensorflow-docs==0.0.0.dev0) (1.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->tensorflow-docs==0.0.0.dev0) (2.0.1)\n",
            "Building wheels for collected packages: tensorflow-docs\n",
            "  Building wheel for tensorflow-docs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tensorflow-docs: filename=tensorflow_docs-0.0.0.dev0-py3-none-any.whl size=173744 sha256=713f3e7c293cced2d8a17a95b789d1743b1596b8d54aaaf67dcea35b06d38c71\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-6a8y8ofy/wheels/cc/c4/d8/5341e93b6376c5c929c49469fce21155eb69cef1a4da4ce32c\n",
            "Successfully built tensorflow-docs\n",
            "Installing collected packages: tensorflow-docs\n",
            "Successfully installed tensorflow-docs-0.0.0.dev0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import imageio\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "from tensorflow.keras import layers\n",
        "import time\n",
        "\n",
        "from IPython import display"
      ],
      "metadata": {
        "id": "aSIKfLg9Xh32"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKy-OjraXpLe",
        "outputId": "da3723a3-990f-4cb0-8279-682f4ddd9704"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype(\"float32\")\n",
        "train_images = (train_images - 127.5)/127.5"
      ],
      "metadata": {
        "id": "W0EvtYIpX3C-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BUFFER_SIZE = 60000\n",
        "BATCH_SIZE = 256\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
      ],
      "metadata": {
        "id": "tIh-DoE-YKT-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_generator():\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100, )))\n",
        "  model.add(layers.BatchNormalization())\n",
        "  model.add(layers.LeakyReLU())\n",
        "\n",
        "  model.add(layers.Reshape((7, 7, 256)))\n",
        "  assert model.output_shape == (None, 7, 7, 256)\n",
        "\n",
        "  model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
        "  assert model.output_shape == (None, 7, 7, 128)\n",
        "  model.add(layers.BatchNormalization())\n",
        "  model.add(layers.LeakyReLU())\n",
        "\n",
        "  model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
        "  assert model.output_shape == (None, 14, 14, 64)\n",
        "  model.add(layers.BatchNormalization())\n",
        "  model.add(layers.LeakyReLU())\n",
        "\n",
        "  model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding=\"same\", use_bias=False, activation=\"tanh\"))\n",
        "  assert model.output_shape == (None, 28, 28, 1)\n",
        "\n",
        "  return model\n",
        "\n"
      ],
      "metadata": {
        "id": "ptFFXlAZYrTd"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = get_generator()\n",
        "noise = tf.random.normal([1, 100])\n",
        "generated_image = generator(noise, training=False)\n",
        "\n",
        "plt.imshow(generated_image[0,:,:, 0], cmap=\"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "AjkzpP-xbpqk",
        "outputId": "5ad10bef-6a30-4073-bce4-3caefd75e658"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9eba4fd590>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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": [
        "def get_discriminator():\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n",
        "                                    input_shape=[28, 28, 1]))\n",
        "  model.add(layers.LeakyReLU())\n",
        "  model.add(layers.Dropout(0.3))\n",
        "\n",
        "  model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
        "  model.add(layers.LeakyReLU())\n",
        "  model.add(layers.Dropout(0.3))\n",
        "\n",
        "  model.add(layers.Flatten())\n",
        "  model.add(layers.Dense(1))\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "SZB2oxiwcVCe"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "descriminator = get_discriminator()"
      ],
      "metadata": {
        "id": "BkEu5UxLcaiV"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
      ],
      "metadata": {
        "id": "A-IQyxneddUY"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def descriminator_loss(real_outputs, fake_outputs):\n",
        "  real_loss = cross_entropy(tf.ones_like(real_outputs), real_outputs)\n",
        "  fake_loss = cross_entropy(tf.zeros_like(fake_outputs), fake_outputs)\n",
        "\n",
        "  return real_loss, fake_loss\n",
        "\n",
        "def generator_loss(fake_outputs):\n",
        "  return cross_entropy(tf.ones_like(fake_outputs), fake_outputs)"
      ],
      "metadata": {
        "id": "59rRokxedNMb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
        "descriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
      ],
      "metadata": {
        "id": "e-0I6VYhenkM"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training loop\n",
        "\n",
        "EPOCHS = 50\n",
        "noise_dim = 100\n",
        "num_examples_to_generate = 16\n",
        "\n",
        "# You will reuse this seed overtime (so it's easier)\n",
        "# to visualize progress in the animated GIF)\n",
        "seed = tf.random.normal([num_examples_to_generate, noise_dim])\n",
        "\n",
        "@tf.function\n",
        "def train_step(images):\n",
        "  noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
        "\n",
        "  with tf.GradientTape() as gen_tape, tf.GradientTape() as desc_tape:\n",
        "    generated_images = generator(noise, training=True)\n",
        "\n",
        "    real_outputs = descriminator(images, training=True)\n",
        "    fake_outputs = descriminator(generated_images, training=True)\n",
        "\n",
        "    gen_loss = generator_loss(fake_outputs)\n",
        "    disc_loss = descriminator_loss(real_outputs, fake_outputs)\n",
        "\n",
        "  generator_gradients = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
        "  descriminator_gradients = desc_tape.gradient(disc_loss, descriminator.trainable_variables)\n",
        "\n",
        "  generator_optimizer.apply_gradients(zip(generator_gradients, generator.trainable_variables))\n",
        "  descriminator_optimizer.apply_gradients(zip(descriminator_gradients, descriminator.trainable_variables))"
      ],
      "metadata": {
        "id": "KA-YbtFue7bL"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(dataset, epochs):\n",
        "  for epoch in range(epochs):\n",
        "    start = time.time()\n",
        "\n",
        "    for image_batch in dataset:\n",
        "      train_step(image_batch)\n",
        "\n",
        "    display.clear_output(wait=True)\n",
        "    generate_and_save_images(generator,\n",
        "                             epoch + 1,\n",
        "                             seed)\n",
        "\n",
        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
        "\n",
        "  # Generate after the final epoch\n",
        "  display.clear_output(wait=True)\n",
        "  generate_and_save_images(generator,\n",
        "                           epochs,\n",
        "                           seed)\n",
        "\n",
        "def generate_and_save_images(model, epoch, test_input):\n",
        "  # Notice `training` is set to False.\n",
        "  # This is so all layers run in inference mode (batchnorm).\n",
        "  predictions = model(test_input, training=False)\n",
        "\n",
        "  fig = plt.figure(figsize=(4, 4))\n",
        "\n",
        "  for i in range(predictions.shape[0]):\n",
        "      plt.subplot(4, 4, i+1)\n",
        "      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
        "      plt.axis('off')\n",
        "\n",
        "  #plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "ZCQ1upSkkFrZ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(train_dataset, EPOCHS)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "tV_ezJVvk2yQ",
        "outputId": "08143332-a529-4696-8256-31381274a510"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seed_ = tf.random.normal([num_examples_to_generate, noise_dim])\n",
        "generate_and_save_images(generator,\n",
        "                              1,\n",
        "                             seed_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "nFeZIW8dk_fB",
        "outputId": "68897dc0-6e89-4c73-a829-60a0e3237254"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mffPfdGBqdUn",
        "outputId": "8f6be96b-4503-4f41-be86-ab2f21d6d6cf"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3cmoYSD9qzV4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}