Learner_Notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Learner Notebook.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rainmaker29/Advanced-Regression/blob/master/Learner_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epSiHdV2pSuh",
        "colab_type": "text"
      },
      "source": [
        "# Understanding Deepfakes with Keras\n",
        "\n",
        "![DCGAN](DCGAN.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fcSNP-9pSvK",
        "colab_type": "text"
      },
      "source": [
        "#Importing Libraries and Helper Functions\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eBoStgCOlD6V",
        "outputId": "3e7e8a4a-212f-43f3-a047-93182af4ff47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%matplotlib notebook\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "import sys\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "from tensorflow.keras.datasets import mnist as mnist\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, BatchNormalization\n",
        "from tensorflow.keras.layers import Conv2DTranspose, Reshape, LeakyReLU\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from PIL import Image\n",
        "\n",
        "print('TensorFlow version:', tf.__version__)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow version: 2.2.0-rc2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qD30ji2YpSxh",
        "colab_type": "text"
      },
      "source": [
        "#Importing and Plotting the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPTj1iJ9pSxq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "57e54d2b-dd7a-4d39-e1e8-0e6db253e71b"
      },
      "source": [
        "\n",
        "(xtrain,ytrain),(xtest,ytest) = mnist.load_data()\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StA2G8Z36Ewx",
        "colab_type": "code",
        "outputId": "457aedbf-608b-41ac-cb5f-a4f2d0a2b4a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "xtrain.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vJrX749GlD6k",
        "colab": {}
      },
      "source": [
        "xtrain = xtrain[np.where(ytrain==0)]\n",
        "xtest = xtest[np.where(ytest==0)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9lLKKQT3qOp",
        "colab_type": "code",
        "outputId": "968c5834-ec24-40c9-a6a3-fd973fd79290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "#Displaying few images\t\n",
        "for i in range(9):\n",
        "  plt.subplot(330 + 1 + i)\n",
        "  plt.imshow(xtrain[i], cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kIgif_VpSz_",
        "colab_type": "text"
      },
      "source": [
        "#Discriminator\n",
        "\n",
        "![Artist and Critic](artist_critic.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JHga1nihlD6q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "4a0775fa-a2d8-4d1f-baf3-1fd391db1d6e"
      },
      "source": [
        "discriminator = Sequential([\n",
        "                            \n",
        "      Conv2D(64,3,strides=2,input_shape=(28,28,1)),\n",
        "      LeakyReLU(),\n",
        "      BatchNormalization(),\n",
        "\n",
        "      Conv2D(128,5,strides=2),\n",
        "      LeakyReLU(),\n",
        "      BatchNormalization(),\n",
        "\n",
        "      Conv2D(256,5,strides=2),\n",
        "      LeakyReLU(),\n",
        "      BatchNormalization(),\n",
        "\n",
        "      Flatten(),\n",
        "      Dense(1,activation='sigmoid')\n",
        "                           \n",
        "])\n",
        "\n",
        "opt = tf.keras.optimizers.Adam(lr=2e-4,beta_1=0.5)\n",
        "\n",
        "discriminator.compile(loss=\"binary_crossentropy\",optimizer=opt,metrics=['accuracy'])\n",
        "discriminator.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 13, 13, 64)        640       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 13, 13, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 5, 5, 128)         204928    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)    (None, 5, 5, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 5, 5, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 1, 1, 256)         819456    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)    (None, 1, 1, 256)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 1, 1, 256)         1024      \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 1,027,073\n",
            "Trainable params: 1,026,177\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30ajgD20pS1e",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "#Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-8mG_PlhlD6s",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pSb3O5MDlD6u",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nMOZU4gpS3y",
        "colab_type": "text"
      },
      "source": [
        "# Task 5: Generative Adversarial Network (GAN)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6YdMQyzclD69",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5C-ChdWpS4Y",
        "colab_type": "text"
      },
      "source": [
        "# Tasks 6 and 7: Training the GAN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HEb3xQ0LlD6_",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}