Applications_of_Autoencoders.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Applications of Autoencoders.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ReKhZpSw7D5",
        "colab_type": "text"
      },
      "source": [
        "<b><h1> Image Denoising with Deep Convolutional AutoEncoder</h1><b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjWzE4Ibw0Cw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reading the Dataset of MNIST\n",
        "# importing necessary libraries\n",
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "\n",
        "(x_train, _), (x_test, _) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2tcHMaqxtTc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d03c38a4-00d5-4c8d-db3d-606077b78e74"
      },
      "source": [
        "# showing image from the data\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "image = x_train[0]\n",
        "plt.imshow(image,cmap='gray')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f35c5781a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I24teSOzyzFE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab0f2a11-3a40-48df-cbec-38815408714d"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwlhkivqyBPR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalizing the input data\n",
        "x_train = x_train/255.\n",
        "x_test = x_test/255.\n",
        "\n",
        "# Reshaping the input\n",
        "x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) \n",
        "x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qsb2XSMayoKB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6455aac4-ec46-4eda-a722-29ae97fe4095"
      },
      "source": [
        "x_train.shape #added color channel to the image"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZWuPojv6rl0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e89c6c2-5278-4063-9685-13b32cef3011"
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBPi9RbvzBXQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding noise to the image \n",
        "noise_factor = 0.3\n",
        "\n",
        "x_train_noise = x_train + noise_factor * np.random.normal(loc=0.0,scale=1.0,size=x_train.shape)\n",
        "x_test_noise = x_test + noise_factor * np.random.normal(loc=0.0,scale=1.0,size=x_test.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8SzZ67oznCi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train_noise = np.clip(x_train_noise, 0., 1.)\n",
        "x_test_noise = np.clip(x_test_noise, 0., 1.)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4TFHF_bzwb1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "79a7960b-4842-4f47-93ef-e6ce95152298"
      },
      "source": [
        "n = 10  # how many digits we will display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display noisy images\n",
        "    ax = plt.subplot(1, n, i + 1)\n",
        "    plt.imshow(x_train_noise[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvetj7W2z3Ld",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# creating our Model \n",
        "from keras.layers import Conv2D,MaxPooling2D,UpSampling2D,Input, Dense\n",
        "from keras.models import Model\n",
        "\n",
        "\n",
        "input_img = Input(shape=(28, 28, 1))\n",
        "\n",
        "# # creating the encoder model here\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
        "\n",
        "# at this point the representation is (7, 7, 32)\n",
        "\n",
        "# # creating the decoder model here\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnyUm4lG5pdC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# combining both Encoder and Decoder to get an Auto Encoder\n",
        "autoencoder = Model(input_img, decoded)\n",
        "# compiling the model\n",
        "from keras.optimizers import adadelta\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3ypOy5i5YbB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "092892f6-ea1c-4117-97ec-95b0e81085e1"
      },
      "source": [
        "# fitting and training the model on our data\n",
        "autoencoder.fit(x_train_noise, x_train,\n",
        "                epochs=100,\n",
        "                batch_size=128,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test_noise, x_test))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/100\n",
            "60000/60000 [==============================] - 9s 154us/step - loss: 0.1483 - val_loss: 0.0987\n",
            "Epoch 2/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0968 - val_loss: 0.0895\n",
            "Epoch 3/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0913 - val_loss: 0.0871\n",
            "Epoch 4/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0885 - val_loss: 0.0877\n",
            "Epoch 5/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0866 - val_loss: 0.0840\n",
            "Epoch 6/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0852 - val_loss: 0.0834\n",
            "Epoch 7/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0843 - val_loss: 0.0831\n",
            "Epoch 8/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0835 - val_loss: 0.0816\n",
            "Epoch 9/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0830 - val_loss: 0.0823\n",
            "Epoch 10/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0824 - val_loss: 0.0811\n",
            "Epoch 11/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0820 - val_loss: 0.0808\n",
            "Epoch 12/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0816 - val_loss: 0.0805\n",
            "Epoch 13/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0813 - val_loss: 0.0815\n",
            "Epoch 14/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0810 - val_loss: 0.0811\n",
            "Epoch 15/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0807 - val_loss: 0.0796\n",
            "Epoch 16/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0805 - val_loss: 0.0804\n",
            "Epoch 17/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0804 - val_loss: 0.0793\n",
            "Epoch 18/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0801 - val_loss: 0.0797\n",
            "Epoch 19/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0801 - val_loss: 0.0789\n",
            "Epoch 20/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0799 - val_loss: 0.0788\n",
            "Epoch 21/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0798 - val_loss: 0.0786\n",
            "Epoch 22/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0796 - val_loss: 0.0789\n",
            "Epoch 23/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0796 - val_loss: 0.0794\n",
            "Epoch 24/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0795 - val_loss: 0.0793\n",
            "Epoch 25/100\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.0793 - val_loss: 0.0791\n",
            "Epoch 26/100\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.0793 - val_loss: 0.0792\n",
            "Epoch 27/100\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.0792 - val_loss: 0.0782\n",
            "Epoch 28/100\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.0791 - val_loss: 0.0781\n",
            "Epoch 29/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0790 - val_loss: 0.0781\n",
            "Epoch 30/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0789 - val_loss: 0.0780\n",
            "Epoch 31/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0789 - val_loss: 0.0790\n",
            "Epoch 32/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0788 - val_loss: 0.0780\n",
            "Epoch 33/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0788 - val_loss: 0.0779\n",
            "Epoch 34/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0787 - val_loss: 0.0791\n",
            "Epoch 35/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0787 - val_loss: 0.0789\n",
            "Epoch 36/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0786 - val_loss: 0.0785\n",
            "Epoch 37/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0785 - val_loss: 0.0786\n",
            "Epoch 38/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0784 - val_loss: 0.0778\n",
            "Epoch 39/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0785 - val_loss: 0.0779\n",
            "Epoch 40/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0784 - val_loss: 0.0784\n",
            "Epoch 41/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0783 - val_loss: 0.0779\n",
            "Epoch 42/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0783 - val_loss: 0.0779\n",
            "Epoch 43/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0782 - val_loss: 0.0782\n",
            "Epoch 44/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0782 - val_loss: 0.0778\n",
            "Epoch 45/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0781 - val_loss: 0.0774\n",
            "Epoch 46/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0782 - val_loss: 0.0774\n",
            "Epoch 47/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0781 - val_loss: 0.0779\n",
            "Epoch 48/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0781 - val_loss: 0.0773\n",
            "Epoch 49/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0780 - val_loss: 0.0775\n",
            "Epoch 50/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0780 - val_loss: 0.0772\n",
            "Epoch 51/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0780 - val_loss: 0.0772\n",
            "Epoch 52/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0780 - val_loss: 0.0775\n",
            "Epoch 53/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0779 - val_loss: 0.0778\n",
            "Epoch 54/100\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.0778 - val_loss: 0.0776\n",
            "Epoch 55/100\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.0779 - val_loss: 0.0777\n",
            "Epoch 56/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0778 - val_loss: 0.0781\n",
            "Epoch 57/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0779 - val_loss: 0.0775\n",
            "Epoch 58/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0778 - val_loss: 0.0771\n",
            "Epoch 59/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0778 - val_loss: 0.0775\n",
            "Epoch 60/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0777 - val_loss: 0.0774\n",
            "Epoch 61/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0777 - val_loss: 0.0774\n",
            "Epoch 62/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0776 - val_loss: 0.0772\n",
            "Epoch 63/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0777 - val_loss: 0.0770\n",
            "Epoch 64/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0776 - val_loss: 0.0772\n",
            "Epoch 65/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0777 - val_loss: 0.0770\n",
            "Epoch 66/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0775 - val_loss: 0.0773\n",
            "Epoch 67/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0775 - val_loss: 0.0769\n",
            "Epoch 68/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0776 - val_loss: 0.0770\n",
            "Epoch 69/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0775 - val_loss: 0.0768\n",
            "Epoch 70/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0775 - val_loss: 0.0781\n",
            "Epoch 71/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0775 - val_loss: 0.0770\n",
            "Epoch 72/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0774 - val_loss: 0.0774\n",
            "Epoch 73/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0774 - val_loss: 0.0771\n",
            "Epoch 74/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0774 - val_loss: 0.0767\n",
            "Epoch 75/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0772\n",
            "Epoch 76/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0774 - val_loss: 0.0771\n",
            "Epoch 77/100\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.0774 - val_loss: 0.0767\n",
            "Epoch 78/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0774 - val_loss: 0.0769\n",
            "Epoch 79/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0767\n",
            "Epoch 80/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0772 - val_loss: 0.0767\n",
            "Epoch 81/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0768\n",
            "Epoch 82/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0766\n",
            "Epoch 83/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0767\n",
            "Epoch 84/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0768\n",
            "Epoch 85/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0773 - val_loss: 0.0766\n",
            "Epoch 86/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0772 - val_loss: 0.0771\n",
            "Epoch 87/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0772 - val_loss: 0.0770\n",
            "Epoch 88/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0772 - val_loss: 0.0769\n",
            "Epoch 89/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0772 - val_loss: 0.0771\n",
            "Epoch 90/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0772 - val_loss: 0.0770\n",
            "Epoch 91/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0771 - val_loss: 0.0771\n",
            "Epoch 92/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0772 - val_loss: 0.0766\n",
            "Epoch 93/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0771 - val_loss: 0.0770\n",
            "Epoch 94/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0771 - val_loss: 0.0772\n",
            "Epoch 95/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0771 - val_loss: 0.0769\n",
            "Epoch 96/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0771 - val_loss: 0.0767\n",
            "Epoch 97/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0771 - val_loss: 0.0765\n",
            "Epoch 98/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0770 - val_loss: 0.0765\n",
            "Epoch 99/100\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.0770 - val_loss: 0.0765\n",
            "Epoch 100/100\n",
            "60000/60000 [==============================] - 3s 51us/step - loss: 0.0771 - val_loss: 0.0768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f35b045c780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejZNlQcE6Olj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "c1ed5b03-d7d5-41a3-a779-60ed04b340e7"
      },
      "source": [
        "decoded_imgs = autoencoder.predict(x_test)\n",
        "\n",
        "n = 10  # how many digits we will display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(x_test_noise[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU4e4fA58ZoG",
        "colab_type": "text"
      },
      "source": [
        "<b><h1> Image Reconstruction with Deep AutoEncoder</h1><b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvCajoQc_M85",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "5385bbd2-0150-4306-dd1a-941bf02e1404"
      },
      "source": [
        "!pip install keract"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keract\n",
            "  Downloading https://files.pythonhosted.org/packages/ad/7f/74c42d4f6fcd09de8a0ebad1bde5484510a80c03df7d0b0db18474aaade5/keract-3.1.0-py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.6/dist-packages (from keract) (1.18.3)\n",
            "Installing collected packages: keract\n",
            "Successfully installed keract-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtxaP-Qj_X_9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "28e50290-9cda-43c1-8e25-e3b4c4c025d2"
      },
      "source": [
        "'''\n",
        "  Visualizing the encoded state of a simple autoencoder created with the Keras Functional API\n",
        "  with Keract.\n",
        "'''\n",
        "import keras\n",
        "from keras.layers import Input, Dense\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Model\n",
        "from keract import get_activations, display_activations\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Model configuration\n",
        "img_width, img_height = 28, 28\n",
        "initial_dimension = img_width * img_height\n",
        "batch_size = 128\n",
        "no_epochs = 10\n",
        "validation_split = 0.2\n",
        "verbosity = 1\n",
        "encoded_dim = 50\n",
        "\n",
        "# Load MNIST dataset\n",
        "(input_train, target_train), (input_test, target_test) = mnist.load_data()\n",
        "\n",
        "# Reshape data\n",
        "input_train = input_train.reshape(input_train.shape[0], initial_dimension)\n",
        "input_test = input_test.reshape(input_test.shape[0], initial_dimension)\n",
        "input_shape = (initial_dimension, )\n",
        "\n",
        "# Parse numbers as floats\n",
        "input_train = input_train.astype('float32')\n",
        "input_test = input_test.astype('float32')\n",
        "\n",
        "# Normalize data\n",
        "input_train = input_train / 255\n",
        "input_test = input_test / 255\n",
        "\n",
        "# Define the layers\n",
        "inputs = Input(shape=input_shape)\n",
        "encoding_layer = Dense(encoded_dim, activation='relu', kernel_initializer='he_normal')(inputs)\n",
        "decoding_layer = Dense(initial_dimension, activation='sigmoid')(encoding_layer)\n",
        "\n",
        "# Instantiate the autoencoder\n",
        "autoencoder = Model(inputs, decoding_layer, name='full_autoencoder')\n",
        "\n",
        "# Instantiate the encoder\n",
        "encoder = Model(inputs, encoding_layer, name='encoder')\n",
        "\n",
        "# Instantiate the decoder\n",
        "encoded_input = Input(shape=(encoded_dim, ))\n",
        "final_ae_layer = autoencoder.layers[-1]\n",
        "decoder = Model(encoded_input, final_ae_layer(encoded_input), name='decoder')\n",
        "\n",
        "# Compile the autoencoder\n",
        "encoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "\n",
        "# Give us some insights\n",
        "autoencoder.summary()\n",
        "encoder.summary()\n",
        "decoder.summary()\n",
        "\n",
        "# Fit data\n",
        "autoencoder.fit(input_train, input_train, epochs=no_epochs, batch_size=batch_size, validation_split=validation_split)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"full_autoencoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_17 (InputLayer)        (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 50)                39250     \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 784)               39984     \n",
            "=================================================================\n",
            "Total params: 79,234\n",
            "Trainable params: 79,234\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"encoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_17 (InputLayer)        (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 50)                39250     \n",
            "=================================================================\n",
            "Total params: 39,250\n",
            "Trainable params: 39,250\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"decoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_18 (InputLayer)        (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 784)               39984     \n",
            "=================================================================\n",
            "Total params: 39,984\n",
            "Trainable params: 39,984\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/10\n",
            "48000/48000 [==============================] - 1s 25us/step - loss: 0.2228 - val_loss: 0.1528\n",
            "Epoch 2/10\n",
            "48000/48000 [==============================] - 1s 24us/step - loss: 0.1350 - val_loss: 0.1209\n",
            "Epoch 3/10\n",
            "48000/48000 [==============================] - 1s 23us/step - loss: 0.1122 - val_loss: 0.1048\n",
            "Epoch 4/10\n",
            "48000/48000 [==============================] - 1s 24us/step - loss: 0.0994 - val_loss: 0.0955\n",
            "Epoch 5/10\n",
            "48000/48000 [==============================] - 1s 24us/step - loss: 0.0912 - val_loss: 0.0888\n",
            "Epoch 6/10\n",
            "48000/48000 [==============================] - 1s 23us/step - loss: 0.0861 - val_loss: 0.0851\n",
            "Epoch 7/10\n",
            "48000/48000 [==============================] - 1s 23us/step - loss: 0.0833 - val_loss: 0.0830\n",
            "Epoch 8/10\n",
            "48000/48000 [==============================] - 1s 24us/step - loss: 0.0817 - val_loss: 0.0818\n",
            "Epoch 9/10\n",
            "48000/48000 [==============================] - 1s 23us/step - loss: 0.0807 - val_loss: 0.0811\n",
            "Epoch 10/10\n",
            "48000/48000 [==============================] - 1s 24us/step - loss: 0.0801 - val_loss: 0.0806\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f356805f908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nnd9rs05AeDK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "outputId": "1712ecfb-472f-4cba-9bec-cfd48f6f0196"
      },
      "source": [
        "# =============================================\n",
        "# Take a sample for visualization purposes\n",
        "# =============================================\n",
        "input_sample = input_test[:1]\n",
        "reconstruction = autoencoder.predict([input_sample])\n",
        "\n",
        "# =============================================\n",
        "# Visualize input-->reconstruction\n",
        "# =============================================\n",
        "fig, axes = plt.subplots(1, 2)\n",
        "fig.set_size_inches(6, 3.5)\n",
        "input_sample_reshaped = input_sample.reshape((img_width, img_height))\n",
        "reconsstruction_reshaped = reconstruction.reshape((img_width, img_height))\n",
        "axes[0].imshow(input_sample_reshaped) \n",
        "axes[0].set_title('Original image')\n",
        "axes[1].imshow(reconsstruction_reshaped)\n",
        "axes[1].set_title('Reconstruction')\n",
        "plt.show()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSq4XH_gBWj2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6176f23c-2443-4c12-a428-9bcb22b8397f"
      },
      "source": [
        "\n",
        "# =============================================\n",
        "# Visualize encoded state with Keract\n",
        "# =============================================\n",
        "activations = get_activations(encoder, input_sample)\n",
        "display_activations(activations, cmap=\"gray\", save=False)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_17 (784,) -> Skipped. First dimension is not 1.\n",
            "dense_7 (1, 50) \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1728x1728 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPthVA_ECTpI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}