AutoEncoder_Simple.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoEncoder_Simple.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPML84svBSuDPTFCYhYkwqL",
      "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/jvishnuvardhan/Stackoverflow_Questions/blob/master/AutoEncoder_Simple.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvpppWe-kQ7_"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Dense\n",
        "from tensorflow.keras.datasets import mnist\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "4V5XYsp3kaIU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoding_dim= 64\n"
      ],
      "metadata": {
        "id": "kWdk9Se_k0tV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_img = Input(shape=(784,))\n",
        "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
        "decoded = Dense(784, activation='sigmoid')(encoded)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "# print('Architecture of AE model')\n",
        "# autoencoder.summary() # architecture of model\n",
        "# Encoder\n",
        "encoder = Model(input_img, encoded)\n",
        "# print('Architecture of Encoder model')\n",
        "# encoder.summary() # architecture of model\n",
        "\n",
        "# Decoder\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "decoder_layer = autoencoder.layers[-1] #Dense(784, activation='sigmoid')(encoded_input)#autoencoder.layers[-1]\n",
        "\n",
        "decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
        "# print('Architecture of Decoder model')\n",
        "# decoder.summary() # architecture of model\n",
        "\n",
        "# decoder2 = Model(autoencoder.layers[-2], autoencoder.layers[-1])"
      ],
      "metadata": {
        "id": "adYPGSA6k4du"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "autoencoder.compile(optimizer='adam', loss = 'binary_crossentropy')"
      ],
      "metadata": {
        "id": "0hhWQ3nHmrLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#load minst data\n",
        "(x_train, _), (x_test, _) = mnist.load_data()\n",
        "# normalize data\n",
        "x_train = x_train.astype('float32')/255.\n",
        "x_test = x_test.astype('float32')/255.\n",
        "\n",
        "x_train = x_train.reshape(len(x_train), np.prod(x_train.shape[1:]))\n",
        "x_test = x_test.reshape(len(x_test), np.prod(x_test.shape[1:]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIxx_hjdpdMh",
        "outputId": "0470a3ec-1e17-4d37-b4d9-7f75074be428"
      },
      "execution_count": null,
      "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": [
        "print(f'x_train shape = {x_train.shape}')\n",
        "print(f'x_test shape = {x_test.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mkaSwhcqlk_",
        "outputId": "6559ae95-75f2-4861-a7e6-a2aa28d811bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape = (60000, 784)\n",
            "x_test shape = (10000, 784)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the data\n",
        "autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True,\n",
        "                validation_data=(x_test,x_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UmK8k57GqndS",
        "outputId": "6231565d-8e5f-4a29-a363-178f79e7e03e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "235/235 [==============================] - 4s 6ms/step - loss: 0.2459 - val_loss: 0.1613\n",
            "Epoch 2/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.1409 - val_loss: 0.1248\n",
            "Epoch 3/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.1168 - val_loss: 0.1075\n",
            "Epoch 4/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.1028 - val_loss: 0.0964\n",
            "Epoch 5/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0938 - val_loss: 0.0892\n",
            "Epoch 6/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0877 - val_loss: 0.0843\n",
            "Epoch 7/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0836 - val_loss: 0.0812\n",
            "Epoch 8/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0808 - val_loss: 0.0789\n",
            "Epoch 9/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0790 - val_loss: 0.0775\n",
            "Epoch 10/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0777 - val_loss: 0.0764\n",
            "Epoch 11/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0767 - val_loss: 0.0756\n",
            "Epoch 12/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0760 - val_loss: 0.0751\n",
            "Epoch 13/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0754 - val_loss: 0.0745\n",
            "Epoch 14/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0750 - val_loss: 0.0742\n",
            "Epoch 15/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0747 - val_loss: 0.0739\n",
            "Epoch 16/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0744 - val_loss: 0.0737\n",
            "Epoch 17/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0742 - val_loss: 0.0735\n",
            "Epoch 18/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0740 - val_loss: 0.0733\n",
            "Epoch 19/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0739 - val_loss: 0.0733\n",
            "Epoch 20/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0738 - val_loss: 0.0732\n",
            "Epoch 21/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0737 - val_loss: 0.0731\n",
            "Epoch 22/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0736 - val_loss: 0.0731\n",
            "Epoch 23/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0735 - val_loss: 0.0729\n",
            "Epoch 24/50\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0735 - val_loss: 0.0729\n",
            "Epoch 25/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0734 - val_loss: 0.0729\n",
            "Epoch 26/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0734 - val_loss: 0.0728\n",
            "Epoch 27/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0733 - val_loss: 0.0728\n",
            "Epoch 28/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0733 - val_loss: 0.0727\n",
            "Epoch 29/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0732 - val_loss: 0.0727\n",
            "Epoch 30/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0732 - val_loss: 0.0726\n",
            "Epoch 31/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0732 - val_loss: 0.0727\n",
            "Epoch 32/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0731 - val_loss: 0.0726\n",
            "Epoch 33/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0731 - val_loss: 0.0726\n",
            "Epoch 34/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0731 - val_loss: 0.0726\n",
            "Epoch 35/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0731 - val_loss: 0.0725\n",
            "Epoch 36/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0730 - val_loss: 0.0726\n",
            "Epoch 37/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0730 - val_loss: 0.0725\n",
            "Epoch 38/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0730 - val_loss: 0.0725\n",
            "Epoch 39/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0730 - val_loss: 0.0725\n",
            "Epoch 40/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0730 - val_loss: 0.0725\n",
            "Epoch 41/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0729 - val_loss: 0.0725\n",
            "Epoch 42/50\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0729 - val_loss: 0.0724\n",
            "Epoch 43/50\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0729 - val_loss: 0.0725\n",
            "Epoch 44/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0729 - val_loss: 0.0724\n",
            "Epoch 45/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0729 - val_loss: 0.0724\n",
            "Epoch 46/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0729 - val_loss: 0.0725\n",
            "Epoch 47/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0729 - val_loss: 0.0724\n",
            "Epoch 48/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0728 - val_loss: 0.0724\n",
            "Epoch 49/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0728 - val_loss: 0.0724\n",
            "Epoch 50/50\n",
            "235/235 [==============================] - 1s 5ms/step - loss: 0.0728 - val_loss: 0.0723\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb7831ae790>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_imgs = encoder.predict(x_test)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)"
      ],
      "metadata": {
        "id": "_UFffqrtrmlk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n=10\n",
        "plt.figure(figsize=(20,4))\n",
        "\n",
        "for i in range(n):\n",
        "  ax=plt.subplot(2, n, i+1)\n",
        "  plt.imshow(x_test[i].reshape(28,28))\n",
        "  # plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "\n",
        "  ax2= plt.subplot(2,n, i+1+n)\n",
        "  plt.imshow(decoded_imgs[i].reshape(28,28))\n",
        "  # plt.gray()\n",
        "  ax2.get_xaxis().set_visible(False)\n",
        "  ax2.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "QE0QlUdVsEp4",
        "outputId": "4ef17ddd-ea60-4d5f-affa-e3e61eae9b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 20 Axes>"
            ],
            "image/png": 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}