econder_decoder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SML_A4_Q3.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Harjeet Singh Yadav\n",
        "## CSAI - 2020561"
      ],
      "metadata": {
        "id": "l3VoUGHnTXBf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing important  libraries"
      ],
      "metadata": {
        "id": "wEENyvsZlDuC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "To91cr7CithG"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf  \n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading MNIST dataset"
      ],
      "metadata": {
        "id": "6LTnzuMRlJkf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()  # loads the popular \"mnist\" training dataset"
      ],
      "metadata": {
        "id": "v3WxRlejlLnf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e638ecd-2227-4437-df2e-9dacdad8de37"
      },
      "execution_count": 2,
      "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": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgaMJ0soleHx",
        "outputId": "1c27f88f-6bad-4250-c75b-09a396bfebe3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmldNOeqljji",
        "outputId": "29bf15f4-1439-4af6-f4bf-07f5d429732e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqR4uZRllnJE",
        "outputId": "9e23f730-92ed-460a-bec7-86b77491b93b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1nF3ia7loZs",
        "outputId": "6411e249-76c8-437f-f1fd-e073744097c4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000,)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Scaling"
      ],
      "metadata": {
        "id": "FUPvdf2LlwgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train/255.0\n",
        "x_test = x_test/255.0"
      ],
      "metadata": {
        "id": "KNT31Srzlsr8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualisation"
      ],
      "metadata": {
        "id": "Kmu6tyq3l4JJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[0], cmap=\"gray\")         # Sample no 1 of test case"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "plnlqXfRl3DP",
        "outputId": "e55db527-cdca-463f-82cc-12b993b37fe8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff1b56e9710>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[56], cmap=\"gray\")    # Sample no 56 of the testcase"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kX3v8HlNmUkw",
        "outputId": "992d9c20-f1f2-4f39-e024-b6d794b037cb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff1b5247950>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[560], cmap=\"gray\")    # Sample no 56 of the testcase"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6vKPHaEEmgwv",
        "outputId": "ee2d8d97-d3d6-4726-b65d-d5e360210402"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff1b51be590>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMo0lEQVR4nO3dYagd9ZnH8d9vNVVM+yIqXkMabS0ixgXtcg0LkUWpDVnfxL7R5kXNQthbtS4t5IXBfVFBFFlsixAo3KI0XbMp1TYYodxtNhbc9UXxKlkTjY1ujCSXa2LNi6ag1muefXEmco3nzLmZOefM5D7fD1zOOfOcmXkY8svMmZlz/o4IAVj8/qbpBgCMBmEHkiDsQBKEHUiCsANJnD/Kldnm1D8wZBHhbtNr7dltr7P9R9tv2d5SZ1kAhstVr7PbPk/SQUnflHRU0kuSNkTE6yXzsGcHhmwYe/bVkt6KiEMR8VdJv5S0vsbyAAxRnbCvkHRk3uujxbTPsD1he9r2dI11Aahp6CfoImJS0qTEYTzQpDp79hlJK+e9/nIxDUAL1Qn7S5Kutv1V21+Q9G1JuwbTFoBBq3wYHxFztu+T9J+SzpP0ZES8NrDOAAxU5UtvlVbGZ3Zg6IZyUw2AcwdhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IInK47NLku3Dkk5K+kTSXESMD6IpAINXK+yFWyLiTwNYDoAh4jAeSKJu2EPS72y/bHui2xtsT9ietj1dc10AanBEVJ/ZXhERM7Yvk7Rb0r9ExAsl76++MgALEhHuNr3Wnj0iZorH45J2SlpdZ3kAhqdy2G0vtf2l088lrZW0f1CNARisOmfjxyTttH16Of8REVMD6eocc+WVV5bW33jjjdL6hRdeWFqv81Hr4MGDpfWLLrqotH7gwIHK6x625557rrS+devWEXVybqgc9og4JOn6AfYCYIi49AYkQdiBJAg7kARhB5Ig7EASte6gO+uVLdI76M4/v/yixp133llaX7duXWn9kksuKa2///77pfW2Gh8v/5LkNddcU1r/+OOPS+vXXnttz9qhQ4dK5z2XDeUOOgDnDsIOJEHYgSQIO5AEYQeSIOxAEoQdSILr7GjM1FT5N6LXrl1bWn/++edL67feeutZ97QYcJ0dSI6wA0kQdiAJwg4kQdiBJAg7kARhB5IYxMCOQE9XXXVVz9qaNWtqLfupp56qNX827NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAmus2OoHnnkkZ61pUuXls779ttvl9Z37NhRqaes+u7ZbT9p+7jt/fOmXWx7t+03i8dlw20TQF0LOYz/uaQzhyzZImlPRFwtaU/xGkCL9Q17RLwg6cQZk9dL2lY83ybp9gH3BWDAqn5mH4uI2eL5u5LGer3R9oSkiYrrATAgtU/QRUSU/ZBkRExKmpT4wUmgSVUvvR2zvVySisfjg2sJwDBUDfsuSRuL5xslPTuYdgAMS9/DeNs7JN0s6VLbRyX9UNKjkn5le5OkdyTdMcwm0V7XX399aX39+vWVl/3444+X1j/66KPKy86ob9gjYkOP0jcG3AuAIeJ2WSAJwg4kQdiBJAg7kARhB5LgK66oZcOGXhdrOi644IKetRMnzvzKxWdt3bq1Uk/ojj07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBdXaU6vdzzxs3biytl3n44YdL66dOnaq8bHwee3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSILr7Ci1du3a0vrYWM+RvyRJH374Yc/aM888U6knVMOeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS4Dp7ckuWLCmt33///bWWPzU11bN25MiRWsvG2em7Z7f9pO3jtvfPm/ag7Rnbe4u/24bbJoC6FnIY/3NJ67pM/0lE3FD8/XawbQEYtL5hj4gXJJWP0wOg9eqcoLvP9qvFYf6yXm+yPWF72vZ0jXUBqKlq2H8q6WuSbpA0K+lHvd4YEZMRMR4R4xXXBWAAKoU9Io5FxCcRcUrSzyStHmxbAAatUthtL5/38luS9vd6L4B26Hud3fYOSTdLutT2UUk/lHSz7RskhaTDkr47xB4xRDfeeGNpffXqegdtTz/9dK35MTh9wx4RG7pMfmIIvQAYIm6XBZIg7EAShB1IgrADSRB2IAlHxOhWZo9uZViQffv2ldavu+660vqLL75YWr/lllt61ubm5krnRTUR4W7T2bMDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL8lPQi99BDD5XWV61aVVo/efJkaX3Lli2lda6ltwd7diAJwg4kQdiBJAg7kARhB5Ig7EAShB1Iguvsi8AVV1zRs7Zp06bSee2uX33+1Pbt20vr/b7PjvZgzw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSXCdfRG49957e9Yuv/zy0nlnZmZK65s3b67UE9qn757d9krbv7f9uu3XbH+/mH6x7d223ywelw2/XQBVLeQwfk7S5ohYJenvJX3P9ipJWyTtiYirJe0pXgNoqb5hj4jZiHileH5S0gFJKyStl7SteNs2SbcPq0kA9Z3VZ3bbX5H0dUl/kDQWEbNF6V1JYz3mmZA0Ub1FAIOw4LPxtr8o6deSfhARf55fi87okF0HbYyIyYgYj4jxWp0CqGVBYbe9RJ2gb4+I3xSTj9leXtSXSzo+nBYBDELfw3h3vgP5hKQDEfHjeaVdkjZKerR4fHYoHUJjY10/IX3qrrvuqrzsnTt3ltY/+OCDystGuyzkM/saSd+RtM/23mLaA+qE/Fe2N0l6R9Idw2kRwCD0DXtE/I+kXr9w8I3BtgNgWLhdFkiCsANJEHYgCcIOJEHYgST4ius54J577imtl32NtXNzY2/9hnTG4sGeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS4Dp7C5QNuSxJd999d+VlT01Nldbfe++9ysvGuYU9O5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwXX2FlizZk1p/bLLLqu87NnZ2f5vQgrs2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgiYWMz75S0i8kjUkKSZMR8bjtByX9s6TTX4h+ICJ+O6xGF7NVq1bVmn9ubq5n7bHHHqu1bCweC7mpZk7S5oh4xfaXJL1se3dR+0lE8K8JOAcsZHz2WUmzxfOTtg9IWjHsxgAM1ll9Zrf9FUlfl/SHYtJ9tl+1/aTtZT3mmbA9bXu6VqcAallw2G1/UdKvJf0gIv4s6aeSvibpBnX2/D/qNl9ETEbEeESMD6BfABUtKOy2l6gT9O0R8RtJiohjEfFJRJyS9DNJq4fXJoC6+obdtiU9IelARPx43vTl8972LUn7B98egEFxvyF9bd8k6b8l7ZN0qpj8gKQN6hzCh6TDkr5bnMwrW1b5ygDUFhHuNr1v2AeJsAPD1yvs3EEHJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IYtRDNv9J0jvzXl9aTGujtvbW1r4keqtqkL1d2asw0u+zf27l9nRbf5uurb21tS+J3qoaVW8cxgNJEHYgiabDPtnw+su0tbe29iXRW1Uj6a3Rz+wARqfpPTuAESHsQBKNhN32Ott/tP2W7S1N9NCL7cO299ne2/T4dMUYesdt75837WLbu22/WTx2HWOvod4etD1TbLu9tm9rqLeVtn9v+3Xbr9n+fjG90W1X0tdIttvIP7PbPk/SQUnflHRU0kuSNkTE6yNtpAfbhyWNR0TjN2DY/gdJf5H0i4j422Lav0k6ERGPFv9RLouI+1vS24OS/tL0MN7FaEXL5w8zLul2Sf+kBrddSV93aATbrYk9+2pJb0XEoYj4q6RfSlrfQB+tFxEvSDpxxuT1krYVz7ep849l5Hr01goRMRsRrxTPT0o6Pcx4o9uupK+RaCLsKyQdmff6qNo13ntI+p3tl21PNN1MF2Pzhtl6V9JYk8100XcY71E6Y5jx1my7KsOf18UJus+7KSL+TtI/SvpecbjaStH5DNama6cLGsZ7VLoMM/6pJrdd1eHP62oi7DOSVs57/eViWitExEzxeFzSTrVvKOpjp0fQLR6PN9zPp9o0jHe3YcbVgm3X5PDnTYT9JUlX2/6q7S9I+rakXQ308Tm2lxYnTmR7qaS1at9Q1LskbSyeb5T0bIO9fEZbhvHuNcy4Gt52jQ9/HhEj/5N0mzpn5P9P0r820UOPvq6S9L/F32tN9yZphzqHdR+rc25jk6RLJO2R9Kak/5J0cYt6+3d1hvZ+VZ1gLW+ot5vUOUR/VdLe4u+2prddSV8j2W7cLgskwQk6IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wHHydpkgz31EwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building AUTO-ENCODERS"
      ],
      "metadata": {
        "id": "IO8T2BDfmkmW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ENCODERS"
      ],
      "metadata": {
        "id": "1xoBujG2mx_2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_input = keras.Input(shape=(28, 28, 1), name='img')          # setting enoder input size layer of encoder\n",
        "x = keras.layers.Flatten()(encoder_input)\n",
        "\n",
        "\n",
        "Input_layer = keras.layers.Dense(512, activation=\"relu\")(x)\n",
        "Hidden_layer = keras.layers.Dense(128, activation=\"relu\")(Input_layer)\n",
        "Latent_space = keras.layers.Dense(64, activation=\"relu\")(Hidden_layer)\n",
        "\n",
        "encoder = keras.Model(encoder_input, Latent_space, name='encoder')\n",
        "\n"
      ],
      "metadata": {
        "id": "vK_dXzE9mjuP"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DECODER PART"
      ],
      "metadata": {
        "id": "Uex93JgaoPdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "decoder_input = keras.layers.Dense(128, activation=\"relu\")(Latent_space)\n",
        "d_Hidden_layer = keras.layers.Dense(512, activation=\"relu\")(decoder_input)\n",
        "d_output = keras.layers.Dense(784, activation=\"relu\")(d_Hidden_layer)\n",
        "\n",
        "decoder_output = keras.layers.Reshape((28, 28, 1))(d_output)"
      ],
      "metadata": {
        "id": "b52DdMJ6oTYd"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model summary"
      ],
      "metadata": {
        "id": "YNO3EBpNv-4G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "autoencoder = keras.Model(encoder_input, decoder_output, name='autoencoder')\n",
        "autoencoder.summary()            # summary of all the layers "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OxgPNa1tHro",
        "outputId": "6777e823-088f-4083-d56a-4d9db72d0183"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"autoencoder\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " img (InputLayer)            [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               401920    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               8320      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               66048     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 784)               402192    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 28, 28, 1)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 952,400\n",
            "Trainable params: 952,400\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the Model"
      ],
      "metadata": {
        "id": "gOO8T8vRu7xp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)        # Adam optimizer\n",
        "autoencoder.compile(opt, loss='mse')                        # Mean Squared error loss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zt-4LXYtahf",
        "outputId": "494c8ac2-80d1-47e3-b995-ac6e4fe6813f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit( x_train, x_train, validation_data=(x_test, x_test), epochs=5,  batch_size=40, validation_split=0.10 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yffICb6GuCMx",
        "outputId": "d5fbe7a9-c356-4a84-9b2f-1ffae51694f7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1350/1350 [==============================] - 23s 17ms/step - loss: 0.0194 - val_loss: 0.0124\n",
            "Epoch 2/5\n",
            "1350/1350 [==============================] - 15s 11ms/step - loss: 0.0115 - val_loss: 0.0107\n",
            "Epoch 3/5\n",
            "1350/1350 [==============================] - 15s 11ms/step - loss: 0.0102 - val_loss: 0.0101\n",
            "Epoch 4/5\n",
            "1350/1350 [==============================] - 15s 11ms/step - loss: 0.0096 - val_loss: 0.0094\n",
            "Epoch 5/5\n",
            "1350/1350 [==============================] - 15s 11ms/step - loss: 0.0091 - val_loss: 0.0091\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_test[0], cmap=\"gray\")             # original data "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ViG7inzKzGYw",
        "outputId": "105db110-2b6f-41ca-d3ca-989b13d8b19c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff1acb12c90>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = encoder.predict([ x_test[0].reshape(-1, 28, 28, 1) ])\n",
        "class_wise = [ 0.87 , 0.95 , 0.97 , 0.96 , 0.90 , 0.98 , 0.96 , 0.97 , 0.89, 0.94]\n",
        "plt.imshow(example[0].reshape((8,8)), cmap=\"gray\")               # ecoded data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "W2BJfqXyy_ZB",
        "outputId": "fe855f99-77ff-4877-e779-799921bd1d50"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff1b125b5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ae_out = autoencoder.predict([ x_test[0].reshape(-1, 28, 28, 1) ])      # decoder data / output\n",
        "plt.imshow(ae_out[0].reshape((28,28)), cmap=\"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-y5lKkL8vWvC",
        "outputId": "aba7b755-aa8b-4b00-f2b8-1bb72222b389"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff1b119a4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "train_acc = autoencoder.evaluate(x_train, x_train, verbose=0)\n",
        "test_acc = autoencoder.evaluate(x_test, x_test, verbose=0)"
      ],
      "metadata": {
        "id": "1liNliKG0L5A"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Loss during training : \", train_acc )\n",
        "print(\"Loss during testing : \", test_acc )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvlY4K060L4Q",
        "outputId": "bc7508e1-7694-4324-c7e2-7d33f8532c24"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss during training :  0.008890422992408276\n",
            "Loss during testing :  0.00897013396024704\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(211)\n",
        "plt.title('Loss')\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "nqOLcENh0y8q",
        "outputId": "fa53a5f6-b7bd-4982-9c0b-6fd9fd6ec607"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff1b10ce190>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MNIST classification modeL"
      ],
      "metadata": {
        "id": "0PEaSDmX1aEw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### using the encoder part again"
      ],
      "metadata": {
        "id": "C7ESzuE62GRB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()  # loads the popular \"mnist\" training dataset"
      ],
      "metadata": {
        "id": "qNIWupocwE3f"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feed_forward = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Flatten(input_shape = (28,28)),\n",
        "                             tf.keras.layers.Dense(512, activation = \"relu\"),\n",
        "                             tf.keras.layers.Dense(128, activation = \"relu\"),\n",
        "                             tf.keras.layers.Dense(64, activation = \"relu\"),\n",
        "                             tf.keras.layers.Dense(32, activation = \"relu\"),\n",
        "                             tf.keras.layers.Dense(10, activation = \"softmax\")\n",
        "])"
      ],
      "metadata": {
        "id": "Ub6KRlaw-eoS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Summary "
      ],
      "metadata": {
        "id": "p4BpXStR33MW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feed_forward.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6r871iyM3gTC",
        "outputId": "30d95af7-806b-4ccf-ff86-cd48fe2e7670"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_2 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 512)               401920    \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 10)                330       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 478,250\n",
            "Trainable params: 478,250\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feed_forward.compile( optimizer= tf.keras.optimizers.SGD(learning_rate=0.1),\n",
        "                      loss = \"categorical_crossentropy\",\n",
        "                      metrics = [\"accuracy\"]\n",
        "                     )\n"
      ],
      "metadata": {
        "id": "5WJAyD8k32KI"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical                # for one hot encoding of output\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "UrZAAKw74EAa"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0daLr_j_GlK",
        "outputId": "771e53c8-8a4b-4fd8-98e0-c0292b426cd8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model"
      ],
      "metadata": {
        "id": "Wr-xFul64YPq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = feed_forward.fit(x_train, y_train,validation_data=(x_test, y_test), epochs = 15, batch_size = 40, verbose = 0)"
      ],
      "metadata": {
        "id": "SVmMjpLQ4WFx"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "train_acc = feed_forward.evaluate(x_train, y_train, verbose=0)\n",
        "test_acc = feed_forward.evaluate(x_test, y_test, verbose=0)"
      ],
      "metadata": {
        "id": "QTIauFt24rLK"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Loss during training : \", train_acc[0])\n",
        "print(\"Accuray of training : \", train_acc[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2SouZWO4rp6",
        "outputId": "76e5b8a7-eda7-46b5-dbf6-c82f5f575f4f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss during training :  nan\n",
            "Accuray of training :  0.9871666878461838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Loss during testing : \", test_acc[0])\n",
        "print(\"Accuray of testing : \", test_acc[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_G_Gdvb4tMc",
        "outputId": "b57e8ddf-70aa-4fd4-fe01-2e8911c6c585"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss during testing :  nan\n",
            "Accuray of testing :  0.9799999743700027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "BHOqUR_10FYU"
      }
    }
  ]
}