AutoEncoders.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoEncoders.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "ejE__mrrH3k1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kTx8ohE8HweQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import make_classification\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LeakyReLU\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.utils import plot_model\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Autoencoder with Compression"
      ],
      "metadata": {
        "id": "1LugtbLPNd-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dataset\n",
        "X, y = make_classification(n_samples = 1000, n_features = 100, n_informative = 10, n_redundant=90, random_state = 1)\n",
        "print('Shape', X.shape)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3peERdbkH21Z",
        "outputId": "08e7899c-9c73-40ad-c67b-d042a79f8e74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape (1000, 100)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5.83689773,  9.48106626, -5.92914747, ...,  5.37136211,\n",
              "         5.7226078 ,  0.51555388],\n",
              "       [-2.71781242, -5.68328851, -0.83430708, ...,  1.25532969,\n",
              "        -6.67717369, -2.55389057],\n",
              "       [-4.02714469, -0.71286757, -0.45087055, ..., -3.09433131,\n",
              "         2.16707373, -5.38783486],\n",
              "       ...,\n",
              "       [-3.07304165, -0.74848055, -3.53713731, ...,  2.02834985,\n",
              "         3.57109914, -3.42184247],\n",
              "       [ 2.58697636,  8.45582302, -4.84465771, ...,  4.36163077,\n",
              "         5.75076495,  3.11705391],\n",
              "       [ 1.72207094,  2.74765656,  3.68745793, ...,  3.03883854,\n",
              "         3.03021905, -1.85702062]])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_inputs = X.shape[1] # Number of inputs"
      ],
      "metadata": {
        "id": "q2sRpvlhH26P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 1)"
      ],
      "metadata": {
        "id": "TYa4CRN8Ocig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Scale data\n",
        "t = MinMaxScaler()\n",
        "t.fit(X_train)\n",
        "X_train = t.transform(X_train)\n",
        "X_test = t.transform(X_test)"
      ],
      "metadata": {
        "id": "ojlKQfMHOcm2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoder"
      ],
      "metadata": {
        "id": "WCyOsUtiP0mp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "visible = Input(shape = (n_inputs,))\n",
        "#Define Encoder\n",
        "#level 1\n",
        "e = Dense(n_inputs*2)(visible) # e = encoder\n",
        "e = BatchNormalization()(e)\n",
        "e = LeakyReLU()(e)\n",
        "\n",
        "# level 2 \n",
        "e = Dense(n_inputs)(e)\n",
        "e = BatchNormalization()(e)\n",
        "e = LeakyReLU()(e)\n",
        "\n",
        "#Bottleneck \n",
        "n_bottleneck = round(float(n_inputs)/2.0)\n",
        "bottleneck = Dense(n_bottleneck)(e)\n",
        "\n",
        "# Define Decoder\n",
        "#level 1\n",
        "d = Dense(n_inputs)(bottleneck) # d = dencoder\n",
        "d = BatchNormalization()(d)\n",
        "d = LeakyReLU()(d)\n",
        "\n",
        "#level 2\n",
        "d = Dense(n_inputs*2)(d) # d = dencoder\n",
        "d = BatchNormalization()(d)\n",
        "d = LeakyReLU()(d)\n",
        "\n",
        "#Output layer\n",
        "output = Dense(n_inputs, activation = 'linear')(d)\n",
        "\n",
        "#Define autoencoder model\n",
        "model = Model(inputs = visible, outputs = output)\n",
        "\n",
        "#compile autoencoder\n",
        "model.compile(optimizer = 'adam', loss = 'mse')"
      ],
      "metadata": {
        "id": "-duxTqo6OcrK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, 'autoencoder_compress.png', show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyfH01odOcu0",
        "outputId": "38334f74-577d-48e7-f88e-19e59bd5b7d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the autoencoder model to reconstruct input\n",
        "history = model.fit(X_train, X_train, epochs = 200, batch_size = 16, verbose = 2, validation_data = (X_test,X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klEvf2_POcyM",
        "outputId": "c4564eb5-bd31-4609-d38a-5c29c8b5d4a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "42/42 - 7s - loss: 0.2384 - val_loss: 0.1801 - 7s/epoch - 165ms/step\n",
            "Epoch 2/200\n",
            "42/42 - 0s - loss: 0.0399 - val_loss: 0.1063 - 330ms/epoch - 8ms/step\n",
            "Epoch 3/200\n",
            "42/42 - 0s - loss: 0.0249 - val_loss: 0.0589 - 353ms/epoch - 8ms/step\n",
            "Epoch 4/200\n",
            "42/42 - 0s - loss: 0.0196 - val_loss: 0.0307 - 338ms/epoch - 8ms/step\n",
            "Epoch 5/200\n",
            "42/42 - 0s - loss: 0.0170 - val_loss: 0.0209 - 346ms/epoch - 8ms/step\n",
            "Epoch 6/200\n",
            "42/42 - 0s - loss: 0.0146 - val_loss: 0.0148 - 351ms/epoch - 8ms/step\n",
            "Epoch 7/200\n",
            "42/42 - 0s - loss: 0.0130 - val_loss: 0.0118 - 344ms/epoch - 8ms/step\n",
            "Epoch 8/200\n",
            "42/42 - 0s - loss: 0.0126 - val_loss: 0.0098 - 341ms/epoch - 8ms/step\n",
            "Epoch 9/200\n",
            "42/42 - 0s - loss: 0.0115 - val_loss: 0.0085 - 348ms/epoch - 8ms/step\n",
            "Epoch 10/200\n",
            "42/42 - 0s - loss: 0.0112 - val_loss: 0.0067 - 341ms/epoch - 8ms/step\n",
            "Epoch 11/200\n",
            "42/42 - 0s - loss: 0.0101 - val_loss: 0.0055 - 349ms/epoch - 8ms/step\n",
            "Epoch 12/200\n",
            "42/42 - 0s - loss: 0.0103 - val_loss: 0.0060 - 344ms/epoch - 8ms/step\n",
            "Epoch 13/200\n",
            "42/42 - 0s - loss: 0.0099 - val_loss: 0.0062 - 346ms/epoch - 8ms/step\n",
            "Epoch 14/200\n",
            "42/42 - 0s - loss: 0.0098 - val_loss: 0.0050 - 350ms/epoch - 8ms/step\n",
            "Epoch 15/200\n",
            "42/42 - 0s - loss: 0.0088 - val_loss: 0.0051 - 345ms/epoch - 8ms/step\n",
            "Epoch 16/200\n",
            "42/42 - 0s - loss: 0.0082 - val_loss: 0.0044 - 338ms/epoch - 8ms/step\n",
            "Epoch 17/200\n",
            "42/42 - 0s - loss: 0.0086 - val_loss: 0.0052 - 340ms/epoch - 8ms/step\n",
            "Epoch 18/200\n",
            "42/42 - 0s - loss: 0.0084 - val_loss: 0.0050 - 351ms/epoch - 8ms/step\n",
            "Epoch 19/200\n",
            "42/42 - 0s - loss: 0.0083 - val_loss: 0.0051 - 358ms/epoch - 9ms/step\n",
            "Epoch 20/200\n",
            "42/42 - 0s - loss: 0.0078 - val_loss: 0.0043 - 344ms/epoch - 8ms/step\n",
            "Epoch 21/200\n",
            "42/42 - 0s - loss: 0.0078 - val_loss: 0.0034 - 332ms/epoch - 8ms/step\n",
            "Epoch 22/200\n",
            "42/42 - 0s - loss: 0.0074 - val_loss: 0.0051 - 340ms/epoch - 8ms/step\n",
            "Epoch 23/200\n",
            "42/42 - 0s - loss: 0.0075 - val_loss: 0.0048 - 344ms/epoch - 8ms/step\n",
            "Epoch 24/200\n",
            "42/42 - 0s - loss: 0.0073 - val_loss: 0.0050 - 351ms/epoch - 8ms/step\n",
            "Epoch 25/200\n",
            "42/42 - 0s - loss: 0.0072 - val_loss: 0.0046 - 338ms/epoch - 8ms/step\n",
            "Epoch 26/200\n",
            "42/42 - 0s - loss: 0.0065 - val_loss: 0.0036 - 348ms/epoch - 8ms/step\n",
            "Epoch 27/200\n",
            "42/42 - 0s - loss: 0.0069 - val_loss: 0.0039 - 343ms/epoch - 8ms/step\n",
            "Epoch 28/200\n",
            "42/42 - 0s - loss: 0.0065 - val_loss: 0.0037 - 333ms/epoch - 8ms/step\n",
            "Epoch 29/200\n",
            "42/42 - 0s - loss: 0.0067 - val_loss: 0.0032 - 369ms/epoch - 9ms/step\n",
            "Epoch 30/200\n",
            "42/42 - 0s - loss: 0.0069 - val_loss: 0.0043 - 345ms/epoch - 8ms/step\n",
            "Epoch 31/200\n",
            "42/42 - 0s - loss: 0.0067 - val_loss: 0.0036 - 346ms/epoch - 8ms/step\n",
            "Epoch 32/200\n",
            "42/42 - 0s - loss: 0.0062 - val_loss: 0.0025 - 340ms/epoch - 8ms/step\n",
            "Epoch 33/200\n",
            "42/42 - 0s - loss: 0.0062 - val_loss: 0.0028 - 347ms/epoch - 8ms/step\n",
            "Epoch 34/200\n",
            "42/42 - 0s - loss: 0.0063 - val_loss: 0.0028 - 341ms/epoch - 8ms/step\n",
            "Epoch 35/200\n",
            "42/42 - 0s - loss: 0.0060 - val_loss: 0.0031 - 346ms/epoch - 8ms/step\n",
            "Epoch 36/200\n",
            "42/42 - 0s - loss: 0.0060 - val_loss: 0.0034 - 346ms/epoch - 8ms/step\n",
            "Epoch 37/200\n",
            "42/42 - 0s - loss: 0.0058 - val_loss: 0.0026 - 343ms/epoch - 8ms/step\n",
            "Epoch 38/200\n",
            "42/42 - 0s - loss: 0.0062 - val_loss: 0.0037 - 355ms/epoch - 8ms/step\n",
            "Epoch 39/200\n",
            "42/42 - 0s - loss: 0.0056 - val_loss: 0.0031 - 351ms/epoch - 8ms/step\n",
            "Epoch 40/200\n",
            "42/42 - 0s - loss: 0.0063 - val_loss: 0.0028 - 333ms/epoch - 8ms/step\n",
            "Epoch 41/200\n",
            "42/42 - 0s - loss: 0.0056 - val_loss: 0.0025 - 350ms/epoch - 8ms/step\n",
            "Epoch 42/200\n",
            "42/42 - 0s - loss: 0.0054 - val_loss: 0.0034 - 342ms/epoch - 8ms/step\n",
            "Epoch 43/200\n",
            "42/42 - 0s - loss: 0.0058 - val_loss: 0.0028 - 333ms/epoch - 8ms/step\n",
            "Epoch 44/200\n",
            "42/42 - 0s - loss: 0.0056 - val_loss: 0.0028 - 336ms/epoch - 8ms/step\n",
            "Epoch 45/200\n",
            "42/42 - 0s - loss: 0.0055 - val_loss: 0.0030 - 331ms/epoch - 8ms/step\n",
            "Epoch 46/200\n",
            "42/42 - 0s - loss: 0.0061 - val_loss: 0.0020 - 340ms/epoch - 8ms/step\n",
            "Epoch 47/200\n",
            "42/42 - 0s - loss: 0.0055 - val_loss: 0.0024 - 362ms/epoch - 9ms/step\n",
            "Epoch 48/200\n",
            "42/42 - 0s - loss: 0.0051 - val_loss: 0.0024 - 356ms/epoch - 8ms/step\n",
            "Epoch 49/200\n",
            "42/42 - 0s - loss: 0.0054 - val_loss: 0.0032 - 347ms/epoch - 8ms/step\n",
            "Epoch 50/200\n",
            "42/42 - 0s - loss: 0.0056 - val_loss: 0.0020 - 345ms/epoch - 8ms/step\n",
            "Epoch 51/200\n",
            "42/42 - 0s - loss: 0.0056 - val_loss: 0.0030 - 343ms/epoch - 8ms/step\n",
            "Epoch 52/200\n",
            "42/42 - 0s - loss: 0.0057 - val_loss: 0.0031 - 325ms/epoch - 8ms/step\n",
            "Epoch 53/200\n",
            "42/42 - 0s - loss: 0.0050 - val_loss: 0.0025 - 330ms/epoch - 8ms/step\n",
            "Epoch 54/200\n",
            "42/42 - 0s - loss: 0.0056 - val_loss: 0.0031 - 331ms/epoch - 8ms/step\n",
            "Epoch 55/200\n",
            "42/42 - 0s - loss: 0.0052 - val_loss: 0.0027 - 328ms/epoch - 8ms/step\n",
            "Epoch 56/200\n",
            "42/42 - 0s - loss: 0.0051 - val_loss: 0.0024 - 353ms/epoch - 8ms/step\n",
            "Epoch 57/200\n",
            "42/42 - 0s - loss: 0.0053 - val_loss: 0.0032 - 337ms/epoch - 8ms/step\n",
            "Epoch 58/200\n",
            "42/42 - 0s - loss: 0.0054 - val_loss: 0.0024 - 342ms/epoch - 8ms/step\n",
            "Epoch 59/200\n",
            "42/42 - 0s - loss: 0.0053 - val_loss: 0.0019 - 326ms/epoch - 8ms/step\n",
            "Epoch 60/200\n",
            "42/42 - 0s - loss: 0.0048 - val_loss: 0.0021 - 342ms/epoch - 8ms/step\n",
            "Epoch 61/200\n",
            "42/42 - 0s - loss: 0.0048 - val_loss: 0.0023 - 331ms/epoch - 8ms/step\n",
            "Epoch 62/200\n",
            "42/42 - 0s - loss: 0.0050 - val_loss: 0.0027 - 340ms/epoch - 8ms/step\n",
            "Epoch 63/200\n",
            "42/42 - 0s - loss: 0.0050 - val_loss: 0.0028 - 336ms/epoch - 8ms/step\n",
            "Epoch 64/200\n",
            "42/42 - 0s - loss: 0.0053 - val_loss: 0.0021 - 320ms/epoch - 8ms/step\n",
            "Epoch 65/200\n",
            "42/42 - 0s - loss: 0.0048 - val_loss: 0.0021 - 358ms/epoch - 9ms/step\n",
            "Epoch 66/200\n",
            "42/42 - 0s - loss: 0.0045 - val_loss: 0.0025 - 337ms/epoch - 8ms/step\n",
            "Epoch 67/200\n",
            "42/42 - 0s - loss: 0.0051 - val_loss: 0.0025 - 339ms/epoch - 8ms/step\n",
            "Epoch 68/200\n",
            "42/42 - 0s - loss: 0.0048 - val_loss: 0.0026 - 324ms/epoch - 8ms/step\n",
            "Epoch 69/200\n",
            "42/42 - 0s - loss: 0.0052 - val_loss: 0.0037 - 339ms/epoch - 8ms/step\n",
            "Epoch 70/200\n",
            "42/42 - 0s - loss: 0.0048 - val_loss: 0.0016 - 345ms/epoch - 8ms/step\n",
            "Epoch 71/200\n",
            "42/42 - 0s - loss: 0.0047 - val_loss: 0.0021 - 329ms/epoch - 8ms/step\n",
            "Epoch 72/200\n",
            "42/42 - 0s - loss: 0.0047 - val_loss: 0.0023 - 333ms/epoch - 8ms/step\n",
            "Epoch 73/200\n",
            "42/42 - 0s - loss: 0.0046 - val_loss: 0.0023 - 324ms/epoch - 8ms/step\n",
            "Epoch 74/200\n",
            "42/42 - 0s - loss: 0.0046 - val_loss: 0.0017 - 337ms/epoch - 8ms/step\n",
            "Epoch 75/200\n",
            "42/42 - 0s - loss: 0.0045 - val_loss: 0.0031 - 334ms/epoch - 8ms/step\n",
            "Epoch 76/200\n",
            "42/42 - 0s - loss: 0.0048 - val_loss: 0.0022 - 342ms/epoch - 8ms/step\n",
            "Epoch 77/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0020 - 330ms/epoch - 8ms/step\n",
            "Epoch 78/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0022 - 323ms/epoch - 8ms/step\n",
            "Epoch 79/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0020 - 323ms/epoch - 8ms/step\n",
            "Epoch 80/200\n",
            "42/42 - 0s - loss: 0.0046 - val_loss: 0.0026 - 330ms/epoch - 8ms/step\n",
            "Epoch 81/200\n",
            "42/42 - 0s - loss: 0.0045 - val_loss: 0.0017 - 327ms/epoch - 8ms/step\n",
            "Epoch 82/200\n",
            "42/42 - 0s - loss: 0.0043 - val_loss: 0.0023 - 313ms/epoch - 7ms/step\n",
            "Epoch 83/200\n",
            "42/42 - 0s - loss: 0.0047 - val_loss: 0.0020 - 332ms/epoch - 8ms/step\n",
            "Epoch 84/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0020 - 329ms/epoch - 8ms/step\n",
            "Epoch 85/200\n",
            "42/42 - 0s - loss: 0.0046 - val_loss: 0.0021 - 340ms/epoch - 8ms/step\n",
            "Epoch 86/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0020 - 326ms/epoch - 8ms/step\n",
            "Epoch 87/200\n",
            "42/42 - 0s - loss: 0.0043 - val_loss: 0.0015 - 333ms/epoch - 8ms/step\n",
            "Epoch 88/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0017 - 350ms/epoch - 8ms/step\n",
            "Epoch 89/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0022 - 337ms/epoch - 8ms/step\n",
            "Epoch 90/200\n",
            "42/42 - 0s - loss: 0.0043 - val_loss: 0.0022 - 325ms/epoch - 8ms/step\n",
            "Epoch 91/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0014 - 314ms/epoch - 7ms/step\n",
            "Epoch 92/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0019 - 361ms/epoch - 9ms/step\n",
            "Epoch 93/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0020 - 345ms/epoch - 8ms/step\n",
            "Epoch 94/200\n",
            "42/42 - 0s - loss: 0.0047 - val_loss: 0.0021 - 336ms/epoch - 8ms/step\n",
            "Epoch 95/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0014 - 344ms/epoch - 8ms/step\n",
            "Epoch 96/200\n",
            "42/42 - 0s - loss: 0.0043 - val_loss: 0.0021 - 327ms/epoch - 8ms/step\n",
            "Epoch 97/200\n",
            "42/42 - 0s - loss: 0.0042 - val_loss: 0.0019 - 331ms/epoch - 8ms/step\n",
            "Epoch 98/200\n",
            "42/42 - 0s - loss: 0.0043 - val_loss: 0.0014 - 340ms/epoch - 8ms/step\n",
            "Epoch 99/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0014 - 342ms/epoch - 8ms/step\n",
            "Epoch 100/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0028 - 363ms/epoch - 9ms/step\n",
            "Epoch 101/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0016 - 335ms/epoch - 8ms/step\n",
            "Epoch 102/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0017 - 338ms/epoch - 8ms/step\n",
            "Epoch 103/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0016 - 429ms/epoch - 10ms/step\n",
            "Epoch 104/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0025 - 332ms/epoch - 8ms/step\n",
            "Epoch 105/200\n",
            "42/42 - 0s - loss: 0.0044 - val_loss: 0.0019 - 335ms/epoch - 8ms/step\n",
            "Epoch 106/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0017 - 345ms/epoch - 8ms/step\n",
            "Epoch 107/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0017 - 322ms/epoch - 8ms/step\n",
            "Epoch 108/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0017 - 337ms/epoch - 8ms/step\n",
            "Epoch 109/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0014 - 355ms/epoch - 8ms/step\n",
            "Epoch 110/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0025 - 341ms/epoch - 8ms/step\n",
            "Epoch 111/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0013 - 356ms/epoch - 8ms/step\n",
            "Epoch 112/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0026 - 373ms/epoch - 9ms/step\n",
            "Epoch 113/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0024 - 357ms/epoch - 8ms/step\n",
            "Epoch 114/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0015 - 355ms/epoch - 8ms/step\n",
            "Epoch 115/200\n",
            "42/42 - 0s - loss: 0.0038 - val_loss: 0.0027 - 357ms/epoch - 8ms/step\n",
            "Epoch 116/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0014 - 424ms/epoch - 10ms/step\n",
            "Epoch 117/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0023 - 351ms/epoch - 8ms/step\n",
            "Epoch 118/200\n",
            "42/42 - 0s - loss: 0.0042 - val_loss: 0.0013 - 346ms/epoch - 8ms/step\n",
            "Epoch 119/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0018 - 316ms/epoch - 8ms/step\n",
            "Epoch 120/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0018 - 332ms/epoch - 8ms/step\n",
            "Epoch 121/200\n",
            "42/42 - 0s - loss: 0.0038 - val_loss: 0.0015 - 340ms/epoch - 8ms/step\n",
            "Epoch 122/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0015 - 322ms/epoch - 8ms/step\n",
            "Epoch 123/200\n",
            "42/42 - 0s - loss: 0.0037 - val_loss: 0.0019 - 338ms/epoch - 8ms/step\n",
            "Epoch 124/200\n",
            "42/42 - 0s - loss: 0.0038 - val_loss: 0.0016 - 348ms/epoch - 8ms/step\n",
            "Epoch 125/200\n",
            "42/42 - 0s - loss: 0.0038 - val_loss: 0.0017 - 329ms/epoch - 8ms/step\n",
            "Epoch 126/200\n",
            "42/42 - 0s - loss: 0.0041 - val_loss: 0.0018 - 332ms/epoch - 8ms/step\n",
            "Epoch 127/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0019 - 346ms/epoch - 8ms/step\n",
            "Epoch 128/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0016 - 321ms/epoch - 8ms/step\n",
            "Epoch 129/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0023 - 348ms/epoch - 8ms/step\n",
            "Epoch 130/200\n",
            "42/42 - 0s - loss: 0.0037 - val_loss: 0.0019 - 344ms/epoch - 8ms/step\n",
            "Epoch 131/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0017 - 334ms/epoch - 8ms/step\n",
            "Epoch 132/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0022 - 339ms/epoch - 8ms/step\n",
            "Epoch 133/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0021 - 341ms/epoch - 8ms/step\n",
            "Epoch 134/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0012 - 340ms/epoch - 8ms/step\n",
            "Epoch 135/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0015 - 345ms/epoch - 8ms/step\n",
            "Epoch 136/200\n",
            "42/42 - 0s - loss: 0.0040 - val_loss: 0.0016 - 320ms/epoch - 8ms/step\n",
            "Epoch 137/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0016 - 345ms/epoch - 8ms/step\n",
            "Epoch 138/200\n",
            "42/42 - 0s - loss: 0.0037 - val_loss: 0.0013 - 317ms/epoch - 8ms/step\n",
            "Epoch 139/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0017 - 325ms/epoch - 8ms/step\n",
            "Epoch 140/200\n",
            "42/42 - 0s - loss: 0.0038 - val_loss: 0.0017 - 334ms/epoch - 8ms/step\n",
            "Epoch 141/200\n",
            "42/42 - 0s - loss: 0.0039 - val_loss: 0.0012 - 331ms/epoch - 8ms/step\n",
            "Epoch 142/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0018 - 329ms/epoch - 8ms/step\n",
            "Epoch 143/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0015 - 318ms/epoch - 8ms/step\n",
            "Epoch 144/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0019 - 332ms/epoch - 8ms/step\n",
            "Epoch 145/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0013 - 329ms/epoch - 8ms/step\n",
            "Epoch 146/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0016 - 355ms/epoch - 8ms/step\n",
            "Epoch 147/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0014 - 343ms/epoch - 8ms/step\n",
            "Epoch 148/200\n",
            "42/42 - 0s - loss: 0.0037 - val_loss: 0.0014 - 348ms/epoch - 8ms/step\n",
            "Epoch 149/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0016 - 351ms/epoch - 8ms/step\n",
            "Epoch 150/200\n",
            "42/42 - 0s - loss: 0.0037 - val_loss: 0.0016 - 325ms/epoch - 8ms/step\n",
            "Epoch 151/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0017 - 326ms/epoch - 8ms/step\n",
            "Epoch 152/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0013 - 324ms/epoch - 8ms/step\n",
            "Epoch 153/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0013 - 317ms/epoch - 8ms/step\n",
            "Epoch 154/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0017 - 333ms/epoch - 8ms/step\n",
            "Epoch 155/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0011 - 340ms/epoch - 8ms/step\n",
            "Epoch 156/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0015 - 339ms/epoch - 8ms/step\n",
            "Epoch 157/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0017 - 340ms/epoch - 8ms/step\n",
            "Epoch 158/200\n",
            "42/42 - 0s - loss: 0.0037 - val_loss: 0.0018 - 335ms/epoch - 8ms/step\n",
            "Epoch 159/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0017 - 343ms/epoch - 8ms/step\n",
            "Epoch 160/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0017 - 356ms/epoch - 8ms/step\n",
            "Epoch 161/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0017 - 325ms/epoch - 8ms/step\n",
            "Epoch 162/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0015 - 337ms/epoch - 8ms/step\n",
            "Epoch 163/200\n",
            "42/42 - 0s - loss: 0.0035 - val_loss: 0.0013 - 345ms/epoch - 8ms/step\n",
            "Epoch 164/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0023 - 340ms/epoch - 8ms/step\n",
            "Epoch 165/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0012 - 339ms/epoch - 8ms/step\n",
            "Epoch 166/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0012 - 349ms/epoch - 8ms/step\n",
            "Epoch 167/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0016 - 335ms/epoch - 8ms/step\n",
            "Epoch 168/200\n",
            "42/42 - 0s - loss: 0.0031 - val_loss: 9.6882e-04 - 359ms/epoch - 9ms/step\n",
            "Epoch 169/200\n",
            "42/42 - 0s - loss: 0.0030 - val_loss: 0.0011 - 321ms/epoch - 8ms/step\n",
            "Epoch 170/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0016 - 337ms/epoch - 8ms/step\n",
            "Epoch 171/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0022 - 344ms/epoch - 8ms/step\n",
            "Epoch 172/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0017 - 332ms/epoch - 8ms/step\n",
            "Epoch 173/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0013 - 343ms/epoch - 8ms/step\n",
            "Epoch 174/200\n",
            "42/42 - 0s - loss: 0.0030 - val_loss: 0.0013 - 336ms/epoch - 8ms/step\n",
            "Epoch 175/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0013 - 351ms/epoch - 8ms/step\n",
            "Epoch 176/200\n",
            "42/42 - 0s - loss: 0.0036 - val_loss: 0.0013 - 349ms/epoch - 8ms/step\n",
            "Epoch 177/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0014 - 348ms/epoch - 8ms/step\n",
            "Epoch 178/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 9.9918e-04 - 329ms/epoch - 8ms/step\n",
            "Epoch 179/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0014 - 334ms/epoch - 8ms/step\n",
            "Epoch 180/200\n",
            "42/42 - 0s - loss: 0.0034 - val_loss: 0.0011 - 340ms/epoch - 8ms/step\n",
            "Epoch 181/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0012 - 336ms/epoch - 8ms/step\n",
            "Epoch 182/200\n",
            "42/42 - 0s - loss: 0.0030 - val_loss: 0.0014 - 328ms/epoch - 8ms/step\n",
            "Epoch 183/200\n",
            "42/42 - 0s - loss: 0.0030 - val_loss: 0.0011 - 342ms/epoch - 8ms/step\n",
            "Epoch 184/200\n",
            "42/42 - 0s - loss: 0.0031 - val_loss: 0.0010 - 348ms/epoch - 8ms/step\n",
            "Epoch 185/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0016 - 336ms/epoch - 8ms/step\n",
            "Epoch 186/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0018 - 339ms/epoch - 8ms/step\n",
            "Epoch 187/200\n",
            "42/42 - 0s - loss: 0.0031 - val_loss: 0.0013 - 337ms/epoch - 8ms/step\n",
            "Epoch 188/200\n",
            "42/42 - 0s - loss: 0.0031 - val_loss: 0.0019 - 349ms/epoch - 8ms/step\n",
            "Epoch 189/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0011 - 347ms/epoch - 8ms/step\n",
            "Epoch 190/200\n",
            "42/42 - 0s - loss: 0.0033 - val_loss: 0.0015 - 339ms/epoch - 8ms/step\n",
            "Epoch 191/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0016 - 346ms/epoch - 8ms/step\n",
            "Epoch 192/200\n",
            "42/42 - 0s - loss: 0.0031 - val_loss: 0.0012 - 346ms/epoch - 8ms/step\n",
            "Epoch 193/200\n",
            "42/42 - 0s - loss: 0.0030 - val_loss: 8.3563e-04 - 328ms/epoch - 8ms/step\n",
            "Epoch 194/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0012 - 348ms/epoch - 8ms/step\n",
            "Epoch 195/200\n",
            "42/42 - 0s - loss: 0.0031 - val_loss: 0.0012 - 340ms/epoch - 8ms/step\n",
            "Epoch 196/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0013 - 338ms/epoch - 8ms/step\n",
            "Epoch 197/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0015 - 337ms/epoch - 8ms/step\n",
            "Epoch 198/200\n",
            "42/42 - 0s - loss: 0.0032 - val_loss: 0.0013 - 340ms/epoch - 8ms/step\n",
            "Epoch 199/200\n",
            "42/42 - 0s - loss: 0.0028 - val_loss: 9.9269e-04 - 346ms/epoch - 8ms/step\n",
            "Epoch 200/200\n",
            "42/42 - 0s - loss: 0.0028 - val_loss: 0.0011 - 345ms/epoch - 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot Loss\n",
        "fig, ax = plt.subplots(figsize=(10,5)) \n",
        "plt.plot(history.history['loss'], label = 'train')\n",
        "plt.plot(history.history['val_loss'], label = 'test')\n",
        "plt.title('Autoencoder loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.savefig('Autoencoderloss.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOaNcZvGOc1e",
        "outputId": "4b4eefa0-1620-4d76-f406-342bd5c16305"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "z0xv1HJXc405"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define an encoder model without the decoder\n",
        "encoder = Model(inputs =visible, outputs = bottleneck)\n",
        "plot_model(model, 'encoder_compress.png', show_shapes = True)"
      ],
      "metadata": {
        "id": "vTvuVVb9ZN60",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4588010-a488-495a-f768-39f29dfe1226"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model\n",
        "encoder.save('encoder.h5')"
      ],
      "metadata": {
        "id": "VaYEG4pUZN-E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c583cce-52af-42a1-fa63-50d1fcc2d114"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Logisticregression"
      ],
      "metadata": {
        "id": "DQ9Svi2udnhL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = make_classification(n_samples=1000, n_features=100, n_informative=10, n_redundant=90, random_state=1)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
        "t = MinMaxScaler()\n",
        "t.fit(X_train)\n",
        "X_train = t.transform(X_train)\n",
        "X_test = t.transform(X_test)\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "metadata": {
        "id": "sM7cpam-ZOA_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39250591-886e-4f47-a512-a07f7e5e6126"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8939393939393939\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the model from file\n",
        "encoder = load_model('encoder.h5')"
      ],
      "metadata": {
        "id": "w3ivYDYqYi9z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9e6cd2e-e893-4254-8a67-f8ea93ba2da1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encode the  data\n",
        "X_train_encode = encoder.predict(X_train)\n",
        "X_test_encode = encoder.predict(X_test)"
      ],
      "metadata": {
        "id": "gEc_Pi7oYjDR"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_encode = LogisticRegression()\n",
        "# fit the model on the training set\n",
        "model_encode.fit(X_train_encode, y_train)\n",
        "# make predictions on the test set\n",
        "y_pred_encode = model_encode.predict(X_test_encode)"
      ],
      "metadata": {
        "id": "FDwj6N3RYjIX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a1f22ad-5cf9-4013-9a65-bf7964e376cc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc_encode = accuracy_score(y_test, y_pred_encode)\n",
        "print(acc_encode)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ike1qLYerRh",
        "outputId": "88c17047-6bc4-46b5-ce3c-d3c001df23b7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9242424242424242\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy LogisticRegression Simple: {}'.format(acc))\n",
        "print('Accuracy LogisticRegression With Autoencoder: {}'.format(acc_encode))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAsnAhnzfDOM",
        "outputId": "69043130-080b-42b4-8d80-eed949d8adac"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy LogisticRegression Simple: 0.8939393939393939\n",
            "Accuracy LogisticRegression With Autoencoder: 0.9242424242424242\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Ih7k8sidgrCx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}