Dropout_For_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tFGgFKexKF1"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAEFOSC8fbQE"
      },
      "source": [
        "# Generate Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWBfjx68OdDL"
      },
      "source": [
        "X_train = np.linspace(-1, 1, 20)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHgAbMkwdqcJ"
      },
      "source": [
        "y_train = np.array([-0.6561 , -0.3099 , -0.59035, -0.50855, -0.285  ,\n",
        "                    -0.2443 , -0.02445,  0.00135, -0.2006 ,  0.07475,\n",
        "                    -0.1422 ,  0.06515,  0.15265,  0.3521 ,  0.28415,\n",
        "                    0.5524 ,  0.23115,  0.20835, 0.4211,  0.60485])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3T0MiSMRU88"
      },
      "source": [
        "X_test = np.linspace(-1, 1, 20)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T13ENwBmRVAC"
      },
      "source": [
        "y_test = np.array([-0.69415, -0.451  , -0.43005, -0.4484 , -0.1475 ,\n",
        "                   -0.5019 , -0.28055,  0.24595, -0.21425, -0.0286 ,\n",
        "                   0.23415,  0.46575, 0.07955,  0.1973 ,  0.0719 ,\n",
        "                   0.3639 ,  0.5536 ,  0.3365 , 0.50705,  0.33435])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XeLcH4EeRVFk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "8c7fadc9-5433-410a-bb34-24a8376404b6"
      },
      "source": [
        "plt.scatter(X_train, y_train, c='red', label='Train')\n",
        "plt.scatter(X_test, y_test, c='blue', label='Test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shGA9BPVfJur"
      },
      "source": [
        "# Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFNJfoqXSZ3A"
      },
      "source": [
        "model_1 = Sequential()\n",
        "model_1.add(Dense(128, input_dim=1, activation=\"relu\"))\n",
        "model_1.add(Dense(128, activation=\"relu\"))\n",
        "model_1.add(Dense(1, activation=\"linear\"))\n",
        "adam = Adam(learning_rate=0.01)\n",
        "\n",
        "model_1.compile(loss='mse', optimizer=adam, metrics=['mse'])\n",
        "\n",
        "history = model_1.fit(X_train, y_train, epochs=500,\n",
        "                    validation_data = (X_test, y_test),\n",
        "                    verbose=False)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5b0Udf8VcB6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25c7037b-0894-451a-ce29-b42dbed736a0"
      },
      "source": [
        "# evaluate the model\n",
        "_, train_mse = model_1.evaluate(X_train, y_train, verbose=0)\n",
        "_, test_mse = model_1.evaluate(X_test, y_test, verbose=0)\n",
        "print('Train: {}, Test: {}'.format(train_mse, test_mse))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 0.001391799421980977, Test: 0.04455260932445526\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ucuCQDuWA8S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b47fca83-3c12-459e-a621-c17b3ebd8624"
      },
      "source": [
        "y_pred_1 = model_1.predict(X_test)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 82ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwWmKGgeWTrf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "47909700-bec4-4de0-c6e1-129224644e51"
      },
      "source": [
        "plt.figure()\n",
        "plt.scatter(X_train, y_train, c='red', label='Train')\n",
        "plt.scatter(X_test, y_test, c='blue', label='Test')\n",
        "plt.plot(X_test, y_pred_1)\n",
        "plt.legend()\n",
        "plt.ylim((-1.5, 1.5))\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrbM_1AZezz_"
      },
      "source": [
        "# Dropout Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vOPbuYHSlZm"
      },
      "source": [
        "model_2 = Sequential()\n",
        "model_2.add(Dense(128, input_dim=1, activation=\"relu\"))\n",
        "model_2.add(Dropout(0.2))\n",
        "model_2.add(Dense(128, activation=\"relu\"))\n",
        "model_2.add(Dropout(0.2))\n",
        "model_2.add(Dense(1, activation=\"linear\"))\n",
        "adam = Adam(learning_rate=0.01)\n",
        "model_2.compile(loss='mse', optimizer=adam, metrics=['mse'])\n",
        "\n",
        "drop_out_history = model_2.fit(X_train, y_train, epochs=500,\n",
        "                               validation_data = (X_test, y_test),\n",
        "                               verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0rofVLkSZ62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3850231d-35a3-4a36-e0b4-a910907b7fbb"
      },
      "source": [
        "# evaluate the model\n",
        "_, train_mse = model_2.evaluate(X_train, y_train, verbose=0)\n",
        "_, test_mse = model_2.evaluate(X_test, y_test, verbose=0)\n",
        "print('Train: {}, Test: {}'.format(train_mse, test_mse))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train: 0.011907287873327732, Test: 0.03752660006284714\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtA4Oj8zV1HU"
      },
      "source": [
        "y_pred_2 = model_2.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lkk2Nf3eV1Kx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "dbc28816-2af8-436e-a005-bcc8decf9afe"
      },
      "source": [
        "plt.figure()\n",
        "plt.scatter(X_train, y_train, c='red', label='Train')\n",
        "plt.scatter(X_test, y_test, c='blue', label='Test')\n",
        "plt.plot(X_test, y_pred_2)\n",
        "plt.legend()\n",
        "plt.ylim((-1.5, 1.5))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dxX6HI77tdw"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}