Regularization_DL.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XEEU0bzZCrec"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_moons\n",
        "import seaborn as sns\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "import tensorflow\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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = make_moons(100, noise=0.25,random_state=2)"
      ],
      "metadata": {
        "id": "lpjYZPGlCsRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:,0], X[:,1], c=y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "tJXFNxw7Cv4A",
        "outputId": "a7cf520b-c593-450a-b253-0fbcd5e39340"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = Sequential()\n",
        "\n",
        "model1.add(Dense(128,input_dim=2, activation=\"relu\"))\n",
        "model1.add(Dense(128, activation=\"relu\"))\n",
        "model1.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wcHQFoH-C14o",
        "outputId": "c340d7ed-16d1-420d-aac6-1919fc95ca96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 128)               384       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,025\n",
            "Trainable params: 17,025\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.01)\n",
        "model1.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "history1 = model1.fit(X, y, epochs=2000, validation_split = 0.2,verbose=0)"
      ],
      "metadata": {
        "id": "vrGZEwyIC9aE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4BVuOYkSmMML"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X, y.astype('int'), clf=model1, legend=2)\n",
        "plt.xlim(-2,3)\n",
        "plt.ylim(-1.5,2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "UfJedHLjDEyc",
        "outputId": "e896777a-14a6-446b-ce10-3a3d0316b8d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "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.plot(history1.history['loss'])\n",
        "plt.plot(history1.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "40lIZPmeVPVw",
        "outputId": "1d72caa4-4b35-47c1-f6e7-7e87f1bf5b94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f653b98e7f0>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = Sequential()\n",
        "\n",
        "model2.add(Dense(128,input_dim=2, activation=\"relu\",kernel_regularizer=tensorflow.keras.regularizers.l1(0.001)))\n",
        "model2.add(Dense(128, activation=\"relu\",kernel_regularizer=tensorflow.keras.regularizers.l1(0.001)))\n",
        "model2.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model2.summary()\n",
        "\n",
        "#we can replace with l2 to get l2 regularisation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8ZnOrbIvinp",
        "outputId": "f9cee348-5038-4aff-bc66-b85e235bb762"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 128)               384       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,025\n",
            "Trainable params: 17,025\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.01)\n",
        "model2.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "history2 = model2.fit(X, y, epochs=2000, validation_split = 0.2,verbose=0)"
      ],
      "metadata": {
        "id": "HNjIkZe8whLV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X, y.astype('int'), clf=model2, legend=2)\n",
        "plt.xlim(-2,3)\n",
        "plt.ylim(-1.5,2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "ioRq1E3awj42",
        "outputId": "9e64e24c-5dad-4e89-d1d6-b73c7e8ab70a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "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.plot(history2.history['loss'])\n",
        "plt.plot(history2.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "njwsZuvWVyIT",
        "outputId": "f1c66c46-8e5b-4e3b-9636-cfd89d903be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f65395e5af0>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1_weight_layer1 = model1.get_weights()[0].reshape(256)\n",
        "model2_weight_layer1 = model2.get_weights()[0].reshape(256)"
      ],
      "metadata": {
        "id": "KyB5EBy8woTH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(model1_weight_layer1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jPX6myb14t2s",
        "outputId": "d8a146fe-bcad-4b15-fa5c-c1bac54d283a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(model2_weight_layer1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "vxusTpdV6icl",
        "outputId": "612a8bb3-d655-4b9b-8dba-a1836b5fca67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1_weight_layer1.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X53hfLJV7bVq",
        "outputId": "e113ea0f-994f-411f-a722-60573229ee2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2.5333333"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2_weight_layer1.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGrQUvX57erb",
        "outputId": "57e44204-9b95-426b-f678-68c3d40529d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2.1728175"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(model1_weight_layer1)\n",
        "sns.distplot(model2_weight_layer1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "xwXhLZecZKHt",
        "outputId": "22084d60-876f-4591-d478-0a1cb7e7e20f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-99b939c7ee98>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(model1_weight_layer1)\n",
            "<ipython-input-16-99b939c7ee98>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(model2_weight_layer1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " model1.get_weights()[0].reshape(256)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soM0qyWFZPdq",
        "outputId": "550e6a7e-9087-4e10-ee45-3d34bb8ee7df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8.36581886e-02,  3.62687036e-02, -4.17765856e-01, -3.93163204e-01,\n",
              "       -3.96005869e-01,  2.13018522e-01,  2.28988439e-01, -3.22141826e-01,\n",
              "        2.23829538e-01,  1.97904125e-01, -5.38365006e-01,  1.70224056e-01,\n",
              "       -1.77298978e-01,  4.41261902e-02,  6.83471262e-02,  2.14902475e-01,\n",
              "        4.95364517e-02,  8.23249444e-02, -5.02141356e-01, -1.56526896e-03,\n",
              "        9.14724991e-02, -2.30933651e-02,  2.13529035e-01,  9.54458341e-02,\n",
              "       -2.91170686e-01,  1.57895297e-01, -5.04917026e-01,  2.12428153e-01,\n",
              "       -2.12982274e-03,  2.03352317e-01,  1.95124984e-01, -5.25625683e-02,\n",
              "       -3.26485872e-01,  2.97886580e-01, -6.85776353e-01,  2.16803566e-01,\n",
              "       -2.26653010e-01,  2.15190932e-01,  2.18419120e-01,  9.44057107e-02,\n",
              "       -3.32020015e-01,  2.72148997e-01, -4.05541629e-01,  2.10304454e-01,\n",
              "       -3.53123620e-02,  2.39017248e-01,  1.97818264e-01,  4.17221524e-02,\n",
              "        2.47686148e-01,  2.60426432e-01,  7.42920935e-02, -1.97996497e-02,\n",
              "        2.49996199e-03,  7.39424825e-02, -3.76863003e-01, -3.45605701e-01,\n",
              "        2.64541715e-01,  1.32041499e-01,  2.02703670e-01, -5.18711984e-01,\n",
              "       -1.18189253e-01,  1.92909852e-01,  6.19973317e-02,  1.69551060e-01,\n",
              "        1.87287569e-01, -4.96824086e-01,  6.72358125e-02, -4.61391211e-01,\n",
              "        2.53824741e-01, -4.05834556e-01,  1.26976356e-01, -5.35438359e-01,\n",
              "        2.17906699e-01, -4.61450815e-01,  1.40626235e-02,  8.39711912e-03,\n",
              "        1.33992940e-01,  2.37961203e-01, -3.23832154e-01,  2.20816463e-01,\n",
              "        1.54505417e-01,  2.60821521e-01, -4.87969033e-02, -1.69064537e-01,\n",
              "       -2.45658420e-02,  2.68627226e-01, -3.08435112e-02, -5.11454821e-01,\n",
              "        1.59141541e-01, -3.70742589e-01,  7.88443834e-02,  4.04660106e-02,\n",
              "        1.14555068e-01,  6.63752928e-02,  9.02682170e-03, -2.60784894e-01,\n",
              "       -4.40374583e-01, -1.14047769e-02, -3.74770425e-02,  1.68620914e-01,\n",
              "        1.78412095e-01,  3.17960113e-01, -1.22944184e-01,  2.22067803e-01,\n",
              "        2.16269627e-01, -1.55968731e-02, -1.53711125e-01,  1.20375909e-01,\n",
              "       -2.82058775e-01, -4.21607405e-01, -2.38318786e-01, -3.87454391e-01,\n",
              "       -5.39283343e-02, -5.19582815e-02,  2.20146533e-02,  2.19618052e-01,\n",
              "       -3.93210024e-01,  5.31659797e-02,  1.20239288e-01, -1.07759438e-01,\n",
              "        6.92361444e-02,  2.54981816e-01,  2.05702752e-01, -4.69989777e-01,\n",
              "       -2.02217568e-02,  2.18708590e-01, -3.57157469e-01, -4.57049876e-01,\n",
              "        1.50024498e+00,  4.97291982e-01,  1.08143896e-01, -2.13980023e-02,\n",
              "       -3.68858159e-01, -1.55548185e-01, -2.20214128e-01, -3.67496133e-01,\n",
              "        3.76471758e-01, -1.12438962e-01, -1.42594591e-01,  2.63052106e-01,\n",
              "        2.86761671e-01, -1.75136283e-01,  1.42654404e-01, -1.64357230e-01,\n",
              "       -2.05702230e-01, -3.31739932e-01, -6.89846743e-03,  1.20316848e-01,\n",
              "        2.39299849e-01,  2.02913046e-01, -1.23616964e-01,  1.96781605e-02,\n",
              "        1.68995097e-01,  8.50129426e-02, -7.56526217e-02, -1.47868231e-01,\n",
              "       -1.22441379e-02,  1.24331288e-01,  8.93589929e-02, -3.18250328e-01,\n",
              "        1.45956382e-01,  1.50910780e-01, -1.24292299e-01, -1.20857731e-01,\n",
              "        3.02440643e-01, -1.56325176e-01, -8.48699063e-02,  1.12520742e+00,\n",
              "        2.29321763e-01, -1.60620332e-01,  2.70279080e-01, -1.20651469e-01,\n",
              "        2.99278051e-01,  4.02042896e-01, -1.20495699e-01, -2.19048098e-01,\n",
              "       -1.40762851e-01, -2.05702767e-01, -4.23372239e-02, -8.96272540e-01,\n",
              "       -1.72349977e+00, -3.21702920e-02, -4.25565869e-01,  1.55981630e-01,\n",
              "       -2.06218705e-01,  2.18112305e-01,  9.11024734e-02, -1.53775085e-02,\n",
              "        2.21733779e-01, -1.11189082e-01, -5.94162345e-02, -7.17675462e-02,\n",
              "       -1.28930464e-01,  1.42400444e-04,  1.16128314e+00,  1.24267630e-01,\n",
              "       -1.96727023e-01,  2.12383211e-01,  3.87428761e-01, -1.30678445e-01,\n",
              "        3.66538137e-01,  2.10566714e-01, -9.19768572e-01,  1.75965977e+00,\n",
              "        2.43212238e-01, -1.89635113e-01,  1.17535554e-01, -1.66406915e-01,\n",
              "       -7.35416561e-02,  4.38676953e-01, -1.25603628e+00,  2.58704990e-01,\n",
              "       -1.68005824e-01, -1.97434500e-01,  1.37823373e-01,  2.18255166e-02,\n",
              "       -6.31580055e-02, -3.56804490e-01, -1.04981947e+00, -4.25514160e-03,\n",
              "        1.34820193e-01, -1.04840231e+00,  2.37947404e-02, -2.71855783e+00,\n",
              "       -9.91729721e-02, -5.63004427e-02, -4.27977107e-02, -1.07557707e-01,\n",
              "        1.92913383e-01,  8.54734406e-02,  3.15448016e-01,  5.29514104e-02,\n",
              "       -1.04846284e-01, -5.43445311e-02,  2.84394890e-01,  2.10982367e-01,\n",
              "       -4.97869343e-01, -2.54125241e-02,  3.14999759e-01,  2.14556679e-01,\n",
              "       -7.29165852e-01,  3.23935524e-02, -4.19065692e-02, -1.65264621e-01,\n",
              "       -1.50705399e-02,  2.35859916e-01,  1.93549111e-01,  2.37957790e-01,\n",
              "        4.22478020e-02,  3.10761720e-01, -1.16883703e-01, -1.06405476e-02,\n",
              "       -5.42359203e-02, -1.25260472e-01, -6.24480210e-02, -3.15924078e-01],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-9V7hE0RbIZt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}