TF2_0_Machine_Learning_and_Neurons.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNGXjaeOMFGsojssAr4n/rc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/selimunal/tensorflow/blob/main/TF2_0_Machine_Learning_and_Neurons.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XfA9hrKMqVV"
      },
      "source": [
        " **TensorFlow Lecture 1**\n",
        "\n",
        "This is regular text\n",
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1E-nzZosM6uo"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9QN92xFNCSO"
      },
      "source": [
        "x = np.linspace(0,10*np.pi,10000)\n",
        "y=np.sin(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "nlLWFI1BNOhE",
        "outputId": "0938d452-626a-4b2b-edd9-acd69bf0db5d"
      },
      "source": [
        "plt.plot(x,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb6d26f2450>]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "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",
      "metadata": {
        "id": "ox3RrLz9NVZd"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1tGD4lwyKH7",
        "outputId": "35f2c162-7ef2-41e5-f685-2dd62126791e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer"
      ],
      "metadata": {
        "id": "Het59SH_yNAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=load_breast_cancer()"
      ],
      "metadata": {
        "id": "TBDixs4myXFH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4kuesY-yboW",
        "outputId": "58ed1db3-3937-4522-9170-460a0e14005d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rv7OFeZBycWW",
        "outputId": "70905727-a021-4e20-ca9a-27ffa334ccb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hA4k5yq3yg-3",
        "outputId": "80ae8ea5-7b6b-4893-8e6b-cfc6f2dcf4e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsPzn1CtyjTX",
        "outputId": "905c20b8-dee3-4952-a6d2-7b8f86c9ac67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBsMncBVzADh",
        "outputId": "088a6528-4a04-40be-a724-37fae114c101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error',\n",
              "       'fractal dimension error', 'worst radius', 'worst texture',\n",
              "       'worst perimeter', 'worst area', 'worst smoothness',\n",
              "       'worst compactness', 'worst concavity', 'worst concave points',\n",
              "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "XkVt5ARLzDhw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data.data,data.target,test_size=0.33)\n",
        "N,D=X_train.shape"
      ],
      "metadata": {
        "id": "T_kfmju4zLsH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi0fs3_rzkp5",
        "outputId": "05da9802-22f4-4830-acc5-92fb4bf77a92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569,)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#NORMALIZATION\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "\n",
        "X_train=scaler.fit_transform(X_train)\n",
        "X_test=scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "MLYu4u9mzm0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#MODEL DEFINITION\n",
        "model=tf.keras.models.Sequential([tf.keras.layers.Input(shape=(D,)),tf.keras.layers.Dense(1,activation='sigmoid')])"
      ],
      "metadata": {
        "id": "rWiYJ_4c026R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can do here\n",
        "\n",
        "model=tf.kearas.models.Sequential()\n",
        "\n",
        "\n",
        "model.add(tf.keras.layers.Dense(1,input_shape(D,),activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "p-9bUGQt2JPW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#COMPILE MODEL\n",
        "\n",
        "model.compile(optimizer=\"adam\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "rplVOL6Z1V3i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r=model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6VSQOIN2gIH",
        "outputId": "c5f4e6c1-fe5a-48b3-f4b5-b41238658330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 1s 20ms/step - loss: 0.8554 - accuracy: 0.4226 - val_loss: 0.8254 - val_accuracy: 0.4734\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7709 - accuracy: 0.5144 - val_loss: 0.7472 - val_accuracy: 0.5319\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.6964 - accuracy: 0.5853 - val_loss: 0.6784 - val_accuracy: 0.5904\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.6319 - accuracy: 0.6457 - val_loss: 0.6190 - val_accuracy: 0.6596\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.5765 - accuracy: 0.7060 - val_loss: 0.5688 - val_accuracy: 0.7394\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.5310 - accuracy: 0.7454 - val_loss: 0.5254 - val_accuracy: 0.7819\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.4912 - accuracy: 0.8005 - val_loss: 0.4887 - val_accuracy: 0.8085\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.4571 - accuracy: 0.8189 - val_loss: 0.4578 - val_accuracy: 0.8298\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.4288 - accuracy: 0.8451 - val_loss: 0.4308 - val_accuracy: 0.8564\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.4037 - accuracy: 0.8530 - val_loss: 0.4072 - val_accuracy: 0.8830\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.3815 - accuracy: 0.8688 - val_loss: 0.3868 - val_accuracy: 0.8936\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.3621 - accuracy: 0.8793 - val_loss: 0.3686 - val_accuracy: 0.9096\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.3446 - accuracy: 0.8845 - val_loss: 0.3526 - val_accuracy: 0.9202\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.3291 - accuracy: 0.9003 - val_loss: 0.3382 - val_accuracy: 0.9255\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.3149 - accuracy: 0.9029 - val_loss: 0.3254 - val_accuracy: 0.9255\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.3022 - accuracy: 0.9134 - val_loss: 0.3137 - val_accuracy: 0.9255\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2904 - accuracy: 0.9291 - val_loss: 0.3033 - val_accuracy: 0.9309\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.2799 - accuracy: 0.9291 - val_loss: 0.2935 - val_accuracy: 0.9309\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2701 - accuracy: 0.9370 - val_loss: 0.2846 - val_accuracy: 0.9309\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2611 - accuracy: 0.9423 - val_loss: 0.2764 - val_accuracy: 0.9309\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2525 - accuracy: 0.9449 - val_loss: 0.2691 - val_accuracy: 0.9309\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 0s 15ms/step - loss: 0.2450 - accuracy: 0.9475 - val_loss: 0.2621 - val_accuracy: 0.9309\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.2379 - accuracy: 0.9475 - val_loss: 0.2556 - val_accuracy: 0.9309\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2310 - accuracy: 0.9501 - val_loss: 0.2496 - val_accuracy: 0.9309\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2248 - accuracy: 0.9501 - val_loss: 0.2440 - val_accuracy: 0.9309\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2189 - accuracy: 0.9501 - val_loss: 0.2388 - val_accuracy: 0.9309\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.2134 - accuracy: 0.9580 - val_loss: 0.2339 - val_accuracy: 0.9309\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2083 - accuracy: 0.9606 - val_loss: 0.2293 - val_accuracy: 0.9309\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.2033 - accuracy: 0.9606 - val_loss: 0.2249 - val_accuracy: 0.9362\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1987 - accuracy: 0.9633 - val_loss: 0.2208 - val_accuracy: 0.9362\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.1946 - accuracy: 0.9633 - val_loss: 0.2168 - val_accuracy: 0.9362\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.1904 - accuracy: 0.9633 - val_loss: 0.2131 - val_accuracy: 0.9362\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1866 - accuracy: 0.9633 - val_loss: 0.2096 - val_accuracy: 0.9362\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1829 - accuracy: 0.9633 - val_loss: 0.2063 - val_accuracy: 0.9415\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1794 - accuracy: 0.9659 - val_loss: 0.2031 - val_accuracy: 0.9415\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1761 - accuracy: 0.9659 - val_loss: 0.2001 - val_accuracy: 0.9415\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1730 - accuracy: 0.9659 - val_loss: 0.1972 - val_accuracy: 0.9415\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1700 - accuracy: 0.9659 - val_loss: 0.1945 - val_accuracy: 0.9415\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1671 - accuracy: 0.9659 - val_loss: 0.1917 - val_accuracy: 0.9415\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1644 - accuracy: 0.9659 - val_loss: 0.1891 - val_accuracy: 0.9415\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1618 - accuracy: 0.9685 - val_loss: 0.1867 - val_accuracy: 0.9415\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1593 - accuracy: 0.9685 - val_loss: 0.1844 - val_accuracy: 0.9415\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1570 - accuracy: 0.9685 - val_loss: 0.1822 - val_accuracy: 0.9468\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1546 - accuracy: 0.9685 - val_loss: 0.1800 - val_accuracy: 0.9468\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1525 - accuracy: 0.9685 - val_loss: 0.1779 - val_accuracy: 0.9468\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1504 - accuracy: 0.9685 - val_loss: 0.1758 - val_accuracy: 0.9468\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1483 - accuracy: 0.9685 - val_loss: 0.1738 - val_accuracy: 0.9468\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1464 - accuracy: 0.9685 - val_loss: 0.1719 - val_accuracy: 0.9468\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1445 - accuracy: 0.9685 - val_loss: 0.1701 - val_accuracy: 0.9468\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1427 - accuracy: 0.9685 - val_loss: 0.1685 - val_accuracy: 0.9521\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1410 - accuracy: 0.9711 - val_loss: 0.1668 - val_accuracy: 0.9574\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1393 - accuracy: 0.9711 - val_loss: 0.1651 - val_accuracy: 0.9574\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1377 - accuracy: 0.9738 - val_loss: 0.1634 - val_accuracy: 0.9574\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1362 - accuracy: 0.9738 - val_loss: 0.1618 - val_accuracy: 0.9574\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1346 - accuracy: 0.9738 - val_loss: 0.1604 - val_accuracy: 0.9574\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1332 - accuracy: 0.9738 - val_loss: 0.1589 - val_accuracy: 0.9574\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1318 - accuracy: 0.9738 - val_loss: 0.1575 - val_accuracy: 0.9574\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1304 - accuracy: 0.9738 - val_loss: 0.1561 - val_accuracy: 0.9574\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1291 - accuracy: 0.9738 - val_loss: 0.1548 - val_accuracy: 0.9574\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1278 - accuracy: 0.9738 - val_loss: 0.1534 - val_accuracy: 0.9574\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1266 - accuracy: 0.9738 - val_loss: 0.1521 - val_accuracy: 0.9628\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1253 - accuracy: 0.9738 - val_loss: 0.1508 - val_accuracy: 0.9628\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.1242 - accuracy: 0.9738 - val_loss: 0.1496 - val_accuracy: 0.9628\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1231 - accuracy: 0.9764 - val_loss: 0.1484 - val_accuracy: 0.9628\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1219 - accuracy: 0.9764 - val_loss: 0.1472 - val_accuracy: 0.9628\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1209 - accuracy: 0.9764 - val_loss: 0.1461 - val_accuracy: 0.9681\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1198 - accuracy: 0.9764 - val_loss: 0.1449 - val_accuracy: 0.9681\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1188 - accuracy: 0.9764 - val_loss: 0.1439 - val_accuracy: 0.9681\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.1178 - accuracy: 0.9764 - val_loss: 0.1428 - val_accuracy: 0.9681\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1169 - accuracy: 0.9764 - val_loss: 0.1418 - val_accuracy: 0.9681\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1159 - accuracy: 0.9764 - val_loss: 0.1408 - val_accuracy: 0.9734\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1150 - accuracy: 0.9764 - val_loss: 0.1398 - val_accuracy: 0.9734\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1141 - accuracy: 0.9764 - val_loss: 0.1388 - val_accuracy: 0.9734\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1133 - accuracy: 0.9764 - val_loss: 0.1378 - val_accuracy: 0.9734\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1124 - accuracy: 0.9764 - val_loss: 0.1369 - val_accuracy: 0.9734\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1116 - accuracy: 0.9764 - val_loss: 0.1359 - val_accuracy: 0.9734\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1108 - accuracy: 0.9764 - val_loss: 0.1350 - val_accuracy: 0.9734\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1100 - accuracy: 0.9790 - val_loss: 0.1342 - val_accuracy: 0.9734\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1092 - accuracy: 0.9790 - val_loss: 0.1333 - val_accuracy: 0.9734\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1085 - accuracy: 0.9790 - val_loss: 0.1325 - val_accuracy: 0.9734\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1078 - accuracy: 0.9790 - val_loss: 0.1317 - val_accuracy: 0.9734\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1070 - accuracy: 0.9790 - val_loss: 0.1308 - val_accuracy: 0.9734\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.1063 - accuracy: 0.9790 - val_loss: 0.1300 - val_accuracy: 0.9734\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1057 - accuracy: 0.9790 - val_loss: 0.1292 - val_accuracy: 0.9734\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1050 - accuracy: 0.9790 - val_loss: 0.1284 - val_accuracy: 0.9734\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1043 - accuracy: 0.9790 - val_loss: 0.1277 - val_accuracy: 0.9734\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1037 - accuracy: 0.9790 - val_loss: 0.1269 - val_accuracy: 0.9734\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1031 - accuracy: 0.9790 - val_loss: 0.1263 - val_accuracy: 0.9734\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1025 - accuracy: 0.9790 - val_loss: 0.1255 - val_accuracy: 0.9734\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1019 - accuracy: 0.9790 - val_loss: 0.1248 - val_accuracy: 0.9734\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1013 - accuracy: 0.9790 - val_loss: 0.1240 - val_accuracy: 0.9734\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1007 - accuracy: 0.9790 - val_loss: 0.1234 - val_accuracy: 0.9734\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1001 - accuracy: 0.9790 - val_loss: 0.1227 - val_accuracy: 0.9734\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0996 - accuracy: 0.9790 - val_loss: 0.1220 - val_accuracy: 0.9734\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.0991 - accuracy: 0.9790 - val_loss: 0.1214 - val_accuracy: 0.9734\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0985 - accuracy: 0.9790 - val_loss: 0.1207 - val_accuracy: 0.9734\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1201 - val_accuracy: 0.9734\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0975 - accuracy: 0.9790 - val_loss: 0.1193 - val_accuracy: 0.9734\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.0970 - accuracy: 0.9790 - val_loss: 0.1187 - val_accuracy: 0.9734\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0965 - accuracy: 0.9790 - val_loss: 0.1181 - val_accuracy: 0.9734\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"train scores:\",model.evaluate(X_train,y_train))\n",
        "print(\"test score:\",model.evaluate(X_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anEwDQIX27nM",
        "outputId": "8cdd5b40-1cf7-4c56-ae8d-eb57e4b30493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 0s 6ms/step - loss: 0.0962 - accuracy: 0.9790\n",
            "train scores: [0.09617641568183899, 0.9790025949478149]\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1181 - accuracy: 0.9734\n",
            "test score: [0.11814715713262558, 0.9734042286872864]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PLOT LOSS\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(r.history[\"loss\"],label=\"loss\")\n",
        "plt.plot(r.history[\"val_loss\"],label=\"val loss\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8rwICaV13Wfj",
        "outputId": "f094b6f6-1562-4886-a47a-1ca218ee1f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb64c711850>"
            ]
          },
          "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": "code",
      "source": [
        "#PLOT ACCURACY\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(r.history[\"accuracy\"],label=\"accuracy\")\n",
        "plt.plot(r.history[\"val_accuracy\"],label=\"val accuracy\")\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "RSO8C0-_3wt6",
        "outputId": "5d5a83cd-08d3-4f3c-be3d-5c2fbf58da54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb64dee0a10>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": [
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9q_JTRlX4H01",
        "outputId": "5e7379ea-086a-4339-a8a7-3e0a23beaa89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.data_module"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "0QL9wJvV4Qu0",
        "outputId": "6d81eb49-d793-40af-bccf-2ee5f9bc5c9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sklearn.datasets.data'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "qG5JnNwb5NzL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_EIZvoTrAFm",
        "outputId": "5e39fe62-c800-461a-bf5d-908c4ba2fa68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrpI3yu4rIr1",
        "outputId": "0d6734af-5b32-46da-c3f0-43622660a6be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Wv_V2qMVrlZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fRN5fyjKNl8W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MAKING PREDICTIONS FOR LINEAR CLASSIFICATION**"
      ],
      "metadata": {
        "id": "biHmbiIIN4is"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#MAKING PREDICTIONS\n",
        "P=model.predict(X_test)\n",
        "print(P) #THIS IS OUTPUT OF SIGMOID, INTERPRETED AS PROBABILITIES\n",
        "#P(Y=1|X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnOBVwmFN_nt",
        "outputId": "b598f983-7703-4db3-ddb9-14e09daa0d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[9.89788890e-01]\n",
            " [7.88065791e-03]\n",
            " [4.97961998e-01]\n",
            " [9.46620405e-02]\n",
            " [8.21492553e-01]\n",
            " [7.37269998e-01]\n",
            " [4.18543816e-04]\n",
            " [9.98957396e-01]\n",
            " [9.99972880e-01]\n",
            " [2.44148672e-02]\n",
            " [9.99661624e-01]\n",
            " [9.82191861e-01]\n",
            " [6.97374344e-04]\n",
            " [7.50212789e-01]\n",
            " [2.23869085e-03]\n",
            " [9.83611703e-01]\n",
            " [4.17312980e-03]\n",
            " [9.97479200e-01]\n",
            " [8.61262679e-01]\n",
            " [1.71207488e-02]\n",
            " [4.01020050e-04]\n",
            " [9.84486699e-01]\n",
            " [9.91891623e-01]\n",
            " [8.81091654e-01]\n",
            " [5.05030155e-04]\n",
            " [2.93578323e-05]\n",
            " [1.15021481e-04]\n",
            " [2.56396830e-02]\n",
            " [9.97287273e-01]\n",
            " [9.85631943e-01]\n",
            " [4.30116415e-01]\n",
            " [9.76921558e-01]\n",
            " [9.39430475e-01]\n",
            " [9.61580157e-01]\n",
            " [8.46344113e-01]\n",
            " [9.96129274e-01]\n",
            " [5.70744276e-04]\n",
            " [2.35210537e-05]\n",
            " [9.21340704e-01]\n",
            " [7.86467314e-01]\n",
            " [9.74135637e-01]\n",
            " [9.94800091e-01]\n",
            " [3.14304233e-03]\n",
            " [1.87622905e-01]\n",
            " [9.67002153e-01]\n",
            " [2.67216146e-01]\n",
            " [9.84718740e-01]\n",
            " [8.39973450e-01]\n",
            " [9.92139578e-01]\n",
            " [3.70717943e-02]\n",
            " [9.89986658e-01]\n",
            " [6.58543289e-01]\n",
            " [4.28923309e-01]\n",
            " [2.63325274e-02]\n",
            " [4.75794077e-04]\n",
            " [9.62932944e-01]\n",
            " [6.16821647e-03]\n",
            " [7.40885735e-04]\n",
            " [9.89761114e-01]\n",
            " [9.97985840e-01]\n",
            " [9.97279882e-01]\n",
            " [6.53094649e-01]\n",
            " [7.88250268e-02]\n",
            " [9.85785723e-01]\n",
            " [9.87235129e-01]\n",
            " [1.04120672e-02]\n",
            " [1.65174633e-01]\n",
            " [2.46346414e-01]\n",
            " [9.89196241e-01]\n",
            " [9.16822374e-01]\n",
            " [9.12485719e-01]\n",
            " [2.89727449e-02]\n",
            " [9.98743832e-01]\n",
            " [5.30079782e-01]\n",
            " [6.08208776e-01]\n",
            " [9.83775973e-01]\n",
            " [1.30936205e-02]\n",
            " [9.97679472e-01]\n",
            " [8.60012770e-01]\n",
            " [9.77055192e-01]\n",
            " [9.95602190e-01]\n",
            " [9.69353557e-01]\n",
            " [2.03254819e-03]\n",
            " [9.92307425e-01]\n",
            " [9.43232735e-05]\n",
            " [8.57323527e-01]\n",
            " [1.04165077e-03]\n",
            " [9.23621356e-01]\n",
            " [1.57976151e-03]\n",
            " [9.71461892e-01]\n",
            " [1.40604109e-01]\n",
            " [4.24104959e-01]\n",
            " [9.82465625e-01]\n",
            " [9.28260684e-01]\n",
            " [1.06975436e-03]\n",
            " [8.45416307e-01]\n",
            " [9.84599948e-01]\n",
            " [9.92125511e-01]\n",
            " [7.44419694e-02]\n",
            " [9.89170015e-01]\n",
            " [9.52371895e-01]\n",
            " [9.92486060e-01]\n",
            " [2.34172434e-01]\n",
            " [6.49050951e-01]\n",
            " [9.97259736e-01]\n",
            " [3.06486189e-02]\n",
            " [9.56559181e-01]\n",
            " [9.99785542e-01]\n",
            " [7.26100564e-01]\n",
            " [3.47090960e-02]\n",
            " [9.99004722e-01]\n",
            " [8.80667567e-03]\n",
            " [9.97349858e-01]\n",
            " [3.43747359e-07]\n",
            " [5.48621058e-01]\n",
            " [7.01201439e-01]\n",
            " [2.04369426e-03]\n",
            " [8.79431013e-07]\n",
            " [9.99817610e-01]\n",
            " [9.98644352e-01]\n",
            " [7.47829080e-01]\n",
            " [3.56087685e-01]\n",
            " [3.59178901e-01]\n",
            " [9.35309827e-01]\n",
            " [9.60504651e-01]\n",
            " [9.46859419e-01]\n",
            " [9.84414458e-01]\n",
            " [9.92631316e-01]\n",
            " [5.93125820e-04]\n",
            " [9.32358265e-01]\n",
            " [9.69766617e-01]\n",
            " [9.86660600e-01]\n",
            " [9.94301796e-01]\n",
            " [3.82162631e-02]\n",
            " [5.49942255e-04]\n",
            " [8.58330727e-01]\n",
            " [5.58464626e-06]\n",
            " [3.80500555e-02]\n",
            " [9.46453452e-01]\n",
            " [6.87658787e-03]\n",
            " [1.48198009e-03]\n",
            " [3.34945619e-02]\n",
            " [9.76559043e-01]\n",
            " [5.39231718e-01]\n",
            " [9.78931725e-01]\n",
            " [4.53685641e-01]\n",
            " [9.99104738e-01]\n",
            " [2.42732465e-02]\n",
            " [9.87422407e-01]\n",
            " [4.46638465e-03]\n",
            " [1.39361024e-02]\n",
            " [5.49896002e-01]\n",
            " [9.70008373e-02]\n",
            " [1.27017498e-04]\n",
            " [9.53133821e-01]\n",
            " [2.86456347e-01]\n",
            " [9.22372937e-03]\n",
            " [9.93865609e-01]\n",
            " [5.39210141e-02]\n",
            " [9.56018448e-01]\n",
            " [1.38815343e-02]\n",
            " [7.91950047e-01]\n",
            " [2.75850296e-03]\n",
            " [9.98322487e-01]\n",
            " [9.95004833e-01]\n",
            " [2.08801627e-01]\n",
            " [5.76114655e-02]\n",
            " [3.56847912e-01]\n",
            " [1.67429447e-04]\n",
            " [9.73424911e-01]\n",
            " [9.67658877e-01]\n",
            " [7.94850826e-01]\n",
            " [9.78576541e-01]\n",
            " [3.31223369e-01]\n",
            " [9.70098615e-01]\n",
            " [9.82400298e-01]\n",
            " [9.97627616e-01]\n",
            " [9.84580040e-01]\n",
            " [6.61724584e-07]\n",
            " [9.43715990e-01]\n",
            " [2.85208225e-04]\n",
            " [9.34471607e-01]\n",
            " [1.09042216e-04]\n",
            " [6.78753793e-01]\n",
            " [6.36904716e-01]\n",
            " [1.82300806e-04]\n",
            " [7.19713509e-01]\n",
            " [8.63358498e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ROUND TO GET THE ACTUAL PREDICTIONS\n",
        "#NOTE:FLATTEDN SINCE THE TARGETS ARE SIZE (N,) WHILE THE PREDUCTIONS\n",
        "#ARE SIZE (N,1)\n",
        "import numpy as np\n",
        "P=np.round(P).flatten()\n",
        "print(P)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wnk2ej9mOGhN",
        "outputId": "557e76d9-4c71-49b3-e765-b4972c665bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1.\n",
            " 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1.\n",
            " 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 1. 0.\n",
            " 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1.\n",
            " 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1.\n",
            " 1. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1.\n",
            " 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0.\n",
            " 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#CALCULATE THE ACCURACY, COMPARE IT TO EVALUATE() OUTPUT\n",
        "print(\"manually calculated accuracy:\",np.mean(P==y_test))\n",
        "print(\"Evaluate output:\", model.evaluate(X_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9wxcDHhOGoF",
        "outputId": "fbced1ed-4511-4c6d-fad6-a217694bf3c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "manually calculated accuracy: 0.973404255319149\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1181 - accuracy: 0.9734\n",
            "Evaluate output: [0.11814715713262558, 0.9734042286872864]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "P.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oq6EfTu4OGtO",
        "outputId": "01c518ed-8ebe-4d1c-f867-83d6b68f8de1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188,)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R14YXG8fSyzn",
        "outputId": "4b17d43a-7f3f-428e-ea81-c10b96feacd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188,)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SAVING AND LOADING A MODEL"
      ],
      "metadata": {
        "id": "SywCyMxCzJC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#LETS NOW SAVE OUR MODEL TO A FILE\n",
        "model.save(\"linearclassifier.h5\")"
      ],
      "metadata": {
        "id": "zDHxky9VzTcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#CHECK THAT THE MODEL FILE EXISTS\n",
        "!ls -lh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrauvMcWzcrm",
        "outputId": "66e662af-177a-4728-fd8f-d95d04b35bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 24K\n",
            "-rw-r--r-- 1 root root  19K Apr  6 16:55 linearclassifier.h5\n",
            "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#LETS LOAD THE MODEL AND CONFIRM THAT IT STILL WORKDS\n",
        "#NOTE: THERE IS A BUG IN KERAS WHERE LOAD/SAVE ONLY WORKS IF YOU DONT\n",
        "#USE THE INPUT() LAYER EXPLICITLY\n",
        "#AT LEAST UNTIL THE BUG IS FIXED\n",
        "model=tf.keras.models.load_model(\"linearclassifier.h5\")\n",
        "print(model.layers)\n",
        "model.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mf9leJvzkBk",
        "outputId": "c5b6f333-d528-426e-eeea-7df736fb584a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<keras.layers.core.dense.Dense object at 0x7fb64a330910>]\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.1181 - accuracy: 0.9734\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11814715713262558, 0.9734042286872864]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It looks like the saved model is working as expected"
      ],
      "metadata": {
        "id": "mLS3cv6K0Qjk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PYTHON CODE TO DOWNLOAD FILE"
      ],
      "metadata": {
        "id": "59TOeQdD0qLt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##DOWNLOAD THE FILE- REQUIRES CHOROME\n",
        "#from google.colab import files\n",
        "#files.download(\"linearclassifier.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "AAUAzqm_0v8D",
        "outputId": "c1f5a237-7162-4e3c-9f8e-a9473c313bce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_08460fd5-cf1f-473a-9c2b-2c6131228010\", \"linearclassifier.h5\", 19312)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LINEAR REGRESSION"
      ],
      "metadata": {
        "id": "ie6x1Tn4NnZ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv"
      ],
      "metadata": {
        "id": "jCRT99HnsHEg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2e551c3-e635-4a92-ab55-ccdd1f8f3954"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-04-06 14:34:25--  https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2302 (2.2K) [text/plain]\n",
            "Saving to: ‘moore.csv.2’\n",
            "\n",
            "moore.csv.2         100%[===================>]   2.25K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-04-06 14:34:25 (58.4 MB/s) - ‘moore.csv.2’ saved [2302/2302]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"moore.csv\",header=None).values"
      ],
      "metadata": {
        "id": "PV7t_FN5sMQH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTX3GSJWbeB2",
        "outputId": "f4d63bc0-771d-4337-b577-d103f1f9eddd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=data[:,0].reshape(-1,1)"
      ],
      "metadata": {
        "id": "Mt6pqQKabwnY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPSZ_Ldfb8V-",
        "outputId": "8ca8fdb7-69c3-463e-d525-eb62e3da5662"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y=data[:,1]"
      ],
      "metadata": {
        "id": "Mh8aM8MYcKJO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k82_5KWncN0I",
        "outputId": "877a2b57-1b06-4f48-a6ff-cffacf9789ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162,)"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "CCuSqa38cWa-",
        "outputId": "f4a2ecb9-5911-4451-ba9e-02e175f15195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8cd82acb90>"
            ]
          },
          "metadata": {},
          "execution_count": 175
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEDCAYAAAAyZm/jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcMklEQVR4nO3df3Bd5X3n8ffXQgbZQISDTbCwMGFcZ8IaMFaxqXeylJkgQlLQQlhwcEN+bJhu09kk22rW3s2EkNLBu+7QbTdtUzalCVtKGH6M1kxoPOxChpYAtYwxxoDBJGAsk9iJsU2CCLL83T/uuebq+jxH9xyd+0vn85rR6N7n/Hruo6Pvee5znvM85u6IiEhxzGh2BkREpLEU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRAqmqYHfzO4ws71m9lwN637EzJ42s8Nm9smqZTeY2cvRzw31y7GISPtrdo3/O8BlNa67C/gM8A+ViWY2B7gJWA5cCNxkZqfkl0URkemlqYHf3R8D9lemmdnZZvYDM9tsZv9kZh+K1n3V3Z8FjlTtph942N33u/ubwMPUfjERESmc45qdgRi3A7/n7i+b2XLgr4BLEtbvAV6veL87ShMRkRgtFfjN7ETgt4B7zaycfHzzciQiMv20VOCn1PR0wN3PT7HNCHBxxfszgB/mmCcRkWml2Td3J3D3Q8BPzOwaACs5b5LNNgKXmtkp0U3dS6M0ERGJ0ezunHcDTwCLzWy3mX0euB74vJltBbYDV0br/qaZ7QauAf7GzLYDuPt+4I+BTdHPN6I0ERGJYRqWWUSkWFqqqUdEROqvaTd3Tz31VF+4cGGzDi8i0pY2b978c3efO5V9NC3wL1y4kOHh4WYdXkSkLZnZa1Pdh5p6REQKRoFfRKRgFPhFRApGgV9EpGAU+EVECqbVxuoREZm2hraMsH7jDvYcGGV+dxeD/YsZWNr4wYQV+EVEGmBoywhrH9jG6Ng4ACMHRln7wDaAhgd/NfWIiDTA+o07jgb9stGxcdZv3NHwvCjwi4g0wJ4Do6nS60mBX0SkAeZ3d6VKrycFfhGRBhjsX0xXZ8eEtK7ODgb7Fzc8L7q5KyLSAOUbuOrVIyJSIANLe5oS6KupqUdEpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKZtLAb2YLzOxRM3vezLab2Zdi1jEz+wsz22lmz5rZBfXJroiITNVxNaxzGPhDd3/azE4CNpvZw+7+fMU6HwMWRT/Lgb+OfouISIuZtMbv7m+4+9PR67eAF4CeqtWuBO70kieBbjM7PffciojIlKVq4zezhcBS4KmqRT3A6xXvd3PsxQEzu9HMhs1seN++felyKiIiuag58JvZicD9wJfd/VCWg7n77e7e5+59c+fOzbILERGZopoCv5l1Ugr6d7n7AzGrjAALKt6fEaWJiEiLqaVXjwF/C7zg7rcFVtsAfDrq3bMCOOjub+SYTxERyUktvXpWAr8LbDOzZ6K0/wL0Arj7t4CHgMuBncDbwGfzz6qISOsb2jLC+o072HNglPndXQz2L2Zg6TG3PJtq0sDv7v8M2CTrOPDFvDIlItKOhraMsPaBbYyOjQMwcmCUtQ9sA2ip4K8nd0VEcrJ+446jQb9sdGyc9Rt3NClH8RT4RURysufAaKr0ZlHgFxHJyfzurlTpzaLALyKSk8H+xXR1dkxI6+rsYLB/cZNyFK+WXj0iIlKD8g3ctu/VIyIitRtY2tNygb6amnpERApGgV9EpGDU1CMikqNp8eSuiIjURk/uiogUjJ7cFREpGD25KyJSMHpyV0SkYPTkrohIwejJXRGRAtKTuyIi0nIU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgJg38ZnaHme01s+cCyy82s4Nm9kz087X8sykiInmpZerF7wDfBO5MWOef3P0TueRIRETqatIav7s/BuxvQF5ERKQB8mrjv8jMtprZP5rZOaGVzOxGMxs2s+F9+/bldGgREUkjj8D/NHCmu58H/E9gKLSiu9/u7n3u3jd37twcDi0iImlNOfC7+yF3/2X0+iGg08xOnXLORESkLqYc+M3sA2Zm0esLo33+Yqr7FRGR+pi0V4+Z3Q1cDJxqZruBm4BOAHf/FvBJ4D+Y2WFgFLjO3b1uORYRkSmZNPC7+6pJln+TUndPERFpA3pyV0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgFPhFRApGgV9EpGAU+EVECkaBX0SkYBT4RUQKRoFfRKRgjptsBTO7A/gEsNfd/1XMcgP+HLgceBv4jLs/nXdGRUQabWjLCOs37mDPgVHmd3cx2L+YgaU9LbO/rGqp8X8HuCxh+ceARdHPjcBfTz1bIiLNNbRlhLUPbGPkwCgOjBwYZe0D2xjaMtIS+5uKSQO/uz8G7E9Y5UrgTi95Eug2s9PzyqCISDOs37iD0bHxCWmjY+Os37ijJfY3FZM29dSgB3i94v3uKO2N6hXN7EZK3wro7e3N4dAiIvWx58BoqvSyUHNO1v3VQx6Bv2bufjtwO0BfX5838tgiImnM7+5iJCYoz+/uCm5Tbs4p1+zLzTlZ91cvefTqGQEWVLw/I0oTEWlbg/2L6ersmJDW1dnBYP/i4DZJzTlZ9lcveQT+DcCnrWQFcNDdj2nmERFpJwNLe7j1qiX0dHdhQE93F7detSSxF05Sc06W/dVLLd057wYuBk41s93ATUAngLt/C3iIUlfOnZS6c362XpkVEWmkgaU9qQLzZM05afdXL5MGfndfNclyB76YW45ERNrUYP/iCW380LzmnCQNvbkrIjKdlWvzoYe0WuUBLgV+EZEchZpzhraMMHjfVsbGSx0aRw6MMnjf1qPbNJLG6hERaYCbH9x+NOiXjY07Nz+4veF5UeAXEWmAN98eS5VeTwr8IiIFo8AvItIAszrjw20ovZ4U+EVEGmDmcR2p0utJgV9EpAEOjsa35YfS60ndOUVEMgj1yf/q0Dbufup1xt3pMGPV8gXcMrCkpQZpU+AXEUkpNArnvcO7ePyV96YvGXfn75/cBbTWU71q6hERSSk0Cmdl0K9091Ovt9cgbSIiMlHayVPGvfTgVqsM0qYav4hISmnb5TvM6pSTbBT4RURSCk2qsvLsObHrr1q+IDa9WRT4RURSCrXX3/WFi1i9ovdoDb/DjNUrerllYElzM1xFbfwiIgFZhlG+ZWBJywX6agr8IiIxkiZOBxKXtcKY+0kU+EVEYiRNnF5+Xb3s5ge3887YkdgLQtLDXY2mwC8iEiNp4vSQuCGWyxeL4df2H32YCyY+3NXo4K+buyIiMUJdNud3d6XuzrnnwCh3P/V67LJQej0p8IuIxAh12RzsXxxc1t3VGbuv+d1dRx/iqhZKrycFfhGRGElDLISWff2Kc4IXi9BDXM14uEtt/CIiAUlDLCQti+vVU93GX9aMh7sU+EVEchS6IJRv4LZCrx7zJrQvAfT19fnw8HBTji0iUi9ZHvpKw8w2u3vfVPahGr+ISE6SHvpqpYe4dHNXRCQnkz301SoU+EVEcpLloa9mUFOPiBReXu3yrTSvbhLV+EWk0Mrt8iMHRnHea5cf2jKSel+D/YuPCaozovTysVaue4Sz1nyfleseyXSMPKjGLyKFltQun7bWP/zafo5UpR2J0iE8omejb/yqxi8ihZZnu3zSeDytdONXgV9ECi1pMLa0ksbjaaUbvzUFfjO7zMx2mNlOM1sTs/wzZrbPzJ6Jfv59/lkVEZlcqB09lJ40GFue8rzATNWkbfxm1gH8JfBRYDewycw2uPvzVave4+5/UIc8iojUJPQA1fBr+7l/80hi+3q9Z80a7F88IW9QnwtMLWq5uXshsNPdfwxgZt8DrgSqA7+ISFOF2tHL4+NUp5dv4CYNuJZGh1lsc0+HWcMuMLWoJfD3AJV3LHYDy2PWu9rMPgK8BHzF3Y+5y2FmNwI3AvT29qbPrYhIglB7eajtPe/29VNP7ORnb70bmw7JI3o2Ul43dx8EFrr7ucDDwHfjVnL32929z9375s6dm9OhRURKQu3loTHv825fjwv6SenNUkvgHwEqB4w+I0o7yt1/4e6/jt5+G1iWT/ZERGoXulG7avmChtzAbRe1NPVsAhaZ2VmUAv51wKcqVzCz0939jejtFcALueZSRKQGSe3ofWfOCbav13so5VYzaeB398Nm9gfARqADuMPdt5vZN4Bhd98A/EczuwI4DOwHPlPHPIuIBKVtR89zKOXTTpoZ26xz2kkzU+2n3moassHdHwIeqkr7WsXrtcDafLMmIpKPpOA+2RO1ab4l/PyXY7HHD6U3i2bgEpFpb+W6R2JHzezp7mJPNDhbnK7OjmP63d96VWmqxLg++dUXkEqvrvt4tsxX0QxcIiI1SBouITSUcodZ4jeBuGUhoV5FzaKxekRk2ksaLiHUEyip73/a/v+rli+YfKUGUuAXkWkvaTyegaU93HrVEnq6uzBKzT/l93Hmd3cFLyQ93V2sXtF7tIbfYcbqFb3cMrAk188zVWrqEZFpb7LhEkI9gZLG1gktG1ja03KBvpoCv4gUQtpunpNdLIZf2390DKAOM65e1hrDMdRCgV9EJCB0sRjaMsI9m94b+G3cnXs2vU7fmXPaIvgr8IvItNKIp3BvfnA7Y+MTb/6OjTs3P7hdgV9EpJGGtowweN/Wo0F55MAog/dtBZKfwg1dLELpb74d/0BWKL3VKPCLyLSRpSaeZfKWdqfALyLTRpaaeJbJWwxin/Ztrce0whT4RaTt5NmOn2XyltAQD80ZACc9BX4RaStJA65lqYl3z+qM/UZgBnGxv/zwVmjsn3agJ3dFpK0kjaaZpSYeGqey67gZwad9k54Ebgeq8YtIy4pr0kkacK0nMOBaUk384Gh8+//o2BH+7NrzE5uU2nXyFgV+EWlJoSadWTM7+NW7x46E2T2rs1Qbr+jOCdDZYYk18dDonPO7uxKf9m2VidOzUFOPiLSkUJPO2zFBHyqabKqbbia54zrYv5jOjol3AcoXi6EtI6xc9whnrfk+K9c9wtCWkcBe2otq/CLSEGl74sTVwiEcxw+OjrF+4w7GjlT14z/ik86mFXexGH5tP/dsej31w2DtQIFfROouy7y2HWbBLpVxumd1Btv/y8cLTb0Yd7H4h6d2UZXcVsMyJFFTj4iklrYJZLJ5beOkCfpQauoJjZOfNJtW6GJRHfTL2mVYhiQK/CKSSrn2PhI9yFSuPScF/6SeOCFp+8QfHB3LNJtW6GIxnSnwi0gqWWrvSVMfhoRuunZ3dQb3NbC0h6uX9UyYAevqZT2Js2mFjjOdKfCLSCpZau+ZH3iKuen6ifNOD+4rNE7+b39obvLxY44zqzM+PIYuPO1EgV9EUslSew/Na5t0kzR00/XRF/fF1uoHlvYER+f8/rNvBI8fOs7M4zronFH1TWCG8fUrzgnmuV2oV4+IpDLYvzhxLtqQtA88hbpzjhwY5f7NIxNq9fdvHqHvzDmJo3OGjh/6pnJwdGzSJ3fblQK/iKQy2Vy0aWUZaTPtPYak42R9credmafsMpWXvr4+Hx4ebsqxRaR+0gTy6v79UPr2cOtVS/jyPc+kOm5oZM7K/cYdBwjmoRWDvpltdve+qexDNX4RyU3Sg1rDr+0/OrlJhxmrli/g0Rf3Zaq9x5nf3cWvfn2YAzGDrhnhbwmPr7kEaN8B17JQjV+k4PKc1GTlukdim01mBwZWyyqp9j5479YJN2s7Z9gxN2/LDPjJuo/nlq9GUI1fRGoWF+CB1EMpJAndkE0b9DvM+MD7TggOsTzYvzh4sar+ZnHthaVvFqF2/CJS4BcpgFATzAmdM4JNIEmB/6tD245ptrllYMmk7ey1GndP7D0Uuuk6tGUktsfP1ct6JkycXrmvIlLgF2lDoeaZUHroadvqtLJy7TguwAP8/ZO7jq477n70fV4Nxz1RjxpI1/Ye+pyPvriPW69a0pB2/DybzupFbfwibSbUE+bqZT0ThhGG0tAD6z95Hl+555lUQbkc5CsD/GRmWHhgs+A2wPGB9voswfKsNd8PzrnbiLb8pF5KeQX/hrXxm9llwJ8DHcC33X1d1fLjgTuBZcAvgGvd/dWpZCxO0pU0tCz0lRTg+v/1BI+/sv/o/leePYe7vnBR4nFC2yQJ5SHLvpLyFjpOlm2S8hZa9tHbfsjLe391NH3RvNk8/J8uTjxOaJsseU76W+e5TZbzI2mbc2/6AYd+/V6gOPn4Dp69+bLgvkK12rue2nXM/LHlYYRDs1aFjLtzV4qgD6Wgn7ap532zOrnpd87JrYac1Ce/EZLGMWqlWv+kNX4z6wBeAj4K7AY2Aavc/fmKdX4fONfdf8/MrgP+rbtfm7TftDX+pCspxPfDvaD3fRP+ccpWr+jlJ/t+Gbts0bzZ7H7zndjj3Du8K3abpID91aFtsbWm006ayc/eejfVvpLKYPi1/bHHWXn2HJ7edTDVNkl5A2LL4IQO453xY8+lRfNms/yD7489zsnHd0wIeJXHP/TOeKo8L5o3e8IFpGz1il6A3LZJKs/Q+ZF0Tn1taFtsGYQC6Mqz58QeYzJpA3LasfDLOmfA2JF027yaY028ETXuJI34xpFHjb+WwH8R8HV374/erwVw91sr1tkYrfOEmR0H/BSY6wk7Txv4Q93EyqPuhXoTxMlyUocmcS4Lnbxnr30o9bFC+0oqg58efCfVcbJsk1XWIFItS57L47nUe5vJzo+8toH8yjNvXZ0zGE0Z9TvMeOXWy3PNRzPb2JP+R8vPC0xVo5p6eoDXK97vBpaH1nH3w2Z2EHg/8PPKlczsRuBGgN7e3lQZzTIiYEiWf5osx8l6rLR52BONi552X40KHXmVQZY8Zzl2o86PRp1T3V2dvPXO4VwvZDOAI1Xvb73q3NRP29bjAtbMYRayjmPUaA0dndPdb3f3Pnfvmzt3bqptk0YETNt+V67R5XH8ehwrbR7md3elPk6WbbLK6zhZ8txh1pBtspwfWc+p0Njy3V2dwdEky71xqq08e05wuOJyk1e11St6ue3a8yeMdHnbteczsLSHU2bFD1kcKs60k620uiyjkDZDLYF/BKg8a86I0mLXiZp63kfpJm9uksbzDi0rt0lXW7V8QXDZonmzg8cJbRNKLx8rzmknzUy9r6QyyPKPnSVvofydEJi4YtG82cHjnHx8R2z6aSfNTJ3nRfNmx6avWr4g122SyjPLORUqg9BlZ+XZc4LnwdevOIf115w3Ieisv+Y8Bpb2cMvAElav6J0wlPHqFb3c9YWLgoEqtM0tA6Xlj6+5hJ+s+ziPr7nkaGC76XfOiZ3U5PrlvdnG429DobJpJbU09WwCFpnZWZQC/HXAp6rW2QDcADwBfBJ4JKl9P4ta+vTWu1fPwNKe1D1xysfKo1dPUhmUl6XphZK0Td69ekLHSdurJynPSX/rPLfJcn4kbZO2V09Z6H8hFGhuGVgy4bOVJTWNhLYJSTpH+86c0/L924uipn78ZnY58D8odee8w93/xMy+AQy7+wYzOwH438BSYD9wnbv/OGmf6scvIpJew/rxu/tDwENVaV+reP0OcM1UMiIiIo2hqRdFRApGgV9EpGAU+EVECkaBX0SkYJo2OqeZ7QNem2S1U6l6+reAil4GRf/8oDIAlQG8VwZnunu6J2CrNC3w18LMhqfabandFb0Miv75QWUAKgPItwzU1CMiUjAK/CIiBdPqgf/2ZmegBRS9DIr++UFlACoDyLEMWrqNX0RE8tfqNX4REcmZAr+ISME0NPCb2R1mttfMnqtIO8/MnjCzbWb2oJmdHKVfb2bPVPwcMbPzo2XLovV3mtlfmDVoRpEcpCyDTjP7bpT+Qnnay2jZZWa2IyqDNc34LFmlLIOZZvZ3UfpWM7u4Ypu2PA/MbIGZPWpmz5vZdjP7UpQ+x8weNrOXo9+nROkWfb6dZvasmV1Qsa8bovVfNrMbmvWZ0spQBh+Kzo9fm9kfVe2rLf8XMpTB9dHff5uZ/cjMzqvYV7oycPeG/QAfAS4AnqtI2wT8m+j154A/jtluCfBKxft/AVZQmq/iH4GPNfJzNKoMKM178L3o9SzgVWAhpeGxXwE+CMwEtgIfbvZnq1MZfBH4u+j1PGAzMKOdzwPgdOCC6PVJwEvAh4H/DqyJ0tcA/y16fXn0+Sz6vE9F6XOAH0e/T4len9Lsz1enMpgH/CbwJ8AfVeynbf8XMpTBb5X/vsDHKs6D1GXQ6KkXH6M0Xn+l3wAei14/DFwds+kq4HsAZnY6cLK7P+mlT30nMFCfHOcvZRk4MNtKs5p1Ae8Ch4ALgZ3u/mN3f5dS2VxZ77znJWUZfBh4JNpuL3AA6Gvn88Dd33D3p6PXbwEvUJq3+krgu9Fq3+W9z3MlcKeXPAl0R5+/H3jY3fe7+5uUyu2yBn6UzNKWgbvvdfdNwFjVrtr2fyFDGfwo+jsDPElpNkTIUAat0Ma/nfcyeQ0Tp3ksuxa4O3rdQ2nC97LdUVo7C5XBfcCvgDeAXcCfuvt+Kia3j0znMtgKXGFmx1lpFrhl0bJpcR6Y2UJKExg9BZzm7m9Ei34KnBa9Dv29p8V5UGMZhBS1DD5P6VsgZCiDVgj8nwN+38w2U/q6827lQjNbDrzt7s/FbTxNhMrgQmAcmA+cBfyhmX2wOVmsu1AZ3EHpRB6mNAvcjyiVSdszsxOB+4Evu/uhymXRt5hp39daZZC+DMzstykF/v+c9Zg1zcBVT+7+InApgJn9BvDxqlWu473aPpTm/T2j4n3c5O9tJaEMPgX8wN3HgL1m9jjQR+nqXvnNaNqWgbsfBr5SXs/MfkSpLfRN2vg8MLNOSv/sd7n7A1Hyz8zsdHd/I2rK2RuljxD/9x4BLq5K/2E9852nlGUQEiqbtpC2DMzsXODblO5n/SJKTl0GTa/xm9m86PcM4KvAtyqWzQD+HVH7PpTaxYBDZrYi6sXxaeD/NDTTOUsog13AJdGy2ZRu7L1I6UboIjM7y8xmUro4bmh0vvMUKgMzmxV9dszso8Bhd3++nc+DKL9/C7zg7rdVLNoAlHvm3MB7n2cD8Omod88K4GD0+TcCl5rZKVHPj0ujtJaXoQxC2vZ/IW0ZmFkv8ADwu+7+UsX66cugwXex76bUXj1G6ev754EvUarBvQSsI3qaOFr/YuDJmP30Ac9RupP9zcptWv0nTRkAJwL3Umr/fh4YrNjP5dH6rwD/tdmfq45lsBDYQenG1/+lNCRtW58HwL+m9PX9WeCZ6Ody4P3A/wNejj7rnGh9A/4y+pzbgL6KfX0O2Bn9fLbZn62OZfCB6Fw5ROkG/25KN/fb9n8hQxl8m9I33fK6wxX7SlUGGrJBRKRgmt7UIyIijaXALyJSMAr8IiIFo8AvIlIwCvwiIgWjwC8iUjAK/CIiBfP/Ae1fVv3FPb7XAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y=np.log(Y)\n",
        "plt.scatter(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ZpE87MjDcb7P",
        "outputId": "77dcd5e0-b084-4049-9c0c-ccb0f6cc2bae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8cd82d7550>"
            ]
          },
          "metadata": {},
          "execution_count": 176
        },
        {
          "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": [
        "#Centering X\n",
        "X=X-X.mean()"
      ],
      "metadata": {
        "id": "S7t2B_NFcvGH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#tensorflow model\n",
        "model=tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Input(shape=(1,)),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])"
      ],
      "metadata": {
        "id": "dGP9d-Xpc3-Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.SGD(0.001,0.9), loss=\"mse\")\n",
        "#mae:mean absolute error\n",
        "#mse:mean squared error"
      ],
      "metadata": {
        "id": "5m4ZoQPbc8gA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#learning rate schedule\n",
        "def schedule(epoch,lr):\n",
        "  if epoch >=50:\n",
        "    return 0.0001\n",
        "  return 0.001"
      ],
      "metadata": {
        "id": "OQcj2gfNd-pB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler=tf.keras.callbacks.LearningRateScheduler(schedule)"
      ],
      "metadata": {
        "id": "67Va4cmueQ8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train the model\n",
        "r=model.fit(X,Y,epochs=300,callbacks=[scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0iXrlGcefbg",
        "outputId": "b868b636-59c4-45eb-e578-9935a5602edc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 407.8728 - lr: 0.0010\n",
            "Epoch 2/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 415.1090 - lr: 0.0010\n",
            "Epoch 3/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 259.2067 - lr: 0.0010\n",
            "Epoch 4/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 210.6771 - lr: 0.0010\n",
            "Epoch 5/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 192.3952 - lr: 0.0010\n",
            "Epoch 6/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 165.3442 - lr: 0.0010\n",
            "Epoch 7/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 112.4394 - lr: 0.0010\n",
            "Epoch 8/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 95.1799 - lr: 0.0010\n",
            "Epoch 9/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 55.4324 - lr: 0.0010\n",
            "Epoch 10/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 37.8941 - lr: 0.0010\n",
            "Epoch 11/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 27.0272 - lr: 0.0010\n",
            "Epoch 12/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 18.7417 - lr: 0.0010\n",
            "Epoch 13/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 18.2216 - lr: 0.0010\n",
            "Epoch 14/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 11.5904 - lr: 0.0010\n",
            "Epoch 15/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 11.8264 - lr: 0.0010\n",
            "Epoch 16/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 5.3449 - lr: 0.0010\n",
            "Epoch 17/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 4.0356 - lr: 0.0010\n",
            "Epoch 18/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 3.5779 - lr: 0.0010\n",
            "Epoch 19/300\n",
            "6/6 [==============================] - 0s 1ms/step - loss: 2.6339 - lr: 0.0010\n",
            "Epoch 20/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 2.2323 - lr: 0.0010\n",
            "Epoch 21/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.8299 - lr: 0.0010\n",
            "Epoch 22/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.6920 - lr: 0.0010\n",
            "Epoch 23/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.5509 - lr: 0.0010\n",
            "Epoch 24/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.2308 - lr: 0.0010\n",
            "Epoch 25/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1212 - lr: 0.0010\n",
            "Epoch 26/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.2378 - lr: 0.0010\n",
            "Epoch 27/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0132 - lr: 0.0010\n",
            "Epoch 28/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0966 - lr: 0.0010\n",
            "Epoch 29/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9735 - lr: 0.0010\n",
            "Epoch 30/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9672 - lr: 0.0010\n",
            "Epoch 31/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1176 - lr: 0.0010\n",
            "Epoch 32/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0285 - lr: 0.0010\n",
            "Epoch 33/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9700 - lr: 0.0010\n",
            "Epoch 34/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9310 - lr: 0.0010\n",
            "Epoch 35/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9202 - lr: 0.0010\n",
            "Epoch 36/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8800 - lr: 0.0010\n",
            "Epoch 37/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8877 - lr: 0.0010\n",
            "Epoch 38/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1543 - lr: 0.0010\n",
            "Epoch 39/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1244 - lr: 0.0010\n",
            "Epoch 40/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.2752 - lr: 0.0010\n",
            "Epoch 41/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.0661 - lr: 0.0010\n",
            "Epoch 42/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0182 - lr: 0.0010\n",
            "Epoch 43/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0132 - lr: 0.0010\n",
            "Epoch 44/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9527 - lr: 0.0010\n",
            "Epoch 45/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9049 - lr: 0.0010\n",
            "Epoch 46/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8850 - lr: 0.0010\n",
            "Epoch 47/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9082 - lr: 0.0010\n",
            "Epoch 48/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0642 - lr: 0.0010\n",
            "Epoch 49/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8909 - lr: 0.0010\n",
            "Epoch 50/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.2123 - lr: 0.0010\n",
            "Epoch 51/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 2.2441 - lr: 1.0000e-04\n",
            "Epoch 52/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.8362 - lr: 1.0000e-04\n",
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            "Epoch 218/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8905 - lr: 1.0000e-04\n",
            "Epoch 219/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8782 - lr: 1.0000e-04\n",
            "Epoch 220/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8706 - lr: 1.0000e-04\n",
            "Epoch 221/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8946 - lr: 1.0000e-04\n",
            "Epoch 222/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 223/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8851 - lr: 1.0000e-04\n",
            "Epoch 224/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8728 - lr: 1.0000e-04\n",
            "Epoch 225/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8760 - lr: 1.0000e-04\n",
            "Epoch 226/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8871 - lr: 1.0000e-04\n",
            "Epoch 227/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8719 - lr: 1.0000e-04\n",
            "Epoch 228/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8759 - lr: 1.0000e-04\n",
            "Epoch 229/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8724 - lr: 1.0000e-04\n",
            "Epoch 230/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8773 - lr: 1.0000e-04\n",
            "Epoch 231/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8776 - lr: 1.0000e-04\n",
            "Epoch 232/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 233/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8723 - lr: 1.0000e-04\n",
            "Epoch 234/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8713 - lr: 1.0000e-04\n",
            "Epoch 235/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8809 - lr: 1.0000e-04\n",
            "Epoch 236/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8798 - lr: 1.0000e-04\n",
            "Epoch 237/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8705 - lr: 1.0000e-04\n",
            "Epoch 238/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8705 - lr: 1.0000e-04\n",
            "Epoch 239/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8699 - lr: 1.0000e-04\n",
            "Epoch 240/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8709 - lr: 1.0000e-04\n",
            "Epoch 241/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8704 - lr: 1.0000e-04\n",
            "Epoch 242/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8709 - lr: 1.0000e-04\n",
            "Epoch 243/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8699 - lr: 1.0000e-04\n",
            "Epoch 244/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8895 - lr: 1.0000e-04\n",
            "Epoch 245/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8962 - lr: 1.0000e-04\n",
            "Epoch 246/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8702 - lr: 1.0000e-04\n",
            "Epoch 247/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8744 - lr: 1.0000e-04\n",
            "Epoch 248/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8718 - lr: 1.0000e-04\n",
            "Epoch 249/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 250/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8785 - lr: 1.0000e-04\n",
            "Epoch 251/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 252/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8720 - lr: 1.0000e-04\n",
            "Epoch 253/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8830 - lr: 1.0000e-04\n",
            "Epoch 254/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8826 - lr: 1.0000e-04\n",
            "Epoch 255/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8779 - lr: 1.0000e-04\n",
            "Epoch 256/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8754 - lr: 1.0000e-04\n",
            "Epoch 257/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8703 - lr: 1.0000e-04\n",
            "Epoch 258/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8696 - lr: 1.0000e-04\n",
            "Epoch 259/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8803 - lr: 1.0000e-04\n",
            "Epoch 260/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8835 - lr: 1.0000e-04\n",
            "Epoch 261/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8780 - lr: 1.0000e-04\n",
            "Epoch 262/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 263/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8727 - lr: 1.0000e-04\n",
            "Epoch 264/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8710 - lr: 1.0000e-04\n",
            "Epoch 265/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8970 - lr: 1.0000e-04\n",
            "Epoch 266/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8984 - lr: 1.0000e-04\n",
            "Epoch 267/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 268/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8755 - lr: 1.0000e-04\n",
            "Epoch 269/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8720 - lr: 1.0000e-04\n",
            "Epoch 270/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8718 - lr: 1.0000e-04\n",
            "Epoch 271/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8828 - lr: 1.0000e-04\n",
            "Epoch 272/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8799 - lr: 1.0000e-04\n",
            "Epoch 273/300\n",
            "6/6 [==============================] - 0s 1ms/step - loss: 0.8808 - lr: 1.0000e-04\n",
            "Epoch 274/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8809 - lr: 1.0000e-04\n",
            "Epoch 275/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8762 - lr: 1.0000e-04\n",
            "Epoch 276/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8843 - lr: 1.0000e-04\n",
            "Epoch 277/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8871 - lr: 1.0000e-04\n",
            "Epoch 278/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9157 - lr: 1.0000e-04\n",
            "Epoch 279/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8878 - lr: 1.0000e-04\n",
            "Epoch 280/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8702 - lr: 1.0000e-04\n",
            "Epoch 281/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 282/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8803 - lr: 1.0000e-04\n",
            "Epoch 283/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8921 - lr: 1.0000e-04\n",
            "Epoch 284/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8763 - lr: 1.0000e-04\n",
            "Epoch 285/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9010 - lr: 1.0000e-04\n",
            "Epoch 286/300\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9704 - lr: 1.0000e-04\n",
            "Epoch 287/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9010 - lr: 1.0000e-04\n",
            "Epoch 288/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8724 - lr: 1.0000e-04\n",
            "Epoch 289/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9037 - lr: 1.0000e-04\n",
            "Epoch 290/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8852 - lr: 1.0000e-04\n",
            "Epoch 291/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8732 - lr: 1.0000e-04\n",
            "Epoch 292/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 293/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8759 - lr: 1.0000e-04\n",
            "Epoch 294/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8838 - lr: 1.0000e-04\n",
            "Epoch 295/300\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8758 - lr: 1.0000e-04\n",
            "Epoch 296/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8710 - lr: 1.0000e-04\n",
            "Epoch 297/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8827 - lr: 1.0000e-04\n",
            "Epoch 298/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8803 - lr: 1.0000e-04\n",
            "Epoch 299/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 300/300\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8740 - lr: 1.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UW2FjHtXmHX4",
        "outputId": "764b0aa4-8990-4ebd-cce6-3e721d9bc7a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'lr'])"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the loss\n",
        "plt.plot(r.history[\"loss\"],label=\"loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WYUGXjrQl1jq",
        "outputId": "1a3ac81d-73a5-4338-dc1b-d36026e411ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8cd8114990>]"
            ]
          },
          "metadata": {},
          "execution_count": 184
        },
        {
          "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": [
        "#get slope of the line\n",
        "#slope of the line is related to the doubling rate of translator count\n",
        "print(model.layers)#1 layer input doesnt count\n",
        "print(model.layers[0].get_weights())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Nt2_NQAmEZ_",
        "outputId": "f552aeaa-fee3-4d7a-8599-0db91a63dd88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<keras.layers.core.dense.Dense object at 0x7f8cd8261290>]\n",
            "[array([[0.3398109]], dtype=float32), array([17.759003], dtype=float32)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#the slope of the line is:\n",
        "a=model.layers[0].get_weights()[0][0,0]"
      ],
      "metadata": {
        "id": "NlKcbne8nLXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Tiem to double:\",np.log(2)/a)\n",
        "#NOT WORKING AS EXPECTED"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqs5O4Vencrz",
        "outputId": "41f9c994-8cfe-455c-d53f-ccdf59dbb077"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tiem to double: 2.039802621300277\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mspquzt1S5_O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MAKING PREDICTIONS ON LINEAR REGRESSION"
      ],
      "metadata": {
        "id": "QJjgpL5XS60v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#MAKE SURE THE LINE FITS OUR DATA\n",
        "Yhat=model.predict(X).flatten()\n",
        "plt.scatter(X,Y)\n",
        "plt.plot(X,Yhat)"
      ],
      "metadata": {
        "id": "r7YM17cgpx3j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "99863c38-f896-4971-f79a-6d3cf955a10c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8cd7d96a10>]"
            ]
          },
          "metadata": {},
          "execution_count": 189
        },
        {
          "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": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32A3Sr8TNTVo",
        "outputId": "79d0ed3e-2c54-4118-a972-3801cd6d2f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aX53zhHITbel",
        "outputId": "532c921a-a6db-46c8-f7ac-40dc8a2d0c11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162,)"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MANUAL CALCUALTION"
      ],
      "metadata": {
        "id": "t2w0z6x8Te1_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#GET THE WEIGHTS\n",
        "w,b=model.layers[0].get_weights()"
      ],
      "metadata": {
        "id": "Gccwyh3MTj6v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[0].get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psuZpSJmTsXG",
        "outputId": "260b0c10-eded-4abf-e19f-71c4071bfab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.3398109]], dtype=float32), array([17.759003], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RESHAPE X BECAUSE WE FLATTENED IT EARLIER (I dont know when)\n",
        "X=X.reshape(-1,1)\n"
      ],
      "metadata": {
        "id": "g-uMCHiMT-02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qlf0_tXUKNd",
        "outputId": "e3671de6-1b17-407b-bb2e-3475a245f752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#(Nx1)==>(Nx1)x(1x1)+(1)\n",
        "Yhat2=(X.dot(w)+b).flatten()"
      ],
      "metadata": {
        "id": "ntNUqv_eUMjt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X.dot(w)+b).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGchrcC1Uv3m",
        "outputId": "c9c2dd38-bf12-450c-9c62-2574355ddc51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Yhat.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGoxQf7zUhNf",
        "outputId": "0b6cf287-ca62-4aee-9356-ae87ae2fc8ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(162,)"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.allclose(Yhat,Yhat2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CI3Lx-D_Uirk",
        "outputId": "49752de9-a25c-44b2-8f05-d6bd5cca3fce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Yhat==Yhat2 #this is not used in floating point numbers"
      ],
      "metadata": {
        "id": "TaBkHVeWUmW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Yhat.shape,Yhat2.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4ANxuBlU3j1",
        "outputId": "9f9abe12-41d9-428b-dd72-7c13c67310d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(162,) (162,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uMT97XdWVBB0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}