Neural Network Regression.ipynb

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    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Anikate-De/TensorFlow-Deep-Learning/blob/main/Neural%20Network%20Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Neural Network Regression\n",
        "\n",
        "Table of Contents\n",
        "* Modelling in TensorFlow\n",
        "* Improving the Model\n",
        "* Evaluating the Model\n",
        "* Tracking Experiments\n",
        "* Saving Models\n",
        "* Loading Models\n",
        "* Download Files from Colab"
      ],
      "metadata": {
        "id": "pqMAf7UV0W_r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Modelling in TensorFlow"
      ],
      "metadata": {
        "id": "enshTI2ii27E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create data to view and fit\n",
        "\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plot\n",
        "\n",
        "# Create features\n",
        "X = tf.constant([-7.0, -4.0, -1.0, 2.0, 5.0, 8.0, 11.0, 14.0])\n",
        "\n",
        "# Create labels\n",
        "y = tf.constant([3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])\n",
        "\n",
        "# Visualise\n",
        "plot.scatter(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oDLinJCqExen",
        "outputId": "26a0df37-f668-400c-a39d-7269ef599f7f"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {},
          "execution_count": 249
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        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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Sw6EYAEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxPw/YhrWmPXy7VoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(4)"
      ],
      "metadata": {
        "id": "zcuqielwjRsz"
      },
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Create a Model using the Sequential API"
      ],
      "metadata": {
        "id": "GS3RxaaUi7PB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])"
      ],
      "metadata": {
        "id": "qrzt4kHXi_Fj"
      },
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Compile the Model"
      ],
      "metadata": {
        "id": "VdwsZl40jpac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])"
      ],
      "metadata": {
        "id": "tCgjm8UYjvQj"
      },
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Fit the model"
      ],
      "metadata": {
        "id": "qcjXcez5kMgU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-HgaYQSkTUW",
        "outputId": "13d4a152-4312-43fc-fc8e-ccafa472d050"
      },
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 0s 414ms/step - loss: 17.1329 - mae: 17.1329\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 16.8517 - mae: 16.8517\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 16.5704 - mae: 16.5704\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 16.2892 - mae: 16.2892\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 16.0079 - mae: 16.0079\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e858d6f70>"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that the Model is fitted, we can use it to make predictions"
      ],
      "metadata": {
        "id": "p7MLvgtIlIWh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oT2ok3avlN2T",
        "outputId": "b9675d8b-3939-45af-9c82-14146753eecf"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 82ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-9.692406]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Improving the Model"
      ],
      "metadata": {
        "id": "4Jn4z6DhllTg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By increasing the number of laps the model takes through the input data"
      ],
      "metadata": {
        "id": "JgfvLEcmpGjg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9IQzi24lSTd",
        "outputId": "0418d45f-ab14-4a70-9989-7523546d36be"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 405ms/step - loss: 13.7283 - mae: 13.7283\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 13.5958 - mae: 13.5958\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 13.4633 - mae: 13.4633\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 13.3308 - mae: 13.3308\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 13.1983 - mae: 13.1983\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 13.0658 - mae: 13.0658\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 12.9333 - mae: 12.9333\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 12.8008 - mae: 12.8008\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.6683 - mae: 12.6683\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 12.5358 - mae: 12.5358\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.4033 - mae: 12.4033\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 12.2708 - mae: 12.2708\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.1383 - mae: 12.1383\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 12.0058 - mae: 12.0058\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.8733 - mae: 11.8733\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 11.7408 - mae: 11.7408\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.6083 - mae: 11.6083\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 11.4758 - mae: 11.4758\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.3433 - mae: 11.3433\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 11.2108 - mae: 11.2108\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 11.0783 - mae: 11.0783\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 10.9458 - mae: 10.9458\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 10.8133 - mae: 10.8133\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.6808 - mae: 10.6808\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 10.5483 - mae: 10.5483\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 10.4158 - mae: 10.4158\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 10.2833 - mae: 10.2833\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 10.1508 - mae: 10.1508\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.0183 - mae: 10.0183\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 9.8858 - mae: 9.8858\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.7533 - mae: 9.7533\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.6208 - mae: 9.6208\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.4883 - mae: 9.4883\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.3558 - mae: 9.3558\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 9.2233 - mae: 9.2233\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.0908 - mae: 9.0908\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.9583 - mae: 8.9583\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.8258 - mae: 8.8258\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.6933 - mae: 8.6933\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 8.5608 - mae: 8.5608\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.4283 - mae: 8.4283\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.2958 - mae: 8.2958\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.1633 - mae: 8.1633\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.0308 - mae: 8.0308\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.8983 - mae: 7.8983\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.7658 - mae: 7.7658\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.6333 - mae: 7.6333\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 7.5008 - mae: 7.5008\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.3683 - mae: 7.3683\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.2358 - mae: 7.2358\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.1250 - mae: 7.1250\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.1194 - mae: 7.1194\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.1138 - mae: 7.1138\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.1081 - mae: 7.1081\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.1025 - mae: 7.1025\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.0969 - mae: 7.0969\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 7.0913 - mae: 7.0913\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0856 - mae: 7.0856\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0800 - mae: 7.0800\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.0744 - mae: 7.0744\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.0687 - mae: 7.0687\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.0631 - mae: 7.0631\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0575 - mae: 7.0575\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0519 - mae: 7.0519\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.0462 - mae: 7.0462\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0406 - mae: 7.0406\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0350 - mae: 7.0350\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.0294 - mae: 7.0294\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0238 - mae: 7.0238\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0181 - mae: 7.0181\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0125 - mae: 7.0125\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.0069 - mae: 7.0069\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.0013 - mae: 7.0013\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.9956 - mae: 6.9956\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.9900 - mae: 6.9900\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.9844 - mae: 6.9844\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9788 - mae: 6.9788\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.9731 - mae: 6.9731\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.9675 - mae: 6.9675\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9619 - mae: 6.9619\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9563 - mae: 6.9563\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9506 - mae: 6.9506\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9450 - mae: 6.9450\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.9394 - mae: 6.9394\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.9338 - mae: 6.9338\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.9281 - mae: 6.9281\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.9225 - mae: 6.9225\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 6.9169 - mae: 6.9169\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.9113 - mae: 6.9113\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.9056 - mae: 6.9056\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.9000 - mae: 6.9000\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.8944 - mae: 6.8944\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.8888 - mae: 6.8888\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.8831 - mae: 6.8831\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8775 - mae: 6.8775\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.8719 - mae: 6.8719\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 6.8663 - mae: 6.8663\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.8606 - mae: 6.8606\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.8550 - mae: 6.8550\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.8494 - mae: 6.8494\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e857c95e0>"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJMIcn-6mRFl",
        "outputId": "348de519-ae0f-420d-9492-eb47e7482d1c"
      },
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 197ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29.516056]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By Adding a Hidden Layer to the model"
      ],
      "metadata": {
        "id": "kbwLYkGaqT_t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzPCgcO3o6xS",
        "outputId": "5f72c775-ffb4-4a12-c6f7-a29c70c6834b"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 842ms/step - loss: 12.5805 - mae: 12.5805\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 12.0266 - mae: 12.0266\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 11.4882 - mae: 11.4882\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 10.9314 - mae: 10.9314\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 10.3546 - mae: 10.3546\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 9.7492 - mae: 9.7492\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 9.1142 - mae: 9.1142\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.4434 - mae: 8.4434\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 7.7284 - mae: 7.7284\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 6.9613 - mae: 6.9613\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 6.1384 - mae: 6.1384\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 5.2539 - mae: 5.2539\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 4.3561 - mae: 4.3561\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9535 - mae: 3.9535\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.9553 - mae: 3.9553\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9275 - mae: 3.9275\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9616 - mae: 3.9616\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9014 - mae: 3.9014\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.9679 - mae: 3.9679\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.9086 - mae: 3.9086\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.9581 - mae: 3.9581\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.9159 - mae: 3.9159\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.9320 - mae: 3.9320\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9222 - mae: 3.9222\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.9058 - mae: 3.9058\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 3.9287 - mae: 3.9287\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8795 - mae: 3.8795\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.9438 - mae: 3.9438\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8775 - mae: 3.8775\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9356 - mae: 3.9356\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8839 - mae: 3.8839\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.9093 - mae: 3.9093\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8904 - mae: 3.8904\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8829 - mae: 3.8829\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8971 - mae: 3.8971\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8649 - mae: 3.8649\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9137 - mae: 3.9137\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8464 - mae: 3.8464\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.9120 - mae: 3.9120\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8530 - mae: 3.8530\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 3.8854 - mae: 3.8854\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8598 - mae: 3.8598\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8588 - mae: 3.8588\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8743 - mae: 3.8743\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8412 - mae: 3.8412\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.8834 - mae: 3.8834\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8165 - mae: 3.8165\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8871 - mae: 3.8871\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8233 - mae: 3.8233\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8603 - mae: 3.8603\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8303 - mae: 3.8303\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8406 - mae: 3.8406\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8472 - mae: 3.8472\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8154 - mae: 3.8154\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8543 - mae: 3.8543\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.7881 - mae: 3.7881\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.8615 - mae: 3.8615\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.7947 - mae: 3.7947\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 3.8339 - mae: 3.8339\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8078 - mae: 3.8078\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8159 - mae: 3.8159\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8189 - mae: 3.8189\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7884 - mae: 3.7884\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8262 - mae: 3.8262\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7608 - mae: 3.7608\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8337 - mae: 3.8337\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7672 - mae: 3.7672\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8114 - mae: 3.8114\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7844 - mae: 3.7844\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7879 - mae: 3.7879\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7917 - mae: 3.7917\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7601 - mae: 3.7601\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7992 - mae: 3.7992\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7333 - mae: 3.7333\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8049 - mae: 3.8049\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7443 - mae: 3.7443\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7864 - mae: 3.7864\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7581 - mae: 3.7581\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7585 - mae: 3.7585\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7656 - mae: 3.7656\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7305 - mae: 3.7305\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7734 - mae: 3.7734\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7077 - mae: 3.7077\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.7775 - mae: 3.7775\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7251 - mae: 3.7251\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7561 - mae: 3.7561\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.7328 - mae: 3.7328\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7278 - mae: 3.7278\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7406 - mae: 3.7406\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.6995 - mae: 3.6995\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7485 - mae: 3.7485\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.6840 - mae: 3.6840\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7528 - mae: 3.7528\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.7006 - mae: 3.7006\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7244 - mae: 3.7244\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7085 - mae: 3.7085\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.6958 - mae: 3.6958\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7165 - mae: 3.7165\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.6672 - mae: 3.6672\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7247 - mae: 3.7247\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e85636880>"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nV-rAuGQrJgj",
        "outputId": "b4c35942-c631-4e52-f358-5fd57c1fb4c2"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 219ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[32.057796]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By Increasing the number of hidden units"
      ],
      "metadata": {
        "id": "0Cvr85YbtJFb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(200, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7Dpd_CotJmg",
        "outputId": "21de3e64-d3ee-4055-b947-28e294575c08"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 2s 2s/step - loss: 14.0378 - mae: 14.0378\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 13.5451 - mae: 13.5451\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 13.0613 - mae: 13.0613\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.5918 - mae: 12.5918\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 12.1290 - mae: 12.1290\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.6578 - mae: 11.6578\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.1748 - mae: 11.1748\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 10.6825 - mae: 10.6825\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 10.1683 - mae: 10.1683\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.6321 - mae: 9.6321\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.0686 - mae: 9.0686\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 8.4695 - mae: 8.4695\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.8308 - mae: 7.8308\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.1468 - mae: 7.1468\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.4591 - mae: 6.4591\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.7163 - mae: 5.7163\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.9076 - mae: 4.9076\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 4.1311 - mae: 4.1311\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.0399 - mae: 4.0399\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9462 - mae: 3.9462\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9003 - mae: 3.9003\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9268 - mae: 3.9268\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.9144 - mae: 3.9144\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9042 - mae: 3.9042\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.9209 - mae: 3.9209\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8791 - mae: 3.8791\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.9276 - mae: 3.9276\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8622 - mae: 3.8622\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.9251 - mae: 3.9251\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8716 - mae: 3.8716\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.9073 - mae: 3.9073\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8834 - mae: 3.8834\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8821 - mae: 3.8821\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8901 - mae: 3.8901\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8567 - mae: 3.8567\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8970 - mae: 3.8970\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8320 - mae: 3.8320\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.9034 - mae: 3.9034\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8468 - mae: 3.8468\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8842 - mae: 3.8842\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 3.8537 - mae: 3.8537\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 3.8587 - mae: 3.8587\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8607 - mae: 3.8607\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 3.8330 - mae: 3.8330\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8678 - mae: 3.8678\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.8072 - mae: 3.8072\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8811 - mae: 3.8811\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.8181 - mae: 3.8181\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8597 - mae: 3.8597\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8252 - mae: 3.8252\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8338 - mae: 3.8338\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8324 - mae: 3.8324\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8079 - mae: 3.8079\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8398 - mae: 3.8398\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7860 - mae: 3.7860\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8551 - mae: 3.8551\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7906 - mae: 3.7906\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8338 - mae: 3.8338\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7978 - mae: 3.7978\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8076 - mae: 3.8076\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8053 - mae: 3.8053\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7814 - mae: 3.7814\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8146 - mae: 3.8146\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7623 - mae: 3.7623\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.8283 - mae: 3.8283\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 3.7641 - mae: 3.7641\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8066 - mae: 3.8066\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7715 - mae: 3.7715\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7802 - mae: 3.7802\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7792 - mae: 3.7792\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7537 - mae: 3.7537\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7939 - mae: 3.7939\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7342 - mae: 3.7342\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8027 - mae: 3.8027\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.7387 - mae: 3.7387\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7781 - mae: 3.7781\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7464 - mae: 3.7464\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.7514 - mae: 3.7514\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7543 - mae: 3.7543\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.7294 - mae: 3.7294\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7701 - mae: 3.7701\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7066 - mae: 3.7066\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.7752 - mae: 3.7752\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7144 - mae: 3.7144\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7483 - mae: 3.7483\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7223 - mae: 3.7223\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7213 - mae: 3.7213\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7323 - mae: 3.7323\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7014 - mae: 3.7014\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7463 - mae: 3.7463\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.6831 - mae: 3.6831\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7444 - mae: 3.7444\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.6910 - mae: 3.6910\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7172 - mae: 3.7172\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.6992 - mae: 3.6992\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.6899 - mae: 3.6899\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7142 - mae: 3.7142\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.6697 - mae: 3.6697\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 3.7235 - mae: 3.7235\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.6604 - mae: 3.6604\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e8554e1c0>"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JHXURCLtZdZ",
        "outputId": "b23ecc31-232d-457f-bd1d-d80586084dca"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 124ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[31.213436]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By Changing the Activation Functions"
      ],
      "metadata": {
        "id": "U3PQ6jKurMcD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, activation = None),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xnxSYt7LsO01",
        "outputId": "c09765cd-60d2-48d9-b84f-5a7fb67fe47a"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 543ms/step - loss: 14.7214 - mae: 14.7214\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 14.2254 - mae: 14.2254\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 13.7345 - mae: 13.7345\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 13.2463 - mae: 13.2463\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 12.7581 - mae: 12.7581\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.2675 - mae: 12.2675\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.7719 - mae: 11.7719\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.2687 - mae: 11.2687\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 10.7554 - mae: 10.7554\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.2293 - mae: 10.2293\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.6877 - mae: 9.6877\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.1279 - mae: 9.1279\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.5469 - mae: 8.5469\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.9419 - mae: 7.9419\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.3096 - mae: 7.3096\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.1124 - mae: 7.1124\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0906 - mae: 7.0906\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0687 - mae: 7.0687\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0467 - mae: 7.0467\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0247 - mae: 7.0247\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0026 - mae: 7.0026\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9805 - mae: 6.9805\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.9582 - mae: 6.9582\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.9359 - mae: 6.9359\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9134 - mae: 6.9134\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.8909 - mae: 6.8909\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8683 - mae: 6.8683\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.8560 - mae: 6.8560\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.8377 - mae: 6.8377\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.8153 - mae: 6.8153\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.7929 - mae: 6.7929\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.7703 - mae: 6.7703\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.7477 - mae: 6.7477\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.7248 - mae: 6.7248\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.7019 - mae: 6.7019\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.6789 - mae: 6.6789\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.6557 - mae: 6.6557\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.6323 - mae: 6.6323\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.6089 - mae: 6.6089\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.5853 - mae: 6.5853\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.5615 - mae: 6.5615\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.5376 - mae: 6.5376\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.5135 - mae: 6.5135\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.4893 - mae: 6.4893\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.4649 - mae: 6.4649\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.4403 - mae: 6.4403\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.4156 - mae: 6.4156\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.3907 - mae: 6.3907\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.3656 - mae: 6.3656\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.3403 - mae: 6.3403\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.3260 - mae: 6.3260\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.3108 - mae: 6.3108\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.2855 - mae: 6.2855\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.2600 - mae: 6.2600\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.2344 - mae: 6.2344\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.2085 - mae: 6.2085\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.1824 - mae: 6.1824\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.1561 - mae: 6.1561\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.1296 - mae: 6.1296\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.1028 - mae: 6.1028\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.0758 - mae: 6.0758\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.0486 - mae: 6.0486\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.0211 - mae: 6.0211\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.9934 - mae: 5.9934\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.9655 - mae: 5.9655\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.9373 - mae: 5.9373\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.9088 - mae: 5.9088\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.8800 - mae: 5.8800\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.8665 - mae: 5.8665\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.8484 - mae: 5.8484\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.8194 - mae: 5.8194\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 5.7902 - mae: 5.7902\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.7607 - mae: 5.7607\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.7309 - mae: 5.7309\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.7007 - mae: 5.7007\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.6703 - mae: 5.6703\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 5.6396 - mae: 5.6396\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.6085 - mae: 5.6085\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 5.5772 - mae: 5.5772\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.5455 - mae: 5.5455\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.5135 - mae: 5.5135\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.4811 - mae: 5.4811\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 5.4484 - mae: 5.4484\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.4159 - mae: 5.4159\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.4554 - mae: 5.4554\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.5202 - mae: 5.5202\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.3423 - mae: 5.3423\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.3083 - mae: 5.3083\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.2740 - mae: 5.2740\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 5.2393 - mae: 5.2393\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 5.2041 - mae: 5.2041\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.1686 - mae: 5.1686\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.1327 - mae: 5.1327\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.0964 - mae: 5.0964\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.0617 - mae: 5.0617\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.1495 - mae: 5.1495\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.1693 - mae: 5.1693\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 4.9781 - mae: 4.9781\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.9402 - mae: 4.9402\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.9019 - mae: 4.9019\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e87f02910>"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rAzn6lHsUpo",
        "outputId": "f92f6024-af31-403c-d7dc-0f470928ef3b"
      },
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 99ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29.57656]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By Changing the Optimisation Functions"
      ],
      "metadata": {
        "id": "EJzwJmDJsWhO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, activation = None),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yft5v6Cts9IH",
        "outputId": "ca148819-6226-4cf9-f6ef-34409a6682be"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 621ms/step - loss: 12.3754 - mae: 12.3754\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.2767 - mae: 12.2767\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 12.1779 - mae: 12.1779\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.0791 - mae: 12.0791\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 11.9801 - mae: 11.9801\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 11.8812 - mae: 11.8812\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.7821 - mae: 11.7821\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.6829 - mae: 11.6829\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 11.5836 - mae: 11.5836\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 11.4843 - mae: 11.4843\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 11.3847 - mae: 11.3847\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 11.2851 - mae: 11.2851\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.1853 - mae: 11.1853\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 11.0853 - mae: 11.0853\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 10.9851 - mae: 10.9851\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.8847 - mae: 10.8847\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.7841 - mae: 10.7841\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.6832 - mae: 10.6832\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.5820 - mae: 10.5820\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.4805 - mae: 10.4805\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 10.3786 - mae: 10.3786\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.2764 - mae: 10.2764\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.1738 - mae: 10.1738\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.0708 - mae: 10.0708\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.9673 - mae: 9.9673\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.8634 - mae: 9.8634\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.7590 - mae: 9.7590\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.6541 - mae: 9.6541\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.5486 - mae: 9.5486\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.4426 - mae: 9.4426\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.3360 - mae: 9.3360\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.2288 - mae: 9.2288\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.1209 - mae: 9.1209\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.0124 - mae: 9.0124\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.9033 - mae: 8.9033\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.7934 - mae: 8.7934\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.6828 - mae: 8.6828\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 8.5714 - mae: 8.5714\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.4593 - mae: 8.4593\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.3464 - mae: 8.3464\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.2327 - mae: 8.2327\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.1181 - mae: 8.1181\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.0028 - mae: 8.0028\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.8865 - mae: 7.8865\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.7694 - mae: 7.7694\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 7.6514 - mae: 7.6514\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.5325 - mae: 7.5325\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.4126 - mae: 7.4126\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.2918 - mae: 7.2918\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.1700 - mae: 7.1700\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0473 - mae: 7.0473\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.9235 - mae: 6.9235\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8752 - mae: 6.8752\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.8604 - mae: 6.8604\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.8458 - mae: 6.8458\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.8315 - mae: 6.8315\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.8174 - mae: 6.8174\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8035 - mae: 6.8035\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.7897 - mae: 6.7897\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.7886 - mae: 6.7886\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8127 - mae: 6.8127\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8265 - mae: 6.8265\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.8312 - mae: 6.8312\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8276 - mae: 6.8276\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8165 - mae: 6.8165\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.7988 - mae: 6.7988\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.7750 - mae: 6.7750\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.7458 - mae: 6.7458\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.7118 - mae: 6.7118\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.6736 - mae: 6.6736\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.6639 - mae: 6.6639\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.6554 - mae: 6.6554\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.6465 - mae: 6.6465\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 6.6373 - mae: 6.6373\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.6278 - mae: 6.6278\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.6180 - mae: 6.6180\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.6080 - mae: 6.6080\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.5976 - mae: 6.5976\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.5871 - mae: 6.5871\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.5764 - mae: 6.5764\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.5654 - mae: 6.5654\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.5543 - mae: 6.5543\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.5430 - mae: 6.5430\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.5315 - mae: 6.5315\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.5198 - mae: 6.5198\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.5080 - mae: 6.5080\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.4961 - mae: 6.4961\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.4840 - mae: 6.4840\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.4718 - mae: 6.4718\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.4594 - mae: 6.4594\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.4469 - mae: 6.4469\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.4343 - mae: 6.4343\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.4216 - mae: 6.4216\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.4087 - mae: 6.4087\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.3957 - mae: 6.3957\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.3827 - mae: 6.3827\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.3695 - mae: 6.3695\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.3561 - mae: 6.3561\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.3427 - mae: 6.3427\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.3292 - mae: 6.3292\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e98bf04c0>"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-8S3wE3tA8p",
        "outputId": "24cde822-c962-4449-fba0-407c06adc15b"
      },
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 79ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30.541237]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By Changing the Learning Rate of the Optimizers"
      ],
      "metadata": {
        "id": "-xqwl48PtC0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, activation = None),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.02),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wN15l7wTtjxk",
        "outputId": "ef5b9118-8a08-40d0-fecc-4074909ceebb"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 855ms/step - loss: 14.8844 - mae: 14.8844\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 13.0418 - mae: 13.0418\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 11.2644 - mae: 11.2644\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.5009 - mae: 9.5009\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.6954 - mae: 7.6954\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.7332 - mae: 6.7332\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.5033 - mae: 7.5033\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 7.6783 - mae: 7.6783\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.2081 - mae: 7.2081\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.5239 - mae: 6.5239\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 5.9439 - mae: 5.9439\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 5.8907 - mae: 5.8907\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.9292 - mae: 5.9292\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 5.6394 - mae: 5.6394\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 5.2595 - mae: 5.2595\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.0408 - mae: 5.0408\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.9388 - mae: 4.9388\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.7326 - mae: 4.7326\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.4315 - mae: 4.4315\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.0430 - mae: 4.0430\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8180 - mae: 3.8180\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7884 - mae: 3.7884\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.4355 - mae: 3.4355\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.7995 - mae: 2.7995\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.4462 - mae: 2.4462\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.1080 - mae: 2.1080\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.6487 - mae: 1.6487\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.9476 - mae: 0.9476\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.7422 - mae: 0.7422\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.6005 - mae: 0.6005\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.0120 - mae: 1.0120\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.4784 - mae: 1.4784\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.3652 - mae: 1.3652\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 1.6319 - mae: 1.6319\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.7692 - mae: 1.7692\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.4248 - mae: 1.4248\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.0174 - mae: 1.0174\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.8855 - mae: 0.8855\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.3583 - mae: 0.3583\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.3750 - mae: 0.3750\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.3549 - mae: 0.3549\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.8907 - mae: 0.8907\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.0283 - mae: 1.0283\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.7899 - mae: 0.7899\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.9854 - mae: 0.9854\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.0201 - mae: 1.0201\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.7213 - mae: 0.7213\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.5990 - mae: 0.5990\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.5271 - mae: 0.5271\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0615 - mae: 0.0615\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2182 - mae: 0.2182\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.3866 - mae: 0.3866\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2739 - mae: 0.2739\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2723 - mae: 0.2723\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.2154 - mae: 0.2154\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1255 - mae: 0.1255\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2254 - mae: 0.2254\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.1668 - mae: 0.1668\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1711 - mae: 0.1711\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1050 - mae: 0.1050\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.2265 - mae: 0.2265\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1245 - mae: 0.1245\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.2062 - mae: 0.2062\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1523 - mae: 0.1523\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0860 - mae: 0.0860\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1232 - mae: 0.1232\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2329 - mae: 0.2329\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.0730 - mae: 0.0730\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.4451 - mae: 0.4451\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.3742 - mae: 0.3742\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1768 - mae: 0.1768\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.2255 - mae: 0.2255\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.2092 - mae: 0.2092\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1172 - mae: 0.1172\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.4413 - mae: 0.4413\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.4791 - mae: 0.4791\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0559 - mae: 0.0559\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.7926 - mae: 0.7926\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.0817 - mae: 1.0817\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.8657 - mae: 0.8657\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.2332 - mae: 0.2332\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.7819 - mae: 0.7819\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.2369 - mae: 1.2369\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.2255 - mae: 1.2255\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.7995 - mae: 0.7995\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0319 - mae: 0.0319\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.4287 - mae: 0.4287\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.3897 - mae: 0.3897\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1529 - mae: 0.1529\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1968 - mae: 0.1968\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1285 - mae: 0.1285\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0631 - mae: 0.0631\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.3658 - mae: 0.3658\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.3128 - mae: 0.3128\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.1454 - mae: 0.1454\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.1640 - mae: 0.1640\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2130 - mae: 0.2130\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.1515 - mae: 0.1515\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.3386 - mae: 0.3386\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.3709 - mae: 0.3709\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e8888f220>"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSQnkm12trVc",
        "outputId": "60c999d5-31e2-4742-b9c5-f2993e7ee982"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 190ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[26.970388]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be understood that the `learning_rate` is the most important amongst all other High Parameters"
      ],
      "metadata": {
        "id": "2MVnGJaOtszu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By fitting on more data\n",
        "\n",
        "(This can be tried out on a real problem)"
      ],
      "metadata": {
        "id": "7pa6UamguQJP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By adding layers"
      ],
      "metadata": {
        "id": "YAwGxWEXudC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, activation = \"relu\"),\n",
        "    tf.keras.layers.Dense(100, activation = \"relu\"),\n",
        "    tf.keras.layers.Dense(100, activation = \"relu\"),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.02),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4e1XFMoSuomh",
        "outputId": "61b1f076-6927-43c9-f37b-70957a4fdf84"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 1s/step - loss: 13.1207 - mae: 13.1207\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 6.6129 - mae: 6.6129\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 10.4211 - mae: 10.4211\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 6.6215 - mae: 6.6215\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 4.2841 - mae: 4.2841\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 5.7321 - mae: 5.7321\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.5517 - mae: 4.5517\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 3.8325 - mae: 3.8325\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 4.9226 - mae: 4.9226\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 4.6746 - mae: 4.6746\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.5408 - mae: 3.5408\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 3.3857 - mae: 3.3857\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.2340 - mae: 3.2340\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 2.4322 - mae: 2.4322\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 2.7511 - mae: 2.7511\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.1624 - mae: 2.1624\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.9717 - mae: 1.9717\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 2.9101 - mae: 2.9101\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 2.1074 - mae: 2.1074\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 1.2777 - mae: 1.2777\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 1.9856 - mae: 1.9856\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.6508 - mae: 0.6508\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.5466 - mae: 1.5466\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.7237 - mae: 0.7237\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 2.1200 - mae: 2.1200\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9795 - mae: 1.9795\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.0000 - mae: 1.0000\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 2.3711 - mae: 2.3711\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 2.2455 - mae: 2.2455\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.8688 - mae: 0.8688\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.7733 - mae: 2.7733\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.4707 - mae: 3.4707\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 1.7487 - mae: 1.7487\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.4045 - mae: 1.4045\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.4100 - mae: 2.4100\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 2.1582 - mae: 2.1582\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.7040 - mae: 0.7040\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 1.6077 - mae: 1.6077\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 2.1315 - mae: 2.1315\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.7601 - mae: 0.7601\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.7832 - mae: 1.7832\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.6851 - mae: 2.6851\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.4571 - mae: 2.4571\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.2543 - mae: 1.2543\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.1108 - mae: 1.1108\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.7419 - mae: 1.7419\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.8210 - mae: 0.8210\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.2763 - mae: 1.2763\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.7848 - mae: 1.7848\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.1302 - mae: 1.1302\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.4589 - mae: 0.4589\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.7792 - mae: 0.7792\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.3483 - mae: 0.3483\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.7785 - mae: 0.7785\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2660 - mae: 0.2660\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.6443 - mae: 0.6443\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.2249 - mae: 0.2249\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.2256 - mae: 0.2256\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.0081 - mae: 1.0081\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.7033 - mae: 0.7033\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.8262 - mae: 0.8262\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.9790 - mae: 0.9790\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2088 - mae: 0.2088\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.8240 - mae: 1.8240\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.0948 - mae: 2.0948\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.0314 - mae: 1.0314\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.3010 - mae: 1.3010\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.9025 - mae: 1.9025\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 1.4811 - mae: 1.4811\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.3306 - mae: 0.3306\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.8022 - mae: 1.8022\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 2.0711 - mae: 2.0711\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.8799 - mae: 0.8799\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.3581 - mae: 1.3581\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 2.1502 - mae: 2.1502\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 2.0447 - mae: 2.0447\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.0521 - mae: 1.0521\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 1.0895 - mae: 1.0895\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.7957 - mae: 1.7957\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.2678 - mae: 1.2678\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.3916 - mae: 0.3916\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.7688 - mae: 0.7688\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2560 - mae: 0.2560\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.2895 - mae: 1.2895\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.4155 - mae: 1.4155\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.5057 - mae: 0.5057\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.4556 - mae: 1.4556\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9930 - mae: 1.9930\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 1.6453 - mae: 1.6453\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.5900 - mae: 0.5900\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.5084 - mae: 1.5084\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.0948 - mae: 2.0948\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.3398 - mae: 1.3398\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.4384 - mae: 0.4384\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.4300 - mae: 1.4300\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.4420 - mae: 1.4420\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.5769 - mae: 0.5769\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.3153 - mae: 1.3153\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9511 - mae: 1.9511\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.3292 - mae: 1.3292\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e88d62dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([21.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARNChGMeuu3D",
        "outputId": "ef8a063b-06b6-49b1-c045-ced3e6c79244"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 103ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[31.377867]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluating the Model"
      ],
      "metadata": {
        "id": "tgVgd623vahr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " - Visualize the Input Data"
      ],
      "metadata": {
        "id": "eE4QuQjHQmWS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make a bigger dataset"
      ],
      "metadata": {
        "id": "0Za0Y9b5PgwR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Input Values\n",
        "X = tf.range(-100, 100, 4)\n",
        "\n",
        "X"
      ],
      "metadata": {
        "id": "cUvooSDqvePy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c23f08f2-35a9-4122-c469-3d99433c3857"
      },
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(50,), dtype=int32, numpy=\n",
              "array([-100,  -96,  -92,  -88,  -84,  -80,  -76,  -72,  -68,  -64,  -60,\n",
              "        -56,  -52,  -48,  -44,  -40,  -36,  -32,  -28,  -24,  -20,  -16,\n",
              "        -12,   -8,   -4,    0,    4,    8,   12,   16,   20,   24,   28,\n",
              "         32,   36,   40,   44,   48,   52,   56,   60,   64,   68,   72,\n",
              "         76,   80,   84,   88,   92,   96], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ground Truth\n",
        "y = X + 10\n",
        "\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzo_qbhzQAk-",
        "outputId": "f9924990-b803-4664-ccc6-e27c37e645f8"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(50,), dtype=int32, numpy=\n",
              "array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42,\n",
              "       -38, -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,\n",
              "        14,  18,  22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,\n",
              "        66,  70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the input data\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yKbJHX-VQd2w",
        "outputId": "19995feb-f737-4395-9d32-b323bed5025c"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6e890b6280>"
            ]
          },
          "metadata": {},
          "execution_count": 271
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " - The Three Sets\n",
        " In Machine Learning, the data is conventionally split into three datasets -\n",
        " 1. The **Training** Dataset - This consists of about **70-80%** of the input labels, it is used for the supervised learning of the model. It is like the learning resources a student utilizes while preparing for an exam.\n",
        " 2. The **Validation** Dataset - The model gets tuned on this data. It is usually **10-15%** of the total input data. It is much like the Practice Questionnaires a student solves in order to prepare well for an exam.\n",
        " 3. The **Testing** Dataset - The model gets tested on this data. It is usually **10-15%** of the total input data. The predictions are tested across the corressponding ground truth input labels. This is much like the final exam or the test the student was preparing for."
      ],
      "metadata": {
        "id": "P_6ccXXsQw3-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the Input Data into Training and Testing Datasets\n",
        "\n",
        "# 80% of the total input data\n",
        "X_train = X[:40]\n",
        "y_train = y[:40]\n",
        "\n",
        "X_test = X[40:]\n",
        "y_test = y[40:]\n",
        "\n",
        "X_train, y_train, X_test, y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23T_p3ZsSTqZ",
        "outputId": "0d9ea21d-9586-4695-c12d-6473638195a6"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
              " array([-100,  -96,  -92,  -88,  -84,  -80,  -76,  -72,  -68,  -64,  -60,\n",
              "         -56,  -52,  -48,  -44,  -40,  -36,  -32,  -28,  -24,  -20,  -16,\n",
              "         -12,   -8,   -4,    0,    4,    8,   12,   16,   20,   24,   28,\n",
              "          32,   36,   40,   44,   48,   52,   56], dtype=int32)>,\n",
              " <tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
              " array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42,\n",
              "        -38, -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,\n",
              "         14,  18,  22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,\n",
              "         66], dtype=int32)>,\n",
              " <tf.Tensor: shape=(10,), dtype=int32, numpy=array([60, 64, 68, 72, 76, 80, 84, 88, 92, 96], dtype=int32)>,\n",
              " <tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualise the new datasets\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "\n",
        "plt.scatter(X_train, y_train, label = \"Training Data\")\n",
        "plt.scatter(X_test, y_test, c = 'y', label = \"Testing Data\")\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "XY-fTfxpTDOl",
        "outputId": "9e408ce3-a0c8-481c-8814-521ea9f421bf"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6e860c6cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 273
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Neural Network that is to be Evaluated\n",
        "\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "model_1 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer', activation = None),\n",
        "    tf.keras.layers.Dense(1, name = 'output_layer')\n",
        "], name = 'model_1')\n",
        "\n",
        "model_1.compile(loss = tf.keras.losses.MeanSquaredError(),\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.02),\n",
        "              metrics = ['mse'])"
      ],
      "metadata": {
        "id": "BHRL3Yo5Tq7p"
      },
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Visualize the Model"
      ],
      "metadata": {
        "id": "2iuKk5TpXU76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.build(input_shape = (40,1))"
      ],
      "metadata": {
        "id": "5-YjMBhvX2-G"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9-SV0-YX6of",
        "outputId": "b693498b-ca8a-4683-a4cb-19869c37b324"
      },
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (40, 100)                 200       \n",
            "                                                                 \n",
            " output_layer (Dense)        (40, 1)                   101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 301\n",
            "Trainable params: 301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Total Params:** The total number of parameters present in the model.\n",
        "\n",
        "**Trainable Params:** The number of parameters that can be updated while training the model.\n",
        "\n",
        "**Non-trainable Params:** The number of parameters that cannot be updated while training the model (These are typically set during Transfer learning) \n"
      ],
      "metadata": {
        "id": "9PLj4XdYZJsk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit our model to the training data\n",
        "\n",
        "model_1.fit(tf.expand_dims(X_train, axis = -1), y_train, epochs = 300, verbose = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6IcrrBAarzV",
        "outputId": "3cd706a9-019b-4c0e-b300-c36863509e79"
      },
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e860b0790>"
            ]
          },
          "metadata": {},
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the Keras plot_model() function to get a more visual insight of the model\n",
        "\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "plot_model(model = model_1, show_shapes = True, show_dtype = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "CjgGEAGVa3vR",
        "outputId": "bf457cc0-c50e-4f6e-d828-d64a8e99b4fd"
      },
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Visualize the model's predictions"
      ],
      "metadata": {
        "id": "gi99ifCCblOu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "\n",
        "y_pred = model_1.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plYUkVZjdAdL",
        "outputId": "5b088e0a-a986-4f21-909e-ae85d0717ad8"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 63ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 69.99999 ],\n",
              "       [ 74.      ],\n",
              "       [ 78.00001 ],\n",
              "       [ 81.999985],\n",
              "       [ 85.99999 ],\n",
              "       [ 90.      ],\n",
              "       [ 94.00001 ],\n",
              "       [ 97.99999 ],\n",
              "       [101.99999 ],\n",
              "       [106.00001 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIhXKFTsdNQ4",
        "outputId": "fb569612-fd81-4f2b-8ee2-f23373cdb770"
      },
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Plotting function for reusability\n",
        "\n",
        "def plot_preds(preds,\n",
        "               train_data = X_train,\n",
        "               train_labels = y_train,\n",
        "               test_data = X_test,\n",
        "               test_labels = y_test):\n",
        "  \"\"\"\n",
        "  Plot training and test data along with predictions made by the model, and compare them to the ground truth (test_labels)\n",
        "  \"\"\"\n",
        "\n",
        "  import matplotlib.pyplot as plt\n",
        "\n",
        "  plt.figure(figsize = (10, 7))\n",
        "\n",
        "  plt.scatter(X_train, y_train, label = \"Training Data\")\n",
        "  plt.scatter(X_test, y_test, c = 'y', label = \"Testing Data\")\n",
        "\n",
        "  # Plot the predictions\n",
        "  plt.scatter(X_test, preds, c = 'r', label = \"Predictions\")\n",
        "\n",
        "  plt.legend()"
      ],
      "metadata": {
        "id": "EXu8dmDTdc1y"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_preds(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "E9NhdDDYe7_-",
        "outputId": "baf870b6-7676-4822-f1b4-7e4152a61e89"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Evaluating Model Based on Common **Regression** Evaluation Metrics\n",
        "\n",
        "The common evaluation metrics for a regression model are -\n",
        "\n",
        "**MAE:** Mean Absolute Error, measures the average deviance from the ground truth values.\n",
        "\n",
        "**MSE:** Mean Squared Error, measures the average of the square of the deviances from the ground truth values. Very effective when making bigger mistakes is not desired. This will tone up the higher values even more (by squaring them) and this will cause the model to adjust more.\n",
        "\n",
        "**Huber:** This is a combination of MSE & MAE, it is less sensitive to outliers than MSE"
      ],
      "metadata": {
        "id": "ABsTzwxHzIyi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using the evaluate() function from keras\n",
        "\n",
        "model_1.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ5OpINIzMQU",
        "outputId": "bd0b418d-bf08-4133-d5ac-9c6966a1424e"
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 127ms/step - loss: 1.1642e-11 - mse: 1.1642e-11\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1641532356165829e-11, 1.1641532356165829e-11]"
            ]
          },
          "metadata": {},
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use the tf.metrics module to get the metrics based on y_pred and y_test"
      ],
      "metadata": {
        "id": "k1lh32Jw1aHk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean Absolute Error\n",
        "\n",
        "mae = tf.metrics.mean_absolute_error(y_true = y_test,\n",
        "                               y_pred = tf.squeeze(y_pred)).numpy()\n",
        "\n",
        "mae"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2op00QHP0iG_",
        "outputId": "c47feb40-8fc2-46c7-e3ed-722e6bf393dc"
      },
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.866455e-06"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean Squared Error\n",
        "\n",
        "mse = tf.metrics.mean_squared_error(y_true = y_test,\n",
        "                               y_pred = tf.squeeze(y_pred)).numpy()\n",
        "mse "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usl-o93G1Ned",
        "outputId": "32d9b7b3-4d13-4cf5-8897-ea1d410b9830"
      },
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.402843e-11"
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Huber\n",
        "\n",
        "huber = tf.keras.losses.huber(y_true = y_test,\n",
        "                               y_pred = tf.squeeze(y_pred)).numpy()\n",
        "huber"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wa4sYOqF1lFd",
        "outputId": "d9c53273-3742-4a4f-9a9d-d31e7e278e51"
      },
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.2014214e-11"
            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.predict([98765432])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYm1JoTs4TM5",
        "outputId": "7001620c-5342-4378-f19d-7930bd6d1e58"
      },
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 89ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[98765440.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tracking Experiments"
      ],
      "metadata": {
        "id": "ouY_hD_n8cOv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Comparing our models and their evaluation metrics"
      ],
      "metadata": {
        "id": "v7GA-aD56g1f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We can use the pandas module to see the output metrics of our models even better\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "model_results = [[\"model_1\", mae, mse, huber],]\n",
        "                # [\"model_2\", mae_2, mse_2, huber_2],\n",
        "                # ...]\n",
        "\n",
        "pd.DataFrame(model_results, columns = ['Model Name', 'MAE', \"MSE\", \"Huber\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "L6kcUY-s6lol",
        "outputId": "b7833cf0-b485-488c-ca04-aad429a51829"
      },
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Model Name       MAE           MSE         Huber\n",
              "0    model_1  0.000007  6.402843e-11  3.201421e-11"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1009b142-23a4-4530-a31b-055d01c0a4cc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model Name</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>Huber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>model_1</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>6.402843e-11</td>\n",
              "      <td>3.201421e-11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1009b142-23a4-4530-a31b-055d01c0a4cc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1009b142-23a4-4530-a31b-055d01c0a4cc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1009b142-23a4-4530-a31b-055d01c0a4cc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Using External Tools\n",
        " - Like **TensorBoard**\n",
        " - and **Weights & Biases**"
      ],
      "metadata": {
        "id": "ZhOT_13f7ZGO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Saving Models"
      ],
      "metadata": {
        "id": "HxK1xHFO9SPr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are two formats of saving TensorFlow models onto the disk\n",
        "\n",
        "1. SavedModel\n",
        "2. HDF5 (.h5 extension)"
      ],
      "metadata": {
        "id": "VclQZHgs9WGt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model using SavedModel format\n",
        "model_1.save(\"Simple Neural Regression Model\")"
      ],
      "metadata": {
        "id": "RR9zFpO-9uVe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "260494f7-ce5b-423d-be9b-99d7c193b4fb"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `.pb` files saved inside the foldername are called **protobuf** files"
      ],
      "metadata": {
        "id": "csaMWNTH-mkz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model in HDF5 format\n",
        "model_1.save(\"Simple Neural Regression Model.h5\")"
      ],
      "metadata": {
        "id": "MNGV6DmT9_lD"
      },
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Loading Models"
      ],
      "metadata": {
        "id": "ULpu4G0w-Pk-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load a model in SavedModel format\n",
        "\n",
        "loaded_model = tf.keras.models.load_model('Simple Neural Regression Model')\n",
        "loaded_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ri9J3NL-98b",
        "outputId": "6ae9f71c-1dc2-48dc-aedb-9a4fbdad4e4f"
      },
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (None, 100)               200       \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 301\n",
            "Trainable params: 301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3jSFt8g_WSy",
        "outputId": "505dc3f8-7708-469d-e291-6235591ad7e1"
      },
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (None, 100)               200       \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 301\n",
            "Trainable params: 301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.squeeze(model_1.predict(X_test)), tf.squeeze(loaded_model.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0pA0hEW_fLp",
        "outputId": "4453f950-4d53-4965-af82-712c89bea838"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 63ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              " array([ 69.99999 ,  74.      ,  78.00001 ,  81.999985,  85.99999 ,\n",
              "         90.      ,  94.00001 ,  97.99999 , 101.99999 , 106.00001 ],\n",
              "       dtype=float32)>, <tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              " array([ 69.99999 ,  74.      ,  78.00001 ,  81.999985,  85.99999 ,\n",
              "         90.      ,  94.00001 ,  97.99999 , 101.99999 , 106.00001 ],\n",
              "       dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.squeeze(model_1.predict(X_test)) == tf.squeeze(loaded_model.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnlbKEfk_ox4",
        "outputId": "4011908d-fb32-43a1-f1e9-ffb9f0ea393b"
      },
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=bool, numpy=\n",
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True])>"
            ]
          },
          "metadata": {},
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load a model in HDF5 (.h5) format\n",
        "\n",
        "loaded_h5_model = tf.keras.models.load_model('Simple Neural Regression Model.h5')\n",
        "loaded_h5_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gnu4CVJyABFg",
        "outputId": "9d2369ab-54e7-47df-9ed1-541b57a51a9a"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (None, 100)               200       \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 301\n",
            "Trainable params: 301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.squeeze(model_1.predict(X_test)) == tf.squeeze(loaded_h5_model.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zo0_ugRDAUm3",
        "outputId": "064c1c40-c3eb-4164-aa19-96d5dfeff785"
      },
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=bool, numpy=\n",
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True])>"
            ]
          },
          "metadata": {},
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Download Files from Colab"
      ],
      "metadata": {
        "id": "v-2_vFboBlZu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"Simple Neural Regression Model.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TYDfbsexBobi",
        "outputId": "9b63ce35-3006-48fb-d7a7-aef2999d843e"
      },
      "execution_count": 297,
      "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_19c908c3-415a-4fdf-892c-844b345e3b3d\", \"Simple Neural Regression Model.h5\", 28416)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save Colab Files to Google Drive\n",
        "\n",
        "!cp \"Simple Neural Regression Model.h5\" \"drive/MyDrive/Colab Notebooks/Neural Network Regression/Models/\"\n",
        "!ls \"drive/MyDrive/Colab Notebooks/Neural Network Regression/Models/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LktD9I6nB6Gu",
        "outputId": "89064e7e-4168-4da2-a38b-cffceef6659b"
      },
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'Simple Neural Regression Model.h5'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# First Large Model"
      ],
      "metadata": {
        "id": "PdI_j6lAHLLW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Get the Dataset and One-Hot Encode it"
      ],
      "metadata": {
        "id": "7jZXEzrVIF8L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0gmFGXMmIKyk",
        "outputId": "3d584dbc-7094-45b5-dbce-ee309da5f154"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
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              "4      32    male  28.880         0     no  northwest   3866.85520\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
              "1333   50    male  30.970         3     no  northwest  10600.54830\n",
              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
              "1335   18  female  36.850         0     no  southeast   1629.83350\n",
              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_one_hot = pd.get_dummies(insurance)\n",
        "insurance_one_hot.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0DWTslttImEm",
        "outputId": "7a606556-ad01-476b-c97b-30cc259740b6"
      },
      "execution_count": 300,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age     bmi  children      charges  sex_female  sex_male  smoker_no  \\\n",
              "0   19  27.900         0  16884.92400           1         0          0   \n",
              "1   18  33.770         1   1725.55230           0         1          1   \n",
              "2   28  33.000         3   4449.46200           0         1          1   \n",
              "3   33  22.705         0  21984.47061           0         1          1   \n",
              "4   32  28.880         0   3866.85520           0         1          1   \n",
              "\n",
              "   smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
              "0           1                 0                 0                 0   \n",
              "1           0                 0                 0                 1   \n",
              "2           0                 0                 0                 1   \n",
              "3           0                 0                 1                 0   \n",
              "4           0                 0                 1                 0   \n",
              "\n",
              "   region_southwest  \n",
              "0                 1  \n",
              "1                 0  \n",
              "2                 0  \n",
              "3                 0  \n",
              "4                 0  "
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Split the Data into Rraining & Test Datasets"
      ],
      "metadata": {
        "id": "qhu4hNHBJLbI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = insurance_one_hot.drop('charges', axis = 1)\n",
        "y = insurance_one_hot['charges']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 4)\n",
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLqVSKWrJVDr",
        "outputId": "0a3d6f0e-b2e5-4569-ea67-3e9045673e1f"
      },
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1070"
            ]
          },
          "metadata": {},
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Build a neural network"
      ],
      "metadata": {
        "id": "dAs8X4ekK4Cy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model.compile(loss = 'mae',\n",
        "              optimizer = 'sgd',\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32E2KP7vMYHH",
        "outputId": "393b129d-3395-4ccc-b9c8-35f6e0f2e03f"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 10)                120       \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 131\n",
            "Trainable params: 131\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the model\n",
        "\n",
        "tf.keras.utils.plot_model(insurance_model, show_shapes = True, show_dtype = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "sZfy0fI8NEhP",
        "outputId": "a925e982-dcc2-44b8-fb7b-1b13b660f58c"
      },
      "execution_count": 303,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model\n",
        "insurance_model.fit(X_train, y_train, epochs = 100, verbose = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lfC3-KuN_Pt",
        "outputId": "3f29971d-deda-45a9-d889-71d61350aa67"
      },
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e88e4fb80>"
            ]
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model.predict(X_test)[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vI1kU05rOiSp",
        "outputId": "e24e0d20-3b07-4bcc-e6a8-b25f7468983f"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[13284.709],\n",
              "       [18644.168],\n",
              "       [ 9077.218],\n",
              "       [12364.906],\n",
              "       [13489.038]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJJg-ZrlO0LI",
        "outputId": "2a2a9440-2443-4f3a-9d28-04d8d3ab65b9"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "726     6664.68595\n",
              "260    11837.16000\n",
              "902     4661.28635\n",
              "566     6373.55735\n",
              "776     6986.69700\n",
              "Name: charges, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIxTlGFbO8Z9",
        "outputId": "9f99d389-7f58-4d38-c8b9-134ed44f214f"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 7501.3091 - mae: 7501.3091\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7501.30908203125, 7501.30908203125]"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Improve the Model"
      ],
      "metadata": {
        "id": "69yVckQPPa7n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model_2')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_2.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_2.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gifRBigZRldl",
        "outputId": "95d71024-db45-492b-b831-3a5e81bb1216"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_105 (Dense)           (1070, 10)                1010      \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,221\n",
            "Trainable params: 2,221\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model\n",
        "insurance_model_2.fit(X_train, y_train, epochs = 100, verbose = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBHdDnckR8c7",
        "outputId": "138b98cc-4a25-45f2-d613-9f9238652b9b"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e8607d7f0>"
            ]
          },
          "metadata": {},
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model_2.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AW0aDj1oSxOy",
        "outputId": "686edd3c-4ca6-4313-c2d5-1f79979b4a03"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 4ms/step - loss: 4561.9707 - mae: 4561.9707\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4561.970703125, 4561.970703125]"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model_3')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_3.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_3.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LesQjigcTN4X",
        "outputId": "7175b122-0950-4201-a95e-b057c270651b"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_106 (Dense)           (1070, 10)                1010      \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,221\n",
            "Trainable params: 2,221\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model and save in history which can later be plotted\n",
        "history = insurance_model_3.fit(X_train, y_train, epochs = 200, verbose = 0)"
      ],
      "metadata": {
        "id": "8FYEyomOTeOr"
      },
      "execution_count": 312,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model_3.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTq7NBR1Tg5m",
        "outputId": "b20238f2-8256-4e85-f8d5-a64365f7944b"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 3815.1538 - mae: 3815.1538\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3815.15380859375, 3815.15380859375]"
            ]
          },
          "metadata": {},
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot history which is also known as loss curve or training curve\n",
        "\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VQIB9o5wTsBV",
        "outputId": "5bbc51fa-a73d-4fd5-a3f8-7fbbb3034e2d"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {},
          "execution_count": 314
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Pre-processing Data (Normalization & Standardization)\n",
        "\n",
        "In terms of scaling values, neural networks tend to prefer normalization."
      ],
      "metadata": {
        "id": "c4REVgE4VUgG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import all libraries\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "VahJf4lXfUi-"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the data\n",
        "\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
      ],
      "metadata": {
        "id": "Gz8SdnVgfcA1"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "insurance"
      ],
      "metadata": {
        "id": "wPv8_H2TW5of",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "2b48c87a-020e-44bd-f8ff-c4ddb3c53bca"
      },
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
              "2      28    male  33.000         3     no  southeast   4449.46200\n",
              "3      33    male  22.705         0     no  northwest  21984.47061\n",
              "4      32    male  28.880         0     no  northwest   3866.85520\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance['bmi'].plot(kind = 'hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "hUX_JRhBefaK",
        "outputId": "143a4c02-6da4-498c-e7b4-5b66897990e4"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e85cc92e0>"
            ]
          },
          "metadata": {},
          "execution_count": 318
        },
        {
          "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": [
        "insurance['age'].plot(kind = 'hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Ro2xBMZ6eiOS",
        "outputId": "94a476f3-4736-45f8-b976-d364c76e5cc3"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e88b9f430>"
            ]
          },
          "metadata": {},
          "execution_count": 319
        },
        {
          "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": [
        "insurance['children'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDshslm0emqT",
        "outputId": "7712624c-9108-4db0-b833-a02470d1ed88"
      },
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    574\n",
              "1    324\n",
              "2    240\n",
              "3    157\n",
              "4     25\n",
              "5     18\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get few modules from sklearn\n",
        "\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder"
      ],
      "metadata": {
        "id": "XrDcnkCpettk"
      },
      "execution_count": 321,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Column Transformer\n",
        "\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(),  ['age', 'bmi', 'children']),\n",
        "    (OneHotEncoder(handle_unknown='ignore'), ['sex', 'smoker', 'region'])\n",
        ")\n",
        "\n",
        "# Create X & y\n",
        "X = insurance.drop('charges', axis = 1)\n",
        "y = insurance['charges']\n",
        "\n",
        "# Split the data in the Training and Testing Datasets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 4)\n",
        "\n",
        "# Fit the column transformer to the training data\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and test data with Normalization (MinMaxScaler) & OneHotEncoder\n",
        "X_train_normal = ct.transform(X_train)\n",
        "X_test_normal = ct.transform(X_test)"
      ],
      "metadata": {
        "id": "CAylKdrLf_ry"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.loc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGw6wdGpg7EZ",
        "outputId": "9639591a-7b95-4797-c167-7da0d2867c2e"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                19\n",
              "sex            female\n",
              "bmi              27.9\n",
              "children            0\n",
              "smoker            yes\n",
              "region      southwest\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_normal[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjwZss31hty9",
        "outputId": "608febb3-fa56-4d06-b59b-4e8f7b122166"
      },
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.73913043, 0.56684566, 0.        , 1.        , 0.        ,\n",
              "       1.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the neural network on the normalized data\n",
        "\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_4 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model_4')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_4.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_4.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FuidzNvhwGL",
        "outputId": "aba041b9-8103-4545-e3ed-d2795813650a"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_107 (Dense)           (1070, 10)                1010      \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,221\n",
            "Trainable params: 2,221\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = insurance_model_4.fit(X_train_normal, y_train, epochs = 200, verbose = 0)\n",
        "\n",
        "insurance_model_4.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "colpkjD9ij3s",
        "outputId": "1460be1d-1ad3-4b8a-a128-75fbde305013"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 3896.3503 - mae: 3896.3503\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3896.350341796875, 3896.350341796875]"
            ]
          },
          "metadata": {},
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot history which is also known as loss curve or training curve\n",
        "\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "OF5Djk-RjF2G",
        "outputId": "844a155f-c3b2-4367-e83f-8f3a7bf48904"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {},
          "execution_count": 327
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercises"
      ],
      "metadata": {
        "id": "jZ0YnVjt2WpX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Create your own regression dataset (or make the one we created in \"Create data to view and fit\" bigger) and build fit a model to it.\n",
        "2. Try building a neural network with 4 Dense layers and fitting it to your own regression dataset, how does it perform?\n",
        "3. Try and improve the results we got on the insurance dataset, some things you might want to try include:\n",
        " - Building a larger model (how does one with 4 dense layers go?).\n",
        " - Increasing the number of units in each layer.\n",
        " - Lookup the documentation of [`Adam`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) and find out what the first parameter is, what happens if you increase it by 10x?\n",
        " - What happens if you train for longer (say 300 epochs instead of 200)?\n",
        "4. Import the [Boston pricing dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/boston_housing/load_data) from TensorFlow [`tf.keras.datasets`](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) and model it."
      ],
      "metadata": {
        "id": "-NFOAmQy2Y5M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Create our own Regression Dataset & Build + Fit a Model to it"
      ],
      "metadata": {
        "id": "mAEIMwE_3Cyt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the required libraries for the exercise\n",
        "\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "oX6hK2rv34l-"
      },
      "execution_count": 328,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Input Values\n",
        "X = tf.range(-10000, 10000, delta=8)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWE_UOkk3G2J",
        "outputId": "c5d3c050-ef3d-4c02-86b6-de4f3e459688"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2500,), dtype=int32, numpy=array([-10000,  -9992,  -9984, ...,   9976,   9984,   9992], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Labels or the \"Ground Truth\"\n",
        "y = X + 5\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mx0LIl834JEh",
        "outputId": "33d1604d-278c-4d97-d461-a005f0c4c3b2"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2500,), dtype=int32, numpy=array([-9995, -9987, -9979, ...,  9981,  9989,  9997], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into Testing and Training Datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = X[:2000], X[2000:], y[:2000], y[2000:]\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3uCC9pm5E5r",
        "outputId": "07a517e9-ad30-488c-963b-c69276fb1f41"
      },
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([2000]), TensorShape([500]))"
            ]
          },
          "metadata": {},
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the Data\n",
        "\n",
        "plt.figure(figsize = (20, 15))\n",
        "\n",
        "plt.scatter(X_train, y_train, label = \"Training Data\")\n",
        "plt.scatter(X_test, y_test, label = \"Testing Data\", c = 'y')\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FMThxzY98acW",
        "outputId": "33089db3-3ff4-480c-cbc6-a78f4a8fdd55"
      },
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6e8545aee0>"
            ]
          },
          "metadata": {},
          "execution_count": 332
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set a global random seed\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss = 'mae',\n",
        "              optimizer = 'sgd',\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Fit the model\n",
        "model.fit(tf.expand_dims(X_train, -1), y_train, epochs = 100, verbose = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NE33cxEo4aIw",
        "outputId": "26dd5ff4-00cb-4082-aff2-4396ebd96ee2"
      },
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e85420220>"
            ]
          },
          "metadata": {},
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nia8CVbF6zDS",
        "outputId": "2f07f1ae-0751-4db9-f7cb-fd3a42ba96d8"
      },
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 2ms/step - loss: 360261.5625 - mae: 360261.5625\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[360261.5625, 360261.5625]"
            ]
          },
          "metadata": {},
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([821])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9ydY0tU6_0w",
        "outputId": "48352d48-371d-4d66-96ee-ae136f14aff8"
      },
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 72ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-36168.52]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Building a Neural Network with 4 Dense layers and fitting it to the regression dataset"
      ],
      "metadata": {
        "id": "4lG09eTK7bjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Fit the model\n",
        "history = model.fit(tf.expand_dims(X_train, -1), y_train, epochs = 100, verbose = 0)"
      ],
      "metadata": {
        "id": "KN5Bpkks7k9c"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kX-zMTq7ul2",
        "outputId": "7e6345b7-ef23-4cf9-b607-716b36a46d2e"
      },
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 2ms/step - loss: 57.5335 - mae: 57.5335\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[57.53351974487305, 57.53351974487305]"
            ]
          },
          "metadata": {},
          "execution_count": 337
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation Metrics"
      ],
      "metadata": {
        "id": "Zv3YT21o7zDA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYjBP4Hh9UiY",
        "outputId": "f4ea63b0-eed8-4373-985f-7061ad93aaee"
      },
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize predictions\n",
        "\n",
        "plt.figure(figsize = (20, 15))\n",
        "\n",
        "plt.scatter(X_train, y_train, label = \"Training Data\")\n",
        "plt.scatter(X_test, y_test, label = \"Testing Data\", c = 'y')\n",
        "plt.scatter(X_test, y_pred, label = \"Predictions\", c = 'r')\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "W6y7x-Aw9C3o",
        "outputId": "b9cf680d-77d8-4445-d901-ab0828476d5a"
      },
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6e850b3fa0>"
            ]
          },
          "metadata": {},
          "execution_count": 339
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MAE\n",
        "mae = tf.keras.losses.mae(y_true = y_test, y_pred= tf.squeeze(y_pred)).numpy()\n",
        "mae"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-h5tJrEx9oG2",
        "outputId": "532d4651-36d1-4c9a-e6f2-244dc99596b1"
      },
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57.533516"
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE\n",
        "mse = tf.keras.losses.mse(y_true = y_test, y_pred= tf.squeeze(y_pred)).numpy()\n",
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0lUarp591DM",
        "outputId": "9cd9cc9b-1951-4aba-b475-a20045e1ccaf"
      },
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3379.1619"
            ]
          },
          "metadata": {},
          "execution_count": 341
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Huber\n",
        "huber = tf.keras.losses.huber(y_true = y_test, y_pred= tf.squeeze(y_pred)).numpy()\n",
        "huber"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8kmopg_9_Gy",
        "outputId": "973e10ee-3a05-4ac1-9bc5-23b69dc857fa"
      },
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57.033516"
            ]
          },
          "metadata": {},
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zz_XU08R-Cbe",
        "outputId": "3c0c3e35-8519-4a1b-817e-21ca47fdcb64"
      },
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 343
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Improve the Insurance Model"
      ],
      "metadata": {
        "id": "uv3sJjoD-TQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
      ],
      "metadata": {
        "id": "XuWzLHaz-_Q6"
      },
      "execution_count": 344,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "\n",
        "# Create a Column Transformer\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(),  ['age', 'bmi', 'children']),\n",
        "    (OneHotEncoder(handle_unknown='ignore'), ['sex', 'smoker', 'region'])\n",
        ")\n",
        "\n",
        "# Create X & y\n",
        "X = insurance.drop('charges', axis = 1)\n",
        "y = insurance['charges']\n",
        "\n",
        "# Split the data in the Training and Testing Datasets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 4)\n",
        "\n",
        "# Fit the column transformer to the training data\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and test data with Normalization (MinMaxScaler) & OneHotEncoder\n",
        "X_train_normal = ct.transform(X_train)\n",
        "X_test_normal = ct.transform(X_test)"
      ],
      "metadata": {
        "id": "Kw0vIduV_bRw"
      },
      "execution_count": 345,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the neural network on the normalized data\n",
        "\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_5 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model_5')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_5.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_5.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_5.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eo8d9uU0_d8w",
        "outputId": "8d758655-8a5b-47e3-9a3b-37101eb30d7e"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_113 (Dense)           (1070, 10)                1010      \n",
            "                                                                 \n",
            " dense_114 (Dense)           (1070, 10)                110       \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,331\n",
            "Trainable params: 2,331\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = insurance_model_5.fit(X_train_normal, y_train, epochs = 200, verbose = 0)\n",
        "\n",
        "insurance_model_5.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nG43I64F_gQD",
        "outputId": "5e8fc976-1f0d-4693-e540-b1002ac0e4e8"
      },
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 3887.8091 - mae: 3887.8091\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3887.80908203125, 3887.80908203125]"
            ]
          },
          "metadata": {},
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "M3YVVEwb_lmc",
        "outputId": "25dca2f6-7ff9-44cd-9b15-dfd087782297"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 348
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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379Wwo4iIjBiVwQDsLZxORUtt2DFEREaMymAA2sefw+TELloO6y2mIjI6qQwGoGDK+UTM2fH6qrCjiIiMiCGVgZn9bzN71cw2mNmPzSzfzKrN7CUzqzWzpWaWG8ybF5yuDc6vSrmeO4LxTWa2cGh3afhNPCu5E/nAa78JOYmIyMgYdBmY2WTgc0CNu88CosA1wLeBe9x9GnAAuDG4yI3AgWD8nmA+zGxmcLlzgCuBfzKz6GBzjYSJU6axPm8OM7Yu4dDBxrDjiIgMu6FuJooBBWYWAwqBXcBlwOPB+YuBjwbTVwenCc6/3MwsGH/U3dvdfStQC8wfYq5hV/DBrzOGw7z6k6+GHUVEZNgNugzcvR74v8BbJEugCXgZOOjuXcFsdcDkYHoysCO4bFcw/7jU8T4ukzGmnfceVpW+n7l1D7NplTYXicjoMpTNRGNI/ldfDZwKFJHczDNizOxmM1tlZqsaGhpG8qb6NO26H7IvMpZx/3kDe+reTPvti4iMlKFsJno/sNXdG9y9E/gpcDFQHmw2AqgE6oPpemAKQHB+GdCYOt7HZY7i7ve7e42711RUVAwh+uCUj59I5ycfId/bOfyjP+FIy+G0ZxARGQlDKYO3gAvNrDDY9n858BrwW+ATwTyLgJ8H08uC0wTn/8bdPRi/Jni3UTUwHVgxhFwjqursGt587z9wetcWNt77aR3NVERGhaHsM3iJ5I7g1cD64LruB74I3GpmtST3CTwQXOQBYFwwfitwe3A9rwKPkSySJ4HPunt8sLnS4bzLrmHFtM8xt/n3vPjQ7WHHEREZMkv+c37yqamp8VWrwvsQmCcSrPrHTzGv6Wleuej7zFm46PgXEhEJmZm97O41vcf1CeRBskiE2X/5EG/EzuSMF25n59bXw44kIjJoKoMhyC8oovjT/4a5c/jh6+jsaA87kojIoKgMhujU6hm8ccE3OatrE6t+9DdhxxERGRSVwTB41wdv4KWxH+GiXUtY97snwo4jInLCVAbD5Lyb7mVr5DQqf/d59u3cHnYcEZETojIYJvmFxUT+5EcUeBu7F19HIp7R744VETmKymAYnXb2u1h/7peY1b6GlU/8fdhxREQGTGUwzOZ97K/ZkHc+M1/9Hg07t4UdR0RkQFQGw8wiEcr/5Ifk0MWOh28JO46IyICoDEZA5bRZvHL6zcxtWc6aZx4JO46IyHGpDEZIzbV3sTVyGpP+8Le0HD4YdhwRkWNSGYyQnNw82q/8eybQyLrHvxl2HBGRY1IZjKAZ8z/AK4UXM2vbYg7u2x12HBGRfqkMRlj5h75CEW1sfOJrYUcREemXymCEVc+cx8vlH2DOzqV6q6mIZCyVQRpM/ujXiJBgyxN3hR1FRKRPKoM0OLV6Bq9UXM3cfb+gfsvGsOOIiLyDyiBNTv/ju3CMul9+J+woIiLvoDJIk4pTq1gzdiHnNfyC/Xvrw44jInIUlUEaTbjyNvKtkzeW6SB2IpJZVAZpdNpZ57Om8CLOrPsJ7W2tYccREemhMkizyPzPMJZDrH/238OOIiLSQ2WQZrMu+Sj1NoHCdUvCjiIi0kNlkGaRaJQd1Z9iZsd6tm98Oew4IiKAyiAU0664mbgbO/9bh7cWkcygMgjB+IlT2JR7DhPrnw07iogIoDIIzaGqhVQntlFXuyHsKCIiKoOwTL34UwDUvfBYyElERFQGoTm16ixqo2dQvv3psKOIiKgMwtRQ+QFmdG1k387tYUcRkSw3pDIws3Ize9zMXjezjWZ2kZmNNbNnzGxz8HtMMK+Z2ffNrNbM1pnZ3JTrWRTMv9nMFg31Tp0sJl7wCQDefF6bikQkXENdM/hH4El3nwGcB2wEbgd+7e7TgV8HpwGuAqYHPzcD9wKY2VjgLuACYD5wV3eBjHZVM95FnU2iYMuvwo4iIllu0GVgZmXAAuABAHfvcPeDwNXA4mC2xcBHg+mrgSWe9CJQbmaTgIXAM+6+390PAM8AVw4218nEIhHqJlzG2UfW0HRgX9hxRCSLDWXNoBpoAH5kZq+Y2b+aWREwwd13BfPsBiYE05OBHSmXrwvG+ht/BzO72cxWmdmqhoaGIUTPHOXv+mNyLM7m558IO4qIZLGhlEEMmAvc6+5zgBbe3iQEgLs74EO4jaO4+/3uXuPuNRUVFcN1taGaPudSmigi8ebvwo4iIllsKGVQB9S5+0vB6cdJlsOeYPMPwe+9wfn1wJSUy1cGY/2NZ4VoLMabRXOZenAlnkiEHUdEstSgy8DddwM7zOysYOhy4DVgGdD9jqBFwM+D6WXAdcG7ii4EmoLNSU8BV5jZmGDH8RXBWNbonHoJE2mgfstrYUcRkSwVG+LlbwEeNrNcYAtwA8mCeczMbgS2A58M5v0l8EGgFmgN5sXd95vZ14CVwXxfdff9Q8x1Upk0ZyFs/Ab1rzxJ5bRZYccRkSw0pDJw9zVATR9nXd7HvA58tp/reRB4cChZTmZTpp3LXsaSs3058DdhxxGRLKRPIGcAi0TYXjaP6uZXtN9AREKhMsgQPvUixnCIHbXrwo4iIllIZZAhJsx6HwC71/8u3CAikpVUBhli6vRzOUAJ7Hjp+DOLiAwzlUGGsEiEbYWzmdS0JuwoIpKFVAYZpH3SPKb4Tvbt3nH8mUVEhpHKIIOUz1gAwI61vwk5iYhkG5VBBqme/W46PEb7Vu03EJH0UhlkkLz8QrbmnEHpfr29VETSS2WQYQ6MOZeq9jfo6uwIO4qIZBGVQYaJTamh0NrZ/vrqsKOISBZRGWSYiTPfA8C+1/8QchIRySYqgwwz+fSZHKAEq18VdhQRySIqgwxjkQhv5c/glEMbwo4iIllEZZCBWk+Zw9T4DpoPHQg7iohkCZVBBiqsnk/EnG3rnw87iohkCZVBBjpt9iUAHK59MeQkIpItVAYZqHz8ROpsEvl7Xgk7iohkCZVBhtpdcg6Vra/pm89EJC1UBhmqa9K7qOAAe+q3hB1FRLKAyiBDjZl+IQA7X9VOZBEZeSqDDFU160I6PEbHVu1EFpGRpzLIUHn5hbyZexZjG18OO4qIZAGVQQY7eMo8Tu+speXwwbCjiMgopzLIYEXTLiFmCba88vuwo4jIKKcyyGDVcy8j7kbz5uVhRxGRUU5lkMFKysayNXY6pXtWhB1FREY5lUGG2zeuhjPaN9J2pCXsKCIyiqkMMlzBjMvIt042r3gq7CgiMoqpDDLcmRf8EW2eQ8urvwo7ioiMYkMuAzOLmtkrZvafwelqM3vJzGrNbKmZ5QbjecHp2uD8qpTruCMY32RmC4eaaTQpKCphU8H5VDY8F3YUERnFhmPN4K+BjSmnvw3c4+7TgAPAjcH4jcCBYPyeYD7MbCZwDXAOcCXwT2YWHYZco0Zb9fup9F3s2Lw27CgiMkoNqQzMrBL4I+Bfg9MGXAY8HsyyGPhoMH11cJrg/MuD+a8GHnX3dnffCtQC84eSa7SZMj+5COtX/EfISURktBrqmsE/ALcB3cdZHgccdPeu4HQdMDmYngzsAAjObwrm7xnv4zJHMbObzWyVma1qaGgYYvSTx6nVM6iNnkHFlp/pkNYiMiIGXQZm9iFgr7un7eA57n6/u9e4e01FRUW6bjYjNM74U86Ib2XT6t+GHUVERqGhrBlcDHzEzLYBj5LcPPSPQLmZxYJ5KoH6YLoemAIQnF8GNKaO93EZCZyz8EZaPJ/Dz90fdhQRGYUGXQbufoe7V7p7FckdwL9x908DvwU+Ecy2CPh5ML0sOE1w/m/c3YPxa4J3G1UD0wF95LaX4tIxbBh/JbMP/pqd2zaFHUdERpmR+JzBF4FbzayW5D6BB4LxB4BxwfitwO0A7v4q8BjwGvAk8Fl3j49ArpPelA9/iS5iHHz4z4l3dR3/AiIiA2TJf85PPjU1Nb5q1aqwY6Tdyv/4IfPWfIlVpR9gyie/zYTKM8KOJCInETN72d1reo/H+ppZMlfNR/4nL+x9nXfVP0zkX2rYlDOd/ZWXUfW+G5h02llhxxORk5TWDE5SO7dtYvuz9zF29x84qyu5D2EP49hZeBZtFedScuZ7mDbnfeQXFoecVEQySX9rBiqDUWDn1td567+XEtuzjlOaN1IZ30nEnA6P8mbuWRw8dQHj53yIM2a/m0hUH+4WyWYqgyzSdGAf2155ltbNzzGuYQXTOjcTMaeRMurzp9Ey5mzKzvsw0+ZcSm5efthxRSSNVAZZbP/eera8uAx/87eUN7/JaV1bybU47Z7D1pxpHBx3HjlT5zHpnEuYNHU6FtHBbEVGK5WB9DjctJ/NLyyjY+uLlO1fS3XHZvKtE4B9lLOjcCZtE+ZQcsaFVJ17CcWlY0JOLCLDRWUg/ersaGf7xpU0vv4HIjtfZsLhDUxNJD8EnnBje3QqDWWzYHINY6fPp7B0PLkFRRQUlVBYVPqONYm21mYAcvMKhnUfhScSHGk9TEFhyZDWXjyRoGn/XnLzCygsLhvQ/O1trXR0tJOTk0s0lkNOTu6Q16AS8Tj7G+opKR9PXn7hoK+jpbmJ4pLyAeXxRIKW5iYg+fgMx/041m11X3fzoQMUFJYQjZ3cb2D0RAJ3H/Dz+kQfn+7baG8/gicS5BcUHXW5zo52Wg4doHz8xEHlB5WBnKCm/Q1sX7ecli0vUbh3Nae1baSc5nfM1+lROokRI06TlWA44zn4jvPbLZdOcgCIkDjqJ+oJDCcanHaMLqLEidBlMeLBOQV+hBI7QofHaLFCIsSJeKLncnGidFmUTnKC64sT9fhR1+sYURLkpawJxYlieE82IJjTidFFobcRs3ceILDLI8nbDG43EWTuuWeW/O1EMBKMTRzAMQ5Eyol5nDF+sCdHsxf03G631Onepx2j3fIo8hZyLc4Rz6XVCnrOS9V92jEK/QjFdqTnvIQbHcTosBw6ySERfA419Tq6l1tqCgOS9z7es5wjOPHgPsc8TiFtHCEPxyi2I3R5hCYr6bmu7vtz9H0+Onlf931A87u/4/xjLdvjzdNFjCOWT4k3k0MXB62UGF3kewcJLHjMDbcIySWRHCv1w+RanFbPo9UKepbl28vUSFgyZa53UOBt5NNO1JK33elRWqyALmIUeSsF1gFAxx17Br2/T58zkBNSNraCcy/9OFz6cSD530rdltfYW7uKriOH8Y4WvL0Fb2vC4h1gESJtBwDYXH4aWBTiHdDVjsU7sHg7Fm9PXpdFwSJgkeR0JAoWxc2Sl8OxRBwSnViiCzyOJbrwWAGJ4onQdohIx6HkZYLLYhHwRDJLojO4/igeifXcVnBHktOlk/COFqJNb0H3P0QWAQv+SC35h0okRiK3GHKLsVguxLvwRCfEuyDR1ZPR4p3g8WQGj2OeOGraMeoKxgMQO7IPj+SwrWAcNmYqiZb9WNvB4Dah90tcT7ZUnsC6jpDILcUKx0DzXqzrCD0viN7rhTY4nYgVQOmpyfvZ1Y51dUBXG8Q7sERHkNvfcT0915Vy2iPdyzcW/DZIBPc3EsVzirDOFswTJEpOhfZDRI7sT7mfA7yvfc1rR5dVX+M98x/v9o5xmx5c1uKdRDpbSOSV4dFcosFj6LGCYMbuxz75uHf/JPLLsYIx0LyHSGfL28vWPWV5Jn8S0Xw8pzB5nbmFyawdzUTaD2GJThI5xXh+GZZfyhwf/qMXqwxkQCwSoXLaLCqnzQo7ioiMAL1tREREVAYiIqIyEBERVAYiIoLKQEREUBmIiAgqAxERQWUgIiKcxIejMLMGYPsgLz4e2DeMcYaLcp24TM2mXCcmU3NB5mYbbK7T3L2i9+BJWwZDYWar+jo2R9iU68RlajblOjGZmgsyN9tw59JmIhERURmIiEj2lsH9YQfoh3KduEzNplwnJlNzQeZmG9ZcWbnPQEREjpatawYiIpJCZSAiItlVBmZ2pZltMrNaM7s95CxTzOy3Zvaamb1qZn8djN9tZvVmtib4+WAI2baZ2frg9lcFY2PN7Bkz2xz8HpPmTGelLJM1ZnbIzD4f1vIyswfNbK+ZbUgZ63MZWdL3g+fdOjObm+Zc3zWz14Pb/pmZlQfjVWZ2JGXZ3ZfmXP0+dmZ2R7C8NpnZwjTnWpqSaZuZrQnG07m8+nt9GLnnmLtnxQ8QBd4ETgdygbBxNswAAATPSURBVLXAzBDzTALmBtMlwBvATOBu4G9CXlbbgPG9xr4D3B5M3w58O+THcjdwWljLC1gAzAU2HG8ZAR8EfkXyOxcvBF5Kc64rgFgw/e2UXFWp84WwvPp87IK/g7VAHlAd/N1G05Wr1/l/D/yfEJZXf68PI/Ycy6Y1g/lArbtvcfcO4FHg6rDCuPsud18dTB8GNgKTw8ozAFcDi4PpxcBHQ8xyOfCmuw/2E+hD5u7Lgf29hvtbRlcDSzzpRaDczCalK5e7P+3uXcHJF4HKkbjtE811DFcDj7p7u7tvBWpJ/v2mNZeZGfBJ4McjcdvHcozXhxF7jmVTGUwGdqScriNDXnzNrAqYA7wUDP1VsKr3YLo3xwQceNrMXjazm4OxCe6+K5jeDUwIIVe3azj6DzTs5dWtv2WUSc+9Pyf5H2S3ajN7xcx+b2aXhJCnr8cuU5bXJcAed9+cMpb25dXr9WHEnmPZVAYZycyKgSeAz7v7IeBe4AzgfGAXydXUdHuPu88FrgI+a2YLUs/05HppKO9JNrNc4CPAT4KhTFhe7xDmMuqPmd0JdAEPB0O7gKnuPge4FXjEzErTGCkjH7sU13L0Px1pX159vD70GO7nWDaVQT0wJeV0ZTAWGjPLIflAP+zuPwVw9z3uHnf3BPAvjNDq8bG4e33wey/wsyDDnu7VzuD33nTnClwFrHb3PUHG0JdXiv6WUejPPTO7HvgQ8OngRYRgM0xjMP0yyW3zZ6Yr0zEeu0xYXjHgj4Gl3WPpXl59vT4wgs+xbCqDlcB0M6sO/ru8BlgWVphge+QDwEZ3/17KeOp2vo8BG3pfdoRzFZlZSfc0yZ2PG0guq0XBbIuAn6czV4qj/lsLe3n10t8yWgZcF7zj40KgKWVVf8SZ2ZXAbcBH3L01ZbzCzKLB9OnAdGBLGnP199gtA64xszwzqw5yrUhXrsD7gdfdva57IJ3Lq7/XB0byOZaOPeOZ8kNyj/sbJBv9zpCzvIfkKt46YE3w80Hg34D1wfgyYFKac51O8p0ca4FXu5cTMA74NbAZeBYYG8IyKwIagbKUsVCWF8lC2gV0ktw+e2N/y4jkOzx+GDzv1gM1ac5VS3J7cvfz7L5g3o8Hj/EaYDXw4TTn6vexA+4Mltcm4Kp05grGHwL+ste86Vxe/b0+jNhzTIejEBGRrNpMJCIi/VAZiIiIykBERFQGIiKCykBERFAZiAyYmTWHnUFkpKgMREREZSAyFGZ2vpm9aG9/V0D38eU/FxyLfp2ZPRqMvTflWPivdH/SWyQT6ENnIgNkZs3uXtxrbB1wi7v/3sy+CpS6++fNbCdQ7e7tZlbu7gfN7BfAt9z9D8EByNr87UNLi4RKawYig2RmZUC5u/8+GFpM8stSIHkYgYfN7M9IHikU4A/A98zsc8HlVASSMVQGIiPjj0geK2YusNLMYu7+LeAmoAD4g5nNCDOgSCqVgcgguXsTcCDlS07+B/B7M4sAU9z9t8AXgTKg2MzOcPf17v5tkkfRVRlIxoiFHUDkJFJoZnUpp79H8jDC95lZIcnDGd9A8jua/z3YjGTA94N9Bl8zs/cBCZJHv/wVIhlCO5BFRESbiURERGUgIiKoDEREBJWBiIigMhAREVQGIiKCykBERID/Dw8zxJKw3OoWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the neural network on the normalized data\n",
        "\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_6 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model_6')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_6.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_6.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_6.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtaxGkD7_0Vr",
        "outputId": "2ab46dcb-a144-4115-d9ed-b9e503a586b5"
      },
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_115 (Dense)           (1070, 100)               10100     \n",
            "                                                                 \n",
            " dense_116 (Dense)           (1070, 100)               10100     \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,501\n",
            "Trainable params: 21,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = insurance_model_6.fit(X_train_normal, y_train, epochs = 200, verbose = 0)\n",
        "\n",
        "insurance_model_6.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kl5uLigaAXOb",
        "outputId": "71d30a4e-f549-49fe-ce06-55afd4509283"
      },
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 3991.8354 - mae: 3991.8354\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3991.83544921875, 3991.83544921875]"
            ]
          },
          "metadata": {},
          "execution_count": 350
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Wa-e0xFYAYD4",
        "outputId": "05685c2c-0979-4e1a-fcc3-48d10bcb2625"
      },
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 351
        },
        {
          "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": [
        "# Build the neural network on the normalized data\n",
        "\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_7 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer'),\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = 'insurance_model_7')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_7.compile(loss = 'mae',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.01),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_7.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_7.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IxjD_BvAfEF",
        "outputId": "2d44da9a-9de9-43ff-e4ae-7ecc3bcd4185"
      },
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_117 (Dense)           (1070, 100)               10100     \n",
            "                                                                 \n",
            " dense_118 (Dense)           (1070, 100)               10100     \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,501\n",
            "Trainable params: 21,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = insurance_model_7.fit(X_train_normal, y_train, epochs = 200, verbose = 0)\n",
        "\n",
        "insurance_model_7.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAL9Rm4WA2Dn",
        "outputId": "eb797e93-1465-4dd3-96c5-1baf74b7cf7c"
      },
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 4004.7136 - mae: 4004.7136\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4004.713623046875, 4004.713623046875]"
            ]
          },
          "metadata": {},
          "execution_count": 353
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "gnhMXzoyA4j6",
        "outputId": "05165dab-f484-4f32-9770-d6914591fecc"
      },
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 354
        },
        {
          "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": [
        "history = insurance_model_7.fit(X_train_normal, y_train, epochs = 300, verbose = 0)\n",
        "\n",
        "insurance_model_7.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExYh_fq4A5cW",
        "outputId": "4e89ae01-7b2a-429d-e823-69463c3b2f2a"
      },
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 3958.0579 - mae: 3958.0579\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3958.057861328125, 3958.057861328125]"
            ]
          },
          "metadata": {},
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Vrzmjg5yBBwh",
        "outputId": "08ca13c8-c1d8-47fe-abb9-c57ddd013a5a"
      },
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 356
        },
        {
          "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": [
        "# Build the neural network on the normalized data\n",
        "\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "insurance_model_7 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer', activation = None),\n",
        "    tf.keras.layers.Dense(100, activation = None),\n",
        "    tf.keras.layers.Dense(100, activation = None),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\", activation = None)\n",
        "], name = 'insurance_model_7')\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_7.compile(loss = 'mse',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.03),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Build the model\n",
        "insurance_model_7.build(input_shape = (1070,11))\n",
        "\n",
        "# Print the Summary of the model\n",
        "insurance_model_7.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8vpX2IlBCct",
        "outputId": "0ee507b6-f1bf-4a7e-b300-6630b67438f7"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"insurance_model_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (1070, 100)               1200      \n",
            "                                                                 \n",
            " dense_119 (Dense)           (1070, 100)               10100     \n",
            "                                                                 \n",
            " dense_120 (Dense)           (1070, 100)               10100     \n",
            "                                                                 \n",
            " output_layer (Dense)        (1070, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,501\n",
            "Trainable params: 21,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = insurance_model_7.fit(X_train_normal, y_train, epochs = 300, verbose = 0)\n",
        "\n",
        "insurance_model_7.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNPfq_EcBwUH",
        "outputId": "530d963e-bafc-479b-fc5e-7b109942d71c"
      },
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 4ms/step - loss: 38592780.0000 - mae: 4091.7043\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[38592780.0, 4091.704345703125]"
            ]
          },
          "metadata": {},
          "execution_count": 358
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "vzyGw1gaCEL-",
        "outputId": "1f1c730f-6572-4219-e239-bc55d64cb5a9"
      },
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 359
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4. Import the Boston pricing dataset and model it."
      ],
      "metadata": {
        "id": "pMz4-lrfCFAx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.boston_housing.load_data(test_split=0.2, seed=4)"
      ],
      "metadata": {
        "id": "UF8Unx2gCLn5"
      },
      "execution_count": 360,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-23Da3vtCtR5",
        "outputId": "219b755e-b957-4948-aebd-178c00efcd2a"
      },
      "execution_count": 361,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[2.11240e-01, 1.25000e+01, 7.87000e+00, ..., 1.52000e+01,\n",
              "         3.86630e+02, 2.99300e+01],\n",
              "        [4.29700e-02, 5.25000e+01, 5.32000e+00, ..., 1.66000e+01,\n",
              "         3.71720e+02, 9.51000e+00],\n",
              "        [1.35540e-01, 1.25000e+01, 6.07000e+00, ..., 1.89000e+01,\n",
              "         3.96900e+02, 1.30900e+01],\n",
              "        ...,\n",
              "        [1.70040e-01, 1.25000e+01, 7.87000e+00, ..., 1.52000e+01,\n",
              "         3.86710e+02, 1.71000e+01],\n",
              "        [5.64600e-02, 0.00000e+00, 1.28300e+01, ..., 1.87000e+01,\n",
              "         3.86400e+02, 1.23400e+01],\n",
              "        [1.42362e+01, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n",
              "         3.96900e+02, 2.03200e+01]]), (404, 13))"
            ]
          },
          "metadata": {},
          "execution_count": 361
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzQcPQWJC0_o",
        "outputId": "2da0f6b2-6cfe-4c91-a167-3b0a79c9947d"
      },
      "execution_count": 362,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16.5, 24.8, 17.4, 19.3, 37.6, 24.2, 35.4, 19.9, 27.5, 17. , 31.2,\n",
              "       24.4, 16.1, 27. , 21. , 14.9, 18.9,  6.3, 16.3, 13.9,  8.8, 19.4,\n",
              "       18.8, 19.8, 17.5, 19.3, 20. , 14.3, 16.1, 19.5, 11. , 21.9, 31. ,\n",
              "       22. , 15.1, 13.3, 28.7, 46.7, 22.2, 22.8, 42.3, 41.3, 16.7, 31.1,\n",
              "       26.7, 19.4, 50. , 16.6, 19.5, 24.4, 28.5, 22.3, 12.1, 28.6, 15.6,\n",
              "       19.2, 27.5, 32. , 20.2, 32.4, 18.4, 19.9, 29.8, 20.1, 43.5, 24.5,\n",
              "       50. ,  7.2, 19.1, 21.2, 22.6, 22.9, 25. , 23.3, 17.3, 33. , 17.8,\n",
              "       23.8, 10.9, 18.6, 19.3, 16.7, 28. , 18.2, 29.1, 11.9, 32.7, 18.3,\n",
              "       22.4, 45.4, 31.5, 48.5, 19.8, 41.7, 22.2, 20.3, 20.7, 50. , 11.8,\n",
              "       19.5,  8.7, 23.3, 36.4, 13.3, 24.8, 20.4, 44. , 29. , 39.8, 22.9,\n",
              "       23. , 15.3, 23.7, 30.5, 33.2, 26.4, 50. , 14.2,  8.1, 16. , 20. ,\n",
              "        8.5, 23.7, 26.4, 18.5, 20. , 50. , 13.4, 13.1, 12.7, 50. , 15.6,\n",
              "       50. , 29.4, 42.8, 21.7, 11.8, 24.8, 19.4, 17.5, 13.4, 13.9, 24.5,\n",
              "       15. , 13.8, 23.1, 12.5, 14.9, 21.9, 18.5, 30.8, 14.6, 23.9, 18.2,\n",
              "       21.7, 13.5, 50. , 23.1, 48.8, 13.8, 20.1, 50. , 34.9,  8.4, 15.2,\n",
              "       23. , 24.7, 25.3, 17.2, 50. , 22.9, 20.2, 17.4, 19.5, 18.5, 14. ,\n",
              "       22.6, 14.1, 15.6, 46. , 20.5, 13.5, 10.4, 21.4, 21.6, 23.2, 23. ,\n",
              "       17.6, 16.1,  5. ,  8.3, 27.5, 18.7, 21.7, 30.7,  5. , 11.3,  7. ,\n",
              "       32.9, 14.6, 12. , 28.1, 18. ,  5.6, 23.6, 24.7, 22.5, 17.7, 13.1,\n",
              "       23.1, 25. , 14.9,  9.7, 22.8, 22. , 23.6, 14.3, 18.8, 19.9, 13.6,\n",
              "       19.4, 16.8, 20. , 43.1, 27.9, 20.1, 19. , 19.2, 21.7, 33.1, 50. ,\n",
              "       33.2, 20.1, 21.1,  8.8, 12.3, 14.5, 23.8, 18.7, 21.8, 21.9, 21.7,\n",
              "       17.1, 23.1, 36.1, 28.2, 11.5, 19. , 22. , 10.5, 21.4, 16.5, 20.6,\n",
              "       23.3, 23.5, 15. , 26.5, 50. , 10.5, 17.5, 13.6, 17.2, 19.1, 16.4,\n",
              "       20.6, 20.9, 30.1, 20.7, 22.2, 24.6, 25.2, 37.9, 20.1, 29.6, 18.7,\n",
              "       23. , 22.9, 24.6, 24.8, 20.8, 22.4, 18.2, 14.4, 23.2, 13. , 19.7,\n",
              "       21.2, 21.7, 24. , 22. , 20.6, 11.9, 24.3, 23.8, 22.8, 13.3, 25. ,\n",
              "       21. , 20.4, 33.1, 48.3, 14.5, 36. , 22.6, 18.4, 18.9, 12.6, 15.2,\n",
              "       24.1, 29.9, 23.9, 31.6, 11.7, 20.3, 16.6, 22.2, 26.6, 36.2, 28.4,\n",
              "       20.8, 15.4, 50. , 18.1, 23.1, 21.5, 13.1, 21.8,  8.5, 15.6, 26.2,\n",
              "       32.2,  9.6, 31.6, 17.8, 34.7, 20. , 21. , 22.7, 28.7, 23.9, 35.4,\n",
              "       13.2, 18.3, 13.1, 23.1, 20.6,  7. , 13.4, 24.1, 30.1, 20.3, 15.6,\n",
              "       26.6, 15. , 37.2, 27.1, 24.4, 17.8, 19.8, 10.2, 23.1, 37.3, 23.2,\n",
              "       19.1, 19.6, 38.7, 25. , 23.7, 22.8, 16.2, 20.3, 24.3, 21.2, 19.3,\n",
              "       20.6, 21.4, 14.4, 19.9, 16.2, 22.5, 19.1, 17.8, 30.1, 14.8, 35.2,\n",
              "       29. , 25.1, 21.5,  8.3, 22. , 44.8, 24.5, 34.9, 17.2, 33.8, 19.6,\n",
              "       14.1,  8.4, 33.3, 23.4, 21.4, 18.9, 21.2,  7.2])"
            ]
          },
          "metadata": {},
          "execution_count": 362
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the neural network\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create a model\n",
        "boston_house_pricing_model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, name = 'input_layer', activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\", activation = None)\n",
        "], name = 'boston_house_pricing_model')\n",
        "\n",
        "# Compile the model\n",
        "boston_house_pricing_model.compile(loss = 'mse',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.05),\n",
        "              metrics = ['mse'])\n",
        "\n",
        "# Build the model\n",
        "boston_house_pricing_model.build(input_shape = (404,13))\n",
        "\n",
        "# Print the Summary of the model\n",
        "boston_house_pricing_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWn8324CC_XP",
        "outputId": "1fdcf686-38f4-4666-913a-27224c9ea9b3"
      },
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"boston_house_pricing_model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (404, 100)                1400      \n",
            "                                                                 \n",
            " dense_121 (Dense)           (404, 10)                 1010      \n",
            "                                                                 \n",
            " dense_122 (Dense)           (404, 10)                 110       \n",
            "                                                                 \n",
            " dense_123 (Dense)           (404, 10)                 110       \n",
            "                                                                 \n",
            " output_layer (Dense)        (404, 1)                  11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,641\n",
            "Trainable params: 2,641\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = boston_house_pricing_model.fit(X_train, y_train, epochs = 300, verbose = 0)\n",
        "boston_house_pricing_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWxrTHWlDZxI",
        "outputId": "a3f2261c-5b4e-42b1-8d94-f074e188ac81"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 6ms/step - loss: 16.7696 - mse: 16.7696\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[16.769601821899414, 16.769601821899414]"
            ]
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.xlabel(\"Loss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "CURnwlJ3DqZ-",
        "outputId": "747f3544-6718-4203-e742-f49871f0059d"
      },
      "execution_count": 365,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 365
        },
        {
          "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": [
        "import numpy as np\n",
        "np.median(X_train), X_train.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uq6FmNd_Drwp",
        "outputId": "fc066aa8-8a3c-4346-8ecb-5cbb888f8c06"
      },
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6.65196, 70.21037013137853)"
            ]
          },
          "metadata": {},
          "execution_count": 366
        }
      ]
    }
  ]
}