01_neural_ntewrok_regression_with_tensorflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "01_neural_ntewrok_regression_with_tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOL234ylLmfOBRPoJZY2yFY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hrushikute/DataAnalytics/blob/master/01_neural_ntewrok_regression_with_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zH2rx0NSvKJ8"
      },
      "source": [
        "# Introduction to NN in Tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEmA6N3PwkLp",
        "outputId": "8bbb83f1-737d-47e1-f5cd-a75236e64280"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJWCVGWww5pm"
      },
      "source": [
        "## Creating  a data to view and fit "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "FzZKr9RBxGk4",
        "outputId": "34321896-79ec-4cbb-af1a-ce1ecaa466af"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#create features\n",
        "\n",
        "X = np.array([-7.,-4.0,-1,2.0,5.0,8,11,14.0])\n",
        "X\n",
        "#Create labels\n",
        "y = np.array([3,6,9.0,12,15.0,18,21,24])\n",
        "plt.scatter(X,y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f479145b7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sw6EYAEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxPw/YhrWmPXy7VoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-7cf1MPyHiI",
        "outputId": "808a586e-c911-4449-fd13-f49f7e8e5958"
      },
      "source": [
        "y ==X+10\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAd-et-dyKW6"
      },
      "source": [
        "** Input and output shapes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJHe_yRAygHU",
        "outputId": "ce0246d9-969a-464f-bf66-e8f001a2d715"
      },
      "source": [
        "##create a demo tensor for out housing price prediction problme\n",
        "house_info = tf.constant([\"bedroom\",\"bathroom\",\"garage\"])\n",
        "house_price = tf.constant([939700])\n",
        "house_info, house_price"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(3,), dtype=string, numpy=array([b'bedroom', b'bathroom', b'garage'], dtype=object)>,\n",
              " <tf.Tensor: shape=(1,), dtype=int32, numpy=array([939700], dtype=int32)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuiEXvgDy7go",
        "outputId": "5a3000ee-9a73-4e82-b9d6-9cf2ad200273"
      },
      "source": [
        "input_shape = X.shape\n",
        "output_shape = y.shape\n",
        "\n",
        "input_shape, output_shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8,), (8,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RT087ZOSzDdC",
        "outputId": "194f1e59-14c4-4af5-b26e-0f8b6261e81c"
      },
      "source": [
        "# trun numpyt array tensors\n",
        "X=tf.constant(X)\n",
        "y=tf.constant(y)\n",
        "X,y"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(8,), dtype=float64, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.])>,\n",
              " <tf.Tensor: shape=(8,), dtype=float64, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.])>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "cLnreoJu0HLc",
        "outputId": "251e204d-b373-42d3-f381-21dfbccb4756"
      },
      "source": [
        "plt.scatter(X,y)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f47913da790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4CUyTUr0J6D"
      },
      "source": [
        "#Steps in modelling with Tensor flow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDgyj_1I0OAt",
        "outputId": "ab59da65-0387-447d-8ed7-ba44dd0c5ca3"
      },
      "source": [
        "#set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model using the Sequential\n",
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Dense(1)\n",
        "                             ])\n",
        "\n",
        "# Compile the model\n",
        "\n",
        "model.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.SGD(),\n",
        "              metrics=['mae']\n",
        "              )\n",
        "\n",
        "#fit the model\n",
        "model.fit(X,y,epochs=5)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 1s 584ms/step - loss: 11.5048 - mae: 11.5048\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 11.3723 - mae: 11.3723\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 11.2398 - mae: 11.2398\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 11.1073 - mae: 11.1073\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 10.9748 - mae: 10.9748\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f47913dae10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKbgUDPg4Mz5",
        "outputId": "ab08daa6-e3ca-458b-a09f-bf42b2b92b74"
      },
      "source": [
        "model.predict([17.0])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12.716021]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BliqFmp4hRZ"
      },
      "source": [
        "#improving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFHg5RE6LsAD",
        "outputId": "b820c564-e9ae-41e3-94cb-2d8bdf90acbb"
      },
      "source": [
        "#lets rebuild out model\n",
        "# 1. Create amodel\n",
        "model_imporve = tf.keras.Sequential([\n",
        "                                     tf.keras.layers.Dense(50,activation=None),\n",
        "                                     tf.keras.layers.Dense(1)\n",
        "                                     ])\n",
        "# 2. Comiple the model\n",
        "model_imporve.compile(loss=tf.keras.losses.mae,\n",
        "                      optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "                      metrics=['mae'])\n",
        "#3. Fit the model\n",
        "model_imporve.fit(X,y,epochs=100)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 335ms/step - loss: 14.6466 - mae: 14.6466\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 13.9532 - mae: 13.9532\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 13.2603 - mae: 13.2603\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 12.5659 - mae: 12.5659\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 11.8675 - mae: 11.8675\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 11.1621 - mae: 11.1621\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 10.4475 - mae: 10.4475\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7214 - mae: 9.7214\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 8.9817 - mae: 8.9817\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.2262 - mae: 8.2262\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.4526 - mae: 7.4526\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.6585 - mae: 6.6585\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.6244 - mae: 6.6244\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9777 - mae: 6.9777\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2653 - mae: 7.2653\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.4820 - mae: 7.4820\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.4879 - mae: 7.4879\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.3301 - mae: 7.3301\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0454 - mae: 7.0454\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.7251 - mae: 6.7251\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.4643 - mae: 6.4643\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.1900 - mae: 6.1900\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.9246 - mae: 5.9246\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.8782 - mae: 5.8782\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.8276 - mae: 5.8276\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.9724 - mae: 5.9724\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.0130 - mae: 6.0130\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.9583 - mae: 5.9583\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.8190 - mae: 5.8190\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.6030 - mae: 5.6030\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.3433 - mae: 5.3433\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.2352 - mae: 5.2352\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.1239 - mae: 5.1239\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0746 - mae: 5.0746\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 5.0523 - mae: 5.0523\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 4.9871 - mae: 4.9871\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 4.8824 - mae: 4.8824\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 4.7409 - mae: 4.7409\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.5658 - mae: 4.5658\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 4.4020 - mae: 4.4020\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.3009 - mae: 4.3009\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.1923 - mae: 4.1923\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 4.0760 - mae: 4.0760\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 4.0086 - mae: 4.0086\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.8364 - mae: 3.8364\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.6514 - mae: 3.6514\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.4858 - mae: 3.4858\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.3568 - mae: 3.3568\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.2428 - mae: 3.2428\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.0760 - mae: 3.0760\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.8591 - mae: 2.8591\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 2.6501 - mae: 2.6501\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 2.4760 - mae: 2.4760\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 2.3457 - mae: 2.3457\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.1065 - mae: 2.1065\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.8523 - mae: 1.8523\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 1.7208 - mae: 1.7208\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 1.5507 - mae: 1.5507\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 1.2700 - mae: 1.2700\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.9323 - mae: 0.9323\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.8599 - mae: 0.8599\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6865 - mae: 0.6865\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.3405 - mae: 0.3405\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.1659 - mae: 0.1659\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.4224 - mae: 0.4224\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - mae: 0.4802\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.6473 - mae: 0.6473\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 0.6601 - mae: 0.6601\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.7372 - mae: 0.7372\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.7022 - mae: 0.7022\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6318 - mae: 0.6318\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.6151 - mae: 0.6151\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.4329 - mae: 0.4329\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.4277 - mae: 0.4277\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.3097 - mae: 0.3097\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.0356 - mae: 0.0356\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.4880 - mae: 0.4880\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 0.6479 - mae: 0.6479\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6174 - mae: 0.6174\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.4529 - mae: 0.4529\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6294 - mae: 0.6294\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.7850 - mae: 0.7850\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.6653 - mae: 0.6653\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.3935 - mae: 0.3935\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.5590 - mae: 0.5590\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.6263 - mae: 0.6263\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.5316 - mae: 0.5316\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2161 - mae: 0.2161\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.3441 - mae: 0.3441\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.5325 - mae: 0.5325\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.4389 - mae: 0.4389\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2560 - mae: 0.2560\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.5795 - mae: 0.5795\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.7585 - mae: 0.7585\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.6773 - mae: 0.6773\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.3691 - mae: 0.3691\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.3135 - mae: 0.3135\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.5528 - mae: 0.5528\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.5451 - mae: 0.5451\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2902 - mae: 0.2902\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f478b466590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpS7eG91Oub-",
        "outputId": "58cbe403-cd3d-44e0-d4bd-3297bd8ed2f8"
      },
      "source": [
        "model_imporve.predict([17.0])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[26.434414]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbEcRU3iegjg"
      },
      "source": [
        "Evaluating a model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DK-weJyCekqK"
      },
      "source": [
        "1. Creating more data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXp3uO9lerzA",
        "outputId": "93a2a82d-a721-41a9-9df6-c793a8938f83"
      },
      "source": [
        "X = tf.range(-100,100,4)\n",
        "X"
      ],
      "execution_count": 13,
      "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": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KX-3-hAMe0uE",
        "outputId": "3d49b16d-2645-40ef-9817-7bd8d914d45c"
      },
      "source": [
        "y = X + 10\n",
        "y"
      ],
      "execution_count": 14,
      "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": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "BRFB2KPee5as",
        "outputId": "4792e818-3e61-4ba5-823f-d5deb691ad7e"
      },
      "source": [
        "# visualize the data \n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X,y)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4789e107d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBNxopQ_idZq"
      },
      "source": [
        "#split the data in Training and  Testing format.\n",
        "* Training data is usually 80% of data\n",
        "* Testing data is usually 20% of data for testing the model \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ve2-nA7i06X"
      },
      "source": [
        "X_train = X[:40]\n",
        "X_test = X[40:]\n",
        "\n",
        "y_train =y[:40]\n",
        "y_test =y[40:]\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "IU9UcGlQjIfR",
        "outputId": "a7200fcb-e880-4cb7-8d11-c3c7601e99a4"
      },
      "source": [
        "#visualize the training and test data\n",
        "\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.scatter(X_train,y_train,c=\"b\", label=\"Training Data\")\n",
        "plt.scatter(X_test,y_test,c=\"g\", label =\" Testing Data\")\n",
        "plt.legend()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4789578790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wwd2_0hFkCYr",
        "outputId": "98591c4b-0625-4aef-a8cf-f13a2a1ab43b"
      },
      "source": [
        "# 1. Create a model\n",
        "\n",
        "model_huge = tf.keras.Sequential([\n",
        "                                  tf.keras.layers.Dense(10,name=\"input_layer\"),\n",
        "                                  tf.keras.layers.Dense(1,name=\"output_layer\"),\n",
        "                                \n",
        "],name=\"huge_model\")\n",
        "\n",
        "#2. Compile the model\n",
        "model_huge.compile(loss=tf.keras.losses.mae,\n",
        "                   optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "                   metrics=[\"mae\"]\n",
        ")\n",
        "\n",
        "#3. Fit the model\n",
        "\n",
        "model_huge.fit(X_train,y_train, epochs =100, verbose=0)\n",
        "model_huge.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"huge_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (Dense)          (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "TtC7PU88cq0x",
        "outputId": "28901172-5c0e-422f-a2bc-fe772722ba52"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model=model_huge, show_shapes=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HnXHZ51Wbbf"
      },
      "source": [
        "## Visualizing our models predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFnMvPz5Wvqm",
        "outputId": "9ea1a098-8314-446b-a871-508823bd1732"
      },
      "source": [
        "y_pred = model_huge.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 69.868454],\n",
              "       [ 73.85556 ],\n",
              "       [ 77.84266 ],\n",
              "       [ 81.829765],\n",
              "       [ 85.816864],\n",
              "       [ 89.80397 ],\n",
              "       [ 93.79108 ],\n",
              "       [ 97.778175],\n",
              "       [101.76529 ],\n",
              "       [105.752396]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dqxu54_TXHEP",
        "outputId": "5fa29907-2ca8-42e0-cf20-8882dea27924"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 21,
      "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": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YE3Cv4jXJKg"
      },
      "source": [
        "# Lets create a plotting function\n",
        "def plot_predictions(train_data = X_train,\n",
        "                     train_labels = y_train,\n",
        "                     test_data = X_test,\n",
        "                     test_labels = y_test,\n",
        "                     predictions = y_pred):\n",
        "  plt.figure(figsize=(10,7))\n",
        "  plt.scatter(train_data,train_labels,c=\"b\", label=\"Training Data\")\n",
        "  plt.scatter(test_data,test_labels,c=\"g\", label=\"Testing Data\")\n",
        "  plt.scatter(test_data,predictions,c=\"r\", label=\"Predictions\")\n",
        "  plt.legend();\n",
        "\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "BlmdTtSDYv_m",
        "outputId": "fcddb132-59ec-439e-d7cc-0116be527851"
      },
      "source": [
        "plot_predictions(train_data = X_train,\n",
        "                     train_labels = y_train,\n",
        "                     test_data = X_test,\n",
        "                     test_labels = y_test,\n",
        "                     predictions = y_pred)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yHQ0NEwa1nt"
      },
      "source": [
        "## Evaluating our models prediction with regression evaluation metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8NwoFhobM2m",
        "outputId": "b40a91de-6e9e-4c61-b19c-0c106c86b774"
      },
      "source": [
        "model_huge.evaluate(X_test,y_test)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 112ms/step - loss: 0.1896 - mae: 0.1896\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.18958130478858948, 0.18958130478858948]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTnCyHDjnluC"
      },
      "source": [
        "#Convert the y_pred to tensor\n",
        "\n",
        "y_test, y_pred\n",
        "\n",
        "y_pred=tf.squeeze(tf.constant(y_pred))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUoHRQqC-H_X",
        "outputId": "a07964b0-8fb5-40bd-b268-8101052c9153"
      },
      "source": [
        "mae_1 = tf.metrics.mean_absolute_error(y_true=y_test,y_pred=y_pred)\n",
        "mae_1"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.18957901>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQMGYNhU-a4R",
        "outputId": "6f5dc5ca-060e-4f83-a0df-7a63dc03bc69"
      },
      "source": [
        "mse_1 = tf.metrics.mean_squared_error(y_true=y_test, y_pred=y_pred)\n",
        "mse_1"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.03731218>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRoXxGYR_EYG"
      },
      "source": [
        "#lets make function for mae and mse"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQFXiA13_H3V"
      },
      "source": [
        "def mae(y_true, y_pred):\n",
        "  return tf.metrics.mean_absolute_error(y_true=y_true, \n",
        "                                        y_pred=y_pred)\n",
        "def mse(y_true, y_pred):\n",
        "  return tf.metrics.mean_squared_error(y_true=y_true, y_pred=y_pred)\n",
        "  "
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKYrU8neYNCc",
        "outputId": "fa42dca3-887d-492e-abcc-a822b2a7deb8"
      },
      "source": [
        "# Create a new improvement model with more epochs\n",
        "#1.Create model\n",
        "model_im_1 = tf.keras.Sequential([\n",
        "                                  tf.keras.layers.Dense(50),\n",
        "                                  tf.keras.layers.Dense(1)\n",
        "],name=\"model_improvement_1\")\n",
        "\n",
        "model_im_1.compile(loss=tf.keras.losses.mae,\n",
        "                   optimizer = tf.keras.optimizers.SGD(),\n",
        "                   metrics=[\"mse\"])\n",
        "model_im_1.fit(X_train,y_train, epochs=100)\n",
        "model_im_1.summary()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 38.5823 - mse: 2061.5188\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 30.0540 - mse: 1181.2529\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 34.5847 - mse: 1817.3695\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 30.0421 - mse: 1336.4307\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.3319 - mse: 299.9452\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.1783 - mse: 183.1089\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.4819 - mse: 160.4214\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.2175 - mse: 175.3141\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 41.8776 - mse: 2796.3372\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 29.0908 - mse: 1196.5226\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.5590 - mse: 96.3929\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 27.4058 - mse: 1032.7471\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.1490 - mse: 176.7777\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 35.0362 - mse: 2020.7500\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 22.8252 - mse: 741.0702\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.9804 - mse: 124.8466\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 18.4021 - mse: 451.7806\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.2963 - mse: 195.0274\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.8328 - mse: 297.1926\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1499 - mse: 145.8904\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.1443 - mse: 415.1303\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.3828 - mse: 325.4500\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.2463 - mse: 118.5424\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 17.3148 - mse: 411.5364\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 15.9730 - mse: 335.6400\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.0137 - mse: 653.3446\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 25.7774 - mse: 1034.9442\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.2996 - mse: 529.6815\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.2350 - mse: 98.4629\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 28.9058 - mse: 1487.7366\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 52.3335 - mse: 4894.1245\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.8522 - mse: 202.2743\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 15.3991 - mse: 326.3807\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 12.5264 - mse: 205.4082\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.1632 - mse: 90.6474\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.3594 - mse: 388.5118\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.1139 - mse: 192.0904\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 18.2465 - mse: 439.4378\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 19.1744 - mse: 536.6580\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.5664 - mse: 617.6783\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.7351 - mse: 270.7925\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.1485 - mse: 179.2370\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.6696 - mse: 160.5603\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 32.1058 - mse: 1693.6031\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.4189 - mse: 198.8683\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.3906 - mse: 452.9273\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.7990 - mse: 340.9338\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.3992 - mse: 112.8466\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.2539 - mse: 284.3045\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.1856 - mse: 309.6557\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.6962 - mse: 257.1215\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.1097 - mse: 462.9853\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 22.7778 - mse: 749.5880\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 26.7840 - mse: 1104.3385\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.4149 - mse: 1060.7009\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 11.3480 - mse: 171.8195\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.3695 - mse: 225.7541\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.9529 - mse: 109.6210\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 13.9264 - mse: 276.2485\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.9953 - mse: 118.8247\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.9828 - mse: 308.6936\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.9323 - mse: 197.0167\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.2905 - mse: 135.2395\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 23.9071 - mse: 840.0677\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.4672 - mse: 130.4158\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 20.9506 - mse: 645.6801\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.4465 - mse: 126.4661\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.1889 - mse: 303.1395\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.5190 - mse: 126.1667\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.5910 - mse: 201.1250\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.0209 - mse: 226.3963\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 19.3776 - mse: 532.8229\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.1322 - mse: 193.1182\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 21.6177 - mse: 726.6817\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.2258 - mse: 71.5725\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.4556 - mse: 149.0653\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 22.1666 - mse: 733.4213\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 17.4724 - mse: 464.0970\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.7106 - mse: 316.6112\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 24.7260 - mse: 935.4324\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.5585 - mse: 165.4418\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 12.3906 - mse: 226.0186\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 17.0418 - mse: 377.6872\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 7.2483 - mse: 75.3861\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 37.3975 - mse: 2252.2263\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 21.3566 - mse: 655.0575\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.0725 - mse: 148.6847\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 25.2307 - mse: 906.1785\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.3982 - mse: 136.1499\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.4842 - mse: 438.7144\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.8471 - mse: 160.6373\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.1721 - mse: 506.8831\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.3552 - mse: 102.2547\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.6004 - mse: 180.6667\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.2157 - mse: 1043.7411\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.2290 - mse: 170.0418\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.8560 - mse: 433.7589\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.6026 - mse: 60.8060\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.5974 - mse: 229.6364\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 19.3961 - mse: 552.4317\n",
            "Model: \"model_improvement_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 50)                100       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 151\n",
            "Trainable params: 151\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "kDfbW7g7g9ZM",
        "outputId": "5fc629a7-ca20-411b-8dea-d98bedb7e48e"
      },
      "source": [
        "y_pred_model_im_1 = model_im_1.predict(X_test)\n",
        "plot_predictions(predictions=y_pred_model_im_1)\n",
        "mae_im1 = mae(y_test,y_pred=tf.squeeze(y_pred_model_im_1))\n",
        "mse_im1 = mse(y_test,y_pred=tf.squeeze(y_pred_model_im_1))\n",
        "mae_im1,mse_im1 "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=float32, numpy=13.30514>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=187.11227>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PPPOMLrnkEj388MNZQyAAAIPSeYSppib939+JE9Ohy4e2EfVb6rV41WK1d6QnYZrbmrV4VfrIl9/LCSZjxivXNKYP05sXX3yxvvvd76qjo0OS9Pvf/15//vOfdf755+vxxx/X4cOHtXfvXj377LNH3XfOnDlav369XnvtNUnSW2+9JSm9dNCBAweO2n/06NEaM2ZM1/lbP/rRj7pmv3J59dVXNXnyZN14441auHChNm/ePKjXCwBATlVV6YWujxxJX/rUq6tmbU1X6OrU3tGumrX+n9ifjBmv2tqe53hJvk1vfuUrX1FTU5POPvtsOec0btw4/fu//7suv/xy/frXv1Z5ebkmTpyoc84556j7jhs3TnV1dbriiit05MgRfeQjH9GvfvUrXXrppfr85z+vlStX6oEHHuhxn0cffVTV1dVqb2/X5MmT9YMf/KDP8T3xxBP60Y9+pGHDhumUU07R7bffXtTXDwBA0Pa0ZT/ClWu7l8w55/uTFqqystL1Xhx6x44dmjp1av4PUl8fyPRmEhX82QAAoi3k/40tu79MzW1Hn9hfOrpUTd9oKvrzmdkm51xlttuScahRCmx6EwCAWItAY9TaebUaMaznif0jho1Q7Tz/F95OTvACAADFF4HGqFUfq1LdpXUqHV0qk6l0dKnqLq3z/cR6KeLneDnnZGZBDwPdROHQNQCgiAJujFq/pV41a2u0p22PJo6eqNp5tVkDVdXHqgIJWr1FdsZr+PDh2r9/P/+hDxHnnPbv36/hw4cHPRQAgF8C7BzQ2Saiua1ZTq6rTURQPbryEdkZrwkTJqilpUWtra1BDwXdDB8+XBMmTAh6GAAAvwTYOaCvNhFhmN3KpijBy8wekfRZSW86587KbDtR0uOSyiQ1SfqCc+5tSx8b/GdJl0hql3Stc+6lQp9z2LBhXR3dAQBAQAJsjBqmNhH5Ktahxh9KWtBr262S1jrnTpe0NnNdkj4t6fTMv8WSvlukMQAAgCAE1Dlg4ujshzNzbQ+DogQv59x6SW/12rxQ0qOZnx+VdFm37Y+5tI2STjCzjxZjHAAAoEjq66WyMmnIkPRliNpDdApTm4h8eXly/cnOub2Zn/9T0smZn8dLer3bfi2ZbT2Y2WIzazCzBs7jAgDARwH35sp3QeswtYnIV9E615tZmaSnu53j9Y5z7oRut7/tnBtjZk9Luss595vM9rWSljnnGrI8rKTsnesBAIBHysrSYau30tL0oUQP9V7QWkrPYoU9UHUXVOf6fZ2HEDOXb2a2vyHptG77TchsAwAAYRBgb64wLWjtBS+D11OSrsn8fI2kld22X21pcyS1dTskCQAAghZgb64oVioWoijBy8x+LOl5SVPMrMXMvizpLkkXmdkuSfMz1yVptaRXJe2W9D1JXyvGGAAAQJHU1qZ7cXXnU2+uKFYqFqIofbycc4ty3DQvy75O0pJiPC8AAPBAgL25aufVZj3HK8yVioWI7JJBAADAQx705sqnWjGKlYqFKFpVo5eoagQAoEjq6wOZyYpDtWK+gqpqBAAAYRJgf664Vyvmi+AFAEBS1NT0XMxaSl+v8T78xL1aMV8ELwAAkiLA/lxxr1bMF8ELAICkCLA/VxTXVfQCwQsAgKQIsD9X3KsV80VVIwAASeJBVWP9lnrVrK3RnrY9mjh6omrn1SYuUHXXV1VjURqoAgCAiKiqKmr7iN5tIprbmrV41eL0UyU4fOXCoUYAAOKgvl4qK5OGDElf+tAiQqJNRKGY8QIAIOo6+3N1toro7M8led4clTYRhWHGCwCAqAuwPxdtIgpD8AIAIOoC7M9Fm4jCELwAAIg6D/pz5bOgtUSbiELRTgIAgKjrfY6XlO7PVVc3oHO8krSgtRdYJBsAgDirqkqHrNJSySx9OcDQJVGp6CWqGgEAiIMi9ueiUtE7zHgBAIAeqFT0DsELAICwCqgpKpWK3iF4AQAQRp0nzDc3S8592BTVh/BFpaJ3qGoEACCMysrSYau30lKpqWnAD8uC1t5jkWwAAKLGg6aoLGgdPA41AgAQRh40RaVNRPAIXgAAhFFtbboJancjRqS3DxBtIoJH8AIAIIyK3BRVok1EGBC8AAAIq6qq9In0R46kLwfZIJU2EcEjeAEA4DcP+nPls6g1bSKCRzsJAAD8VOQFrSUWtQ6bvtpJELwAAPCTB/25yu4vU3Pb0Y9ZOrpUTd8Y2GNi4PoKXhxqBADATx7056JaMToIXgAA+MmD/lxUK0YHwQsAAD950J+LasXoIHgBAOAnD/pzUa0YHZxcDwBASLGgdTSxSDYAABHDgtbxxKFGAABCiAWt44ngBQBACNEiIp4IXgAAhBAtIuKJ4AUAQAjRIiKeCF4AAPiMBa2Ti3YSAAD4iAWt44+1GgEACAmqFZON4AUAgI+oVkw2ghcAAD6iWjHZCF4AAPiIasVkI3gBAOAjqhWTjapGAACKpL5eqqmR9uyRJk6UamulKvJU4rBINgAAHquvlxYvltozBYvNzenrEuELH+JQIwAARVBT82Ho6tTent4OdCJ4AQBQBHtydIPItR3JRPACAKAIJuboBpFrO5KJ4AUAQBHU1kojenaJ0IgR6e1AJ4IXAAB9qK+XysqkIUPSl/VHr2ctKX0CfV2dVFoqmaUv6+o4sR49UdUIAEAOhVYqVlURtNA3ZrwAAMiBSkUUG8ELAIAcqFREsRG8AADIgUpFFBvBCwCAHKhURLERvAAAyIFKRRQbwQsAkEiFtIloapKOHElfErowGLSTAAAkDgtaIyjMeAEAEoc2EQgKwQsAkDi0iUBQCF4AgMShTQSCQvACACQObSIQFIIXACBW8qlWpE0EgkJVIwAgNgqpVmRBawSBGS8AQGxQrYiwI3gBAGKDakWEHcELABAbVCsi7AheAIDYoFoRYed58DKzJjPbYmaNZtaQ2Xaimf3KzHZlLsd4PQ4AQHQVsq4i1YoIM3POefsEZk2SKp1zf+y27R5Jbznn7jKzWyWNcc4ty/UYlZWVrqGhwdNxAgDCqXelopSexSJQIazMbJNzrjLbbUEdalwo6dHMz49KuiygcQAAQo5KRcSJH8HLSfqlmW0ys0w3FZ3snNub+fk/JZ3c+05mttjMGsysobW11YdhAgDCiEpFxIkfweuTzrmzJX1a0hIzO7/7jS59rPOo453OuTrnXKVzrnLcuHE+DBMAEEZUKiJOPA9ezrk3MpdvSnpS0ixJ+8zso5KUuXzT63EAAKKJSkXEiafBy8yON7NRnT9L+pSkrZKeknRNZrdrJK30chwAgOiiUhFx4vWM18mSfmNmv5P0gqRnnHO/kHSXpIvMbJek+ZnrAICEKaRNRFOTdORI+pLQhajydJFs59yrkqZn2b5f0jwvnxsAEG6FLGgNxAWd6wEAgaBNBJKI4AUACARtIpBEBC8AQCBoE4EkIngBAAJBmwgkEcELAFBULGgN5OZpVSMAIFkKrVSsqiJoIVmY8QIAFA2VikDfCF4AgKKhUhHoG8ELAFA0VCoCfSN4AQCKhkpFoG8ELwBA0VCpCPSN4AUAyAsLWgODRzsJAEC/WNAaKA5mvAAA/aJNBFAcBC8AQL9oEwEUB8ELANAv2kQAxUHwAgD0izYRQHEQvAAg4fKpVqRNBFAcVDUCQIIVUq3IgtbA4DHjBQAJRrUi4C+CFwAkGNWKgL8IXgCQYFQrAv4ieAFAglGtCPiL4AUACUa1IuAvghcAxFC+C1pLLGoN+Il2EgAQMyxoDYQXM14AEDO0iADCi+AFADFDiwggvAheABAztIgAwovgBQAxQ4sIILwIXgAQISxoDUQbVY0AEBEsaA1EHzNeABARVCsC0UfwAoCIoFoRiD6CFwBEBNWKQPQRvAAgIqhWBKKP4AUAEUG1IhB9BC8ACIF8F7VmQWsg2mgnAQABY1FrIDmY8QKAgNEmAkgOghcABIw2EUByELwAIGC0iQCSg+AFAAGjTQSQHAQvAPBIIZWKtIkAkoGqRgDwQKGViixqDSQDM14A4AEqFQFkQ/ACAA9QqQggG4IXAHiASkUA2RC8AMADVCoCyIbgBQAeoFIRQDYELwAoEAtaAxgo2kkAQAFY0BrAYDDjBQAFoE0EgMEgeAFAAWgTAWAwCF4AUADaRAAYDIIXABSANhEABoPgBQAZ+VQr0iYCwGBQ1QgAKqxakQWtAQwUM14AIKoVAfiD4AUAoloRgD8IXgAgqhUB+IPgBQCiWhGAPwheACCqFQH4g+AFINbyXdBaYlFrAN6jnQSA2GJBawBhw4wXgNiiRQSAsCF4AYgtWkQACBuCF4DYokUEgLAheAGILVpEAAgbgheASGJBawBRRFUjgMhhQWsAUcWMF4DIoVoRQFQFFrzMbIGZ7TSz3WZ2a1DjABA9VCsCiKpAgpeZDZX0L5I+Lalc0iIzKw9iLACih2pFAFEV1IzXLEm7nXOvOucOSvqJpIUBjQVAxFCtCCCqggpe4yW93u16S2ZbFzNbbGYNZtbQ2trq6+AABIdqRQBxFtqqRudcnaQ6SaqsrHQBDweAD6hWBBB3Qc14vSHptG7XJ2S2AUgwqhUBxF1QwetFSaeb2SQzO1bSVZKeCmgsAEKCakUAcRdI8HLOHZK0VNJ/SNoh6Qnn3LYgxgIgPKhWBBB3gfXxcs6tds6d4Zz7C+cctUgAqFYEEHt0rgcQGlQrAog7ghcAz+XTIqJTVZXU1CQdOZK+JHQBiJPQtpMAEA+FtIgAgLhjxguAp2gRAQAfIngB8BQtIgDgQwQvAJ6iRQQAfIjgBcBTtIgAgA8RvAAMGAtaA0BhqGoEMCAsaA0AhWPGC8CAUK0IAIUjeAEYEKoVAaBwBC8AA0K1IgAUjuAFYECoVgSAwhG8AAwI1YoAUDiCF4Cj5LuoNQtaA0BhaCcBoAcWtQYA7zDjBaAH2kQAgHcIXgB6oE0EAHiH4AWgB9pEAIB3CF4AeqBNBAB4h+AFJEQhlYq0iQAAb1DVCCRAoZWKLGoNAN5gxgtIACoVASAcCF5AAlCpCADhQPACEoBKRQAIB4IXkABUKgJAOBC8gASgUhEAwoHgBUQcC1oDQHTQTgKIMBa0BoBoYcYLiDDaRABAtBC8gAijTQQARAvBC4gw2kQAQLQQvIAIo00EAEQLwQsIIRa0BoB4oqoRCBkWtAaA+GLGCwgZKhUBIL4IXkDIUKkIAPFF8AJChkpFAIgvghcQMlQqAkB8EbyAkKFSEQDii+AF+IgFrQEg2WgnAfiEBa0BAMx4AT6hTQQAgOAF+IQ2EQAAghfgE9pEAAAIXoBPaBMBACB4AUWQT7UibSIAAFQ1AoNUSLUiC1oDQLIx4wUMEtWKAIB8EbyAQaJaEQCQL4IXMEhUKwIA8kXwAgaJakUAQL4IXsAgUa0IAMgXwQvIId8FrSUWtQYA5Id2EkAWLGgNAPACM15AFrSIAAB4geAFZEGLCACAFwheQBa0iAAAeIHgBWRBiwgAgBcIXkgcFrQGAASFqkYkCgtaAwCCxIwXEoVqRQBAkAheSBSqFQEAQSJ4IVGoVgQABInghUShWhEAECSCF2KDakUAQNhR1YhYoFoRABAFzHghFqhWBABEAcELsUC1IgAgCgheiAWqFQEAUUDwQixQrQgAiAKCF2KBakUAQBR4FrzM7A4ze8PMGjP/Lul2221mttvMdprZxV6NAdGXT4uITlVVUlOTdORI+pLQBQAIG6/bSdznnLu3+wYzK5d0laRpkk6VtMbMznDOHfZ4LIiYQlpEAAAQBUEcalwo6SfOuQ+cc69J2i1pVgDjQMjRIgIAEDdeB6+lZrbZzB4xszGZbeMlvd5tn5bMth7MbLGZNZhZQ2trq8fDRBjRIgIAEDeDCl5mtsbMtmb5t1DSdyX9haSUpL2SvlPIYzvn6pxzlc65ynHjxg1mmIgoWkQAAOJmUOd4Oefm57OfmX1P0tOZq29IOq3bzRMy24Aeamt7nuMl0SICABBtXlY1frTb1cslbc38/JSkq8ysxMwmSTpd0gtejQPhxILWAIAk8rKq8R4zS0lykpokXS9JzrltZvaEpO2SDklaQkVjsrCgNQAgqcw5F/QY+lVZWekaGhqCHgaKpKwsHbZ6Ky1N998CACDKzGyTc5z63qgAAAloSURBVK4y2210rofvqFYEACQVwQu+o1oRAJBUBC/4jgWtAQBJRfCC76hWBAAkFcELRZXvotYsaA0ASCKvF8lGgrCoNQAAfWPGC0XDotYAAPSN4IWioU0EAAB9I3ihaGgTAQBA3wheKBraRAAA0DeCF/pVSKUibSIAAMiNqkb0qdBKRRa1BgAgN2a80CcqFQEAKB6CF/pEpSIAAMVD8EKfqFQEAKB4CF7oE5WKAAAUD8ELfaJSEQCA4iF4JRgLWgMA4C/aSSQUC1oDAOA/ZrwSijYRAAD4j+CVULSJAADAfwSvhKJNBAAA/iN4JRRtIgAA8B/BK4byqVakTQQAAP6jqjFmCqlWZEFrAAD8xYxXzFCtCABAeBG8YoZqRQAAwovgFTNUKwIAEF4Er5ihWhEAgPAieMUM1YoAAIQXwSsi8l3QWmJRawAAwop2EhHAgtYAAMQDM14RQIsIAADigeAVAbSIAAAgHgheEUCLCAAA4oHgFQG0iAAAIB4IXgFjQWsAAJKDqsYAsaA1AADJwoxXgKhWBAAgWQheAaJaEQCAZCF4BYhqRQAAkoXgFSCqFQEASBaCV4CoVgQAIFkIXh7Jd1FrFrQGACA5aCfhARa1BgAA2TDj5QHaRAAAgGwIXh6gTQQAAMiG4OUB2kQAAIBsCF4eoE0EAADIhuBVgEIqFWkTAQAAeqOqMU+FViqyqDUAAOiNGa88UakIAAAGi+CVJyoVAQDAYBG88kSlIgAAGCyCV56oVAQAAINF8MoTlYoAAGCwCF5iQWsAAOCPxLeTYEFrAADgl8TPeNEmAgAA+CXxwYs2EQAAwC+JD160iQAAAH5JfPCiTQQAAPBL4oMXbSIAAIBfEl/VKLGgNQAA8EfiZ7wAAAD8QvACAADwCcELAADAJwQvAAAAnxC8AAAAfELwAgAA8AnBCwAAwCcELwAAAJ8MKniZ2ZVmts3MjphZZa/bbjOz3Wa208wu7rZ9QWbbbjO7dTDPDwAAECWDnfHaKukKSeu7bzSzcklXSZomaYGkh8xsqJkNlfQvkj4tqVzSosy+AAAAsTeoJYOcczskycx637RQ0k+ccx9Ies3Mdkualbltt3Pu1cz9fpLZd/tgxgEAABAFXp3jNV7S692ut2S25dp+FDNbbGYNZtbQ2trq0TABAAD80++Ml5mtkXRKlptqnHMriz+kNOdcnaS6zBhazazZq+fq5iRJf/ThecIs6e9B0l+/xHsg8R4k/fVLvAcS78FgXn9prhv6DV7OufkDeMI3JJ3W7fqEzDb1sb2vMYwbwBgKZmYNzrnK/veMr6S/B0l//RLvgcR7kPTXL/EeSLwHXr1+rw41PiXpKjMrMbNJkk6X9IKkFyWdbmaTzOxYpU/Af8qjMQAAAITKoE6uN7PLJT0gaZykZ8ys0Tl3sXNum5k9ofRJ84ckLXHOHc7cZ6mk/5A0VNIjzrltg3oFAAAAETHYqsYnJT2Z47ZaSbVZtq+WtHowz+uhuqAHEAJJfw+S/vol3gOJ9yDpr1/iPZB4Dzx5/eac8+JxAQAA0AtLBgEAAPiE4AUAAOCTRAYv1pjsycweN7PGzL8mM2vMbC8zs/e63bYi6LF6xczuMLM3ur3WS7rdlvU7ESdm9m0ze8XMNpvZk2Z2QmZ7Yr4DUrx/z3Mxs9PM7Fkz2575u/j1zPacvxNxlPnbtyXzWhsy2040s1+Z2a7M5Zigx+kFM5vS7XNuNLM/mdk34v4dMLNHzOxNM9vabVvWz9zSlmf+Nmw2s7MH/LxJPMfLzKZKOiLpYUk3O+c6f8nKJf1Y6eWNTpW0RtIZmbv9XtJFSnfbf1HSIudc7JY6MrPvSGpzzv2DmZVJeto5d1awo/Kemd0h6V3n3L29tmf9TnRW6caFmX1K0q+dc4fM7G5Jcs4tS9h3YKgS8nvenZl9VNJHnXMvmdkoSZskXSbpC8ryOxFXZtYkqdI598du2+6R9JZz7q5MEB/jnFsW1Bj9kPk9eEPSbEn/TTH+DpjZ+ZLelfRY59+4XJ95JnTeIOkSpd+bf3bOzR7I8yZyxss5t8M5tzPLTV1rTDrnXpPUucbkLGXWmHTOHZTUucZkrJiZKf3H9sdBjyVEcn0nYsU590vn3KHM1Y1KNzdOmkT8nvfmnNvrnHsp8/MBSTuUYym3BFoo6dHMz48qHUjjbp6kPzjn/FgtJlDOufWS3uq1OddnvlDpgOaccxslnZD5n5aCJTJ49WHQa0xG3HmS9jnndnXbNsnMXjaz/2tm5wU1MJ8szUwhP9LtkEJSPvvurpP0827Xk/IdSOJn3UNmhnOGpP+X2ZTtdyKunKRfmtkmM1uc2Xayc25v5uf/lHRyMEPz1VXq+T/fSfoOSLk/86L9fYht8DKzNWa2Ncu/2P8fbDZ5vh+L1PMXbq+kic65GZL+u6R/M7P/4ue4i6mf9+C7kv5CUkrp1/2dQAfrgXy+A2ZWo3TT4/rMplh9B5CbmY2U9DNJ33DO/UkJ+J3o5ZPOubMlfVrSksxhqC4ufV5OrM/NsfSKMp+T9L8zm5L2HejBq898UA1UwywMa0yGSX/vh5kdI+kKSTO73ecDSR9kft5kZn9Q+py3Bg+H6pl8vxNm9j1JT2eu9vWdiJQ8vgPXSvqspHmZPzix+w70IzafdaHMbJjSoaveOfd/JMk5t6/b7d1/J2LJOfdG5vJNM3tS6UPP+8zso865vZnDSm8GOkjvfVrSS52ffdK+Axm5PvOi/X2I7YzXACV5jcn5kl5xzrV0bjCzcZkTLWVmk5V+P14NaHye6nWs/nJJnVUuub4TsWJmCyTdIulzzrn2btsT8x1QMn7Pj5I5t/N/SdrhnPuf3bbn+p2IHTM7PlNYIDM7XtKnlH69T0m6JrPbNZJWBjNC3/Q46pGk70A3uT7zpyRdnalunKN0EdrebA/Qn9jOePXFWGMym97H9SXpfEn/YGYdSleBVjvnep+IGBf3mFlK6WnlJknXS1Jf34mYeVBSiaRfpf87rI3OuWol6DuQqeiM++95NudK+q+StlimlYyk2yUtyvY7EVMnS3oy890/RtK/Oed+YWYvSnrCzL4sqVnp4qNYygTOi9Tzc876dzEuzOzHkuZKOsnMWiT9D0l3KftnvlrpisbdktqVrvgc2PMmsZ0EAABAEDjUCAAA4BOCFwAAgE8IXgAAAD4heAEAAPiE4AUAAOATghcAAIBPCF4AAAA++f+Ks37j7UvKmwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6Iac0W4tgUg",
        "outputId": "a7384e26-8901-4208-877f-8ad32a444b37"
      },
      "source": [
        "# Create a model with 500 epochs\n",
        "\n",
        "model_im_2 = tf.keras.Sequential([\n",
        "                                  tf.keras.layers.Dense(50),\n",
        "                                  tf.keras.layers.Dense(1)\n",
        "])\n",
        "model_im_2.compile(loss=tf.keras.losses.mae,\n",
        "                   optimizer=tf.keras.optimizers.SGD(),\n",
        "                   metrics=[\"mse\"])\n",
        "model_im_2.fit(X_train,y_train,epochs=500)\n",
        "model.summary()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 27.4863 - mse: 1358.1165\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.9833 - mse: 878.3484\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 29.9332 - mse: 1348.0040\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.3894 - mse: 385.6161\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 22.5929 - mse: 747.9679\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.9811 - mse: 133.4425\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.0116 - mse: 181.2243\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.9151 - mse: 132.9997\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 37.8055 - mse: 2201.5361\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.4254 - mse: 907.2096\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.2573 - mse: 147.6745\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 25.3941 - mse: 898.4086\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.9493 - mse: 397.5302\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 25.8481 - mse: 1042.8611\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.6774 - mse: 432.8918\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.0803 - mse: 124.1935\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.6802 - mse: 467.0157\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.7986 - mse: 214.1149\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.5210 - mse: 437.7364\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.2386 - mse: 93.3044\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.4069 - mse: 288.1172\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.8194 - mse: 245.3365\n",
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            "Epoch 367/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 32.7095 - mse: 1608.1097\n",
            "Epoch 368/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 6.7690 - mse: 101.9715\n",
            "Epoch 369/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 7.6868 - mse: 81.2054\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.9888 - mse: 423.5811\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 6.5048 - mse: 82.8963\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 6.3580 - mse: 79.6903\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 23.6756 - mse: 827.5114\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.3478 - mse: 206.2933\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.9685 - mse: 236.9613\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.8602 - mse: 323.2561\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.7076 - mse: 295.2972\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.2053 - mse: 415.3347\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 20.7340 - mse: 592.4501\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 33.8228 - mse: 1715.8962\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.6261 - mse: 119.1463\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.4319 - mse: 160.0051\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 5.5572 - mse: 48.7718\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 8.7654 - mse: 125.4361\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 5.6905 - mse: 63.6520\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.9259 - mse: 832.2921\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 17.9923 - mse: 476.2461\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 5.9842 - mse: 77.8136\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.7167 - mse: 157.8651\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.0068 - mse: 68.4780\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.3830 - mse: 240.5251\n",
            "Epoch 392/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 8.2629 - mse: 177.8657\n",
            "Epoch 393/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 19.2814 - mse: 583.6108\n",
            "Epoch 394/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.8693 - mse: 932.5449\n",
            "Epoch 395/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 19.7691 - mse: 556.9648\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.4711 - mse: 114.8882\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.3369 - mse: 232.7708\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 6.8667 - mse: 77.7125\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.2420 - mse: 316.7984\n",
            "Epoch 400/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.6747 - mse: 61.0829\n",
            "Epoch 401/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.4167 - mse: 133.0698\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.9558 - mse: 333.9449\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 12.2759 - mse: 296.7574\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 22.8310 - mse: 733.2965\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 18.3030 - mse: 510.1836\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.0382 - mse: 76.5185\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.7792 - mse: 273.5864\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 5.6040 - mse: 41.9198\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 31.6146 - mse: 1548.8711\n",
            "Epoch 410/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.1272 - mse: 222.4091\n",
            "Epoch 411/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 14.7810 - mse: 283.5932\n",
            "Epoch 412/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 21.6179 - mse: 716.3934\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.9748 - mse: 262.3860\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.2809 - mse: 180.3745\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.7033 - mse: 240.5079\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.6648 - mse: 917.3312\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.6302 - mse: 373.0489\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.5976 - mse: 232.1197\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 15.7161 - mse: 361.9624\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 24.4518 - mse: 831.6743\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 18.2049 - mse: 482.8632\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.5847 - mse: 201.1669\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.6375 - mse: 861.0783\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.8831 - mse: 438.6650\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 7.2461 - mse: 77.3223\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 21.0766 - mse: 614.9274\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 6.4432 - mse: 81.3365\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.5406 - mse: 301.5028\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.5223 - mse: 216.5070\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.2725 - mse: 215.7764\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.5636 - mse: 247.5543\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.9666 - mse: 187.7931\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 3.4791 - mse: 16.9056\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 37.3350 - mse: 2206.5417\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.1516 - mse: 323.5082\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.3636 - mse: 217.7946\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 16.2452 - mse: 346.2302\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 13.9256 - mse: 283.3562\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 31.9835 - mse: 1422.2478\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.4610 - mse: 257.1582\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.7539 - mse: 346.1712\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.6724 - mse: 830.5516\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 20.1869 - mse: 571.0022\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.8075 - mse: 145.0104\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.9873 - mse: 273.5395\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.6497 - mse: 439.7879\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 6.7724 - mse: 68.0983\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 5.9932 - mse: 99.4754\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.2159 - mse: 379.0424\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.1382 - mse: 318.0272\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.7965 - mse: 464.4365\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 46.3610 - mse: 3795.6528\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 26.7492 - mse: 1038.2166\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 0s 2ms/step - loss: 23.4255 - mse: 800.7764\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.8227 - mse: 152.6138\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.9224 - mse: 265.6943\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.2042 - mse: 387.0935\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 13.1297 - mse: 293.0237\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.1870 - mse: 859.6810\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 19.4875 - mse: 543.3801\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.6672 - mse: 264.5860\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.9494 - mse: 240.6241\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 28.8185 - mse: 1164.1667\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 7.4871 - mse: 113.3609\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 15.8110 - mse: 368.2714\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.3086 - mse: 259.6674\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 21.4326 - mse: 713.0658\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.0424 - mse: 816.1470\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 21.4812 - mse: 654.5864\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.7192 - mse: 182.6497\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.2748 - mse: 402.7622\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.8546 - mse: 301.9553\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.1335 - mse: 515.0904\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 42.4419 - mse: 2843.1177\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 18.1343 - mse: 468.1024\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.1620 - mse: 242.4464\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 29.2122 - mse: 1194.2977\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 5.7929 - mse: 72.8212\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 6.3467 - mse: 110.1568\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 20.0176 - mse: 621.3151\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.1343 - mse: 945.9955\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.7046 - mse: 287.2496\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.7319 - mse: 206.9406\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 27.7787 - mse: 1094.1404\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.2595 - mse: 181.7203\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 4.2764 - mse: 37.3886\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.3816 - mse: 288.6179\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 27.0981 - mse: 1068.7654\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 5.2149 - mse: 76.5981\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 5.9868 - mse: 94.8564\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.9389 - mse: 510.9944\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 7.4010 - mse: 170.2054\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 18.2777 - mse: 488.5852\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 18.4784 - mse: 537.6639\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.2882 - mse: 57.4851\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 6.3559 - mse: 86.2533\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.7619 - mse: 299.9114\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 6.7244 - mse: 75.2695\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 17.6583 - mse: 449.7725\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.2464 - mse: 266.7622\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 2\n",
            "Trainable params: 2\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "IUrKEgJpu8a3",
        "outputId": "3f4f7736-9057-4124-cdfd-c4f4067afeca"
      },
      "source": [
        "y_pred_im_2 = model_im_2.predict(X_test)\n",
        "plot_predictions(predictions=y_pred_im_2)\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f47870e7290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNGmBe5LoTGD",
        "outputId": "b6995d54-717e-4cf7-de63-412486c160f4"
      },
      "source": [
        "mae_3 = mae(y_true=y_test, y_pred=tf.squeeze(y_pred_im_2))\n",
        "mse_3 = mse(y_true=y_test, y_pred=tf.squeeze(y_pred_im_2))\n",
        "mae_3,mse_3"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=float32, numpy=67.42881>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=4630.2188>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "LYQxlNXrsd1_",
        "outputId": "5483e4d6-ebb6-4889-d66f-9b8539864bf5"
      },
      "source": [
        "import pandas as pd\n",
        "model_results = [[\"model_huge\",mae_1.numpy(),mse_1.numpy()],\n",
        "                 [\"model_im_1\",mae_im1.numpy(), mse_im1.numpy()],\n",
        "                 [\"model_im_2\",mae_3.numpy(),mse_3.numpy()]]\n",
        "all_models=pd.DataFrame(model_results,columns=[\"Model\",\"mae\",\"mse\"])\n",
        "all_models"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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</th>\n",
              "      <th>mae</th>\n",
              "      <th>mse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>model_huge</td>\n",
              "      <td>0.189579</td>\n",
              "      <td>0.037312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>model_im_1</td>\n",
              "      <td>13.305140</td>\n",
              "      <td>187.112274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>model_im_2</td>\n",
              "      <td>67.428810</td>\n",
              "      <td>4630.218750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Model        mae          mse\n",
              "0  model_huge   0.189579     0.037312\n",
              "1  model_im_1  13.305140   187.112274\n",
              "2  model_im_2  67.428810  4630.218750"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75iEeUoruQOX",
        "outputId": "d07ffe81-8e99-43e7-9711-55ec80d892bb"
      },
      "source": [
        "model_huge.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"huge_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (Dense)          (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xu1hHY9muUY4"
      },
      "source": [
        "# Saving our models -- to use them outside colab - eg: in Web apps or mobile apps.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFDhXpuVuo-7",
        "outputId": "9407a261-7eb9-4dd4-9180-5d2c8e37f4e6"
      },
      "source": [
        "model_huge.save(\"best_model_till_now\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: best_model_till_now/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNV_n6GWvhS5"
      },
      "source": [
        "model_huge.save(\"best_model_till_now.h5\")"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxIko9ohxD2i",
        "outputId": "ab643557-46a8-4a54-cc91-d1e12e4b4658"
      },
      "source": [
        "#Loading the saved model\n",
        "loaded_SavedModel_format=tf.keras.models.load_model (\"/content/best_model_till_now\")\n",
        "loaded_SavedModel_format.summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"huge_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (Dense)          (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Lekz_2h1-jz"
      },
      "source": [
        "#Lets work on larger models now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q28jPwic2CeR"
      },
      "source": [
        "#import necessary libs\n",
        "\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "sGdQ5T6P2Oz8",
        "outputId": "8c3dc5b9-65e3-4b2b-88f2-dc9dff59e930"
      },
      "source": [
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>male</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>10600.54830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northeast</td>\n",
              "      <td>2205.98080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>female</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "      <td>2007.94500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>northwest</td>\n",
              "      <td>29141.36030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 7 columns</p>\n",
              "</div>"
            ],
            "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",
              "...   ...     ...     ...       ...    ...        ...          ...\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",
              "[1338 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "zqqXysxM2_n5",
        "outputId": "580ab8f5-085f-466f-8c76-a6771c6acea5"
      },
      "source": [
        "#One hot encode the data using pandas dummies function\n",
        "\n",
        "insurance_one_hot=pd.get_dummies(insurance)\n",
        "insurance_one_hot.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>16884.92400</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>1725.55230</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>3866.85520</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     bmi  children  ...  region_northwest  region_southeast  region_southwest\n",
              "0   19  27.900         0  ...                 0                 0                 1\n",
              "1   18  33.770         1  ...                 0                 1                 0\n",
              "2   28  33.000         3  ...                 0                 1                 0\n",
              "3   33  22.705         0  ...                 1                 0                 0\n",
              "4   32  28.880         0  ...                 1                 0                 0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3fBXbtW3crz",
        "outputId": "6fe97a1a-4f25-434c-99e5-c328995e6554"
      },
      "source": [
        "#1. Create X and Y features and Lables.\n",
        "X = insurance_one_hot.drop(\"charges\", axis=1)\n",
        "y = insurance[\"charges\"]\n",
        "X.head(),y.head()\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(   age     bmi  children  ...  region_northwest  region_southeast  region_southwest\n",
              " 0   19  27.900         0  ...                 0                 0                 1\n",
              " 1   18  33.770         1  ...                 0                 1                 0\n",
              " 2   28  33.000         3  ...                 0                 1                 0\n",
              " 3   33  22.705         0  ...                 1                 0                 0\n",
              " 4   32  28.880         0  ...                 1                 0                 0\n",
              " \n",
              " [5 rows x 11 columns], 0    16884.92400\n",
              " 1     1725.55230\n",
              " 2     4449.46200\n",
              " 3    21984.47061\n",
              " 4     3866.85520\n",
              " Name: charges, dtype: float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JimIy9Ix4GQt",
        "outputId": "ed107962-1f1c-4e47-86f3-21f2a76a2c39"
      },
      "source": [
        "#2. Create training and test data \n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "len(X),len(X_train),len(X_test),"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 1070, 268)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "JB_vQ2zR4iOA",
        "outputId": "aa50d02f-c691-4e10-bd66-4184cf3125b8"
      },
      "source": [
        "X_train\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>560</th>\n",
              "      <td>46</td>\n",
              "      <td>19.950</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1285</th>\n",
              "      <td>47</td>\n",
              "      <td>24.320</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>52</td>\n",
              "      <td>24.860</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>969</th>\n",
              "      <td>39</td>\n",
              "      <td>34.320</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>54</td>\n",
              "      <td>21.470</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1095</th>\n",
              "      <td>18</td>\n",
              "      <td>31.350</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>39</td>\n",
              "      <td>23.870</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>58</td>\n",
              "      <td>25.175</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>37</td>\n",
              "      <td>47.600</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1126</th>\n",
              "      <td>55</td>\n",
              "      <td>29.900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1070 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      age     bmi  ...  region_southeast  region_southwest\n",
              "560    46  19.950  ...                 0                 0\n",
              "1285   47  24.320  ...                 0                 0\n",
              "1142   52  24.860  ...                 1                 0\n",
              "969    39  34.320  ...                 1                 0\n",
              "486    54  21.470  ...                 0                 0\n",
              "...   ...     ...  ...               ...               ...\n",
              "1095   18  31.350  ...                 0                 0\n",
              "1130   39  23.870  ...                 1                 0\n",
              "1294   58  25.175  ...                 0                 0\n",
              "860    37  47.600  ...                 0                 1\n",
              "1126   55  29.900  ...                 0                 1\n",
              "\n",
              "[1070 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSBs2Ul05iMJ",
        "outputId": "ea3889e7-b0e1-4ce6-80a6-89a975dff82a"
      },
      "source": [
        "# Build neural network model\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "#1. Create a model\n",
        "insurance_model = tf.keras.Sequential([\n",
        "                                       tf.keras.layers.Dense(10),\n",
        "                                       tf.keras.layers.Dense(1)\n",
        "],name = \"insuarance_model\")\n",
        "\n",
        "#2. Compile a model\n",
        "insurance_model.compile(loss=tf.keras.losses.mae,\n",
        "                         optimizer=tf.keras.optimizers.SGD(),\n",
        "                         metrics=[\"mae\"])\n",
        "#fit the model\n",
        "insurance_model.fit(X_train,y_train,epochs=100)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 8637.1006 - mae: 8637.1006\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7886.7759 - mae: 7886.7759\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7558.1470 - mae: 7558.1470\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7792.0225 - mae: 7792.0225\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7748.3887 - mae: 7748.3887\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7595.3940 - mae: 7595.3940\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7589.9849 - mae: 7589.9849\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7698.5591 - mae: 7698.5591\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7496.7783 - mae: 7496.7783\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7493.1743 - mae: 7493.1743\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7769.7310 - mae: 7769.7310\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7706.9048 - mae: 7706.9048\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7687.7227 - mae: 7687.7227\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7689.8999 - mae: 7689.8999\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7393.5332 - mae: 7393.5332\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7780.6982 - mae: 7780.6982\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7578.5112 - mae: 7578.5112\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7750.8354 - mae: 7750.8354\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7739.2148 - mae: 7739.2148\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7875.0654 - mae: 7875.0654\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7466.6772 - mae: 7466.6772\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7941.2319 - mae: 7941.2319\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7640.2725 - mae: 7640.2725\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7539.2666 - mae: 7539.2666\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7619.9663 - mae: 7619.9663\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7644.1719 - mae: 7644.1719\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7709.0376 - mae: 7709.0376\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7366.8662 - mae: 7366.8662\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7444.3159 - mae: 7444.3159\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7616.4097 - mae: 7616.4097\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7686.3853 - mae: 7686.3853\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7548.0991 - mae: 7548.0991\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7501.5537 - mae: 7501.5537\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7363.4155 - mae: 7363.4155\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7295.4473 - mae: 7295.4473\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7569.8818 - mae: 7569.8818\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7548.1997 - mae: 7548.1997\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7424.3979 - mae: 7424.3979\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7529.7734 - mae: 7529.7734\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7467.3237 - mae: 7467.3237\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7635.9292 - mae: 7635.9292\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7536.8394 - mae: 7536.8394\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7616.5854 - mae: 7616.5854\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7439.4941 - mae: 7439.4941\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7538.0161 - mae: 7538.0161\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7415.1465 - mae: 7415.1465\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7420.6938 - mae: 7420.6938\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7509.9844 - mae: 7509.9844\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7541.1128 - mae: 7541.1128\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7467.8638 - mae: 7467.8638\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7389.3550 - mae: 7389.3550\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7499.7754 - mae: 7499.7754\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7523.9282 - mae: 7523.9282\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7243.3120 - mae: 7243.3120\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7429.5859 - mae: 7429.5859\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7313.4004 - mae: 7313.4004\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7526.3887 - mae: 7526.3887\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7542.2666 - mae: 7542.2666\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7576.9282 - mae: 7576.9282\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7546.4058 - mae: 7546.4058\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7351.2275 - mae: 7351.2275\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7302.1440 - mae: 7302.1440\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7393.0884 - mae: 7393.0884\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7442.2886 - mae: 7442.2886\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7492.6792 - mae: 7492.6792\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7561.9170 - mae: 7561.9170\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7340.5142 - mae: 7340.5142\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7496.0859 - mae: 7496.0859\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7617.0322 - mae: 7617.0322\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7641.1953 - mae: 7641.1953\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7084.2749 - mae: 7084.2749\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7240.4907 - mae: 7240.4907\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7283.4888 - mae: 7283.4888\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7335.5078 - mae: 7335.5078\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7275.6396 - mae: 7275.6396\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7313.1855 - mae: 7313.1855\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7485.7598 - mae: 7485.7598\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7352.2798 - mae: 7352.2798\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7520.5708 - mae: 7520.5708\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7279.3779 - mae: 7279.3779\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7273.8472 - mae: 7273.8472\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7176.5215 - mae: 7176.5215\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7425.6304 - mae: 7425.6304\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7403.1294 - mae: 7403.1294\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7356.0078 - mae: 7356.0078\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7484.7271 - mae: 7484.7271\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7217.6079 - mae: 7217.6079\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7261.0000 - mae: 7261.0000\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7134.1562 - mae: 7134.1562\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7083.4360 - mae: 7083.4360\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7254.1782 - mae: 7254.1782\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7268.7456 - mae: 7268.7456\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7470.5215 - mae: 7470.5215\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7210.9536 - mae: 7210.9536\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7395.6816 - mae: 7395.6816\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7328.0874 - mae: 7328.0874\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7230.4395 - mae: 7230.4395\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7261.3940 - mae: 7261.3940\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7342.5679 - mae: 7342.5679\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7106.1714 - mae: 7106.1714\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f47d7c3d310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k9qX3Kna8gr",
        "outputId": "47ea1301-4720-4a39-f9b8-d930d4e83efe"
      },
      "source": [
        "insurance_model.summary()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"insuarance_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_7 (Dense)              (None, 10)                120       \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 131\n",
            "Trainable params: 131\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QaMl3g_1bXb2",
        "outputId": "ca11506d-fccd-4089-9abf-e631a1e76b1a"
      },
      "source": [
        "#Check the against test data\n",
        "insurance_model.evaluate(X_test,y_test)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7023.3291 - mae: 7023.3291\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7023.3291015625, 7023.3291015625]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXyTJYkRbyQq"
      },
      "source": [
        "#Right  now model is not performing well\n",
        "  1. Try changin optimizer \n",
        "  2. Train for longer epochs\n",
        "  \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AE_fwmFAkm9j",
        "outputId": "1e01c09e-2d12-42ad-8a6e-547b2cb752c0"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "# Create a model\n",
        "insurance_model_2 = tf.keras.Sequential([\n",
        "                                         tf.keras.layers.Dense(100),\n",
        "                                         tf.keras.layers.Dense(10),\n",
        "                                         tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "insurance_model_2.compile(loss=tf.keras.losses.mae,\n",
        "                          optimizer=tf.keras.optimizers.Adam(),\n",
        "                          metrics=[\"mae\"])\n",
        "\n",
        "#fit the model\n",
        "history_2=insurance_model_2.fit(X_train,y_train,epochs=100)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13273.1602 - mae: 13273.1602\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13104.4297 - mae: 13104.4297\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 12749.5420 - mae: 12749.5420\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 12055.7510 - mae: 12055.7510\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10905.8154 - mae: 10905.8154\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9457.7217 - mae: 9457.7217\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8147.6543 - mae: 8147.6543\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7528.8408 - mae: 7528.8408\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7429.1528 - mae: 7429.1528\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7409.0811 - mae: 7409.0811\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7390.8042 - mae: 7390.8042\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7368.9180 - mae: 7368.9180\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7348.5190 - mae: 7348.5190\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7326.4893 - mae: 7326.4893\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7307.5815 - mae: 7307.5815\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7285.7734 - mae: 7285.7734\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7265.7100 - mae: 7265.7100\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7242.5488 - mae: 7242.5488\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7220.5068 - mae: 7220.5068\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7197.1978 - mae: 7197.1978\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7179.0195 - mae: 7179.0195\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7151.2104 - mae: 7151.2104\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7126.4639 - mae: 7126.4639\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7101.9189 - mae: 7101.9189\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7084.3379 - mae: 7084.3379\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7052.3291 - mae: 7052.3291\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7024.3506 - mae: 7024.3506\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6996.6963 - mae: 6996.6963\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6969.0112 - mae: 6969.0112\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6942.1899 - mae: 6942.1899\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6911.7275 - mae: 6911.7275\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6884.0205 - mae: 6884.0205\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6853.4648 - mae: 6853.4648\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6823.0674 - mae: 6823.0674\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6789.6855 - mae: 6789.6855\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6755.7646 - mae: 6755.7646\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6720.2026 - mae: 6720.2026\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6689.7158 - mae: 6689.7158\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6652.4614 - mae: 6652.4614\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6618.1016 - mae: 6618.1016\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6585.8643 - mae: 6585.8643\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6559.4956 - mae: 6559.4956\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6530.0444 - mae: 6530.0444\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6506.8071 - mae: 6506.8071\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6493.5718 - mae: 6493.5718\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6475.9258 - mae: 6475.9258\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6458.8979 - mae: 6458.8979\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6445.1494 - mae: 6445.1494\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6430.9634 - mae: 6430.9634\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6417.7510 - mae: 6417.7510\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6403.2759 - mae: 6403.2759\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6392.4141 - mae: 6392.4141\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6378.7451 - mae: 6378.7451\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6364.9131 - mae: 6364.9131\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6351.5273 - mae: 6351.5273\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6337.6602 - mae: 6337.6602\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6324.8374 - mae: 6324.8374\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6310.1943 - mae: 6310.1943\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6295.6035 - mae: 6295.6035\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6284.8696 - mae: 6284.8696\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6265.6411 - mae: 6265.6411\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6253.0103 - mae: 6253.0103\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6234.9292 - mae: 6234.9292\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6218.0430 - mae: 6218.0430\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6201.1899 - mae: 6201.1899\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6183.9595 - mae: 6183.9595\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6171.2993 - mae: 6171.2993\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6148.8398 - mae: 6148.8398\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6132.5981 - mae: 6132.5981\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6112.3848 - mae: 6112.3848\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6092.7202 - mae: 6092.7202\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6073.7422 - mae: 6073.7422\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6059.4873 - mae: 6059.4873\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6031.3848 - mae: 6031.3848\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6010.3350 - mae: 6010.3350\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5995.2178 - mae: 5995.2178\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5963.0718 - mae: 5963.0718\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5940.0605 - mae: 5940.0605\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5915.1064 - mae: 5915.1064\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5887.9990 - mae: 5887.9990\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5861.6987 - mae: 5861.6987\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5834.3071 - mae: 5834.3071\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5805.8242 - mae: 5805.8242\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5772.3232 - mae: 5772.3232\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5745.1514 - mae: 5745.1514\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5711.3481 - mae: 5711.3481\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5674.5215 - mae: 5674.5215\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5639.4927 - mae: 5639.4927\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5600.6650 - mae: 5600.6650\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5559.4326 - mae: 5559.4326\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5523.6187 - mae: 5523.6187\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5474.1250 - mae: 5474.1250\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5432.2656 - mae: 5432.2656\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5386.0527 - mae: 5386.0527\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5333.1812 - mae: 5333.1812\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5288.8159 - mae: 5288.8159\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5234.6792 - mae: 5234.6792\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5170.9351 - mae: 5170.9351\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5112.9438 - mae: 5112.9438\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5059.8447 - mae: 5059.8447\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQPAzU-Wl6c5",
        "outputId": "c7581b1a-523a-461a-ff1d-5d0aaf2dc34f"
      },
      "source": [
        "insurance_model_2.evaluate(X_test,y_test)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 4924.4961 - mae: 4924.4961\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4924.49609375, 4924.49609375]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "jMdWIRA0mDhj",
        "outputId": "2c10aa18-22f3-4c0b-a9cc-f46d80eaa67c"
      },
      "source": [
        "pd.DataFrame(history_2.history).plot()\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLwmmtEAmZVc",
        "outputId": "d123b548-fb02-4d8a-d68e-329345108ada"
      },
      "source": [
        "#Now since the loss is decreasing lets train the model for larger epochs.\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "#Create a model\n",
        "insurance_model_3 = tf.keras.Sequential([\n",
        "                                         tf.keras.layers.Dense(100),\n",
        "                                         tf.keras.layers.Dense(10),\n",
        "                                         tf.keras.layers.Dense(1)\n",
        "], name=\"insurance_model_3\")\n",
        "\n",
        "#compile the model\n",
        "\n",
        "insurance_model_3.compile(loss=tf.keras.losses.mae,\n",
        "                          optimizer=tf.keras.optimizers.Adam(),\n",
        "                          metrics=[\"mae\"])\n",
        "#fit the model\n",
        "history_3=insurance_model_3.fit(X_train,y_train,epochs=200)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13273.1602 - mae: 13273.1602\n",
            "Epoch 2/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13104.4297 - mae: 13104.4297\n",
            "Epoch 3/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12749.5420 - mae: 12749.5420\n",
            "Epoch 4/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 12055.7510 - mae: 12055.7510\n",
            "Epoch 5/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 10905.8154 - mae: 10905.8154\n",
            "Epoch 6/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 9457.7217 - mae: 9457.7217\n",
            "Epoch 7/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 8147.6543 - mae: 8147.6543\n",
            "Epoch 8/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7528.8408 - mae: 7528.8408\n",
            "Epoch 9/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7429.1528 - mae: 7429.1528\n",
            "Epoch 10/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7409.0811 - mae: 7409.0811\n",
            "Epoch 11/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7390.8042 - mae: 7390.8042\n",
            "Epoch 12/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7368.9180 - mae: 7368.9180\n",
            "Epoch 13/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7348.5190 - mae: 7348.5190\n",
            "Epoch 14/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7326.4893 - mae: 7326.4893\n",
            "Epoch 15/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7307.5815 - mae: 7307.5815\n",
            "Epoch 16/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7285.7734 - mae: 7285.7734\n",
            "Epoch 17/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7265.7100 - mae: 7265.7100\n",
            "Epoch 18/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7242.5488 - mae: 7242.5488\n",
            "Epoch 19/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7220.5068 - mae: 7220.5068\n",
            "Epoch 20/200\n",
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            "34/34 [==============================] - 0s 2ms/step - loss: 3694.2781 - mae: 3694.2781\n",
            "Epoch 186/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3696.6104 - mae: 3696.6104\n",
            "Epoch 187/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3689.0247 - mae: 3689.0247\n",
            "Epoch 188/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3694.2283 - mae: 3694.2283\n",
            "Epoch 189/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3683.9485 - mae: 3683.9485\n",
            "Epoch 190/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3682.8477 - mae: 3682.8477\n",
            "Epoch 191/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3699.1458 - mae: 3699.1458\n",
            "Epoch 192/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3684.8535 - mae: 3684.8535\n",
            "Epoch 193/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3675.5303 - mae: 3675.5303\n",
            "Epoch 194/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3675.5764 - mae: 3675.5764\n",
            "Epoch 195/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3673.1931 - mae: 3673.1931\n",
            "Epoch 196/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3682.2253 - mae: 3682.2253\n",
            "Epoch 197/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3666.2061 - mae: 3666.2061\n",
            "Epoch 198/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3671.8359 - mae: 3671.8359\n",
            "Epoch 199/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3680.2205 - mae: 3680.2205\n",
            "Epoch 200/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3666.9414 - mae: 3666.9414\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "68Af9LO95b3W",
        "outputId": "0b16a6b6-9b37-441d-8230-c5330473bd26"
      },
      "source": [
        "pd.DataFrame(history_3.history).plot()\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6A2I_OAYh7-"
      },
      "source": [
        "# Lets pre process the data \n",
        "--> Normalizing it or Standardising it "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ss_tsHbNYyv0"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "npMrs0eYY6sq",
        "outputId": "8051adfd-79f9-4981-a262-6613dfc060f8"
      },
      "source": [
        "insurance_data = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance_data"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>male</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>10600.54830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northeast</td>\n",
              "      <td>2205.98080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>female</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "      <td>2007.94500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>northwest</td>\n",
              "      <td>29141.36030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 7 columns</p>\n",
              "</div>"
            ],
            "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",
              "...   ...     ...     ...       ...    ...        ...          ...\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",
              "[1338 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4822VLQZQB5"
      },
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mu7yJqtOaNHd"
      },
      "source": [
        "column_transformer = make_column_transformer(\n",
        "    (MinMaxScaler(),[\"age\",\"bmi\",\"children\"]),\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"),[\"sex\",\"smoker\",\"region\"])\n",
        ")"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTU9tWombCl4"
      },
      "source": [
        "#Create X and y (features and lables.)\n",
        "X=insurance_data.drop(\"charges\",axis=1)\n",
        "y=insurance_data[\"charges\"]\n"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VcxnGkObVIG"
      },
      "source": [
        "#Create testing and trianing data"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4xQu8pibzzU"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hhd2aIEfcQhp",
        "outputId": "5109affc-83c4-42e7-f0f8-c84916f5c732"
      },
      "source": [
        "# # Fit column transformer on the training data only (doing so on test data would result in data leakage)\n",
        "column_transformer.fit(X_train)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
              "                  transformer_weights=None,\n",
              "                  transformers=[('minmaxscaler',\n",
              "                                 MinMaxScaler(copy=True, feature_range=(0, 1)),\n",
              "                                 ['age', 'bmi', 'children']),\n",
              "                                ('onehotencoder',\n",
              "                                 OneHotEncoder(categories='auto', drop=None,\n",
              "                                               dtype=<class 'numpy.float64'>,\n",
              "                                               handle_unknown='ignore',\n",
              "                                               sparse=True),\n",
              "                                 ['sex', 'smoker', 'region'])],\n",
              "                  verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMxRmxXUcYT4"
      },
      "source": [
        "X_train_normal = column_transformer.transform(X_train)\n",
        "X_test_normal = column_transformer.transform(X_test)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "DvGzOjRics2G",
        "outputId": "c9854661-3572-482d-8cf7-79133cf3a0b1"
      },
      "source": [
        "X_train[\"age\"].plot(kind=\"hist\")\n"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4786630110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oZNrz_ydAml"
      },
      "source": [
        "# Build the tensor flow model ob pre process data\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "#Create a model\n",
        "insurance_model_4 = tf.keras.Sequential([\n",
        "                                         tf.keras.layers.Dense(100),\n",
        "                                         tf.keras.layers.Dense(10),\n",
        "                                         tf.keras.layers.Dense(1)\n",
        "], name=\"Insurance_pre_processed_model\")\n",
        "\n",
        "#Compile the model\n",
        "insurance_model_4.compile(loss=tf.keras.losses.mae,\n",
        "                          optimizer=tf.keras.optimizers.Adam(),\n",
        "                          metrics=[\"mae\"])\n",
        "#fit the model\n",
        "\n",
        "history_4=insurance_model_4.fit(X_train_normal,y_train,epochs=200,verbose=0)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZR2wcAYdHF0",
        "outputId": "1b745201-edbd-4222-9803-746d5bafce38"
      },
      "source": [
        "insurance_model_4.evaluate(X_test_normal,y_test)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 1ms/step - loss: 3171.5776 - mae: 3171.5776\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3171.57763671875, 3171.57763671875]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "s3UhlaRUeZ-r",
        "outputId": "50c62b3b-7d56-4089-d898-df7c6fefad1b"
      },
      "source": [
        "pd.DataFrame(history_4.history).plot()\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}