01_neural_network_regression.ipynb

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  "metadata": {
    "colab": {
      "name": "01_neural_network_regression.ipynb",
      "provenance": [],
      "mount_file_id": "1tQQLvIyEqwYE11IeSvzCh7PgjbI2NGZB",
      "authorship_tag": "ABX9TyPQ1MqmcKguP7ILlu4Bel0h",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prachuryanath/TF-Learning/blob/main/01_neural_network_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYALxTD1vcw2"
      },
      "source": [
        "# Introduction to Regression with Neural Networks in Tensorflow\n",
        "\n",
        "There are many definitions for a regression problem but in our case, we're going to simplfy it; predicting a numerical variable based on some other combination of variables, even shorter ... predicting a number."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gmV8mZLwL7S",
        "outputId": "dcc0208e-7e90-42ad-988e-3d1378396f7a"
      },
      "source": [
        "# Import Tensorflow\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4VvylWqyj_K"
      },
      "source": [
        "### Creating data to view and fit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "QZbs5kXSyssx",
        "outputId": "a225dec1-b2c5-4103-bf08-658065c85262"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create features\n",
        "X = np.array([-7.0,-4.0,-1.0,2.0,5.0,8.0,11.0,14.0])\n",
        "\n",
        "# Create labels\n",
        "y = np.array([3.0,6.0,9.0,12.0,15.0,18.0,21.0,24.0])\n",
        "\n",
        "# Visualise it\n",
        "plt.scatter(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbe04655710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "iTVHK7fCznWW"
      },
      "source": [
        "### Input and Output Shapes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJr_7D6U030A",
        "outputId": "10fe4aab-4a06-489a-cff2-437263f325cc"
      },
      "source": [
        "# Create a demo tensor for our housing price prediction problem\n",
        "house_info = tf.constant([\"bedroom\",\"bathroom\",\"garage\"])\n",
        "house_price = tf.constant ([939700])\n",
        "house_info, house_price"
      ],
      "execution_count": null,
      "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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ae2Ie2b1Y0L",
        "outputId": "5f2f3a45-6eab-4ba0-d510-c082537d3c8f"
      },
      "source": [
        "input_shape = X.shape\n",
        "output_shape = y.shape\n",
        "input_shape, output_shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8,), (8,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjGEs-1Z1q3g",
        "outputId": "db129bf7-a5c3-48b8-8339-6d450ecd3626"
      },
      "source": [
        "# Turn our NumPy arrays into tensors\n",
        "X = tf.constant(X)\n",
        "y = tf.constant(y)\n",
        "X, y"
      ],
      "execution_count": null,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVNzc_kjL4zP"
      },
      "source": [
        "## Steps in modelling with Tensorflow\n",
        "\n",
        "1. **Creating a model** - *define the input and output layers, as well as the hidden layers of a deep learning model.*\n",
        "2. **Compiling a model** - *define the loss function( in other words, the function which tells our model how wrong it is ) and the optimizer ( tells our model how to improve the patterns in its learning ) and evaluation metrics ( what we can use to interpret the performance of our model).*\n",
        "3. **Fitting a model** - *letting the model try to find patters between X & y(features and labels)* "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGED5Y_wNbcP",
        "outputId": "ab9ca16c-8bd5-421f-d8b2-a098c92a8f06"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model using the Sequential API\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model.fit(X, y, epochs= 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 3s 3s/step - loss: 11.5048 - mae: 11.5048\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 11.3723 - mae: 11.3723\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 11.2398 - mae: 11.2398\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 11.1073 - mae: 11.1073\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 10.9748 - mae: 10.9748\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbe04e810d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyZSlS9CPwhD",
        "outputId": "53a95a32-bc38-45b7-c4bb-3549a99d2b5c"
      },
      "source": [
        "# Check out X and y\n",
        "X, y"
      ],
      "execution_count": null,
      "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/"
        },
        "id": "Ha45M74eRSQ3",
        "outputId": "2d8dd71e-c7da-4b5d-abe2-5cdf548a167e"
      },
      "source": [
        "# Try and make a prediction using our model\n",
        "model.predict([8.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.0104804]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imCA0515Rd0L"
      },
      "source": [
        "### Improving our model\n",
        "\n",
        "We can improve our model, by altering the steps we took to create a model.\n",
        "1. **Creating a model** - here we might add more layers, increase the number of hidden units ( all called neurons) withing each of the hidden layers, change the activation function of each layer.\n",
        "2. **Compiling a model** - here we might change the optimization function or perhaps the learning rate of the optimization function.\n",
        "3. **Fitting a model** - here we might fit a model for more epochs (leave it training for longerr) or on more data( give the model more examples to learn from)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m51Gsa-ASKdX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "189aebec-b848-4cad-c318-46371101c0d3"
      },
      "source": [
        "# 1. Create the model\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model (this time we'll train for longer)\n",
        "model.fit(X, y, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 11.2219 - mae: 11.2219\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 11.0894 - mae: 11.0894\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.9569 - mae: 10.9569\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 10.8244 - mae: 10.8244\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 10.6919 - mae: 10.6919\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.5594 - mae: 10.5594\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.4269 - mae: 10.4269\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.2944 - mae: 10.2944\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.1619 - mae: 10.1619\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.0294 - mae: 10.0294\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8969 - mae: 9.8969\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7644 - mae: 9.7644\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.6319 - mae: 9.6319\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.4994 - mae: 9.4994\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.3669 - mae: 9.3669\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.2344 - mae: 9.2344\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.1019 - mae: 9.1019\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 8.9694 - mae: 8.9694\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.8369 - mae: 8.8369\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 8.7044 - mae: 8.7044\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.5719 - mae: 8.5719\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.4394 - mae: 8.4394\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.3069 - mae: 8.3069\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.1744 - mae: 8.1744\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 8.0419 - mae: 8.0419\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.9094 - mae: 7.9094\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.7769 - mae: 7.7769\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.6444 - mae: 7.6444\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.5119 - mae: 7.5119\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.3794 - mae: 7.3794\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2750 - mae: 7.2750\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2694 - mae: 7.2694\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.2638 - mae: 7.2638\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.2581 - mae: 7.2581\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2525 - mae: 7.2525\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.2469 - mae: 7.2469\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.2413 - mae: 7.2413\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.2356 - mae: 7.2356\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.2300 - mae: 7.2300\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2244 - mae: 7.2244\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.2188 - mae: 7.2188\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.2131 - mae: 7.2131\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2075 - mae: 7.2075\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2019 - mae: 7.2019\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.1962 - mae: 7.1962\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.1906 - mae: 7.1906\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.1850 - mae: 7.1850\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.1794 - mae: 7.1794\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 7.1737 - mae: 7.1737\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.1681 - mae: 7.1681\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.1625 - mae: 7.1625\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.1569 - mae: 7.1569\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.1512 - mae: 7.1512\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1456 - mae: 7.1456\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.1400 - mae: 7.1400\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.1344 - mae: 7.1344\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1287 - mae: 7.1287\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.1231 - mae: 7.1231\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.1175 - mae: 7.1175\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 7.1119 - mae: 7.1119\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 7.1062 - mae: 7.1062\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.1006 - mae: 7.1006\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.0950 - mae: 7.0950\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.0894 - mae: 7.0894\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.0838 - mae: 7.0838\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.0781 - mae: 7.0781\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0725 - mae: 7.0725\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.0669 - mae: 7.0669\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.0613 - mae: 7.0613\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 7.0556 - mae: 7.0556\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0500 - mae: 7.0500\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.0444 - mae: 7.0444\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 7.0388 - mae: 7.0388\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0331 - mae: 7.0331\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 7.0275 - mae: 7.0275\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.0219 - mae: 7.0219\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.0163 - mae: 7.0163\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.0106 - mae: 7.0106\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0050 - mae: 7.0050\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9994 - mae: 6.9994\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9938 - mae: 6.9938\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9881 - mae: 6.9881\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.9825 - mae: 6.9825\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.9769 - mae: 6.9769\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.9713 - mae: 6.9713\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9656 - mae: 6.9656\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9600 - mae: 6.9600\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9544 - mae: 6.9544\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9488 - mae: 6.9488\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9431 - mae: 6.9431\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9375 - mae: 6.9375\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.9319 - mae: 6.9319\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9262 - mae: 6.9262\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9206 - mae: 6.9206\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9150 - mae: 6.9150\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9094 - mae: 6.9094\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.9038 - mae: 6.9038\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8981 - mae: 6.8981\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.8925 - mae: 6.8925\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.8869 - mae: 6.8869\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbdf04809d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AU52bpm4xiB3",
        "outputId": "df5473eb-f5a4-4669-c0bd-7970de626197"
      },
      "source": [
        "# Let's see if our model's predicton has improved ...\n",
        "model.predict([8.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14.431991]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubPCgySdxq2b",
        "outputId": "51f9fd7e-8572-41fb-dea4-2b0612e046fc"
      },
      "source": [
        "# Let's see if we can make another to improve our model\n",
        "\n",
        "# 1. Create the model (this time with an extra hidden layer with 100 hidden units)\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(100, activation = \"relu\"),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model.fit(X, y, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 314ms/step - loss: 12.3193 - mae: 12.3193\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 11.5062 - mae: 11.5062\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.6739 - mae: 10.6739\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.8419 - mae: 9.8419\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 8.9961 - mae: 8.9961\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 8.1346 - mae: 8.1346\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.4052 - mae: 7.4052\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 6.6763 - mae: 6.6763\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 5.9140 - mae: 5.9140\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 5.1124 - mae: 5.1124\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.2652 - mae: 4.2652\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.8460 - mae: 3.8460\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.9257 - mae: 3.9257\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.0900 - mae: 4.0900\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.4781 - mae: 4.4781\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 4.7607 - mae: 4.7607\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 4.9264 - mae: 4.9264\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.9894 - mae: 4.9894\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 4.9672 - mae: 4.9672\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.8677 - mae: 4.8677\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 4.7046 - mae: 4.7046\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.4897 - mae: 4.4897\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.2326 - mae: 4.2326\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.9418 - mae: 3.9418\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7886 - mae: 3.7886\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.6632 - mae: 3.6632\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.5393 - mae: 3.5393\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.5531 - mae: 3.5531\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.6208 - mae: 3.6208\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.6576 - mae: 3.6576\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7506 - mae: 3.7506\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.7092 - mae: 3.7092\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.5338 - mae: 3.5338\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.4270 - mae: 3.4270\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.3004 - mae: 3.3004\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.1545 - mae: 3.1545\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.0648 - mae: 3.0648\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.0459 - mae: 3.0459\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.0209 - mae: 3.0209\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.0347 - mae: 3.0347\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 2.9867 - mae: 2.9867\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 2.8888 - mae: 2.8888\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 2.8175 - mae: 2.8175\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.7250 - mae: 2.7250\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 2.6173 - mae: 2.6173\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 2.5069 - mae: 2.5069\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 2.4002 - mae: 2.4002\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 2.2894 - mae: 2.2894\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.1712 - mae: 2.1712\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 2.0403 - mae: 2.0403\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 1.9989 - mae: 1.9989\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 1.8724 - mae: 1.8724\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.7025 - mae: 1.7025\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 1.5747 - mae: 1.5747\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 1.4753 - mae: 1.4753\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 1.3545 - mae: 1.3545\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 1.2393 - mae: 1.2393\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 1.0315 - mae: 1.0315\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.8339 - mae: 0.8339\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.6625 - mae: 0.6625\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.5088 - mae: 0.5088\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.3310 - mae: 0.3310\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.3307 - mae: 0.3307\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.3330 - mae: 0.3330\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2128 - mae: 0.2128\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6744 - mae: 0.6744\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6279 - mae: 0.6279\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.6084 - mae: 0.6084\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.7256 - mae: 0.7256\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.6237 - mae: 0.6237\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.3927 - mae: 0.3927\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.5721 - mae: 0.5721\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6973 - mae: 0.6973\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.4928 - mae: 0.4928\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.5584 - mae: 0.5584\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.5706 - mae: 0.5706\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.4548 - mae: 0.4548\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.2731 - mae: 0.2731\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.6350 - mae: 0.6350\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.7159 - mae: 0.7159\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.4848 - mae: 0.4848\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.1989 - mae: 0.1989\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.3395 - mae: 0.3395\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.2128 - mae: 0.2128\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.3692 - mae: 0.3692\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.4717 - mae: 0.4717\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.3324 - mae: 0.3324\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.2551 - mae: 0.2551\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 0.3310 - mae: 0.3310\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2381 - mae: 0.2381\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2594 - mae: 0.2594\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2367 - mae: 0.2367\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1750 - mae: 0.1750\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2303 - mae: 0.2303\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.1337 - mae: 0.1337\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1930 - mae: 0.1930\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1099 - mae: 0.1099\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2055 - mae: 0.2055\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1987 - mae: 0.1987\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2265 - mae: 0.2265\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbdf03c60d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLPaUkz3y9Tu",
        "outputId": "92ea1571-11a5-419e-f72c-b0e171e00a3a"
      },
      "source": [
        "model.predict([8.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18.031555]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOAAQt9izIE7"
      },
      "source": [
        "## Evaluating a model\n",
        "\n",
        "In practice, a typical workflow you'll go through when building neural network is :\n",
        "```\n",
        "Build a model -> Fit it -> Evaluate it -> Tweak a model -> Fit it -> Evaluate it -> Tweak it ...\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hotW3WO03FD0"
      },
      "source": [
        "When it comes to evaluation... there are 3 words you should memorize :\n",
        "\n",
        "`\"Visualize Visualize Visualize\"`\n",
        "\n",
        "It's a good idea to visualize :\n",
        "* The data -  what data are we working with ? What does it look like ?\n",
        "* The model itself - what does our model look like ?\n",
        "* The training of a model - how does a model perform while it learns ?\n",
        "* The predictions of the model - how do the predictions of a model line up against the ground truth ( the original labels )?\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwWQFaOqEi4k",
        "outputId": "019c6802-75f1-4bbd-ee2e-f0b7d2c30a90"
      },
      "source": [
        "# Make a bigger dataset\n",
        "X = tf.range(-100,100,4)\n",
        "X"
      ],
      "execution_count": null,
      "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": "jffajWyYErF0",
        "outputId": "95e076d2-100c-41b0-e88e-e4e3b5c04350"
      },
      "source": [
        "# Make labels for the dataset\n",
        "y = X+10\n",
        "y"
      ],
      "execution_count": null,
      "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": 282
        },
        "id": "fniYBdh0E4DG",
        "outputId": "355b60ce-8848-4148-afee-ac9dc8bb4f43"
      },
      "source": [
        "# Visualze the plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbdf02d3910>]"
            ]
          },
          "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": "T0d1ebAvFBkg"
      },
      "source": [
        "### The 3 sets...\n",
        "\n",
        "* **Training set** - *the model learns from this data, which is typically 70-80% of the total data available.*\n",
        "* **Validation set** -*the model gets tuned on this data, which is typically 10-15% of the data available.*\n",
        "* **Test set** *- the model gets evaluated on this data to test what it has learnt, this set is typically 10-15% of the total data available.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOXpc12uF_Hm",
        "outputId": "fa00c49f-7880-4aeb-c968-a4aa2c3179eb"
      },
      "source": [
        "# Check the length of how many samples we have\n",
        "len(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGbQBQffGYd2"
      },
      "source": [
        "# Split the data into train and test sets\n",
        "X_train = X[:40]  # 80% of the data\n",
        "y_train = y[:40]  # 80% of the data\n",
        "\n",
        "X_test = X[40:]   # 20% of the data\n",
        "y_test = y[40:]   # 20% of the data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlvsqGaPHfyx"
      },
      "source": [
        "### Visualizing the data\n",
        "\n",
        "Now we've got our data in training and test sets... let's visualise it again !"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fybSRAStH4ms",
        "outputId": "2dae7110-afae-4233-c54d-5b46647ca354"
      },
      "source": [
        "plt.figure(figsize=(7,6))\n",
        "# Plot training data in blue\n",
        "plt.scatter(X_train, y_train, c=\"b\", label=\"Training data\")\n",
        "# Plot training data in green\n",
        "plt.scatter(X_test, y_test, c=\"g\", label=\"Testing data\")\n",
        "# Show a legend\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbdf022d690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfZTjXZVIa8m"
      },
      "source": [
        "# Let's have a look at how to build a neural network for our data\n",
        "\n",
        "# 1. Create the model\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# # 3. Fit the model\n",
        "# model.fit(X_train, y_train, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bd0pOhS1zh7X"
      },
      "source": [
        "### Visualizing our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "033SnCmczxR6",
        "outputId": "99a7983c-3065-4926-ff8e-5b938834c399"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-5f15418b3570>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, line_length, positions, print_fn)\u001b[0m\n\u001b[1;32m   2374\u001b[0m     \"\"\"\n\u001b[1;32m   2375\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2376\u001b[0;31m       raise ValueError('This model has not yet been built. '\n\u001b[0m\u001b[1;32m   2377\u001b[0m                        \u001b[0;34m'Build the model first by calling `build()` or calling '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2378\u001b[0m                        \u001b[0;34m'`fit()` with some data, or specify '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: This model has not yet been built. Build the model first by calling `build()` or calling `fit()` with some data, or specify an `input_shape` argument in the first layer(s) for automatic build."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFxgDqddz0eh"
      },
      "source": [
        "# Let's create a model which builds automatically by defining the input_shape argument in the first layer\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model (same as above)\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(10, input_shape=[1], name=\"input_layer\"),\n",
        "  tf.keras.layers.Dense(1, name=\"output_layer\")\n",
        "], name = \"model_1\")\n",
        "\n",
        "# Compile the model\n",
        "model.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUUv5_Pvz4AQ",
        "outputId": "bb2afab9-b2d9-43dc-c027-55fb51e0d69f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\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": "AuNLW0fS21lV"
      },
      "source": [
        "* Total params : total parameters in the model\n",
        "* Trainable parameters : these are the parameters(patterns) the model can update as it trains\n",
        "* Non-trainable : these parameters aren't updated during training ( this is typical when you bring parameters from other models during **transfer learning**)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZpLUTVG4Swa",
        "outputId": "5757eea3-8fb9-4a56-e793-62a3f62b507a"
      },
      "source": [
        "# Fit the model\n",
        "model.fit(X_train, y_train, epochs= 100, verbose=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbdf0081450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agbEVS-yA2aZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "95f0f5df-48e9-40f0-9954-56faf2f94d3c"
      },
      "source": [
        "\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "plot_model(model=model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCCGE18TSAbp"
      },
      "source": [
        "### Visualizing our model's predictions\n",
        "\n",
        "To visualize predictions, it's a good idea to plot them against the ground truth labels.\n",
        "\n",
        "Often you'll see this in the form of `y_test` or `y_true` versus `y_pred` (ground truth versus your model's predictions)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLQI_7M6VHro",
        "outputId": "41566fbb-4615-4cc0-c0bb-072fc773de26"
      },
      "source": [
        "# Make some predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred, y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 70.55221 ],\n",
              "        [ 75.13992 ],\n",
              "        [ 79.72765 ],\n",
              "        [ 84.31538 ],\n",
              "        [ 88.9031  ],\n",
              "        [ 93.49082 ],\n",
              "        [ 98.07855 ],\n",
              "        [102.666275],\n",
              "        [107.254005],\n",
              "        [111.84173 ]], dtype=float32),\n",
              " <tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmlR4TbvWKTJ"
      },
      "source": [
        "**Note** : If you feel like you're going to reuse some kind of functionality in the future, it's a good idea tu turn it into a function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUu4m7m4WmhI"
      },
      "source": [
        "# Let's 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=X_test,\n",
        "                     predictions=y_pred):\n",
        "  plt.figure(figsize=(10,7))\n",
        "  # Plot training data in blue\n",
        "  plt.scatter(train_data,train_labels, c=\"b\", label=\"Training data\")\n",
        "  # Plot training data in green\n",
        "  plt.scatter(test_data,test_labels, c=\"g\", label=\"Testing data\")\n",
        "  # Plot model's predictions in red\n",
        "  plt.scatter(test_data, predictions, c=\"r\", label=\"Predictions\")\n",
        "  # Show a legend\n",
        "  plt.legend();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "sTV6FaKcYp7i",
        "outputId": "163bec24-c53f-4e95-a52c-cce302ce72a8"
      },
      "source": [
        "plot_predictions()"
      ],
      "execution_count": null,
      "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": "jQxLW-SIYwtV"
      },
      "source": [
        "### Evaluating our model's predictions with regression evaluation metrics\n",
        "\n",
        "Depending on the problem you're working on, there will be different evaluation metrics to evaluate your model's performance.\n",
        "\n",
        "Since we're working on a regression, two of the main metrics :\n",
        "* MAE - mean average error, \"on average, how wrong is each of my model's predictions.\"\n",
        "* MSE - mean square error, \"square the average errors\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dq3HgxdJdyNR",
        "outputId": "56477b1a-d705-4bc1-c59c-f84818509ea2"
      },
      "source": [
        "# Evaluate the model on the test\n",
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 104ms/step - loss: 3.1970 - mae: 3.1970\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.1969642639160156, 3.1969642639160156]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCyMKYEweNqg",
        "outputId": "24b7bbaa-b4d1-41df-af34-dc19e76b5b70"
      },
      "source": [
        "# Calculate the mean absolute error\n",
        "mae = tf.metrics.mean_absolute_error(y_true=y_test,\n",
        "                                     y_pred=tf.constant(y_pred))\n",
        "mae"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([17.558233, 14.116046, 11.708939, 10.336925, 10.      , 10.698164,\n",
              "       12.447131, 15.33302 , 19.254005, 23.841728], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyCOrbOOe6yE",
        "outputId": "d004879e-f4ac-4cb3-fab3-ee1768ccf5fd"
      },
      "source": [
        "tf.squeeze(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([ 70.55221 ,  75.13992 ,  79.72765 ,  84.31538 ,  88.9031  ,\n",
              "        93.49082 ,  98.07855 , 102.666275, 107.254005, 111.84173 ],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khA1CkyypO4B",
        "outputId": "9b9e0776-8123-43e6-8aa3-ac56a1eb6ac3"
      },
      "source": [
        "# Calculate the mean absolute error\n",
        "mae = tf.metrics.mean_absolute_error(y_true=y_test,\n",
        "                                     y_pred=tf.squeeze(y_pred))\n",
        "mae"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=3.1969643>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfBNkOsLpYrJ",
        "outputId": "2e097e91-bbff-4112-e0b4-04f9d2876be4"
      },
      "source": [
        "# Calculate the mean square error\n",
        "mse = tf.metrics.mean_squared_error(y_true=y_test,\n",
        "                                     y_pred=tf.squeeze(y_pred))\n",
        "mse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=13.0703>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVDgSCECpiHm"
      },
      "source": [
        "# Make some functions to reuse MAE and MSE\n",
        "def mae(y_true, y_pred):\n",
        "  return tf.metrics.mean_absolute_error(y_true=y_test,\n",
        "                                        y_pred=tf.squeeze(y_pred))\n",
        "def mse(y_true, y_pred):\n",
        "  return tf.metrics.mean_squared_error(y_true=y_test,\n",
        "                                        y_pred=tf.squeeze(y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_455gDmqb9g"
      },
      "source": [
        "## Running experiments to improve our model\n",
        "\n",
        "```\n",
        "Build a model -> Fit it -> Evaluate it -> Tweak it -> Fit it -> Evaluate it -> Tweak it ...\n",
        "```\n",
        "\n",
        "1. Get more data - get more examples for your model to train on ( more opportunities to learn patterns or relationships between features and labels).\n",
        "2. Make your model larger(using a more complex model) - this might come in the form of more layers or more hidden units in each layer.\n",
        "3. Train for longer - give your model more of a chance to find patterns in the data\n",
        "\n",
        "Let's do 3 modelling experiments :\n",
        "\n",
        "1. `model_1` - same as the original model, 1 layer, trained for 100 epochs.\n",
        "2. `model_2` - 2 layers, trained for 100 epochs.\n",
        "3. `model_3` - 2 layers, trained for 500 epochs.\n",
        "\n",
        "**Build `model_1`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-ua3kV3qdNf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7a7e74f-b818-48a1-9b8d-c6a26163cdfa"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "model_1 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_1.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_1.fit(X_train, y_train, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.4124 - mae: 16.4124\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.0829 - mae: 11.0829\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.1242 - mae: 11.1242\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.6945 - mae: 8.6945\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.8003 - mae: 9.8003\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5263 - mae: 9.5263\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.4102 - mae: 8.4102\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.1449 - mae: 9.1449\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.4932 - mae: 19.4932\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.6061 - mae: 9.6061\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.5992 - mae: 8.5992\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.9324 - mae: 10.9324\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.0574 - mae: 10.0574\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.5679 - mae: 16.5679\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.4537 - mae: 11.4537\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.4056 - mae: 8.4056\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.8417 - mae: 13.8417\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.6621 - mae: 11.6621\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.7623 - mae: 18.7623\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.5702 - mae: 15.5702\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.9959 - mae: 10.9959\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.0023 - mae: 8.0023\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.8593 - mae: 9.8593\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.5796 - mae: 7.5796\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.7226 - mae: 13.7226\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.1740 - mae: 17.1740\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.4233 - mae: 13.4233\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.5473 - mae: 14.5473\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.8753 - mae: 9.8753\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 17.1187 - mae: 17.1187\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.5949 - mae: 24.5949\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.6765 - mae: 7.6765\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.2971 - mae: 9.2971\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.1703 - mae: 14.1703\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.8635 - mae: 10.8635\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.5689 - mae: 13.5689\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.3448 - mae: 9.3448\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.4804 - mae: 10.4804\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.0178 - mae: 10.0178\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.8005 - mae: 10.8005\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 7.7789 - mae: 7.7789\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.2844 - mae: 10.2844\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.9628 - mae: 8.9628\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.6243 - mae: 12.6243\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.1236 - mae: 14.1236\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.2681 - mae: 8.2681\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.0126 - mae: 9.0126\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.6882 - mae: 10.6882\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.7431 - mae: 7.7431\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.3950 - mae: 9.3950\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.9267 - mae: 8.9267\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 16.9818 - mae: 16.9818\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 14.8677 - mae: 14.8677\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 22.0413 - mae: 22.0413\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 17.0640 - mae: 17.0640\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.9520 - mae: 9.9520\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.7522 - mae: 9.7522\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.4378 - mae: 9.4378\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.3373 - mae: 8.3373\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.6425 - mae: 9.6425\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.7590 - mae: 11.7590\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.7187 - mae: 11.7187\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.2316 - mae: 7.2316\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 17.7848 - mae: 17.7848\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.6255 - mae: 12.6255\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 13.2840 - mae: 13.2840\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.8471 - mae: 7.8471\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.9708 - mae: 9.9708\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.4903 - mae: 12.4903\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.5001 - mae: 8.5001\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.9996 - mae: 9.9996\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.1797 - mae: 10.1797\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.0024 - mae: 13.0024\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 10.3812 - mae: 10.3812\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 9.7621 - mae: 9.7621\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.5186 - mae: 11.5186\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.3070 - mae: 8.3070\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.4436 - mae: 9.4436\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 20.3897 - mae: 20.3897\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.4751 - mae: 15.4751\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.0208 - mae: 9.0208\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.3869 - mae: 13.3869\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.9649 - mae: 7.9649\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.5702 - mae: 7.5702\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.9706 - mae: 9.9706\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.0533 - mae: 9.0533\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.2670 - mae: 12.2670\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.6052 - mae: 10.6052\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.1986 - mae: 7.1986\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.0379 - mae: 13.0379\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 7.1968 - mae: 7.1968\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.5386 - mae: 7.5386\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.0932 - mae: 7.0932\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.8860 - mae: 12.8860\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.9620 - mae: 9.9620\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 8.7739 - mae: 8.7739\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.0861 - mae: 13.0861\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.3848 - mae: 8.3848\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 9.7828 - mae: 9.7828\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.5252 - mae: 8.5252\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbdb8088d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD99mLpl7VAF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "73529c2f-2fcb-4660-8dab-be6759c7ad4e"
      },
      "source": [
        "# Make and plot predictions for model_1\n",
        "y_preds_1 = model_1.predict(X_test)\n",
        "plot_predictions(predictions=y_preds_1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fbdaa7d5b00> 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": "BfGBpPq5WOXL",
        "outputId": "5c39393e-7581-4e4a-b6e8-c523ac5446a2"
      },
      "source": [
        "# Calculate model_1 evaluation metrics\n",
        "mae_1 = mae(y_test,y_preds_1)\n",
        "mse_1 = mse(y_test,y_preds_1)\n",
        "mae_1,mse_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=float32, numpy=18.745327>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=353.5734>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rOUHGbYW6yn"
      },
      "source": [
        "**Build `model_2`**\n",
        "\n",
        "* 2 dense layers, trained for 100 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq6YUIUjhzDc",
        "outputId": "fd7c73e4-c0cb-4952-be00-67cabead72d8"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "model_2 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(10),\n",
        "  tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_2.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mse\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_2.fit(X_train, y_train, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 26.8627 - mse: 1015.8977\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 24.4175 - mse: 767.5333\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 31.5187 - mse: 1433.3077\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 27.8490 - mse: 1141.6676\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.6465 - mse: 267.4541\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.9682 - mse: 169.5530\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.0874 - mse: 141.6589\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.3978 - mse: 167.3422\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 42.4087 - mse: 2772.5435\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 28.3537 - mse: 1129.8184\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.6590 - mse: 132.9751\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.8820 - mse: 933.4346\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 16.7682 - mse: 398.1735\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.6019 - mse: 1102.2362\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.7361 - mse: 441.0354\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.4550 - mse: 81.2729\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6974 - mse: 163.8850\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 20.7691 - mse: 610.4132\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.6535 - mse: 146.6566\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.4600 - mse: 487.0983\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.2352 - mse: 362.8489\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.5012 - mse: 290.6679\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.6493 - mse: 88.0710\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.4430 - mse: 131.0228\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.8060 - mse: 237.9439\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.8011 - mse: 1075.4352\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.0333 - mse: 202.1000\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.4573 - mse: 871.2022\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.1266 - mse: 92.4710\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 31.1804 - mse: 1657.8944\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 56.8211 - mse: 5441.4536\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 12.2909 - mse: 221.1292\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.3303 - mse: 330.0118\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.8145 - mse: 215.9795\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.4721 - mse: 95.5154\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.6526 - mse: 404.8036\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.4634 - mse: 169.1310\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.7760 - mse: 459.8316\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 20.1198 - mse: 569.0896\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 21.4944 - mse: 654.6217\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.5667 - mse: 268.1427\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.2999 - mse: 188.8542\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.1115 - mse: 175.7054\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.5895 - mse: 869.7918\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.9461 - mse: 113.3928\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.0273 - mse: 188.4638\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.0557 - mse: 132.7937\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 17.8711 - mse: 427.6239\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.5485 - mse: 96.8224\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.7912 - mse: 256.8745\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.2969 - mse: 145.4000\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 32.1753 - mse: 1732.7579\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.1257 - mse: 300.5091\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.3921 - mse: 897.0252\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.5967 - mse: 834.7570\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0101 - mse: 137.5419\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.4333 - mse: 186.3947\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.4491 - mse: 96.3009\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.6438 - mse: 218.5186\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.3168 - mse: 208.3240\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.1832 - mse: 422.8599\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.4860 - mse: 128.6800\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.8072 - mse: 160.7223\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.2892 - mse: 945.1689\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.7923 - mse: 145.3389\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 22.0328 - mse: 725.3465\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6505 - mse: 132.1477\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.8455 - mse: 155.3466\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.0960 - mse: 770.1608\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.7402 - mse: 139.4087\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.8968 - mse: 339.6096\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 6.7092 - mse: 66.6670\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.6475 - mse: 146.9912\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 24.6152 - mse: 948.2745\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.7602 - mse: 119.7264\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.9799 - mse: 172.5533\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.6488 - mse: 432.3298\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.2784 - mse: 98.6556\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 24.4604 - mse: 902.0727\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 27.4405 - mse: 1158.6084\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.7139 - mse: 170.4368\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.6757 - mse: 196.1245\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.9045 - mse: 414.3997\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.0376 - mse: 70.0240\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.1558 - mse: 321.1296\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.1583 - mse: 311.4817\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.8683 - mse: 554.4749\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 30.7657 - mse: 1357.5465\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.7059 - mse: 108.4963\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 21.5531 - mse: 674.2971\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5827 - mse: 128.0419\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.2158 - mse: 463.7718\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.1935 - mse: 75.5075\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.4261 - mse: 438.1335\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1722 - mse: 164.3358\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 18.9207 - mse: 491.7027\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.2012 - mse: 208.9879\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.2931 - mse: 158.5949\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.4563 - mse: 254.5626\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 20.1754 - mse: 612.1698\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbda9ebc550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "dQ-G6CaWiuLR",
        "outputId": "7a4b82cb-1fad-4ab7-ca4b-f0e85b15810e"
      },
      "source": [
        "# Make and plot\n",
        "y_preds_2 = model_2.predict(X_test)\n",
        "plot_predictions(predictions=y_preds_2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fbda9e274d0> 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": "oMi77nPYibvK",
        "outputId": "7f02098f-f69f-4705-a572-5d72ebbd1c5a"
      },
      "source": [
        "# Calculate model_2 evaluation metrics\n",
        "mae_2 = mae(y_test,y_preds_2)\n",
        "mse_2 = mse(y_test,y_preds_2)\n",
        "mae_2,mse_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=float32, numpy=3.1969643>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=13.0703>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8OL63MXjAGa"
      },
      "source": [
        "**Build `model_3`**\n",
        "* 2 layers, trained for 500 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVHboLvkkpCr",
        "outputId": "dc343e2f-16fa-47e0-f46e-a96e2e65625f"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "model_3 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(10),\n",
        "  tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_3.fit(X_train, y_train, epochs= 500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 26.8627 - mae: 26.8627\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.4175 - mae: 24.4175\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 31.5187 - mae: 31.5187\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 27.8490 - mae: 27.8490\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.6465 - mae: 14.6465\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.9682 - mae: 11.9682\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.0874 - mae: 11.0874\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.3978 - mae: 11.3978\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 42.4087 - mae: 42.4087\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 28.3537 - mae: 28.3537\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.6590 - mae: 9.6590\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.8820 - mae: 25.8820\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.7682 - mae: 16.7682\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.6019 - mae: 26.6019\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.7361 - mae: 17.7361\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.4550 - mae: 7.4550\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6974 - mae: 10.6974\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 20.7691 - mae: 20.7691\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.6535 - mae: 9.6535\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.4600 - mae: 18.4600\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.2352 - mae: 16.2352\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.5012 - mae: 14.5012\n",
            "Epoch 23/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.6493 - mae: 8.6493\n",
            "Epoch 24/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.4430 - mae: 10.4430\n",
            "Epoch 25/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.8060 - mae: 12.8060\n",
            "Epoch 26/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.8011 - mae: 26.8011\n",
            "Epoch 27/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.0333 - mae: 12.0333\n",
            "Epoch 28/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.4573 - mae: 23.4573\n",
            "Epoch 29/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.1266 - mae: 9.1266\n",
            "Epoch 30/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 31.1804 - mae: 31.1804\n",
            "Epoch 31/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 56.8211 - mae: 56.8211\n",
            "Epoch 32/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.2909 - mae: 12.2909\n",
            "Epoch 33/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 15.3303 - mae: 15.3303\n",
            "Epoch 34/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.8145 - mae: 12.8145\n",
            "Epoch 35/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.4721 - mae: 9.4721\n",
            "Epoch 36/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.6526 - mae: 16.6526\n",
            "Epoch 37/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.4634 - mae: 10.4634\n",
            "Epoch 38/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 18.7760 - mae: 18.7760\n",
            "Epoch 39/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.1198 - mae: 20.1198\n",
            "Epoch 40/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 21.4944 - mae: 21.4944\n",
            "Epoch 41/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.5667 - mae: 14.5667\n",
            "Epoch 42/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 12.2999 - mae: 12.2999\n",
            "Epoch 43/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1115 - mae: 11.1115\n",
            "Epoch 44/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.5895 - mae: 23.5895\n",
            "Epoch 45/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.9461 - mae: 9.9461\n",
            "Epoch 46/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.0273 - mae: 12.0273\n",
            "Epoch 47/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.0557 - mae: 9.0557\n",
            "Epoch 48/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.8711 - mae: 17.8711\n",
            "Epoch 49/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.5485 - mae: 9.5485\n",
            "Epoch 50/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.7912 - mae: 13.7912\n",
            "Epoch 51/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.2969 - mae: 11.2969\n",
            "Epoch 52/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 32.1753 - mae: 32.1753\n",
            "Epoch 53/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.1257 - mae: 14.1257\n",
            "Epoch 54/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.3921 - mae: 24.3921\n",
            "Epoch 55/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.5967 - mae: 23.5967\n",
            "Epoch 56/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0101 - mae: 10.0101\n",
            "Epoch 57/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.4333 - mae: 12.4333\n",
            "Epoch 58/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.4491 - mae: 9.4491\n",
            "Epoch 59/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.6438 - mae: 12.6438\n",
            "Epoch 60/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.3168 - mae: 12.3168\n",
            "Epoch 61/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.1832 - mae: 17.1832\n",
            "Epoch 62/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.4860 - mae: 10.4860\n",
            "Epoch 63/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.8072 - mae: 10.8072\n",
            "Epoch 64/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 25.2892 - mae: 25.2892\n",
            "Epoch 65/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.7923 - mae: 10.7923\n",
            "Epoch 66/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 22.0328 - mae: 22.0328\n",
            "Epoch 67/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.6505 - mae: 10.6505\n",
            "Epoch 68/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.8455 - mae: 10.8455\n",
            "Epoch 69/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.0960 - mae: 23.0960\n",
            "Epoch 70/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.7402 - mae: 8.7402\n",
            "Epoch 71/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.8968 - mae: 15.8968\n",
            "Epoch 72/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.7092 - mae: 6.7092\n",
            "Epoch 73/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6475 - mae: 10.6475\n",
            "Epoch 74/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.6152 - mae: 24.6152\n",
            "Epoch 75/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.7602 - mae: 8.7602\n",
            "Epoch 76/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.9799 - mae: 11.9799\n",
            "Epoch 77/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.6488 - mae: 16.6488\n",
            "Epoch 78/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.2784 - mae: 9.2784\n",
            "Epoch 79/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.4604 - mae: 24.4604\n",
            "Epoch 80/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 27.4405 - mae: 27.4405\n",
            "Epoch 81/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.7139 - mae: 11.7139\n",
            "Epoch 82/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.6757 - mae: 11.6757\n",
            "Epoch 83/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.9045 - mae: 17.9045\n",
            "Epoch 84/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.0376 - mae: 7.0376\n",
            "Epoch 85/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.1558 - mae: 15.1558\n",
            "Epoch 86/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.1583 - mae: 15.1583\n",
            "Epoch 87/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 19.8683 - mae: 19.8683\n",
            "Epoch 88/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 30.7657 - mae: 30.7657\n",
            "Epoch 89/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.7059 - mae: 9.7059\n",
            "Epoch 90/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 21.5531 - mae: 21.5531\n",
            "Epoch 91/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.5827 - mae: 9.5827\n",
            "Epoch 92/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.2158 - mae: 18.2158\n",
            "Epoch 93/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.1935 - mae: 7.1935\n",
            "Epoch 94/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.4261 - mae: 17.4261\n",
            "Epoch 95/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1722 - mae: 11.1722\n",
            "Epoch 96/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.9207 - mae: 18.9207\n",
            "Epoch 97/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.2012 - mae: 12.2012\n",
            "Epoch 98/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.2931 - mae: 11.2931\n",
            "Epoch 99/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.4563 - mae: 13.4563\n",
            "Epoch 100/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.1754 - mae: 20.1754\n",
            "Epoch 101/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.4395 - mae: 11.4395\n",
            "Epoch 102/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.0888 - mae: 17.0888\n",
            "Epoch 103/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.9455 - mae: 6.9455\n",
            "Epoch 104/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.3779 - mae: 24.3779\n",
            "Epoch 105/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.8800 - mae: 16.8800\n",
            "Epoch 106/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.5557 - mae: 8.5557\n",
            "Epoch 107/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 26.5426 - mae: 26.5426\n",
            "Epoch 108/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.2375 - mae: 13.2375\n",
            "Epoch 109/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.1284 - mae: 9.1284\n",
            "Epoch 110/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.9256 - mae: 8.9256\n",
            "Epoch 111/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.0550 - mae: 14.0550\n",
            "Epoch 112/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.7418 - mae: 9.7418\n",
            "Epoch 113/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.5151 - mae: 17.5151\n",
            "Epoch 114/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.0304 - mae: 17.0304\n",
            "Epoch 115/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.9228 - mae: 10.9228\n",
            "Epoch 116/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.5453 - mae: 23.5453\n",
            "Epoch 117/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.8340 - mae: 8.8340\n",
            "Epoch 118/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.2978 - mae: 10.2978\n",
            "Epoch 119/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.3192 - mae: 8.3192\n",
            "Epoch 120/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 30.7265 - mae: 30.7265\n",
            "Epoch 121/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.1436 - mae: 8.1436\n",
            "Epoch 122/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 29.4327 - mae: 29.4327\n",
            "Epoch 123/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 34.2183 - mae: 34.2183\n",
            "Epoch 124/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 19.6697 - mae: 19.6697\n",
            "Epoch 125/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 6.5959 - mae: 6.5959\n",
            "Epoch 126/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.8683 - mae: 21.8683\n",
            "Epoch 127/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.0892 - mae: 8.0892\n",
            "Epoch 128/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.1330 - mae: 21.1330\n",
            "Epoch 129/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.3481 - mae: 9.3481\n",
            "Epoch 130/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.2634 - mae: 24.2634\n",
            "Epoch 131/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.9302 - mae: 9.9302\n",
            "Epoch 132/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.3634 - mae: 18.3634\n",
            "Epoch 133/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.2677 - mae: 7.2677\n",
            "Epoch 134/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 18.4166 - mae: 18.4166\n",
            "Epoch 135/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.7401 - mae: 9.7401\n",
            "Epoch 136/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.8516 - mae: 17.8516\n",
            "Epoch 137/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 22.9936 - mae: 22.9936\n",
            "Epoch 138/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.2013 - mae: 8.2013\n",
            "Epoch 139/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.0097 - mae: 8.0097\n",
            "Epoch 140/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.9624 - mae: 15.9624\n",
            "Epoch 141/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.6422 - mae: 8.6422\n",
            "Epoch 142/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 39.2127 - mae: 39.2127\n",
            "Epoch 143/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.9599 - mae: 25.9599\n",
            "Epoch 144/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.6134 - mae: 8.6134\n",
            "Epoch 145/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 27.7680 - mae: 27.7680\n",
            "Epoch 146/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.3428 - mae: 8.3428\n",
            "Epoch 147/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.0730 - mae: 15.0730\n",
            "Epoch 148/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 19.1933 - mae: 19.1933\n",
            "Epoch 149/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.4993 - mae: 7.4993\n",
            "Epoch 150/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.4097 - mae: 7.4097\n",
            "Epoch 151/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.1762 - mae: 18.1762\n",
            "Epoch 152/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.9065 - mae: 9.9065\n",
            "Epoch 153/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 30.6868 - mae: 30.6868\n",
            "Epoch 154/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.6497 - mae: 9.6497\n",
            "Epoch 155/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.0887 - mae: 16.0887\n",
            "Epoch 156/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 18.0462 - mae: 18.0462\n",
            "Epoch 157/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 34.4868 - mae: 34.4868\n",
            "Epoch 158/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.3002 - mae: 10.3002\n",
            "Epoch 159/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.5288 - mae: 8.5288\n",
            "Epoch 160/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 22.2562 - mae: 22.2562\n",
            "Epoch 161/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.2986 - mae: 11.2986\n",
            "Epoch 162/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.5576 - mae: 21.5576\n",
            "Epoch 163/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.8923 - mae: 18.8923\n",
            "Epoch 164/500\n",
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            "Epoch 495/500\n",
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            "Epoch 497/500\n",
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            "Epoch 498/500\n",
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            "Epoch 499/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.9942 - mae: 8.9942\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.1579 - mae: 15.1579\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbda95a8650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "hK8J_XNkk1Fj",
        "outputId": "5ed7f300-d92b-4850-a1c5-c42014f6be2a"
      },
      "source": [
        "# Make and plot\n",
        "y_preds_3 = model_3.predict(X_test)\n",
        "plot_predictions(predictions=y_preds_3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:7 out of the last 7 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fbdaa654a70> 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": {
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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": "cYaHeaBGk_t2",
        "outputId": "70c28346-e922-42b5-e8e4-8442025f37ce"
      },
      "source": [
        "# Calculate model_3 evaluation metrics\n",
        "mae_3 = mae(y_test,y_preds_3)\n",
        "mse_3 = mse(y_test,y_preds_3)\n",
        "mae_3,mse_3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=float32, numpy=68.71362>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=4808.0283>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oOTjBoclJ7y"
      },
      "source": [
        "### Comparing the results of our experiments\n",
        "\n",
        "We've run a few experiments, let's compare the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "fBTH6VGtmE7U",
        "outputId": "c01bf835-b151-4ee5-8828-8b40a0497a69"
      },
      "source": [
        "# Let's compare our model's results using a pandas dataframe\n",
        "import pandas as pd\n",
        "\n",
        "model_results = [[\"model_1\",mae_1.numpy(),mse_1.numpy()],\n",
        "                 [\"model_2\",mae_2.numpy(),mse_2.numpy()],\n",
        "                 [\"model_3\",mae_3.numpy(),mse_3.numpy()]]\n",
        "\n",
        "all_results = pd.DataFrame(model_results, columns=[\"model\",\"mae\",\"mse\"])\n",
        "all_results"
      ],
      "execution_count": null,
      "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_1</td>\n",
              "      <td>18.745327</td>\n",
              "      <td>353.573395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>model_2</td>\n",
              "      <td>3.196964</td>\n",
              "      <td>13.070300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>model_3</td>\n",
              "      <td>68.713623</td>\n",
              "      <td>4808.028320</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     model        mae          mse\n",
              "0  model_1  18.745327   353.573395\n",
              "1  model_2   3.196964    13.070300\n",
              "2  model_3  68.713623  4808.028320"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Ou73kqUpzcn"
      },
      "source": [
        "Looks like `model_2` performed best..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12k73E3qpTec",
        "outputId": "6c276253-895e-4947-d277-38e18b64ec84"
      },
      "source": [
        "model_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_7 (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": "wE09ZslnpsFf"
      },
      "source": [
        "### Tracking your experiments\n",
        "\n",
        "One really good habit in machine learning modelling is to track the results of experiments.\n",
        "\n",
        "And when doing so, it can be tedious if you're running lots of experiments.\n",
        "\n",
        "Luckily, there are tools to help us !\n",
        "\n",
        "**Resource** : As you build more models, you'll want to look into using :\n",
        "* TensorBoard - a component of the TensorFlow library to help track modelling experiments.\n",
        "* Weights & Biases - a tool for tracking all kinds of machine learning experiments ( plugs straight into TensorBoard)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJEmmJPOrsBW"
      },
      "source": [
        " ### Saving our models\n",
        "\n",
        " Saving our models allow us to use them outside of Google Colab (or wherever they were trained) such as in a web application or a mobile app.\n",
        "\n",
        " There are two main formats we can save our model's too :\n",
        " \n",
        " 1. The SavedModel format\n",
        " 2. The HDF5 format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUzEiPWz6W5B",
        "outputId": "58e1db0b-e987-4c2a-91b4-b3a1dd2ce69e"
      },
      "source": [
        "model_2.save(\"best_model_SavedModel_format\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: best_model_SavedModel_format/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTjfMBd07-eb"
      },
      "source": [
        "model_2.save(\"best_model_hdf5_format.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nrw6WACX85jj"
      },
      "source": [
        "### Loading in a saved model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WBu23j99q3X",
        "outputId": "ffaf412a-d81e-47ee-fdcb-beb9b9c5b53f"
      },
      "source": [
        "# Load in the SavedModel format model\n",
        "loaded_SavedModel_format = tf.keras.models.load_model('/content/best_model_SavedModel_format')\n",
        "loaded_SavedModel_format.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_7 (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/"
        },
        "id": "rSydPxZm-PKp",
        "outputId": "14c56373-9f6c-49c5-cf33-b3909ec56f4c"
      },
      "source": [
        "# Compare model_2 predictions with SavedModel format model predictions\n",
        "model_2_preds = model_2.predict(X_test)\n",
        "loaded_SavedModel_format_preds = loaded_SavedModel_format.predict(X_test)\n",
        "model_2_preds == loaded_SavedModel_format_preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:8 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fbdaa5a4c20> 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": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APdZEamn--3d",
        "outputId": "701e10f0-e809-4716-975e-f96f2479b93d"
      },
      "source": [
        "# Compare the MAE of model_2_preds and loaded_SavedModel_preds\n",
        "mae(y_true = y_test,y_pred=model_2_preds) == mae(y_true = y_test,y_pred=loaded_SavedModel_format_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=bool, numpy=True>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZaaCM4X_seV"
      },
      "source": [
        "### Download a model(or any other file) from Google Colab\n",
        "\n",
        "1. You can go the \"files\" tab and right click on the file you're after and click 'download'.\n",
        "2. Use code (see the cell below)\n",
        "3. Save it to Google Drive by connecting Google Drive and copying it there (see 2nd code cell below)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16
        },
        "id": "3JgT4vnoCdRh",
        "outputId": "505e8a25-f7b4-440a-e48b-4cf9aaf12a24"
      },
      "source": [
        "# Download a file from Google Colab\n",
        "from google.colab import files\n",
        "files.download(\"/content/model.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_d092f8df-5f73-4643-acbd-6124f6d32f40\", \"model.png\", 18927)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxgwDTtWDB1c"
      },
      "source": [
        "# Save a file from Google Colab to Google Drive (requires mounting Google Drive)\n",
        "!cp /content/best_model_hdf5_format.h5 /content/drive/MyDrive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E__FfLkFHJKb",
        "outputId": "9e6a03ff-624f-4b0e-f81a-049448408013"
      },
      "source": [
        "!ls /content/drive/MyDrive"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'1913032 MIM.pdf'\n",
            " 1913032.pdf\n",
            " 2013024_PARBATI_KUMARI_DAS.gdoc\n",
            " 2013024_PARBATI_KUMARI_DAS.pdf\n",
            "'2013086_Rituporna Das.gdoc'\n",
            "'2013086_Rituporna Das.pdf'\n",
            " 20210415_213024.jpg\n",
            "'AE Lab 1913032 Prachurya Nath.pdf'\n",
            "'AE Lab EXP3 1913032.pdf'\n",
            "'AE Lab Exp4 1913032.pdf'\n",
            "'AE Lab Exp5 1913032.pdf'\n",
            " best_model_hdf5_format.h5\n",
            " Christmas.gdoc\n",
            " Classroom\n",
            " COD.gform\n",
            "'COD:M Tournament.gsheet'\n",
            "'Colab Notebooks'\n",
            "'Constitution Day.gdoc'\n",
            "'Contact Information (1).gform'\n",
            "'Contact Information.gform'\n",
            "'DP 2 Relation to Industry.gdoc'\n",
            "'DP 3 Background  Motivation.gdoc'\n",
            "'DP 4 Literature Survey.gdoc'\n",
            "'Electrical Facts.docx'\n",
            " EMFT.docx\n",
            "'eoi-drvq-ikd - Dec 13, 2020.gjam'\n",
            "'Event Registration (1).gform'\n",
            "'Event Registration.gform'\n",
            " Exp10_2.gdoc\n",
            "'EXP3 GR9 PSIM LAB REPORT.pdf'\n",
            "'EXP5 GR9 PSL (1).pdf'\n",
            "'EXP5 GR9 PSL.pdf'\n",
            " image_837b4184-ccca-45cf-9101-d548a0d5ed5420210414_184004.jpg\n",
            " IMG-20210123-WA0025.jpg\n",
            "'Introduction Video '\n",
            "'Meet Attendance 08 28 2020 (1).gsheet'\n",
            "'Meet Attendance 08 28 2020.gsheet'\n",
            "'MIM 1913032.pdf'\n",
            "'New Year.gdoc'\n",
            "'Photography Contest.gdoc'\n",
            "'Prachurya Nath (1).rar'\n",
            " prachuryanath.pdf\n",
            "'Prachurya Nath.rar'\n",
            " Screenshot_20210414-163648_Instagram.jpg\n",
            " Screenshot_20210414-163841_Instagram.jpg\n",
            " Screenshot_20210415-015001_WhatsApp.jpg\n",
            " Screenshot_20210415-122844_WhatsApp.jpg\n",
            " Screenshot_20210415-141008_Instagram.jpg\n",
            " Screenshot_20210415-200952_Instagram.jpg\n",
            " Screenshot_20210415-215923_WhatsApp.jpg\n",
            " Screenshot_20210416-100619_Instagram.jpg\n",
            " Screenshot_20210416-231206_WhatsApp.jpg\n",
            "'Screenshot_20210418-220443_Stock Trainer (1).jpg'\n",
            "'Screenshot_20210418-220443_Stock Trainer.jpg'\n",
            "'Screenshot_20210420-155521_Stock Trainer.jpg'\n",
            "'Screenshot_20210420-155554_Stock Trainer.jpg'\n",
            " TESLA.docx\n",
            "'THE MESH.gdoc'\n",
            "'THE MESH.pdf'\n",
            "'Untitled form (Responses).gsheet'\n",
            "'Voice of Electra.gdoc'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96YHcnEeHfAT"
      },
      "source": [
        "## A larger example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNaskNLSHt1V"
      },
      "source": [
        "# Import required libraries\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "e7qppm8WJA19",
        "outputId": "1fca0867-7aab-416c-f1ac-05b527a47dfa"
      },
      "source": [
        "# Read in the insurance dataset\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance"
      ],
      "execution_count": null,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZxXcf8KwJMGK",
        "outputId": "aa98efa5-6827-4d1d-a5e8-0159c8a29010"
      },
      "source": [
        "insurance.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "Lx_CBaAJJPrs",
        "outputId": "a3f42c94-3c71-479a-8f16-b39f57078fa7"
      },
      "source": [
        "# Let's try one-hot encode our DataFrame so it's all numbers\n",
        "insurance_one_hot = pd.get_dummies(insurance)\n",
        "insurance_one_hot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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",
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              "      <th>0</th>\n",
              "      <td>19</td>\n",
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              "      <td>1725.55230</td>\n",
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              "      <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",
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              "      <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",
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              "      <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>10600.54830</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>2205.98080</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>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>1629.83350</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>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>2007.94500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>29141.36030</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      age     bmi  ...  region_southeast  region_southwest\n",
              "0      19  27.900  ...                 0                 1\n",
              "1      18  33.770  ...                 1                 0\n",
              "2      28  33.000  ...                 1                 0\n",
              "3      33  22.705  ...                 0                 0\n",
              "4      32  28.880  ...                 0                 0\n",
              "...   ...     ...  ...               ...               ...\n",
              "1333   50  30.970  ...                 0                 0\n",
              "1334   18  31.920  ...                 0                 0\n",
              "1335   18  36.850  ...                 1                 0\n",
              "1336   21  25.800  ...                 0                 1\n",
              "1337   61  29.070  ...                 0                 0\n",
              "\n",
              "[1338 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-XmsywmLE_x"
      },
      "source": [
        "# Create X & y values (features and labels)\n",
        "X = insurance_one_hot.drop(\"charges\", axis = 1)\n",
        "y = insurance[\"charges\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "D9JIth1IMEYK",
        "outputId": "5926e51e-40c4-461f-ff67-fde4db706362"
      },
      "source": [
        "# View X\n",
        "X.head()"
      ],
      "execution_count": null,
      "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>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>0</th>\n",
              "      <td>19</td>\n",
              "      <td>27.900</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",
              "      <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>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>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>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>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 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2D66omKnMI5i",
        "outputId": "cd70a413-8107-4111-91d3-b6ee81b825a8"
      },
      "source": [
        "# View y\n",
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvbZOgLBMNyo",
        "outputId": "267a2793-b5f0-4468-f2b5-270c17f54195"
      },
      "source": [
        "# Create training and test sets\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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 1070, 268)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ATKiO09M8Kx",
        "outputId": "99abffe1-a6c9-44f0-e686-21b2e461e810"
      },
      "source": [
        "# Build a neural network\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "insurance_model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(10),\n",
        "  tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.SGD(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model.fit(X_train, y_train, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10200.7270 - mae: 10200.7270\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7718.1916 - mae: 7718.1916\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6983.7825 - mae: 6983.7825\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8083.4820 - mae: 8083.4820\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7793.8447 - mae: 7793.8447\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7618.4113 - mae: 7618.4113\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7678.4979 - mae: 7678.4979\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7780.4289 - mae: 7780.4289\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7588.8132 - mae: 7588.8132\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7520.6834 - mae: 7520.6834\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8309.7824 - mae: 8309.7824\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7748.0188 - mae: 7748.0188\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7888.3562 - mae: 7888.3562\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7731.1164 - mae: 7731.1164\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7786.1038 - mae: 7786.1038\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8343.8846 - mae: 8343.8846\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7142.3610 - mae: 7142.3610\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8070.0509 - mae: 8070.0509\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7604.8298 - mae: 7604.8298\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7945.1268 - mae: 7945.1268\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6864.6822 - mae: 6864.6822\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7505.7913 - mae: 7505.7913\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7621.7371 - mae: 7621.7371\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7262.7541 - mae: 7262.7541\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8156.1936 - mae: 8156.1936\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7246.2702 - mae: 7246.2702\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7562.7815 - mae: 7562.7815\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7842.6210 - mae: 7842.6210\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7153.8770 - mae: 7153.8770\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7723.4947 - mae: 7723.4947\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8310.0628 - mae: 8310.0628\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7734.9661 - mae: 7734.9661\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7403.4652 - mae: 7403.4652\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7482.6279 - mae: 7482.6279\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7198.8914 - mae: 7198.8914\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7484.1008 - mae: 7484.1008\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7701.6285 - mae: 7701.6285\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6923.6147 - mae: 6923.6147\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7673.7907 - mae: 7673.7907\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7670.9200 - mae: 7670.9200\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7774.5884 - mae: 7774.5884\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7413.0885 - mae: 7413.0885\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7793.8659 - mae: 7793.8659\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7239.3577 - mae: 7239.3577\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7402.6554 - mae: 7402.6554\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7167.7292 - mae: 7167.7292\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7716.9014 - mae: 7716.9014\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7509.9134 - mae: 7509.9134\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7216.6766 - mae: 7216.6766\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7354.2576 - mae: 7354.2576\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7587.3340 - mae: 7587.3340\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7697.7622 - mae: 7697.7622\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7084.7633 - mae: 7084.7633\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7366.1162 - mae: 7366.1162\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7354.5021 - mae: 7354.5021\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7201.4706 - mae: 7201.4706\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7612.1703 - mae: 7612.1703\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7684.3348 - mae: 7684.3348\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7315.3652 - mae: 7315.3652\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7381.6652 - mae: 7381.6652\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7842.5246 - mae: 7842.5246\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7346.2638 - mae: 7346.2638\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7634.5367 - mae: 7634.5367\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6976.2131 - mae: 6976.2131\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7608.7969 - mae: 7608.7969\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7275.7896 - mae: 7275.7896\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7244.1642 - mae: 7244.1642\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7553.0941 - mae: 7553.0941\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8028.1956 - mae: 8028.1956\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7150.7134 - mae: 7150.7134\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7137.5187 - mae: 7137.5187\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6708.9296 - mae: 6708.9296\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7554.2734 - mae: 7554.2734\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7060.4084 - mae: 7060.4084\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7564.8157 - mae: 7564.8157\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7016.7826 - mae: 7016.7826\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7936.9758 - mae: 7936.9758\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6828.1387 - mae: 6828.1387\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6875.0799 - mae: 6875.0799\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6770.6312 - mae: 6770.6312\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7216.2089 - mae: 7216.2089\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7774.2740 - mae: 7774.2740\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7156.2549 - mae: 7156.2549\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7436.0036 - mae: 7436.0036\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6855.7934 - mae: 6855.7934\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7768.8276 - mae: 7768.8276\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7014.5802 - mae: 7014.5802\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7525.7779 - mae: 7525.7779\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6994.6074 - mae: 6994.6074\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7677.9603 - mae: 7677.9603\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7210.0507 - mae: 7210.0507\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7335.2229 - mae: 7335.2229\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7235.0909 - mae: 7235.0909\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6728.8877 - mae: 6728.8877\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7811.5165 - mae: 7811.5165\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7430.2760 - mae: 7430.2760\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6876.5969 - mae: 6876.5969\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7070.8703 - mae: 7070.8703\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7118.6875 - mae: 7118.6875\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6880.6736 - mae: 6880.6736\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbe40af3790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKm7RGCCNYii",
        "outputId": "15f2d7a6-1a7d-40df-8291-3ce8c8750d54"
      },
      "source": [
        "# Check the results of the insurance model on the test data\n",
        "insurance_model.evaluate(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7023.3267 - mae: 7023.3267\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7023.32666015625, 7023.32666015625]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2k954mGOstD"
      },
      "source": [
        "Right now it looks like our model isn't performing too well... let's try and improve it !\n",
        "\n",
        "To (try) improve our model, we'll run 2 experiments :\n",
        "\n",
        "1. Add an extra layer with more hidden units and use the Adam optimizer.\n",
        "2. Same as above but Train for longer (200 epochs)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hek6F_waPE4o",
        "outputId": "18c89a0f-54e9-4f45-ddd0-a7b5184b4da7"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the 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",
        "# 2. Compile the model\n",
        "insurance_model_2.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.Adam(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model_2.fit(X_train, y_train, epochs= 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13251.7398 - mae: 13251.7398\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12768.7722 - mae: 12768.7722\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12248.2842 - mae: 12248.2842\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12353.0233 - mae: 12353.0233\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11243.3955 - mae: 11243.3955\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9718.5233 - mae: 9718.5233\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8354.8463 - mae: 8354.8463\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7732.2947 - mae: 7732.2947\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7537.6736 - mae: 7537.6736\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7652.7183 - mae: 7652.7183\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7802.4479 - mae: 7802.4479\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7370.3450 - mae: 7370.3450\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7651.9583 - mae: 7651.9583\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7404.5586 - mae: 7404.5586\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7639.0610 - mae: 7639.0610\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7569.4714 - mae: 7569.4714\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6946.3936 - mae: 6946.3936\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7137.7243 - mae: 7137.7243\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7165.3119 - mae: 7165.3119\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7063.6224 - mae: 7063.6224\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6472.3193 - mae: 6472.3193\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6674.0547 - mae: 6674.0547\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7112.2984 - mae: 7112.2984\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6838.1525 - mae: 6838.1525\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7496.9556 - mae: 7496.9556\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6718.1682 - mae: 6718.1682\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7107.2975 - mae: 7107.2975\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7357.2011 - mae: 7357.2011\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6668.5721 - mae: 6668.5721\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7119.4992 - mae: 7119.4992\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7360.1237 - mae: 7360.1237\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6817.2566 - mae: 6817.2566\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6991.0291 - mae: 6991.0291\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6829.4904 - mae: 6829.4904\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6895.2924 - mae: 6895.2924\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6762.5130 - mae: 6762.5130\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6910.4820 - mae: 6910.4820\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6405.0329 - mae: 6405.0329\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6799.7922 - mae: 6799.7922\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6696.0554 - mae: 6696.0554\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6632.0484 - mae: 6632.0484\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6501.1591 - mae: 6501.1591\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6518.8887 - mae: 6518.8887\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6287.3744 - mae: 6287.3744\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6331.2564 - mae: 6331.2564\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6080.3173 - mae: 6080.3173\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6665.8197 - mae: 6665.8197\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6424.2352 - mae: 6424.2352\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6154.8290 - mae: 6154.8290\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6426.9844 - mae: 6426.9844\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6338.3254 - mae: 6338.3254\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6335.6011 - mae: 6335.6011\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5995.7964 - mae: 5995.7964\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6431.7972 - mae: 6431.7972\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6224.0666 - mae: 6224.0666\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6289.4115 - mae: 6289.4115\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6578.3136 - mae: 6578.3136\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6365.6013 - mae: 6365.6013\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5914.7986 - mae: 5914.7986\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6239.8725 - mae: 6239.8725\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6751.6416 - mae: 6751.6416\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6350.5866 - mae: 6350.5866\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6374.7378 - mae: 6374.7378\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5877.7619 - mae: 5877.7619\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6260.5316 - mae: 6260.5316\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6007.1753 - mae: 6007.1753\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6106.7940 - mae: 6106.7940\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6035.6674 - mae: 6035.6674\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6523.5446 - mae: 6523.5446\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5708.9529 - mae: 5708.9529\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6110.2176 - mae: 6110.2176\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5773.3155 - mae: 5773.3155\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6297.5182 - mae: 6297.5182\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6112.9398 - mae: 6112.9398\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6449.7248 - mae: 6449.7248\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5688.1449 - mae: 5688.1449\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6402.3403 - mae: 6402.3403\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5499.5555 - mae: 5499.5555\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5108.3484 - mae: 5108.3484\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5226.6402 - mae: 5226.6402\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5817.3267 - mae: 5817.3267\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6548.7334 - mae: 6548.7334\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5722.6893 - mae: 5722.6893\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5570.2340 - mae: 5570.2340\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5121.8592 - mae: 5121.8592\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5889.9371 - mae: 5889.9371\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5472.2757 - mae: 5472.2757\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5770.0220 - mae: 5770.0220\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5392.7433 - mae: 5392.7433\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5951.3932 - mae: 5951.3932\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5577.1117 - mae: 5577.1117\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5633.8437 - mae: 5633.8437\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5209.0324 - mae: 5209.0324\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5018.4450 - mae: 5018.4450\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5419.2092 - mae: 5419.2092\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5547.8991 - mae: 5547.8991\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4931.7103 - mae: 4931.7103\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4912.5443 - mae: 4912.5443\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4650.0277 - mae: 4650.0277\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4770.3930 - mae: 4770.3930\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbe40965fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPAMTZu4Pr2k",
        "outputId": "487b8848-769d-4595-fd62-2d6a46e19cdc"
      },
      "source": [
        "# Evaluate the larger model\n",
        "insurance_model_2.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 4924.5107 - mae: 4924.5107\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4924.5107421875, 4924.5107421875]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vI_xrir3QmDp"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the 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",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model_3.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.Adam(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = insurance_model_3.fit(X_train, y_train, epochs= 500, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4V2O8oFQ7aB",
        "outputId": "af10b6b7-564f-4d15-968b-8621739b3440"
      },
      "source": [
        "# Evaluate the third model\n",
        "insurance_model_3.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3491.0034 - mae: 3491.0034\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3491.00341796875, 3491.00341796875]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "BATjOkHqRFXv",
        "outputId": "851e4518-c168-4232-c8ed-f607299da21f"
      },
      "source": [
        "# Plot history ( also known as a loss curve or a training curve)\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "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": "n-DQwiMGONGq"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "insurance_model_4 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(500, activation=tf.keras.layers.LeakyReLU()),\n",
        "  tf.keras.layers.Dense(400, activation=tf.keras.layers.LeakyReLU()),\n",
        "  tf.keras.layers.Dense(300, activation=tf.keras.layers.LeakyReLU()),\n",
        "  tf.keras.layers.Dense(200, activation=tf.keras.layers.LeakyReLU()),\n",
        "  tf.keras.layers.Dense(100, activation=tf.keras.layers.LeakyReLU()),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model_4.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.Adam(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = insurance_model_4.fit(X_train, y_train, epochs= 1000, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vX_YmLIgOgQC",
        "outputId": "fc2de0a3-62cb-41f2-feaa-353a57d83ea2"
      },
      "source": [
        "# Evaluate the fourth model\n",
        "insurance_model_4.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 1366.6139 - mae: 1366.6139\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1366.6138916015625, 1366.6138916015625]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "3gf2UcvIOch_",
        "outputId": "ba4df812-4745-45f2-d630-dff7c4342614"
      },
      "source": [
        "# Plot history ( also known as a loss curve or a training curve)\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        },
        {
          "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": "LMgATF-5Rg0n"
      },
      "source": [
        "## Preprocessing data (normalization and standardization)\n",
        "\n",
        "* In terms of scaling values, neural networks tend to prefer normalization.\n",
        "* If you're not sure on which to use, you should try both and see which performs better."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "LAfCvKwdS8kD",
        "outputId": "166ca553-cce6-48af-ed5a-d93c0d56f7ea"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance.head()"
      ],
      "execution_count": null,
      "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",
              "  </tbody>\n",
              "</table>\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZtKLO_CUj_U"
      },
      "source": [
        "To prepare our data, we can borrow a few classes from Scikit-learn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYQdIYnRVgf6"
      },
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "\n",
        "# Create column transformer (this will help us normalize/preprocess our data)\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]), # get all values between 0 and 1\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
        ")\n",
        "\n",
        "# Create X & y\n",
        "X = insurance.drop(\"charges\", axis=1)\n",
        "y = insurance[\"charges\"]\n",
        "\n",
        "# Build our train and test sets (use random state to ensure same split as before)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit column transformer on the training data only (doing so on test data would result in data leakage)\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and test data with normalization (MinMaxScalar) and one hot encoding (OneHotEncoder)\n",
        "X_train_normal = ct.transform(X_train)\n",
        "X_test_normal = ct.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dMC9iLqaMhp",
        "outputId": "2fab5146-08b6-471a-933c-8a9a8aea7eaa"
      },
      "source": [
        "# What does our data look like now ?\n",
        "X_train_normal "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.60869565, 0.10734463, 0.4       , ..., 1.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.63043478, 0.22491256, 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.73913043, 0.23944041, 0.        , ..., 0.        , 1.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.86956522, 0.24791499, 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.41304348, 0.85122411, 0.4       , ..., 0.        , 0.        ,\n",
              "        1.        ],\n",
              "       [0.80434783, 0.37503363, 0.        , ..., 0.        , 0.        ,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHHr-0TWaTqH",
        "outputId": "ee5c1b07-cbc7-4a6e-ab73-4d879ceaee56"
      },
      "source": [
        "X_train.shape, X_train_normal.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1070, 6), (1070, 11))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-aFLUC3za0Mo"
      },
      "source": [
        "Beautiful ! Our data has been normalized and one hot encoded. Now let's build a neural network model on it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijqv9gQlbJof"
      },
      "source": [
        "# Build a neural network model to fit on our normalized data\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "insurance_model_normal = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(500, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(400, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(300, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(200, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(100, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model_normal.compile( loss = tf.keras.losses.mae,\n",
        "               optimizer = tf.keras.optimizers.Adam(),\n",
        "               metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history_normal = insurance_model_normal.fit(X_train_normal, y_train, epochs= 500, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwjDfW7rcEhr",
        "outputId": "3476ffa4-08ca-49e1-e4a8-02d57ffae619"
      },
      "source": [
        "# Evaluate the normalized model\n",
        "insurance_model_normal.evaluate(X_test_normal, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 1476.7100 - mae: 1476.7100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1476.7099609375, 1476.7099609375]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "2aGNPSyacT6W",
        "outputId": "e5bfddb1-dab7-4c15-d30e-a09ff1ba91f7"
      },
      "source": [
        "# Plot history ( also known as a loss curve or a training curve)\n",
        "pd.DataFrame(history_normal.history).plot()\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "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": "1GEsTp6Cca4H"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}