01_neural_network_regression_in_tensorflow.ipynb

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  "metadata": {
    "colab": {
      "name": "01_neural_network_regression_in_tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1QnEFDhmyS-16_64czVPsSZUAReFy1ahF",
      "authorship_tag": "ABX9TyNtRnOTJgd2zw74qN0aYQgT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ihsraham/TensorFlow/blob/main/01_neural_network_regression_in_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWYOlLd4w2zk"
      },
      "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 simplify 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": "HJzYVVYuy2Kj",
        "outputId": "009a60fc-ab0f-42fa-f9d0-84820e749283"
      },
      "source": [
        "# Import TensorFlow\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QqZvq6GezeMy"
      },
      "source": [
        "## Create data to view and fit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "3CkClpZEzmH8",
        "outputId": "d5aa7773-1f48-46a8-c923-f9f323ac32ff"
      },
      "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",
        "#Visualize it\n",
        "plt.scatter(X, y);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sw6EYAEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxPw/YhrWmPXy7VoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vqhdDe60KWD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bdbdff0-7e46-4adb-db47-14f1991df767"
      },
      "source": [
        "y == X + 10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbbls6Bm1Gzm"
      },
      "source": [
        "## Input and output shapes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYiWr5kz1RfX",
        "outputId": "d23219eb-1f03-47b4-906e-3363b25936b3"
      },
      "source": [
        " # Create a demo tensor for our price prediction problem\n",
        " house_info = tf.constant([\"bedroom\", \"bathroom\", \"garage\"])\n",
        " house_price = tf.constant([93700])\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([93700], dtype=int32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCBlbgf52tPR",
        "outputId": "fe04bca3-a8e0-4d70-af25-e22d7e918dbe"
      },
      "source": [
        "X[0], y[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-7.0, 3.0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATPaWmfy2zKu",
        "outputId": "ecab900e-b6be-4aba-c351-f55619dc6cdb"
      },
      "source": [
        "X[1], y[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-4.0, 6.0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duM8wTam2ajh",
        "outputId": "f762daa0-fa9a-4107-9af5-1526194c986a"
      },
      "source": [
        "input_shape = X[0].shape\n",
        "output_shape = y[0].shape\n",
        "input_shape, output_shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((), ())"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WI9uLiMT4Im3",
        "outputId": "dc3166d9-5098-4b39-9647-ec84434e229d"
      },
      "source": [
        "X[0].ndim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2s9FWmUx4WKb",
        "outputId": "a6d5646b-c611-4f55-c35c-36eb4f473205"
      },
      "source": [
        "X[0], y[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-7.0, 3.0)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gpL9QLhO4Z8C",
        "outputId": "d7d8df7f-b4e5-4f06-fb6c-fb7bd0d16969"
      },
      "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": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgor_GMn429A",
        "outputId": "4b14622a-2dce-45c7-de3e-90ee83e9baee"
      },
      "source": [
        "input_shape = X[0].shape\n",
        "output_shape = y[0].shape\n",
        "input_shape, output_shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([]), TensorShape([]))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "jt42BLpu4_2b",
        "outputId": "a19dc311-6055-4e0d-b57d-e639a09a42a3"
      },
      "source": [
        "plt.scatter(X, y);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sw6EYAEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxBB2AEgMYQeAxPw/YhrWmPXy7VoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZj5iF6B5E0v"
      },
      "source": [
        "## Steps in modelling with TensorFlow\n",
        "\n",
        "\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 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 patterns between X & y (features and labels). \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bQS7qbDAE53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "686363a8-f47a-4e93-a0a3-7395f17dc0ad"
      },
      "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, # mae is short for mean absolute error\n",
        "              optimizer=tf.keras.optimizers.SGD(), # sgd is short for stochastic gradient descent\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model.fit(X, y, epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 1s 677ms/step - loss: 11.5048 - mae: 11.5048\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.3723 - mae: 11.3723\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.2398 - mae: 11.2398\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 11.1073 - mae: 11.1073\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 10.9748 - mae: 10.9748\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3982f0bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1B9F1ISJKVq",
        "outputId": "a20eb53a-82e2-41a3-bfd3-ce08376b60ae"
      },
      "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": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DhlYFKHJ9eA",
        "outputId": "a31ab912-dae0-4e0b-c150-36fe3a0fbc2f"
      },
      "source": [
        "# Try and make a prediction using our model\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12.716021]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n38a-YsqKLdE"
      },
      "source": [
        "## Improving a model\n",
        "\n",
        "We can improve our model, by altering the steps we took to crete a model.\n",
        "\n",
        "1. **Creating a model** - here we might add more layers, increase the number of hidden units(all called neurons) within each of the hidden layers, change the activation functionof 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 longer) or on more data (give the model more examples to learn from)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTtV8OBMKRGa",
        "outputId": "449dc2f8-988f-4e1a-9f9b-0c37c5619f5d"
      },
      "source": [
        "# Let's rebuild our model\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. Fir the model (this time we'll train for longer)\n",
        "model.fit(X, y, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 264ms/step - loss: 11.2219 - mae: 11.2219\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.0894 - mae: 11.0894\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 10.9569 - mae: 10.9569\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.8244 - mae: 10.8244\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.6919 - mae: 10.6919\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 10.5594 - mae: 10.5594\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.4269 - mae: 10.4269\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 5ms/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 10ms/step - loss: 10.0294 - mae: 10.0294\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8969 - mae: 9.8969\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.7644 - mae: 9.7644\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.6319 - mae: 9.6319\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.4994 - mae: 9.4994\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.3669 - mae: 9.3669\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.2344 - mae: 9.2344\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.1019 - mae: 9.1019\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.9694 - mae: 8.9694\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.8369 - mae: 8.8369\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.7044 - mae: 8.7044\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 7ms/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 7ms/step - loss: 8.3069 - mae: 8.3069\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 15ms/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 6ms/step - loss: 7.7769 - mae: 7.7769\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 16ms/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 4ms/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 11ms/step - loss: 7.2694 - mae: 7.2694\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.2638 - mae: 7.2638\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2581 - mae: 7.2581\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.2525 - mae: 7.2525\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2469 - mae: 7.2469\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2412 - mae: 7.2412\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.2356 - mae: 7.2356\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.2300 - mae: 7.2300\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.2244 - mae: 7.2244\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.2188 - mae: 7.2188\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.2131 - mae: 7.2131\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 16ms/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 6ms/step - loss: 7.1962 - mae: 7.1962\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1906 - mae: 7.1906\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 8ms/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 11ms/step - loss: 7.1737 - mae: 7.1737\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1681 - mae: 7.1681\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.1625 - mae: 7.1625\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.1569 - mae: 7.1569\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.1512 - mae: 7.1512\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.1456 - mae: 7.1456\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.1400 - mae: 7.1400\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.1344 - mae: 7.1344\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.1287 - mae: 7.1287\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.1231 - mae: 7.1231\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.1175 - mae: 7.1175\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.1119 - mae: 7.1119\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.1062 - mae: 7.1062\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1006 - mae: 7.1006\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.0950 - mae: 7.0950\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.0894 - mae: 7.0894\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.0838 - mae: 7.0838\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 5ms/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 8ms/step - loss: 7.0613 - mae: 7.0613\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0556 - mae: 7.0556\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.0500 - mae: 7.0500\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.0444 - mae: 7.0444\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.0388 - mae: 7.0388\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.0331 - mae: 7.0331\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 4ms/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 10ms/step - loss: 7.0163 - mae: 7.0163\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 7.0106 - mae: 7.0106\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0050 - mae: 7.0050\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 6ms/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 12ms/step - loss: 6.9881 - mae: 6.9881\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9825 - mae: 6.9825\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.9769 - mae: 6.9769\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 6.9713 - mae: 6.9713\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.9656 - mae: 6.9656\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.9600 - mae: 6.9600\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.9544 - mae: 6.9544\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9488 - mae: 6.9488\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9431 - mae: 6.9431\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9375 - mae: 6.9375\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.9319 - mae: 6.9319\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.9263 - mae: 6.9263\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.9206 - mae: 6.9206\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.9150 - mae: 6.9150\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.9094 - mae: 6.9094\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.9038 - mae: 6.9038\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.8981 - mae: 6.8981\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8925 - mae: 6.8925\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 6.8869 - mae: 6.8869\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa39822ec90>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQ-m4GLDMPaI",
        "outputId": "60cb533f-31c1-463b-e924-1b6e26255410"
      },
      "source": [
        "# Remind ourselves of the data\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": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-rL6Y_zMlqV",
        "outputId": "b53b8cec-0335-4f2c-8be6-be794dfcd810"
      },
      "source": [
        "# Let's see if our model's prediction has improved..\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29.739855]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoRFl0XEMwdJ",
        "outputId": "97e5dc51-f788-45e3-c91d-12323ac3399f"
      },
      "source": [
        "# Let's rebuild our model\n",
        "\n",
        "# 1. Create a model (This time with an extra layer with 100 hidden units)\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile our model \n",
        "model.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.SGD(),\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# #.Fit the model\n",
        "model.fit(X, y, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 334ms/step - loss: 12.3193 - mae: 12.3193\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 11.7804 - mae: 11.7804\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.2324 - mae: 11.2324\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.6601 - mae: 10.6601\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.0632 - mae: 10.0632\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.4503 - mae: 9.4503\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.7991 - mae: 8.7991\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.1072 - mae: 8.1072\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.3691 - mae: 7.3691\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.5758 - mae: 6.5758\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.7205 - mae: 5.7205\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.7947 - mae: 4.7947\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.3581 - mae: 4.3581\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.3134 - mae: 4.3134\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.2550 - mae: 4.2550\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.2442 - mae: 4.2442\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.1520 - mae: 4.1520\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.1739 - mae: 4.1739\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 4.0681 - mae: 4.0681\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 4.0807 - mae: 4.0807\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9954 - mae: 3.9954\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.9739 - mae: 3.9739\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9208 - mae: 3.9208\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9047 - mae: 3.9047\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.9267 - mae: 3.9267\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.8797 - mae: 3.8797\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9341 - mae: 3.9341\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8678 - mae: 3.8678\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9274 - mae: 3.9274\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8751 - mae: 3.8751\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.9080 - mae: 3.9080\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8893 - mae: 3.8893\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8834 - mae: 3.8834\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.8969 - mae: 3.8969\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8581 - mae: 3.8581\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.9046 - mae: 3.9046\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.8386 - mae: 3.8386\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.9054 - mae: 3.9054\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8482 - mae: 3.8482\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8862 - mae: 3.8862\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8605 - mae: 3.8605\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8608 - mae: 3.8608\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8683 - mae: 3.8683\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.8352 - mae: 3.8352\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8762 - mae: 3.8762\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8106 - mae: 3.8106\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8821 - mae: 3.8821\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8234 - mae: 3.8234\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8626 - mae: 3.8626\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8328 - mae: 3.8328\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8369 - mae: 3.8369\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8408 - mae: 3.8408\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8111 - mae: 3.8111\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8489 - mae: 3.8489\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7850 - mae: 3.7850\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8585 - mae: 3.8585\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7982 - mae: 3.7982\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8377 - mae: 3.8377\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8062 - mae: 3.8062\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.8117 - mae: 3.8117\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8144 - mae: 3.8144\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7856 - mae: 3.7856\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.8227 - mae: 3.8227\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7593 - mae: 3.7593\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.8352 - mae: 3.8352\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7725 - mae: 3.7725\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8115 - mae: 3.8115\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7807 - mae: 3.7807\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7853 - mae: 3.7853\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7891 - mae: 3.7891\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7588 - mae: 3.7588\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7975 - mae: 3.7975\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7337 - mae: 3.7337\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8105 - mae: 3.8105\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7478 - mae: 3.7478\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7840 - mae: 3.7840\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 3.7563 - mae: 3.7563\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7575 - mae: 3.7575\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7648 - mae: 3.7648\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7307 - mae: 3.7307\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7735 - mae: 3.7735\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7125 - mae: 3.7125\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7820 - mae: 3.7820\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7242 - mae: 3.7242\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7552 - mae: 3.7552\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7329 - mae: 3.7329\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7284 - mae: 3.7284\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7416 - mae: 3.7416\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7013 - mae: 3.7013\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7505 - mae: 3.7505\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.6921 - mae: 3.6921\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7522 - mae: 3.7522\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7016 - mae: 3.7016\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7251 - mae: 3.7251\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7105 - mae: 3.7105\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.6979 - mae: 3.6979\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.7194 - mae: 3.7194\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.6705 - mae: 3.6705\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7299 - mae: 3.7299\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.6711 - mae: 3.6711\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa39811d590>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPTMcLFRNyHH",
        "outputId": "99c1024c-4da6-461c-dc14-f59245b68d6c"
      },
      "source": [
        "# Let's see if our model's prediction has improved\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[31.223137]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vc6eYnMvOtKA",
        "outputId": "5358e398-ef4d-4405-c961-e02bdaa62d8f"
      },
      "source": [
        "# Let's rebuild our model\n",
        "\n",
        "# 1. Creating a model\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model \n",
        "model.fit(X, y, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 345ms/step - loss: 9.8244 - mae: 9.8244\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.8240 - mae: 9.8240\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8235 - mae: 9.8235\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8231 - mae: 9.8231\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8226 - mae: 9.8226\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8222 - mae: 9.8222\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.8217 - mae: 9.8217\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.8213 - mae: 9.8213\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8208 - mae: 9.8208\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.8204 - mae: 9.8204\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.8199 - mae: 9.8199\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.8195 - mae: 9.8195\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8190 - mae: 9.8190\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8186 - mae: 9.8186\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8181 - mae: 9.8181\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8177 - mae: 9.8177\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8172 - mae: 9.8172\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8168 - mae: 9.8168\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8163 - mae: 9.8163\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.8159 - mae: 9.8159\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.8154 - mae: 9.8154\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8150 - mae: 9.8150\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8145 - mae: 9.8145\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8141 - mae: 9.8141\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.8136 - mae: 9.8136\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8132 - mae: 9.8132\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8127 - mae: 9.8127\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.8123 - mae: 9.8123\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.8118 - mae: 9.8118\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8114 - mae: 9.8114\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8109 - mae: 9.8109\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8105 - mae: 9.8105\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8100 - mae: 9.8100\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8096 - mae: 9.8096\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8091 - mae: 9.8091\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8087 - mae: 9.8087\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8082 - mae: 9.8082\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.8078 - mae: 9.8078\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8073 - mae: 9.8073\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.8069 - mae: 9.8069\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 9.8064 - mae: 9.8064\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.8060 - mae: 9.8060\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8055 - mae: 9.8055\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8051 - mae: 9.8051\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.8046 - mae: 9.8046\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.8042 - mae: 9.8042\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8037 - mae: 9.8037\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8033 - mae: 9.8033\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8028 - mae: 9.8028\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8024 - mae: 9.8024\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8019 - mae: 9.8019\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8015 - mae: 9.8015\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.8010 - mae: 9.8010\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.8006 - mae: 9.8006\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.8001 - mae: 9.8001\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.7997 - mae: 9.7997\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7992 - mae: 9.7992\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7988 - mae: 9.7988\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.7983 - mae: 9.7983\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7979 - mae: 9.7979\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.7974 - mae: 9.7974\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.7970 - mae: 9.7970\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.7965 - mae: 9.7965\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 9.7961 - mae: 9.7961\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.7956 - mae: 9.7956\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.7952 - mae: 9.7952\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.7947 - mae: 9.7947\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7943 - mae: 9.7943\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 9.7938 - mae: 9.7938\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.7934 - mae: 9.7934\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.7929 - mae: 9.7929\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7925 - mae: 9.7925\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.7920 - mae: 9.7920\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7916 - mae: 9.7916\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7911 - mae: 9.7911\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.7907 - mae: 9.7907\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7902 - mae: 9.7902\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 9.7898 - mae: 9.7898\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7893 - mae: 9.7893\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.7889 - mae: 9.7889\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7884 - mae: 9.7884\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7880 - mae: 9.7880\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7875 - mae: 9.7875\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7871 - mae: 9.7871\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.7866 - mae: 9.7866\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.7862 - mae: 9.7862\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7857 - mae: 9.7857\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.7853 - mae: 9.7853\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7848 - mae: 9.7848\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7844 - mae: 9.7844\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7839 - mae: 9.7839\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7835 - mae: 9.7835\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7830 - mae: 9.7830\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7826 - mae: 9.7826\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7821 - mae: 9.7821\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.7817 - mae: 9.7817\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7812 - mae: 9.7812\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7808 - mae: 9.7808\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 9.7803 - mae: 9.7803\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.7799 - mae: 9.7799\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa39733a890>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKbMSM1qPXSm",
        "outputId": "3f303399-8d18-4f4d-d37b-78476e9f2245"
      },
      "source": [
        "# Let's see how much our model has improved\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18.032803]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DPFjok4PgZZ",
        "outputId": "4ca2f9c1-4596-4958-a9a8-7f7f2fd7e579"
      },
      "source": [
        "# Let's rebuild our model\n",
        "\n",
        "# 1. Creating a model\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model.fit(X, y, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 350ms/step - loss: 13.5267 - mae: 13.5267\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 13.4372 - mae: 13.4372\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 13.3477 - mae: 13.3477\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 13.2581 - mae: 13.2581\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 13.1685 - mae: 13.1685\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 13.0788 - mae: 13.0788\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.9891 - mae: 12.9891\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 12.8994 - mae: 12.8994\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.8098 - mae: 12.8098\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 12.7209 - mae: 12.7209\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.6318 - mae: 12.6318\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 12.5427 - mae: 12.5427\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 12.4536 - mae: 12.4536\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 12.3643 - mae: 12.3643\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 12.2750 - mae: 12.2750\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 12.1855 - mae: 12.1855\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 12.0960 - mae: 12.0960\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 12.0062 - mae: 12.0062\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.9162 - mae: 11.9162\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.8261 - mae: 11.8261\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.7360 - mae: 11.7360\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 11.6460 - mae: 11.6460\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 11.5558 - mae: 11.5558\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.4656 - mae: 11.4656\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.3752 - mae: 11.3752\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.2845 - mae: 11.2845\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.1935 - mae: 11.1935\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 11.1023 - mae: 11.1023\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.0108 - mae: 11.0108\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.9190 - mae: 10.9190\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.8269 - mae: 10.8269\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.7346 - mae: 10.7346\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.6419 - mae: 10.6419\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.5489 - mae: 10.5489\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.4657 - mae: 10.4657\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.3975 - mae: 10.3975\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.3287 - mae: 10.3287\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.2594 - mae: 10.2594\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.1896 - mae: 10.1896\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.1194 - mae: 10.1194\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.0486 - mae: 10.0486\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.9775 - mae: 9.9775\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.9060 - mae: 9.9060\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8343 - mae: 9.8343\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.7621 - mae: 9.7621\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.6895 - mae: 9.6895\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.6164 - mae: 9.6164\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.5427 - mae: 9.5427\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.4686 - mae: 9.4686\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.3940 - mae: 9.3940\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.3189 - mae: 9.3189\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.2433 - mae: 9.2433\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.1673 - mae: 9.1673\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.0908 - mae: 9.0908\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.0138 - mae: 9.0138\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.9363 - mae: 8.9363\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.8583 - mae: 8.8583\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.7799 - mae: 8.7799\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.7010 - mae: 8.7010\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.6218 - mae: 8.6218\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.5422 - mae: 8.5422\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.4622 - mae: 8.4622\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.3815 - mae: 8.3815\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.3003 - mae: 8.3003\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.2185 - mae: 8.2185\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.1362 - mae: 8.1362\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.0533 - mae: 8.0533\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.9698 - mae: 7.9698\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.8858 - mae: 7.8858\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.8012 - mae: 7.8012\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.7161 - mae: 7.7161\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.6304 - mae: 7.6304\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.5441 - mae: 7.5441\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.4573 - mae: 7.4573\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 7.3700 - mae: 7.3700\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.2822 - mae: 7.2822\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1937 - mae: 7.1937\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.1046 - mae: 7.1046\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.0148 - mae: 7.0148\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.9244 - mae: 6.9244\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8333 - mae: 6.8333\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.7416 - mae: 6.7416\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.6493 - mae: 6.6493\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.5563 - mae: 6.5563\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.4626 - mae: 6.4626\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.3684 - mae: 6.3684\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.2735 - mae: 6.2735\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.1779 - mae: 6.1779\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.0816 - mae: 6.0816\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.9845 - mae: 5.9845\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.8868 - mae: 5.8868\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.7883 - mae: 5.7883\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.6890 - mae: 5.6890\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.5890 - mae: 5.5890\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.4883 - mae: 5.4883\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.3869 - mae: 5.3869\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.2848 - mae: 5.2848\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.1819 - mae: 5.1819\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0783 - mae: 5.0783\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.9739 - mae: 4.9739\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa39508f7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlL9QyrGQT1V",
        "outputId": "e8d649df-df91-42e0-8f7b-2112a7b34e53"
      },
      "source": [
        "# Let's see how much our model has improved\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa394f74950> 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[25.852007]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCv9Ls4RQb14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c58a79b-cbaa-49cd-99b3-75510e0fdecc"
      },
      "source": [
        "# Let's see if we can make another change to improve our model\n",
        "\n",
        "# 1. Create the model (this time with an extra hidden layer wih 100 hidden units)\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Dense(1))\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=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 305ms/step - loss: 12.5326 - mae: 12.5326\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 11.8956 - mae: 11.8956\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 11.2464 - mae: 11.2464\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.5791 - mae: 10.5791\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.8874 - mae: 9.8874\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 9.1649 - mae: 9.1649\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 8.4050 - mae: 8.4050\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 7.6117 - mae: 7.6117\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.7798 - mae: 6.7798\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.8888 - mae: 5.8888\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.9302 - mae: 4.9302\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 4.1685 - mae: 4.1685\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.0209 - mae: 4.0209\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.9298 - mae: 3.9298\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9596 - mae: 3.9596\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.9400 - mae: 3.9400\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9405 - mae: 3.9405\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.9523 - mae: 3.9523\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.9142 - mae: 3.9142\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9599 - mae: 3.9599\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8896 - mae: 3.8896\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.9648 - mae: 3.9648\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8971 - mae: 3.8971\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.9391 - mae: 3.9391\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9123 - mae: 3.9123\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9192 - mae: 3.9192\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9199 - mae: 3.9199\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.8926 - mae: 3.8926\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9277 - mae: 3.9277\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8659 - mae: 3.8659\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9356 - mae: 3.9356\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.8655 - mae: 3.8655\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.9214 - mae: 3.9214\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8808 - mae: 3.8808\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8966 - mae: 3.8966\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8887 - mae: 3.8887\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8697 - mae: 3.8697\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8967 - mae: 3.8967\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8428 - mae: 3.8428\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.9048 - mae: 3.9048\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 3.8370 - mae: 3.8370\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8996 - mae: 3.8996\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8505 - mae: 3.8505\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8727 - mae: 3.8727\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8586 - mae: 3.8586\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8456 - mae: 3.8456\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8668 - mae: 3.8668\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 3.8184 - mae: 3.8184\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8751 - mae: 3.8751\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8119 - mae: 3.8119\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8747 - mae: 3.8747\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 3.8214 - mae: 3.8214\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8475 - mae: 3.8475\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8296 - mae: 3.8296\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8202 - mae: 3.8202\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8380 - mae: 3.8380\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7926 - mae: 3.7926\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8494 - mae: 3.8494\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7849 - mae: 3.7849\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.8486 - mae: 3.8486\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7933 - mae: 3.7933\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.8211 - mae: 3.8211\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8017 - mae: 3.8017\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7934 - mae: 3.7934\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8103 - mae: 3.8103\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.7656 - mae: 3.7656\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8265 - mae: 3.8265\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7577 - mae: 3.7577\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.8211 - mae: 3.8211\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7662 - mae: 3.7662\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 3.7933 - mae: 3.7933\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7749 - mae: 3.7749\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.7654 - mae: 3.7654\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7837 - mae: 3.7837\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.7411 - mae: 3.7411\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8001 - mae: 3.8001\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7315 - mae: 3.7315\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7923 - mae: 3.7923\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7402 - mae: 3.7402\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.7642 - mae: 3.7642\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7491 - mae: 3.7491\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7359 - mae: 3.7359\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.7583 - mae: 3.7583\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7143 - mae: 3.7143\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7746 - mae: 3.7746\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7063 - mae: 3.7063\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7622 - mae: 3.7622\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.7152 - mae: 3.7152\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.7338 - mae: 3.7338\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7243 - mae: 3.7243\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7052 - mae: 3.7052\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.7373 - mae: 3.7373\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.6832 - mae: 3.6832\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7502 - mae: 3.7502\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.6821 - mae: 3.6821\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.7308 - mae: 3.7308\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.6912 - mae: 3.6912\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7020 - mae: 3.7020\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7005 - mae: 3.7005\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.6736 - mae: 3.6736\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa39474e450>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxToS7BN9qxq",
        "outputId": "e57d2313-a2d0-4bde-9192-64d2a0a87663"
      },
      "source": [
        "# Let's remind ourselves of the data\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": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8iRlwTF_Ns3",
        "outputId": "fb735f1c-828a-4505-dc6a-726f4298a21e"
      },
      "source": [
        "# Let's try to make a prediction\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa394753200> 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[32.787727]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMgNItGJ_TpA",
        "outputId": "a7253f4c-3643-4b88-d434-6e0e6f390322"
      },
      "source": [
        "# Let's see if we can make another change to improve our model\n",
        "\n",
        "# 1. Creating a model\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(50, activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile(loss=\"mae\",\n",
        "              optimizer=tf.keras.optimizers.Adam(lr=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",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 345ms/step - loss: 13.4588 - mae: 13.4588\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 12.8717 - mae: 12.8717\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.2913 - mae: 12.2913\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 11.7929 - mae: 11.7929\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.4167 - mae: 11.4167\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 11.0371 - mae: 11.0371\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 10.6558 - mae: 10.6558\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 10.2756 - mae: 10.2756\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.8942 - mae: 9.8942\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 9.5081 - mae: 9.5081\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 9.1140 - mae: 9.1140\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.7103 - mae: 8.7103\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.2971 - mae: 8.2971\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 7.8755 - mae: 7.8755\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.4403 - mae: 7.4403\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.9898 - mae: 6.9898\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.5229 - mae: 6.5229\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 6.0379 - mae: 6.0379\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.5340 - mae: 5.5340\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0105 - mae: 5.0105\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 4.4662 - mae: 4.4662\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9095 - mae: 3.9095\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7192 - mae: 3.7192\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.6367 - mae: 3.6367\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.7214 - mae: 3.7214\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7926 - mae: 3.7926\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 3.8508 - mae: 3.8508\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 4.0062 - mae: 4.0062\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 4.1463 - mae: 4.1463\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 4.2058 - mae: 4.2058\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.1842 - mae: 4.1842\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.1508 - mae: 4.1508\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 4.0788 - mae: 4.0788\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.9731 - mae: 3.9731\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.8376 - mae: 3.8376\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.6902 - mae: 3.6902\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.6195 - mae: 3.6195\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.5464 - mae: 3.5464\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.4770 - mae: 3.4770\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.4084 - mae: 3.4084\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.3605 - mae: 3.3605\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.2755 - mae: 3.2755\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.2687 - mae: 3.2687\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.2923 - mae: 3.2923\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.2976 - mae: 3.2976\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 3ms/step - loss: 3.2851 - mae: 3.2851\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.2735 - mae: 3.2735\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.2235 - mae: 3.2235\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.1752 - mae: 3.1752\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.1131 - mae: 3.1131\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.0379 - mae: 3.0379\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 3.0129 - mae: 3.0129\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 3.0182 - mae: 3.0182\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 2.9947 - mae: 2.9947\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.9757 - mae: 2.9757\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.9484 - mae: 2.9484\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.9134 - mae: 2.9134\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.8713 - mae: 2.8713\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.8221 - mae: 2.8221\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.7667 - mae: 2.7667\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 2.7052 - mae: 2.7052\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.6379 - mae: 2.6379\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.5982 - mae: 2.5982\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 2.5772 - mae: 2.5772\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.5269 - mae: 2.5269\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.4667 - mae: 2.4667\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 2.3861 - mae: 2.3861\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 2.3369 - mae: 2.3369\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.3076 - mae: 2.3076\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 2.2408 - mae: 2.2408\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.1883 - mae: 2.1883\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 2.1274 - mae: 2.1274\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.0533 - mae: 2.0533\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 1.9664 - mae: 1.9664\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.8736 - mae: 1.8736\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 1.7791 - mae: 1.7791\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 1.7302 - mae: 1.7302\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 1.6535 - mae: 1.6535\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.5418 - mae: 1.5418\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.4912 - mae: 1.4912\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.3832 - mae: 1.3832\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 1.2565 - mae: 1.2565\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.1659 - mae: 1.1659\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 1.0942 - mae: 1.0942\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.9972 - mae: 0.9972\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.8965 - mae: 0.8965\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.7915 - mae: 0.7915\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.6592 - mae: 0.6592\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.5738 - mae: 0.5738\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.5023 - mae: 0.5023\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.5062 - mae: 0.5062\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.4316 - mae: 0.4316\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.3446 - mae: 0.3446\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.3823 - mae: 0.3823\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.4264 - mae: 0.4264\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.4631 - mae: 0.4631\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.3652 - mae: 0.3652\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.4558 - mae: 0.4558\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.4573 - mae: 0.4573\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.4196 - mae: 0.4196\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa394f888d0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "096aVbVFJWOQ",
        "outputId": "b0c0d31c-75cf-4406-f699-5a81fe233dcf"
      },
      "source": [
        "# Let's see how good our model is\n",
        "model.predict([17.0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27.758755]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "neHgSL5B4mk3"
      },
      "source": [
        "## Evaluating a model\n",
        "\n",
        "In practice, a typical workflow you'll go through when building neural networks is:\n",
        "\n",
        "```\n",
        "Build a model -> fit -> evaluate it -> tweak a model -> fit it -> evaluate it -> tweak a model -> fit it -> evaluate it...\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFCgcLOVJg2L"
      },
      "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)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewdWVqsF8HPq",
        "outputId": "6e1d6dcb-0635-47d8-b232-e5fff5344181"
      },
      "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": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXuOEzV08RNO",
        "outputId": "fb819b45-6ebe-4cb8-fc14-1dbd5bf44ebb"
      },
      "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": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ECNuMQIi8zIG",
        "outputId": "9585412f-aca0-44cc-ed89-230718e1932a"
      },
      "source": [
        "# Visualize the data\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X,y);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WDiu8jn8_Fi"
      },
      "source": [
        "### The 3 sets...\n",
        "\n",
        "* **Training set** - the model learns from this data, which is typically 70-80% of the total data you have 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 learned, this set is typically 10-15% of the total data available."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-UWJKo__fcI",
        "outputId": "9d24ab06-856f-4f1c-b2d5-a4419be46b11"
      },
      "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": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOuszBDz_rfw",
        "outputId": "80f80115-1f54-4ea2-a7cc-c76156961224"
      },
      "source": [
        "# Split the data into training and test sets\n",
        "X_train = X[:40] # first 40 are training samples (80% of the data)\n",
        "y_train = y[:40]\n",
        "\n",
        "X_test = X[40:] # last 10 are testing samples (20% of the data)\n",
        "y_test = y[40:]\n",
        "\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 10, 40, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TI1GeBYABOnl"
      },
      "source": [
        "### Visualising the data\n",
        "\n",
        "Now we've got our data in training and test sets... let's visualize it again!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "unTpKK1tB1XP",
        "outputId": "afe16bd4-93a5-43c3-beb4-7cff0c8dae0d"
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "# Plot training data in blue \n",
        "plt.scatter(X_train, y_train, c=\"b\", label=\"Training data\")\n",
        "# Plot test 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": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPdSrQHYCi4p"
      },
      "source": [
        "# Let's have a look at how to build a neural network for our data\n",
        "\n",
        "# 1. Create a model\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(1))\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": "CQkK06hfFI5v"
      },
      "source": [
        "### Visualizing the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2u7RcLTnFZ8j"
      },
      "source": [
        "model.summary()\n",
        "---------------------------------------------------------------------------\n",
        "\n",
        "ValueError                                Traceback (most recent call last)\n",
        "\n",
        "<ipython-input-38-5f15418b3570> in <module>()\n",
        "----> 1 model.summary()\n",
        "\n",
        "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py in summary(self, line_length, positions, print_fn)\n",
        "   2519     \"\"\"\n",
        "   2520     if not self.built:\n",
        "-> 2521       raise ValueError('This model has not yet been built. '\n",
        "   2522                        'Build the model first by calling `build()` or calling '\n",
        "   2523                        '`fit()` with some data, or specify '\n",
        "\n",
        "ValueError: 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": "RbxHZxtlFcmX"
      },
      "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",
        "# 1. Create a model (same as above)\n",
        "model = tf.keras.Sequential(name=\"model_1\")\n",
        "model.add(tf.keras.layers.Dense(10, input_shape=[1], name=\"input_layer\"))\n",
        "model.add(tf.keras.layers.Dense(1, name=\"output_layer\"))\n",
        "\n",
        "# 2. Compile a model\n",
        "model.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.SGD(),\n",
        "              metrics=[\"mae\"])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmQVorQgGBAl",
        "outputId": "1ac458ff-68a0-4713-afab-1463916fbb31"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzpgE0ZRMxhK"
      },
      "source": [
        "* Total params - total number of parameters in the model.\n",
        "* Trainable parameters - these are the parameters (patterns) the model can update as it trains.\n",
        "* Non-trainable params - these parameters are not updated during training (this is typical when you bring in already learned patterns or parameters from other models during **transfer learning**).\n",
        "\n",
        "**Resource:** For a more in-depth overview of the trainable parameters  within a layer, check out MIT's Introduction to Deep Learning video.\n",
        "\n",
        "**Exercise:** Try playing around with the number of hidden units in the dense layer, see how that affects the number of parameters (total and trainable) by calling `model.summary()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZE9QJiVvOlnq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9272c08f-e5ab-44c8-cb12-6aba5139b7ff"
      },
      "source": [
        "# Let's fit our model to the training data \n",
        "model.fit(X_train, y_train, epochs=100, verbose=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa399483fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMByxQPQUary",
        "outputId": "b94f371f-ef97-4386-868d-dcaec67524c3"
      },
      "source": [
        "# Let's get a summary of our model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImEuSp1AG4fA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "93e00032-b1d8-4ed4-e1b2-b874944d6616"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model=model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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p06dTSUmJ0nYePXpE7733HonFYhoyZAh9/vnntHTpUgJALi4uisfmz549S4MHDyaJREKTJk2i+/fvd2o//vKXv5CdnR0BIAsLCwoMDCQiopiYGOrbty9ZW1vTjBkzaOvWrQSA5HK50qP6RERjx46luLi4Ntf/7NkziomJIScnJzIxMaEBAwZQUFAQFRUV0YYNG0gikRAAGjRokNolXPThHBw5coRkMhklJye3ux+nTp2iESNGkJGREQEge3t7WrNmjU7tyxdffEFyuZwAdPjav3+/Ylv68Bki4qEVXcFDK/RLR0MrBK9nqG/1yzoybdo0unbtmtBhqK03nQN93xchP0OcDNXHyVC/6Hw9Q32tX/biLbMLFy5ALBZjyJAhAkbUdfp6DtqiT/vSmz5DjOkznUiGPeXSpUuKqa46enW1GGVMTAyKi4tx5coVzJ07F0lJSXoTO9MNPfkZYrppwYIFSn/DbZUAKygoQFxcnErJsE8++USlr7e3N2QyGYyNjTFixAicPXtWG7vRbS0tLUhNTW1zIvxDhw5hw4YNKv+xPXDggNKx69+/v+YCUuMyUuPi4uLIzMyMANDrr79OOTk5WtmupixbtoyMjIxo0KBBStNm6RN9Pwcv0sd90aXPEN8mVV9Xvi9bb+Xn5+fT5cuXqb6+Xmn5ypUrydfXlyorKxVtcrmc+vXrRwDo8OHDKuvMz88nf3//ru2EAK5cuUITJ04kADR69Og2+6SlpdHkyZOVpmVsaWmh27dv0/Hjx2natGlK0xp2hk7/ZsgY0w26kAxra2vJw8NDb7bR1WTo6OjY5rJ169bRsGHDqK6uTqldLpfTnj17yMjIiBwdHenp06dKy/UpGRYWFlJgYCDt3r2bxowZ024yJCKKiIggDw8PamxsVFkWGRmp0WTYq2+TMsb0izZKZulqWa6rV69ixYoVWL16NcRiscpyT09PREVF4c6dO1iyZIkAEWrG6NGjkZubi9mzZ8Pc3LzDvgkJCSgsLGx3oLwmcTJkjHUZESElJUUxMbqNjQ2mT5+uNF9qd0pm6UOJMU1JT08HEcHPz6/dPsnJyRg2bBh27tyJgoKCDtfXmXPT2fJoQMclxHqKjY0NJk+ejLS0NBBRj26LkyFjrMsSEhIQFxeHZcuWoaysDMePH8etW7fg5eWFBw8eAHj+Jf/ydGXbtm3D6tWrldrS0tLg6+sLuVwOIsLVq1cRERGB0NBQ1NbWIjIyEqWlpTh79iyamprwwQcf4NatW93eBvDbE8gtLS2aOzhqysvLg6urK6RSabt9JBIJvvrqKxgZGWH+/PmKOWvb0plzs3DhQkRHR6Ourg4ymQxZWVkoKSmBs7Mz5s+fr/S0c2xsLDZu3IjU1FTcu3cPvr6+mDVrFn7++WfNHYQ2jB07Fnfu3MH58+d7dDucDBljXVJXV4eUlBQEBgZizpw5sLKygru7O7Zv347y8nLs2LFDY9vSlxJjXVVTU4Pr169DLpe/sq+Hhweio6NRWlqK2NjYNvt05dx0VB5NnRJimjZ06FAAz6c27EmcDBljXVJUVITq6mqMGzdOqX38+PEwMzNrd9o5TdC1slzdVVZWBiLq8KrwRcnJyXB1dcW2bdtw4sQJleXdPTcvl0cTsgxd6zFpvZrtKZwMGWNd8vTpUwBAnz59VJZZW1ujqqqqR7ffm0qM1dfXA8ArHyhpJRaLkZmZCZFIhHnz5qGurk5puabPzYslxF4c53fjxg3U1taqtS51SSQSAL8do57CyZAx1iXW1tYA0OYXa0+XzOpNJcaA377w1Zk9ycPDA4sWLUJxcbHKZA2aPjcvlhAjIqXXyZMn1VqXuhoaGgD8dox6CidDxliXjBw5En369FF5gOL06dNoaGjAW2+9pWjTdMms3lRiDABsbW0hEolQUVGh1vuSkpIwfPhwnDt3TqldnXPTGUKWEGs9JnZ2dj26HU6GjLEuEYvFWLx4Mfbv34/du3ejsrISFy9eRHh4OBwcHBAWFqbo292SWb2pxFhbpFIpnJ2dcfv2bbXe13q71NjYWKW9s+ems9t5VQmxkJAQ2NnZaXw6uNZj4u7urtH1voyTIWOsy1atWoW1a9ciMTER/fv3x+TJk/H6668r1XQEnj/C/9577+Hjjz+Gq6srkpKSFLe9PDw8FEMkwsPDYWtrCzc3N0ybNg2PHz8G8Pz3Ind3d0gkEnh5eWHYsGH45z//qfQbW3e3ITQfHx8UFRUp/f7397//HS4uLigpKcH48ePx+eefq7xvwoQJWLRokUp7Z85NRkYGUlNTAQCjRo3CtWvX8Ne//hWLFy8GAHz44YcoLi4G8HxYSnR0NDZs2IB+/frBwcEBUVFRePLkCYDntzPLyspw8ODBDvfz1KlTmDRpEl577TWcPn0a58+fh4ODAyZOnIjjx4+r9D9z5gwcHR0xatSozhzGrlNjuhrGWC+mC9OxtUWXy3Jpcjq24uJiMjEx6VItSl3Q3NxMXl5etGvXLo2ts7y8nMRiMW3evFllGU/HxhgzOPpUlqsz6urq8P3336O4uFjxgIiLiwsSExORmJiI6upqgSNUT3NzMw4cOICqqiqNVtJJSEjAmDFjEBERAeD5rDp3797FiRMnFBMmaAonQ8YY07LHjx/jww8/xLBhwzBv3jxFe1xcHGbMmIGQkBC1H6YR0tGjR5Gbm4v8/PxOj5V8lZSUFBQWFuLIkSMwNTUFABw8eBCOjo7w8vJCXl6eRrbTipMhY0xnxcfHIzMzExUVFRgyZAj27dsndEjdtn37dqWhCbt371ZavmbNGkRERGDdunUCRai+KVOmYM+ePUpzw3bHwYMH8ezZMxw9ehQ2NjaK9unTpysdu9Y5ZzXBRGNrYowxDVu7di3Wrl0rdBha5+3tDW9vb6HDEIy/vz/8/f21uk2+MmSMMWbwOBkyxhgzeJwMGWOMGTxOhowxxgxeuw/QZGdnazMOxpjAWqe94r/9zmudpJqPmX7oaFJxERHRiw3Z2dmYOXNmjwfFGGOMCeGltAcAOSrJkDGmPa3/+eQ/Q8YElcO/GTLGGDN4nAwZY4wZPE6GjDHGDB4nQ8YYYwaPkyFjjDGDx8mQMcaYweNkyBhjzOBxMmSMMWbwOBkyxhgzeJwMGWOMGTxOhowxxgweJ0PGGGMGj5MhY4wxg8fJkDHGmMHjZMgYY8zgcTJkjDFm8DgZMsYYM3icDBljjBk8ToaMMcYMHidDxhhjBo+TIWOMMYPHyZAxxpjB42TIGGPM4HEyZIwxZvA4GTLGGDN4nAwZY4wZPE6GjDHGDB4nQ8YYYwaPkyFjjDGDx8mQMcaYweNkyBhjzOBxMmSMMWbwOBkyxhgzeCZCB8CYobh9+zb+8Ic/oLm5WdH25MkTyGQyvPvuu0p9XV1d8b//+79ajpAxw8XJkDEtGThwIG7cuIGSkhKVZceOHVP69zvvvKOtsBhj4NukjGnVp59+ClNT01f2CwkJ0UI0jLFWnAwZ06LZs2ejqampwz4jRoyAm5ubliJijAGcDBnTKrlcjlGjRkEkErW53NTUFH/4wx+0HBVjjJMhY1r26aefwtjYuM1lTU1NmDFjhpYjYoxxMmRMyz7++GO0tLSotBsZGWHChAl4/fXXtR8UYwaOkyFjWubg4ICJEyfCyEj5z8/IyAiffvqpQFExZtg4GTImgE8++USljYgQGBgoQDSMMU6GjAkgODhY6XdDY2NjvP/++7C1tRUwKsYMFydDxgRgY2ODDz74QJEQiQhz5swROCrGDBcnQ8YEMmfOHMWDNKamppg+fbrAETFmuDgZMiYQPz8/mJubAwB8fX3Rp08fgSNizHBxMmRMIBYWFoqrQb5FypiwREREQgfRXTNmzMC+ffuEDoMxxgxOVlYWPvroI6HD6K6cXlO1YsKECYiOjhY6DMaUzJw5E1FRUfDw8GhzeXNzM7KysjBr1iwtR6a7UlNTAYD/nvXAzJkzhQ5BY3pNMhw4cGBv+N8J62VmzpwJDw+PDj+bAQEBEIvFWoxKt+Xk5AAA/z3rgd6UDPk3Q8YExomQMeFxMmSMMWbwOBkyxhgzeJwMGWOMGTxOhowxxgweJ0PG9MCRI0dgZWWFb7/9VuhQ9FJBQQHi4uKQm5sLZ2dniEQiiESiNquHeHt7QyaTwdjYGCNGjMDZs2cFiFh9LS0tSE1Nhaenp8qyQ4cOYcOGDWhubhYgMv3AyZAxPdAL5sYQzKpVq5Ceno74+HgEBQXh2rVrkMvl6NevH3bv3o28vDyl/j/88ANycnLg6+uLoqIivPnmmwJF3nnFxcV45513sGjRItTW1qos9/Pzg1gsxpQpU/D06VMBItR9nAwZ0wM+Pj6oqKiAr6+v0KGgrq6uzasPXbR+/Xrs3bsX2dnZkMlkSsvS09NhZGSEsLAwVFRUCBRh950/fx6xsbEIDw/HmDFj2u0XGRmJ0aNHY9q0aWhqatJihPqBkyFjTC27du1CWVmZ0GG80tWrV7FixQqsXr26zbGcnp6eiIqKwp07d7BkyRIBItSM0aNHIzc3F7Nnz1ZM/N6ehIQEFBYWIi0tTUvR6Q9OhozpuBMnTsDJyQkikQhbt24FAGRkZMDCwgJSqRQHDx7E1KlTYWlpiYEDB+Kbb75RvDc9PR1isRi2trZYsGABHBwcIBaL4enpidOnTyv6RUREwMzMDPb29oq2P/3pT7CwsIBIJEJ5eTkAICoqCosXL0ZJSQlEIhFcXFwAAN999x0sLS2xZs0abRySTklPTwcRwc/Pr90+ycnJGDZsGHbu3ImCgoIO10dESElJwRtvvAFzc3PY2Nhg+vTpuHTpkqJPZ88L8HwqvpUrV8LJyQkSiQSjRo1CVlZW93b6FWxsbDB58mSkpaXxrfeXcDJkTMdNmjQJP/30k1LbwoULER0djbq6OshkMmRlZaGkpATOzs6YP38+GhsbATxPcqGhoaitrUVkZCRKS0tx9uxZNDU14YMPPsCtW7cAPE8cL09/tm3bNqxevVqpLS0tDb6+vpDL5SAiXL16FQAUD2a01mfUBXl5eXB1dYVUKm23j0QiwVdffQUjIyPMnz8fNTU17fZNSEhAXFwcli1bhrKyMhw/fhy3bt2Cl5cXHjx4AKDz5wUAYmNjsXHjRqSmpuLevXvw9fXFrFmz8PPPP2vuILRh7NixuHPnDs6fP9+j29E3nAwZ03Oenp6wtLTEgAEDEBISgpqaGty8eVOpj4mJieKKxs3NDRkZGaiqqkJmZqZGYvDx8UFlZSVWrFihkfV1V01NDa5fvw65XP7Kvh4eHoiOjkZpaSliY2Pb7FNXV4eUlBQEBgZizpw5sLKygru7O7Zv347y8nLs2LFD5T0dnZf6+npkZGQgICAAQUFBsLa2xvLly2Fqaqqxc9KeoUOHAgAuXrzYo9vRN5wMGetFzMzMAEDpCqQt48aNg1QqVbrF15uUlZWBiDq8KnxRcnIyXF1dsW3bNpw4cUJleVFREaqrqzFu3Dil9vHjx8PMzEzplnNbXj4vly9fRm1tLUaOHKnoI5FIYG9v3+PnpPWYtF7Nsuc4GTJmoMzNzfHw4UOhw+gR9fX1APDKB0paicViZGZmQiQSYd68eairq1Na3jocoU+fPirvtba2RlVVlVrxtd6OXb58uWLMo0gkwo0bN9ocGqFJEokEwG/HiD3HyZAxA9TY2IinT59i4MCBQofSI1q/8NUZZO7h4YFFixahuLgYSUlJSsusra0BoM2k15XjOGDAAADPazcSkdLr5MmTaq1LXQ0NDQB+O0bsOU6GjBmgo0ePgogwYcIERZuJickrb6/qC1tbW4hEIrXHDyYlJWH48OE4d+6cUvvIkSPRp08flYdbTp8+jYaGBrz11ltqbWfQoEEQi8UoLCxU632a0HpM7OzstL5tXcbJkDED0NLSgidPnqCpqQkXLlxAVFQUnJycEBoaqujj4uKCx48f48CBA2hsbMTDhw9x48YNlXX17dsXd+/eRWlpKaqqqtDY2Ij8/HydGlohlfrVXhcAACAASURBVErh7OyM27dvq/W+1tulxsbGKu2LFy/G/v37sXv3blRWVuLixYsIDw+Hg4MDwsLC1N7O3Llz8c033yAjIwOVlZVobm7G7du3ce/ePQBASEgI7OzsND4dXOsxcXd31+h69R71AsHBwRQcHCx0GIypAEBZWVndWseWLVvI3t6eAJBUKiU/Pz/atm0bSaVSAkBDhw6lkpIS2rFjB1laWhIAGjx4MF25coWIiMLCwsjU1JQcHR3JxMSELC0tafr06VRSUqK0nUePHtF7771HYrGYhgwZQp9//jktXbqUAJCLiwvdvHmTiIjOnj1LgwcPJolEQpMmTaL79+/TkSNHSCaTUXJycrf2lUhzf88RERFkampKtbW1irb9+/eTXC4nANS/f3/67LPP2nzv0qVLyd/fX6mtpaWFNm3aREOHDiVTU1OysbGhgIAAunz5sqKPOufl2bNnFBMTQ05OTmRiYkIDBgygoKAgKioqIiKigIAAAkArV67scD9PnjxJEydOJAcHBwJAAMje3p48PT3p2LFjKv19fHzI0dGRWlpaOncgO6CJz7eOyOZkyFgP0oUvi7CwMOrbt6+gMahDU3/PxcXFZGJiQl9//bUGotK+5uZm8vLyol27dmlsneXl5SQWi2nz5s0aWZ8ufL41JJtvkzJmAAyxWoGLiwsSExORmJiI6upqocNRS3NzMw4cOICqqiqEhIRobL0JCQkYM2YMIiIiNLbO3sJgk2FvKImzefNmxYMC27dvFzoctbxcSqf1ZWZmBltbW7z77rvYtGkTnjx5InSoTI/FxcVhxowZCAkJ0avJuI8ePYrc3Fzk5+d3eqzkq6SkpKCwsBBHjhyBqampRtbZmxhsMqReMC/fkiVLVKbp0hcvltKxsrICEaGlpQVlZWXIzs7GkCFDEBMTgxEjRvT49FS9WXx8PDIzM1FRUYEhQ4Zg3759QoekdWvWrEFERATWrVsndCidNmXKFOzZs0dprtjuOHjwIJ49e4ajR4/CxsZGI+vsbUyEDkAorSVxdEFdXR2mTJmit4lNU0QiEaytrfHuu+/i3XffhY+PD2bOnAkfHx9cuXIFVlZWQoeod9auXYu1a9cKHYbgvL294e3tLXQYgvH394e/v7/QYeg0g70y1CX6UhJH24KDgxEaGoqysjK9uw3MGNMvBpkM9aEkTnf861//gpubG6ysrCAWi+Hu7o7vv/8eAPDHP/5R8fucXC5XDC6eO3cupFIprKyscOjQIQAdl5jZuHEjpFIpZDIZysrKsHjxYjg6OuLy5csaLefTOg4uPz9f0dZRXOqU0Dl27BjefvttSKVSWFpawt3dHZWVla/cBmOsFxL6eVZN6Mqj2Ldu3SIAtGXLFkXbsmXLCAD9+OOPVFFRQWVlZeTl5UUWFhbU0NCg6BcWFkYWFhb0yy+/UH19PRUVFdH48eNJJpMpxmIREc2ePZvs7OyUtrtp0yYCQA8fPlS0BQUFkVwuV3e3iej54+MA6IsvvlC05eTkUEJCAj1+/JgePXpEEyZMoH79+iltz9jYmO7cuaO0rlmzZtGhQ4cU/16yZAmZm5vTvn376MmTJxQfH09GRkZ05swZpeMVGRlJW7ZsocDAQPr111/p8OHDJJPJKDEx8ZXxy+VysrKyand5ZWUlAaBBgwapHVdH57G6uposLS1pw4YNVFdXR/fv36fAwEDFeXnVNjoLvefRc63hoVL6oxd9vnloRVt0oSROdwQHB2PVqlWwsbFB37594efnh0ePHikmZQ4PD0dzc7NSrJWVlThz5gymTZsGQL0SM+vXr8dnn32G3NxcDB8+XKPlfGQyGUQikWJOSHXi6ug8lpaWorKyEiNGjIBYLIadnR1yc3PRv39/QcvrMMaEYbAP0HRWbyiJ0/oYdetYs9/97ncYNmwYvvzyS8THx0MkEmHv3r0ICQlRTEMlZImZF9XU1ICIYGlp2a24Xj6Pzs7OsLW1xZw5cxAZGYnQ0FC8/vrr3dpGe3p64uXepnW6sOzsbIEjYYaEk6EG6UpJnLy8PGzatAlFRUWorKxUSeQikQgLFizAokWL8OOPP+L999/H3/72N+zZs0fR58USM8uXL1d6v4ODQ8/vxP935coVAMDw4cM1GpdEIsE//vEPxMbGYs2aNUhMTMRHH32EzMxMje97Wloa0tLS1H6foZs5c6bQITADwrdJNURXSuLcvHkTAQEBsLe3x+nTp1FRUYENGzao9AsNDYVYLMbOnTtx+fJlWFpaYvDgwYrlQpaYedF3330HAJg6darG4xoxYgS+/fZb3L17FzExMcjKysLmzZs1vu9ZWVkq6+FX+6/g4GAEBwcLHge/Xv3qTfjKUEN0pSTOxYsX0djYiIULF8LZ2RnA8yvBl9nY2GDmzJnYu3cvZDIZ5s+fr7RcyBIzre7fv4/U1FQMHDgQ8+bN02hcd+/exdOnT+Hm5oYBAwZg3bp1+OGHH/DLL7/oxL4zxrSLrwy7qKdL4nSVk5MTAKCgoAD19fUoLi5WGvLxovDwcDx79gyHDx+Gr6+v0rLOlJhpj7rlfIgI1dXVaGlpARHh4cOHyMrKwsSJE2FsbIwDBw4ofjPsTlwvunv3LhYsWIBLly6hoaEB586dw40bNzBhwgSNbYMxpkeoF1D3UWx9KInTGX/5y1/Izs6OAJCFhQUFBgYSEVFMTAz17duXrK2tacaMGbR161YCQHK5XGnoBxHR2LFjKS4urs31d1RiZsOGDSSRSBTDHl6sDNCZcj6HDh2iUaNGkVQqJTMzMzIyMiIAJBKJyNramt5++21KTEykR48eqRVXZ89jaWkpeXp6ko2NDRkbG9Nrr71Gy5Yto6amplduQx3oPY+eaw0PrdAfvejznS0i0v8bvzNmzAAA5OTkaGV7CxYsQE5ODh49eqSV7fUkHx8fbN26FUOGDBE6lF5JJBIhKysLH330kdCh6A1t/z2zrutFn+8cvk3aRfpaEufFW7AXLlyAWCzmRMgYM3icDHXMpUuXVMoatfXqao2zmJgYFBcX48qVK5g7dy6SkpI0vAeMMaZ/OBmqqadL4gwfPrxTjzTv3bu3S+uXSqUYPnw43n//fSQkJMDNzU2j8TMmtIKCAsTFxanUzPzkk09U+np7e0Mmk8HY2BgjRozA2bNnBYhYfS0tLUhNTYWnp2e7fU6cOIGJEydCKpXCwcEBMTExePbsmWL5oUOHsGHDBr29y6VxAv1YqVH8gzvTVeg9DxhoTXf+nleuXEm+vr5UWVmpaJPL5dSvXz8CQIcPH1Z5T35+Pvn7+3c5Xm27cuUKTZw4kQDQ6NGj2+zzn//8hyQSCa1YsYKqq6vpp59+ov79+9PcuXOV+qWlpdHkyZPpyZMnXYqlF32+eW5Sxnqzurq6Dq8e9GUbnbF+/Xrs3bsX2dnZkMlkSsvS09NhZGSEsLAwnalj2hXnz59HbGwswsPDMWbMmHb7JSUlwd7eHqtXr4aFhQU8PDwQExODr776SmlKwcjISIwePRrTpk1DU1OTNnZBZ3EyZKwX00atTF2ox3n16lWsWLECq1evhlgsVlnu6emJqKgo3LlzB0uWLBEgQs0YPXo0cnNzMXv2bJibm7fZp6mpCXl5eZg8ebLShBtTp04FEeHgwYNK/RMSElBYWGjwUwZyMmRMhxARUlJSFBVRbGxsMH36dKX/zXenVqa26nFqsqZlZ6Snp4OI4Ofn126f5ORkDBs2DDt37kRBQUGH6+vMeVCndqY262Neu3YN1dXVigk4WsnlcgDPnyJ/kY2NDSZPnoy0tLReN8WaOjgZMqZDEhISEBcXh2XLlqGsrAzHjx/HrVu34OXlhQcPHgB4/sX/8riubdu2YfXq1UptaWlp8PX1hVwuBxHh6tWriIiIQGhoKGpraxEZGYnS0lKcPXsWTU1N+OCDD3Dr1q1ubwP4behRS0uL5g5OB/Ly8uDq6gqpVNpuH4lEgq+++gpGRkaYP3++YkL2tnTmPCxcuBDR0dGoq6uDTCZDVlYWSkpK4OzsjPnz5ysNY4qNjcXGjRuRmpqKe/fuwdfXF7NmzcLPP/+suYPw/92/fx8AVG4Vi8ViSCQSRfwvGjt2LO7cuYPz589rPB59wcmQMR1RV1eHlJQUBAYGYs6cObCysoK7uzu2b9+O8vJy7NixQ2Pb6ul6nJqsafkqNTU1uH79uuLKpyMeHh6Ijo5GaWkpYmNj2+zTlfPQUe1MbdfHbH1itLUc24tMTU1RV1en0j506FAAz+c2NlScDBnTEUVFRaiursa4ceOU2sePHw8zM7N255jVBF2ux/kqZWVlIKIOrwpflJycDFdXV2zbtg0nTpxQWd7d8/By7Uxt1wZt/c20rQdiGhoaIJFIVNpbj11bV42GgpMhYzri6dOnAIA+ffqoLLO2tkZVVVWPbl9X6nGqq76+HgDafaDkZWKxGJmZmRCJRJg3b57KlZKmz8OL9TFfnDjjxo0bqK2tVWtdndH6O29lZaVSe21tLerr69usydmaIFuPpSHiZMiYjrC2tgaANr9se7pWpq7U4+yK1i9ydQaPe3h4YNGiRSguLlaZhUnT50HbtUGHDBkCmUymUiGn9ffcUaNGqbynoaEBANq8ajQUnAwZ0xEjR45Enz59VB6qOH36NBoaGvDWW28p2jRdK1NX6nF2ha2tLUQikdrjB5OSkjB8+HCcO3dOqV2d89AZ2q6PaWJigmnTpuH48eNKDzDl5+dDJBK1+cRt67Gzs7PTSoy6iJMhYzpCLBZj8eLF2L9/P3bv3o3KykpcvHgR4eHhcHBwQFhYmKJvd2tl9nQ9TnVrWnaHVCqFs7Mzbt++rdb7Wm+XvvygiTrnobPbeVV9zJCQENjZ2WlsOrgVK1bgwYMHWLVqFWpqanDy5Els2rQJoaGhcHV1Venfeuzc3d01sn29JMjENxrG07ExXQU1p6tqaWmhTZs20dChQ8nU1JRsbGwoICCALl++rNSvO7UytVGPszM1LdvTlb/niIgIMjU1pdraWkXb/v37SS6XEwDq378/ffbZZ22+d+nSpSrTsXXmPKhTA/VV9TEDAgIIAK1cubLD/Tx58iRNnDiRHBwcCAABIHt7e/L09KRjx44p9T127Bi9/fbbZG5uTg4ODrR06VKqr69vc70+Pj7k6OhILS0tHW7/Zep+vnVYNidDxnqQLn5ZhIWFUd++fYUOo11d+XsuLi4mExMTpSLT+qS5uZm8vLxo165dWt92eXk5icVi2rx5s9rv1cXPdxfx3KSMGaLeVqnAxcUFiYmJSExMRHV1tdDhqKW5uRkHDhxAVVVVl0uzdUdCQgLGjBmDiIgIrW9bl3AyZIz1CnFxcZgxYwZCQkL0ajLuo0ePIjc3F/n5+Z0eK6kpKSkpKCwsxJEjR2BqaqrVbesaToaMGZCerscptDVr1iAiIgLr1q0TOpROmzJlCvbs2aM0D6w2HDx4EM+ePcPRo0dhY2Oj1W3rIhOhA2CMac/atWuxdu1aocPoUd7e3vD29hY6DJ3n7+8Pf39/ocPQGXxlyBhjzOBxMmSMMWbwOBkyxhgzeJwMGWOMGbxe8wDNqVOnMGPGDKHDYExFamoqcnJyhA5Db5w6dQoA+O+ZaVWvSIYeHh5Ch8BYm4KDgztcfv/+fZw7dw5Tp07VUkS678XJwpluCw4OxqBBg4QOQyNERERCB8GYocrOzsbMmTPBf4aMCSqHfzNkjDFm8DgZMsYYM3icDBljjBk8ToaMMcYMHidDxhhjBo+TIWOMMYPHyZAxxpjB42TIGGPM4HEyZIwxZvA4GTLGGDN4nAwZY4wZPE6GjDHGDB4nQ8YYYwaPkyFjjDGDx8mQMcaYweNkyBhjzOBxMmSMMWbwOBkyxhgzeJwMGWOMGTxOhowxxgweJ0PGGGMGj5MhY4wxg8fJkDHGmMHjZMgYY8zgcTJkjDFm8DgZMsYYM3icDBljjBk8ToaMMcYMHidDxhhjBo+TIWOMMYPHyZAxxpjB42TIGGPM4JkIHQBjhqKxsRHV1dVKbTU1NQCAJ0+eKLWLRCJYW1trLTbGDB0nQ8a05PHjx3B0dERzc7PKsr59+yr9+7333sM//vEPbYXGmMHj26SMaYmdnR3eeecdGBl1/GcnEonw8ccfaykqxhjAyZAxrfrkk09e2cfY2BiBgYFaiIYx1oqTIWNaFBQUBBOT9n+dMDY2xocffoh+/fppMSrGGCdDxrTI0tISU6dObTchEhHmzJmj5agYY5wMGdOyOXPmtPkQDQCYmZnh97//vZYjYoxxMmRMy37/+99DKpWqtJuamiIgIAAWFhYCRMWYYeNkyJiWicViBAYGwtTUVKm9sbERs2fPFigqxgwbJ0PGBDBr1iw0NjYqtVlaWuKDDz4QKCLGDBsnQ8YE8P777ysNtDc1NcXHH38MMzMzAaNizHBxMmRMACYmJvj4448Vt0obGxsxa9YsgaNizHBxMmRMIB9//LHiVqmdnR0mTZokcESMGS5OhowJxNPTE46OjgCATz/99JXTtDHGeo7eTdR9+/Zt/PTTT0KHwZhGjB8/Hnfu3EG/fv2QnZ0tdDiMacRHH30kdAhqExERCR2EOrKzszFz5kyhw2CMMdYOPUsrAJCjd1eGrfTwYDOm+M/ci5/fffv2ITg4WMCodJ9IJEJWVpZeXnEYEn2+WOEfKRgTGCdCxoTHyZAxxpjB42TIGGPM4HEyZIwxZvA4GTLGGDN4nAwZY4wZPE6GjOmhI0eOwMrKCt9++63Qoei8goICxMXFITc3F87OzhCJRBCJRPjkk09U+np7e0Mmk8HY2BgjRozA2bNnBYhYfS0tLUhNTYWnp2e7fU6cOIGJEydCKpXCwcEBMTExePbsmWL5oUOHsGHDhnYLT/d2nAwZ00M8zrZzVq1ahfT0dMTHxyMoKAjXrl2DXC5Hv379sHv3buTl5Sn1/+GHH5CTkwNfX18UFRXhzTffFCjyzisuLsY777yDRYsWoba2ts0+RUVF8Pb2xpQpU/Dw4UPs378fX375JcLDwxV9/Pz8IBaLMWXKFDx9+lRb4esMToaM6SEfHx9UVFTA19dX6FBQV1fX4RWJUNavX4+9e/ciOzsbMplMaVl6ejqMjIwQFhaGiooKgSLsvvPnzyM2Nhbh4eEYM2ZMu/2SkpJgb2+P1atXw8LCAh4eHoiJicFXX32FS5cuKfpFRkZi9OjRmDZtGpqamrSxCzqDkyFjrFt27dqFsrIyocNQcvXqVaxYsQKrV6+GWCxWWe7p6YmoqCjcuXMHS5YsESBCzRg9ejRyc3Mxe/ZsmJubt9mnqakJeXl5mDx5MkQikaJ96tSpICIcPHhQqX9CQgIKCwuRlpbWo7HrGk6GjOmZEydOwMnJCSKRCFu3bgUAZGRkwMLCAlKpFAcPHsTUqVNhaWmJgQMH4ptvvlG8Nz09HWKxGLa2tliwYAEcHBwgFovh6emJ06dPK/pFRETAzMwM9vb2irY//elPsLCwgEgkQnl5OQAgKioKixcvRklJCUQiEVxcXAAA3333HSwtLbFmzRptHBIV6enpICL4+fm12yc5ORnDhg3Dzp07UVBQ0OH6iAgpKSl44403YG5uDhsbG0yfPl3pqqqz5wAAmpubsXLlSjg5OUEikWDUqFHIysrq3k6349q1a6iuroaTk5NSu1wuBwBcuHBBqd3GxgaTJ09GWlqaQd2O52TImJ6ZNGmSSuWWhQsXIjo6GnV1dZDJZMjKykJJSQmcnZ0xf/58Rd3EiIgIhIaGora2FpGRkSgtLcXZs2fR1NSEDz74ALdu3QLwPJm8PA/otm3bsHr1aqW2tLQ0+Pr6Qi6Xg4hw9epVAFA8hNHS0tIjx+BV8vLy4OrqCqlU2m4fiUSCr776CkZGRpg/fz5qamra7ZuQkIC4uDgsW7YMZWVlOH78OG7dugUvLy88ePAAQOfPAQDExsZi48aNSE1Nxb179+Dr64tZs2bh559/1txB+P/u378PACq3isViMSQSiSL+F40dOxZ37tzB+fPnNR6PruJkyFgv4+npCUtLSwwYMAAhISGoqanBzZs3lfqYmJgornLc3NyQkZGBqqoqZGZmaiQGHx8fVFZWYsWKFRpZnzpqampw/fp1xZVPRzw8PBAdHY3S0lLExsa22aeurg4pKSkIDAzEnDlzYGVlBXd3d2zfvh3l5eXYsWOHyns6Ogf19fXIyMhAQEAAgoKCYG1tjeXLl8PU1FRjx/9FrU+MGhsbqywzNTVFXV2dSvvQoUMBABcvXtR4PLqKkyFjvZiZmRkAKF2VtGXcuHGQSqVKt/30VVlZGYiow6vCFyUnJ8PV1RXbtm3DiRMnVJYXFRWhuroa48aNU2ofP348zMzMlG4vt+Xlc3D58mXU1tZi5MiRij4SiQT29vY9cvxbfzNt64GYhoYGSCQSlfbWY9fWVWNvxcmQMQYAMDc3x8OHD4UOo9vq6+sBoN0HSl4mFouRmZkJkUiEefPmqVwptQ4z6NOnj8p7ra2tUVVVpVZ8rbdjly9frhjzKBKJcOPGjXaHRnRH6+++lZWVSu21tbWor6+Hg4ODyntaE2TrsTQEnAwZY2hsbMTTp08xcOBAoUPpttYvcnUGj3t4eGDRokUoLi5GUlKS0jJra2sAaDPpdeWYDRgwAACQmpoKIlJ6nTx5Uq11dcaQIUMgk8lw48YNpfbW33dHjRql8p6GhgYAaPOqsbfiZMgYw9GjR0FEmDBhgqLNxMTklbdXdZGtrS1EIpHa4weTkpIwfPhwnDt3Tql95MiR6NOnj8rDLadPn0ZDQwPeeusttbYzaNAgiMViFBYWqvW+rjIxMcG0adNw/PhxpQea8vPzIRKJ2nzitvXY2dnZaSVGXcDJkDED1NLSgidPnqCpqQkXLlxAVFQUnJycEBoaqujj4uKCx48f48CBA2hsbMTDhw9Vri4AoG/fvrh79y5KS0tRVVWFxsZG5OfnCza0QiqVwtnZGbdv31brfa23S19+0EQsFmPx4sXYv38/du/ejcrKSly8eBHh4eFwcHBAWFiY2tuZO3cuvvnmG2RkZKCyshLNzc24ffs27t27BwAICQmBnZ2dxqaDW7FiBR48eIBVq1ahpqYGJ0+exKZNmxAaGgpXV1eV/q3Hzt3dXSPb1wukZ7KyskgPw2aMiDTz+d2yZQvZ29sTAJJKpeTn50fbtm0jqVRKAGjo0KFUUlJCO3bsIEtLSwJAgwcPpitXrhARUVhYGJmampKjoyOZmJiQpaUlTZ8+nUpKSpS28+jRI3rvvfdILBbTkCFD6PPPP6elS5cSAHJxcaGbN28SEdHZs2dp8ODBJJFIaNKkSXT//n06cuQIyWQySk5O7ta+tgJAWVlZne4fERFBpqamVFtbq2jbv38/yeVyAkD9+/enzz77rM33Ll26lPz9/ZXaWlpaaNOmTTR06FAyNTUlGxsbCggIoMuXLyv6qHMOnj17RjExMeTk5EQmJiY0YMAACgoKoqKiIiIiCggIIAC0cuXKDvfz5MmTNHHiRHJwcCAABIDs7e3J09OTjh07ptT32LFj9Pbbb5O5uTk5ODjQ0qVLqb6+vs31+vj4kKOjI7W0tHS4/Zfp8fdztt5FrccHmzGd+PyGhYVR3759BY1BXeomw+LiYjIxMaGvv/66B6PqOc3NzeTl5UW7du3S+rbLy8tJLBbT5s2b1X6vLny+uyibb5MyZoB6e2UCFxcXJCYmIjExEdXV1UKHo5bm5mYcOHAAVVVVCAkJ0fr2ExISMGbMGERERGh920LiZNjLbN68WfEAwfbt24UORy0vl9hpfZmZmcHW1hbvvvsuNm3ahCdPnggdKtMDcXFxmDFjBkJCQvRqMu6jR48iNzcX+fn5nR4rqSkpKSkoLCzEkSNHYGpqqtVtC42TYS+zZMkSlam69MWLJXasrKxARGhpaUFZWRmys7MxZMgQxMTEYMSIET0ybZUhiI+PR2ZmJioqKjBkyBDs27dP6JB61Jo1axAREYF169YJHUqnTZkyBXv27FGaF1YbDh48iGfPnuHo0aOwsbHR6rZ1ASfDLtBGyRpdLYujbSKRCNbW1nj33XeRmZmJ7OxsPHjwQFHCiKln7dq1ePbsGYgI169fR3BwsNAh9Thvb2+sX79e6DB0nr+/P+Li4tqcts0QcDLsAm2UrNHFsji6IDg4GKGhoSgrK9O728CMMd1lEMmQOlF+pTsla7RVFqc7/vWvf8HNzQ1WVlYQi8Vwd3fH999/DwD44x//qPh9Ti6XKwYdz507F1KpFFZWVjh06BCAjkvPbNy4EVKpFDKZDGVlZVi8eDEcHR1x+fJljZb0aR0Ll5+fr2jrKC51SuscO3YMb7/9NqRSKSwtLeHu7q6YxkqbZXcYY1om8OOsauvKo7srV64kMzMz+vrrr+np06d04cIFevPNN6l///50//59Rb/Zs2eTnZ2d0ns3bdpEAOjhw4eKtqCgIJLL5Ur9wsLCyMLCgn755Reqr6+noqIiGj9+PMlkMsV4rO5uo7OKi4sJAH3xxReKtpycHEpISKDHjx/To0ePaMKECdSvXz+l7RkbG9OdO3eU1jVr1iw6dOiQ4t9Lliwhc3Nz2rdvHz158oTi4+PJyMiIzpw5Q0REy5YtIwAUGRlJW7ZsocDAQPr111/p8OHDJJPJKDEx8ZXxy+VysrKyand5ZWUlAaBBgwapHdePP/5IFRUVVFZWRl5eXmRhYUENDQ1ERFRdXU2Wlpa0YcMGqquro/v371NgYKDivLxqG52hx4+eCwpqDq1gwtDjz3fvH2dYW1tLffr0oZCQEKX2//u//yMASl/O3U2GL3+BnzlzhgDQ6tWrNbKNzmorGb5s7dq1BIDKysqIiKigoIAAKA2SrqiooKFDh1JTUxMREdXV1ZFUKlU6lrW1tWRubk4LFy4kot+STl1dXZdiJ3p1MiQiEolEZG1t3a24tm3bnesWPgAACYxJREFURgDo6tWrRET0n//8hwDQ4cOHVbbXmW10hh5/WQiKk6F+0OPPd7aJ1i5BBdLd8ivdoctlcVofm24db/a73/0Ow4YNw5dffon4+HiIRCLs3bsXISEhih/UtV16pj01NTUgIlhaWnYrrpdL6zg7O8PW1hZz5sxBZGQkQkND8frrr3drG+2ZMWOG2u8xdKmpqcjJyRE6DNYBdafA0yW9/jdDTZdfUZeulMXJy8vDu+++iwEDBsDc3Bx//vOflZaLRCIsWLAA165dw48//ggA+Nvf/ob/+Z//UfTRdumZ9ly5cgUAMHz4cI3GJZFI8I9//AOTJk3CmjVr4OzsjJCQENTV1enMvjPGekavvzLUdPkVdehKWZybN28iICAAgYGB+PLLL/Haa69hy5YtKgkxNDQU8fHx2LlzJwYNGgRLS0sMHjxYsfzF0jNRUVFa3YcXfffddwCAqVOnajyuESNG4Ntvv8XDhw+RkpKC9evXY8SIEYqZQDS173yFox6RSITo6Gh89NFHQofCOpCdnY2ZM2cKHUaX9PpkqE75FU2XrNGVsjgXL15EY2MjFi5cCGdnZwDPv1xeZmNjg5kzZ2Lv3r2QyWSYP3++0nJtl55py/3795GamoqBAwdi3rx5Go3r7t27ePr0Kdzc3DBgwACsW7cOP/zwA3755Red2HfGWM/p9bdJ1Sm/0p2SNUDPl8XpKicnJwBAQUEB6uvrUVxc3O5vpeHh4Xj27BkOHz4MX19fpWWdKT3THnVL+hARqqur0dLSAiLCw4cPkZWVhYkTJ8LY2BgHDhxQ/GbYnbhedPfuXSxYsACXLl1CQ0MDzp07hxs3bmDChAka2wZjTEcJ+wCP+rrytFJnyq8Qda9kjTbK4nTGX/7yF7KzsyMAZGFhQYGBgUREFBMTQ3379iVra2uaMWMGbd26lQCQXC5XGvpBRDR27FiKi4trc/0dlZ7ZsGEDSSQSxbCHFysGdKakz6FDh2jUqFEklUrJzMyMjIyMCIDiydG3336bEhMT6dGjR2rF1dnSOqWlpeTp6Uk2NjZkbGxMr732Gi1btkzxNO2ryu50hh4/bSco8NOkekGPP9/ZIiIiYdJw17Tek9a1sBcsWICcnBw8evRI6FC6zcfHB1u3bsWQIUOEDqXX0dXPr64TiUTIysri3wx1nB5/vnN6/W1SbdLXsjgv3oK9cOECxGIxJ0LGmEHhZKgHLl26pFLWqK1XV2ufxcTEoLi4GFeuXMHcuXORlJSk4T1gTPcUFBQgLi5OpXTYJ598otLX29sbMpkMxsbGGDFiBM6ePStAxOpraWlBampqm5P+Hzp0CBs2bNDb/8RrGidDDejpsjjDhw8HEb3ytXfv3i6tXyqVYvjw4Xj//feRkJAANzc3jcbPmK5ZtWoV0tPTER8fr1Q6rF+/fti9ezfy8vKU+v/www/IycmBr68vioqK8OabbwoUeecVFxfjnXfewaJFi9ocC+vn5wexWIwpU6YoxmMbMk6GGqDvZXGSk5PR3NyMmzdvqjxBynofQy9Btn79euzduxfZ2dmQyWRKy9LT02FkZISwsDC9LhF2/vx5xMbGIjw8HGPGjGm3X2RkJEaPHo1p06ahqalJixHqHk6GjBkYQy5BdvXqVaxYsQKrV6+GWCxWWe7p6YmoqCjcuXMHS5YsESBCzRg9ejRyc3Mxe/ZsmJubd9g3ISEBhYWFSEtL01J0uomTIWM6jnpJCTJNlvHqqvT0dBAR/Pz82u2TnJyMYcOGYefOnSgoKOhwfZ05N+qUEBOiTJiNjQ0mT56MtLQ0fXwKVHO0PJaj2/R4HAtjBl2CTJ0yXi+DhsYZOjs7k5ubW5vL5HI5Xb9+nYiIfvrpJzIyMqLXX3+dqquriYgoPz+f/P39ld7T2XPTmRJiRJopE/ay//qv/6LRo0d32CcuLo4A0Llz57q8HSK9/n7O5itDxnRYXV0dUlJSEBgYiDlz5sDKygru7u7Yvn07ysvLsWPHDo1ty8TERHGF4+bmhoyMDFRVVSEzM1Mj6/fx8UFlZSVWrFihkfWpq6amBtevX4dcLn9lXw8PD0RHR6O0tBSxsbFt9unKufH09ISlpSUGDBiAkJAQ1NTU4ObNmwCA+vp6ZGRkICAgAEFBQbC2tsby5cthamqqsXPQnqFDhwJ4PnWjoeJkyJgO4xJkmlNWVgYiglQq7VT/5OT/194dg6QWhXEA/19SsLYismirwJagNaMpcGmQBtHZxSXuEDS0RNjTlmhrDKeGXhS12KoQ+LbaXV2EtiIt0O8ND1/PrOe9derc2/n/xmue+3XOxY/snvv/gUgkgoODA1xdXfW8/tG1eRkhpjMirTMn9Xr9U8/jZWyGRB7GCDJ1ms0mAPS9oaQjFAqhUCjAsiyk02k0Go2u11Wvjc6YsMHBQQDPc2QiNkMiD2MEmTqdD3w3m8wXFhawvr6OarXa8zAK1WvzbxSZvNhDXKlUXI3l1tPTE4DnOTIRmyGRhzGCTJ2xsTFYluV6/+DOzg5mZ2dxfX3dddzN2jihMyasMyfhcPjLz+0VbIZEHvadIsjcxnipNjQ0hKmpKdRqNVfv63xdOjAw0HPc6do4PU+/mLBUKoVwOKz8cXCdOZmbm1M6rq/ovJf1PXx86y6R0RFkTmK83gJFWyts25ZgMCgPDw9/j52dncn09LQAkNHRUVlbW3v1vRsbGz1bK5ysjdMIMZH+MWGrq6sCQLa2tv77e1YqFVlcXJSJiQkBIABkfHxcotGolMvlnp9fWVmRyclJabfbzibyDT7+fP7pu6p9PNlEnr1+M5mMjIyM6C7jTaqaYbValUAg0JW16SetVkuWlpbk8PBQ2Zi3t7cSCoVkb2/vw2N59fp2gPsMiegPE9ILZmZmkM1mkc1mcX9/r7scV1qtFs7Pz3F3d/fuhJrXbG9vY35+HrZtKxvTj9gMicgom5ubSCQSSKVSvnoYd6lUwunpKS4vLx3vlexnf38fNzc3KBaLCAaDSsb0KzZDIsN9dgSZF+VyOdi2jd3dXd2lOLa8vIyjo6OuZ8N+xMXFBR4fH1EqlTA8PKxkTD8L6C6AiPTK5/PI5/O6y/hysVgMsVhMdxnaxONxxONx3WV4Bv8yJCIi47EZEhGR8dgMiYjIeGyGRERkPDZDIiIynm/vJrUsS3cJRO/G69e9ZDKJZDKpuwz6pnzXDKPRKI6Pj3WXQURE34glIqK7CCIiIo1O+D9DIiIyHpshEREZj82QiIiMFwBworsIIiIijX79BsnByZjpyWYFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8KPpdgJVj_v"
      },
      "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` vs `y_pred` (ground truth versus your model's predictions)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7g9JobSIXGy8",
        "outputId": "994c1e18-d902-4eb0-9e29-cc2762ae553f"
      },
      "source": [
        " # make some predictions\n",
        " y_pred = model.predict(X_test)\n",
        " y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 70.55218 ],\n",
              "       [ 75.13991 ],\n",
              "       [ 79.72763 ],\n",
              "       [ 84.31535 ],\n",
              "       [ 88.903076],\n",
              "       [ 93.49081 ],\n",
              "       [ 98.07853 ],\n",
              "       [102.66625 ],\n",
              "       [107.253975],\n",
              "       [111.8417  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26fhQWC3YY2a",
        "outputId": "a5bf5e8d-7a09-4f56-a97d-7ba7b8853888"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeaUKP-hYfcL"
      },
      "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=y_test,\n",
        "                     predictions=y_pred):\n",
        "  \"\"\"\n",
        "  Plots training data, test data and compares predictions to ground truth labels.\n",
        "  \"\"\"\n",
        "  \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 test data in green\n",
        "  plt.scatter(test_data, test_labels, c=\"g\", label=\"Testing data\")\n",
        "  # Plot model's prediction in red\n",
        "  plt.scatter(test_data, predictions, c=\"r\", label=\"Predictions\")\n",
        "  # show the legend\n",
        "  plt.legend();\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "gLUXauNEmQY0",
        "outputId": "a36ff464-24e7-4f2e-9360-d7448b6f8f9a"
      },
      "source": [
        "plot_predictions()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pl7vP7eKm2Snu65JuyMTrpi3N7cp9tW/fPj333HM699xEo8arr76quro6jR07VlVVVRo5cqT+8Ic/6OOPP9bMmTP1uc99Tq+99po2b96sjRs3aseOHZo0aZKuvvrqDs/b2Nioa6+9VqtWrdLYsWP1/vvv68gjj9T8+fM1fPhw3XrrrZKkFStWtD3miiuu0H333aczzzxTd955p+666y7dc889bfNcs2aNli1bprvuukvLly/XkiVLdNNNN6miokJ79uxRS0tLv98PAAAOkmY/V+Xsyg77eEm5XxHRG7E4V2NvzvvUGx999JFKS0tVVlamwsJCffWrX5UkTZ8+XWPHjpUkvfDCC3rkkUdUWlqqGTNmaOfOndqyZYtWrVqluXPnauDAgTr++OP12c9+9qDnf+WVV3TGGWe0PdeRRx7Z7Xyampr0l7/8RWeeeaYk6corr9SqVavabr/kkkskSdOmTVN9fb0k6dRTT9U///M/67vf/a4aGhp06KGH9us9AQAgpTT7ufKxIqI34nHKoDS3K/dW6z5enR122GFtl91d9913n84555wO91m2bFm/XrsvhgwZIilxUEDr/mdf+cpXNGPGDD377LM677zz9JOf/CRlCAQAoL+qS6RFN0tbm6TCkVJliZQqTkXhNH9diceKV5rblYNwzjnn6IEHHtDevXslSX/84x/117/+VWeccYaWLl2qlpYWbd++XS+99NJBjz3llFO0atUqvf3225Kk999PdNSOGDFCH3744UH3HzlypI444oi2/bd+/vOft61+deWtt97SuHHjtGDBAl100UVat25dv35eAEAMpbEfdWtNRENTg1zeVhPRXUdXFMVjxasX533KtGuuuUb19fWaOnWq3F0FBQX69a9/rTlz5ug3v/mNJk2apMLCQp166qkHPbagoEBVVVW65JJLtH//fh199NF68cUXdeGFF+qLX/yinnrqKd13330dHvPwww9r/vz5am5u1rhx4/Tv//7v3c7vscce089//nMNHjxYxx57rO64446M/vwAgIhLcz/q7moiorq6lYol9nXPbWVlZd755NCbNm3SxIkT03+S6urEPl1btyZWuiorM7JjPTrq9e8FAJDfiosTYauzoqLE6X6SBtw1QK6DM4fJtP9b+4ObXxaY2Vp3L0t1WzxWvKREyCJoAQCQWWnuR104slANTQcHtKjURKQrHvt4AQCAYKS5H3Xl7EoNGzysw1iUaiLSldfBKx82k8YJvw8AiKHKysR+0+2l2I866jUR6crbTY1Dhw7Vzp07NWrUKJlZtqcTe+6unTt3aujQodmeCgAgTBUV+u07v1Px96p0/J9b9KcjBqr+tiv1tyl274lyTUS68jZ4jRkzRtu2bVNjY2O2p4KkoUOHasyYMdmeBgAgRNXrqzVv/8Nqvqn1zCctGrb/YVWtnxn7kJVK3h7VCAAAApRmG0DxPcUpd5ovGlmk+pvrQ5ho7uGoRgAAkL5enON4a1Pqoxq7Go+7vN65HgAABKAX5zjuqg4ibjUR6SJ4AQCAjnpxjmNqInqH4AUAADrqxTmOqYnoHfbxAgAAHVVWat81V2vQ7j1tQ/uGHqJBXZzjmJqI9LHiBQAAOqguka690FU/UtovqX5k4np1SbZnlv+okwAAAB1QEdE/3dVJsOIFAECcVFdLxcXSgAGJ79XVB92FiojgELwAAIiL1n6uhgbJ/UA/V6fwRUVEcAheAADERZr9XFREBIfgBQBAXKTZz0VFRHCokwAAIC4KCxObF1ONd0JFRDBY8QIAICZ+O/88/XVwx7G/Dk6MIxwELwAAYuLyoct07YXq1M+VGEc42NQIAEBMbG3aqoYS6dFORahGTURoWPECACAK0ujnoiYi+wheAADkuzT7uaiJyD6CFwAA+S7Nfi5qIrKPczUCAJDvBgxIrHR1Zibt3x/+fGKOczUCABBhu449slfjyB6CFwAAee6OzyplP9cdn83OfNA1ghcAAHnux+PfT9nP9ePx72d7auiE4AUAQK5KoyJCStRBPFoijb1FGrg48f3REmoiclGgwcvMTjSz2nZfH5jZzWa22MzebTfOuQoAAGgvzYoIiZqIfBLaUY1mNlDSu5JmSPp7Sbvc/e50HstRjQCA2CkuTn1C66Iiqb7+oOHq9dVatGKRtjZtVeHIQlXOrqQmIku6O6oxzFMGzZb0prs3mFmILwsAQP7xrQ1K9V/LrsYrPl1B0MoDYe7jdZmkR9tdv9HM1pnZQ2Z2ROc7m9k8M6sxs5rGxsbwZgkAQA549/CBvRpHfggleJnZIZK+IOk/kkMPSPqkpFJJ2yV9v/Nj3L3K3cvcvaygoCCMaQIAkDMWlrekrIhYWN6SnQkhI8Ja8fq8pFfdfYckufsOd29x9/2SHpQ0PaR5AACQF353elHKiojfnV6U7amhH8Lax2uu2m1mNLPj3H178uocSXUhzQMAgLxQObtS85rn6dGSA+dgHDZ4mKo4UjGvBb7iZWaHSTpb0q/aDX/PzNab2TpJ5ZJuCXoeAADkjDT6uTihdTRxkmwAAMJUXa1911ytQbv3tA3tG3qIBv30IamCUBUFnCQbAIAcsesbN3UIXZI0aPce7frGTVmaEcJE8AIAIETDtu/s1TiiheAFAECIto7s3TiiheAFAECIfnDBqJT9XD+4YFR2JoRQEbwAAAjRjIU/0o0XD+7Qz3XjxYM1Y+GPsj01hCDMczUCABB7FZ+ukL4pzTqNE1rHEXUSAABkSHW1tGiRtHWrVFgoVVbSEBFH3dVJsOIFAEAGVFdL8+ZJzcmi+YaGxHWJ8IUD2McLAIAMWLToQOhq1dycGAdaEbwAAMiArVt7N454IngBAJABhYW9G0c8EbwAAMiAykpp2LCOY8OGJcaBVgQvAAAyoKJCqqqSiooks8T3qip2rEdHBC8AALpRXS0VF0sDBiS+V1d3fd+KCqm+Xtq/P/Gd0IXOqJMAAKALVEQg01jxAgCgC1REINMIXgAAdIGKCGQawQsAgC5QEYFMI3gBANAFKiKQaQQvAAC6QEUEMo3gBQCIpXRrIqiIQCZRJwEAiB1qIpAtrHgBAGKHmghkC8ELABA71EQgWwheAIDYoSYC2ULwAgDEDjURyBaCFwAgdqiJQLYQvAAAkUJNBHIZdRIAgMigJgK5jhUvAEBkUBOBXEfwAgBEBjURyHUELwBAZFATgVxH8AIARAY1Ech1gQcvM6s3s/VmVmtmNcmxI83sRTPbkvx+RNDzAABEHzURyHVhrXiVu3upu5clr/+jpBXuPl7SiuR1AABSSrciQqImArktW5saL5L0cPLyw5IuztI8AAA5rrUioqFBcj9QEdFd+AJyVRjByyW9YGZrzSzZpqJj3H178vL/SjomhHkAAPIQFRGIkjAKVP/W3d81s6MlvWhmr7e/0d3dzLzzg5IhbZ4kFXI4CgDEFhURiJLAV7zc/d3k9/ckPSlpuqQdZnacJCW/v5ficVXuXubuZQUFBUFPEwCQo6iIQJQEGrzM7DAzG9F6WdLnJNVJelrSlcm7XSnpqSDnAQDIX1REIEqCXvE6RtJvzez/SVoj6Vl3f17SdySdbWZbJJ2VvA4AiJl0jlakIgJRYu4H7V6Vc8rKyrympibb0wAAZFDnE1pLiZUsQhXynZmtbVeh1QHN9QCArOBoRcQRwQsAkBUcrYg4IngBALKCoxURRwQvAEBWcLQi4ojgBQDICo5WRBwRvAAAGcUJrYGuhXHKIABATHSuiGg9obVEqAIkVrwAABlERQTQPYIXACBjqIgAukfwAgBkDBURQPcIXgCAjKEiAugewQsAkDFURADdI3gBANKSbk0EFRFA16iTAAD0iJoIIDNY8QIA9IiaCCAzCF4AgB5REwFkBsELANAjaiKAzCB4AQB6RE0EkBkELwBAj6iJADKD4AUAMUdNBBAe6iQAIMaoiQDCxYoXAMQYNRFAuAheABBj1EQA4SJ4AUCMURMBhIvgBQAxRk0EEC6CFwDEGDURQLgIXgAQQelWREjURABhok4CACKGigggd7HiBQARQ0UEkLsIXgAQMVREALmL4AUAEUNFBJC7CF4AEDFURAC5i+AFABFDRQSQuwheAJBH0q2JoCICyE2BBS8z+4SZvWRmG81sg5ndlBxfbGbvmllt8uu8oOYAAFHSWhPR0CC5H6iJ6K6jC0BuMXcP5onNjpN0nLu/amYjJK2VdLGkL0na5e53p/tcZWVlXlNTE8g8ASBfFBcnwlZnRUWJVS0AucHM1rp7WarbAitQdfftkrYnL39oZpskjQ7q9QAg6qiJAPJfKPt4mVmxpCmS/ic5dKOZrTOzh8zsiC4eM8/MasysprGxMYxpAkBOoyYCyH+BBy8zGy7pCUk3u/sHkh6Q9ElJpUqsiH0/1ePcvcrdy9y9rKCgIOhpAkDOoyYCyH+BBi8zG6xE6Kp2919JkrvvcPcWd98v6UFJ04OcAwBEBTURQP4L8qhGk/Rvkja5+w/ajR/X7m5zJNUFNQcAyBfURADxENjO9ZJmSvo7SevNrDY5doekuWZWKskl1Uu6LsA5AEDOa62JaD2xdWtNhESwAqImsDqJTKJOAkCUURMBREt3dRI01wNAllETAcQHwQsAsoyaCCA+CF4AkGXURADxQfACgID05khFaiKAeAjyqEYAiK3eHqlYUUHQAuKAFS8ACMCiRQdCV6vm5sQ4gPgieAFAADhSEUAqBC8ACABHKgJIheAFAAHgSEUAqRC8ACAAHKkIIBWCFwD0Eie0BtBX1EkAQC9wQmsA/cGKFwD0AjURAPqD4AUAvUBNBID+IHgBQC9QEwGgPwheANAL1EQA6A+CFwD0AjURAPqD4AUASdREAAgadRIAIGoiAISDFS8AEDURAMJB8AIAURMBIBwELwAQNREAwkHwAgBREwEgHAQvABA1EQDCQfACEGnpVkRI1EQACB51EgAii4oIALmGFS8AkUVFBIBcQ/ACEFlURADINQQvAJFFRQSAXEPwAhBZVEQAyDUELwCRRUUEgFxD8AKQl9KtiaAiAkAuoU4CQN6hJgJAvmLFC0DeoSYCQL7KWvAys3PNbLOZvWFm/5iteQDIP9REAMhXWQleZjZQ0r9K+rykSZLmmtmkbMwFQP6hJgJAvsrWitd0SW+4+1vuvkfSLyVdlKW5AMgz1EQAyFfZCl6jJb3T7vq25FgbM5tnZjVmVtPY2Bjq5ADkNmoiAOSrnN253t2r3L3M3csKCgqyPR0AIaEmAkCUZatO4l1Jn2h3fUxyDECMURMBIOqyteL1B0njzWysmR0i6TJJT2dpLgByBDURAKIuKyte7r7PzG6U9F+SBkp6yN03ZGMuAHIHNREAoi5rzfXuvkzSsmy9PoDcU1iY2LyYahwAoiBnd64HED/URACIOoIXgJxBTQSAqCN4AQhcuhUREjURAKIta/t4AYgHKiIA4ABWvAAEiooIADiA4AUgUFREAMABBC8AgeqqCoKKCABxRPACECgqIgDgAIIXgD5L52hFKiIA4ACOagTQJ705WrGigqAFABIrXgD6iKMVAaD3CF4A+oSjFQGg9wheAPqEoxUBoPcIXgD6hKMVAaD3CF4A+oSjFQGg9wheAA6S7kmtOaE1APQOdRIAOuCk1gAQHFa8AHRATQQABIfgBaADaiIAIDgELwAdUBMBAMEheAHogJoIAAgOwQtAB9REAEBwCF5ATKRbESFREwEAQaFOAogBKiIAIDew4gXEABURAJAbCF5ADFARAQC5geAFxAAVEQCQGwheQAxQEQEAuYHgBcQAFREAkBsIXkCeS7cmgooIAMg+6iSAPEZNBADkF1a8gDxGTQQA5BeCF5DHqIkAgPxC8ALyGDURAJBfAgleZvYvZva6ma0zsyfN7PDkeLGZfWRmtcmvJUG8PhAX1EQAQH4JasXrRUknuXuJpD9Kur3dbW+6e2nya35Arw/EAjURAJBfAgle7v6Cu+9LXn1F0pggXgeIqnQrIiRqIgAgn4Sxj9fVkp5rd32smb1mZv9tZqd39SAzm2dmNWZW09jYGPwsgRzRWhHR0CC5H6iI6C58AQDyg7l73x5otlzSsSluWuTuTyXvs0hSmaRL3N3NbIik4e6+08ymSVDj/QEAAA3bSURBVPq1pMnu/kF3r1VWVuY1NTV9mieQb4qLE2Grs6KixIoWACC3mdlady9LdVufC1Td/aweXvQqSRdImu3JdOfuH0v6OHl5rZm9KekESaQqIImKCACIrqCOajxX0m2SvuDuze3GC8xsYPLyOEnjJb0VxByAfEVFBABEV1D7eP1Y0ghJL3aqjThD0jozq5X0uKT57v5+QHMA8hIVEQAQXYGcq9HdP9XF+BOSngjiNYGoaD0qcdGixObFwsJE6OJoRQDIfzTXAyFKtyaCiggAiKZAVrwAHKy1JqL1pNatNRESwQoA4oIVLyAkixYdCF2tmpsT4wCAeCB4ASGhJgIAQPACQkJNBACA4AWEhJoIAADBCwhJRYVUVZU49Y9Z4ntVFTvWA0CcELyADKAmAgCQDuokgH6iJgIAkC5WvIB+oiYCAJAughfQT9REAADSRfAC+omaCABAugheQD9REwEASBfBC+hCb45UpCYCAJAOjmoEUujtkYoVFQQtAEDPWPECUuBIRQBAEAheQAocqQgACALBC0iBIxUBAEEgeAEpcKQiACAIBC8gBY5UBAAEgeCF2OGE1gCAbKFOArHCCa0BANnEihdihZoIAEA2EbwQK9REAACyieCFWKEmAgCQTQQvxAo1EQCAbCJ4IVaoiQAAZBPBC5FBTQQAINdRJ4FIoCYCAJAPWPFCJFATAQDIBwQvRAI1EQCAfEDwQiRQEwEAyAcEL0QCNREAgHxA8EIkUBMBAMgHgQUvM1tsZu+aWW3y67x2t91uZm+Y2WYzOyeoOSD/pVsRIVETAQDIfUHXSfzQ3e9uP2BmkyRdJmmypOMlLTezE9y9JeC5IM9QEQEAiJpsbGq8SNIv3f1jd39b0huSpmdhHshxVEQAAKIm6OB1o5mtM7OHzOyI5NhoSe+0u8+25FgHZjbPzGrMrKaxsTHgaSIXUREBAIiafgUvM1tuZnUpvi6S9ICkT0oqlbRd0vd789zuXuXuZe5eVlBQ0J9pIk9REQEAiJp+7ePl7melcz8ze1DSfyavvivpE+1uHpMcAzqorOy4j5dERQQAIL8FeVTjce2uzpFUl7z8tKTLzGyImY2VNF7SmqDmgfxFRQQAIGqC3Mfre2a23szWSSqXdIskufsGSY9J2ijpeUk3cERj/KRbE0FFBAAgSgKrk3D3v+vmtkpJbDCKKWoiAABxRXM9QkdNBAAgrgheCB01EQCAuCJ4IXTURAAA4orghdBVViZqIdqjJgIAEAcEL4SOmggAQFwRvJBR1EQAANC1wOokED/URAAA0D1WvJAx1EQAANA9ghcyhpoIAAC6R/BCxlATAQBA9wheyBhqIgAA6B7BCxlDTQQAAN0jeKFH6VZESNREAADQHeok0C0qIgAAyBxWvNAtKiIAAMgcghe6RUUEAACZQ/BCt6iIAAAgcwhe6BYVEQAAZA7BK8bSOVqRiggAADKHoxpjqjdHK1ZUELQAAMgEVrxiiqMVAQAIH8ErpjhaEQCA8BG8YoqjFQEACB/BK6Y4WhEAgPARvGKKoxUBAAgfwSuC0j2pNSe0BgAgXNRJRAwntQYAIHex4hUx1EQAAJC7CF4RQ00EAAC5i+AVMdREAACQuwheEUNNBAAAuYvgFTHURAAAkLsIXnki3YoIiZoIAAByFXUSeYCKCAAAoiGQFS8zW2pmtcmvejOrTY4Xm9lH7W5bEsTrRw0VEQAAREMgK17u/uXWy2b2fUlN7W5+091Lg3jdqKIiAgCAaAh0Hy8zM0lfkvRokK8TdVREAAAQDUHvXH+6pB3uvqXd2Fgze83M/tvMTu/qgWY2z8xqzKymsbEx4GnmNioiAACIhj4HLzNbbmZ1Kb4uane3ueq42rVdUqG7T5H0D5J+YWZ/k+r53b3K3cvcvaygoKCv04wEKiIAAIiGPgcvdz/L3U9K8fWUJJnZIEmXSFra7jEfu/vO5OW1kt6UdEL/foT8lm5NBBURAADkvyDrJM6S9Lq7b2sdMLMCSe+7e4uZjZM0XtJbAc4hp1ETAQBAvAS5j9dlOnin+jMkrUvWSzwuab67vx/gHHIaNREAAMRLYCte7n5VirEnJD0R1GvmG2oiAACIF04ZlEXURAAAEC8EryyiJgIAgHgheGURNREAAMQLwSsg1EQAAIDOgqyTiC1qIgAAQCqseAWAmggAAJAKwSsA1EQAAIBUCF4BoCYCAACkQvAKADURAAAgFYJXAKiJAAAAqRC8eiHdigiJmggAAHAw6iTSREUEAADoL1a80kRFBAAA6C+CV5qoiAAAAP1F8EoTFREAAKC/CF5poiICAAD0F8ErTVREAACA/iJ4Kf2aCCoiAABAf8S+ToKaCAAAEJbYr3hREwEAAMIS++BFTQQAAAhL7IMXNREAACAssQ9e1EQAAICwxD54URMBAADCEvujGqVEyCJoAQCAoMV+xQsAACAsBC8AAICQELwAAABCQvACAAAICcELAAAgJAQvAACAkBC8AAAAQkLwAgAACAnBCwAAICT9Cl5mdqmZbTCz/WZW1um2283sDTPbbGbntBs/Nzn2hpn9Y39eHwAAIJ/0d8WrTtIlkla1HzSzSZIukzRZ0rmS7jezgWY2UNK/Svq8pEmS5ibvCwAAEHn9Oleju2+SJDPrfNNFkn7p7h9LetvM3pA0PXnbG+7+VvJxv0zed2N/5gEAAJAPgjpJ9mhJr7S7vi05JknvdBqfkeoJzGyepHnJq7vMbHOmJ5nCUZL+L4TXyWVxfw/i/vNLvAcS70Hcf36J90DiPejPz1/U1Q09Bi8zWy7p2BQ3LXL3p/o4oR65e5WkqqCePxUzq3H3sp7vGV1xfw/i/vNLvAcS70Hcf36J90DiPQjq5+8xeLn7WX143nclfaLd9THJMXUzDgAAEGlB1Uk8LekyMxtiZmMljZe0RtIfJI03s7FmdogSO+A/HdAcAAAAckq/9vEyszmS7pNUIOlZM6t193PcfYOZPabETvP7JN3g7i3Jx9wo6b8kDZT0kLtv6NdPkFmhbtrMUXF/D+L+80u8BxLvQdx/fon3QOI9COTnN3cP4nkBAADQCc31AAAAISF4AQAAhCSWwYtTHXVkZkvNrDb5VW9mtcnxYjP7qN1tS7I916CY2WIze7fdz3peu9tSfiaixMz+xcxeN7N1ZvakmR2eHI/NZ0CK9t95V8zsE2b2kpltTP67eFNyvMu/iShK/tu3Pvmz1iTHjjSzF81sS/L7EdmeZxDM7MR2v+daM/vAzG6O+mfAzB4ys/fMrK7dWMrfuSXcm/y3YZ2ZTe3z68ZxHy8zmyhpv6SfSLrV3Vv/yCZJelSJlv3jJS2XdELyYX+UdLYSpa9/kDTX3SPXuG9m35fU5O7fNrNiSf/p7idld1bBM7PFkna5+92dxlN+JloPFokKM/ucpN+4+z4z+64kufvCmH0GBiomf+ftmdlxko5z91fNbISktZIulvQlpfibiCozq5dU5u7/127se5Led/fvJIP4Ee6+MFtzDEPy7+BdJcrN/14R/gyY2RmSdkl6pPXfuK5+58nQ+XVJ5ynx3vzI3VMWwPcklite7r7J3VM14bed6sjd35bUeqqj6Uqe6sjd90hqPdVRpJiZKfGP7aPZnksO6eozESnu/oK770tefUWJjr24icXfeWfuvt3dX01e/lDSJh0400jcXSTp4eTlh5UIpFE3W9Kb7t6Q7YkEzd1XSXq/03BXv/OLlAho7u6vSDo8+T8tvRbL4NWN0Tr4lEajuxmPmtMl7XD3Le3GxprZa2b232Z2erYmFpIbk0vID7XbpBCX3317V0t6rt31uHwG4vi77iC5wjlF0v8kh1L9TUSVS3rBzNZa4pR1knSMu29PXv5fScdkZ2qhukwd/+c7Tp8Bqevfecb+fYhs8DKz5WZWl+Ir8v8Hm0qa78dcdfyD2y6p0N2nSPoHSb8ws78Jc96Z1MN78ICkT0oqVeLn/n5WJxuAdD4DZrZIie696uRQpD4D6JqZDZf0hKSb3f0DxeBvopO/dfepkj4v6YbkZqg2ntgvJ9L75lii2PwLkv4jORS3z0AHQf3OgzpJdtZxqqOOeno/zGyQpEskTWv3mI8lfZy8vNbM3lRin7eaAKcamHQ/E2b2oKT/TF7t7jORV9L4DFwl6QJJs5P/4ETuM9CDyPyue8vMBisRuqrd/VeS5O472t3e/m8iktz93eT398zsSSU2Pe8ws+PcfXtys9J7WZ1k8D4v6dXW333cPgNJXf3OM/bvQ2RXvPoozqc6OkvS6+6+rXXAzAqSO1rKzMYp8X68laX5BarTtvo5klqPcunqMxEpZnaupNskfcHdm9uNx+YzoHj8nR8kuW/nv0na5O4/aDfe1d9E5JjZYckDC2Rmh0n6nBI/79OSrkze7UpJT2VnhqHpsNUjTp+Bdrr6nT8t6Yrk0Y2nKHEQ2vZUT9CTyK54dceid6qjTOi8XV+SzpD0bTPbq8RRoPPdvfOOiFHxPTMrVWJZuV7SdZLU3WciYn4saYikFxP/HdYr7j5fMfoMJI/ojPrfeSozJf2dpPWWrJKRdIekuan+JiLqGElPJj/7gyT9wt2fN7M/SHrMzL4qqUGJg48iKRk4z1bH33PKfxejwswelTRL0lFmtk3StyR9R6l/58uUOKLxDUnNShzx2bfXjWOdBAAAQDawqREAACAkBC8AAICQELwAAABCQvACAAAICcELAAAgJAQvAACAkBC8AAAAQvL/AX4emgnuNQtBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snsSSxIpmT_0"
      },
      "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 absolute error, \"on average, how wrong is each of my model's predictions\"\n",
        "* MSE - mean square error, \"square the average errors\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruDiwS2y5Aeg",
        "outputId": "e2e93914-68c1-46f5-eccb-554c92f34a5b"
      },
      "source": [
        "# Evaluate the model on test set\n",
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 128ms/step - loss: 3.1969 - mae: 3.1969\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.196942090988159, 3.196942090988159]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88XAMUiP7gy-",
        "outputId": "232fbb72-1bf8-466c-ebc7-6cfcb6fddf5f"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 70.55218 ],\n",
              "       [ 75.13991 ],\n",
              "       [ 79.72763 ],\n",
              "       [ 84.31535 ],\n",
              "       [ 88.903076],\n",
              "       [ 93.49081 ],\n",
              "       [ 98.07853 ],\n",
              "       [102.66625 ],\n",
              "       [107.253975],\n",
              "       [111.8417  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIASID4U7tXD",
        "outputId": "649d5dc4-ea52-4861-fa1e-f3177de01acd"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0He50Dj7uld",
        "outputId": "bf08dcec-d6de-43a3-996d-e98f5b4c9c4b"
      },
      "source": [
        "# Calculate the mean absolute error\n",
        "tf.cast(y_test, float)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([ 70.,  74.,  78.,  82.,  86.,  90.,  94.,  98., 102., 106.],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQaikhlS8Grf",
        "outputId": "d4640eea-7a17-4101-a5b1-69013a04363f"
      },
      "source": [
        "y_pred.T - tf.cast(y_test, float)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 10), dtype=float32, numpy=\n",
              "array([[0.5521774, 1.1399078, 1.7276306, 2.3153534, 2.9030762, 3.4908066,\n",
              "        4.0785294, 4.666252 , 5.253975 , 5.8416977]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzyylN7d8i7i",
        "outputId": "bb7fc4ef-ebc8-4404-8935-7570fcb75f56"
      },
      "source": [
        "abs(y_pred.T - tf.cast(y_test, float))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 10), dtype=float32, numpy=\n",
              "array([[0.5521774, 1.1399078, 1.7276306, 2.3153534, 2.9030762, 3.4908066,\n",
              "        4.0785294, 4.666252 , 5.253975 , 5.8416977]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2cgwLzI9r06",
        "outputId": "5569cae5-31bc-446a-a3be-27c60e236f7a"
      },
      "source": [
        "(abs(y_pred.T - tf.cast(y_test, float)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 10), dtype=float32, numpy=\n",
              "array([[0.5521774, 1.1399078, 1.7276306, 2.3153534, 2.9030762, 3.4908066,\n",
              "        4.0785294, 4.666252 , 5.253975 , 5.8416977]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDt8sX38AREO",
        "outputId": "6f46a1de-bacd-4d4c-94d5-045c9c8de68f"
      },
      "source": [
        "len(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNJcTWzK952Z",
        "outputId": "5ab86d5b-6fc3-4f18-e064-def0fd5e236b"
      },
      "source": [
        "sum = 0\n",
        "for val in (abs(y_pred.T - tf.cast(y_test, float))):\n",
        "  for val1 in val:\n",
        "    sum+=val1\n",
        "print(sum)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(31.969406, shape=(), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMM6ykH4AWu-",
        "outputId": "42d30675-bb4c-4df9-d089-e85a6b61141c"
      },
      "source": [
        "mae = sum/len(y_pred)\n",
        "mae"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=3.1969407>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6P5MihQKpoO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4108c99-ab6f-4e3d-a1df-d0c9286a876b"
      },
      "source": [
        "# Calculate the men absolute error\n",
        "mae = tf.metrics.mean_absolute_error(y_true=y_test,\n",
        "                               y_pred=y_pred)\n",
        "mae"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([17.558258 , 14.1160555, 11.708948 , 10.336929 , 10.       ,\n",
              "       10.698161 , 12.447118 , 15.333002 , 19.253975 , 23.841698 ],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgG9rBj7rPE5",
        "outputId": "53a9f972-806f-4f2f-deae-50c7cbd9a537"
      },
      "source": [
        "tf.constant(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
              "array([[ 70.55218 ],\n",
              "       [ 75.13991 ],\n",
              "       [ 79.72763 ],\n",
              "       [ 84.31535 ],\n",
              "       [ 88.903076],\n",
              "       [ 93.49081 ],\n",
              "       [ 98.07853 ],\n",
              "       [102.66625 ],\n",
              "       [107.253975],\n",
              "       [111.8417  ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sRvNfJYr1M9",
        "outputId": "65812543-ff40-4278-b31c-1db98e437318"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKqjsFBjr92w",
        "outputId": "1787bf72-c598-489a-c772-2c95dbdbdafe"
      },
      "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([ 70.55218 ,  75.13991 ,  79.72763 ,  84.31535 ,  88.903076,\n",
              "        93.49081 ,  98.07853 , 102.66625 , 107.253975, 111.8417  ],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Akrv0AWEsG2f"
      },
      "source": [
        "Now our tensors `y_pred` and `y_test` are of same shape. We can compare them now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYl5aplOsCHf",
        "outputId": "6039a2ad-8577-424b-9988-29555f582849"
      },
      "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=3.1969407>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivkVhJ_zssB_",
        "outputId": "69bdf61a-16da-48eb-c99e-e10467524c45"
      },
      "source": [
        "# Calculate the mean squared error\n",
        "sum = 0\n",
        "for val in ((y_pred.T - tf.cast(y_test, float))**2):\n",
        "  for val1 in val:\n",
        "    sum+=val1\n",
        "print(sum)\n",
        "mse = sum/len(y_pred)\n",
        "print(mse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(130.70143, shape=(), dtype=float32)\n",
            "tf.Tensor(13.070143, shape=(), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzXtXfO5tm4l",
        "outputId": "3ddc78c4-db4e-4992-c076-e09d5589db21"
      },
      "source": [
        "# Calculate the mean squared 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=13.070143>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTkNEJTZuAqw",
        "outputId": "83a72134-6875-45e8-f189-302a8c81ab87"
      },
      "source": [
        "huber = tf.keras.losses.huber(y_true=y_test,\n",
        "                         y_pred=tf.squeeze(y_pred))\n",
        "huber"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=2.7069678>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMbamP5KuvrA"
      },
      "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_true,\n",
        "                                        y_pred=tf.squeeze(y_pred))\n",
        "  \n",
        "def mse(y_true, y_pred):\n",
        "  return tf.metrics.mean_squared_error(y_true=y_true,\n",
        "                                       y_pred=tf.squeeze(y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WQR4NXivf4L"
      },
      "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 -> fit it -> evaluate it ...\n",
        "```\n",
        "\n",
        "\n",
        "1. Get more data - get more examples for your model to train on (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 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 your 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`**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7G1LVyYu0goD",
        "outputId": "b32bcefb-4142-41bd-d486-a260de4f6d19"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "model_1 = tf.keras.Sequential()\n",
        "model_1.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile the model\n",
        "model_1.compile(loss=\"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",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.9024 - mae: 15.9024\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.2837 - mae: 11.2837\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.1074 - mae: 11.1074\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.2991 - mae: 9.2991\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.1677 - mae: 10.1677\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.4303 - mae: 9.4303\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.5704 - mae: 8.5704\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.0442 - mae: 9.0442\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 18.7517 - mae: 18.7517\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.1142 - mae: 10.1142\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.3980 - mae: 8.3980\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6639 - mae: 10.6639\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 9.7977 - mae: 9.7977\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.0103 - mae: 16.0103\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.4068 - mae: 11.4068\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.5393 - mae: 8.5393\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 13.6348 - mae: 13.6348\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.4629 - mae: 11.4629\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.9148 - mae: 17.9148\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 15.0494 - mae: 15.0494\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.0216 - mae: 11.0216\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.1558 - mae: 8.1558\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.5138 - mae: 9.5138\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.6617 - mae: 7.6617\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 13.1859 - mae: 13.1859\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.4211 - mae: 16.4211\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.1660 - mae: 13.1660\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.2559 - mae: 14.2559\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.0670 - mae: 10.0670\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.3409 - mae: 16.3409\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.6444 - mae: 23.6444\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.6215 - mae: 7.6215\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.3221 - mae: 9.3221\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.7313 - mae: 13.7313\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.1276 - mae: 11.1276\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 13.3222 - mae: 13.3222\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 9.4763 - mae: 9.4763\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.1381 - mae: 10.1381\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.1793 - mae: 10.1793\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.9137 - mae: 10.9137\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.9063 - mae: 7.9063\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0914 - mae: 10.0914\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.7006 - mae: 8.7006\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 12.2047 - mae: 12.2047\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.7970 - mae: 13.7970\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.4687 - mae: 8.4687\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.1330 - mae: 9.1330\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.6190 - mae: 10.6190\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.7503 - mae: 7.7503\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5407 - mae: 9.5407\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.1584 - mae: 9.1584\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.3630 - mae: 16.3630\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.1299 - mae: 14.1299\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.1247 - mae: 21.1247\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.3961 - mae: 16.3961\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.9806 - mae: 9.9806\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.9606 - mae: 9.9606\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.2209 - mae: 9.2209\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.4239 - mae: 8.4239\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.4869 - mae: 9.4869\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.4355 - mae: 11.4355\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 11.6887 - mae: 11.6887\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.0838 - mae: 7.0838\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.9675 - mae: 16.9675\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.4599 - mae: 12.4599\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.0184 - mae: 13.0184\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.0600 - mae: 8.0600\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.1888 - mae: 10.1888\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.3633 - mae: 12.3633\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.0516 - mae: 9.0516\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 10.0378 - mae: 10.0378\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0516 - mae: 10.0516\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.6151 - mae: 12.6151\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.3819 - mae: 10.3819\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.7229 - mae: 9.7229\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.2252 - mae: 11.2252\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.3642 - mae: 8.3642\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.1274 - mae: 9.1274\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 19.5039 - mae: 19.5039\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 14.8945 - mae: 14.8945\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.0034 - mae: 9.0034\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.0206 - mae: 13.0206\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.9299 - mae: 7.9299\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.6872 - mae: 7.6872\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.0328 - mae: 10.0328\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.2433 - mae: 9.2433\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.0209 - mae: 12.0209\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.6389 - mae: 10.6389\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.2667 - mae: 7.2667\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.7786 - mae: 12.7786\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.3481 - mae: 7.3481\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.7175 - mae: 7.7175\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 7.1263 - mae: 7.1263\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.6190 - mae: 12.6190\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.0912 - mae: 10.0912\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.3558 - mae: 9.3558\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.6834 - mae: 12.6834\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.6762 - mae: 8.6762\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.4693 - mae: 9.4693\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.7067 - mae: 8.7067\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3aa36c150>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "Cfepr3wYwIlt",
        "outputId": "264f28d7-947f-4fca-db27-a10d9639c471"
      },
      "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": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dwo43SAM34iq",
        "outputId": "03d29b6e-c8e1-421e-a47b-8eac646d9f19"
      },
      "source": [
        "y_preds_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[53.57109 ],\n",
              "       [57.05633 ],\n",
              "       [60.541573],\n",
              "       [64.02681 ],\n",
              "       [67.512054],\n",
              "       [70.99729 ],\n",
              "       [74.48254 ],\n",
              "       [77.96777 ],\n",
              "       [81.45301 ],\n",
              "       [84.938255]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2rLaQNQzqQF",
        "outputId": "c92317af-5c8f-4a12-d82e-f55f23da9ead"
      },
      "source": [
        "# Calculate model_1 evaluation metrics\n",
        "mae_1 = mae(y_test, y_preds_1)\n",
        "mae_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=18.745327>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAoUn_Lb2Gd3",
        "outputId": "f9a06b93-1396-496f-f215-abb62f814fd3"
      },
      "source": [
        "mse_1 = mse(y_test, y_preds_1)\n",
        "mse_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=353.57336>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7rjO66O4QDv"
      },
      "source": [
        "**Build `model_2`**\n",
        "\n",
        "* 2 dense layers, trained for 100 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYAsBUyn4pzT",
        "outputId": "e6c8b3aa-286a-488e-a796-a037ab51a617"
      },
      "source": [
        "#  Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "model_2 = tf.keras.Sequential()\n",
        "model_2.add(tf.keras.layers.Dense(units=10))\n",
        "model_2.add(tf.keras.layers.Dense(1)) \n",
        "\n",
        "# 2. Compile the model\n",
        "model_2.compile(loss=\"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",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 27.4058 - mse: 1084.1482\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 24.6339 - mse: 777.9203\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 29.8935 - mse: 1334.8956\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 27.4055 - mse: 1106.8035\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 14.9463 - mse: 281.1077\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 11.8819 - mse: 168.6621\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.1988 - mse: 151.3509\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.0910 - mse: 160.3745\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 40.4763 - mse: 2586.0090\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 27.8688 - mse: 1094.4382\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.2473 - mse: 147.9359\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 25.2803 - mse: 890.3866\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 16.9897 - mse: 399.9678\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 25.9217 - mse: 1049.5515\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.9948 - mse: 450.2580\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.3510 - mse: 80.6206\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.8636 - mse: 174.7868\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.5304 - mse: 565.8053\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.3469 - mse: 167.7749\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.6985 - mse: 455.7096\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 15.8984 - mse: 347.1929\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.1991 - mse: 285.1767\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.7720 - mse: 91.7852\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.0570 - mse: 153.7430\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.6838 - mse: 233.2949\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.1877 - mse: 1024.6091\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.7432 - mse: 194.8454\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 22.8730 - mse: 835.6074\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.2459 - mse: 96.7786\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 29.2641 - mse: 1535.1349\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 53.0225 - mse: 5030.2988\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.9951 - mse: 211.7025\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.6357 - mse: 337.3666\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.6925 - mse: 214.4824\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.2398 - mse: 92.9126\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 16.6497 - mse: 403.6573\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.0382 - mse: 192.3919\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.1634 - mse: 433.6717\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.1013 - mse: 529.6439\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.4324 - mse: 610.1324\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.9102 - mse: 279.6183\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.2809 - mse: 186.6180\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 10.7333 - mse: 167.0952\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 23.0260 - mse: 830.4244\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.3897 - mse: 128.9549\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.7904 - mse: 181.9212\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.6438 - mse: 153.8708\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.2335 - mse: 402.8494\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.5729 - mse: 99.8337\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.8185 - mse: 260.3670\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 11.5958 - mse: 154.7956\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 30.5538 - mse: 1613.0886\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 14.3541 - mse: 302.5293\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.9713 - mse: 859.3983\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 23.1938 - mse: 805.5452\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 10.8837 - mse: 170.9834\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.7445 - mse: 198.7015\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.5995 - mse: 102.5890\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.5172 - mse: 216.3367\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 12.3200 - mse: 208.6371\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 17.4604 - mse: 428.6393\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.6052 - mse: 136.9777\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.4893 - mse: 152.4555\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 24.8450 - mse: 911.7512\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.6761 - mse: 142.7374\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 21.7809 - mse: 704.4492\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 10.7136 - mse: 136.0194\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 10.6397 - mse: 149.2300\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 22.6914 - mse: 742.1761\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.3316 - mse: 166.1628\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.4355 - mse: 323.0843\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 6.7437 - mse: 67.0210\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 11.6891 - mse: 183.7296\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 24.0400 - mse: 908.8992\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.5896 - mse: 149.3948\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.4371 - mse: 188.3310\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.6489 - mse: 429.2708\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.0614 - mse: 95.4870\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.9675 - mse: 864.0864\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 26.7463 - mse: 1104.4032\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.6714 - mse: 170.7055\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 12.0228 - mse: 211.9191\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.4218 - mse: 395.5589\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.2629 - mse: 73.0935\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.9650 - mse: 312.8361\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 15.2862 - mse: 315.3605\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 19.1086 - mse: 521.2534\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 29.8229 - mse: 1287.1907\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.1742 - mse: 124.1342\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 21.5240 - mse: 663.8611\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 10.5716 - mse: 161.7467\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.3977 - mse: 464.1326\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 7.4138 - mse: 81.9820\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.7380 - mse: 445.7379\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1144 - mse: 164.0820\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 19.4346 - mse: 510.5842\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.1593 - mse: 209.9755\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.5653 - mse: 169.4052\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.8827 - mse: 265.4630\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.2277 - mse: 608.8218\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3aa1bc510>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEQzPdgm68WH",
        "outputId": "ced5beb2-583f-4cd0-f445-05812ddbed4b"
      },
      "source": [
        "y_preds_2 = model_2.predict(X_test)\n",
        "y_preds_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 70.55218 ],\n",
              "       [ 75.13991 ],\n",
              "       [ 79.72763 ],\n",
              "       [ 84.31535 ],\n",
              "       [ 88.903076],\n",
              "       [ 93.49081 ],\n",
              "       [ 98.07853 ],\n",
              "       [102.66625 ],\n",
              "       [107.253975],\n",
              "       [111.8417  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpuTgwc17w4E",
        "outputId": "50598e75-c704-47a1-da89-77d541073b97"
      },
      "source": [
        "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=3.1969407>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=13.070143>)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "MNIsc9zy8AEg",
        "outputId": "1a5a3f86-cfc3-462b-db88-b89144e36cd4"
      },
      "source": [
        "plot_predictions(predictions=y_preds_2)"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32wT9RYv8Il3"
      },
      "source": [
        "**Build `model_3`**\n",
        "\n",
        "* 2 layers, trained for 500 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmnuyMpi9zLz",
        "outputId": "f2aa3394-e6f9-482b-a6ea-bb35e6ad7511"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "model_3 = tf.keras.Sequential()\n",
        "model_3.add(tf.keras.layers.Dense(10))\n",
        "model_3.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile(loss=tf.keras.losses.mae,\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=[\"mse\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_3.fit(X_train, y_train, epochs=500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 27.4058 - mse: 1084.1482\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 24.6339 - mse: 777.9203\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 29.8935 - mse: 1334.8956\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 27.4055 - mse: 1106.8035\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.9463 - mse: 281.1077\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 11.8819 - mse: 168.6621\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1988 - mse: 151.3509\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 11.0910 - mse: 160.3745\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 40.4763 - mse: 2586.0090\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 27.8688 - mse: 1094.4382\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.2473 - mse: 147.9359\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 25.2803 - mse: 890.3866\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 16.9897 - mse: 399.9678\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.9217 - mse: 1049.5515\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 17.9948 - mse: 450.2580\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 7.3510 - mse: 80.6206\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.8636 - mse: 174.7868\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 19.5304 - mse: 565.8053\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.3469 - mse: 167.7749\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.6985 - mse: 455.7096\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 15.8984 - mse: 347.1929\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.1991 - mse: 285.1767\n",
            "Epoch 23/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.7720 - mse: 91.7852\n",
            "Epoch 24/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.0570 - mse: 153.7430\n",
            "Epoch 25/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 12.6838 - mse: 233.2949\n",
            "Epoch 26/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 26.1877 - mse: 1024.6091\n",
            "Epoch 27/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.7432 - mse: 194.8454\n",
            "Epoch 28/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 22.8730 - mse: 835.6074\n",
            "Epoch 29/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.2459 - mse: 96.7786\n",
            "Epoch 30/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 29.2641 - mse: 1535.1349\n",
            "Epoch 31/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 53.0225 - mse: 5030.2988\n",
            "Epoch 32/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 11.9951 - mse: 211.7025\n",
            "Epoch 33/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.6357 - mse: 337.3666\n",
            "Epoch 34/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 12.6925 - mse: 214.4824\n",
            "Epoch 35/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.2398 - mse: 92.9126\n",
            "Epoch 36/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.6497 - mse: 403.6573\n",
            "Epoch 37/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.0382 - mse: 192.3919\n",
            "Epoch 38/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.1634 - mse: 433.6717\n",
            "Epoch 39/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 19.1013 - mse: 529.6439\n",
            "Epoch 40/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 20.4324 - mse: 610.1324\n",
            "Epoch 41/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.9102 - mse: 279.6183\n",
            "Epoch 42/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.2809 - mse: 186.6180\n",
            "Epoch 43/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.7333 - mse: 167.0952\n",
            "Epoch 44/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.0260 - mse: 830.4244\n",
            "Epoch 45/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.3897 - mse: 128.9549\n",
            "Epoch 46/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.7904 - mse: 181.9212\n",
            "Epoch 47/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 9.6438 - mse: 153.8708\n",
            "Epoch 48/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 17.2335 - mse: 402.8494\n",
            "Epoch 49/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.5729 - mse: 99.8337\n",
            "Epoch 50/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.8185 - mse: 260.3670\n",
            "Epoch 51/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 11.5958 - mse: 154.7956\n",
            "Epoch 52/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 30.5538 - mse: 1613.0886\n",
            "Epoch 53/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.3541 - mse: 302.5293\n",
            "Epoch 54/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 23.9713 - mse: 859.3983\n",
            "Epoch 55/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.1938 - mse: 805.5452\n",
            "Epoch 56/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 10.8837 - mse: 170.9834\n",
            "Epoch 57/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.7445 - mse: 198.7015\n",
            "Epoch 58/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.5995 - mse: 102.5890\n",
            "Epoch 59/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 12.5172 - mse: 216.3367\n",
            "Epoch 60/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.3200 - mse: 208.6371\n",
            "Epoch 61/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.4604 - mse: 428.6393\n",
            "Epoch 62/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.6052 - mse: 136.9777\n",
            "Epoch 63/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.4893 - mse: 152.4555\n",
            "Epoch 64/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 24.8450 - mse: 911.7512\n",
            "Epoch 65/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6761 - mse: 142.7374\n",
            "Epoch 66/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.7809 - mse: 704.4492\n",
            "Epoch 67/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.7136 - mse: 136.0194\n",
            "Epoch 68/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.6397 - mse: 149.2300\n",
            "Epoch 69/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 22.6914 - mse: 742.1761\n",
            "Epoch 70/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.3316 - mse: 166.1628\n",
            "Epoch 71/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.4355 - mse: 323.0843\n",
            "Epoch 72/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.7437 - mse: 67.0210\n",
            "Epoch 73/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 11.6891 - mse: 183.7296\n",
            "Epoch 74/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 24.0400 - mse: 908.8992\n",
            "Epoch 75/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5896 - mse: 149.3948\n",
            "Epoch 76/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.4371 - mse: 188.3310\n",
            "Epoch 77/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.6489 - mse: 429.2708\n",
            "Epoch 78/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.0614 - mse: 95.4870\n",
            "Epoch 79/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.9675 - mse: 864.0864\n",
            "Epoch 80/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 26.7463 - mse: 1104.4032\n",
            "Epoch 81/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.6714 - mse: 170.7055\n",
            "Epoch 82/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.0228 - mse: 211.9191\n",
            "Epoch 83/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.4218 - mse: 395.5589\n",
            "Epoch 84/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.2629 - mse: 73.0935\n",
            "Epoch 85/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.9650 - mse: 312.8361\n",
            "Epoch 86/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 15.2862 - mse: 315.3605\n",
            "Epoch 87/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.1086 - mse: 521.2534\n",
            "Epoch 88/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 29.8229 - mse: 1287.1907\n",
            "Epoch 89/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.1742 - mse: 124.1342\n",
            "Epoch 90/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 21.5240 - mse: 663.8611\n",
            "Epoch 91/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.5716 - mse: 161.7467\n",
            "Epoch 92/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 18.3977 - mse: 464.1326\n",
            "Epoch 93/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.4138 - mse: 81.9820\n",
            "Epoch 94/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.7380 - mse: 445.7379\n",
            "Epoch 95/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.1144 - mse: 164.0820\n",
            "Epoch 96/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 19.4346 - mse: 510.5842\n",
            "Epoch 97/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.1593 - mse: 209.9755\n",
            "Epoch 98/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.5653 - mse: 169.4052\n",
            "Epoch 99/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.8827 - mse: 265.4630\n",
            "Epoch 100/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 20.2277 - mse: 608.8218\n",
            "Epoch 101/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.4479 - mse: 177.1446\n",
            "Epoch 102/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.4842 - mse: 426.5329\n",
            "Epoch 103/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.0217 - mse: 65.2649\n",
            "Epoch 104/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.5789 - mse: 757.4911\n",
            "Epoch 105/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 16.8932 - mse: 443.0951\n",
            "Epoch 106/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 9.2954 - mse: 144.6570\n",
            "Epoch 107/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 25.3749 - mse: 934.2098\n",
            "Epoch 108/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.4621 - mse: 269.8598\n",
            "Epoch 109/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.5238 - mse: 108.7908\n",
            "Epoch 110/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 9.6722 - mse: 128.8767\n",
            "Epoch 111/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.5987 - mse: 295.9862\n",
            "Epoch 112/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.5670 - mse: 123.4113\n",
            "Epoch 113/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.8092 - mse: 460.3867\n",
            "Epoch 114/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 17.1782 - mse: 441.2208\n",
            "Epoch 115/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 11.1182 - mse: 155.0961\n",
            "Epoch 116/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 23.3071 - mse: 791.7657\n",
            "Epoch 117/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.6144 - mse: 126.1895\n",
            "Epoch 118/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 10.6899 - mse: 140.2945\n",
            "Epoch 119/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 8.0355 - mse: 78.3508\n",
            "Epoch 120/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 29.6859 - mse: 1315.8645\n",
            "Epoch 121/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.0714 - mse: 70.6648\n",
            "Epoch 122/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 28.3086 - mse: 1233.3849\n",
            "Epoch 123/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 32.9014 - mse: 1591.9503\n",
            "Epoch 124/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.6291 - mse: 571.6960\n",
            "Epoch 125/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.0095 - mse: 72.1091\n",
            "Epoch 126/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 21.8056 - mse: 666.4340\n",
            "Epoch 127/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.9812 - mse: 69.5627\n",
            "Epoch 128/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.0585 - mse: 653.6486\n",
            "Epoch 129/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.0107 - mse: 120.8763\n",
            "Epoch 130/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.0502 - mse: 815.7742\n",
            "Epoch 131/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 9.7537 - mse: 133.0194\n",
            "Epoch 132/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 18.3052 - mse: 500.9214\n",
            "Epoch 133/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.5833 - mse: 80.1913\n",
            "Epoch 134/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 18.5755 - mse: 506.3341\n",
            "Epoch 135/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.5360 - mse: 148.5977\n",
            "Epoch 136/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.2694 - mse: 472.4460\n",
            "Epoch 137/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.1658 - mse: 724.0381\n",
            "Epoch 138/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.1362 - mse: 139.5299\n",
            "Epoch 139/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 8.9181 - mse: 144.6079\n",
            "Epoch 140/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.4732 - mse: 389.2471\n",
            "Epoch 141/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.4208 - mse: 91.7413\n",
            "Epoch 142/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 36.9540 - mse: 2404.8650\n",
            "Epoch 143/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 25.5820 - mse: 927.2343\n",
            "Epoch 144/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.5392 - mse: 144.3266\n",
            "Epoch 145/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 26.6058 - mse: 957.1338\n",
            "Epoch 146/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.7248 - mse: 109.2954\n",
            "Epoch 147/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 15.6172 - mse: 311.2310\n",
            "Epoch 148/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 18.3065 - mse: 464.7855\n",
            "Epoch 149/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.1994 - mse: 106.4583\n",
            "Epoch 150/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.4964 - mse: 66.4456\n",
            "Epoch 151/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.3374 - mse: 485.9982\n",
            "Epoch 152/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.2895 - mse: 130.5616\n",
            "Epoch 153/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 29.6425 - mse: 1268.4631\n",
            "Epoch 154/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.5556 - mse: 201.2335\n",
            "Epoch 155/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 15.4537 - mse: 347.2169\n",
            "Epoch 156/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.0174 - mse: 438.8495\n",
            "Epoch 157/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 32.8218 - mse: 1744.8142\n",
            "Epoch 158/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.7038 - mse: 151.0813\n",
            "Epoch 159/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.9054 - mse: 97.7705\n",
            "Epoch 160/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 22.1321 - mse: 709.4396\n",
            "Epoch 161/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.7113 - mse: 202.9613\n",
            "Epoch 162/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.5734 - mse: 670.2861\n",
            "Epoch 163/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.2485 - mse: 533.4074\n",
            "Epoch 164/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.0156 - mse: 177.6826\n",
            "Epoch 165/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.6187 - mse: 179.8633\n",
            "Epoch 166/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 21.5908 - mse: 681.9667\n",
            "Epoch 167/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 26.2851 - mse: 1048.8446\n",
            "Epoch 168/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.8525 - mse: 118.2892\n",
            "Epoch 169/500\n",
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            "Epoch 341/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.7625 - mse: 156.3229\n",
            "Epoch 342/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 11.9500 - mse: 188.5579\n",
            "Epoch 343/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.3943 - mse: 145.3949\n",
            "Epoch 344/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.6071 - mse: 44.6760\n",
            "Epoch 345/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 37.4876 - mse: 2291.7795\n",
            "Epoch 346/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 16.8830 - mse: 411.1353\n",
            "Epoch 347/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.8748 - mse: 281.5983\n",
            "Epoch 348/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 8.1960 - mse: 162.6964\n",
            "Epoch 349/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 13.5568 - mse: 261.1368\n",
            "Epoch 350/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 15.4354 - mse: 333.2080\n",
            "Epoch 351/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 32.9626 - mse: 1524.7932\n",
            "Epoch 352/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.2040 - mse: 282.7506\n",
            "Epoch 353/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.9196 - mse: 374.1915\n",
            "Epoch 354/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 19.0878 - mse: 534.9771\n",
            "Epoch 355/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 34.1178 - mse: 1782.7025\n",
            "Epoch 356/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.6798 - mse: 90.9118\n",
            "Epoch 357/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 25.2287 - mse: 974.0947\n",
            "Epoch 358/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 22.6759 - mse: 742.0939\n",
            "Epoch 359/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.8765 - mse: 198.2591\n",
            "Epoch 360/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 21.4709 - mse: 687.3278\n",
            "Epoch 361/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.6073 - mse: 616.7942\n",
            "Epoch 362/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 7.0611 - mse: 69.3817\n",
            "Epoch 363/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 25.8117 - mse: 994.8185\n",
            "Epoch 364/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 32.2247 - mse: 1527.6050\n",
            "Epoch 365/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.0204 - mse: 161.6848\n",
            "Epoch 366/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.6722 - mse: 227.5722\n",
            "Epoch 367/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 30.4171 - mse: 1294.6047\n",
            "Epoch 368/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.5020 - mse: 236.3134\n",
            "Epoch 369/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.9909 - mse: 330.8544\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 14.6580 - mse: 308.0736\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 23.3672 - mse: 789.0358\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 13.1025 - mse: 290.2268\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 9.2586 - mse: 140.7053\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 9.6648 - mse: 194.1147\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 13.0041 - mse: 237.9867\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 14.8863 - mse: 324.7536\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 14.7932 - mse: 297.2056\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 16.2751 - mse: 418.5632\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 20.8307 - mse: 596.8133\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 33.5317 - mse: 1684.8943\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 8.2166 - mse: 100.6385\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 13.0960 - mse: 282.0384\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 8.3999 - mse: 126.0928\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 7.1283 - mse: 72.4399\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.9390 - mse: 249.4376\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 19.7654 - mse: 601.5795\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 24.8625 - mse: 896.3691\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 8.7422 - mse: 128.0917\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.9488 - mse: 48.1568\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 24.4400 - mse: 881.1037\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 5.9771 - mse: 73.2800\n",
            "Epoch 392/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.3250 - mse: 379.2592\n",
            "Epoch 393/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 6.0917 - mse: 76.7833\n",
            "Epoch 394/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.0963 - mse: 204.7650\n",
            "Epoch 395/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.9601 - mse: 336.1563\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 7.6462 - mse: 103.3247\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.7654 - mse: 136.5433\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 14.5991 - mse: 320.4537\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.3166 - mse: 280.6889\n",
            "Epoch 400/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 21.9080 - mse: 741.3461\n",
            "Epoch 401/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 14.8653 - mse: 348.6721\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.4970 - mse: 115.5853\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 10.3957 - mse: 191.0402\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 10.2556 - mse: 207.5718\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.3392 - mse: 68.2857\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.4602 - mse: 460.2048\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.4627 - mse: 284.7277\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 20.7294 - mse: 662.1500\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 31.3338 - mse: 1522.7683\n",
            "Epoch 410/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.2542 - mse: 221.9487\n",
            "Epoch 411/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.8621 - mse: 285.8415\n",
            "Epoch 412/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 21.7182 - mse: 721.0389\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 12.6615 - mse: 248.9694\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 6.0687 - mse: 88.3708\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 13.2201 - mse: 239.8954\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 27.4244 - mse: 1037.9579\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 10.6407 - mse: 207.4821\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 12.8230 - mse: 236.7907\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 15.8836 - mse: 374.5482\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 24.7510 - mse: 849.5329\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 17.3753 - mse: 444.1830\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 7.8241 - mse: 157.5328\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 25.3789 - mse: 907.6139\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.1031 - mse: 369.3289\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.1643 - mse: 75.3718\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 20.3318 - mse: 572.0355\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 6.3283 - mse: 84.9745\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.9961 - mse: 288.4218\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 10.7869 - mse: 192.7742\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 11.4007 - mse: 248.2724\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.6152 - mse: 218.3398\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.4582 - mse: 239.2198\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.3851 - mse: 296.6783\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 30.3986 - mse: 1296.5632\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.5052 - mse: 283.6812\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 28.8810 - mse: 1216.2336\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.5916 - mse: 209.5433\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.7378 - mse: 264.4510\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 33.6754 - mse: 1570.2361\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 15.0963 - mse: 294.7132\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 17.4813 - mse: 487.0573\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 22.3049 - mse: 750.9305\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 23.5841 - mse: 784.1378\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.0008 - mse: 203.7582\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 14.9175 - mse: 316.4790\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.9979 - mse: 515.1712\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 5.4482 - mse: 51.4485\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.0527 - mse: 253.4828\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.0052 - mse: 277.3353\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 16.7782 - mse: 424.4229\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.2937 - mse: 303.4478\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 30.6193 - mse: 1329.8268\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.6541 - mse: 159.0376\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 28.1428 - mse: 1104.4142\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 8.0017 - mse: 119.3274\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.3933 - mse: 289.6392\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 15.0242 - mse: 318.3535\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 16.5653 - mse: 439.8007\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 26.8566 - mse: 1033.5485\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.4852 - mse: 251.1162\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.4784 - mse: 272.9071\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 13.3186 - mse: 245.2335\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 29.5524 - mse: 1219.8660\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 3.4664 - mse: 22.0145\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 15.2136 - mse: 352.9369\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 20.8327 - mse: 651.1593\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 30.5108 - mse: 1421.0009\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.0597 - mse: 236.2266\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 12.8372 - mse: 278.2045\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 3.2398 - mse: 14.0655\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.6964 - mse: 372.9566\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 13.3883 - mse: 253.4232\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 0s 3ms/step - loss: 15.2771 - mse: 396.6107\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.7448 - mse: 305.3250\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 16.4113 - mse: 397.1613\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.8785 - mse: 273.7976\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 30.6702 - mse: 1309.4625\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.5880 - mse: 188.9073\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.7384 - mse: 261.5203\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 17.9051 - mse: 484.4154\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 15.8095 - mse: 368.1044\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 21.3054 - mse: 698.1634\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 25.3845 - mse: 955.1250\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.9815 - mse: 811.2782\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 5.7734 - mse: 53.7612\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 20.0010 - mse: 565.2876\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.0419 - mse: 286.2684\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 30.6088 - mse: 1334.5730\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.9409 - mse: 237.4729\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 12.7352 - mse: 252.5674\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 23.6139 - mse: 837.7737\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 20.5365 - mse: 592.2181\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 4.9942 - mse: 48.0282\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.7986 - mse: 247.7645\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.3772 - mse: 239.0901\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 12.6727 - mse: 241.5636\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 17.6192 - mse: 515.3860\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 23.5629 - mse: 816.5500\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.3755 - mse: 136.1007\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 14.6316 - mse: 295.2644\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3aa09a150>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxxVo3cJ-oCu",
        "outputId": "66ea3268-bf56-4fc7-beff-48dd614ee37c"
      },
      "source": [
        "y_preds_3 = model_3.predict(X_test)\n",
        "y_preds_3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15.854742],\n",
              "       [16.617329],\n",
              "       [17.379915],\n",
              "       [18.142504],\n",
              "       [18.905092],\n",
              "       [19.667677],\n",
              "       [20.430264],\n",
              "       [21.192854],\n",
              "       [21.95544 ],\n",
              "       [22.718027]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "vouZiYZn_C2l",
        "outputId": "76a575dc-5851-4615-8766-f65fca7d3675"
      },
      "source": [
        "plot_predictions(predictions=y_preds_3)"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsh-qIPi_KEI",
        "outputId": "60a56c4f-c993-4cd9-d52d-b95ad7c5b418"
      },
      "source": [
        "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=68.713615>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=4808.0273>)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhFKErfrGPgN"
      },
      "source": [
        "**Note:** You want to start with small experiments (small models) and make sure they work and then increase their scale when necessary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93DacATD_26a"
      },
      "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": 143
        },
        "id": "9DVMMfzFGKhQ",
        "outputId": "1b3fd9f3-a296-4509-b3e2-b37129f4a1f7"
      },
      "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>353.573364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>model_2</td>\n",
              "      <td>3.196941</td>\n",
              "      <td>13.070143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>model_3</td>\n",
              "      <td>68.713615</td>\n",
              "      <td>4808.027344</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     model        mae          mse\n",
              "0  model_1  18.745327   353.573364\n",
              "1  model_2   3.196941    13.070143\n",
              "2  model_3  68.713615  4808.027344"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNjPtahYHuEd"
      },
      "source": [
        "Looks like `model_2` performed the best..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGht5852HL1y",
        "outputId": "9fc5399c-7870-48ef-8086-9e242d6bb69b"
      },
      "source": [
        "model_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Irk9Y_CNHiUC"
      },
      "source": [
        "> **Note:** One of the main goals should be to minimize the time between your experiments. The more experiments you do, the more things you'll figure out which don't work and in turn, get closer to figuring out what does work. Remember the machine learning practitioner's motto: \"experiment, experiment, experiment\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ml_PN7fJIa2x"
      },
      "source": [
        "## Tracking your experiments\n",
        "\n",
        "One really good habit in machine learning modelling is to track the results of your 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",
        "\n",
        "* TensorBoard - a component of the TensorFlow library to help track modelling experiments (we'll see this one later).\n",
        "* Weights & Biases - a tool for tracking all kinds of machine learning experiments (plugs straight into TensorBoard)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7N_eh2iwK7iL"
      },
      "source": [
        "## Saving our models\n",
        "\n",
        "Saving our models allows 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 models to:\n",
        "\n",
        "1. The SavedModel format\n",
        "2. The HDF5 format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_-rxXfMK6_-",
        "outputId": "b771316f-8a84-48c6-e682-4fe031b92c49"
      },
      "source": [
        "# Save model using SavedModel format\n",
        "model_2.save(\"best_model_SavedModel_format\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: best_model_SavedModel_format/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1USWtwvnR5x"
      },
      "source": [
        "# Save model using the HDF5 format\n",
        "model_2.save(\"best_model_HDF5_format.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqNK40gAodTz"
      },
      "source": [
        "## Loading in a saved model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z36-9XhuetET",
        "outputId": "09beef3a-48f3-4d52-a096-c30aa8d0a839"
      },
      "source": [
        "# Load in the SavedModel format model\n",
        "loaded_SavedModel_format = tf.keras.models.load_model(\"best_model_SavedModel_format\")\n",
        "loaded_SavedModel_format.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTX3NZetgsZi",
        "outputId": "d5e4b661-776f-4127-9ebf-57efadee3fde"
      },
      "source": [
        "model_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YyBD_AdgyaX",
        "outputId": "f1ff56f6-4b7e-4733-e7c3-41937492ca7a"
      },
      "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": "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": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qpXop7cqhdGB",
        "outputId": "55ca00f8-c858-4aff-a3a4-d40d95e3d322"
      },
      "source": [
        "# Load in a model using the .h5 format\n",
        "loaded_h5_model = tf.keras.models.load_model(\"best_model_HDF5_format.h5\")\n",
        "loaded_h5_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZBKdzmPj3Ih",
        "outputId": "32c50e51-e3b9-4c64-a335-84c5f654defe"
      },
      "source": [
        "model_2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9t7NFg5aj6TR",
        "outputId": "35ded429-5aa2-4389-c8d5-24f53452b040"
      },
      "source": [
        "# Check to see if loaded .h5 model predictions match model_2\n",
        "model_2_preds = model_2.predict(X_test)\n",
        "loaded_h5_model_preds = loaded_h5_model.predict(X_test)\n",
        "model_2_preds == loaded_h5_model_preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nRQAjkLkqMx"
      },
      "source": [
        "## Download a model (or any other file) from Google Colab\n",
        "\n",
        "If you want to download your files from Google Colab:\n",
        "\n",
        "1. You can go to 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": 17
        },
        "id": "eL039-R6nDbz",
        "outputId": "a8992031-a17d-43a4-843c-b463284648cd"
      },
      "source": [
        "# Download a file from Google Colab \n",
        "from google.colab import files\n",
        "files.download(\"/content/best_model_HDF5_format.h5\")"
      ],
      "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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_47d7a490-2f54-4f87-8fcc-5ea49f533163\", \"best_model_HDF5_format.h5\", 17040)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqY_LOOcnVW8"
      },
      "source": [
        "# Save a file from Google colab to Google drive (requires Google Drive)\n",
        "!cp /content/best_model_HDF5_format.h5 /content/drive/MyDrive/1234"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1M39JrktPN2",
        "outputId": "1eaf5fed-a880-450f-c005-ef42b538a773"
      },
      "source": [
        "!ls /content/drive/MyDrive/1234"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"2021-05-09 15.47.07 Sangita Vishwakarma's Personal Meeting Room 7387491608.mp4\"\n",
            "'2021-06-23 17.06.43 TTT schedule - Dawn to Dusk 71976213255.mp4'\n",
            "\"2021-07-16 17.14.46 Sony Mishra's Zoom Meeting 81387526320.mp4\"\n",
            "\"2021-07-16 17.39.50 Sony Mishra's Zoom Meeting 81387526320.mp4\"\n",
            "\"2021-07-19 17.01.36 Sony Mishra's Zoom Meeting 86189689070.mp4\"\n",
            " anvSyncDroid\n",
            " best_model_HDF5_format.h5\n",
            " BTP_Report.docx\n",
            " BTP_Report.gdoc\n",
            " Camera\n",
            " Camera.zip\n",
            " CamScanner\n",
            " Certificate-F-2019-E46D2A68261.pdf\n",
            "'Copy of 6384720-ALANCE.part1.rar'\n",
            "'Copy of 6384720-ALANCE.part2.rar'\n",
            "'Copy of 6384720-ALANCE.part3.rar'\n",
            "'Copy of OG0938-STE2BS.part1.rar'\n",
            "'Copy of OG0977-PIETFN.rar'\n",
            "'Copy of [Ttorial.Com] Beginner Mobile Photography.rar'\n",
            "'Data Structures and Algorithms'\n",
            " IBP_presentation_Group5.pptx\n",
            " Lectures\n",
            "'Manas documents'\n",
            "'Microsoft Excel Certificates'\n",
            "'NCERT Economics'\n",
            " OG0977-PIETFN.rar\n",
            " PhoneRecord\n",
            " Photos\n",
            " Photos.zip\n",
            "'Resume 1.1.docx'\n",
            "'Resume 1.5.pdf'\n",
            "'Resume 1.5 uscreen.pdf'\n",
            "'Resume 1.7.pdf'\n",
            "'Resume 1.8.pdf'\n",
            "'Resume 1.9.pdf'\n",
            "'Screenshot from 2021-07-30 18-07-00.png'\n",
            "'Screenshot from 2021-07-30 18-09-44.png'\n",
            "'Special 26 2013 Hindi 720p DvDRip (Moviez32.net)'\n",
            "'Test Series 2019'\n",
            " TF\n",
            "'[Ttorial.Com] Beginner Mobile Photography.rar'\n",
            "'Untitled spreadsheet.gsheet'\n",
            " upsc\n",
            " UPSC\n",
            " Video\n",
            " Video.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rIAe14watVkv"
      },
      "source": [
        "## A larger example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ7sq0G83FJB"
      },
      "source": [
        "# Import required libraries\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw_ZjQwv3HmN"
      },
      "source": [
        "# Read in the insurance dataset\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "elaj1y6b4Z2A",
        "outputId": "02524f14-29d6-4183-f82e-cbd9c458ea14"
      },
      "source": [
        "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": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lBamnyK-4bEc",
        "outputId": "bc00e3d8-a6ff-4c33-abb6-0609cc99426f"
      },
      "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.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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>16884.92400</td>\n",
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              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     bmi  children  ...  region_northwest  region_southeast  region_southwest\n",
              "0   19  27.900         0  ...                 0                 0                 1\n",
              "1   18  33.770         1  ...                 0                 1                 0\n",
              "2   28  33.000         3  ...                 0                 1                 0\n",
              "3   33  22.705         0  ...                 1                 0                 0\n",
              "4   32  28.880         0  ...                 1                 0                 0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iItWFn8Cxeq"
      },
      "source": [
        "# Create X & y values (features and labels)\n",
        "X = insurance_one_hot.drop(\"charges\", axis=1)\n",
        "y = insurance_one_hot[\"charges\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7JXv3-t4Ddsz",
        "outputId": "1019cc96-1281-4090-fdb5-b7b911dbecb3"
      },
      "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",
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              "\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",
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              "      <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": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPJS5vB0D30j",
        "outputId": "4cddf17d-68b2-4703-96e3-3b9840156a67"
      },
      "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": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_WCVUXOC55k",
        "outputId": "d07aa83a-4339-4ff1-af35-70e114611804"
      },
      "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": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLUmoJn5C_mk",
        "outputId": "17b3438d-8cd5-43ef-8ce7-ed9154f09e48"
      },
      "source": [
        "# Build a neural network (sort of like model_2 above)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model \n",
        "insurance_model = tf.keras.Sequential()\n",
        "insurance_model.add(tf.keras.layers.Dense(10))\n",
        "insurance_model.add(tf.keras.layers.Dense(1))\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",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 8637.1006 - mae: 8637.1006\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7886.7759 - mae: 7886.7759\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7558.1470 - mae: 7558.1470\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7792.0220 - mae: 7792.0220\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7748.3887 - mae: 7748.3887\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7595.3940 - mae: 7595.3940\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7589.9844 - mae: 7589.9844\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7698.5576 - mae: 7698.5576\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7496.7778 - mae: 7496.7778\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7493.1743 - mae: 7493.1743\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7769.7295 - mae: 7769.7295\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7706.9028 - mae: 7706.9028\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7687.7231 - mae: 7687.7231\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7689.9004 - mae: 7689.9004\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7393.5327 - mae: 7393.5327\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7780.6987 - mae: 7780.6987\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7578.5098 - mae: 7578.5098\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7750.8354 - mae: 7750.8354\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7739.2144 - mae: 7739.2144\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7875.0654 - mae: 7875.0654\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7466.6768 - mae: 7466.6768\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7941.2329 - mae: 7941.2329\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7640.2725 - mae: 7640.2725\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7539.2671 - mae: 7539.2671\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7619.9653 - mae: 7619.9653\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7644.1719 - mae: 7644.1719\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7709.0371 - mae: 7709.0371\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7366.8662 - mae: 7366.8662\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7444.3154 - mae: 7444.3154\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7616.4077 - mae: 7616.4077\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7686.3853 - mae: 7686.3853\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7548.0977 - mae: 7548.0977\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7501.5532 - mae: 7501.5532\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7363.4160 - mae: 7363.4160\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7295.4478 - mae: 7295.4478\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7569.8813 - mae: 7569.8813\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7548.1997 - mae: 7548.1997\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7424.3975 - mae: 7424.3975\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7529.7734 - mae: 7529.7734\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7467.3232 - mae: 7467.3232\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7635.9292 - mae: 7635.9292\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7536.8398 - mae: 7536.8398\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7616.5859 - mae: 7616.5859\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7439.4941 - mae: 7439.4941\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7538.0151 - mae: 7538.0151\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7415.1470 - mae: 7415.1470\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7420.6938 - mae: 7420.6938\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7509.9839 - mae: 7509.9839\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7541.1133 - mae: 7541.1133\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7467.8643 - mae: 7467.8643\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7389.3560 - mae: 7389.3560\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7499.7749 - mae: 7499.7749\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7523.9282 - mae: 7523.9282\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7243.3115 - mae: 7243.3115\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7429.5864 - mae: 7429.5864\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7313.3999 - mae: 7313.3999\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7526.3877 - mae: 7526.3877\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7542.2666 - mae: 7542.2666\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7576.9277 - mae: 7576.9277\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7546.4048 - mae: 7546.4048\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7351.2261 - mae: 7351.2261\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7302.1436 - mae: 7302.1436\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7393.0879 - mae: 7393.0879\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7442.2881 - mae: 7442.2881\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7492.6782 - mae: 7492.6782\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7561.9165 - mae: 7561.9165\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7340.5137 - mae: 7340.5137\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7496.0845 - mae: 7496.0845\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7617.0303 - mae: 7617.0303\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7641.1948 - mae: 7641.1948\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7084.2744 - mae: 7084.2744\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7240.4902 - mae: 7240.4902\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7283.4888 - mae: 7283.4888\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7335.5083 - mae: 7335.5083\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7275.6392 - mae: 7275.6392\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7313.1860 - mae: 7313.1860\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7485.7588 - mae: 7485.7588\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7352.2803 - mae: 7352.2803\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7520.5703 - mae: 7520.5703\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7279.3779 - mae: 7279.3779\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7273.8477 - mae: 7273.8477\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7176.5215 - mae: 7176.5215\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7425.6289 - mae: 7425.6289\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7403.1294 - mae: 7403.1294\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7356.0088 - mae: 7356.0088\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7484.7271 - mae: 7484.7271\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7217.6074 - mae: 7217.6074\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7261.0000 - mae: 7261.0000\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7134.1562 - mae: 7134.1562\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7083.4360 - mae: 7083.4360\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7254.1782 - mae: 7254.1782\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7268.7456 - mae: 7268.7456\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7470.5220 - mae: 7470.5220\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7210.9536 - mae: 7210.9536\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7395.6816 - mae: 7395.6816\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7328.0884 - mae: 7328.0884\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7230.4380 - mae: 7230.4380\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7261.3936 - mae: 7261.3936\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7342.5684 - mae: 7342.5684\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7106.1714 - mae: 7106.1714\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3a9c5c1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H8ZQm8V1lNl",
        "outputId": "0c4e8c8e-9df4-4e48-83ec-868f0fb8ed07"
      },
      "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",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7023.3291 - mae: 7023.3291\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7023.3291015625, 7023.3291015625]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQatMBNL2J__",
        "outputId": "03d7c93f-25d6-407c-f4c6-84a9e1353fe6"
      },
      "source": [
        "y_train.median(), y_train.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9575.4421, 13346.089736364489)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5vA3xgr2ecd"
      },
      "source": [
        "Right now it looks like our model isn't performing too well.. let's try to improve it!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92BZ_c2KTMXf"
      },
      "source": [
        "cb_reducelr = tf.keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor='loss',\n",
        "    mode='min',\n",
        "    factor=0.1,\n",
        "    patience=10,\n",
        "    verbose=1,\n",
        "    min_lr=0.000001\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK8br8NgTYnc"
      },
      "source": [
        "cb_earlystop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='loss',\n",
        "    mode='min',\n",
        "    min_delta=0.001,\n",
        "    patience=10,\n",
        "    verbose=1\n",
        ")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3NuJEd13FE_",
        "outputId": "3fcb32f6-4132-4438-c69e-3af665420997"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "i_model_1 = tf.keras.Sequential()\n",
        "i_model_1.add(tf.keras.layers.Dense(50, activation=\"tanh\"))\n",
        "i_model_1.add(tf.keras.layers.Dense(10, activation=\"relu\"))\n",
        "i_model_1.add(tf.keras.layers.Dense(10, activation=\"relu\"))\n",
        "i_model_1.add(tf.keras.layers.Dense(1, activation=\"relu\"))\n",
        "\n",
        "# 2. Compile the model\n",
        "i_model_1.compile(loss=tf.keras.losses.mae,\n",
        "                  optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "                  metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "I_model_fit = i_model_1.fit(X_train, \n",
        "                            y_train, \n",
        "                            epochs=1000, \n",
        "                            callbacks=[cb_reducelr, cb_earlystop])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "34/34 [==============================] - 1s 2ms/step - loss: 13344.4561 - mae: 13344.4561\n",
            "Epoch 2/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13339.9482 - mae: 13339.9482\n",
            "Epoch 3/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13330.8193 - mae: 13330.8193\n",
            "Epoch 4/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13317.1133 - mae: 13317.1133\n",
            "Epoch 5/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13296.6328 - mae: 13296.6328\n",
            "Epoch 6/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13266.9346 - mae: 13266.9346\n",
            "Epoch 7/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 13224.7520 - mae: 13224.7520\n",
            "Epoch 8/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13166.6328 - mae: 13166.6328\n",
            "Epoch 9/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 13090.1416 - mae: 13090.1416\n",
            "Epoch 10/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12993.0439 - mae: 12993.0439\n",
            "Epoch 11/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12873.1182 - mae: 12873.1182\n",
            "Epoch 12/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12728.1748 - mae: 12728.1748\n",
            "Epoch 13/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12556.1152 - mae: 12556.1152\n",
            "Epoch 14/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12354.8926 - mae: 12354.8926\n",
            "Epoch 15/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12124.8145 - mae: 12124.8145\n",
            "Epoch 16/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11871.3154 - mae: 11871.3154\n",
            "Epoch 17/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 11599.0947 - mae: 11599.0947\n",
            "Epoch 18/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 11328.1758 - mae: 11328.1758\n",
            "Epoch 19/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 11060.6514 - mae: 11060.6514\n",
            "Epoch 20/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 10800.6787 - mae: 10800.6787\n",
            "Epoch 21/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10537.0723 - mae: 10537.0723\n",
            "Epoch 22/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 10268.2539 - mae: 10268.2539\n",
            "Epoch 23/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9986.7822 - mae: 9986.7822\n",
            "Epoch 24/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9681.4326 - mae: 9681.4326\n",
            "Epoch 25/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9365.5400 - mae: 9365.5400\n",
            "Epoch 26/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 9047.6328 - mae: 9047.6328\n",
            "Epoch 27/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8743.6494 - mae: 8743.6494\n",
            "Epoch 28/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8446.2832 - mae: 8446.2832\n",
            "Epoch 29/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8139.7412 - mae: 8139.7412\n",
            "Epoch 30/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7863.4458 - mae: 7863.4458\n",
            "Epoch 31/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7623.7827 - mae: 7623.7827\n",
            "Epoch 32/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7363.8447 - mae: 7363.8447\n",
            "Epoch 33/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7113.3726 - mae: 7113.3726\n",
            "Epoch 34/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6885.7393 - mae: 6885.7393\n",
            "Epoch 35/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6693.4395 - mae: 6693.4395\n",
            "Epoch 36/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6451.0278 - mae: 6451.0278\n",
            "Epoch 37/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6225.7070 - mae: 6225.7070\n",
            "Epoch 38/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6014.1709 - mae: 6014.1709\n",
            "Epoch 39/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5835.1699 - mae: 5835.1699\n",
            "Epoch 40/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5620.6357 - mae: 5620.6357\n",
            "Epoch 41/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5430.2812 - mae: 5430.2812\n",
            "Epoch 42/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5259.0161 - mae: 5259.0161\n",
            "Epoch 43/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5087.3120 - mae: 5087.3120\n",
            "Epoch 44/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4934.5020 - mae: 4934.5020\n",
            "Epoch 45/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4788.5645 - mae: 4788.5645\n",
            "Epoch 46/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4652.6470 - mae: 4652.6470\n",
            "Epoch 47/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4521.4458 - mae: 4521.4458\n",
            "Epoch 48/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4426.1611 - mae: 4426.1611\n",
            "Epoch 49/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4296.6577 - mae: 4296.6577\n",
            "Epoch 50/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4204.4990 - mae: 4204.4990\n",
            "Epoch 51/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4097.1206 - mae: 4097.1206\n",
            "Epoch 52/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4043.0813 - mae: 4043.0813\n",
            "Epoch 53/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3979.9580 - mae: 3979.9580\n",
            "Epoch 54/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3915.8950 - mae: 3915.8950\n",
            "Epoch 55/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3855.3503 - mae: 3855.3503\n",
            "Epoch 56/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3802.4104 - mae: 3802.4104\n",
            "Epoch 57/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3753.3779 - mae: 3753.3779\n",
            "Epoch 58/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3777.3467 - mae: 3777.3467\n",
            "Epoch 59/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3724.0566 - mae: 3724.0566\n",
            "Epoch 60/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3692.5837 - mae: 3692.5837\n",
            "Epoch 61/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3653.4575 - mae: 3653.4575\n",
            "Epoch 62/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3641.3538 - mae: 3641.3538\n",
            "Epoch 63/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3654.6643 - mae: 3654.6643\n",
            "Epoch 64/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3599.5725 - mae: 3599.5725\n",
            "Epoch 65/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3585.9897 - mae: 3585.9897\n",
            "Epoch 66/1000\n",
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            "Epoch 286/1000\n",
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            "Epoch 288/1000\n",
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            "\n",
            "Epoch 00288: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 289/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1802.9260 - mae: 1802.9260\n",
            "Epoch 290/1000\n",
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            "Epoch 291/1000\n",
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            "Epoch 292/1000\n",
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            "Epoch 293/1000\n",
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            "Epoch 294/1000\n",
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            "Epoch 295/1000\n",
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            "Epoch 296/1000\n",
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            "Epoch 297/1000\n",
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            "Epoch 298/1000\n",
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            "Epoch 299/1000\n",
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            "Epoch 300/1000\n",
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            "Epoch 301/1000\n",
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            "Epoch 302/1000\n",
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            "Epoch 303/1000\n",
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            "Epoch 304/1000\n",
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            "Epoch 305/1000\n",
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            "Epoch 306/1000\n",
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            "Epoch 308/1000\n",
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            "Epoch 310/1000\n",
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            "Epoch 313/1000\n",
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            "Epoch 368/1000\n",
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            "Epoch 369/1000\n",
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            "Epoch 370/1000\n",
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            "Epoch 371/1000\n",
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            "Epoch 372/1000\n",
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            "Epoch 373/1000\n",
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            "Epoch 375/1000\n",
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            "Epoch 376/1000\n",
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            "Epoch 377/1000\n",
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            "Epoch 378/1000\n",
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            "Epoch 379/1000\n",
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            "Epoch 380/1000\n",
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            "\n",
            "Epoch 00380: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Epoch 381/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1770.0614 - mae: 1770.0614\n",
            "Epoch 382/1000\n",
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            "Epoch 383/1000\n",
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            "Epoch 384/1000\n",
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            "Epoch 385/1000\n",
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            "Epoch 386/1000\n",
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            "Epoch 387/1000\n",
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            "Epoch 388/1000\n",
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            "Epoch 389/1000\n",
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            "Epoch 390/1000\n",
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            "Epoch 391/1000\n",
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            "Epoch 392/1000\n",
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            "Epoch 393/1000\n",
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            "Epoch 394/1000\n",
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            "Epoch 395/1000\n",
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            "Epoch 396/1000\n",
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            "Epoch 397/1000\n",
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            "Epoch 398/1000\n",
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            "Epoch 426/1000\n",
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            "Epoch 429/1000\n",
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            "Epoch 430/1000\n",
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            "Epoch 434/1000\n",
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            "Epoch 435/1000\n",
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            "Epoch 436/1000\n",
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            "Epoch 439/1000\n",
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            "Epoch 440/1000\n",
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            "Epoch 442/1000\n",
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            "Epoch 443/1000\n",
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            "Epoch 444/1000\n",
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            "Epoch 445/1000\n",
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            "Epoch 446/1000\n",
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            "Epoch 447/1000\n",
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            "\n",
            "Epoch 00447: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "Epoch 448/1000\n",
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            "Epoch 449/1000\n",
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            "Epoch 450/1000\n",
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            "Epoch 451/1000\n",
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            "Epoch 452/1000\n",
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            "Epoch 453/1000\n",
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            "Epoch 454/1000\n",
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            "Epoch 455/1000\n",
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            "Epoch 456/1000\n",
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            "Epoch 457/1000\n",
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            "Epoch 458/1000\n",
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            "Epoch 459/1000\n",
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            "Epoch 460/1000\n",
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            "Epoch 461/1000\n",
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            "Epoch 462/1000\n",
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            "Epoch 464/1000\n",
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            "Epoch 465/1000\n",
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            "Epoch 466/1000\n",
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            "Epoch 467/1000\n",
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            "Epoch 468/1000\n",
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            "Epoch 469/1000\n",
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            "Epoch 470/1000\n",
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            "Epoch 471/1000\n",
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            "Epoch 472/1000\n",
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            "Epoch 473/1000\n",
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            "Epoch 474/1000\n",
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            "Epoch 475/1000\n",
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            "Epoch 476/1000\n",
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            "Epoch 477/1000\n",
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            "Epoch 500/1000\n",
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            "Epoch 501/1000\n",
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            "Epoch 505/1000\n",
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            "Epoch 506/1000\n",
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            "Epoch 507/1000\n",
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            "Epoch 508/1000\n",
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            "Epoch 509/1000\n",
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            "Epoch 510/1000\n",
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            "Epoch 512/1000\n",
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            "Epoch 520/1000\n",
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            "Epoch 521/1000\n",
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            "Epoch 528/1000\n",
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            "Epoch 531/1000\n",
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            "Epoch 541/1000\n",
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            "Epoch 550/1000\n",
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            "Epoch 554/1000\n",
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            "Epoch 555/1000\n",
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            "Epoch 556/1000\n",
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            "Epoch 557/1000\n",
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            "Epoch 558/1000\n",
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            "Epoch 559/1000\n",
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            "Epoch 560/1000\n",
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            "Epoch 561/1000\n",
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            "Epoch 562/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0953 - mae: 1767.0953\n",
            "Epoch 563/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0903 - mae: 1767.0903\n",
            "Epoch 564/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0847 - mae: 1767.0847\n",
            "Epoch 565/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0703 - mae: 1767.0703\n",
            "Epoch 566/1000\n",
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            "Epoch 567/1000\n",
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            "Epoch 568/1000\n",
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            "Epoch 569/1000\n",
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            "Epoch 570/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0878 - mae: 1767.0878\n",
            "Epoch 571/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0511 - mae: 1767.0511\n",
            "Epoch 572/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0741 - mae: 1767.0741\n",
            "Epoch 573/1000\n",
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            "Epoch 574/1000\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 1767.1050 - mae: 1767.1050\n",
            "Epoch 575/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0881 - mae: 1767.0881\n",
            "Epoch 576/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0677 - mae: 1767.0677\n",
            "Epoch 577/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0591 - mae: 1767.0591\n",
            "Epoch 578/1000\n",
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            "Epoch 579/1000\n",
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            "Epoch 580/1000\n",
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            "Epoch 581/1000\n",
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            "Epoch 582/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0245 - mae: 1767.0245\n",
            "Epoch 583/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0393 - mae: 1767.0393\n",
            "Epoch 584/1000\n",
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            "Epoch 585/1000\n",
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            "Epoch 586/1000\n",
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            "Epoch 587/1000\n",
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            "Epoch 588/1000\n",
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            "Epoch 589/1000\n",
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            "Epoch 590/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0427 - mae: 1767.0427\n",
            "Epoch 591/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0205 - mae: 1767.0205\n",
            "Epoch 592/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0413 - mae: 1767.0413\n",
            "Epoch 593/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0217 - mae: 1767.0217\n",
            "Epoch 594/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0363 - mae: 1767.0363\n",
            "Epoch 595/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9865 - mae: 1766.9865\n",
            "Epoch 596/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9882 - mae: 1766.9882\n",
            "Epoch 597/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0322 - mae: 1767.0322\n",
            "Epoch 598/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0013 - mae: 1767.0013\n",
            "Epoch 599/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0168 - mae: 1767.0168\n",
            "Epoch 600/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0239 - mae: 1767.0239\n",
            "Epoch 601/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9836 - mae: 1766.9836\n",
            "Epoch 602/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9865 - mae: 1766.9865\n",
            "Epoch 603/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0171 - mae: 1767.0171\n",
            "Epoch 604/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9801 - mae: 1766.9801\n",
            "Epoch 605/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9832 - mae: 1766.9832\n",
            "Epoch 606/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9768 - mae: 1766.9768\n",
            "Epoch 607/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9922 - mae: 1766.9922\n",
            "Epoch 608/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9846 - mae: 1766.9846\n",
            "Epoch 609/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9944 - mae: 1766.9944\n",
            "Epoch 610/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0377 - mae: 1767.0377\n",
            "Epoch 611/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9575 - mae: 1766.9575\n",
            "Epoch 612/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9775 - mae: 1766.9775\n",
            "Epoch 613/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0029 - mae: 1767.0029\n",
            "Epoch 614/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9752 - mae: 1766.9752\n",
            "Epoch 615/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9878 - mae: 1766.9878\n",
            "Epoch 616/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9930 - mae: 1766.9930\n",
            "Epoch 617/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9777 - mae: 1766.9777\n",
            "Epoch 618/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9767 - mae: 1766.9767\n",
            "Epoch 619/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9912 - mae: 1766.9912\n",
            "Epoch 620/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0007 - mae: 1767.0007\n",
            "Epoch 621/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9456 - mae: 1766.9456\n",
            "Epoch 622/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9642 - mae: 1766.9642\n",
            "Epoch 623/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0276 - mae: 1767.0276\n",
            "Epoch 624/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9569 - mae: 1766.9569\n",
            "Epoch 625/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9390 - mae: 1766.9390\n",
            "Epoch 626/1000\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 1766.9785 - mae: 1766.9785\n",
            "Epoch 627/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9590 - mae: 1766.9590\n",
            "Epoch 628/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9828 - mae: 1766.9828\n",
            "Epoch 629/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9387 - mae: 1766.9387\n",
            "Epoch 630/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9313 - mae: 1766.9313\n",
            "Epoch 631/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9377 - mae: 1766.9377\n",
            "Epoch 632/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0177 - mae: 1767.0177\n",
            "Epoch 633/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9470 - mae: 1766.9470\n",
            "Epoch 634/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9640 - mae: 1766.9640\n",
            "Epoch 635/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9481 - mae: 1766.9481\n",
            "Epoch 636/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9445 - mae: 1766.9445\n",
            "Epoch 637/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9415 - mae: 1766.9415\n",
            "Epoch 638/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9208 - mae: 1766.9208\n",
            "Epoch 639/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9567 - mae: 1766.9567\n",
            "Epoch 640/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9143 - mae: 1766.9143\n",
            "Epoch 641/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9087 - mae: 1766.9087\n",
            "Epoch 642/1000\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 1766.9335 - mae: 1766.9335\n",
            "Epoch 643/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9293 - mae: 1766.9293\n",
            "Epoch 644/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1767.0129 - mae: 1767.0129\n",
            "Epoch 645/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9755 - mae: 1766.9755\n",
            "Epoch 646/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9276 - mae: 1766.9276\n",
            "Epoch 647/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9045 - mae: 1766.9045\n",
            "Epoch 648/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9417 - mae: 1766.9417\n",
            "Epoch 649/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9043 - mae: 1766.9043\n",
            "Epoch 650/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9005 - mae: 1766.9005\n",
            "Epoch 651/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8892 - mae: 1766.8892\n",
            "Epoch 652/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8986 - mae: 1766.8986\n",
            "Epoch 653/1000\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 1766.8981 - mae: 1766.8981\n",
            "Epoch 654/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8953 - mae: 1766.8953\n",
            "Epoch 655/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9094 - mae: 1766.9094\n",
            "Epoch 656/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9037 - mae: 1766.9037\n",
            "Epoch 657/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9171 - mae: 1766.9171\n",
            "Epoch 658/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9117 - mae: 1766.9117\n",
            "Epoch 659/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8885 - mae: 1766.8885\n",
            "Epoch 660/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8691 - mae: 1766.8691\n",
            "Epoch 661/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8867 - mae: 1766.8867\n",
            "Epoch 662/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9656 - mae: 1766.9656\n",
            "Epoch 663/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9080 - mae: 1766.9080\n",
            "Epoch 664/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9374 - mae: 1766.9374\n",
            "Epoch 665/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8831 - mae: 1766.8831\n",
            "Epoch 666/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9578 - mae: 1766.9578\n",
            "Epoch 667/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9259 - mae: 1766.9259\n",
            "Epoch 668/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9065 - mae: 1766.9065\n",
            "Epoch 669/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8445 - mae: 1766.8445\n",
            "Epoch 670/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8644 - mae: 1766.8644\n",
            "Epoch 671/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8878 - mae: 1766.8878\n",
            "Epoch 672/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8613 - mae: 1766.8613\n",
            "Epoch 673/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.9081 - mae: 1766.9081\n",
            "Epoch 674/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8215 - mae: 1766.8215\n",
            "Epoch 675/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1767.0142 - mae: 1767.0142\n",
            "Epoch 676/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8828 - mae: 1766.8828\n",
            "Epoch 677/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8175 - mae: 1766.8175\n",
            "Epoch 678/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8662 - mae: 1766.8662\n",
            "Epoch 679/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.9052 - mae: 1766.9052\n",
            "Epoch 680/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8575 - mae: 1766.8575\n",
            "Epoch 681/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8522 - mae: 1766.8522\n",
            "Epoch 682/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8856 - mae: 1766.8856\n",
            "Epoch 683/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1766.8240 - mae: 1766.8240\n",
            "Epoch 684/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8342 - mae: 1766.8342\n",
            "Epoch 685/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8434 - mae: 1766.8434\n",
            "Epoch 686/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8359 - mae: 1766.8359\n",
            "Epoch 687/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8340 - mae: 1766.8340\n",
            "\n",
            "Epoch 00687: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "Epoch 688/1000\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 1766.8740 - mae: 1766.8740\n",
            "Epoch 00688: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "rmuxZu2tD6tW",
        "outputId": "4e953d84-6f48-41ad-e1a0-ccbfd8f7594a"
      },
      "source": [
        "pd.DataFrame(I_model_fit.history).plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa392d61250>"
            ]
          },
          "metadata": {},
          "execution_count": 221
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ok-OXah14C-V",
        "outputId": "086f589f-b998-4bf9-c847-27ac65256d63"
      },
      "source": [
        "i_model_1.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 1560.7059 - mae: 1560.7059\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1560.7059326171875, 1560.7059326171875]"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3U1KDGG4K5L"
      },
      "source": [
        "Right now it look like our model isn't performing too well... let's try and improve it!\n",
        "To (try) improve our model, we'll run 2 experiments:\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": "URPxxn-HVH_t",
        "outputId": "b23be78b-7ebd-4d85-ba15-1c96ab6e2b94"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "insurance_model_2 = tf.keras.Sequential()\n",
        "insurance_model_2.add(tf.keras.layers.Dense(100))\n",
        "insurance_model_2.add(tf.keras.layers.Dense(10))\n",
        "insurance_model_2.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model_2.compile(loss=\"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",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13273.1602 - mae: 13273.1602\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13104.4297 - mae: 13104.4297\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12749.5420 - mae: 12749.5420\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12055.7510 - mae: 12055.7510\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 10905.8154 - mae: 10905.8154\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9457.7217 - mae: 9457.7217\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8147.6543 - mae: 8147.6543\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7528.8408 - mae: 7528.8408\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7429.1528 - mae: 7429.1528\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7409.0811 - mae: 7409.0811\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7390.8042 - mae: 7390.8042\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7368.9180 - mae: 7368.9180\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7348.5195 - mae: 7348.5195\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7326.4893 - mae: 7326.4893\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7307.5815 - mae: 7307.5815\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7285.7734 - mae: 7285.7734\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7265.7104 - mae: 7265.7104\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7242.5488 - mae: 7242.5488\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7220.5068 - mae: 7220.5068\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7197.1978 - mae: 7197.1978\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7179.0195 - mae: 7179.0195\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7151.2104 - mae: 7151.2104\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7126.4639 - mae: 7126.4639\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7101.9199 - mae: 7101.9199\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7084.3379 - mae: 7084.3379\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7052.3291 - mae: 7052.3291\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7024.3501 - mae: 7024.3501\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6996.6963 - mae: 6996.6963\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6969.0112 - mae: 6969.0112\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6942.1899 - mae: 6942.1899\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6911.7280 - mae: 6911.7280\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6884.0205 - mae: 6884.0205\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6853.4648 - mae: 6853.4648\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6823.0674 - mae: 6823.0674\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6789.6855 - mae: 6789.6855\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6755.7646 - mae: 6755.7646\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6720.2026 - mae: 6720.2026\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6689.7158 - mae: 6689.7158\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6652.4614 - mae: 6652.4614\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6618.1006 - mae: 6618.1006\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6585.8643 - mae: 6585.8643\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6559.4956 - mae: 6559.4956\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6530.0444 - mae: 6530.0444\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6506.8071 - mae: 6506.8071\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6493.5718 - mae: 6493.5718\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6475.9258 - mae: 6475.9258\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6458.8979 - mae: 6458.8979\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6445.1494 - mae: 6445.1494\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6430.9639 - mae: 6430.9639\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6417.7510 - mae: 6417.7510\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6403.2759 - mae: 6403.2759\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6392.4141 - mae: 6392.4141\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6378.7451 - mae: 6378.7451\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6364.9131 - mae: 6364.9131\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6351.5269 - mae: 6351.5269\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6337.6602 - mae: 6337.6602\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6324.8369 - mae: 6324.8369\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6310.1948 - mae: 6310.1948\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6295.6035 - mae: 6295.6035\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6284.8696 - mae: 6284.8696\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6265.6411 - mae: 6265.6411\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6253.0103 - mae: 6253.0103\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6234.9292 - mae: 6234.9292\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6218.0430 - mae: 6218.0430\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6201.1899 - mae: 6201.1899\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6183.9590 - mae: 6183.9590\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6171.2993 - mae: 6171.2993\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6148.8398 - mae: 6148.8398\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6132.5981 - mae: 6132.5981\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6112.3848 - mae: 6112.3848\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6092.7202 - mae: 6092.7202\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6073.7422 - mae: 6073.7422\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6059.4873 - mae: 6059.4873\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6031.3848 - mae: 6031.3848\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6010.3350 - mae: 6010.3350\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5995.2178 - mae: 5995.2178\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5963.0718 - mae: 5963.0718\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5940.0605 - mae: 5940.0605\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5915.1064 - mae: 5915.1064\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5887.9990 - mae: 5887.9990\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5861.6992 - mae: 5861.6992\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5834.3066 - mae: 5834.3066\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5805.8237 - mae: 5805.8237\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5772.3232 - mae: 5772.3232\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5745.1514 - mae: 5745.1514\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5711.3477 - mae: 5711.3477\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5674.5215 - mae: 5674.5215\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5639.4927 - mae: 5639.4927\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5600.6655 - mae: 5600.6655\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5559.4326 - mae: 5559.4326\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5523.6187 - mae: 5523.6187\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5474.1250 - mae: 5474.1250\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5432.2661 - mae: 5432.2661\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5386.0527 - mae: 5386.0527\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5333.1812 - mae: 5333.1812\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5288.8159 - mae: 5288.8159\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5234.6792 - mae: 5234.6792\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5170.9360 - mae: 5170.9360\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5112.9443 - mae: 5112.9443\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5059.8643 - mae: 5059.8643\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3aa238110>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDvNBczEWAkI",
        "outputId": "fa8037a8-af1a-4e27-b39e-634ff8c495c7"
      },
      "source": [
        "insurance_model_2.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 4924.3477 - mae: 4924.3477\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4924.34765625, 4924.34765625]"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEN3jVS-WcNV",
        "outputId": "81473445-f479-4da0-807b-7fcbb90aacaf"
      },
      "source": [
        "insurance_model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7023.3291 - mae: 7023.3291\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7023.3291015625, 7023.3291015625]"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEiASc2BgJU7",
        "outputId": "7b728874-9bfa-4b1e-9f8f-9789ea7d1bb1"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model (same as above)\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=\"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=200)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13273.1602 - mae: 13273.1602\n",
            "Epoch 2/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13104.4297 - mae: 13104.4297\n",
            "Epoch 3/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12749.5420 - mae: 12749.5420\n",
            "Epoch 4/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12055.7510 - mae: 12055.7510\n",
            "Epoch 5/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10905.8154 - mae: 10905.8154\n",
            "Epoch 6/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9457.7217 - mae: 9457.7217\n",
            "Epoch 7/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8147.6543 - mae: 8147.6543\n",
            "Epoch 8/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7528.8408 - mae: 7528.8408\n",
            "Epoch 9/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7429.1528 - mae: 7429.1528\n",
            "Epoch 10/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7409.0811 - mae: 7409.0811\n",
            "Epoch 11/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7390.8042 - mae: 7390.8042\n",
            "Epoch 12/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7368.9180 - mae: 7368.9180\n",
            "Epoch 13/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7348.5195 - mae: 7348.5195\n",
            "Epoch 14/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7326.4893 - mae: 7326.4893\n",
            "Epoch 15/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7307.5815 - mae: 7307.5815\n",
            "Epoch 16/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7285.7734 - mae: 7285.7734\n",
            "Epoch 17/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7265.7104 - mae: 7265.7104\n",
            "Epoch 18/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7242.5488 - mae: 7242.5488\n",
            "Epoch 19/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7220.5068 - mae: 7220.5068\n",
            "Epoch 20/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7197.1978 - mae: 7197.1978\n",
            "Epoch 21/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7179.0195 - mae: 7179.0195\n",
            "Epoch 22/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7151.2104 - mae: 7151.2104\n",
            "Epoch 23/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7126.4639 - mae: 7126.4639\n",
            "Epoch 24/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7101.9199 - mae: 7101.9199\n",
            "Epoch 25/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7084.3379 - mae: 7084.3379\n",
            "Epoch 26/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7052.3291 - mae: 7052.3291\n",
            "Epoch 27/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7024.3501 - mae: 7024.3501\n",
            "Epoch 28/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6996.6963 - mae: 6996.6963\n",
            "Epoch 29/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6969.0112 - mae: 6969.0112\n",
            "Epoch 30/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6942.1899 - mae: 6942.1899\n",
            "Epoch 31/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6911.7280 - mae: 6911.7280\n",
            "Epoch 32/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6884.0205 - mae: 6884.0205\n",
            "Epoch 33/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6853.4648 - mae: 6853.4648\n",
            "Epoch 34/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6823.0674 - mae: 6823.0674\n",
            "Epoch 35/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6789.6855 - mae: 6789.6855\n",
            "Epoch 36/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6755.7646 - mae: 6755.7646\n",
            "Epoch 37/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6720.2026 - mae: 6720.2026\n",
            "Epoch 38/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6689.7158 - mae: 6689.7158\n",
            "Epoch 39/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6652.4614 - mae: 6652.4614\n",
            "Epoch 40/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6618.1006 - mae: 6618.1006\n",
            "Epoch 41/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6585.8643 - mae: 6585.8643\n",
            "Epoch 42/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6559.4956 - mae: 6559.4956\n",
            "Epoch 43/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6530.0444 - mae: 6530.0444\n",
            "Epoch 44/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6506.8071 - mae: 6506.8071\n",
            "Epoch 45/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6493.5718 - mae: 6493.5718\n",
            "Epoch 46/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6475.9258 - mae: 6475.9258\n",
            "Epoch 47/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6458.8979 - mae: 6458.8979\n",
            "Epoch 48/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6445.1494 - mae: 6445.1494\n",
            "Epoch 49/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6430.9639 - mae: 6430.9639\n",
            "Epoch 50/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6417.7510 - mae: 6417.7510\n",
            "Epoch 51/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6403.2759 - mae: 6403.2759\n",
            "Epoch 52/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6392.4141 - mae: 6392.4141\n",
            "Epoch 53/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6378.7451 - mae: 6378.7451\n",
            "Epoch 54/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6364.9131 - mae: 6364.9131\n",
            "Epoch 55/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6351.5269 - mae: 6351.5269\n",
            "Epoch 56/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6337.6602 - mae: 6337.6602\n",
            "Epoch 57/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6324.8369 - mae: 6324.8369\n",
            "Epoch 58/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6310.1948 - mae: 6310.1948\n",
            "Epoch 59/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6295.6035 - mae: 6295.6035\n",
            "Epoch 60/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6284.8696 - mae: 6284.8696\n",
            "Epoch 61/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6265.6411 - mae: 6265.6411\n",
            "Epoch 62/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6253.0103 - mae: 6253.0103\n",
            "Epoch 63/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6234.9292 - mae: 6234.9292\n",
            "Epoch 64/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6218.0430 - mae: 6218.0430\n",
            "Epoch 65/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6201.1899 - mae: 6201.1899\n",
            "Epoch 66/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6183.9590 - mae: 6183.9590\n",
            "Epoch 67/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6171.2993 - mae: 6171.2993\n",
            "Epoch 68/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6148.8398 - mae: 6148.8398\n",
            "Epoch 69/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6132.5981 - mae: 6132.5981\n",
            "Epoch 70/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6112.3848 - mae: 6112.3848\n",
            "Epoch 71/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6092.7202 - mae: 6092.7202\n",
            "Epoch 72/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6073.7422 - mae: 6073.7422\n",
            "Epoch 73/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6059.4873 - mae: 6059.4873\n",
            "Epoch 74/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6031.3848 - mae: 6031.3848\n",
            "Epoch 75/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6010.3350 - mae: 6010.3350\n",
            "Epoch 76/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5995.2178 - mae: 5995.2178\n",
            "Epoch 77/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5963.0718 - mae: 5963.0718\n",
            "Epoch 78/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5940.0605 - mae: 5940.0605\n",
            "Epoch 79/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5915.1064 - mae: 5915.1064\n",
            "Epoch 80/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5887.9990 - mae: 5887.9990\n",
            "Epoch 81/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5861.6992 - mae: 5861.6992\n",
            "Epoch 82/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5834.3066 - mae: 5834.3066\n",
            "Epoch 83/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5805.8237 - mae: 5805.8237\n",
            "Epoch 84/200\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5772.3232 - mae: 5772.3232\n",
            "Epoch 85/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5745.1514 - mae: 5745.1514\n",
            "Epoch 86/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5711.3477 - mae: 5711.3477\n",
            "Epoch 87/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5674.5215 - mae: 5674.5215\n",
            "Epoch 88/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5639.4927 - mae: 5639.4927\n",
            "Epoch 89/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5600.6655 - mae: 5600.6655\n",
            "Epoch 90/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5559.4326 - mae: 5559.4326\n",
            "Epoch 91/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5523.6187 - mae: 5523.6187\n",
            "Epoch 92/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5474.1250 - mae: 5474.1250\n",
            "Epoch 93/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5432.2661 - mae: 5432.2661\n",
            "Epoch 94/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5386.0527 - mae: 5386.0527\n",
            "Epoch 95/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5333.1812 - mae: 5333.1812\n",
            "Epoch 96/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5288.8159 - mae: 5288.8159\n",
            "Epoch 97/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5234.6792 - mae: 5234.6792\n",
            "Epoch 98/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5170.9360 - mae: 5170.9360\n",
            "Epoch 99/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5112.9443 - mae: 5112.9443\n",
            "Epoch 100/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5059.8643 - mae: 5059.8643\n",
            "Epoch 101/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4987.6191 - mae: 4987.6191\n",
            "Epoch 102/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4915.2910 - mae: 4915.2910\n",
            "Epoch 103/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4847.3599 - mae: 4847.3599\n",
            "Epoch 104/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4768.0151 - mae: 4768.0151\n",
            "Epoch 105/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4683.4727 - mae: 4683.4727\n",
            "Epoch 106/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4600.5054 - mae: 4600.5054\n",
            "Epoch 107/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4513.1436 - mae: 4513.1436\n",
            "Epoch 108/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4422.2983 - mae: 4422.2983\n",
            "Epoch 109/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4339.9600 - mae: 4339.9600\n",
            "Epoch 110/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4254.3916 - mae: 4254.3916\n",
            "Epoch 111/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4173.1797 - mae: 4173.1797\n",
            "Epoch 112/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4102.2939 - mae: 4102.2939\n",
            "Epoch 113/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4031.9592 - mae: 4031.9592\n",
            "Epoch 114/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3986.0220 - mae: 3986.0220\n",
            "Epoch 115/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3943.2346 - mae: 3943.2346\n",
            "Epoch 116/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3918.8977 - mae: 3918.8977\n",
            "Epoch 117/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3895.5610 - mae: 3895.5610\n",
            "Epoch 118/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3869.5679 - mae: 3869.5679\n",
            "Epoch 119/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3850.2136 - mae: 3850.2136\n",
            "Epoch 120/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3834.7349 - mae: 3834.7349\n",
            "Epoch 121/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3827.0952 - mae: 3827.0952\n",
            "Epoch 122/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3821.6382 - mae: 3821.6382\n",
            "Epoch 123/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3813.8315 - mae: 3813.8315\n",
            "Epoch 124/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3805.7307 - mae: 3805.7307\n",
            "Epoch 125/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3794.7090 - mae: 3794.7090\n",
            "Epoch 126/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3804.4946 - mae: 3804.4946\n",
            "Epoch 127/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3796.0596 - mae: 3796.0596\n",
            "Epoch 128/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3791.0422 - mae: 3791.0422\n",
            "Epoch 129/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3800.0696 - mae: 3800.0696\n",
            "Epoch 130/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3788.5005 - mae: 3788.5005\n",
            "Epoch 131/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3780.8442 - mae: 3780.8442\n",
            "Epoch 132/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3774.5413 - mae: 3774.5413\n",
            "Epoch 133/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3771.0156 - mae: 3771.0156\n",
            "Epoch 134/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3769.3762 - mae: 3769.3762\n",
            "Epoch 135/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3766.7610 - mae: 3766.7610\n",
            "Epoch 136/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3765.5508 - mae: 3765.5508\n",
            "Epoch 137/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3774.5032 - mae: 3774.5032\n",
            "Epoch 138/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3785.3909 - mae: 3785.3909\n",
            "Epoch 139/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3761.1299 - mae: 3761.1299\n",
            "Epoch 140/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3764.1753 - mae: 3764.1753\n",
            "Epoch 141/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3763.9250 - mae: 3763.9250\n",
            "Epoch 142/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3762.7959 - mae: 3762.7959\n",
            "Epoch 143/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3754.4397 - mae: 3754.4397\n",
            "Epoch 144/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3750.3347 - mae: 3750.3347\n",
            "Epoch 145/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3750.4006 - mae: 3750.4006\n",
            "Epoch 146/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3755.4736 - mae: 3755.4736\n",
            "Epoch 147/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3750.3223 - mae: 3750.3223\n",
            "Epoch 148/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3758.1089 - mae: 3758.1089\n",
            "Epoch 149/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3743.4858 - mae: 3743.4858\n",
            "Epoch 150/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3738.5342 - mae: 3738.5342\n",
            "Epoch 151/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3740.1384 - mae: 3740.1384\n",
            "Epoch 152/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3742.4954 - mae: 3742.4954\n",
            "Epoch 153/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3744.4399 - mae: 3744.4399\n",
            "Epoch 154/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3737.1824 - mae: 3737.1824\n",
            "Epoch 155/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3737.6541 - mae: 3737.6541\n",
            "Epoch 156/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3737.1663 - mae: 3737.1663\n",
            "Epoch 157/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3733.1101 - mae: 3733.1101\n",
            "Epoch 158/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3729.5813 - mae: 3729.5813\n",
            "Epoch 159/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3725.9053 - mae: 3725.9053\n",
            "Epoch 160/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3733.2817 - mae: 3733.2817\n",
            "Epoch 161/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3728.2559 - mae: 3728.2559\n",
            "Epoch 162/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3724.5825 - mae: 3724.5825\n",
            "Epoch 163/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3723.0806 - mae: 3723.0806\n",
            "Epoch 164/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3726.9475 - mae: 3726.9475\n",
            "Epoch 165/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3716.5430 - mae: 3716.5430\n",
            "Epoch 166/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3721.9155 - mae: 3721.9155\n",
            "Epoch 167/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3721.1814 - mae: 3721.1814\n",
            "Epoch 168/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3715.2456 - mae: 3715.2456\n",
            "Epoch 169/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3713.9756 - mae: 3713.9756\n",
            "Epoch 170/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3707.9922 - mae: 3707.9922\n",
            "Epoch 171/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3707.4158 - mae: 3707.4158\n",
            "Epoch 172/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3710.6833 - mae: 3710.6833\n",
            "Epoch 173/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3703.3618 - mae: 3703.3618\n",
            "Epoch 174/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3710.9385 - mae: 3710.9385\n",
            "Epoch 175/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3713.0417 - mae: 3713.0417\n",
            "Epoch 176/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3705.0571 - mae: 3705.0571\n",
            "Epoch 177/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3698.9333 - mae: 3698.9333\n",
            "Epoch 178/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3697.9983 - mae: 3697.9983\n",
            "Epoch 179/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3704.9150 - mae: 3704.9150\n",
            "Epoch 180/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3710.3679 - mae: 3710.3679\n",
            "Epoch 181/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3696.6482 - mae: 3696.6482\n",
            "Epoch 182/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3692.7329 - mae: 3692.7329\n",
            "Epoch 183/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3691.1655 - mae: 3691.1655\n",
            "Epoch 184/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3699.2437 - mae: 3699.2437\n",
            "Epoch 185/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3693.2480 - mae: 3693.2480\n",
            "Epoch 186/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3696.1387 - mae: 3696.1387\n",
            "Epoch 187/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3687.8640 - mae: 3687.8640\n",
            "Epoch 188/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3693.3562 - mae: 3693.3562\n",
            "Epoch 189/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3682.7324 - mae: 3682.7324\n",
            "Epoch 190/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3683.2891 - mae: 3683.2891\n",
            "Epoch 191/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3697.6536 - mae: 3697.6536\n",
            "Epoch 192/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3684.6665 - mae: 3684.6665\n",
            "Epoch 193/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3675.5154 - mae: 3675.5154\n",
            "Epoch 194/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3676.3923 - mae: 3676.3923\n",
            "Epoch 195/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3672.8452 - mae: 3672.8452\n",
            "Epoch 196/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3682.0283 - mae: 3682.0283\n",
            "Epoch 197/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3665.7961 - mae: 3665.7961\n",
            "Epoch 198/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3671.7419 - mae: 3671.7419\n",
            "Epoch 199/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3680.5464 - mae: 3680.5464\n",
            "Epoch 200/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3665.6401 - mae: 3665.6401\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTvoGKcg4oES",
        "outputId": "77ebe699-1388-4c50-a2bb-fe5fbb3594b4"
      },
      "source": [
        "# Evaluate our third model\n",
        "insurance_model_3.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3491.2961 - mae: 3491.2961\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3491.296142578125, 3491.296142578125]"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qBBvmsZ4zpC",
        "outputId": "7bc233b5-5df5-4941-bf39-dedef37b60e0"
      },
      "source": [
        "insurance_model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7023.3291 - mae: 7023.3291\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7023.3291015625, 7023.3291015625]"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "fCS_oIU444pr",
        "outputId": "98c29624-a8c9-4cf3-9071-0339a9f69c27"
      },
      "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": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vx44SsF3DXBC"
      },
      "source": [
        "> 🤔 **Question:** How long should you train for?\n",
        "\n",
        "It depends. Really... it depends on the problem you're working on. However, many people have asked this question before... so TensorFlow has a solution! It's called the [EarlyStopping Callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping), which is a TensorFlow component you can add to your model to stop training once it stops improving a certain metric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrNKNw9dHEyf"
      },
      "source": [
        "## Preprocessing data (normalization and standardization)\n",
        "\n",
        "In terms of scaling values, neural networks tend to prefer normalization.\n",
        "\n",
        "If you're not sure on which to use, you could try both and see which performs better."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tgMfJbEEz50P",
        "outputId": "0304ca1c-23f0-44aa-9170-9264e9f60911"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>30.970</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>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>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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>29.070</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 11 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 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "k3-JwTpj0wUZ",
        "outputId": "c4cad034-beea-46c4-f389-c850de4219a6"
      },
      "source": [
        "X[\"age\"].plot(kind=\"hist\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa383c43e10>"
            ]
          },
          "metadata": {},
          "execution_count": 224
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qpClxf6k036y",
        "outputId": "f8d506ec-a5f2-4d11-e8b2-30691693034c"
      },
      "source": [
        "X[\"bmi\"].plot(kind=\"hist\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa383cf76d0>"
            ]
          },
          "metadata": {},
          "execution_count": 225
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxYOcACx087H",
        "outputId": "6fdc6f85-3f33-4970-b897-afe4c2a59f9b"
      },
      "source": [
        "X[\"children\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    574\n",
              "1    324\n",
              "2    240\n",
              "3    157\n",
              "4     25\n",
              "5     18\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "VuT_CjaU1DYh",
        "outputId": "f37caa29-f55e-4553-c8bf-a32b0df97a7c"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# Read in the insurance dataframe\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": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_dYoKiZ2-HN"
      },
      "source": [
        "To prepare our data, we can borrow a few classes from Scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSG8WX-M3apV"
      },
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Create a column transformer\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]),  # turn all values in these columns between 0 and 1\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
        ")\n",
        "\n",
        "# Create our X & y values\n",
        "X = insurance.drop(\"charges\", axis=1)\n",
        "y = insurance[\"charges\"]\n",
        "\n",
        "# Build our train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit the the column transformer to our training data\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and test data with normalization (MinMaxScaler) and OneHotEncode\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": "LYsiiFOP36Nv",
        "outputId": "638c4bbc-4957-46e1-9f9c-e15f855e537a"
      },
      "source": [
        "# What does our data look like now?\n",
        "X_train.loc[0] "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                19\n",
              "sex            female\n",
              "bmi              27.9\n",
              "children            0\n",
              "smoker            yes\n",
              "region      southwest\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ft6XljZX6r4N",
        "outputId": "91fed2b4-ada0-4d42-9395-9fcbee18023c"
      },
      "source": [
        "X_train_normal[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.60869565, 0.10734463, 0.4       , 1.        , 0.        ,\n",
              "       1.        , 0.        , 0.        , 1.        , 0.        ,\n",
              "       0.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EhBf1YK6wqt",
        "outputId": "7ed2b2ab-1183-499c-e1bf-80ce8eaf995e"
      },
      "source": [
        "X_train.shape, X_train_normal.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1070, 6), (1070, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9NpCtld7K3g"
      },
      "source": [
        "Beautiful! Our data has been normalized and one hot encoded. Now let's build a neural network model on it and see how it goes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QeBotM5E7khg",
        "outputId": "11da5bf2-5503-49eb-8fda-efdefc260600"
      },
      "source": [
        "# Build a neural network model to fit on our normalized data\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "insurance_model_4 = tf.keras.Sequential(\n",
        "    [tf.keras.layers.Dense(100),\n",
        "     tf.keras.layers.Dense(10),\n",
        "     tf.keras.layers.Dense(1)]\n",
        ")\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model_4.compile(loss=\"mae\",\n",
        "                          optimizer=tf.keras.optimizers.Adam(),\n",
        "                          metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model_4.fit(X_train_normal, y_train, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 1s 3ms/step - loss: 13342.6475 - mae: 13342.6475\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13333.4785 - mae: 13333.4785\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13312.0234 - mae: 13312.0234\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13267.7930 - mae: 13267.7930\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13189.5850 - mae: 13189.5850\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13066.4502 - mae: 13066.4502\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12888.1953 - mae: 12888.1953\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12644.6523 - mae: 12644.6523\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12325.5469 - mae: 12325.5469\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11925.9658 - mae: 11925.9658\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11454.3350 - mae: 11454.3350\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10949.8086 - mae: 10949.8086\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10448.9404 - mae: 10448.9404\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9951.6250 - mae: 9951.6250\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9482.7422 - mae: 9482.7422\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 9066.7461 - mae: 9066.7461\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8721.9854 - mae: 8721.9854\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8441.2002 - mae: 8441.2002\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8227.5117 - mae: 8227.5117\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8081.9775 - mae: 8081.9775\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7973.8945 - mae: 7973.8945\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7899.1597 - mae: 7899.1597\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7840.3916 - mae: 7840.3916\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7787.9619 - mae: 7787.9619\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7749.2622 - mae: 7749.2622\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7697.9595 - mae: 7697.9595\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7656.0273 - mae: 7656.0273\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7613.4780 - mae: 7613.4780\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7570.9482 - mae: 7570.9482\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7527.4175 - mae: 7527.4175\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7483.5947 - mae: 7483.5947\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7439.4424 - mae: 7439.4424\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7395.0552 - mae: 7395.0552\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7346.8125 - mae: 7346.8125\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7300.0493 - mae: 7300.0493\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7249.8452 - mae: 7249.8452\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7199.5303 - mae: 7199.5303\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7148.4805 - mae: 7148.4805\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7093.6660 - mae: 7093.6660\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7038.1797 - mae: 7038.1797\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6981.7393 - mae: 6981.7393\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6922.7847 - mae: 6922.7847\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6860.1724 - mae: 6860.1724\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6793.7979 - mae: 6793.7979\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6726.6201 - mae: 6726.6201\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6657.4683 - mae: 6657.4683\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6586.3086 - mae: 6586.3086\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6507.5063 - mae: 6507.5063\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6428.6025 - mae: 6428.6025\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6342.7100 - mae: 6342.7100\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6258.0718 - mae: 6258.0718\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6164.7046 - mae: 6164.7046\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6068.6748 - mae: 6068.6748\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5970.0981 - mae: 5970.0981\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5862.5625 - mae: 5862.5625\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5753.9531 - mae: 5753.9531\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5638.0942 - mae: 5638.0942\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5519.8687 - mae: 5519.8687\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5401.3198 - mae: 5401.3198\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5277.3506 - mae: 5277.3506\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5149.7642 - mae: 5149.7642\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5019.3540 - mae: 5019.3540\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4889.6865 - mae: 4889.6865\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4756.8560 - mae: 4756.8560\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4629.4370 - mae: 4629.4370\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4503.5991 - mae: 4503.5991\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4392.9922 - mae: 4392.9922\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4284.3862 - mae: 4284.3862\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4182.6182 - mae: 4182.6182\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4089.5720 - mae: 4089.5720\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4003.3901 - mae: 4003.3901\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3929.0093 - mae: 3929.0093\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3866.3110 - mae: 3866.3110\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3813.7144 - mae: 3813.7144\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3773.0317 - mae: 3773.0317\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3744.1995 - mae: 3744.1995\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3719.6870 - mae: 3719.6870\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3702.9109 - mae: 3702.9109\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3691.8792 - mae: 3691.8792\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3682.8350 - mae: 3682.8350\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3676.9763 - mae: 3676.9763\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3673.9492 - mae: 3673.9492\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3667.8452 - mae: 3667.8452\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3664.5757 - mae: 3664.5757\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3661.8562 - mae: 3661.8562\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3660.3049 - mae: 3660.3049\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3657.5134 - mae: 3657.5134\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3655.2200 - mae: 3655.2200\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3653.8831 - mae: 3653.8831\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3652.0195 - mae: 3652.0195\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3648.9990 - mae: 3648.9990\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3648.4463 - mae: 3648.4463\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3646.2297 - mae: 3646.2297\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3644.4377 - mae: 3644.4377\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3645.8772 - mae: 3645.8772\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3642.2573 - mae: 3642.2573\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3640.1184 - mae: 3640.1184\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3638.0649 - mae: 3638.0649\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3637.2051 - mae: 3637.2051\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3636.1707 - mae: 3636.1707\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa3854c8cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yH0K_-4p9GxG",
        "outputId": "cdc4567f-bcf9-4dda-ff51-c222b3219bfd"
      },
      "source": [
        "# Evaluate our insurance model trained on normalized data\n",
        "insurance_model_4.evaluate(X_test_normal, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3438.7844 - mae: 3438.7844\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3438.784423828125, 3438.784423828125]"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pesy010G9YE7",
        "outputId": "f3429c33-fb78-463d-cd18-1692e3083ad5"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "i_model_1 = tf.keras.Sequential()\n",
        "i_model_1.add(tf.keras.layers.Dense(50, activation=\"tanh\"))\n",
        "i_model_1.add(tf.keras.layers.Dense(10, activation=\"relu\"))\n",
        "i_model_1.add(tf.keras.layers.Dense(10, activation=\"relu\"))\n",
        "i_model_1.add(tf.keras.layers.Dense(1, activation=\"relu\"))\n",
        "\n",
        "# 2. Compile the model\n",
        "i_model_1.compile(loss=tf.keras.losses.mae,\n",
        "                  optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "                  metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "I_model_fit = i_model_1.fit(X_train_normal, \n",
        "                            y_train, \n",
        "                            epochs=1000, \n",
        "                            callbacks=[cb_reducelr, cb_earlystop])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "34/34 [==============================] - 1s 2ms/step - loss: 13345.6611 - mae: 13345.6611\n",
            "Epoch 2/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13343.0762 - mae: 13343.0762\n",
            "Epoch 3/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13335.3584 - mae: 13335.3584\n",
            "Epoch 4/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13317.0195 - mae: 13317.0195\n",
            "Epoch 5/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13281.7500 - mae: 13281.7500\n",
            "Epoch 6/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13224.3691 - mae: 13224.3691\n",
            "Epoch 7/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13141.7803 - mae: 13141.7803\n",
            "Epoch 8/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13031.5576 - mae: 13031.5576\n",
            "Epoch 9/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12891.2002 - mae: 12891.2002\n",
            "Epoch 10/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12718.0264 - mae: 12718.0264\n",
            "Epoch 11/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12509.2080 - mae: 12509.2080\n",
            "Epoch 12/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12262.3086 - mae: 12262.3086\n",
            "Epoch 13/1000\n",
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            "Epoch 675/1000\n",
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            "Epoch 680/1000\n",
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            "Epoch 681/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1757.2319 - mae: 1757.2319\n",
            "Epoch 682/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1760.2758 - mae: 1760.2758\n",
            "Epoch 683/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1756.2441 - mae: 1756.2441\n",
            "Epoch 684/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1760.7120 - mae: 1760.7120\n",
            "Epoch 685/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1761.8219 - mae: 1761.8219\n",
            "Epoch 686/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1758.7781 - mae: 1758.7781\n",
            "Epoch 687/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1754.8892 - mae: 1754.8892\n",
            "Epoch 688/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1758.9858 - mae: 1758.9858\n",
            "Epoch 689/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1754.2811 - mae: 1754.2811\n",
            "Epoch 690/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1753.4656 - mae: 1753.4656\n",
            "Epoch 691/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1759.7405 - mae: 1759.7405\n",
            "Epoch 692/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1755.3566 - mae: 1755.3566\n",
            "Epoch 693/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1756.3726 - mae: 1756.3726\n",
            "Epoch 694/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1754.0914 - mae: 1754.0914\n",
            "Epoch 695/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1754.0231 - mae: 1754.0231\n",
            "Epoch 696/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1754.9261 - mae: 1754.9261\n",
            "Epoch 697/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1759.7410 - mae: 1759.7410\n",
            "Epoch 698/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1754.5027 - mae: 1754.5027\n",
            "Epoch 699/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1751.3593 - mae: 1751.3593\n",
            "Epoch 700/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1753.9951 - mae: 1753.9951\n",
            "Epoch 701/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1751.7679 - mae: 1751.7679\n",
            "Epoch 702/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1755.2849 - mae: 1755.2849\n",
            "Epoch 703/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1754.4115 - mae: 1754.4115\n",
            "Epoch 704/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1750.9456 - mae: 1750.9456\n",
            "Epoch 705/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1753.0372 - mae: 1753.0372\n",
            "Epoch 706/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1749.6281 - mae: 1749.6281\n",
            "Epoch 707/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1756.2196 - mae: 1756.2196\n",
            "Epoch 708/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1751.0496 - mae: 1751.0496\n",
            "Epoch 709/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1755.4823 - mae: 1755.4823\n",
            "Epoch 710/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1750.2001 - mae: 1750.2001\n",
            "Epoch 711/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1753.4596 - mae: 1753.4596\n",
            "Epoch 712/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1752.8656 - mae: 1752.8656\n",
            "Epoch 713/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1749.6827 - mae: 1749.6827\n",
            "Epoch 714/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1748.0408 - mae: 1748.0408\n",
            "Epoch 715/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1753.5043 - mae: 1753.5043\n",
            "Epoch 716/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1750.8174 - mae: 1750.8174\n",
            "Epoch 717/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1749.3538 - mae: 1749.3538\n",
            "Epoch 718/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1752.7125 - mae: 1752.7125\n",
            "Epoch 719/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1750.3228 - mae: 1750.3228\n",
            "Epoch 720/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1748.3137 - mae: 1748.3137\n",
            "Epoch 721/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1747.8975 - mae: 1747.8975\n",
            "Epoch 722/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1748.1930 - mae: 1748.1930\n",
            "Epoch 723/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1745.6212 - mae: 1745.6212\n",
            "Epoch 724/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1746.0706 - mae: 1746.0706\n",
            "Epoch 725/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1744.7283 - mae: 1744.7283\n",
            "Epoch 726/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1745.7382 - mae: 1745.7382\n",
            "Epoch 727/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1748.6517 - mae: 1748.6517\n",
            "Epoch 728/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1745.6228 - mae: 1745.6228\n",
            "Epoch 729/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1746.1427 - mae: 1746.1427\n",
            "Epoch 730/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1743.0542 - mae: 1743.0542\n",
            "Epoch 731/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1748.6034 - mae: 1748.6034\n",
            "Epoch 732/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1743.8602 - mae: 1743.8602\n",
            "Epoch 733/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1756.1344 - mae: 1756.1344\n",
            "Epoch 734/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1748.0549 - mae: 1748.0549\n",
            "Epoch 735/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1744.8777 - mae: 1744.8777\n",
            "Epoch 736/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1744.5336 - mae: 1744.5336\n",
            "Epoch 737/1000\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 1747.1947 - mae: 1747.1947\n",
            "Epoch 738/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1749.6335 - mae: 1749.6335\n",
            "Epoch 739/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1743.1570 - mae: 1743.1570\n",
            "Epoch 740/1000\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 1747.9629 - mae: 1747.9629\n",
            "\n",
            "Epoch 00740: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 00740: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "3YmHQYSg_XOG",
        "outputId": "3ae2e00a-bf09-42e5-88f8-a05562a38619"
      },
      "source": [
        "pd.DataFrame(I_model_fit.history).plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa393a2c350>"
            ]
          },
          "metadata": {},
          "execution_count": 258
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnoixsoO_XOL",
        "outputId": "7ba6d814-986d-449c-8dcf-b581e0ff566e"
      },
      "source": [
        "i_model_1.evaluate(X_test_normal, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 1613.5618 - mae: 1613.5618\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1613.561767578125, 1613.561767578125]"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5bB0jNF_aE_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}