neural_network_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d0da4dc6-0437-4670-bc75-6a6fc87dc30d",
   "metadata": {
    "id": "d0da4dc6-0437-4670-bc75-6a6fc87dc30d"
   },
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6626be4d-ccc7-4515-a321-a3dc939b5fe8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3053,
     "status": "ok",
     "timestamp": 1620755418062,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "6626be4d-ccc7-4515-a321-a3dc939b5fe8",
    "outputId": "8220f0fc-11c1-4e31-ca2d-fc21f1c3d6bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b554da27-c896-4c75-a616-3dfbf96eb60c",
   "metadata": {
    "id": "b554da27-c896-4c75-a616-3dfbf96eb60c"
   },
   "source": [
    "### Creating Data to view and fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f0c8ecb-f31f-4368-9ff4-3b3da82ecb55",
   "metadata": {
    "executionInfo": {
     "elapsed": 337,
     "status": "ok",
     "timestamp": 1620755426158,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "5f0c8ecb-f31f-4368-9ff4-3b3da82ecb55"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5084ace9-c5b7-48c2-a7fc-a104ee36d3c7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "executionInfo": {
     "elapsed": 196,
     "status": "ok",
     "timestamp": 1620755426992,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
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    },
    "id": "5084ace9-c5b7-48c2-a7fc-a104ee36d3c7",
    "outputId": "cf533cf1-ab9f-4832-a53d-880d8f52e7b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iCDsAJIawA0BiCDsAJIawA0BiCDsAJOb/AWIa1pguLY/fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating Features\n",
    "X = np.array([-7.0, -4.0, -1.0, 2.0, 5.0, 8.0, 11.0, 14.0])\n",
    "Y = np.array([3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])\n",
    "\n",
    "plt.scatter(X, Y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ab53fdd-d277-4243-a65c-afe710cc9a92",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 47,
     "status": "ok",
     "timestamp": 1620755427801,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "8ab53fdd-d277-4243-a65c-afe710cc9a92",
    "outputId": "50b82988-4276-4c1e-80da-220bce1ec0c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y == X + 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fb7a03-2384-43f2-b880-272231ffe91e",
   "metadata": {
    "id": "60fb7a03-2384-43f2-b880-272231ffe91e"
   },
   "source": [
    "## Input and Output Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64703153-bed4-4135-bab3-93b70cbda6c3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1514,
     "status": "ok",
     "timestamp": 1620755431608,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "64703153-bed4-4135-bab3-93b70cbda6c3",
    "outputId": "0dedebe7-9af1-4eca-d649-ec337057bf89"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(3,), dtype=string, numpy=array([b'bedroom', b'bathroom', b'garage'], dtype=object)>,\n",
       " <tf.Tensor: shape=(1,), dtype=int32, numpy=array([939700])>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_info = tf.constant([\"bedroom\", \"bathroom\", \"garage\"])\n",
    "house_price = tf.constant([939700])\n",
    "\n",
    "house_info, house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1244a16e-5693-43a0-809c-e2e28588c9de",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 56,
     "status": "ok",
     "timestamp": 1620755436455,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "1244a16e-5693-43a0-809c-e2e28588c9de",
    "outputId": "ba884720-0192-4fe4-83f0-ae78109a1e2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8,), (8,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = X.shape\n",
    "output_shape = Y.shape\n",
    "\n",
    "input_shape, output_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80d65f84-06f1-4d3c-bd24-f9d774874a99",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 48,
     "status": "ok",
     "timestamp": 1620755437587,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "80d65f84-06f1-4d3c-bd24-f9d774874a99",
    "outputId": "e1e61aac-c471-43d0-cea2-0f44f5e533f4"
   },
   "outputs": [
    {
     "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.])>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.constant(X)\n",
    "Y = tf.constant(Y)\n",
    "\n",
    "X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ec888a3-10c9-4b51-ab33-c3f9c4217d64",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 242,
     "status": "ok",
     "timestamp": 1620755438605,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "0ec888a3-10c9-4b51-ab33-c3f9c4217d64",
    "outputId": "baa79db5-1e98-482e-fcac-89f77dc6d1a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([]), TensorShape([]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = X[0].shape\n",
    "output_shape = Y[0].shape\n",
    "\n",
    "input_shape, output_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d2c815-36e8-4c59-b474-d63b78d28646",
   "metadata": {
    "id": "85d2c815-36e8-4c59-b474-d63b78d28646"
   },
   "source": [
    "## Steps in modeling wiht TF\n",
    "\n",
    "1. creating a model\n",
    "2. compiling a model\n",
    "3. fitting a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a420499-b124-40da-9876-c374b1701751",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1893,
     "status": "ok",
     "timestamp": 1620755442301,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "2a420499-b124-40da-9876-c374b1701751",
    "outputId": "77d2656b-4d8b-4acc-9fe6-a56094bdcc3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 2s 2s/step - loss: 11.5048 - mae: 11.5048\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 11.3723 - mae: 11.3723\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 11.2398 - mae: 11.2398\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 11.1073 - mae: 11.1073\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 10.9748 - mae: 10.9748\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 10.8423 - mae: 10.8423\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 10.7098 - mae: 10.7098\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 10.5773 - mae: 10.5773\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 10.4448 - mae: 10.4448\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 10.3123 - mae: 10.3123\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 10.1798 - mae: 10.1798\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 10.0473 - mae: 10.0473\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 9.9148 - mae: 9.9148\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 9.7823 - mae: 9.7823\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 9.6498 - mae: 9.6498\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 9.5173 - mae: 9.5173\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 9.3848 - mae: 9.3848\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 9.2523 - mae: 9.2523\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 9.1198 - mae: 9.1198\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 8.9873 - mae: 8.9873\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 8.8548 - mae: 8.8548\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 8.7223 - mae: 8.7223\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 8.5898 - mae: 8.5898\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 8.4573 - mae: 8.4573\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 8.3248 - mae: 8.3248\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.1923 - mae: 8.1923\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.0598 - mae: 8.0598\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.9273 - mae: 7.9273\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 7.7948 - mae: 7.7948\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.6623 - mae: 7.6623\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.5298 - mae: 7.5298\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.3973 - mae: 7.3973\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2648 - mae: 7.2648\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2525 - mae: 7.2525\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2469 - mae: 7.2469\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2413 - mae: 7.2413\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2356 - mae: 7.2356\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2300 - mae: 7.2300\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.2244 - mae: 7.2244\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.2188 - mae: 7.2188\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.2131 - mae: 7.2131\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.2075 - mae: 7.2075\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 7.2019 - mae: 7.2019\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.1962 - mae: 7.1962\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 7.1906 - mae: 7.1906\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1850 - mae: 7.1850\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.1794 - mae: 7.1794\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1738 - mae: 7.1738\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 7.1681 - mae: 7.1681\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 7.1625 - mae: 7.1625\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 7.1569 - mae: 7.1569\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.1512 - mae: 7.1512\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.1456 - mae: 7.1456\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1400 - mae: 7.1400\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1344 - mae: 7.1344\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1287 - mae: 7.1287\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1231 - mae: 7.1231\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1175 - mae: 7.1175\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.1119 - mae: 7.1119\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.1063 - mae: 7.1063\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.1006 - mae: 7.1006\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.0950 - mae: 7.0950\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0894 - mae: 7.0894\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 7.0838 - mae: 7.0838\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 7.0781 - mae: 7.0781\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 7.0725 - mae: 7.0725\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.0669 - mae: 7.0669\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.0613 - mae: 7.0613\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.0556 - mae: 7.0556\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0500 - mae: 7.0500\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0444 - mae: 7.0444\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.0388 - mae: 7.0388\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0331 - mae: 7.0331\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 7.0275 - mae: 7.0275\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0219 - mae: 7.0219\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 7.0163 - mae: 7.0163\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0106 - mae: 7.0106\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0050 - mae: 7.0050\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 6.9994 - mae: 6.9994\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9938 - mae: 6.9938\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 6.9881 - mae: 6.9881\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.9825 - mae: 6.9825\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.9769 - mae: 6.9769\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9713 - mae: 6.9713\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 6.9656 - mae: 6.9656\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9600 - mae: 6.9600\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9544 - mae: 6.9544\n",
      "Epoch 88/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9488 - mae: 6.9488\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.9431 - mae: 6.9431\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 6.9375 - mae: 6.9375\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.9319 - mae: 6.9319\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9263 - mae: 6.9263\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 6.9206 - mae: 6.9206\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 6.9150 - mae: 6.9150\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.9094 - mae: 6.9094\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 6.9038 - mae: 6.9038\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.8981 - mae: 6.8981\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.8925 - mae: 6.8925\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.8869 - mae: 6.8869\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 6.8813 - mae: 6.8813\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d236c7da00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "# 1. Creating Model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# 2. Compuiling a 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) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc15fcac-980a-4613-8fdd-000543220589",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 363,
     "status": "ok",
     "timestamp": 1620755463102,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "bc15fcac-980a-4613-8fdd-000543220589",
    "outputId": "094f8f1e-5fcd-4b8a-b537-50d3509c8bd7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.457797]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try and make prediction\n",
    "y_pred = model.predict([5.0])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "IMI9jYWFTdoI",
   "metadata": {
    "id": "IMI9jYWFTdoI"
   },
   "source": [
    "## Improve the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "hLSn1UK0X3mq",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 68,
     "status": "ok",
     "timestamp": 1620755467213,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "hLSn1UK0X3mq",
    "outputId": "b6f5c144-1085-4261-fac4-54f505142f9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(500,), dtype=float32, numpy=\n",
       " array([6.87891235e+01, 4.84478836e+01, 9.30994415e+01, 2.52187004e+01,\n",
       "        7.31153946e+01, 8.92568207e+01, 9.46748276e+01, 7.49334106e+01,\n",
       "        3.49256287e+01, 5.47182541e+01, 2.61603947e+01, 6.97343216e+01,\n",
       "        1.19625807e+01, 5.34843445e+01, 7.14896774e+01, 8.75017776e+01,\n",
       "        3.39674950e+01, 1.73776264e+01, 4.41852112e+01, 9.00826111e+01,\n",
       "        1.38038635e+01, 1.22179747e+01, 5.75449104e+01, 9.41718140e+01,\n",
       "        9.18658524e+01, 5.97084770e+01, 6.10948219e+01, 8.20862656e+01,\n",
       "        8.32697906e+01, 8.91584854e+01, 1.37722492e+00, 4.98074646e+01,\n",
       "        5.75036659e+01, 6.85619507e+01, 7.59727859e+01, 9.08944016e+01,\n",
       "        4.09002190e+01, 8.76515350e+01, 5.38900261e+01, 4.27330971e+01,\n",
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       "        9.70401764e+01, 6.13973141e+00, 5.30347481e+01, 9.86999359e+01,\n",
       "        4.74694481e+01, 8.64675369e+01, 2.03010674e+01, 1.14484549e+01,\n",
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       "        6.56516571e+01, 9.25825577e+01, 2.39171505e+01, 8.36595764e+01,\n",
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       "        1.49196033e+01, 8.59481583e+01, 9.16732788e+01, 5.04495392e+01,\n",
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       "        8.49067001e+01, 2.05561638e+01, 3.90226479e+01, 5.90919952e+01,\n",
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       "        9.94888992e+01, 6.34980431e+01, 9.07959900e+01, 6.83556213e+01,\n",
       "        3.41896057e+01, 3.70069618e+01, 3.83494034e+01, 9.61771011e+01,\n",
       "        5.28049011e+01, 2.03329811e+01, 2.80536537e+01, 6.86178665e+01,\n",
       "        8.30521545e+01, 1.72950630e+01, 3.83119583e+01, 3.60430832e+01,\n",
       "        4.31462517e+01, 9.20127182e+01, 9.38506851e+01, 5.35561562e+01,\n",
       "        2.89724350e+01, 6.31305466e+01, 9.27119980e+01, 5.59410820e+01,\n",
       "        4.58888893e+01, 3.00925026e+01, 8.44438782e+01, 7.94572449e+01,\n",
       "        1.68455486e+01, 5.00970497e+01, 5.34514771e+01, 8.16843414e+01,\n",
       "        2.67147903e+01, 7.51518936e+01, 3.15358047e+01, 6.23232727e+01,\n",
       "        2.10112457e+01, 9.21640244e+01, 9.60234680e+01, 2.69160385e+01,\n",
       "        1.73444977e+01, 8.77467422e+01, 1.79169426e+01, 9.50680695e+01,\n",
       "        7.22839584e+01, 5.96815338e+01, 5.28050194e+01, 6.94626570e+00,\n",
       "        2.23957300e+00, 5.25833015e+01, 4.71002235e+01, 3.82860899e+00,\n",
       "        2.45585079e+01, 6.40271454e+01, 1.18741989e+01, 3.70935783e+01,\n",
       "        7.57660980e+01, 4.23809052e+01, 4.01034355e+01, 6.27136688e+01,\n",
       "        1.00385780e+01, 2.23830948e+01, 2.90045738e+01, 5.77788353e+00,\n",
       "        8.05728226e+01, 9.77212448e+01, 7.73264999e+01, 5.83416710e+01,\n",
       "        2.39693642e+01, 5.83573112e+01, 7.85795441e+01, 2.47143021e+01,\n",
       "        4.67370872e+01, 9.81726913e+01, 1.97929382e+01, 7.46305695e+01,\n",
       "        3.54431381e+01, 1.55549049e+01, 5.44155350e+01, 4.96847878e+01,\n",
       "        4.75833054e+01, 1.98220978e+01, 7.86604080e+01, 8.87897720e+01,\n",
       "        4.42264099e+01, 9.18925781e+01, 8.56256943e+01, 7.05313416e+01,\n",
       "        9.60019684e+01, 9.58736649e+01, 3.52309723e+01, 7.51322784e+01,\n",
       "        6.39195786e+01, 5.19311790e+01, 8.11165543e+01, 4.46854591e+01,\n",
       "        7.10866318e+01, 1.33754730e+01, 2.90071125e+01, 6.97024002e+01,\n",
       "        2.04237576e+01, 1.28851414e+01, 5.99576225e+01, 2.07826385e+01,\n",
       "        1.62568092e+00, 3.88608208e+01, 4.99403114e+01, 3.99644737e+01,\n",
       "        8.61331253e+01, 8.54347000e+01, 1.84130669e-01, 7.63949509e+01,\n",
       "        7.93247604e+01, 8.01684265e+01, 4.22909393e+01, 7.30639343e+01,\n",
       "        7.54046173e+01, 2.43059750e+01, 4.20184135e+00, 1.76515579e+01,\n",
       "        3.21204529e+01, 7.95956116e+01, 2.82718887e+01, 3.89244194e+01,\n",
       "        7.63261318e-01, 6.52570953e+01, 9.42623596e+01, 8.48012695e+01,\n",
       "        1.45654564e+01, 5.02259026e+01, 5.00802383e+01, 6.67071915e+01,\n",
       "        7.95574570e+01, 1.97914715e+01, 3.20254669e+01, 3.00641060e+01,\n",
       "        9.45493011e+01, 8.47681732e+01, 7.82128296e+01, 4.04308434e+01,\n",
       "        9.04588089e+01, 4.47835579e+01, 4.50701103e+01, 5.42919998e+01,\n",
       "        8.70293884e+01, 8.10094604e+01, 6.43266439e+00, 1.86014538e+01,\n",
       "        1.05375051e+00, 3.83579483e+01, 6.49020309e+01, 1.34482746e+01,\n",
       "        8.79745483e+00, 7.76373901e+01, 3.08268070e+01, 1.03683834e+01,\n",
       "        5.16151314e+01, 3.38107452e+01, 6.18588676e+01, 1.55141716e+01,\n",
       "        2.67027626e+01, 6.92076187e+01, 3.31802139e+01, 9.21256332e+01,\n",
       "        2.73699284e+01, 1.06318588e+01, 6.21861687e+01, 6.06759071e+01,\n",
       "        3.87469177e+01, 3.52707253e+01, 3.51033096e+01, 8.38424454e+01,\n",
       "        2.79881954e+01, 7.51965179e+01, 4.05955086e+01, 7.94499130e+01,\n",
       "        6.52922897e+01, 2.51003742e+01, 2.53449078e+01, 6.14570389e+01,\n",
       "        4.42690353e+01, 6.84108276e+01, 5.59784889e+01, 6.07075577e+01,\n",
       "        9.67483139e+01, 4.89061356e+01, 2.92356853e+01, 1.61170731e+01,\n",
       "        1.17256165e+01, 3.73268242e+01, 1.35183334e+01, 6.00394020e+01,\n",
       "        1.70706635e+01, 5.89730740e+00, 9.82264023e+01, 8.04528046e+00,\n",
       "        9.21477890e+01, 3.33182106e+01, 6.49059906e+01, 3.28217278e+01,\n",
       "        9.85384140e+01, 3.05630569e+01, 3.40560913e+01, 9.86031418e+01,\n",
       "        3.37176552e+01, 5.83687286e+01, 6.74362640e+01, 1.66871910e+01,\n",
       "        3.23037643e+01, 1.52338028e+01, 6.68100739e+01, 7.41268396e+00,\n",
       "        1.42855883e+01, 2.77285461e+01, 6.62159920e-01, 9.96887665e+01,\n",
       "        3.01415558e+01, 1.66073322e+01, 4.14191742e+01, 3.29615364e+01,\n",
       "        5.52117844e+01, 1.19099255e+01, 7.43775368e+00, 1.35934353e-01,\n",
       "        7.33972168e+01, 8.96839828e+01, 5.86493263e+01, 4.30403709e+01,\n",
       "        7.11433411e+01, 7.43950500e+01, 1.93560715e+01, 5.60738907e+01,\n",
       "        3.70011559e+01, 1.37199163e+01, 9.50171356e+01, 6.57345505e+01,\n",
       "        7.27276688e+01, 6.93294678e+01, 4.42266006e+01, 8.51637878e+01,\n",
       "        6.08756905e+01, 3.62926254e+01, 8.04646988e+01, 9.87497940e+01,\n",
       "        6.34331093e+01, 8.70843430e+01, 3.27320442e+01, 9.46720810e+01,\n",
       "        6.10910072e+01, 7.39611511e+01, 3.45691910e+01, 6.76299210e+01,\n",
       "        3.09837818e+01, 6.77535782e+01, 4.70373993e+01, 9.99832993e+01,\n",
       "        9.30252991e+01, 7.89380569e+01, 6.47870407e+01, 1.07782955e+01,\n",
       "        3.00210114e+01, 7.22780457e+01, 3.65051498e+01, 4.14835091e+01,\n",
       "        8.46639786e+01, 6.06443176e+01, 1.02692842e+01, 4.07418137e+01,\n",
       "        3.93046722e+01, 4.87184868e+01, 7.85524826e+01, 4.68659286e+01,\n",
       "        5.23207436e+01, 6.57168884e+01, 9.75894012e+01, 2.01012497e+01,\n",
       "        4.44096327e+00, 7.88080902e+01, 8.11438904e+01, 7.31760712e+01,\n",
       "        7.80967484e+01, 5.85267181e+01, 2.98103333e+01, 2.69530659e+01,\n",
       "        6.20468750e+01, 3.56676579e+00, 5.86907387e+01, 8.28085632e+01,\n",
       "        7.08811493e+01, 2.26290474e+01, 3.50184326e+01, 2.74954071e+01,\n",
       "        8.56859665e+01, 1.42935514e+01, 2.99245110e+01, 4.86261482e+01,\n",
       "        3.17039490e+00, 4.29527512e+01, 3.22818756e-02, 1.35606880e+01,\n",
       "        3.52038727e+01, 4.16317101e+01, 1.69781570e+01, 8.82592087e+01,\n",
       "        9.00375843e-01, 2.67494564e+01, 6.70172806e+01, 4.19482841e+01,\n",
       "        5.07242928e+01, 9.27755508e+01, 7.69033279e+01, 3.49684601e+01,\n",
       "        1.17130871e+01, 7.76648521e-01, 7.66167908e+01, 2.73753052e+01,\n",
       "        3.05392036e+01, 9.78371048e+01, 8.68108749e+01, 2.61708260e+01,\n",
       "        1.79281120e+01, 4.63037376e+01, 8.87690887e+01, 9.84267120e+01,\n",
       "        8.05071106e+01, 3.23668594e+01, 6.21531143e+01, 1.25585794e+00,\n",
       "        3.71219292e+01, 1.25539541e+01, 5.66347733e+01, 2.63186226e+01,\n",
       "        5.61105003e+01, 5.76070442e+01, 6.69041901e+01, 2.28801498e+01,\n",
       "        9.65771332e+01, 3.63649712e+01, 7.38461456e+01, 7.37625351e+01,\n",
       "        2.23124733e+01, 7.75348663e+01, 8.28531265e+01, 6.46296616e+01,\n",
       "        5.48890839e+01, 3.82241745e+01, 9.46414719e+01, 7.69985580e+01,\n",
       "        4.60815773e+01, 2.32892265e+01, 8.40965118e+01, 2.20620880e+01,\n",
       "        1.19577045e+01, 9.87456818e+01, 6.06616135e+01, 5.50765266e+01,\n",
       "        4.43664789e+00, 3.42448463e+01, 1.23397827e+01, 6.43181686e+01,\n",
       "        5.77013130e+01, 8.05149231e+01, 9.42119217e+01, 6.95896149e+01],\n",
       "       dtype=float32)>,\n",
       " <tf.Tensor: shape=(500,), dtype=float32, numpy=\n",
       " array([ 78.78912  ,  58.447884 , 103.09944  ,  35.2187   ,  83.115395 ,\n",
       "         99.25682  , 104.67483  ,  84.93341  ,  44.92563  ,  64.718254 ,\n",
       "         36.160393 ,  79.73432  ,  21.962582 ,  63.484344 ,  81.48968  ,\n",
       "         97.50178  ,  43.967495 ,  27.377626 ,  54.18521  , 100.08261  ,\n",
       "         23.803864 ,  22.217976 ,  67.54491  , 104.171814 , 101.86585  ,\n",
       "         69.70848  ,  71.09482  ,  92.086266 ,  93.26979  ,  99.158485 ,\n",
       "         11.377225 ,  59.807465 ,  67.50366  ,  78.56195  ,  85.972786 ,\n",
       "        100.8944   ,  50.90022  ,  97.651535 ,  63.890026 ,  52.733097 ,\n",
       "         50.1173   ,  76.623245 ,  26.348064 ,  28.220245 , 107.04018  ,\n",
       "         16.139732 ,  63.034748 , 108.699936 ,  57.469448 ,  96.46754  ,\n",
       "         30.301067 ,  21.448456 ,  45.100246 ,  78.22718  ,  28.108868 ,\n",
       "         91.63481  ,  75.65166  , 102.58256  ,  33.917152 ,  93.65958  ,\n",
       "         14.765272 ,  90.07157  ,  92.992676 ,  24.328373 ,  96.16878  ,\n",
       "        107.072014 ,  77.79593  ,  66.41242  ,  72.537    ,  96.53159  ,\n",
       "         26.104567 ,  21.101364 ,  71.647224 ,  70.355606 ,  73.03868  ,\n",
       "         62.13008  ,  91.86623  ,  30.56997  ,  29.041788 ,  24.648687 ,\n",
       "         24.919603 ,  95.94816  , 101.67328  ,  60.44954  ,  96.56684  ,\n",
       "         80.33374  ,  63.43715  ,  45.093033 ,  73.313484 ,  94.159874 ,\n",
       "         53.46044  ,  39.658783 ,  94.9067   ,  30.556164 ,  49.022648 ,\n",
       "         69.091995 ,  97.87719  ,  81.97267  ,  19.947216 , 104.80752  ,\n",
       "         54.62725  ,  43.6092   ,  54.68707  ,  88.97918  ,  71.74626  ,\n",
       "         64.82606  , 103.60252  ,  29.89354  ,  95.55646  ,  39.720078 ,\n",
       "         22.980974 ,  69.032135 ,  17.226849 ,  89.90017  ,  47.418842 ,\n",
       "         90.04397  ,  17.435453 ,  56.83442  ,  49.76704  ,  53.65393  ,\n",
       "         33.95352  ,  82.49464  ,  53.66442  ,  64.28084  , 105.01494  ,\n",
       "         61.135265 , 104.95735  ,  20.583961 ,  88.03346  , 103.132935 ,\n",
       "        101.63221  ,  84.1234   ,  43.278156 ,  99.80862  ,  44.247505 ,\n",
       "         67.605576 , 109.4889   ,  73.49805  , 100.79599  ,  78.35562  ,\n",
       "         44.189606 ,  47.00696  ,  48.349403 , 106.1771   ,  62.8049   ,\n",
       "         30.332981 ,  38.053654 ,  78.61787  ,  93.052155 ,  27.295063 ,\n",
       "         48.31196  ,  46.043083 ,  53.14625  , 102.01272  , 103.850685 ,\n",
       "         63.556156 ,  38.972435 ,  73.13055  , 102.712    ,  65.941086 ,\n",
       "         55.88889  ,  40.092503 ,  94.44388  ,  89.457245 ,  26.845549 ,\n",
       "         60.09705  ,  63.451477 ,  91.68434  ,  36.71479  ,  85.15189  ,\n",
       "         41.535805 ,  72.32327  ,  31.011246 , 102.164024 , 106.02347  ,\n",
       "         36.91604  ,  27.344498 ,  97.74674  ,  27.916943 , 105.06807  ,\n",
       "         82.28396  ,  69.68153  ,  62.80502  ,  16.946266 ,  12.239573 ,\n",
       "         62.5833   ,  57.100224 ,  13.828609 ,  34.55851  ,  74.027145 ,\n",
       "         21.874199 ,  47.09358  ,  85.7661   ,  52.380905 ,  50.103436 ,\n",
       "         72.71367  ,  20.038578 ,  32.383095 ,  39.004574 ,  15.777884 ,\n",
       "         90.57282  , 107.721245 ,  87.3265   ,  68.341675 ,  33.969364 ,\n",
       "         68.357315 ,  88.579544 ,  34.714302 ,  56.737087 , 108.17269  ,\n",
       "         29.792938 ,  84.63057  ,  45.44314  ,  25.554905 ,  64.415535 ,\n",
       "         59.684788 ,  57.583305 ,  29.822098 ,  88.66041  ,  98.78977  ,\n",
       "         54.22641  , 101.89258  ,  95.625694 ,  80.53134  , 106.00197  ,\n",
       "        105.873665 ,  45.230972 ,  85.13228  ,  73.91958  ,  61.93118  ,\n",
       "         91.116554 ,  54.68546  ,  81.08663  ,  23.375473 ,  39.00711  ,\n",
       "         79.7024   ,  30.423758 ,  22.885141 ,  69.95763  ,  30.782639 ,\n",
       "         11.625681 ,  48.86082  ,  59.94031  ,  49.964474 ,  96.133125 ,\n",
       "         95.4347   ,  10.184131 ,  86.39495  ,  89.32476  ,  90.16843  ,\n",
       "         52.29094  ,  83.063934 ,  85.40462  ,  34.305977 ,  14.201841 ,\n",
       "         27.651558 ,  42.120453 ,  89.59561  ,  38.27189  ,  48.92442  ,\n",
       "         10.763262 ,  75.257095 , 104.26236  ,  94.80127  ,  24.565456 ,\n",
       "         60.225903 ,  60.08024  ,  76.70719  ,  89.55746  ,  29.791471 ,\n",
       "         42.025467 ,  40.064106 , 104.5493   ,  94.76817  ,  88.21283  ,\n",
       "         50.430843 , 100.45881  ,  54.783558 ,  55.07011  ,  64.292    ,\n",
       "         97.02939  ,  91.00946  ,  16.432665 ,  28.601454 ,  11.053751 ,\n",
       "         48.35795  ,  74.90203  ,  23.448275 ,  18.797455 ,  87.63739  ,\n",
       "         40.826805 ,  20.368383 ,  61.61513  ,  43.810745 ,  71.85887  ,\n",
       "         25.514172 ,  36.702763 ,  79.20762  ,  43.180214 , 102.12563  ,\n",
       "         37.369926 ,  20.631859 ,  72.18617  ,  70.6759   ,  48.746918 ,\n",
       "         45.270725 ,  45.10331  ,  93.842445 ,  37.988197 ,  85.19652  ,\n",
       "         50.59551  ,  89.44991  ,  75.29229  ,  35.100372 ,  35.34491  ,\n",
       "         71.45704  ,  54.269035 ,  78.41083  ,  65.978485 ,  70.70756  ,\n",
       "        106.748314 ,  58.906136 ,  39.235687 ,  26.117073 ,  21.725616 ,\n",
       "         47.326824 ,  23.518333 ,  70.0394   ,  27.070663 ,  15.897307 ,\n",
       "        108.2264   ,  18.04528  , 102.14779  ,  43.31821  ,  74.90599  ,\n",
       "         42.821728 , 108.538414 ,  40.563057 ,  44.05609  , 108.60314  ,\n",
       "         43.717655 ,  68.36873  ,  77.436264 ,  26.687191 ,  42.303764 ,\n",
       "         25.233803 ,  76.810074 ,  17.412683 ,  24.285587 ,  37.728546 ,\n",
       "         10.66216  , 109.68877  ,  40.141556 ,  26.607332 ,  51.419174 ,\n",
       "         42.961536 ,  65.211784 ,  21.909925 ,  17.437754 ,  10.135935 ,\n",
       "         83.39722  ,  99.68398  ,  68.64932  ,  53.04037  ,  81.14334  ,\n",
       "         84.39505  ,  29.356071 ,  66.07389  ,  47.001156 ,  23.719917 ,\n",
       "        105.017136 ,  75.73455  ,  82.72767  ,  79.32947  ,  54.2266   ,\n",
       "         95.16379  ,  70.87569  ,  46.292625 ,  90.4647   , 108.749794 ,\n",
       "         73.433105 ,  97.08434  ,  42.732044 , 104.67208  ,  71.091    ,\n",
       "         83.96115  ,  44.56919  ,  77.62992  ,  40.98378  ,  77.75358  ,\n",
       "         57.0374   , 109.9833   , 103.0253   ,  88.93806  ,  74.78704  ,\n",
       "         20.778296 ,  40.02101  ,  82.278046 ,  46.50515  ,  51.48351  ,\n",
       "         94.66398  ,  70.64432  ,  20.269283 ,  50.741814 ,  49.304672 ,\n",
       "         58.718487 ,  88.55248  ,  56.86593  ,  62.320744 ,  75.71689  ,\n",
       "        107.5894   ,  30.10125  ,  14.440964 ,  88.80809  ,  91.14389  ,\n",
       "         83.17607  ,  88.09675  ,  68.52672  ,  39.810333 ,  36.953064 ,\n",
       "         72.046875 ,  13.566766 ,  68.690735 ,  92.80856  ,  80.88115  ,\n",
       "         32.629047 ,  45.018433 ,  37.495407 ,  95.68597  ,  24.293552 ,\n",
       "         39.92451  ,  58.62615  ,  13.170395 ,  52.95275  ,  10.032282 ,\n",
       "         23.560688 ,  45.203873 ,  51.63171  ,  26.978157 ,  98.25921  ,\n",
       "         10.900375 ,  36.74946  ,  77.01728  ,  51.948284 ,  60.724293 ,\n",
       "        102.77555  ,  86.90333  ,  44.96846  ,  21.713087 ,  10.7766485,\n",
       "         86.61679  ,  37.375305 ,  40.539204 , 107.837105 ,  96.810875 ,\n",
       "         36.170826 ,  27.928112 ,  56.303738 ,  98.76909  , 108.42671  ,\n",
       "         90.50711  ,  42.36686  ,  72.153114 ,  11.255857 ,  47.12193  ,\n",
       "         22.553955 ,  66.63477  ,  36.318623 ,  66.110504 ,  67.60704  ,\n",
       "         76.90419  ,  32.88015  , 106.57713  ,  46.36497  ,  83.846146 ,\n",
       "         83.762535 ,  32.312473 ,  87.53487  ,  92.85313  ,  74.62966  ,\n",
       "         64.889084 ,  48.224174 , 104.64147  ,  86.99856  ,  56.081577 ,\n",
       "         33.289227 ,  94.09651  ,  32.062088 ,  21.957705 , 108.74568  ,\n",
       "         70.66161  ,  65.07652  ,  14.436647 ,  44.244846 ,  22.339783 ,\n",
       "         74.31817  ,  67.70131  ,  90.51492  , 104.21192  ,  79.589615 ],\n",
       "       dtype=float32)>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.random.uniform(\n",
    "    shape=(500,), minval=0, maxval=100, dtype=tf.float32\n",
    ")\n",
    "Y = X + 10\n",
    "\n",
    "\n",
    "X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a82f285b-25cc-4e7b-ad81-bb29c1ddf5eb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 9808,
     "status": "error",
     "timestamp": 1620755484764,
     "user": {
      "displayName": "Pritesh K",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhF6__ePTHwH6FOGfpMnlYyZAb3k7dPrjsUF9LtUg=s64",
      "userId": "10569277111666665261"
     },
     "user_tz": -330
    },
    "id": "a82f285b-25cc-4e7b-ad81-bb29c1ddf5eb",
    "outputId": "8609d664-6812-4b36-ee03-9f06be06ff71",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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      "16/16 [==============================] - 0s 4ms/step - loss: 0.2383 - mae: 0.2383\n",
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      "16/16 [==============================] - 0s 4ms/step - loss: 0.1131 - mae: 0.1131\n",
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      "Epoch 179/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2080 - mae: 0.2080\n",
      "Epoch 180/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1322 - mae: 0.1322\n",
      "Epoch 181/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.1353 - mae: 0.1353\n",
      "Epoch 182/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2377 - mae: 0.2377\n",
      "Epoch 183/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.1230 - mae: 0.1230\n",
      "Epoch 184/300\n",
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      "Epoch 185/300\n",
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      "16/16 [==============================] - 0s 4ms/step - loss: 0.2418 - mae: 0.2418\n",
      "Epoch 187/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.0775 - mae: 0.0775\n",
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      "Epoch 190/300\n",
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      "Epoch 193/300\n",
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      "16/16 [==============================] - 0s 6ms/step - loss: 0.1069 - mae: 0.1069\n",
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      "16/16 [==============================] - 0s 4ms/step - loss: 0.0955 - mae: 0.0955\n",
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      "Epoch 218/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2008 - mae: 0.2008\n",
      "Epoch 219/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0755 - mae: 0.0755\n",
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      "Epoch 227/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1831 - mae: 0.1831\n",
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      "16/16 [==============================] - 0s 4ms/step - loss: 0.2468 - mae: 0.2468\n",
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      "16/16 [==============================] - 0s 4ms/step - loss: 0.1042 - mae: 0.1042\n",
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      "Epoch 231/300\n",
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      "Epoch 255/300\n",
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      "Epoch 256/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0862 - mae: 0.0862\n",
      "Epoch 257/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1148 - mae: 0.1148A: 0s - loss: 0.1112 - mae: 0.111\n",
      "Epoch 258/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1339 - mae: 0.1339\n",
      "Epoch 259/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0564 - mae: 0.0564\n",
      "Epoch 260/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1225 - mae: 0.1225\n",
      "Epoch 261/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2122 - mae: 0.2122\n",
      "Epoch 262/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.3430 - mae: 0.3430\n",
      "Epoch 263/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2217 - mae: 0.2217\n",
      "Epoch 264/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0981 - mae: 0.0981\n",
      "Epoch 265/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1884 - mae: 0.1884\n",
      "Epoch 266/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.1874 - mae: 0.1874\n",
      "Epoch 267/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.3078 - mae: 0.3078\n",
      "Epoch 268/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.3475 - mae: 0.3475\n",
      "Epoch 269/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.2121 - mae: 0.2121\n",
      "Epoch 270/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.2041 - mae: 0.2041\n",
      "Epoch 271/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.3895 - mae: 0.3895\n",
      "Epoch 272/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.2022 - mae: 0.2022\n",
      "Epoch 273/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.2412 - mae: 0.2412\n",
      "Epoch 274/300\n",
      "16/16 [==============================] - 0s 6ms/step - loss: 0.3113 - mae: 0.3113\n",
      "Epoch 275/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.3361 - mae: 0.3361\n",
      "Epoch 276/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2262 - mae: 0.2262\n",
      "Epoch 277/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1567 - mae: 0.1567\n",
      "Epoch 278/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1309 - mae: 0.1309\n",
      "Epoch 279/300\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.3247 - mae: 0.3247\n",
      "Epoch 280/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1255 - mae: 0.1255\n",
      "Epoch 281/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.3054 - mae: 0.3054\n",
      "Epoch 282/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2113 - mae: 0.2113\n",
      "Epoch 283/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.3391 - mae: 0.3391\n",
      "Epoch 284/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2469 - mae: 0.2469\n",
      "Epoch 285/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1897 - mae: 0.1897\n",
      "Epoch 286/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0714 - mae: 0.0714\n",
      "Epoch 287/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1104 - mae: 0.1104\n",
      "Epoch 288/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1093 - mae: 0.1093\n",
      "Epoch 289/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1270 - mae: 0.1270\n",
      "Epoch 290/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0999 - mae: 0.0999\n",
      "Epoch 291/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1398 - mae: 0.1398\n",
      "Epoch 292/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2415 - mae: 0.2415\n",
      "Epoch 293/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1898 - mae: 0.1898\n",
      "Epoch 294/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1976 - mae: 0.1976\n",
      "Epoch 295/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.2390 - mae: 0.2390\n",
      "Epoch 296/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.2353 - mae: 0.2353\n",
      "Epoch 297/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.1973 - mae: 0.1973\n",
      "Epoch 298/300\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.1207 - mae: 0.1207\n",
      "Epoch 299/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0830 - mae: 0.0830\n",
      "Epoch 300/300\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1035 - mae: 0.1035\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d2444919d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "# 1. Creating Model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100, activation=None),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# 2. Compuiling a model\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "              optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "#               optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mae\"])\n",
    "\n",
    "# 3. Fit the model\n",
    "model.fit(X, Y, epochs=300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cb2cc1aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5024.3804]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([5000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66c59d5",
   "metadata": {},
   "source": [
    "## Evaluating a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62aacf15",
   "metadata": {},
   "outputs": [
    {
     "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])>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.range(-100, 100, 4)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46b7aa13",
   "metadata": {},
   "outputs": [
    {
     "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])>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = X +10\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "878b98b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d241920cd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVDElEQVR4nO3df+xldX3n8edr8UeItQuWWToOTGdwgV1MswN8w5qgJgoWIa2Api5s4uJqOjUr2brdpR1k05htTFGWmjRtdIeUFDcquuWHpKWLIG672yzWGWc6DALLDIXI13EYdRGzEir43j++54t3xnvnO9/vPffXuc9HcnPP/Zx773nPuZf3nHndD+ekqpAkddM/mHQBkqTRsclLUofZ5CWpw2zyktRhNnlJ6rCXTbqAXieddFJt2rRp0mVI0kzZuXPnd6pqXb91U9XkN23axI4dOyZdhiTNlCRPDlpnXCNJHWaTl6QOs8lLUofZ5CWpw2zyktRhUzW7RpLmzZ27Frnhnkf51jPP8doTjueai87ksrM3tPb+NnlJmpA7dy1y7e0P8tyPXgRg8ZnnuPb2BwFaa/TGNZI0ITfc8+hLDX7Zcz96kRvuebS1bdjkJWlCvvXMc6saXwvjGkkag37Z+2tPOJ7FPg39tScc39p2PZKXpBFbzt4Xn3mO4ifZ+1v+yTqOf/lxhz33+JcfxzUXndnatlfV5JPcnOTpJHt7xl6T5N4kjzX3JzbjSfIHSfYl2ZPknNaqlqQZMih7/8ojh/i9d/4iG044ngAbTjie33vnL050ds2fAH8IfLpnbBvw5aq6Psm25vFvAxcDpze3fw58srmXpLlytOz9srM3tNrUj7SqI/mq+ivge0cMXwrc0izfAlzWM/7pWvIAcEKS9UPUKklT7c5di5x//f1s3vbnnH/9/dy5axEYnLG3mb0P0kYmf3JVHWiWvw2c3CxvAL7Z87ynmrHDJNmaZEeSHYcOHWqhHEkav0G5+527FrnmojNHnr0P0uoPr1VVQK3yNduraqGqFtat63vOe0maekeb837Z2RtGnr0P0sYUyoNJ1lfVgSaOeboZXwRO7XneKc2YJHXOSnPeR529D9JGk78LuAq4vrn/Ys/41UluZekH1+/3xDqSNLMmNed9LVY7hfJzwP8GzkzyVJL3s9Tc35bkMeDC5jHA3cDjwD7gJuDftFa1JE3IJOe8r8WqjuSr6soBqy7o89wCPriWoiRpWq00532UZ5RcC09rIEmrMMk572thk5ekAWYpex/Ec9dIUh+zlr0PYpOXpD4meb6ZNhnXSFIfs5a9D2KTlzT3upC9D2JcI2mudSV7H8QmL2mudSV7H8S4RtJc60r2PohNXtLc6HL2PohxjaS50PXsfRCbvKS50PXsfRDjGklzoevZ+yA2eUmd0i93v+zsDZ3P3gcxrpHUGdN6ndVJsslL6oxpvc7qJA0d1yQ5E/h8z9BpwO8AJwC/Bhxqxj9cVXcPuz1JGmRar7M6SUMfyVfVo1W1paq2AOcCPwTuaFZ/YnmdDV7SqA3K17ueux9N2z+8XgDsr6onk7T81pL0E/1+YL3mojO59vYHD4ts5iF3P5q2M/krgM/1PL46yZ4kNyc5seVtSZpTg35gBeYydz+aLF1vu4U3Sl4BfAt4fVUdTHIy8B2ggN8F1lfV+/q8biuwFWDjxo3nPvnkk63UI6m7zr/+/r7TITeccDx/ve2tE6hospLsrKqFfuvaPJK/GPh6VR0EqKqDVfViVf0YuAk4r9+Lqmp7VS1U1cK6detaLEdSV630A6t+os1M/kp6opok66vqQPPwcmBvi9uSNCfm8aRibWrlSD7Jq4C3Abf3DH88yYNJ9gBvAf5dG9uSND/m9aRibWrlSL6q/h/wc0eMvaeN95Y0v1Y6qVi/0xfocJ67RtLUmteTirXJJi9pKpi9j4bnrpE0cWbvo2OTlzRx83pBj3EwrpE0cWbvo2OTlzRWZu/jZVwjaWzM3sfPJi9pbMzex8+4RtLYmL2Pn01eUuu8mPb0MK6R1Covpj1dbPKSWuXFtKeLcY2kVnkx7elik5e0Zs55n37GNZLWxDnvs8EmL2lNnPM+G4xrJK2Jc95nQ2tNPskTwA+AF4EXqmohyWuAzwObgCeAd1fV/21rm5LGw+x9drUd17ylqrZU1ULzeBvw5ao6Hfhy81jSDDF7n22jzuQvBW5plm8BLhvx9iS1zOx9trWZyRfwpSQF/Jeq2g6cXFUHmvXfBk4+8kVJtgJbATZu3NhiOZLaYPY+29ps8m+sqsUk/wi4N8kjvSurqpq/ADhifDuwHWBhYeGn1ksaH7P37mktrqmqxeb+aeAO4DzgYJL1AM39021tT1K7zN67qZUmn+RVSV69vAz8ErAXuAu4qnnaVcAX29iepPaZvXdTW3HNycAdSZbf87NV9d+TfA34QpL3A08C725pe5JaZvbeTa00+ap6HPhnfca/C1zQxjYktcfsfX54WgNpzpi9zxebvDRnzN7ni+eukeaM2ft8sclLHeV1VgXGNVIneZ1VLbPJSx3kdVa1zLhG6iCvs6plNnlpxjnnXUdjXCPNMOe8ayU2eWmGOeddKzGukWaYc961Epu8NCPM3rUWxjXSDDB711rZ5KUZYPautTKukWaA2bvWyiYvTRmzd7Vp6LgmyalJvpLkG0keSvIbzfhHkiwm2d3cLhm+XKnbzN7VtjYy+ReAf19VZwFvAD6Y5Kxm3Seqaktzu7uFbUmdZvautg0d11TVAeBAs/yDJA8DfvOkNTB7V9tanV2TZBNwNvDVZujqJHuS3JzkxAGv2ZpkR5Idhw4darMcaWrduWuR86+/n83b/pzzr7+fO3ctAoMzdrN3rVVrTT7JzwC3AR+qqmeBTwKvA7awdKR/Y7/XVdX2qlqoqoV169a1VY40tTzXu8aplSaf5OUsNfjPVNXtAFV1sKperKofAzcB57WxLWnWea53jdPQmXySAH8MPFxVv98zvr7J6wEuB/YOuy2pCzzXu8apjXny5wPvAR5MsrsZ+zBwZZItQAFPAL/ewrakmeKcd01aG7Nr/heQPqucMqm5tpy9L0czy9n7u87dwG07Fw+LbMzdNSqeu0YaEee8axp4WgNpRJzzrmlgk5daYPauaWVcIw3J881omtnkpSGZvWuaGddIQzJ71zSzyUurYPauWWNcIx0js3fNIpu8dIzM3jWLjGukY2T2rllkk5f6MHtXVxjXSEcwe1eX2OSlI5i9q0uMa6QjmL2rS2zymlv9cvfLzt5g9q5OMa7RXPI6q5oXI2/ySd6e5NEk+5JsG/X2pGPhdVY1L0Ya1yQ5Dvgj4G3AU8DXktxVVd8Y5XallXidVc2LUWfy5wH7qupxgCS3ApcCNnmNjXPeNc9GHddsAL7Z8/ipZuwlSbYm2ZFkx6FDh0ZcjuaNc9417yb+w2tVba+qhapaWLdu3aTLUcc4513zbtRxzSJwas/jU5oxaSyc8655N+om/zXg9CSbWWruVwD/csTb1Jwye5d+2kjjmqp6AbgauAd4GPhCVT00ym1qPpm9S/2NPJOvqrur6oyqel1VfXTU29N8MnuX+vO0BuoEs3epP5u8Zo7Zu3TsJj6FUloNs3dpdWzymilm79LqGNdoppi9S6tjk9fUMnuXhmdco6lk9i61wyavqWT2LrXDuEZTyexdaodNXhPldVal0TKu0cR4nVVp9GzymhivsyqNnnGNJsbrrEqjZ5PXWDjnXZoM4xqNnHPepcmxyWvknPMuTc5QcU2SG4BfAf4e2A/866p6Jskmlq4E9Wjz1Aeq6gPDbEuzyznv0uQMm8nfC1xbVS8k+RhwLfDbzbr9VbVlyPfXjDF7l6bLUHFNVX2puY4rwAPAKcOXpFll9i5NnzYz+fcBf9HzeHOSXUn+MsmbBr0oydYkO5LsOHToUIvlaNzM3qXps2Jck+Q+4Of7rLquqr7YPOc64AXgM826A8DGqvpuknOBO5O8vqqePfJNqmo7sB1gYWGh1vbH0DQwe5emz4pNvqouPNr6JO8Ffhm4oKqqec3zwPPN8s4k+4EzgB3DFqzpYPYuzYah4pokbwd+C3hHVf2wZ3xdkuOa5dOA04HHh9mWpofZuzQ7hs3k/xB4NXBvkt1JPtWMvxnYk2Q38KfAB6rqe0NuS1PC7F2aHUNNoayqfzxg/DbgtmHeW9PL7F2aHZ67Rkdl9i7NNk9roIHM3qXZZ5PXQGbv0uwzrtFAZu/S7LPJy+usSh1mXDPnvM6q1G02+TnndValbjOumXNeZ1XqNo/k59ygfN3cXeoGj+TnSL8fWK+56Eyuvf3BwyIbc3epOzySnxODfmAFzN2lDvNIfk4c7QfWv972Vpu61FEeyc+JlX5gldRNHsl3kCcVk7TMI/mO8aRiknrZ5DvGk4pJ6jVUXJPkI8CvAYeaoQ9X1d3NumuB9wMvAv+2qu4ZZls6Np5UTFKvNjL5T1TVf+4dSHIWcAXweuC1wH1JzqiqF/u9gdbG7F3SSkYV11wK3FpVz1fV3wH7gPNGtK25ZPYu6Vi00eSvTrInyc1JTmzGNgDf7HnOU82YWmL2LulYrBjXJLkP+Pk+q64DPgn8LlDN/Y3A+1ZTQJKtwFaAjRs3rualc83sXdKxWLHJV9WFx/JGSW4C/qx5uAic2rP6lGas3/tvB7YDLCws1LFsa554QQ9Jwxgqrkmyvufh5cDeZvku4Iokr0yyGTgd+JthtjWPvKCHpGENO7vm40m2sBTXPAH8OkBVPZTkC8A3gBeADzqzZvVWOt/M8nOOPMqXpGVDNfmqes9R1n0U+Ogw7z/vvKCHpGF57pop4Zx3SaPgaQ2mgHPeJY2KTX4KOOdd0qgY10wB57xLGhWb/JiZvUsaJ+OaMTJ7lzRuNvkxMnuXNG7GNWNk9i5p3GzyI2L2LmkaGNeMgNm7pGlhkx8Bs3dJ08K4ZgTM3iVNC5v8kMzeJU0z45ohmL1LmnY2+SGYvUuadsY1QzB7lzTtbPLHwOusSppVw17j9fNJdje3J5LsbsY3JXmuZ92nWql2ArzOqqRZNuzl//7F8nKSG4Hv96zeX1Vbhnn/aeB1ViXNslbimiQB3g28tY33myZeZ1XSLGsrk38TcLCqHusZ25xkF/As8B+r6n/2e2GSrcBWgI0bN7ZUzto4511S16yYySe5L8nePrdLe552JfC5nscHgI1VdTbwm8Bnk/xsv/evqu1VtVBVC+vWrRvmzzIU57xL6qIVj+Sr6sKjrU/yMuCdwLk9r3keeL5Z3plkP3AGsGOoakdopTnv5u6SZlEbcc2FwCNV9dTyQJJ1wPeq6sUkpwGnA4+3sK2Rcc67pC5qo8lfweFRDcCbgf+U5EfAj4EPVNX3WthWK8zeJc2LoZt8Vb23z9htwG3DvvcoLGfvy9HMcvb+rnM3cNvOxcMiG7N3SbNu7s5d4/lmJM2TuTutgdm7pHnS6SZv9i5p3nU2rnHeuyR1uMmbvUtSh+Mas3dJ6kiTN3uXpP5mPq4xe5ekwWa+yZu9S9JgMx/XmL1L0mAzfyQ/KGM3e5ekDjR5r7MqSYPNfFyzHMd4vndJ+mkz3+TB66xK0iAzH9dIkgazyUtSh9nkJanDbPKS1GE2eUnqsFTVpGt4SZJDwJNDvMVJwHdaKqdN01oXWNtaWdvqTWtdMPu1/UJVreu3Yqqa/LCS7KiqhUnXcaRprQusba2sbfWmtS7odm3GNZLUYTZ5SeqwrjX57ZMuYIBprQusba2sbfWmtS7ocG2dyuQlSYfr2pG8JKmHTV6SOmwmm3ySX03yUJIfJ1k4Yt21SfYleTTJRT3jb2/G9iXZNqY6P59kd3N7IsnuZnxTkud61n1qHPUcUdtHkiz21HBJz7q++3CMtd2Q5JEke5LckeSEZnwa9tvYv0dHqeXUJF9J8o3mv4ffaMYHfrZjru+JJA82Nexoxl6T5N4kjzX3J06grjN79s3uJM8m+dCk9luSm5M8nWRvz1jf/ZQlf9B8//YkOWfFDVTVzN2AfwqcCfwPYKFn/Czgb4FXApuB/cBxzW0/cBrwiuY5Z4255huB32mWNwF7J7wPPwL8hz7jfffhmGv7JeBlzfLHgI9Nw36bhu/REfWsB85pll8N/J/m8+v72U6gvieAk44Y+ziwrVnetvzZTvgz/TbwC5Pab8CbgXN6v9uD9hNwCfAXQIA3AF9d6f1n8ki+qh6uqkf7rLoUuLWqnq+qvwP2Aec1t31V9XhV/T1wa/PcsUgS4N3A58a1zSEM2odjU1VfqqoXmocPAKeMc/tHMdHv0ZGq6kBVfb1Z/gHwMDDtF1a4FLilWb4FuGxypQBwAbC/qob5P+2HUlV/BXzviOFB++lS4NO15AHghCTrj/b+M9nkj2ID8M2ex081Y4PGx+VNwMGqeqxnbHOSXUn+MsmbxlhLr6ubf/Ld3PPP5knvqyO9j6Ujl2WT3G/Ttm9ekmQTcDbw1Wao32c7bgV8KcnOJFubsZOr6kCz/G3g5MmU9pIrOPzgaxr2GwzeT6v+Dk5tk09yX5K9fW4TO3Lq5xjrvJLDv0gHgI1VdTbwm8Bnk/zsmGv7JPA6YEtTz41tb3+I2pafcx3wAvCZZmgs+23WJPkZ4DbgQ1X1LBP+bHu8sarOAS4GPpjkzb0rayl/mNgc7iSvAN4B/LdmaFr222GG3U9Te/m/qrpwDS9bBE7teXxKM8ZRxoeyUp1JXga8Ezi35zXPA883yzuT7AfOAHa0UdOx1tZT403AnzUPj7YPW3MM++29wC8DFzRf8rHtt6MYy75ZjSQvZ6nBf6aqbgeoqoM963s/27GqqsXm/ukkd7AUdx1Msr6qDjQxw9OTqK1xMfD15f01LfutMWg/rfo7OLVH8mt0F3BFklcm2QycDvwN8DXg9CSbm7+9r2ieOw4XAo9U1VPLA0nWJTmuWT6tqfPxMdWzXENvjnc5sPzL/qB9OM7a3g78FvCOqvphz/ik99skv0c/pfmt54+Bh6vq93vGB32246ztVUlevbzM0o/pe1naX1c1T7sK+OK4a+tx2L+wp2G/9Ri0n+4C/lUzy+YNwPd7Yp3+JvnL9hC/Rl/OUhb1PHAQuKdn3XUszYB4FLi4Z/wSlmYf7AeuG2OtfwJ84IixdwEPAbuBrwO/MoF9+F+BB4E9zRdn/Ur7cIy17WMpd9zd3D41RfttIt+jAbW8kaV/xu/p2VeXHO2zHWNtp7E0++hvm8/sumb854AvA48B9wGvmdC+exXwXeAf9oxNZL+x9BfNAeBHTV97/6D9xNKsmj9qvn8P0jO7cNDN0xpIUod1La6RJPWwyUtSh9nkJanDbPKS1GE2eUnqMJu8JHWYTV6SOuz/AxoNPqtYbk+wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdaa81f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "82aaadd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 10)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data\n",
    "x_train = X[:40]\n",
    "y_train = Y[:40]\n",
    "\n",
    "x_test = X[40:]\n",
    "y_test = Y[40:]\n",
    "\n",
    "len(x_train), len(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fc0013",
   "metadata": {},
   "source": [
    "### Visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "88cfa929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d342326a60>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(x_train, y_train, c=\"b\", label=\"Training Data\")\n",
    "plt.scatter(x_test, y_test, c=\"g\", label=\"Testing Data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0f312208",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mae\"])\n",
    "\n",
    "# model.fit(x_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9ab2f9a0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "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.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-5f15418b3570>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\prite\\.virtualenvs\\machine-learning\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36msummary\u001b[1;34m(self, line_length, positions, print_fn)\u001b[0m\n\u001b[0;32m   2374\u001b[0m     \"\"\"\n\u001b[0;32m   2375\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2376\u001b[1;33m       raise ValueError('This model has not yet been built. '\n\u001b[0m\u001b[0;32m   2377\u001b[0m                        \u001b[1;34m'Build the model first by calling `build()` or calling '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2378\u001b[0m                        \u001b[1;34m'`fit()` with some data, or specify '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: This model has not yet been built. Build the model first by calling `build()` or calling `fit()` with some data, or specify an `input_shape` argument in the first layer(s) for automatic build."
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e28b3c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(10, input_shape=[1], name=\"input_layer\"),\n",
    "    tf.keras.layers.Dense(1, name=\"output_layer\") \n",
    "])\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mae\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e5f17867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\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"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1aee7ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 26.8627 - mae: 26.8627\n",
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      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 28.3537 - mae: 28.3537\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.6590 - mae: 9.6590\n",
      "Epoch 12/100\n",
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      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.7682 - mae: 16.7682\n",
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      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.4550 - mae: 7.4550\n",
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      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 20.7691 - mae: 20.7691\n",
      "Epoch 19/100\n",
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      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.4600 - mae: 18.4600\n",
      "Epoch 21/100\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 14.5012 - mae: 14.5012\n",
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      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 26.8011 - mae: 26.8011\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.0333 - mae: 12.0333\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 23.4573 - mae: 23.4573\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.1266 - mae: 9.1266\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 31.1804 - mae: 31.1804\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 56.8211 - mae: 56.8211\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.2909 - mae: 12.2909\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.3303 - mae: 15.3303\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.8145 - mae: 12.8145\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.4721 - mae: 9.4721\n",
      "Epoch 36/100\n",
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      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.4634 - mae: 10.4634\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 18.7760 - mae: 18.7760\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 20.1198 - mae: 20.1198\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 21.4944 - mae: 21.4944\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.5667 - mae: 14.5667\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 12.2999 - mae: 12.2999\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.1115 - mae: 11.1115\n",
      "Epoch 44/100\n",
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      "Epoch 45/100\n",
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      "Epoch 46/100\n",
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      "Epoch 49/100\n",
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      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.7912 - mae: 13.7912\n",
      "Epoch 51/100\n",
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      "Epoch 52/100\n",
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      "Epoch 53/100\n",
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      "Epoch 54/100\n",
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      "Epoch 55/100\n",
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      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.0101 - mae: 10.0101\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.4333 - mae: 12.4333\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.4491 - mae: 9.4491\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.6438 - mae: 12.6438\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.3168 - mae: 12.3168\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.1832 - mae: 17.1832\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.4860 - mae: 10.4860\n",
      "Epoch 63/100\n",
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      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 25.2892 - mae: 25.2892\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.7923 - mae: 10.7923\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 22.0328 - mae: 22.0328\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.6505 - mae: 10.6505\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.8455 - mae: 10.8455\n",
      "Epoch 69/100\n",
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      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.7402 - mae: 8.7402\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.8968 - mae: 15.8968\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.7092 - mae: 6.7092\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.6475 - mae: 10.6475\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 24.6152 - mae: 24.6152\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.7602 - mae: 8.7602\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9799 - mae: 11.9799\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.6488 - mae: 16.6488\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.2784 - mae: 9.2784\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 24.4604 - mae: 24.4604\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 27.4405 - mae: 27.4405\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7139 - mae: 11.7139\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 11.6757 - mae: 11.6757\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.9045 - mae: 17.9045\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.0376 - mae: 7.0376\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.1558 - mae: 15.1558\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.1583 - mae: 15.1583\n",
      "Epoch 87/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 5ms/step - loss: 19.8683 - mae: 19.8683\n",
      "Epoch 88/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 30.7657 - mae: 30.7657\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.7059 - mae: 9.7059\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 21.5531 - mae: 21.5531\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.5827 - mae: 9.5827\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 18.2158 - mae: 18.2158\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.1935 - mae: 7.1935\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.4261 - mae: 17.4261\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.1722 - mae: 11.1722\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.9207 - mae: 18.9207\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.2012 - mae: 12.2012\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.2931 - mae: 11.2931\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.4563 - mae: 13.4563\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 20.1754 - mae: 20.1754\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d3436c9940>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a91390e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "plot_model(model=model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a19cbb",
   "metadata": {},
   "source": [
    "## visualizing model's prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6db4924f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D3422B4430> 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 70.55221 ],\n",
       "       [ 75.13992 ],\n",
       "       [ 79.72765 ],\n",
       "       [ 84.31538 ],\n",
       "       [ 88.9031  ],\n",
       "       [ 93.49082 ],\n",
       "       [ 98.07855 ],\n",
       "       [102.666275],\n",
       "       [107.254005],\n",
       "       [111.84173 ]], dtype=float32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a predictions\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "280ea7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106])>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "510265f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a plotting function\n",
    "\n",
    "def plot_predictions(train_data=x_train, train_labels=y_train,\n",
    "                    test_data=x_test, test_labels=y_test, predictions=y_pred):\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    plt.scatter(train_data, train_labels, c=\"b\", label=\"Training Data\")\n",
    "    plt.scatter(test_data, test_labels, c=\"g\", label=\"Testing Data\")\n",
    "    plt.scatter(test_data, predictions, c=\"r\", label=\"Predictions\")\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c7e26293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(train_data=x_train,\n",
    "                 train_labels=y_train,\n",
    "                 test_data=x_test,\n",
    "                 test_labels=y_test,\n",
    "                 predictions=y_pred\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7bae92",
   "metadata": {},
   "source": [
    "## Evaluating model's predictions with regression evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "810ac898",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 102ms/step - loss: 3.1970 - mae: 3.1970\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.1969642639160156, 3.1969642639160156]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "77ea2ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 70.55221 ],\n",
       "       [ 75.13992 ],\n",
       "       [ 79.72765 ],\n",
       "       [ 84.31538 ],\n",
       "       [ 88.9031  ],\n",
       "       [ 93.49082 ],\n",
       "       [ 98.07855 ],\n",
       "       [102.666275],\n",
       "       [107.254005],\n",
       "       [111.84173 ]], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "74d9c4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106])>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2f672794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=3.1969643>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae = tf.metrics.mean_absolute_error(y_test, tf.squeeze(tf.constant(y_pred)))\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4a953916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=13.0703>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = tf.metrics.mean_squared_error(y_test, tf.squeeze(tf.constant(y_pred)))\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0e5e24c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae(y_true, y_pred):\n",
    "    return tf.metrics.mean_absolute_error(\n",
    "        y_true=y_true,\n",
    "        y_pred=tf.squeeze(tf.constant(y_pred))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f46a634d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mse(y_true, y_pred):\n",
    "    return tf.metrics.mean_squared_error(\n",
    "        y_true=y_true, \n",
    "        y_pred=tf.squeeze(tf.constant(y_pred))\n",
    "    ) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9abbf15e",
   "metadata": {},
   "source": [
    "## running experiment to improve model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "766af1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
       " array([-100,  -96,  -92,  -88,  -84,  -80,  -76,  -72,  -68,  -64,  -60,\n",
       "         -56,  -52,  -48,  -44,  -40,  -36,  -32,  -28,  -24,  -20,  -16,\n",
       "         -12,   -8,   -4,    0,    4,    8,   12,   16,   20,   24,   28,\n",
       "          32,   36,   40,   44,   48,   52,   56])>,\n",
       " <tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
       " array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42,\n",
       "        -38, -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,\n",
       "         14,  18,  22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,\n",
       "         66])>)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2017069",
   "metadata": {},
   "source": [
    "### Build `model_1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2552e899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 16.4124 - mae: 16.4124\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.0829 - mae: 11.0829\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.1242 - mae: 11.1242\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.6945 - mae: 8.6945\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.8003 - mae: 9.8003\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.5263 - mae: 9.5263\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.4102 - mae: 8.4102\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.1449 - mae: 9.1449\n",
      "Epoch 9/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 19.4932 - mae: 19.4932\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.6061 - mae: 9.6061\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5992 - mae: 8.5992\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.9324 - mae: 10.9324\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.0574 - mae: 10.0574\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.5679 - mae: 16.5679\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.4537 - mae: 11.4537\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.4056 - mae: 8.4056\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.8417 - mae: 13.8417\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.6621 - mae: 11.6621\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.7623 - mae: 18.7623\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 15.5702 - mae: 15.5702\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.9959 - mae: 10.9959\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.0023 - mae: 8.0023\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.8593 - mae: 9.8593\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.5796 - mae: 7.5796\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.7226 - mae: 13.7226\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.1740 - mae: 17.1740\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.4233 - mae: 13.4233\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.5473 - mae: 14.5473\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.8753 - mae: 9.8753\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.1187 - mae: 17.1187\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 24.5949 - mae: 24.5949\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.6765 - mae: 7.6765\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.2971 - mae: 9.2971\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.1703 - mae: 14.1703\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.8635 - mae: 10.8635\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.5689 - mae: 13.5689\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3448 - mae: 9.3448\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.4804 - mae: 10.4804\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.0178 - mae: 10.0178\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.8005 - mae: 10.8005\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 7.7789 - mae: 7.7789\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.2844 - mae: 10.2844\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.9628 - mae: 8.9628\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.6243 - mae: 12.6243\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.1236 - mae: 14.1236\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.2681 - mae: 8.2681\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.0126 - mae: 9.0126\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.6882 - mae: 10.6882\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.7431 - mae: 7.7431\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3950 - mae: 9.3950\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.9267 - mae: 8.9267\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.9818 - mae: 16.9818\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.8677 - mae: 14.8677\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 22.0413 - mae: 22.0413\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.0640 - mae: 17.0640\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.9520 - mae: 9.9520\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.7522 - mae: 9.7522\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.4378 - mae: 9.4378\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.3373 - mae: 8.3373\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.6425 - mae: 9.6425\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.7590 - mae: 11.7590\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7187 - mae: 11.7187\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.2316 - mae: 7.2316\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.7848 - mae: 17.7848\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.6255 - mae: 12.6255\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.2840 - mae: 13.2840\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.8471 - mae: 7.8471\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.9708 - mae: 9.9708\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.4903 - mae: 12.4903\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.5001 - mae: 8.5001\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.9996 - mae: 9.9996\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.1797 - mae: 10.1797\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.0024 - mae: 13.0024\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.3812 - mae: 10.3812\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 9.7621 - mae: 9.7621\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.5186 - mae: 11.5186\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.3070 - mae: 8.3070\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.4436 - mae: 9.4436\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 20.3897 - mae: 20.3897\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.4751 - mae: 15.4751\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.0208 - mae: 9.0208\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.3869 - mae: 13.3869\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 7.9649 - mae: 7.9649\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.5702 - mae: 7.5702\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.9706 - mae: 9.9706\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.0533 - mae: 9.0533\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 12.2670 - mae: 12.2670\n",
      "Epoch 88/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 6ms/step - loss: 10.6052 - mae: 10.6052\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.1986 - mae: 7.1986\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.0379 - mae: 13.0379\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.1968 - mae: 7.1968\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.5386 - mae: 7.5386\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.0932 - mae: 7.0932\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.8860 - mae: 12.8860\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.9620 - mae: 9.9620\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.7739 - mae: 8.7739\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.0861 - mae: 13.0861\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.3848 - mae: 8.3848\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.7828 - mae: 9.7828\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.5252 - mae: 8.5252\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d343e64f40>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "model_1 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model_1.compile(loss=tf.keras.losses.mae,\n",
    "               optimizer=tf.keras.optimizers.SGD(),\n",
    "               metrics=[\"mae\"])\n",
    "\n",
    "model_1.fit(x_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5302ca52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:9 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D3412CBC10> 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"
     ]
    },
    {
     "data": {
      "image/png": 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SS3rqqad04403atmyZVqyZImuuOIK1dXV6dNPP9WuXbvyfj0AAPFC/UL2ojWDVV//Wbjq1tmZ2J6Hjz76SDU1NaqtrdXo0aP1zW9+U5I0depUjRkzRpL09NNP68EHH1RNTY2mTZum7du3a9OmTVq5cqXmzZun/v3768gjj9Sf/umf7vX4L774ok477bTUYx1yyCF9jqejo0Pvv/++Tj/9dEnSRRddpJUrV6auP++88yRJU6ZMUWtrqyTp5JNP1g9+8APdfPPNamtr0+DBg/N6TQAA8dJdv9DW0SaXp+oXmtYGu6coKqIVsDb3coRCb9uz1L0Gq6WlRXfeeWfqtDUHHXRQ6jburjvvvDN1u7feektnnXVWXs+7vwYOHCgpsTi/e33YX/zFX+jxxx/X4MGDdc455+jZZ58tydgAAOFE/UJuohWwRvdyhEJv2wN09tln65577lFXV5ck6Xe/+53++Mc/6rTTTtNDDz2kXbt2aevWrXruuef2uu9JJ52klStX6q233pIkvffee5ISp8zZsWPHXrevqKjQ8OHDU+urfvKTn6Rms3rz5ptvauzYsbr88ss1Z84crVmzJq/fFwAQL9Qv5CZaa7AaGvZcgyVJQ4YkthfYt771LbW2turEE0+Uu6uyslI///nPde655+rZZ5/VhAkTNHr0aJ188sl73beyslKNjY0677zztHv3bn3uc5/TM888o9mzZ+trX/uaHnvsMd1555173OeBBx7QggUL1NnZqbFjx+pf//Vf+xzfww8/rJ/85CcaMGCADj/8cN1www2B/v4AgGgbXTFabR1tGbdjb+bupR5DSm1trfc8yfHGjRs1fvz47B+kqSmx5mrz5sTMVUNDIAvcsbec3xsAQGj1PAWOlKhfiOMRgt3MbLW712a6LlozWFIiTBGoAAAIVHeIyuYoQkQxYAEAgKxlW70gUb+Qi1AELHeXmZV6GEhTTruWAQD7p+duv+7qBUkEqTyV/VGEgwYN0vbt2/kPehlxd23fvl2DBg0q9VAAAHmIZPVCgc/okq2yn8EaNWqUtmzZovb29lIPBWkGDRqkUaNGlXoYAIA8RK56ocBndMlF2QesAQMGpBrOAQBAcCJXvdDXGV2KHLDKfhchAAAojIYZDRoyYMge24YMGKKGGYXvjyyIAp3RZX8QsAAAiKm64+vUOLtRVRVVMpmqKqrC3WtVwjO69ETAAgAggprWNqn6jmr1u7Gfqu+o7vWkzHXH16n1ylbt/t5utV7ZGt5wJSXKxYfsOSNXrDO69ETAAgAgYrrrF9o62uTyVP1CbyErFLI5OrCuTmpslKqqJLPE98bGkhSQl/2pcgAAQG6q76jOuHi9qqJKrVe2Fn9A+ep5dKCUmJkqUXjq1tepcpjBAgAgYiJXv9DX0YFlioAFAEDE9FazENr6hTI6OjBbBCwAACImcvULZXR0YLYIWAAAREzk6hfK6OjAbBGwAAAIiWyrF6SQ1C9ke97AMjo6MFscRQgAQAh0Vy+kn5x5yIAh4Z2ZKtMjA3PR11GEBCwAAEIgctUL1dWJkzH3VFUltbYWezT7hZoGAABCLnLVCyE8MjAXBCwAAEIgctULITwyMBd5BywzG2dmLWlfH5jZlWa2yMzeSdt+ThADBgAgjiJXvRDCIwNzkXfAcvfX3L3G3WskTZHUKWlp8urbu69z96fyfS4AAOIqVNULITtvYCEEusjdzM6S9D13P8XMFkn60N1vzfb+LHIHAMRR09om1S+v1+aOzRpdMVoNMxrKMzhlIwJHB2armIvcL5D007TLC81sjZndb2bDexncfDNrNrPm9vb2gIcDAEB5665faOtok8vV1tGm+U/M77PjqqyF8LyBhRDYDJaZHSjp/0ma6O7bzOwwSb+X5JK+L+kId7+kr8dgBgsAEDeRq1/o10/KlC3MpN27iz+eAirWDNaXJb3s7tskyd23ufsud98t6T5JUwN8LgAAIiFy9QsRPzowW0EGrHlK2z1oZkekXXeupHUBPhcAAJEQufqFiB8dmK1AApaZHSTpTEn/nrb5FjNba2ZrJJ0h6aogngsAgCgJVf0CRwdmjVPlAABQYqE4ijBGRwdmi3MRAgBQAqEITtmKwLkDg9ZXwDqg2IMBACAOuusXOrsSMz7d9QuSwhmyIn7uwKBxLkIAAAqgfnl9Klx16+zqVP3ykPZBcXRgTghYAAAUQOTqFzg6MCcELAAACiBy9QscHZgTAhYAAAUQmvqFbKoXutXVJRa0796d+E646hUBCwCAAqg7vk6NsxtVVVElk6mqokqNsxvLa4F7d/VCW1vi9DZtbYnLfYUsZIWaBgAActDUlDhv8ebNifXdDQ0hnsiheiEv1DQAABCAnl2b3RM+UkhDFtULBcMuQgAAslRfv2eRuZS4XB/S5gWqFwqHgAUAQJYiN+FD9ULBELAAAMhSqCZ8ODFzSRGwAADIUmgmfHI5OpDqhYIgYAEAkKXQTPhEbrFY+BCwAABQ9n2boZjwidxisfAhYAEAYi9yfZuhWiwWTQQsAEDsRW6PWmgWi0UXAQsAEHuh2aOWy37MUCwWiy6a3AEAsTd6dOYzxpTVHrVca+Tr6ghUJcQMFgAg9kKxRy1y+zGjjYAFAIi9UOxRC81+TEgELABAxEWmfoEjA0OFgAUAiKxI1S+EYj8muhGwAACRFZplS5w3MHLM3Us9hpTa2lpvbm4u9TAAABHRr19i5qons8SuwLLQ8+hAKTEzRXgqe2a22t1rM13HDBYAILJCsWwpNNNsyAUBCwAQWaFYtsTRgZFEwAIARFYoli2FYpoNuSJgAQBCJ9vqBSkE9QuhmGZDrghYAIBQCVX1AkcHxhZHEQIAQqW6OvN5A6uqEjNUZYOjAyOPowgBAJERmjXhHB0YawQsAECohGZNeGiSIAqBgAUACJXQrAkPTRJEIRCwAAChEpo14aFJgiiEwAKWmbWa2VozazGz5uS2Q8zsGTPblPw+PKjnAwBET7b1C2VfvSCFKAmiEAI7itDMWiXVuvvv07bdIuk9d7/JzK6TNNzdr+3tMTiKEADii4PuEDalPIpwjqQHkj8/IGlugZ8PABBSHHSHKAkyYLmkp81stZnNT247zN23Jn/+H0mH9byTmc03s2Yza25vbw9wOACAMOGgO0RJkAHrS+5+oqQvS7rMzE5Lv9IT+yL32h/p7o3uXuvutZWVlQEOBwAQJhx0hygJLGC5+zvJ7+9KWippqqRtZnaEJCW/vxvU8wEAooWD7hAlgQQsMzvIzIZ1/yzpLEnrJD0u6aLkzS6S9FgQzwcAiB4OukOUBDWDdZikX5vZbyW9JOkX7v4rSTdJOtPMNkmambwMAIiZSNUvAFk4IIgHcfc3JZ2QYft2STOCeA4AQDj1rF9oa0tclghQiC6a3AEABUX9AuKIgAUAKCjqFxBHBCwAQEFRv4A4ImABAAqK+gXEEQELAFBQ1C8gjgI5ihAAgL7U1RGoEC/MYAEA9ku23VZAHDGDBQDIGd1WQN+YwQIA5IxuK6BvBCwAQM7otgL6RsACAOSMbiugbwQsAEDO6LYC+kbAAgDkjG4roG8ELADAHrKtX6irk1pbpd27E98JV8BnqGkAAKRQvwAEgxksAEAK9QtAMAhYAIAU6heAYBCwAAAp1C8AwSBgAQBSqF8AgkHAAgCkUL8ABIOABQAxQf0CUDzUNABADFC/ABQXM1gAEAPULwDFRcACgBigfgEoLgIWAMQA9QtAcRGwACAGqF8AiouABQAxQP0CUFwELAAIsWyrFyTqF4BioqYBAEKK6gWgfDGDBQAhRfUCUL4IWAAQUlQvAOWLgAUAIUX1AlC+CFgAEFJULwDli4AFACFF9QJQvghYAFCGsq1foHoBKE95BywzO8rMnjOzDWa23syuSG5fZGbvmFlL8uuc/IcLANHXXb/Q1ia5f1a/0FfHFYDyYu6e3wOYHSHpCHd/2cyGSVotaa6kr0v60N1vzfaxamtrvbm5Oa/xAEDYVVcnQlVPVVWJWSoA5cHMVrt7babr8i4adfetkrYmf95hZhsljcz3cQEgrqhfAMIv0DVYZlYtabKk/05uWmhma8zsfjMbHuRzAUBUUb8AhF9gAcvMhkp6VNKV7v6BpHskfV5SjRIzXLf1cr/5ZtZsZs3t7e1BDQcAQov6BSD8AglYZjZAiXDV5O7/Lknuvs3dd7n7bkn3SZqa6b7u3ujute5eW1lZGcRwACDUqF8Awi+IowhN0r9I2uju/5S2/Yi0m50raV2+zwUAYUf9AhAPeS9yl3SKpL+StNbMWpLbbpA0z8xqJLmkVkmXBvBcABBa3fUL3Sdo7q5fkAhQQNTkXdMQJGoaAEQZ9QtAtPRV00CTOwAUCfULQHwQsACgSKhfAOKDgAUARUL9AhAfBCwAKBLqF4D4IGABQJ6yrV6QqF8A4iKImgYAiC2qFwBkwgwWAOShvv6zcNWtszOxHUB8EbAAIA9ULwDIhIAFAHmgegFAJgQsAMgD1QsAMiFgAUAeqF4AkAkBCwB6kW39AtULAHqipgEAMqB+AUA+mMECgAyoXwCQDwIWAGRA/QKAfBCwACAD6hcA5IOABQAZUL8AIB8ELADIgPoFAPkgYAGIHeoXABQaNQ0AYoX6BQDFwAwWgFihfgFAMRCwAMQK9QsAioGABSBWqF8AUAwELACxQv0CgGIgYAGIFeoXABQDAQtAJGRbvSBRvwCg8KhpABB6VC8AKDfMYAEIPaoXAJQbAhaA0KN6AUC5IWABCD2qFwCUGwIWgNCjegFAuSFgAQg9qhcAlBsCFoCylm39AtULAMoJNQ0Ayhb1CwDCihksAGWL+gUAYUXAAlC2qF8AEFYFD1hmNsvMXjOz183sukI/H4DooH4BQFgVNGCZWX9J/0fSlyVNkDTPzCYU8jkBRAf1CwDCqtAzWFMlve7ub7r7p5J+JmlOgZ8TQERQvwAgrAodsEZKejvt8pbkthQzm29mzWbW3N7eXuDhACgH2VYvSNQvAAinki9yd/dGd69199rKyspSDwdAgXVXL7S1Se6fVS/0FbIAIGwKHbDekXRU2uVRyW0AYorqBQBxUOiA9RtJR5vZGDM7UNIFkh4v8HMCKGNULwCIg4IGLHffKWmhpP+QtFHSw+6+vpDPCaC8Ub0AIA4KvgbL3Z9y92Pc/fPuzsHVQMxRvQAgDkq+yB1AvFC9ACAOCFgAApNt/QLVCwCi7oBSDwBANHTXL3QfIdhdvyARoADEDzNYAAJB/QIAfIaABSAQ1C8AwGcIWAACQf0CAHyGgAUgENQvAMBnCFgAAkH9AgB8hoAFYJ+oXwCA3FDTAKBP1C8AQO6YwQLQJ+oXACB3BCwAfaJ+AQByR8AC0CfqFwAgdwQsAH2ifgEAckfAAtAn6hcAIHcELCCmsq1ekKhfAIBcUdMAxBDVCwBQWMxgATFE9QIAFBYBC4ghqhcAoLAIWEAMUb0AAIVFwAJiiOoFACgsAhYQQ1QvAEBhEbCAiMm2foHqBQAoHGoagAihfgEAygMzWECEUL8AAOWBgAVECPULAFAeCFhAhFC/AADlgYAFRAj1CwBQHghYQIRQvwAA5YGABYQE9QsAEB7UNAAhQP0CAIQLM1hACFC/AADhQsACQoD6BQAIFwIWEALULwBAuBCwgBCgfgEAwiWvgGVmPzSzV81sjZktNbODk9urzewjM2tJfi0JZLRATFG/AADhYu6+/3c2O0vSs+6+08xuliR3v9bMqiU96e7H5fJ4tbW13tzcvN/jAQAAKBYzW+3utZmuy2sGy92fdvedyYsvShqVz+MBcZNttxUAIFyCXIN1iaRfpl0eY2avmNl/mtmpvd3JzOabWbOZNbe3twc4HKC8dXdbtbVJ7p91WxGyACD89rmL0MyWSTo8w1X17v5Y8jb1kmolnefubmYDJQ119+1mNkXSzyVNdPcP+noudhEiTqqrE6Gqp6qqRAM7AKC89bWLcJ9N7u4+cx8PfrGkr0ia4cm05u6fSPok+fNqM3tD0jGSSE9AEt1WABBd+R5FOEvSNZK+6u6dadsrzax/8uexko6W9GY+zwVEDd1WABBd+a7BukvSMEnP9KhjOE3SGjNrkfSIpAXu/l6ezwVECt1WABBdeZ3s2d3/Vy/bH5X0aD6PDURdd4dVfX1it+Do0YlwRbcVAIQfTe5AAWRbv1BXl1jQvnt34jvhCgCiIa8ZLAB7665f6EyuSuyuX5AIUAAQF8xgAQGrr/8sXHXr7ExsBwDEAwELCBj1CwAAAhYQMOoXAAAELCBg1C8AAAhYQMDq6qTGxsQpb8wS3xsbWeAOAHFCwAJyQP0CACAb1DQAWaJ+AQCQLWawgCxRvwAAyBYBC8gS9QsAgGwRsIAsUb8AAMgWAQvIEvULAIBsEbCALFG/AADIFgELsZdt9YJE/QIAIDvUNCDWqF4AABQCM1iINaoXAACFQMBCrFG9AAAoBAIWYo3qBQBAIRCwEGtULwAACoGAhVijegEAUAgELERWtvULVC8AAIJGTQMiifoFAEApMYOFSKJ+AQBQSgQsRBL1CwCAUiJgIZKoXwAAlBIBC5FE/QIAoJQIWIgk6hcAAKVEwELoUL8AACh31DQgVKhfAACEATNYCBXqFwAAYUDAQqhQvwAACAMCFkKF+gUAQBgQsBAq1C8AAMKAgIVQoX4BABAGeQUsM1tkZu+YWUvy65y06643s9fN7DUzOzv/oSLKsq1ekKhfAACUvyBqGm5391vTN5jZBEkXSJoo6UhJy8zsGHffFcDzIWKoXgAARE2hdhHOkfQzd//E3d+S9LqkqQV6LoQc1QsAgKgJImAtNLM1Zna/mQ1Pbhsp6e2022xJbtuLmc03s2Yza25vbw9gOAgbqhcAAFGzz4BlZsvMbF2GrzmS7pH0eUk1krZKui3XAbh7o7vXunttZWVlrndHBFC9AACImn2uwXL3mdk8kJndJ+nJ5MV3JB2VdvWo5DZgLw0Ne67BkqheAACEW75HER6RdvFcSeuSPz8u6QIzG2hmYyQdLemlfJ4L0UX1AgAgavJdg3WLma01szWSzpB0lSS5+3pJD0vaIOlXki7jCMJ4yrZ+geoFAECU5FXT4O5/1cd1DZLYyRNj1C8AAOKKJncUDPULAIC4ImChYKhfAADEFQELBUP9AgAgrghYKJiGhkTdQjrqFwAAcUDAQsFQvwAAiCsCFvYL9QsAAPQur5oGxBP1CwAA9I0ZLOSM+gUAAPpGwELOqF8AAKBvBCzkjPoFAAD6RsBCzqhfAACgbwQs5Iz6BQAA+kbAQkq21QsS9QsAAPSFmgZIonoBAIAgMYMFSVQvAAAQJAIWJFG9AABAkAhYkET1AgAAQSJgQRLVCwAABImABUlULwAAECQCVgxkW79A9QIAAMGgpiHiqF8AAKD4mMGKOOoXAAAoPgJWxFG/AABA8RGwIo76BQAAio+AFXHULwAAUHwErIijfgEAgOIjYIVUttULEvULAAAUGzUNIUT1AgAA5Y0ZrBCiegEAgPJGwAohqhcAAChvBKwQonoBAIDyRsAKIaoXAAAobwSsEKJ6AQCA8kbAKjPZ1i9QvQAAQPmipqGMUL8AAEA05DWDZWYPmVlL8qvVzFqS26vN7KO065YEMtqIo34BAIBoyGsGy92/0f2zmd0mqSPt6jfcvSafx48b6hcAAIiGQNZgmZlJ+rqknwbxeHFF/QIAANEQ1CL3UyVtc/dNadvGmNkrZvafZnZqb3c0s/lm1mxmze3t7QENJ5yoXwAAIBr2GbDMbJmZrcvwNSftZvO05+zVVkmj3X2ypL+V9G9m9ieZHt/dG9291t1rKysr8/ldQo/6BQAAomGfAcvdZ7r7cRm+HpMkMztA0nmSHkq7zyfuvj3582pJb0g6pjC/QjhQvwAAQHwEUdMwU9Kr7r6le4OZVUp6z913mdlYSUdLejOA5wol6hcAAIiXINZgXaC9F7efJmlNsrbhEUkL3P29AJ4rlKhfAAAgXvKewXL3izNse1TSo/k+dlRQvwAAQLxwqpwioH4BAIB4IWAVAfULAADECwGrCKhfAAAgXghYeci2ekGifgEAgDgJoqYhlqheAAAAvWEGaz9RvQAAAHpDwNpPVC8AAIDeELD2E9ULAACgNwSs/UT1AgAA6A0Baz9RvQAAAHpDwMog2/oFqhcAAEAm1DT0QP0CAADIFzNYPVC/AAAA8kXA6oH6BQAAkC8CVg/ULwAAgHwRsHqgfgEAAOSLgNUD9QsAACBfHEWYQV0dgQoAAOy/WM1gZdtvBQAAkI/YzGDRbwUAAIolNjNY9FsBAIBiiU3Aot8KAAAUS2wCFv1WAACgWGITsOi3AgAAxRKbgEW/FQAAKJbYHEUo0W8FAACKIzYzWAAAAMVCwAIAAAgYAQsAACBgBCwAAICAEbAAAAACRsACAAAIGAELAAAgYAQsAACAgBGwAAAAAkbAAgAACBgBCwAAIGAELAAAgICZu5d6DClm1i6prQhPdaik3xfhecpV3H9/iddA4jWQeA3i/vtLvAYSr0E+v3+Vu1dmuqKsAlaxmFmzu9eWehylEvffX+I1kHgNJF6DuP/+Eq+BxGtQqN+fXYQAAAABI2ABAAAELK4Bq7HUAyixuP/+Eq+BxGsg8RrE/feXeA0kXoOC/P6xXIMFAABQSHGdwQIAACgYAhYAAEDAIh2wzOx8M1tvZrvNrLbHddeb2etm9pqZnZ22fVZy2+tmdl3xR104ZvaQmbUkv1rNrCW5vdrMPkq7bkmJh1owZrbIzN5J+13PSbsu42ciSszsh2b2qpmtMbOlZnZwcntsPgNStP/Oe2NmR5nZc2a2Ifnv4hXJ7b3+TURN8t+9tcnfszm57RAze8bMNiW/Dy/1OAvFzMalvc8tZvaBmV0Z9c+Amd1vZu+a2bq0bRnfd0tYnPy3YY2ZnbjfzxvlNVhmNl7Sbkn3Srra3bv/oCZI+qmkqZKOlLRM0jHJu/1O0pmStkj6jaR57r6hyEMvODO7TVKHu/+jmVVLetLdjyvxsArOzBZJ+tDdb+2xPeNnwt13FX2QBWRmZ0l61t13mtnNkuTu18bsM9BfMfk7T2dmR0g6wt1fNrNhklZLmivp68rwNxFFZtYqqdbdf5+27RZJ77n7TcmwPdzdry3VGIsl+XfwjqRpkv63IvwZMLPTJH0o6cHuf+N6e9+T4fI7ks5R4rX5Z3eftj/PG+kZLHff6O6vZbhqjqSfufsn7v6WpNeV+A/rVEmvu/ub7v6ppJ8lbxspZmZK/KP601KPpYz09pmIFHd/2t13Ji++KGlUKcdTIrH4O+/J3be6+8vJn3dI2ihpZGlHVRbmSHog+fMDSoTOOJgh6Q13L8bZU0rK3VdKeq/H5t7e9zlKBDF39xclHZz8n5OcRTpg9WGkpLfTLm9Jbutte9ScKmmbu29K2zbGzF4xs/80s1NLNbAiWZic+r0/bXdAXN77dJdI+mXa5bh8BuL4Xu8hOWM5WdJ/Jzdl+puIIpf0tJmtNrP5yW2HufvW5M//I+mw0gyt6C7Qnv+THZfPQLfe3vfA/n0IfcAys2Vmti7DV+T/jzSTLF+PedrzD2urpNHuPlnS30r6NzP7k2KOO0j7eA3ukfR5STVK/N63lXKshZDNZ8DM6iXtlNSU3BSpzwB6Z2ZDJT0q6Up3/0Ax+JtI8yV3P1HSlyVdltx1lOKJNTPRXTeTZGYHSvqqpP+b3BSnz8BeCvW+HxD0Axabu8/cj7u9I+motMujktvUx/ZQ2NfrYWYHSDpP0pS0+3wi6ZPkz6vN7A0l1qQ1F3CoBZPtZ8LM7pP0ZPJiX5+JUMniM3CxpK9ImpH8hyVyn4F9iMx7nSszG6BEuGpy93+XJHfflnZ9+t9E5Lj7O8nv75rZUiV2F28zsyPcfWtyV9C7JR1kcXxZ0svd732cPgNpenvfA/v3IfQzWPvpcUkXmNlAMxsj6WhJLymx2PVoMxuTTPgXJG8bJTMlveruW7o3mFllcsGjzGysEq/HmyUaX0H12Jd+rqTuo0p6+0xEipnNknSNpK+6e2fa9th8BhSPv/O9JNde/oukje7+T2nbe/ubiBQzOyi5uF9mdpCks5T4XR+XdFHyZhdJeqw0IyyqPfZixOUz0ENv7/vjki5MHk14khIHg23N9AD7EvoZrL6Y2bmS7pRUKekXZtbi7me7+3oze1jSBiV2k1zWfbSYmS2U9B+S+ku6393Xl2j4hdJzv7sknSbpH82sS4mjLhe4e88FgVFxi5nVKDEd3CrpUknq6zMRMXdJGijpmcR/b/Wiuy9QjD4DySMoo/53nskpkv5K0lpLVrRIukHSvEx/ExF0mKSlyc/9AZL+zd1/ZWa/kfSwmX1TUpsSBwBFVjJcnqk93+eM/y5GhZn9VNJ0SYea2RZJ35N0kzK/708pcQTh65I6lTjCcv+eN8o1DQAAAKUQ112EAAAABUPAAgAACBgBCwAAIGAELAAAgIARsAAAAAJGwAIAAAgYAQsAACBg/x/oBSyuZiF/mgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make and plot predictions\n",
    "\n",
    "y_pred_1 = model_1.predict(x_test)\n",
    "plot_predictions(predictions=y_pred_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "336cb0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=18.745327>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=353.5734>)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate model_1 evaluation metrics\n",
    "mae_1 = mae(y_test, y_pred_1)\n",
    "mse_1 = mse(y_test, y_pred_1)\n",
    "\n",
    "mae_1, mse_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4c0cc1",
   "metadata": {},
   "source": [
    "### Build `model_2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d792a324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 39.0788 - mae: 39.0788\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.9383 - mae: 14.9383\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.7671 - mae: 10.7671\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.1174 - mae: 8.1174\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.9726 - mae: 8.9726\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.0027 - mae: 9.0027\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.2701 - mae: 11.2701\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.5040 - mae: 14.5040\n",
      "Epoch 9/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 23.5506 - mae: 23.5506\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.5859 - mae: 11.5859\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.8314 - mae: 9.8314\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.2191 - mae: 11.2191\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.4406 - mae: 7.4406\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3295 - mae: 9.3295\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.4579 - mae: 6.4579\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.3920 - mae: 13.3920\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.7755 - mae: 10.7755\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.9332 - mae: 13.9332\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 20.6667 - mae: 20.6667\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.2473 - mae: 5.2473\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.8761 - mae: 13.8761\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.6955 - mae: 19.6955\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.2666 - mae: 11.2666\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.6121 - mae: 11.6121\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.9498 - mae: 17.9498\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.7060 - mae: 17.7060\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.6004 - mae: 12.6004\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.2098 - mae: 12.2098\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.9853 - mae: 4.9853\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 21.0522 - mae: 21.0522\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.5875 - mae: 9.5875\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.2936 - mae: 12.2936\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5199 - mae: 8.5199\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.8148 - mae: 15.8148\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.1640 - mae: 7.1640\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 18.5625 - mae: 18.5625\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.9393 - mae: 6.9393\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.1962 - mae: 12.1962\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.7124 - mae: 15.7124\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.8550 - mae: 12.8550\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.5289 - mae: 6.5289\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.9887 - mae: 11.9887\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.7104 - mae: 9.7104\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 24.9217 - mae: 24.9217\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.1240 - mae: 14.1240\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 6.0990 - mae: 6.0990\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.4420 - mae: 8.4420\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.2822 - mae: 12.2822\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3563 - mae: 9.3563\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.0370 - mae: 14.0370\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.1891 - mae: 9.1891\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.1275 - mae: 19.1275\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.7562 - mae: 7.7562\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.2977 - mae: 14.2977\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 23.5370 - mae: 23.5370\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.3691 - mae: 13.3691\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.3706 - mae: 6.3706\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 5.9765 - mae: 5.9765\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.2622 - mae: 13.2622\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.2073 - mae: 13.2073\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.9229 - mae: 15.9229\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.5687 - mae: 7.5687\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.5599 - mae: 6.5599\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.6429 - mae: 15.6429\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.4262 - mae: 12.4262\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 11.3451 - mae: 11.3451\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.9988 - mae: 5.9988\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.7063 - mae: 11.7063\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 21.2788 - mae: 21.2788\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.0830 - mae: 8.0830\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 8.4334 - mae: 8.4334\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.6131 - mae: 10.6131\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7788 - mae: 11.7788\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 26.4460 - mae: 26.4460\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.9574 - mae: 4.9574\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.0466 - mae: 15.0466\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.0873 - mae: 13.0873\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.9623 - mae: 7.9623\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 21.8326 - mae: 21.8326\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.4087 - mae: 13.4087\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.3192 - mae: 5.3192\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.8730 - mae: 6.8730\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.7992 - mae: 11.7992\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.3296 - mae: 8.3296\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.2279 - mae: 16.2279\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.8007 - mae: 11.8007\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.8681 - mae: 9.8681\n",
      "Epoch 88/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 5ms/step - loss: 8.9641 - mae: 8.9641\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.7158 - mae: 6.7158\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.3038 - mae: 16.3038\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7185 - mae: 11.7185\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.5317 - mae: 8.5317\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.1947 - mae: 9.1947\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.0506 - mae: 13.0506\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.9540 - mae: 15.9540\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.5734 - mae: 7.5734\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.9518 - mae: 8.9518\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.7399 - mae: 7.7399\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.1221 - mae: 9.1221\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.5636 - mae: 17.5636\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d348b8c970>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "model_2 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model_2.compile(loss=tf.keras.losses.mae,\n",
    "               optimizer=tf.keras.optimizers.SGD(),\n",
    "               metrics=[\"mae\"])\n",
    "\n",
    "model_2.fit(x_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5db92613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D348CC8430> 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"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_2 = model_2.predict(x_test)\n",
    "\n",
    "plot_predictions(predictions=y_pred_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c2fcfe7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=3.5793526>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=13.517387>)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_2 = mae(y_test, y_pred_2)\n",
    "mse_2 = mse(y_test, y_pred_2)\n",
    "\n",
    "mae_2, mse_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91e3ca1",
   "metadata": {},
   "source": [
    "### Build `model_3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "135a6e10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 39.0788 - mae: 39.0788\n",
      "Epoch 2/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.9383 - mae: 14.9383\n",
      "Epoch 3/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.7671 - mae: 10.7671\n",
      "Epoch 4/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.1174 - mae: 8.1174\n",
      "Epoch 5/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.9726 - mae: 8.9726\n",
      "Epoch 6/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.0027 - mae: 9.0027\n",
      "Epoch 7/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 11.2701 - mae: 11.2701\n",
      "Epoch 8/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 14.5040 - mae: 14.5040\n",
      "Epoch 9/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 23.5506 - mae: 23.5506\n",
      "Epoch 10/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.5859 - mae: 11.5859\n",
      "Epoch 11/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.8314 - mae: 9.8314\n",
      "Epoch 12/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.2191 - mae: 11.2191\n",
      "Epoch 13/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 7.4406 - mae: 7.4406\n",
      "Epoch 14/500\n",
      "2/2 [==============================] - 0s 14ms/step - loss: 9.3295 - mae: 9.3295\n",
      "Epoch 15/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.4579 - mae: 6.4579\n",
      "Epoch 16/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.3920 - mae: 13.3920\n",
      "Epoch 17/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.7755 - mae: 10.7755\n",
      "Epoch 18/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.9332 - mae: 13.9332\n",
      "Epoch 19/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 20.6667 - mae: 20.6667\n",
      "Epoch 20/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.2473 - mae: 5.2473\n",
      "Epoch 21/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.8761 - mae: 13.8761\n",
      "Epoch 22/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.6955 - mae: 19.6955\n",
      "Epoch 23/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.2666 - mae: 11.2666\n",
      "Epoch 24/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.6121 - mae: 11.6121\n",
      "Epoch 25/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.9498 - mae: 17.9498\n",
      "Epoch 26/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.7060 - mae: 17.7060\n",
      "Epoch 27/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.6004 - mae: 12.6004\n",
      "Epoch 28/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.2098 - mae: 12.2098\n",
      "Epoch 29/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.9853 - mae: 4.9853\n",
      "Epoch 30/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 21.0522 - mae: 21.0522\n",
      "Epoch 31/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.5875 - mae: 9.5875\n",
      "Epoch 32/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.2936 - mae: 12.2936\n",
      "Epoch 33/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5199 - mae: 8.5199\n",
      "Epoch 34/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.8148 - mae: 15.8148\n",
      "Epoch 35/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.1640 - mae: 7.1640\n",
      "Epoch 36/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 18.5625 - mae: 18.5625\n",
      "Epoch 37/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 6.9393 - mae: 6.9393\n",
      "Epoch 38/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.1962 - mae: 12.1962\n",
      "Epoch 39/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 15.7124 - mae: 15.7124\n",
      "Epoch 40/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.8550 - mae: 12.8550\n",
      "Epoch 41/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.5289 - mae: 6.5289\n",
      "Epoch 42/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.9887 - mae: 11.9887\n",
      "Epoch 43/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 9.7104 - mae: 9.7104\n",
      "Epoch 44/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 24.9217 - mae: 24.9217\n",
      "Epoch 45/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.1240 - mae: 14.1240\n",
      "Epoch 46/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.0990 - mae: 6.0990\n",
      "Epoch 47/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.4420 - mae: 8.4420\n",
      "Epoch 48/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 12.2822 - mae: 12.2822\n",
      "Epoch 49/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3563 - mae: 9.3563\n",
      "Epoch 50/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 14.0370 - mae: 14.0370\n",
      "Epoch 51/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.1891 - mae: 9.1891\n",
      "Epoch 52/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.1275 - mae: 19.1275\n",
      "Epoch 53/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.7562 - mae: 7.7562\n",
      "Epoch 54/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.2977 - mae: 14.2977\n",
      "Epoch 55/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 23.5370 - mae: 23.5370\n",
      "Epoch 56/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.3691 - mae: 13.3691\n",
      "Epoch 57/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.3706 - mae: 6.3706\n",
      "Epoch 58/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 5.9765 - mae: 5.9765\n",
      "Epoch 59/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.2622 - mae: 13.2622\n",
      "Epoch 60/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.2073 - mae: 13.2073\n",
      "Epoch 61/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.9229 - mae: 15.9229\n",
      "Epoch 62/500\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 7.5687 - mae: 7.5687\n",
      "Epoch 63/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.5599 - mae: 6.5599\n",
      "Epoch 64/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.6429 - mae: 15.6429\n",
      "Epoch 65/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.4262 - mae: 12.4262\n",
      "Epoch 66/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.3451 - mae: 11.3451\n",
      "Epoch 67/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.9988 - mae: 5.9988\n",
      "Epoch 68/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7063 - mae: 11.7063\n",
      "Epoch 69/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 21.2788 - mae: 21.2788\n",
      "Epoch 70/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.0830 - mae: 8.0830\n",
      "Epoch 71/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.4334 - mae: 8.4334\n",
      "Epoch 72/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.6131 - mae: 10.6131\n",
      "Epoch 73/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7788 - mae: 11.7788\n",
      "Epoch 74/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 26.4460 - mae: 26.4460\n",
      "Epoch 75/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.9574 - mae: 4.9574\n",
      "Epoch 76/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.0466 - mae: 15.0466\n",
      "Epoch 77/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.0873 - mae: 13.0873\n",
      "Epoch 78/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.9623 - mae: 7.9623\n",
      "Epoch 79/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 21.8326 - mae: 21.8326\n",
      "Epoch 80/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.4087 - mae: 13.4087\n",
      "Epoch 81/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.3192 - mae: 5.3192\n",
      "Epoch 82/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.8730 - mae: 6.8730\n",
      "Epoch 83/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7992 - mae: 11.7992\n",
      "Epoch 84/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.3296 - mae: 8.3296\n",
      "Epoch 85/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.2279 - mae: 16.2279\n",
      "Epoch 86/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.8007 - mae: 11.8007\n",
      "Epoch 87/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.8681 - mae: 9.8681\n",
      "Epoch 88/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 5ms/step - loss: 8.9641 - mae: 8.9641\n",
      "Epoch 89/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.7158 - mae: 6.7158\n",
      "Epoch 90/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.3038 - mae: 16.3038\n",
      "Epoch 91/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.7185 - mae: 11.7185\n",
      "Epoch 92/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5317 - mae: 8.5317\n",
      "Epoch 93/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.1947 - mae: 9.1947\n",
      "Epoch 94/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.0506 - mae: 13.0506\n",
      "Epoch 95/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.9540 - mae: 15.9540\n",
      "Epoch 96/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.5734 - mae: 7.5734\n",
      "Epoch 97/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.9518 - mae: 8.9518\n",
      "Epoch 98/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.7399 - mae: 7.7399\n",
      "Epoch 99/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.1221 - mae: 9.1221\n",
      "Epoch 100/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 17.5636 - mae: 17.5636\n",
      "Epoch 101/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.9276 - mae: 8.9276\n",
      "Epoch 102/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 7.1666 - mae: 7.1666\n",
      "Epoch 103/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.6745 - mae: 6.6745\n",
      "Epoch 104/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 18.0526 - mae: 18.0526\n",
      "Epoch 105/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.8674 - mae: 4.8674\n",
      "Epoch 106/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.4683 - mae: 6.4683\n",
      "Epoch 107/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.3050 - mae: 6.3050\n",
      "Epoch 108/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.2116 - mae: 7.2116\n",
      "Epoch 109/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.5275 - mae: 13.5275\n",
      "Epoch 110/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.8683 - mae: 9.8683\n",
      "Epoch 111/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.8898 - mae: 7.8898\n",
      "Epoch 112/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.8152 - mae: 10.8152\n",
      "Epoch 113/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.6388 - mae: 15.6388\n",
      "Epoch 114/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 13.9973 - mae: 13.9973\n",
      "Epoch 115/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.2185 - mae: 9.2185\n",
      "Epoch 116/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.4469 - mae: 4.4469\n",
      "Epoch 117/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.7898 - mae: 9.7898\n",
      "Epoch 118/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.5749 - mae: 11.5749\n",
      "Epoch 119/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.6952 - mae: 8.6952\n",
      "Epoch 120/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.0001 - mae: 9.0001\n",
      "Epoch 121/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.1308 - mae: 9.1308\n",
      "Epoch 122/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.9473 - mae: 16.9473\n",
      "Epoch 123/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 24.0955 - mae: 24.0955\n",
      "Epoch 124/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 25.5351 - mae: 25.5351\n",
      "Epoch 125/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.4140 - mae: 7.4140\n",
      "Epoch 126/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.8882 - mae: 8.8882\n",
      "Epoch 127/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9409 - mae: 11.9409\n",
      "Epoch 128/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 14.0377 - mae: 14.0377\n",
      "Epoch 129/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.3539 - mae: 8.3539\n",
      "Epoch 130/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.1871 - mae: 9.1871\n",
      "Epoch 131/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.5591 - mae: 5.5591\n",
      "Epoch 132/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.6148 - mae: 11.6148\n",
      "Epoch 133/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.8762 - mae: 4.8762\n",
      "Epoch 134/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.1634 - mae: 11.1634\n",
      "Epoch 135/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.3606 - mae: 6.3606\n",
      "Epoch 136/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.9064 - mae: 12.9064\n",
      "Epoch 137/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.6005 - mae: 14.6005\n",
      "Epoch 138/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.9769 - mae: 9.9769\n",
      "Epoch 139/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.5372 - mae: 8.5372\n",
      "Epoch 140/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.4704 - mae: 12.4704\n",
      "Epoch 141/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.2776 - mae: 10.2776\n",
      "Epoch 142/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 18.0000 - mae: 18.0000\n",
      "Epoch 143/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.5772 - mae: 14.5772\n",
      "Epoch 144/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.4462 - mae: 8.4462\n",
      "Epoch 145/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.0891 - mae: 19.0891\n",
      "Epoch 146/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.7040 - mae: 4.7040\n",
      "Epoch 147/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.0641 - mae: 10.0641\n",
      "Epoch 148/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.8161 - mae: 4.8161\n",
      "Epoch 149/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.8376 - mae: 11.8376\n",
      "Epoch 150/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.6165 - mae: 9.6165\n",
      "Epoch 151/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.6343 - mae: 19.6343\n",
      "Epoch 152/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.6463 - mae: 6.6463\n",
      "Epoch 153/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.8982 - mae: 16.8982\n",
      "Epoch 154/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 5.3248 - mae: 5.3248\n",
      "Epoch 155/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.1511 - mae: 11.1511\n",
      "Epoch 156/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.1080 - mae: 17.1080\n",
      "Epoch 157/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 18.1827 - mae: 18.1827\n",
      "Epoch 158/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.2026 - mae: 11.2026\n",
      "Epoch 159/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.6645 - mae: 8.6645\n",
      "Epoch 160/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.6226 - mae: 17.6226\n",
      "Epoch 161/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.6622 - mae: 9.6622\n",
      "Epoch 162/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3440 - mae: 9.3440\n",
      "Epoch 163/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.1071 - mae: 11.1071\n",
      "Epoch 164/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.2014 - mae: 6.2014\n",
      "Epoch 165/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.6171 - mae: 4.6171\n",
      "Epoch 166/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.7224 - mae: 13.7224\n",
      "Epoch 167/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9675 - mae: 11.9675\n",
      "Epoch 168/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.9539 - mae: 4.9539\n",
      "Epoch 169/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.4744 - mae: 15.4744\n",
      "Epoch 170/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.1832 - mae: 7.1832\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 5ms/step - loss: 13.8504 - mae: 13.8504\n",
      "Epoch 347/500\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 6ms/step - loss: 8.6268 - mae: 8.6268\n",
      "Epoch 433/500\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 7.3330 - mae: 7.3330\n",
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      "2/2 [==============================] - 0s 4ms/step - loss: 23.3711 - mae: 23.3711\n",
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      "2/2 [==============================] - 0s 7ms/step - loss: 13.1972 - mae: 13.1972\n",
      "Epoch 441/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 8.7849 - mae: 8.7849\n",
      "Epoch 442/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.4490 - mae: 9.4490\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 6.6127 - mae: 6.6127\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 11.7467 - mae: 11.7467\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 14.1529 - mae: 14.1529\n",
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      "2/2 [==============================] - 0s 6ms/step - loss: 29.3024 - mae: 29.3024\n",
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      "2/2 [==============================] - 0s 4ms/step - loss: 16.7751 - mae: 16.7751\n",
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      "2/2 [==============================] - 0s 6ms/step - loss: 4.5279 - mae: 4.5279\n",
      "Epoch 461/500\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 9.5848 - mae: 9.5848\n",
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      "Epoch 475/500\n",
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      "Epoch 476/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.3826 - mae: 7.3826\n",
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      "Epoch 479/500\n",
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      "2/2 [==============================] - 0s 6ms/step - loss: 8.4696 - mae: 8.4696\n",
      "Epoch 484/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 21.6710 - mae: 21.6710\n",
      "Epoch 485/500\n",
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      "Epoch 486/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.1850 - mae: 19.1850\n",
      "Epoch 487/500\n",
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      "Epoch 488/500\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 21.2504 - mae: 21.2504\n",
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      "2/2 [==============================] - 0s 5ms/step - loss: 13.0994 - mae: 13.0994\n",
      "Epoch 490/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5593 - mae: 8.5593\n",
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      "Epoch 492/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.8815 - mae: 12.8815\n",
      "Epoch 493/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 7.0745 - mae: 7.0745\n",
      "Epoch 494/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 6.2857 - mae: 6.2857\n",
      "Epoch 495/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7268 - mae: 3.7268\n",
      "Epoch 496/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.6134 - mae: 3.6134\n",
      "Epoch 497/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.5896 - mae: 12.5896\n",
      "Epoch 498/500\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 15.9115 - mae: 15.9115\n",
      "Epoch 499/500\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.8865 - mae: 7.8865\n",
      "Epoch 500/500\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.2900 - mae: 6.2900\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d348e9cbb0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "model_3 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model_3.compile(loss=tf.keras.losses.mae,\n",
    "               optimizer=tf.keras.optimizers.SGD(),\n",
    "               metrics=[\"mae\"])\n",
    "\n",
    "model_3.fit(x_train, y_train, epochs=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6db6f283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D3490EF1F0> 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"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_3 = model_3.predict(x_test)\n",
    "\n",
    "plot_predictions(predictions=y_pred_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6b8ab23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=2.8092332>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=9.772123>)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_3 = mae(y_test, y_pred_3)\n",
    "mse_3 = mse(y_test, y_pred_3)\n",
    "\n",
    "mae_3, mse_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9564a69a",
   "metadata": {},
   "source": [
    "## Comparing the results of experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6d0e88a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>model_1</td>\n",
       "      <td>18.745327</td>\n",
       "      <td>353.573395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>model_2</td>\n",
       "      <td>3.579353</td>\n",
       "      <td>13.517387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>model_3</td>\n",
       "      <td>2.809233</td>\n",
       "      <td>9.772123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model        mae         mse\n",
       "0  model_1  18.745327  353.573395\n",
       "1  model_2   3.579353   13.517387\n",
       "2  model_3   2.809233    9.772123"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compate model's results using pandas\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9684812b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_31 (Dense)             (None, 100)               200       \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 301\n",
      "Trainable params: 301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36712d70",
   "metadata": {},
   "source": [
    " ## Saving a Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cd042d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/model_3\\assets\n"
     ]
    }
   ],
   "source": [
    "model_3.save(\"saved_models/model_3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4d28cd2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3.save(\"saved_models/model_3.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1e65ff",
   "metadata": {},
   "source": [
    "## Loading a saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "932f6dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = tf.keras.models.load_model(\"saved_models/model_3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "8e383992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_31 (Dense)             (None, 100)               200       \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 301\n",
      "Trainable params: 301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "loaded_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "65a93d24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001D345CFD8B0> 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compate model predictions with loaded models\n",
    "\n",
    "model_3_preds = model_3.predict(x_test)\n",
    "loaded_model_preds = loaded_model.predict(x_test)\n",
    "\n",
    "model_3_preds == loaded_model_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36d6948",
   "metadata": {},
   "source": [
    "## A larger Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "688ce8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required library\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "baa1e524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   charges   1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
    "insurance.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b3d2fa20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>charges</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>10600.54830</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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>2205.98080</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>1629.83350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>2007.94500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>29141.36030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     bmi  children      charges  sex_female  sex_male  smoker_no  \\\n",
       "0      19  27.900         0  16884.92400           1         0          0   \n",
       "1      18  33.770         1   1725.55230           0         1          1   \n",
       "2      28  33.000         3   4449.46200           0         1          1   \n",
       "3      33  22.705         0  21984.47061           0         1          1   \n",
       "4      32  28.880         0   3866.85520           0         1          1   \n",
       "...   ...     ...       ...          ...         ...       ...        ...   \n",
       "1333   50  30.970         3  10600.54830           0         1          1   \n",
       "1334   18  31.920         0   2205.98080           1         0          1   \n",
       "1335   18  36.850         0   1629.83350           1         0          1   \n",
       "1336   21  25.800         0   2007.94500           1         0          1   \n",
       "1337   61  29.070         0  29141.36030           1         0          0   \n",
       "\n",
       "      smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
       "0              1                 0                 0                 0   \n",
       "1              0                 0                 0                 1   \n",
       "2              0                 0                 0                 1   \n",
       "3              0                 0                 1                 0   \n",
       "4              0                 0                 1                 0   \n",
       "...          ...               ...               ...               ...   \n",
       "1333           0                 0                 1                 0   \n",
       "1334           0                 1                 0                 0   \n",
       "1335           0                 0                 0                 1   \n",
       "1336           0                 0                 0                 0   \n",
       "1337           1                 0                 1                 0   \n",
       "\n",
       "      region_southwest  \n",
       "0                    1  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  \n",
       "...                ...  \n",
       "1333                 0  \n",
       "1334                 0  \n",
       "1335                 0  \n",
       "1336                 1  \n",
       "1337                 0  \n",
       "\n",
       "[1338 rows x 12 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insu_one_hot = pd.get_dummies(insurance)\n",
    "insu_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "8825a4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       16884.92400\n",
       " 1        1725.55230\n",
       " 2        4449.46200\n",
       " 3       21984.47061\n",
       " 4        3866.85520\n",
       "            ...     \n",
       " 1333    10600.54830\n",
       " 1334     2205.98080\n",
       " 1335     1629.83350\n",
       " 1336     2007.94500\n",
       " 1337    29141.36030\n",
       " Name: charges, Length: 1338, dtype: float64,\n",
       "       age     bmi  children  sex_female  sex_male  smoker_no  smoker_yes  \\\n",
       " 0      19  27.900         0           1         0          0           1   \n",
       " 1      18  33.770         1           0         1          1           0   \n",
       " 2      28  33.000         3           0         1          1           0   \n",
       " 3      33  22.705         0           0         1          1           0   \n",
       " 4      32  28.880         0           0         1          1           0   \n",
       " ...   ...     ...       ...         ...       ...        ...         ...   \n",
       " 1333   50  30.970         3           0         1          1           0   \n",
       " 1334   18  31.920         0           1         0          1           0   \n",
       " 1335   18  36.850         0           1         0          1           0   \n",
       " 1336   21  25.800         0           1         0          1           0   \n",
       " 1337   61  29.070         0           1         0          0           1   \n",
       " \n",
       "       region_northeast  region_northwest  region_southeast  region_southwest  \n",
       " 0                    0                 0                 0                 1  \n",
       " 1                    0                 0                 1                 0  \n",
       " 2                    0                 0                 1                 0  \n",
       " 3                    0                 1                 0                 0  \n",
       " 4                    0                 1                 0                 0  \n",
       " ...                ...               ...               ...               ...  \n",
       " 1333                 0                 1                 0                 0  \n",
       " 1334                 1                 0                 0                 0  \n",
       " 1335                 0                 0                 1                 0  \n",
       " 1336                 0                 0                 0                 1  \n",
       " 1337                 0                 1                 0                 0  \n",
       " \n",
       " [1338 rows x 11 columns])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = insu_one_hot.drop(\"charges\", axis=1)\n",
    "y = insu_one_hot[\"charges\"]\n",
    "\n",
    "y, x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "98cf428a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1003, 335, 1003, 335)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, \n",
    "                                                    random_state=42)\n",
    "\n",
    "len(x_train), len(x_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c6e86e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = tf.constant(x_train)\n",
    "y_train = tf.constant(y_train)\n",
    "x_test = tf.constant(x_test)\n",
    "y_test = tf.constant(y_test)\n",
    "\n",
    "# type(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "ad225b34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "32/32 [==============================] - 2s 14ms/step - loss: 11522.6781 - mae: 11522.6781\n",
      "Epoch 2/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 6654.1016 - mae: 6654.1016\n",
      "Epoch 3/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 4896.3157 - mae: 4896.3157\n",
      "Epoch 4/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 4388.4317 - mae: 4388.4317\n",
      "Epoch 5/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 3800.9566 - mae: 3800.9566\n",
      "Epoch 6/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3637.5061 - mae: 3637.5061\n",
      "Epoch 7/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 3502.7959 - mae: 3502.7959\n",
      "Epoch 8/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3521.3535 - mae: 3521.3535\n",
      "Epoch 9/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3984.7264 - mae: 3984.7264\n",
      "Epoch 10/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 3181.5239 - mae: 3181.5239\n",
      "Epoch 11/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 3453.2114 - mae: 3453.2114\n",
      "Epoch 12/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 3567.5535 - mae: 3567.5535\n",
      "Epoch 13/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3127.4631 - mae: 3127.4631\n",
      "Epoch 14/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2753.3336 - mae: 2753.3336\n",
      "Epoch 15/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3419.7566 - mae: 3419.7566\n",
      "Epoch 16/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2845.6657 - mae: 2845.6657\n",
      "Epoch 17/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2640.6591 - mae: 2640.6591\n",
      "Epoch 18/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3569.7080 - mae: 3569.7080\n",
      "Epoch 19/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 3547.2120 - mae: 3547.2120\n",
      "Epoch 20/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2985.1985 - mae: 2985.1985\n",
      "Epoch 21/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2574.4620 - mae: 2574.4620\n",
      "Epoch 22/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2593.5796 - mae: 2593.5796\n",
      "Epoch 23/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2430.5378 - mae: 2430.5378\n",
      "Epoch 24/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2984.6811 - mae: 2984.6811\n",
      "Epoch 25/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2656.0659 - mae: 2656.0659\n",
      "Epoch 26/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2491.4350 - mae: 2491.4350\n",
      "Epoch 27/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2881.4747 - mae: 2881.4747\n",
      "Epoch 28/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2605.6299 - mae: 2605.6299\n",
      "Epoch 29/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2525.5926 - mae: 2525.5926\n",
      "Epoch 30/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2643.4325 - mae: 2643.4325\n",
      "Epoch 31/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2382.3275 - mae: 2382.3275\n",
      "Epoch 32/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2309.4145 - mae: 2309.4145\n",
      "Epoch 33/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2524.5976 - mae: 2524.5976\n",
      "Epoch 34/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2430.1669 - mae: 2430.1669\n",
      "Epoch 35/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2462.0781 - mae: 2462.0781\n",
      "Epoch 36/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2401.7048 - mae: 2401.7048\n",
      "Epoch 37/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2529.7419 - mae: 2529.7419\n",
      "Epoch 38/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2002.0435 - mae: 2002.0435\n",
      "Epoch 39/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2554.7965 - mae: 2554.7965\n",
      "Epoch 40/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2476.0630 - mae: 2476.0630\n",
      "Epoch 41/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2521.1946 - mae: 2521.1946\n",
      "Epoch 42/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2326.5556 - mae: 2326.5556\n",
      "Epoch 43/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2512.9753 - mae: 2512.9753\n",
      "Epoch 44/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2535.3569 - mae: 2535.3569\n",
      "Epoch 45/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2554.8336 - mae: 2554.8336\n",
      "Epoch 46/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2393.4265 - mae: 2393.4265\n",
      "Epoch 47/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2704.0394 - mae: 2704.0394\n",
      "Epoch 48/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2628.9397 - mae: 2628.9397\n",
      "Epoch 49/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2490.9029 - mae: 2490.9029\n",
      "Epoch 50/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2318.7462 - mae: 2318.7462\n",
      "Epoch 51/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2175.5112 - mae: 2175.5112\n",
      "Epoch 52/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2548.5249 - mae: 2548.5249\n",
      "Epoch 53/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2582.1640 - mae: 2582.1640\n",
      "Epoch 54/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2649.6277 - mae: 2649.6277\n",
      "Epoch 55/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2280.4860 - mae: 2280.4860\n",
      "Epoch 56/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2462.0196 - mae: 2462.0196\n",
      "Epoch 57/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2249.0422 - mae: 2249.0422\n",
      "Epoch 58/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2109.9680 - mae: 2109.9680\n",
      "Epoch 59/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2097.0044 - mae: 2097.0044\n",
      "Epoch 60/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2960.0306 - mae: 2960.0306\n",
      "Epoch 61/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2098.0955 - mae: 2098.0955\n",
      "Epoch 62/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2254.1483 - mae: 2254.1483\n",
      "Epoch 63/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2259.9897 - mae: 2259.9897\n",
      "Epoch 64/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2535.5059 - mae: 2535.5059\n",
      "Epoch 65/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2525.7644 - mae: 2525.7644\n",
      "Epoch 66/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2829.6541 - mae: 2829.6541\n",
      "Epoch 67/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2569.5658 - mae: 2569.5658\n",
      "Epoch 68/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2294.6272 - mae: 2294.6272\n",
      "Epoch 69/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2623.7321 - mae: 2623.7321\n",
      "Epoch 70/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2513.4881 - mae: 2513.4881\n",
      "Epoch 71/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2135.5465 - mae: 2135.5465\n",
      "Epoch 72/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1990.6216 - mae: 1990.6216\n",
      "Epoch 73/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 3001.9853 - mae: 3001.9853\n",
      "Epoch 74/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1908.5350 - mae: 1908.5350\n",
      "Epoch 75/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2453.9940 - mae: 2453.9940\n",
      "Epoch 76/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2375.6707 - mae: 2375.6707\n",
      "Epoch 77/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2217.5211 - mae: 2217.5211\n",
      "Epoch 78/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2350.5148 - mae: 2350.5148\n",
      "Epoch 79/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2302.0839 - mae: 2302.0839\n",
      "Epoch 80/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2451.1736 - mae: 2451.1736\n",
      "Epoch 81/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2366.3739 - mae: 2366.3739\n",
      "Epoch 82/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 7ms/step - loss: 2303.4203 - mae: 2303.4203\n",
      "Epoch 83/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2227.4470 - mae: 2227.4470\n",
      "Epoch 84/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2147.3886 - mae: 2147.3886\n",
      "Epoch 85/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2779.7879 - mae: 2779.7879\n",
      "Epoch 86/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2848.3270 - mae: 2848.3270\n",
      "Epoch 87/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2233.9863 - mae: 2233.9863\n",
      "Epoch 88/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2408.8977 - mae: 2408.8977\n",
      "Epoch 89/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2172.2300 - mae: 2172.2300\n",
      "Epoch 90/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2268.3527 - mae: 2268.3527\n",
      "Epoch 91/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2283.8283 - mae: 2283.8283\n",
      "Epoch 92/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2330.6464 - mae: 2330.6464\n",
      "Epoch 93/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2259.4564 - mae: 2259.4564\n",
      "Epoch 94/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2468.5944 - mae: 2468.5944\n",
      "Epoch 95/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2427.1004 - mae: 2427.1004\n",
      "Epoch 96/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2172.0629 - mae: 2172.0629\n",
      "Epoch 97/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2356.6222 - mae: 2356.6222\n",
      "Epoch 98/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2152.2429 - mae: 2152.2429\n",
      "Epoch 99/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2304.5208 - mae: 2304.5208\n",
      "Epoch 100/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1876.9592 - mae: 1876.9592\n",
      "Epoch 101/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2317.8061 - mae: 2317.8061\n",
      "Epoch 102/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2188.6827 - mae: 2188.6827\n",
      "Epoch 103/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2099.8880 - mae: 2099.8880\n",
      "Epoch 104/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2220.3759 - mae: 2220.3759\n",
      "Epoch 105/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1949.7101 - mae: 1949.7101\n",
      "Epoch 106/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2079.9698 - mae: 2079.9698\n",
      "Epoch 107/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2287.2301 - mae: 2287.2301\n",
      "Epoch 108/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2300.3316 - mae: 2300.3316\n",
      "Epoch 109/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1966.9896 - mae: 1966.9896\n",
      "Epoch 110/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2276.3206 - mae: 2276.3206\n",
      "Epoch 111/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2196.0598 - mae: 2196.0598\n",
      "Epoch 112/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2811.6773 - mae: 2811.6773\n",
      "Epoch 113/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2227.8607 - mae: 2227.8607\n",
      "Epoch 114/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1954.2986 - mae: 1954.2986\n",
      "Epoch 115/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2042.5222 - mae: 2042.5222\n",
      "Epoch 116/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2114.5213 - mae: 2114.5213\n",
      "Epoch 117/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2036.8940 - mae: 2036.8940\n",
      "Epoch 118/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2675.1303 - mae: 2675.1303\n",
      "Epoch 119/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1941.0082 - mae: 1941.0082\n",
      "Epoch 120/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2267.1822 - mae: 2267.1822\n",
      "Epoch 121/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2022.2217 - mae: 2022.2217\n",
      "Epoch 122/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1960.1443 - mae: 1960.1443\n",
      "Epoch 123/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 1999.7484 - mae: 1999.7484\n",
      "Epoch 124/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2296.6031 - mae: 2296.6031\n",
      "Epoch 125/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2320.1186 - mae: 2320.1186\n",
      "Epoch 126/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2458.2384 - mae: 2458.2384\n",
      "Epoch 127/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1814.9202 - mae: 1814.9202\n",
      "Epoch 128/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2071.7464 - mae: 2071.7464\n",
      "Epoch 129/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2210.6531 - mae: 2210.6531\n",
      "Epoch 130/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1988.3223 - mae: 1988.3223\n",
      "Epoch 131/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1902.7470 - mae: 1902.7470\n",
      "Epoch 132/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2072.3822 - mae: 2072.3822\n",
      "Epoch 133/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2445.6384 - mae: 2445.6384\n",
      "Epoch 134/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2010.1280 - mae: 2010.1280\n",
      "Epoch 135/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1984.5552 - mae: 1984.5552\n",
      "Epoch 136/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1945.6890 - mae: 1945.6890\n",
      "Epoch 137/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2387.1643 - mae: 2387.1643\n",
      "Epoch 138/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2319.3959 - mae: 2319.3959\n",
      "Epoch 139/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2019.5436 - mae: 2019.5436\n",
      "Epoch 140/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2215.1353 - mae: 2215.1353\n",
      "Epoch 141/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2018.3646 - mae: 2018.3646\n",
      "Epoch 142/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1747.8150 - mae: 1747.8150\n",
      "Epoch 143/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2081.2003 - mae: 2081.2003\n",
      "Epoch 144/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2049.0733 - mae: 2049.0733\n",
      "Epoch 145/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1833.4734 - mae: 1833.4734\n",
      "Epoch 146/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2164.0741 - mae: 2164.0741\n",
      "Epoch 147/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2050.1661 - mae: 2050.1661\n",
      "Epoch 148/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1856.1690 - mae: 1856.1690\n",
      "Epoch 149/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2199.0040 - mae: 2199.0040\n",
      "Epoch 150/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1879.4556 - mae: 1879.4556\n",
      "Epoch 151/200\n",
      "32/32 [==============================] - 0s 5ms/step - loss: 1959.7901 - mae: 1959.7901\n",
      "Epoch 152/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2109.1741 - mae: 2109.1741\n",
      "Epoch 153/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1887.2562 - mae: 1887.2562\n",
      "Epoch 154/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2053.8455 - mae: 2053.8455\n",
      "Epoch 155/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2114.3283 - mae: 2114.3283\n",
      "Epoch 156/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2289.2256 - mae: 2289.2256\n",
      "Epoch 157/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1794.4306 - mae: 1794.4306\n",
      "Epoch 158/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2098.3610 - mae: 2098.3610\n",
      "Epoch 159/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2325.2419 - mae: 2325.2419\n",
      "Epoch 160/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2223.3389 - mae: 2223.3389\n",
      "Epoch 161/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2181.1929 - mae: 2181.1929\n",
      "Epoch 162/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 6ms/step - loss: 1968.5176 - mae: 1968.5176\n",
      "Epoch 163/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2003.4069 - mae: 2003.4069\n",
      "Epoch 164/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1879.3757 - mae: 1879.3757\n",
      "Epoch 165/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2117.1501 - mae: 2117.1501\n",
      "Epoch 166/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1817.3892 - mae: 1817.3892\n",
      "Epoch 167/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1919.3858 - mae: 1919.3858\n",
      "Epoch 168/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1924.4997 - mae: 1924.4997\n",
      "Epoch 169/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 2267.6168 - mae: 2267.6168\n",
      "Epoch 170/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1983.8171 - mae: 1983.8171\n",
      "Epoch 171/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2136.5123 - mae: 2136.5123\n",
      "Epoch 172/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2148.4697 - mae: 2148.4697\n",
      "Epoch 173/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1636.7313 - mae: 1636.7313\n",
      "Epoch 174/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1824.5831 - mae: 1824.5831\n",
      "Epoch 175/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2078.3109 - mae: 2078.3109\n",
      "Epoch 176/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2251.2347 - mae: 2251.2347\n",
      "Epoch 177/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1921.1734 - mae: 1921.1734\n",
      "Epoch 178/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1841.1331 - mae: 1841.1331\n",
      "Epoch 179/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1997.7624 - mae: 1997.7624\n",
      "Epoch 180/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1569.1698 - mae: 1569.1698\n",
      "Epoch 181/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2232.4101 - mae: 2232.4101\n",
      "Epoch 182/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1880.1898 - mae: 1880.1898\n",
      "Epoch 183/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 1900.1925 - mae: 1900.1925\n",
      "Epoch 184/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2326.4593 - mae: 2326.4593\n",
      "Epoch 185/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2152.1571 - mae: 2152.1571\n",
      "Epoch 186/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 1762.5895 - mae: 1762.5895\n",
      "Epoch 187/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1958.3375 - mae: 1958.3375\n",
      "Epoch 188/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1851.8533 - mae: 1851.8533\n",
      "Epoch 189/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2205.3031 - mae: 2205.3031\n",
      "Epoch 190/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1807.3361 - mae: 1807.3361\n",
      "Epoch 191/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2267.1000 - mae: 2267.1000\n",
      "Epoch 192/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2259.4241 - mae: 2259.4241\n",
      "Epoch 193/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1621.8563 - mae: 1621.8563\n",
      "Epoch 194/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2000.6147 - mae: 2000.6147A: 0s - loss: 1990.5964 - mae: 1990.596\n",
      "Epoch 195/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1986.0790 - mae: 1986.0790\n",
      "Epoch 196/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2091.2164 - mae: 2091.2164\n",
      "Epoch 197/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2178.6763 - mae: 2178.6763\n",
      "Epoch 198/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2045.5417 - mae: 2045.5417\n",
      "Epoch 199/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2226.9536 - mae: 2226.9536\n",
      "Epoch 200/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2228.8088 - mae: 2228.8088\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "insu_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "insu_model.compile(loss=tf.keras.losses.mae,\n",
    "                  optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
    "                  metrics=[\"mae\"])\n",
    "\n",
    "history = insu_model.fit(x_train, y_train, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "a340ed6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 4ms/step - loss: 1728.0939 - mae: 1728.0939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1728.0938720703125, 1728.0938720703125]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insu_model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "d9993057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epochs')"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot()\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epochs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c614059",
   "metadata": {},
   "source": [
    "## Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "f883a076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "insu_one_hot[\"age\"].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "06740d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "insu_one_hot[\"bmi\"].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "43fec0ff",
   "metadata": {},
   "outputs": [
    {
     "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]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "77cec0d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "\n",
    "# Column transformer\n",
    "ct = make_column_transformer(\n",
    "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]),\n",
    "    (OneHotEncoder(handle_unknown=\"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
    ")\n",
    "\n",
    "X = insurance.drop(\"charges\", axis=1)\n",
    "Y = insurance[\"charges\"]\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, \n",
    "                                                    random_state=42)\n",
    "\n",
    "ct.fit(X_train)\n",
    "\n",
    "X_train_normal = ct.transform(X_train)\n",
    "X_test_normal = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "34abf4f6",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "fc126a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_normal = tf.constant(X_train_normal)\n",
    "Y_train = tf.constant(Y_train)\n",
    "X_test_normal = tf.constant(X_train_normal)\n",
    "Y_test = tf.constant(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "cf7f8212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1003, 6), TensorShape([1003, 11]))"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_train_normal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "a5860d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 50/200\n",
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      "Epoch 73/200\n",
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      "Epoch 78/200\n",
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      "Epoch 79/200\n",
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      "Epoch 80/200\n",
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      "Epoch 81/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 6ms/step - loss: 1893.0512 - mae: 1893.0512\n",
      "Epoch 82/200\n",
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      "Epoch 83/200\n",
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      "Epoch 84/200\n",
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      "Epoch 85/200\n",
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      "Epoch 86/200\n",
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      "Epoch 87/200\n",
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      "Epoch 89/200\n",
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      "Epoch 90/200\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 1644.2137 - mae: 1644.2137A: 0s - loss: 1621.9900 - mae: 1621.990\n",
      "Epoch 91/200\n",
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      "32/32 [==============================] - 0s 6ms/step - loss: 1994.5223 - mae: 1994.5223\n",
      "Epoch 159/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1781.3582 - mae: 1781.3582\n",
      "Epoch 160/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1622.7691 - mae: 1622.7691\n",
      "Epoch 161/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 6ms/step - loss: 1705.0749 - mae: 1705.0749\n",
      "Epoch 162/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1603.0696 - mae: 1603.0696\n",
      "Epoch 163/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1665.0826 - mae: 1665.0826\n",
      "Epoch 164/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1498.0851 - mae: 1498.0851\n",
      "Epoch 165/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1748.5336 - mae: 1748.5336\n",
      "Epoch 166/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1625.4506 - mae: 1625.4506\n",
      "Epoch 167/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1547.4230 - mae: 1547.4230\n",
      "Epoch 168/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1658.8790 - mae: 1658.8790\n",
      "Epoch 169/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1505.1741 - mae: 1505.1741\n",
      "Epoch 170/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1493.4725 - mae: 1493.4725\n",
      "Epoch 171/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1636.2613 - mae: 1636.2613\n",
      "Epoch 172/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1524.5795 - mae: 1524.5795\n",
      "Epoch 173/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1373.8482 - mae: 1373.8482\n",
      "Epoch 174/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1690.0890 - mae: 1690.0890\n",
      "Epoch 175/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 2156.5944 - mae: 2156.5944\n",
      "Epoch 176/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1608.8806 - mae: 1608.8806\n",
      "Epoch 177/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1549.1332 - mae: 1549.1332\n",
      "Epoch 178/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1489.4379 - mae: 1489.4379\n",
      "Epoch 179/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1739.0919 - mae: 1739.0919\n",
      "Epoch 180/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1553.5978 - mae: 1553.5978\n",
      "Epoch 181/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1787.5991 - mae: 1787.5991\n",
      "Epoch 182/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1577.8781 - mae: 1577.8781\n",
      "Epoch 183/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1718.1747 - mae: 1718.1747\n",
      "Epoch 184/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1705.9906 - mae: 1705.9906\n",
      "Epoch 185/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1830.3975 - mae: 1830.3975\n",
      "Epoch 186/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1504.1433 - mae: 1504.1433\n",
      "Epoch 187/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1755.3388 - mae: 1755.3388\n",
      "Epoch 188/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1547.5282 - mae: 1547.5282\n",
      "Epoch 189/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1665.5789 - mae: 1665.5789\n",
      "Epoch 190/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1431.6249 - mae: 1431.6249\n",
      "Epoch 191/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1576.7586 - mae: 1576.7586\n",
      "Epoch 192/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1410.0563 - mae: 1410.0563\n",
      "Epoch 193/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1849.0376 - mae: 1849.0376\n",
      "Epoch 194/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1724.6882 - mae: 1724.6882\n",
      "Epoch 195/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1401.1942 - mae: 1401.1942\n",
      "Epoch 196/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1543.4546 - mae: 1543.4546\n",
      "Epoch 197/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1748.8571 - mae: 1748.8571\n",
      "Epoch 198/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1858.9270 - mae: 1858.9270\n",
      "Epoch 199/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1876.9443 - mae: 1876.9443\n",
      "Epoch 200/200\n",
      "32/32 [==============================] - 0s 6ms/step - loss: 1740.6846 - mae: 1740.6846\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "insu_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(100, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "insu_model.compile(loss=tf.keras.losses.mae,\n",
    "                  optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
    "                  metrics=[\"mae\"])\n",
    "\n",
    "history = insu_model.fit(X_train_normal, Y_train, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "efc6257d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 4ms/step - loss: 1468.8141 - mae: 1468.8141\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1468.8140869140625, 1468.8140869140625]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insu_model.evaluate(X_test_normal, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "9cb64f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epochs')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot()\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epochs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bb7645a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "neural_network_regression.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "machine-learning",
   "language": "python",
   "name": "machine-learning"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}