01 Neural Network Regression by Jean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0508a54d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-24 00:06:07.734993: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-01-24 00:06:07.735006: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8.0-rc0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7868512a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f69abe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5fa3128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QGMIOAIkh7ACQmP8HZ8fRmwFzBQ0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create features\n",
    "X = np.array([-7.0, -4.0, -1.0, 2.0, 5.0, 8.0, 11.0, 14.0])\n",
    "\n",
    "# Create labels\n",
    "Y = np.array([3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])\n",
    "\n",
    "# Visualize it\n",
    "plt.scatter(X, Y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61b01534",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-24 00:06:14.312243: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-01-24 00:06:14.312260: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-01-24 00:06:14.312278: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (jean): /proc/driver/nvidia/version does not exist\n",
      "2022-01-24 00:06:14.313399: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff598ac8460>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# create features (using tensors)\n",
    "X = tf.constant ([-7.0, -4.0, -1.0, 2.0, 5.0, 8.0, 11.0, 14.0])\n",
    "\n",
    "# create labels (using tensors)\n",
    "y = tf.constant ([3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])\n",
    "\n",
    "# Visualize it\n",
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0734d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([]), TensorShape([]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a single example of X\n",
    "input_shape = X[0].shape \n",
    "\n",
    "# Take a single example of y\n",
    "output_shape = y[0].shape\n",
    "\n",
    "input_shape, output_shape # these are both scalars (no shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eeaf7d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=-7.0>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=3.0>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the single examples invidually\n",
    "X[0], y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e08a1494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 11.5048 - mae: 11.5048\n",
      "Epoch 2/5\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 11.3723 - mae: 11.3723\n",
      "Epoch 3/5\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.2398 - mae: 11.2398\n",
      "Epoch 4/5\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.1073 - mae: 11.1073\n",
      "Epoch 5/5\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.9748 - mae: 10.9748\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff598b32770>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Create a model using the sequential API\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1))\n",
    "model.compile(loss='mae', optimizer='sgd', metrics=[\"mae\"])\n",
    "\n",
    "# model.compile(loss=tf.keras.losses.mae, # mae is short for mean absolute error\n",
    "#               optimizer=tf.keras.optimizers.SGD(), # SGD is short for stochastic gradient descent\n",
    "#               metrics=[\"mae\"])\n",
    "\n",
    "# Compile the model\n",
    "# model.compile(loss=tf.keras.losses.mae,\n",
    "#               optimizer=tf.keras.optimizers.SGD(),\n",
    "#               metrics=[\"mae\"])\n",
    "\n",
    "# Fit the model\n",
    "model.fit(tf.expand_dims(X, axis=-1), y, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bca25ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float32, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.], dtype=float32)>,\n",
       " <tf.Tensor: shape=(8,), dtype=float32, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.], dtype=float32)>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3086e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12.716021]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([17.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc4736f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 133ms/step - loss: 11.5048 - mae: 11.5048\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.3723 - mae: 11.3723\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.2398 - mae: 11.2398\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.1073 - mae: 11.1073\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 10.9748 - mae: 10.9748\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.8423 - mae: 10.8423\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.7098 - mae: 10.7098\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.5773 - mae: 10.5773\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 10.4448 - mae: 10.4448\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.3123 - mae: 10.3123\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.1798 - mae: 10.1798\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.0473 - mae: 10.0473\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.9148 - mae: 9.9148\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.7823 - mae: 9.7823\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.6498 - mae: 9.6498\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.5173 - mae: 9.5173\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.3848 - mae: 9.3848\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.2523 - mae: 9.2523\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.1198 - mae: 9.1198\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.9873 - mae: 8.9873\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.8548 - mae: 8.8548\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.7223 - mae: 8.7223\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.5898 - mae: 8.5898\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.4573 - mae: 8.4573\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.3248 - mae: 8.3248\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.1923 - mae: 8.1923\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.0598 - mae: 8.0598\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.9273 - mae: 7.9273\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.7948 - mae: 7.7948\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.6623 - mae: 7.6623\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.5298 - mae: 7.5298\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.3973 - mae: 7.3973\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2648 - mae: 7.2648\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2525 - mae: 7.2525\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2469 - mae: 7.2469\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2413 - mae: 7.2413\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2356 - mae: 7.2356\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2300 - mae: 7.2300\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2244 - mae: 7.2244\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2188 - mae: 7.2188\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2131 - mae: 7.2131\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2075 - mae: 7.2075\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.2019 - mae: 7.2019\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1963 - mae: 7.1963\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1906 - mae: 7.1906\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1850 - mae: 7.1850\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1794 - mae: 7.1794\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1738 - mae: 7.1738\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.1681 - mae: 7.1681\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1625 - mae: 7.1625\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1569 - mae: 7.1569\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1512 - mae: 7.1512\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1456 - mae: 7.1456\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1400 - mae: 7.1400\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1344 - mae: 7.1344\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1287 - mae: 7.1287\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1231 - mae: 7.1231\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1175 - mae: 7.1175\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1119 - mae: 7.1119\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1063 - mae: 7.1063\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1006 - mae: 7.1006\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0950 - mae: 7.0950\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0894 - mae: 7.0894\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0838 - mae: 7.0838\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.0781 - mae: 7.0781\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0725 - mae: 7.0725\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0669 - mae: 7.0669\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0613 - mae: 7.0613\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0556 - mae: 7.0556\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0500 - mae: 7.0500\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0444 - mae: 7.0444\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0388 - mae: 7.0388\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0331 - mae: 7.0331\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0275 - mae: 7.0275\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0219 - mae: 7.0219\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0163 - mae: 7.0163\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0106 - mae: 7.0106\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0050 - mae: 7.0050\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9994 - mae: 6.9994\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.9938 - mae: 6.9938\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9881 - mae: 6.9881\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9825 - mae: 6.9825\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9769 - mae: 6.9769\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9713 - mae: 6.9713\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9656 - mae: 6.9656\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9600 - mae: 6.9600\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.9544 - mae: 6.9544\n",
      "Epoch 88/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9488 - mae: 6.9488\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9431 - mae: 6.9431\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9375 - mae: 6.9375\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9319 - mae: 6.9319\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9263 - mae: 6.9263\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9206 - mae: 6.9206\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9150 - mae: 6.9150\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9094 - mae: 6.9094\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9038 - mae: 6.9038\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.8981 - mae: 6.8981\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.8925 - mae: 6.8925\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.8869 - mae: 6.8869\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8813 - mae: 6.8813\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff598b6c790>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# create the model\n",
    "model = tf.keras.Sequential([\n",
    "  tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# compile the model\n",
    "model.compile(loss='mae',\n",
    "optimizer='sgd',\n",
    "metrics=['mae'])\n",
    "\n",
    "# fit the model\n",
    "model.fit(tf.expand_dims(X, axis=-1), y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b2f7ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float32, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.], dtype=float32)>,\n",
       " <tf.Tensor: shape=(8,), dtype=float32, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.], dtype=float32)>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34022c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30.158512 ],\n",
       "       [ 4.2826185]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([17.0,2.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf63548b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 11.9988 - mae: 11.9988\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.3442 - mae: 6.3442\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5.3751 - mae: 5.3751\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.5065 - mae: 6.5065\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.9955 - mae: 4.9955\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.5475 - mae: 3.5475\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.3469 - mae: 4.3469\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.4267 - mae: 4.4267\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1921 - mae: 3.1921\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1096 - mae: 3.1096\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1257 - mae: 3.1257\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.3730 - mae: 2.3730\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.9958 - mae: 1.9958\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.7725 - mae: 1.7725\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1797 - mae: 1.1797\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9620 - mae: 0.9620\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.8400 - mae: 0.8400\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9749 - mae: 0.9749\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0729 - mae: 1.0729\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.2278 - mae: 1.2278\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0835 - mae: 1.0835\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1830 - mae: 1.1830\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.4047 - mae: 1.4047\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.8017 - mae: 0.8017\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.7976 - mae: 0.7976\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5301 - mae: 0.5301\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6867 - mae: 0.6867\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6154 - mae: 0.6154\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9254 - mae: 0.9254\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.7040 - mae: 0.7040\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5232 - mae: 0.5232\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5846 - mae: 0.5846\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.4689 - mae: 0.4689\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3465 - mae: 0.3465\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.7699 - mae: 0.7699\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3467 - mae: 0.3467\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.8225 - mae: 0.8225\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.5537 - mae: 0.5537\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.2440 - mae: 1.2440\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1420 - mae: 1.1420\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.7077 - mae: 0.7077\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6353 - mae: 0.6353\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6526 - mae: 0.6526\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.6171 - mae: 0.6171\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9721 - mae: 0.9721\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5367 - mae: 0.5367\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0025 - mae: 1.0025\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.2608 - mae: 1.2608\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5446 - mae: 0.5446\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9097 - mae: 0.9097\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0498 - mae: 1.0498\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3973 - mae: 0.3973\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.6513 - mae: 0.6513\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2517 - mae: 0.2517\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.4136 - mae: 0.4136\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.8244 - mae: 0.8244\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.5051 - mae: 0.5051\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.8283 - mae: 0.8283\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9971 - mae: 0.9971\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.5045 - mae: 0.5045\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.9145 - mae: 0.9145\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0192 - mae: 1.0192\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1598 - mae: 0.1598\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1857 - mae: 0.1857\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.7522 - mae: 0.7522\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5157 - mae: 0.5157\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6354 - mae: 0.6354\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.6591 - mae: 0.6591\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3902 - mae: 0.3902\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5606 - mae: 0.5606\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.3861 - mae: 0.3861\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1769 - mae: 0.1769\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.4797 - mae: 0.4797\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1597 - mae: 0.1597\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0220 - mae: 1.0220\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.2096 - mae: 1.2096\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.6946 - mae: 0.6946\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.7723 - mae: 0.7723\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0864 - mae: 1.0864\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.4090 - mae: 0.4090\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1595 - mae: 1.1595\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.5945 - mae: 1.5945\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.4018 - mae: 1.4018\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6078 - mae: 0.6078\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1390 - mae: 1.1390\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.7020 - mae: 1.7020\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.2580 - mae: 1.2580\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0816 - mae: 0.0816\n",
      "Epoch 89/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1710 - mae: 1.1710\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.4753 - mae: 1.4753\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0191 - mae: 1.0191\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2318 - mae: 0.2318\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5647 - mae: 0.5647\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2287 - mae: 0.2287\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6878 - mae: 0.6878\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5896 - mae: 0.5896\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2895 - mae: 0.2895\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2935 - mae: 0.2935\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5500 - mae: 0.5500\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.6254 - mae: 0.6254\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff5706628f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Create a model using the sequential API\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(50, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(1))\n",
    "# model.compile(loss='mae', optimizer='sgd', metrics=[\"mae\"])\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae, # mae is short for mean absolute error\n",
    "              optimizer=tf.keras.optimizers.Adam(learning_rate=0.1), # SGD is short for stochastic gradient descent\n",
    "              metrics=[\"mae\"])\n",
    "\n",
    "# Compile the model\n",
    "# model.compile(loss=tf.keras.losses.mae,\n",
    "#               optimizer=tf.keras.optimizers.SGD(),\n",
    "#               metrics=[\"mae\"])\n",
    "\n",
    "# Fit the model\n",
    "model.fit(tf.expand_dims(X, axis=-1), y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6892b5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 134ms/step - loss: 11.5048 - mae: 11.5048\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.3723 - mae: 11.3723\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.2398 - mae: 11.2398\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 11.1073 - mae: 11.1073\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 10.9748 - mae: 10.9748\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.8423 - mae: 10.8423\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.7098 - mae: 10.7098\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 10.5773 - mae: 10.5773\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.4448 - mae: 10.4448\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.3123 - mae: 10.3123\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 10.1798 - mae: 10.1798\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 10.0473 - mae: 10.0473\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.9148 - mae: 9.9148\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.7823 - mae: 9.7823\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.6498 - mae: 9.6498\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 9.5173 - mae: 9.5173\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.3848 - mae: 9.3848\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.2523 - mae: 9.2523\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.1198 - mae: 9.1198\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.9873 - mae: 8.9873\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.8548 - mae: 8.8548\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.7223 - mae: 8.7223\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.5898 - mae: 8.5898\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.4573 - mae: 8.4573\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.3248 - mae: 8.3248\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1923 - mae: 8.1923\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.0598 - mae: 8.0598\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.9273 - mae: 7.9273\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.7948 - mae: 7.7948\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.6623 - mae: 7.6623\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.5298 - mae: 7.5298\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.3973 - mae: 7.3973\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2648 - mae: 7.2648\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2525 - mae: 7.2525\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2469 - mae: 7.2469\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2413 - mae: 7.2413\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2356 - mae: 7.2356\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.2300 - mae: 7.2300\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2244 - mae: 7.2244\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2188 - mae: 7.2188\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2131 - mae: 7.2131\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.2075 - mae: 7.2075\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.2019 - mae: 7.2019\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1963 - mae: 7.1963\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1906 - mae: 7.1906\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1850 - mae: 7.1850\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1794 - mae: 7.1794\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1738 - mae: 7.1738\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1681 - mae: 7.1681\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1625 - mae: 7.1625\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1569 - mae: 7.1569\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1512 - mae: 7.1512\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1456 - mae: 7.1456\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1400 - mae: 7.1400\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1344 - mae: 7.1344\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1287 - mae: 7.1287\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1231 - mae: 7.1231\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.1175 - mae: 7.1175\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1119 - mae: 7.1119\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1063 - mae: 7.1063\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.1006 - mae: 7.1006\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0950 - mae: 7.0950\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0894 - mae: 7.0894\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0838 - mae: 7.0838\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0781 - mae: 7.0781\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0725 - mae: 7.0725\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0669 - mae: 7.0669\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0613 - mae: 7.0613\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0556 - mae: 7.0556\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0500 - mae: 7.0500\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0444 - mae: 7.0444\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0388 - mae: 7.0388\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0331 - mae: 7.0331\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0275 - mae: 7.0275\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.0219 - mae: 7.0219\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0163 - mae: 7.0163\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0106 - mae: 7.0106\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0050 - mae: 7.0050\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9994 - mae: 6.9994\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9938 - mae: 6.9938\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9881 - mae: 6.9881\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9825 - mae: 6.9825\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9769 - mae: 6.9769\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9713 - mae: 6.9713\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9656 - mae: 6.9656\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9600 - mae: 6.9600\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9544 - mae: 6.9544\n",
      "Epoch 88/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9488 - mae: 6.9488\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9431 - mae: 6.9431\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9375 - mae: 6.9375\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9319 - mae: 6.9319\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9263 - mae: 6.9263\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9206 - mae: 6.9206\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9150 - mae: 6.9150\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9094 - mae: 6.9094\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.9038 - mae: 6.9038\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8981 - mae: 6.8981\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8925 - mae: 6.8925\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8869 - mae: 6.8869\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8813 - mae: 6.8813\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff570535780>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Create a model using the sequential API\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1, activation=None))\n",
    "# model.add(tf.keras.layers.Dense(1))\n",
    "# model.add(tf.keras.layers.Dense(1))\n",
    "# model.add(tf.keras.layers.Dense(1))\n",
    "model.compile(loss='mae', optimizer='sgd', metrics=[\"mae\"])\n",
    "\n",
    "# model.compile(loss=tf.keras.losses.mae, # mae is short for mean absolute error\n",
    "#               optimizer=tf.keras.optimizers.SGD(), # SGD is short for stochastic gradient descent\n",
    "#               metrics=[\"mae\"])\n",
    "\n",
    "# Compile the model\n",
    "# model.compile(loss=tf.keras.losses.mae,\n",
    "#               optimizer=tf.keras.optimizers.SGD(),\n",
    "#               metrics=[\"mae\"])\n",
    "\n",
    "# Fit the model\n",
    "model.fit(tf.expand_dims(X, axis=-1), y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ab107787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30.158512]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([17.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7147c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.arange(-100, 100, 4)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fddad715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.arange(-90, 110, 4)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e6edc4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "306f56ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37e30afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f03e04ab070>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "060b010b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 40, 10, 10)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### the 3 sets\n",
    "# train\n",
    "# practice\n",
    "# test\n",
    "\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(y_train), len(X_test), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "364c029d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZWcA3DQBAPOVbv1B/dj2BKiKCPgZrvqSfZ51eYmabzGy1mY3OdQUzW2hmzWbW3NHREfB0AAAIt0z9QtveNrm8u36haXNTuaeGAgRWNGpmx0h6Q9JUd99tZidL+pskl3S7pLHufk1ft0HRKAAgaap/WK22vW1HjFeNqlLrja2lnxDy1lfRaJArWJ+W9Bd33y1J7r7b3Q+4+0FJ90uaEeC2AACIBeoX4inIgLVAWbsHzWxs1nlfkLQlwG0BABAL1C/EUyABy8yGS7pY0q+yhleY2WYz2yRptqSlQWwLAIA4oX4hngJ5F6G7d0oa02PsK0HcNgAAcZZ5VyAf4hwvgR3kHgQOcgcAxEm+9QuIpr4Ocg+6BwsAAOhQ/ULmA5oz9QuSCFkJwGcRAgBQBA3rGrrDVUZnV6ca1jWUaUYoJQIWAABFQP1CshGwAAAoAuoXko2ABQBAEVC/kGwELAAAiqD+7Ho1zm1U1agqmUxVo6rUOLeRA9wTgpoGAAAGoKlJamiQdu6UJkyQli+X6slMiURNAwAAAWhqkhYulDrTbw5sa0udlghZOBy7CAEAyFNDw6FwldHZmRoHshGwAADI085eGhZ6G0dyEbAAAMjThF4aFnobR3IRsAAAyNPy5dLww5sXNHx4ahzIRsACACBP9fVSY6NUVSWZpb43NnKAO45EwAIAQKl3CFZXS4MGpb43NeW+XH291NoqHTyY+k64Qi7UNAAAEo/6BQSNFSwAQOJRv4CgEbAAAIlH/QKCRsACACQe9QsIGgELAJB41C8gaAQsAEDiUb+AoBGwAACxRv0CyoGaBgBAbFG/gHJhBQsAEFvUL6BcCFgAgNiifgHlQsACAMQW9QsoFwIWACC2qF9AuRCwAACxRf0CyoWABQCInHyrFyTqF1Ae1DQAACKF6gVEAStYAIBIoXoBUUDAAgBECtULiAICFgAgUqheQBQQsAAAkUL1AqKAgAUAiBSqFxAFgQQsM2s1s81m1mJmzemxE8zsd2a2I/19dBDbAgDEV771C1QvIOyCXMGa7e617l6XPn2zpHXuPknSuvRpAAByytQvtLVJ7ofqF/rquALCqpi7CC+T9ED65wckzSvitgAAEUf9AuIkqIDlkn5rZhvNLF33ppPdfZckpb+flOuKZrbQzJrNrLmjoyOg6QAAoob6BcRJUAHrfHf/uKRPS1psZhfme0V3b3T3Onevq6ysDGg6AICooX4BcRJIwHL3N9Lf35S0RtIMSbvNbKwkpb+/GcS2AADxRP0C4qTggGVmx5nZyMzPkj4laYukRyVdnb7Y1ZIeKXRbAID4on4BcRLECtbJkv5oZs9L2iDp1+7+G0l3SLrYzHZIujh9GgCQQNQvIGmGFHoD7v6qpHNyjO+RNKfQ2wcARFumfiHzDsFM/YJEgEJ80eQOACgq6heQRAQsAEBRUb+AJCJgAQCKivoFJBEBCwBQVNQvIIkIWACAoqJ+AUlU8LsIAQDoT309gQrJwgoWAOCo5NttBSQRK1gAgAGj2wroGytYAIABo9sK6BsBCwAwYHRbAX0jYAEABoxuK6BvBCwAwIDRbQX0jYAFABgwuq2AvhGwAACHybd+ob5eam2VDh5MfSdcAYdQ0wAA6Eb9AhAMVrAAAN2oXwCCQcACAHSjfgEIBgELANCN+gUgGAQsAEA36heAYBCwAADdqF8AgkHAAoCEoH4BKB1qGgAgAahfAEqLFSwASADqF4DSImABQAJQvwCUFgELABKA+gWgtAhYAJAA1C8ApUXAAoAEoH4BKC0CFgBEWL7VCxL1C0ApUdMAABFF9QIQXqxgAUBEUb0AhBcBCwAiiuoFILwIWAAQUVQvAOFFwAKAiKJ6AQgvAhYARBTVC0B4EbAAIITyrV+gegEIp4IDlpmdama/N7NtZrbVzG5Ij99mZn81s5b012cKny4AxF+mfqGtTXI/VL/QV8cVgHAxdy/sBszGShrr7n8xs5GSNkqaJ+lKSe+6+5353lZdXZ03NzcXNB8AiLrq6lSo6qmqKrVKBSAczGyju9flOq/golF33yVpV/rnfWa2TdK4Qm8XAJKK+gUg+gI9BsvMqiVNk/Q/6aElZrbJzFab2eggtwUAcUX9AhB9gQUsMxsh6WFJN7r7O5Luk/QxSbVKrXDd1cv1FppZs5k1d3R0BDUdAIgs6heA6AskYJnZUKXCVZO7/0qS3H23ux9w94OS7pc0I9d13b3R3evcva6ysjKI6QBApFG/AERfEO8iNEn/IWmbu/9b1vjYrIt9QdKWQrcFAFFH/QKQDAUf5C7pfElfkbTZzFrSY9+WtMDMaiW5pFZJ1wawLQCIrEz9QuYDmjP1CxIBCoibgmsagkRNA4A4o34BiJe+ahpocgeAEqF+AUgOAhYAlAj1C0ByELAAoESoXwCSg4AFACVC/QKQHAQsAChQvtULEvULQFIEUdMAAIlF9QKAXFjBAoACNDQcClcZnZ2pcQDJRcACgAJQvQAgFwIWABSA6gUAuRCwAKAAVC8AyIWABQAFoHoBQC4ELADoRb71C1QvAOiJmgYAyIH6BQCFYAULAHKgfgFAIQhYAJAD9QsACkHAAoAcqF8AUAgCFgDkQP0CgEIQsAAgB+oXABSCgAUgcahfAFBs1DQASBTqFwCUAitYABKF+gUApUDAApAo1C8AKAUCFoBEoX4BQCkQsAAkCvULAEqBgAUgUahfAFAKBCwAsZBv9YJE/QKA4qOmAUDkUb0AIGxYwQIQeVQvAAgbAhaAyKN6AUDYELAARB7VCwDChoAFIPKoXgAQNgQsAJFH9QKAsCFgAQi1fOsXqF4AECbUNAAILeoXAEQVK1gAQov6BQBRRcACEFrULwCIqqIHLDO71MxeNLOXzezmYm8PQHxQvwAgqooasMxssKT/I+nTks6UtMDMzizmNgHEB/ULAKKq2CtYMyS97O6vuvuHkn4h6bIibxNATFC/ACCqih2wxkl6Pet0e3qsm5ktNLNmM2vu6Ogo8nQAhEG+1QsS9QsAoqnYActyjPlhJ9wb3b3O3esqKyuLPB0A5ZapXmhrk9wPVS/0FbIAIGqKHbDaJZ2adXq8pDeKvE0AIUb1AoAkKHbA+rOkSWY20cyOkTRf0qNF3iaAEKN6AUASFDVguft+SUsk/ZekbZIecvetxdwmgHCjegFAEhS9B8vdn3D30939Y+7Om6uBhKN6AUAS0OQOoKSoXgCQBAQsAIHJt36B6gUAcTek3BMAEA+Z+oXMOwQz9QsSAQpA8rCCBSAQ1C8AwCEELACBoH4BAA4hYAEIBPULAHAIAQtAIKhfAIBDCFgAAkH9AgAcQsAC0C/qFwBgYKhpANAn6hcAYOBYwQLQJ+oXAGDgCFgA+kT9AgAMHAELQJ+oXwCAgSNgAegT9QsAMHAELAB9on4BAAaOgAUkVL7VCxL1CwAwUNQ0AAlE9QIAFBcrWEACUb0AAMVFwAISiOoFACguAhaQQFQvAEBxEbCABKJ6AQCKi4AFJBDVCwBQXAQsIGbyrV+gegEAioeaBiBGqF8AgHBgBQuIEeoXACAcCFhAjFC/AADhQMACYoT6BQAIBwIWECPULwBAOBCwgBihfgEAwoGABUQE9QsAEB3UNAARQP0CAEQLK1hABFC/AADRQsACIoD6BQCIFgIWEAHULwBAtBCwgAigfgEAoqWggGVmPzCz7Wa2yczWmNnx6fFqM3vPzFrSX6sCmS2QUNQvAEC0mLsf/ZXNPiXpv919v5l9X5LcfZmZVUt63N3PGsjt1dXVeXNz81HPBwAAoFTMbKO71+U6r6AVLHf/rbvvT598VtL4Qm4PSJp8u60AANES5DFY10j6z6zTE83sOTP7g5ld0NuVzGyhmTWbWXNHR0eA0wHCLdNt1dYmuR/qtiJkAUD09buL0MzWSjolx1kN7v5I+jINkuokXe7ubmYVkka4+x4zmy7p/0ma6u7v9LUtdhEiSaqrU6Gqp6qqVAM7ACDc+tpF2G+Tu7tf1M+NXy3pc5LmeDqtufsHkj5I/7zRzF6RdLok0hOQRrcVAMRXoe8ivFTSMkmfd/fOrPFKMxuc/vk0SZMkvVrItoC4odsKAOKr0GOw7pE0UtLvetQxXChpk5k9L+mXkha5+1sFbguIFbqtACC+CvqwZ3f/X72MPyzp4UJuG4i7TIdVQ0Nqt+CECalwRbcVAEQfTe5AEeRbv1Bfnzqg/eDB1HfCFQDEQ0ErWACOlKlf6EwflZipX5AIUACQFKxgAQFraDgUrjI6O1PjAIBkIGABAaN+AQBAwAICRv0CAICABQSM+gUAAAELCFh9vdTYmPrIG7PU98ZGDnAHgCQhYAEDQP0CACAf1DQAeaJ+AQCQL1awgDxRvwAAyBcBC8gT9QsAgHwRsIA8Ub8AAMgXAQvIE/ULAIB8EbCAPFG/AADIFwELiZdv9YJE/QIAID/UNCDRqF4AABQDK1hINKoXAADFQMBColG9AAAoBgIWEo3qBQBAMRCwkGhULwAAioGAhUSjegEAUAwELMRWvvULVC8AAIJGTQNiifoFAEA5sYKFWKJ+AQBQTgQsxBL1CwCAciJgIZaoXwAAlBMBC7FE/QIAoJwIWIgl6hcAAOVEwELkUL8AAAg7ahoQKdQvAACigBUsRAr1CwCAKCBgIVKoXwAARAEBC5FC/QIAIAoIWIgU6hcAAFFAwEKkUL8AAIiCggKWmd1mZn81s5b012eyzrvFzF42sxfN7JLCp4o4y7d6QaJ+AQAQfkHUNNzt7ndmD5jZmZLmS5oq6aOS1prZ6e5+IIDtIWaoXgAAxE2xdhFeJukX7v6Bu78m6WVJM4q0LUQc1QsAgLgJImAtMbNNZrbazEanx8ZJej3rMu3psSOY2UIzazaz5o6OjgCmg6ihegEAEDf9BiwzW2tmW3J8XSbpPkkfk1QraZekuzJXy3FTnuv23b3R3evcva6ysvLo7gUijeoFAEDc9HsMlrtflM8Nmdn9kh5Pn2yXdGrW2eMlvTHg2SERli8//BgsieoFAEC0FfouwrFZJ78gaUv650clzTezCjObKGmSpA2FbAvxRfUCACBuCj0Ga4WZbTazTZJmS1oqSe6+VdJDkl6Q9BtJi3kHYTLlW79A9QIAIE4Kqmlw96/0cd5ySezkSTDqFwAASUWTO4qG+gUAQFIRsFA01C8AAJKKgIWioX4BAJBUBCwUzfLlqbqFbNQvAACSgICFoqF+AQCQVAQsHBXqFwAA6F1BNQ1IJuoXAADoGytYGDDqFwAA6BsBCwNG/QIAAH0jYGHAqF8AAKBvBCwMGPULAAD0jYCFAaN+AQCAvhGw0C3f6gWJ+gUAAPpCTQMkUb0AAECQWMGCJKoXAAAIEgELkqheAAAgSAQsSKJ6AQCAIBGwIInqBQAAgkTAgiSqFwAACBIBKwHyrV+gegEAgGBQ0xBz1C8AAFB6rGDFHPULAACUHgEr5qhfAACg9AhYMUf9AgAApUfAijnqFwAAKD0CVsxRvwAAQOkRsCIq3+oFifoFAABKjZqGCKJ6AQCAcGMFK4KoXgAAINwIWBFE9QIAAOFGwIogqhcAAAg3AlYEUb0AAEC4EbAiiOoFAADCjYAVMvnWL1C9AABAeFHTECLULwAAEA8FrWCZ2YNm1pL+ajWzlvR4tZm9l3XeqkBmG3PULwAAEA8FrWC5+1WZn83sLkl7s85+xd1rC7n9pKF+AQCAeAjkGCwzM0lXSvp5ELeXVNQvAAAQD0Ed5H6BpN3uviNrbKKZPWdmfzCzC3q7opktNLNmM2vu6OgIaDrRRP0CAADx0G/AMrO1ZrYlx9dlWRdboMNXr3ZJmuDu0yR9U9LPzOwjuW7f3Rvdvc7d6yorKwu5L5FH/QIAAPHQb8By94vc/awcX49IkpkNkXS5pAezrvOBu+9J/7xR0iuSTi/OXYgG6hcAAEiOIGoaLpK03d3bMwNmVinpLXc/YGanSZok6dUAthVJ1C8AAJAsQRyDNV9HHtx+oaRNZva8pF9KWuTubwWwrUiifgEAgGQpeAXL3b+WY+xhSQ8XettxQf0CAADJwkfllAD1CwAAJAsBqwSoXwAAIFkIWCVA/QIAAMlCwCpAvtULEvULAAAkSRA1DYlE9QIAAOgNK1hHieoFAADQGwLWUaJ6AQAA9IaAdZSoXgAAAL0hYB0lqhcAAEBvCFhHieoFAADQGwJWDvnWL1C9AAAAcqGmoQfqFwAAQKFYweqB+gUAAFAoAlYP1C8AAIBCEbB6oH4BAAAUioDVA/ULAACgUASsHqhfAAAAheJdhDnU1xOoAADA0UvUCla+/VYAAACFSMwKFv1WAACgVBKzgkW/FQAAKJXEBCz6rQAAQKkkJmDRbwUAAEolMQGLfisAAFAqiQlY9FsBAIBSScy7CCX6rQAAQGkkZgULAACgVAhYAAAAASNgAQAABIyABQAAEDACFgAAQMAIWAAAAAEjYAEAAASMgAUAABAwAhYAAEDACFgAAAABI2ABAAAEjIAFAAAQMHP3cs+hm5l1SGorwaZOlPS3EmwnrJJ+/yUeA4nHQOIxSPr9l3gMJB6DQu5/lbtX5jojVAGrVMys2d3ryj2Pckn6/Zd4DCQeA4nHIOn3X+IxkHgMinX/2UUIAAAQMAIWAABAwJIasBrLPYEyS/r9l3gMJB4Diccg6fdf4jGQeAyKcv8TeQwWAABAMSV1BQsAAKBoCFgAAAABi3XAMrMrzGyrmR00s7oe591iZi+b2YtmdknW+HQz25w+b6WZWelnXhxm9qCZtaS/Ws2sJT1ebWbvZZ23qsxTLRozu83M/pp1Xz+TdV7O10ScmNkPzGy7mW0yszVmdnx6PDGvAUkys0vTz/PLZnZzuedTCmZ2qpn93sy2pf8u3pAe7/V3Im7Sf/c2p+9nc3rsBDP7nZntSH8fXe55FouZTc56nlvM7B0zuzHurwEzW21mb5rZlqyxXp/3oP4tiPUxWGZ2hqSDkn4k6SZ3z/xCnSnp55JmSPqopLWSTnf3A2a2QdINkp6V9ISkle7+n+WYfzGZ2V2S9rr7v5pZtaTH3f2sMk+r6MzsNknvuvudPcZ7fU2UfJJFZGafkvTf7r7fzL4vSe6+LGGvgcGSXpJ0saR2SX+WtMDdXyjrxIrMzMZKGuvufzGzkZI2Spon6Url+J2IIzNrlVTn7n/LGlsh6S13vyMdtke7+7JyzbFU0r8Hf5V0rqT/rRi/BszsQknvSvpp5m9cb897kP8WxHoFy923ufuLOc66TNIv3P0Dd39N0suSZqT/AH3E3Z/xVPL8qVJ/gGIlvSp3pVIvIqTkfE2UeU6Bc/ffuvv+9MlnJY0v53zKZIakl939VXf/UNIvlHr+Y83dd7n7X9I/75O0TdK48s4qFC6T9ED65wcUw7/5vZgj6RV3L8Wnp5SVuz8p6a0ew70974H9WxDrgNWHcZJezzrdnh4bl/6553jcXCBpt7vvyBqbaGbPmdkfzOyCck2sRJakd5GtzloW7u01EWfXSMpenU3KayCJz/Vh0iuW0yT9T3oo1+9EHLmk35rZRjNbmB472d13SakQKumkss2utObr8P9kJ+U1kNHb8x7Y34fIBywzW2tmW3J89fU/0lzHVXkf45GR5+OxQIf/Yu2SNMHdp0n6pqSfmdlHSjnvIPXzGNwn6WOSapW633dlrpbjpiL13Gfk8xowswZJ+yU1pYdi9RroR2ye66NhZiMkPSzpRnd/R73/TsTR+e7+cUmflrQ4vesocczsGEmfl/R/00NJeg30J7C/D0MKnEjZuftFR3G1dkmnZp0eL+mN9Pj4HOOR0d/jYWZDJF0uaXrWdT6Q9EH6541m9oqk0yU1F3GqRZPva8LM7pf0ePpkb6+JyMnjNXC1pM9JmpPeFR6710A/YvNcD5SZDVUqXDW5+68kyd13Z52f/TsRO+7+Rvr7m2a2RqldP7vNbKy770ofJvJmWSdZGp+W9JfMc5+k10CW3p73wP4+RH4F6yg9Kmm+mVWY2URJkyRtSC8T7jOzT6aPU/qqpEfKOdEiuEjSdnfv3hVqZpXpAx5lZqcp9Xi8Wqb5FVX6FynjC5Iy7yrJ+Zoo9fyKzcwulbRM0ufdvTNrPDGvAaUOap9kZhPT/5Ofr9TzH2vpv2n/IWmbu/9b1nhvvxOxYmbHpQ/ul5kdJ+lTSt3XRyVdnb7Y1Yrf3/xcDtuLkZTXQA+9Pe+B/VsQ+RWsvpjZFyT9u6RKSb82sxZ3v8Tdt5rZQ5JeUGo3yeKsdwh8XdJPJB2r1PEpcXsHYc/97pJ0oaR/NbP9kg5IWuTuPQ8IjIsVZlar1JJvq6RrJamf10Sc3COpQtLvUv/e6ll3X6QEvQbS76BcIum/JA2WtNrdt5Z5WqVwvqSvSNps6YoWSd+WtCDX70QMnSxpTfp1P0TSz9z9N2b2Z0kPmdk/S9op6YoyzrHozGy4Uu+gzX6ec/5djAsz+7mkWZJONLN2Sf8i6Q7leN6D/Lcg1jUNAAAA5ZDUXYQAAABFQ8ACAAAIGAELAAAgYAQsAACAgBGwAAAAAkbAAgAACBgBCwAAIGD/H2e8bRla2ltfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "plt.scatter(X_train, y_train, c='b', label='Training Data')\n",
    "\n",
    "plt.scatter(X_test, y_test, c='g', label='Testing Data')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64b5c5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "# Build the model\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model.compile(loss='mae', optimizer='sgd', metrics=['mae'])\n",
    "\n",
    "# fit the model\n",
    "# model.fit(tf.expand_dims(X_train, axis=-1), y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae2ea200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.build([1])\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8939d9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f03e0395a20>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "# Build the model\n",
    "model = tf.keras.Sequential(name='model1')\n",
    "model.add(tf.keras.layers.Dense(10, input_shape=[1], name='inputLayer'))\n",
    "model.add(tf.keras.layers.Dense(2, name='hidden'))\n",
    "model.add(tf.keras.layers.Dense(1, name='outputLayer'))\n",
    "\n",
    "# compile the model\n",
    "model.compile(loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=.01),\n",
    "    metrics=['mae','mse'])\n",
    "\n",
    "# fit the model\n",
    "model.fit(tf.expand_dims(X_train, axis=-1), y_train, epochs=100, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6bcb1b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " inputLayer (Dense)          (None, 10)                20        \n",
      "                                                                 \n",
      " hidden (Dense)              (None, 2)                 22        \n",
      "                                                                 \n",
      " outputLayer (Dense)         (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 45\n",
      "Trainable params: 45\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9cfe7db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "plot_model(model,show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4cea7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 69.72706 ],\n",
       "       [ 73.71803 ],\n",
       "       [ 77.70901 ],\n",
       "       [ 81.69998 ],\n",
       "       [ 85.690956],\n",
       "       [ 89.68194 ],\n",
       "       [ 93.67291 ],\n",
       "       [ 97.663895],\n",
       "       [101.65486 ],\n",
       "       [105.645836]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = model.predict(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5bf5fbfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73179988",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(train_data=X_train,\n",
    "                    train_labels=y_train,\n",
    "                    test_data=X_test,\n",
    "                    test_labels=y_test,\n",
    "                    predictions=y_preds):\n",
    "    \"\"\"\n",
    "    Plots training data, test data and compares predictions.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    # Plot training data in blue\n",
    "    plt.scatter(train_data, train_labels, c=\"b\", label=\"Training data\")\n",
    "    # Plot test data in green\n",
    "    plt.scatter(test_data, test_labels, c=\"g\", label=\"Testing data\")\n",
    "    # Plot the predictions in red (predictions were made on the test data)\n",
    "    plt.scatter(test_data, predictions, c=\"r\", label=\"Predictions\")\n",
    "    # Show the legend\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50844d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c8b3f802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 97ms/step - loss: 0.3136 - mae: 0.3136 - mse: 0.0990\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3135520815849304, 0.3135520815849304, 0.09898675978183746]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32209d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae(y_test, y_pred):\n",
    "    return tf.metrics.mean_absolute_error(y_test,\n",
    "                                         y_pred.squeeze())\n",
    "\n",
    "def mse(y_test, y_pred):\n",
    "    return tf.metrics.mean_squared_error(y_test,\n",
    "                                        y_pred.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "63e470fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.31355208>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "58f4ee7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.09898676>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc78fb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f03e0e0c2e0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "# build the model\n",
    "model_1 = tf.keras.Sequential(name='bestResults')\n",
    "model_1.add(tf.keras.layers.Dense(10, input_shape=[1]))\n",
    "model_1.add(tf.keras.layers.Dense(2))\n",
    "model_1.add(tf.keras.layers.Dense(5))\n",
    "# model_1.add(tf.keras.layers.Dense(4))\n",
    "model_1.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model_1.compile(loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=.01),\n",
    "    metrics=['mae'])\n",
    "\n",
    "# fit the model\n",
    "model_1.fit(X_train, y_train, epochs=500, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ac8e428c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f03e0f38820> 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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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds_1 = model_1.predict(X_test)\n",
    "plot_predictions(predictions=y_preds_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dd8098cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=0.09623871>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=0.009433456>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_1=mae(y_test, y_preds_1)\n",
    "mse_1=mse(y_test, y_preds_1)\n",
    "mae_1, mse_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7cf5634f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-23 18:40:18.811536: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_1_best_results/assets\n"
     ]
    }
   ],
   "source": [
    "model_1.save('model_1_best_results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fa75c9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1.save('model_1_best_results.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1739b6aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_1_best_results.h5\n"
     ]
    }
   ],
   "source": [
    "!ls model_1_best_results.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8d6e8cc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"bestResults\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_6 (Dense)             (None, 10)                20        \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 2)                 22        \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 5)                 15        \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 63\n",
      "Trainable params: 63\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "loaded_model = tf.keras.models.load_model('model_1_best_results')\n",
    "loaded_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bac3cb6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"bestResults\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_6 (Dense)             (None, 10)                20        \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 2)                 22        \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 5)                 15        \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 63\n",
      "Trainable params: 63\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "loaded_model_h5 = tf.keras.models.load_model('model_1_best_results.h5')\n",
    "loaded_model_h5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d52a735",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-23 18:46:59.076730: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-01-23 18:46:59.076745: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "371cc6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the insurance dataset\n",
    "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dea7debc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0db4070c",
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7768486e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <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",
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       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>19</td>\n",
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       "      <td>1725.55230</td>\n",
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       "      <td>1</td>\n",
       "      <td>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",
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       "    <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",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
       "0           1                 0                 0                 0   \n",
       "1           0                 0                 0                 1   \n",
       "2           0                 0                 0                 1   \n",
       "3           0                 0                 1                 0   \n",
       "4           0                 0                 1                 0   \n",
       "\n",
       "   region_southwest  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_one_hot = pd.get_dummies(insurance)\n",
    "insurance_one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6bea3bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   age               1338 non-null   int64  \n",
      " 1   bmi               1338 non-null   float64\n",
      " 2   children          1338 non-null   int64  \n",
      " 3   charges           1338 non-null   float64\n",
      " 4   sex_female        1338 non-null   uint8  \n",
      " 5   sex_male          1338 non-null   uint8  \n",
      " 6   smoker_no         1338 non-null   uint8  \n",
      " 7   smoker_yes        1338 non-null   uint8  \n",
      " 8   region_northeast  1338 non-null   uint8  \n",
      " 9   region_northwest  1338 non-null   uint8  \n",
      " 10  region_southeast  1338 non-null   uint8  \n",
      " 11  region_southwest  1338 non-null   uint8  \n",
      "dtypes: float64(2), int64(2), uint8(8)\n",
      "memory usage: 52.4 KB\n"
     ]
    }
   ],
   "source": [
    "insurance_one_hot.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6f798b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = insurance_one_hot.drop('charges', axis=1)\n",
    "y = insurance_one_hot['charges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78e0c72a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>27.900</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     bmi  children  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",
       "   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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "258c275b",
   "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",
       "Name: charges, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f787d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.2,\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9572e15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff584649480>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# create the model\n",
    "model = tf.keras.Sequential(name='InsuranceModel')\n",
    "model.add(tf.keras.layers.Dense(1))\n",
    "model.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=.01),\n",
    "    metrics=['mae']\n",
    ")\n",
    "\n",
    "# fit the model\n",
    "model.fit(X_train, y_train, epochs=100, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "067f8b3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"InsuranceModel\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_2 (Dense)             (None, 1)                 12        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14\n",
      "Trainable params: 14\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5193f1ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 647us/step - loss: 6731.2715 - mae: 6731.2720\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[6731.271484375, 6731.27197265625]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the results of model\n",
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4bea0b71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 911us/step - loss: 5139.2612 - mae: 5139.2612\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[5139.26123046875, 5139.26123046875]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# create the model\n",
    "model_1 = tf.keras.Sequential(name='InsuranceModel')\n",
    "model_1.add(tf.keras.layers.Dense(1))\n",
    "model_1.add(tf.keras.layers.Dense(1))\n",
    "model_1.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model_1.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=.01),\n",
    "    metrics=['mae']\n",
    ")\n",
    "\n",
    "# fit the model\n",
    "model_1.fit(X_train, y_train, epochs=100, verbose=0)\n",
    "\n",
    "# check the results of model\n",
    "model_1.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ced081b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 552us/step - loss: 1779.0431 - mae: 1779.0431\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1779.0430908203125, 1779.0430908203125]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# create the model\n",
    "model_2 = tf.keras.Sequential(name='InsuranceModel')\n",
    "model_2.add(tf.keras.layers.Dense(100, activation='relu'))\n",
    "# model_2.add(tf.keras.layers.Dense(10))\n",
    "model_2.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model_2.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
    "    metrics=['mae']\n",
    ")\n",
    "\n",
    "# fit the model\n",
    "history = model_2.fit(X_train, y_train, epochs=100, verbose=0)\n",
    "\n",
    "# check the results of model\n",
    "model_2.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "586004e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 699us/step - loss: 7229.7812 - mae: 7229.7812\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[7229.78125, 7229.78125]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# create the model\n",
    "model_3 = tf.keras.Sequential(name='InsuranceModel')\n",
    "model_3.add(tf.keras.layers.Dense(100, activation='relu'))\n",
    "# model_3.add(tf.keras.layers.Dense(1))\n",
    "model_3.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model_3.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n",
    "    metrics=['mae']\n",
    ")\n",
    "\n",
    "# fit the model\n",
    "model_3.fit(X_train, y_train, epochs=100, verbose=0)\n",
    "\n",
    "# check the results of model\n",
    "model_3.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8c5a6a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 84,
   "id": "0f3f23d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "\n",
    "# create column transformer\n",
    "ct = make_column_transformer(\n",
    "    (MinMaxScaler(), ['age', 'bmi', 'children']),# gets all values between 0 and 1\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(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.2,\n",
    "    random_state=42\n",
    ")\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": 85,
   "id": "7270c460",
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0d6f8410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60869565, 0.10734463, 0.4       , 1.        , 0.        ,\n",
       "       1.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_normal[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "703eb845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1070, 11), (1070, 6))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice the normalized/one-hot encoded shape is larger because of the extra columns\n",
    "X_train_normal.shape, X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "39a81344",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 759us/step - loss: 1767.7408 - mae: 1767.7408\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1767.7408447265625, 1767.7408447265625]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# create the model\n",
    "model_normal = tf.keras.Sequential(name='InsuranceModelNormal')\n",
    "model_normal.add(tf.keras.layers.Dense(10, activation='relu'))\n",
    "model_normal.add(tf.keras.layers.Dense(1))\n",
    "# model_normal.add(tf.keras.layers.Dense(1))\n",
    "model_normal.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "# compile the model\n",
    "model_normal.compile(\n",
    "    loss='mae',\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
    "    metrics=['mae']\n",
    ")\n",
    "\n",
    "# fit the model\n",
    "history = model_normal.fit(X_train_normal, y_train, epochs=100, verbose=0)\n",
    "\n",
    "# check the results of model\n",
    "model_normal.evaluate(X_test_normal, y_test)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
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 "nbformat_minor": 5
}