ANN V2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c687af21",
   "metadata": {},
   "source": [
    "# ANN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b13fc427",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten\n",
    "from tensorflow.keras.optimizers import SGD, Adam, RMSprop\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.layers import Dropout\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "094303e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './archive'\n",
    "\n",
    "kc_data = pd.read_csv(path + '/kc_preprocessed.csv')\n",
    "kc_data = kc_data.drop(['Unnamed: 0'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe2467c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_mean: 540083.5187858597\n",
      "y_sigma: 367135.0612686228\n"
     ]
    }
   ],
   "source": [
    "columns = kc_data.columns\n",
    "kc_data_norm=(kc_data-kc_data.mean())/kc_data.std()\n",
    "y_mean = kc_data['price'].mean()\n",
    "y_sigma = kc_data['price'].std()\n",
    "def y_unnorm(y_norm):\n",
    "    y = y_norm * y_sigma + y_mean\n",
    "    return y\n",
    "print('y_mean: '+str(y_mean))\n",
    "print('y_sigma: '+str(y_sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd56d074",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = kc_data_norm['price']\n",
    "data = kc_data_norm.drop(['price'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fa78ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_final_test, y_train, y_final_test = train_test_split(data, target, test_size = 0.2)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bf40352",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_12\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_99 (Dense)             (None, 17)                323       \n",
      "_________________________________________________________________\n",
      "dense_100 (Dense)            (None, 100)               1800      \n",
      "_________________________________________________________________\n",
      "dense_101 (Dense)            (None, 50)                5050      \n",
      "_________________________________________________________________\n",
      "dense_102 (Dense)            (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "dense_103 (Dense)            (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 9,774\n",
      "Trainable params: 9,774\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Base Structure\n",
    "\n",
    "NN_model = Sequential()\n",
    "NN_model.add(Dense(17,input_dim = x_train.shape[1], activation='relu'))\n",
    "\n",
    "# The Hidden Layers :\n",
    "NN_model.add(Dense(100,activation='sigmoid'))\n",
    "\n",
    "NN_model.add(Dense(50, activation='sigmoid'))\n",
    "\n",
    "NN_model.add(Dense(50, activation='sigmoid'))\n",
    "\n",
    "\n",
    "\n",
    "# The Output Layer :\n",
    "NN_model.add(Dense(1,))\n",
    "\n",
    "# Compile the network :\n",
    "opt = SGD(learning_rate=0.001, momentum=1)\n",
    "\n",
    "NN_model.compile(loss ='mean_squared_error', optimizer='RMSprop',)\n",
    "NN_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa5dd8b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hidden layer examination\n",
    "Models= {}\n",
    "Models['layer-Dense50']={}\n",
    "\n",
    "for i in range(1,6):\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation='relu'))\n",
    "    NN.add(Dense(100,activation='relu'))\n",
    "    for j in range(i):\n",
    "        NN.add(Dense(50, activation='relu'))\n",
    "        \n",
    "    NN.add(Dense(1,))\n",
    "    NN.compile(loss ='mean_squared_error', optimizer='RMSprop',)\n",
    "\n",
    "    Models['layer-Dense50'][i] = NN\n",
    "    \n",
    "        \n",
    "counter = 0\n",
    "for i, model in Models['layer-Dense50'].items():\n",
    "    print('training model:')\n",
    "    history = model.fit(x = x_train, y = y_train, epochs=60, verbose = 0)\n",
    "    plt.figure()\n",
    "    plt.plot(history.history['loss'],)\n",
    "    plt.title('Number of layer-Dense50:{}'.format(i))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71703225",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VF013WSIiKVPQp2DKuCJ+cftF3HHZNO5f2ciHv/ucunJEJGMo6FOUF87hC4tm8eObL2BXWw93aaoEEckQCvqTdPH0cfz3K2bw5IbdPL2xJd3liIickIL+FHzykimcOa6Iv354A73RWLrLERF5Rwr6U5AfDvG/PzKHbXs6ufe57ekuR0TkHSnoT9GlMytZOHs83/3tZna26cKsiIxeCvoh+OqHZxOLO3//6BvpLkVE5LgU9ENQO6aQOy6bxsNrmnlhy950lyMiMigF/RDdfuk0aioKuOuh9XT1RWnc18XK7ft4eE0z//7cNl7feTDdJYpIlgunu4BMF8kN8dUPz+a2H61i9teWv217UV6IH33qAhZMrkhDdSIiCvphsXD2eO76g9m090QZXxZhQmmE8aURwiHj5vtW8qf3vsxPP3Uh59SUpbtUEclClspTlMxsEfDPQAi4x92/ccz2WcAPgAXAl939W0nbtgPtQAyIunvdib6vrq7O6+vrT6IZo9eOA918fMkLdPZF+dktF3L2xNJ0lyQiAWRmq46XryfsozezEHA3cBUwG7jRzGYfs9s+4M+BbzG4y919XiohHzTV5QX87JYLiYRD/PE9L9HQ0p7ukkQky6RyMfZ8oMHdt7p7H3A/sDh5B3dvcfeVQP8I1JjxJo8t5Ke3XICZcdO/vcTW1o50lyQiWSSVoK8GGpOWmxLrUuXAE2a2ysxuPd5OZnarmdWbWX1ra+tJfHxmOLOymJ/ecgHRuHPlt59l8d2/55+e2MjL2/bRH4unuzwRCbBULsbaIOtO5tl6F7t7s5lVAU+a2RvuvuJtH+i+FFgKA330J/H5GWPm+BJ+/emLefDVJn63eQ/fe2YL3/1tA8X5Ya6YVcVfXX02E8oi6S5TRAImlaBvAmqTlmuA5lS/wN2bEz9bzGwZA11Bbwv6bDF5bCGfu3Imn7tyJm3d/bywZS8rNrfy4CtNPLOxhbs+Modr51djNtjvVxGRk5dK181KYIaZTTWzPOAG4KFUPtzMisys5NB7YCGw7lSLDZqyglwWvWsCf3/tOTz22fcxc3wJf/nAGm754Spa2nvSXZ6IBMQJg97do8CdwHLgdeABd19vZreb2e0AZjbBzJqAvwS+YmZNZlYKjAeeM7M1wMvAI+7++Eg1JpNNHVfEz297D1/50Nn8bnMrC//vCn6zeke6yxKRAEhpHP3pFqRx9KdiS2sH//MXa3j1rQPcdumZfOGDs8jJUVeOiBzfkMbRy+k3rbKYX9z2Hj5x4Rn8v2e38rmfr9YDTkTklGkKhFEqHMrhrxfPYWJ5hH94fCOt7b0s+cS7KSvITXdpIpJhFPSjmJnx6cumM7Eswud/uZaPL3mBH/y386gsyWdtUxsvbdvLi1v3sabxADOqirn6nIlcdc4EJpYVpLt0ERlF1EefIX7fsIfbfrSKUI7RF43T3T/QlTOjqph5teW8tqONN3YNTK+wYHI5V58zkY8uqKGiKC+dZYvIafJOffQK+gyyofkg335yIzUVhVwwdQznTx3D2OL8w9u3tnbw2LpdPLJ2Jxt2HqSmooAf3XwBU8cVpbFqETkdFPRZaNWb+7nlhwP/DX/wZ+cxt7Y8vQWJyIjSqJss9O4zKvjVHRdRlB/ixn97kWc3BW/+IBFJjYI+wKaOK+JXd1zEGWOLuPm+lSx7tSndJYlIGmjUTcBVlUT4+W0XctsPV/EXP1/Df73eQmkkl9yQkRvKIRwyZk0o4Zp5ml9HJKgU9FmgNJLLfZ88j6/9ej2/29xKf9zpj8WJxpy+aJy+WJzXd7bzpatmKexFAkhBnyXywyG++bFz37Y+Hnfueng9S1dspaM3yt8ufpemWxAJGAV9lsvJMb7+kTkU5Yf5/jNb6OqN8o/XzyU3dPTlm82723ltRxsfnDOBonz9sRHJJPobK5gZX1g0i+L8MP+4fCOdfTH+9ab57O/s5+E1zfx69Q7WNx8EYNaErfzbn9RRO6YwzVWLSKo0jl6Oct/vt3HXwxuYVBZh58Ee3GFuTRmL51UzoSzClx58DTP43k0LuGj6uHSXKyIJ7zSOXmf0cpQ/u3gqxZFc7nt+Gx+rq+WaeZM4s7L48PbZE0u55Yf1fOLel/nKh87mzy6aogu4IqOczujlpHX0RvmLn6/myQ27+eiCGs6bUsGOA93s2N9N04FudrZ1M2VsEQvnTGDh7PGML9VzcEVGmqZAkGEXjzvfeWoz//LUZgByDCaWFVBdXsD4sgjrdrSxbU8nAPNqy1k4ZzwLZ09gelXxO32siJwiBb2MmMZ9XZjBhNII4aSROu7OltYOlq/fzRPrd7GmqQ2AMyuLWDh7AgvnjGdeTbmGcooMEwW9pN3Otm7+a8Nuntiwmxe27CUad6pK8vnQuRO55b1nMqlcc+iLDIWCXkaVtq5+nt7YwvL1u3hyw27M4Pq6Wu64dJqGbYqcIgW9jFpN+7tY8uwWHljZRNyda+dX80cXnoEBnX1RunpjdPZFibszr7aCKWMLNcpHZBAKehn1drX1sOTZLfzs5bfojcaPu9+ksggXTR/HxdPHctG0cRrRI5KgoJeM0dLew8vb9lGQG6IwL0xRfoii/DDRmPPy9n0837CHF7bu5UBXPzAw7/4186v58DkT9dhEyWoKegmUeNzZsPMgz25q5Terd7Bpdwe5IeOys6q4bn41l8+qIpIbSneZIqfVkIPezBYB/wyEgHvc/RvHbJ8F/ABYAHzZ3b+V6rGDUdBLqtwHQn/ZKzv4zZpmWtt7KY2E+dC5k7huQTV1Z1SoT1+ywpCC3sxCwCbgA0ATsBK40d03JO1TBZwBXAPsPxT0qRw7GAW9nIpY3Pl9wx6WvbqDx9ftors/Ru2YAq6dV83ksUXsPNDNzoM97GrroflAN939MfLDOURyQ0TCIfJzc6ipKOTTl2n0j2Seoc51cz7Q4O5bEx92P7AYOBzW7t4CtJjZh072WJHhEsox3jezkvfNrORvr4myfP0uHnxlB999uoFD5zNji/KYUBahpqKAovwwvf1xeqIxevpjdPRGWfZqE796pYlPXjyVz1w+jZJIbnobJTIMUgn6aqAxabkJuCDFz0/5WDO7FbgVYPLkySl+vMjgivLDXLeghusW1NDS3kN3X4zxpZET9t3vauvhH5dvZMmzW/hFfSN/uXAmf1hXe9RdvyKZJpU/vYN1cKZ6BTflY919qbvXuXtdZWVlih8vcmJVJRHOGFuU0gXaCWUR/unjc3n4zkuYVlnMl5etY+F3VvCNx95gxaZWuvqip6FikeGVyhl9E1CbtFwDNKf4+UM5ViRtzqkp4+e3Xcjy9bv49+e2cc/vtrLk2S3khoy5NeVcNG0s75tZybzacp3ty6iXStCvBGaY2VRgB3ADcFOKnz+UY0XSysxY9K6JLHrXRLr6otRv38/zW/bywta9/OvTDfzLbxsoK8jlkhnjuPysKi6dWUllSX66yxZ5mxMGvbtHzexOYDkDQyTvdff1ZnZ7YvsSM5sA1AOlQNzMPgfMdveDgx07Qm0RGTGFeeHDF3oB2rr7+X3DHp5+o4VnN7XyyNqdAFSXFzC9qpgZVcXMGF/M9Kpi+qLOW/s6eXNvF2/u6+KtvV2ML83njsum8+4zKtLZLMkSumFKZIgOjeVfsWkPb+w6SENLBw0tHW+byiGcY9RUFFA7ppD1zQfZ19nHe2eM47Pvn0HdlDFpql6CQo8SFBlBZsacSWXMmVR2eF0s7uzY383mlnbywjmcMaaISeVH5uzv7I3y4xffZOmKrXxsyQtcPH0s182vob2nn72dfezp6KW1vQ93Z/H8ahbNmUBeWNcC5NTojF4kjbr6jgT+no4+YOBpXWOK8hhXnE97T5QdB7qpLMnnpvMnc9MFk4d1IrddbT18/5kGtrR2ct6UMVw0fSxza8r1SyUDaa4bkVGuuy9G4/4uxhTlUVGYRyjx5K143Hl2Uyv/8cJ2ntnYSjjHuGJWFdUVBYfv6I3kDtzdW1GUR2VxPpUleVQWRygtCB93+oeW9h6+/8wWfvLSW8TjzrTKYja1tOMOhXkhzpsyhsvPquTGCyaTH9a8QZlAQS8SANv3dPLjF9/kkdd20t4Tpac/RjR+/L+/eaEcaioKmDKuiKmJ15SxRfyuoZX/eH47/THnuvnV/Pn7Z1A7ppADXX28uHUvz2/Zy+8b9rCltZMzK4v4u2vO4T3Txp7GlsqpUNCLBFQ0Fqc3Gqe7P8a+zj72tPfS2tFLa/vA6619XWzb08n2vZ309A9cHDaDxXMn8dkrZzJ1XNFxP/uZjS189TfraNzXzXULqvny1WcztljDR0crBb1IlovHnV0He9i2p5NJ5QXvGPDJuvtifPe3m1m6YivFkTCf/+Asrj5nAuWFmvt/tFHQi8iQbNrdzpeXvcbK7fsBmFZZxLvPqGDB5ArOqSmjoydK4/5uGvd10bi/i+YD3YwvjTCvtpz5kyuYPbFUF3hHmIJeRIYsHh94yteqN/fzypv7WfXW/sNP+jrEDCaURphUXsCO/d3sOtgDQF44hzmTSjmnuoyZ40s4a0IJM6tKKCscfHbQWNxp7+mnrfvIK+5wwdQxeqjMcWgcvYgMWU6OceGZY7nwzIELs+7Otj2drG8+SFlBLrVjCplUHjlqlM7Otm5Wv3WAVxsP8Opb+3nwlR109B6ZGG58aT6VJfn09Mfp7huYLrq7P0ZXX2zQGioKc7m+rpYbz5+ccveT6IxeRE4jd6e5rYdNu9vZtKudjbva2d/VR0FeiILcMAV5OYefF1xWkHvkVZhLR0+UB+obeWLDbmJx5+LpY7nx/MmcN2UMVSX5Wf8kMXXdiEhgtBzs4YH6Rn72ciM7DnQDUJIfZvr4gTmGplcVM740wpiivMOvisK8wHf5KOhFJHBicad++z427m5n8+4ONre009DSyZ6O3kH3N4OQGTk5RsiMUI4xsSzCZWdVcvlZVdRNGZPRF4wV9CKSNdq6+mnt6GFfZz/7OvvY39XHvs4+evpjxOJOzJ143InFYXNLOy9t3UdfLE5xfphLpo9jwRnl5IZyCOUYOYlfCIV5IeZMKuPMcUXk5IzOLiJdjBWRrFFWmHvc0TyD6eyNDkw5vbGVp99o4fH1u467b0kkzNyacubWlnFuTTmTygqoLMlnbHEeuaP4ATQKehHJakX5YRbOmcDCORNwdw52R4l70pl/Yt3apgOsbhx4LXl2K7Fjpp8oL8xlXHE+YwrzKCvMpbwgl/LCXMoL84jFnY7eKO09/bT3ROnojVKUH2bW+BJmTihh1oQSaisKR+xfC+q6ERE5Sd19MTbubqflYA+tHb3sae+jtaOHPe19HOju40DXwNj/A139dPcPDBWN5OZQEsmlJD9MUX6Ytu5+3trXdfgzB7qHSnngtvec0ggidd2IiAyjgrwQ82rLU9q3pz9GKMcG7drp7I2yaffAMNONu9vp7ouNyDBRBb2IyAh6p2GdRflh5k+uYP7kkX2k5Oi9eiAiIsNCQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiIgGnoBcRCTgFvYhIwI3KKRDMrBV48xQPHwfsGcZy0ilIbQG1ZzQLUlsgWO1JtS1nuHvlYBtGZdAPhZnVH2++h0wTpLaA2jOaBaktEKz2DEdb1HUjIhJwCnoRkYALYtAvTXcBwyhIbQG1ZzQLUlsgWO0ZclsC10cvIiJHC+IZvYiIJFHQi4gEXGCC3swWmdlGM2swsy+mu56TZWb3mlmLma1LWjfGzJ40s82JnyP7dIJhYma1Zva0mb1uZuvN7LOJ9ZnanoiZvWxmaxLt+XpifUa2B8DMQmb2qpn9Z2I5k9uy3cxeM7PVZlafWJfJ7Sk3s1+a2RuJv0PvGWp7AhH0ZhYC7gauAmYDN5rZ7PRWddLuAxYds+6LwFPuPgN4KrGcCaLA/3D3s4ELgc8k/n9kant6gSvcfS4wD1hkZheSue0B+CzwetJyJrcF4HJ3n5c03jyT2/PPwOPuPguYy8D/p6G1x90z/gW8B1ietPwl4EvprusU2jEFWJe0vBGYmHg/EdiY7hpPsV2/AT4QhPYAhcArwAWZ2h6gJhEWVwD/mViXkW1J1LsdGHfMuoxsD1AKbCMxUGa42hOIM3qgGmhMWm5KrMt04919J0DiZ1Wa6zlpZjYFmA+8RAa3J9HVsRpoAZ5090xuz3eAzwPxpHWZ2hYAB54ws1VmdmtiXaa250ygFfhBomvtHjMrYojtCUrQD/bYdI0bTTMzKwZ+BXzO3Q+mu56hcPeYu89j4Gz4fDN7V5pLOiVm9mGgxd1XpbuWYXSxuy9goOv2M2b2vnQXNARhYAHwfXefD3QyDN1OQQn6JqA2abkGaE5TLcNpt5lNBEj8bElzPSkzs1wGQv4n7v5gYnXGtucQdz8APMPA9ZRMbM/FwEfMbDtwP3CFmf2YzGwLAO7enPjZAiwDzidz29MENCX+xQjwSwaCf0jtCUrQrwRmmNlUM8sDbgAeSnNNw+Eh4E8T7/+Ugb7uUc/MDPh34HV3/3bSpkxtT6WZlSfeFwBXAm+Qge1x9y+5e427T2Hg78lv3f2PycC2AJhZkZmVHHoPLATWkaHtcfddQKOZnZVY9X5gA0NtT7ovPgzjRYyrgU3AFuDL6a7nFOr/GbAT6Gfgt/rNwFgGLpptTvwck+46U2zLJQx0na0FVideV2dwe84FXk20Zx3wtcT6jGxPUrsu48jF2IxsCwN92msSr/WH/u5nansStc8D6hN/3n4NVAy1PZoCQUQk4ILSdSMiIsehoBcRCTgFvYhIwCnoRUQCTkEvIhJwCnoRkYBT0IuIBNz/B+Tnn/A+ZgxSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# activation function examination\n",
    "Models['activation'] = {}\n",
    "\n",
    "activation_func = ['relu','sigmoid','tanh','elu','selu','softmax']\n",
    "for func in activation_func:\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation=func))\n",
    "    NN.add(Dense(100,activation=func))\n",
    "    NN.add(Dense(50, activation=func))\n",
    "    for j in range(5):\n",
    "        NN.add(Dense(50, activation=func))\n",
    "        \n",
    "    NN.add(Dense(1,))\n",
    "    NN.compile(loss ='mean_squared_error', optimizer='RMSprop',)\n",
    "\n",
    "    Models['activation'][func]=NN\n",
    "    \n",
    "counter = 0\n",
    "for i, model in Models['activation'].items():\n",
    "    print('training model:')\n",
    "    \n",
    "    history = model.fit(x = x_train, y = y_train, epochs=60, verbose = 0)\n",
    "    plt.figure()\n",
    "    plt.title(activation_func[counter])\n",
    "    plt.plot(history.history['loss'],)\n",
    "    counter += 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ced3da8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimizer examination\n",
    "Models['optimizer']={}\n",
    "\n",
    "optimizers = ['SGD','RMSprop','Adam','Adadelta','Adagrad','Adamax','Nadam','Ftrl']\n",
    "for opt in optimizers:\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation='relu'))\n",
    "    NN.add(Dense(100,activation='relu'))\n",
    "    NN.add(Dense(50, activation='relu'))\n",
    "    for j in range(5):\n",
    "        NN.add(Dense(50, activation='relu'))\n",
    "        \n",
    "    NN.add(Dense(1,))\n",
    "    NN.compile(loss ='mean_squared_error', optimizer=opt,)\n",
    "\n",
    "    Models['optimizer'][opt]=NN\n",
    "    \n",
    "counter = 0\n",
    "for i, model in Models['optimizer'].items():\n",
    "    print('training model:')\n",
    "    \n",
    "    history = model.fit(x = x_train, y = y_train, epochs=60, verbose = 0)\n",
    "    plt.figure()\n",
    "    plt.title(optimizers[counter])\n",
    "    plt.plot(history.history['loss'],)\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0e3c561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# learning rate examination\n",
    "Models['learning_rate'] = {}\n",
    "\n",
    "learning_rates = [0.1,0.01,0.001,0.0001,0.00001,0.000001,0.0000001]\n",
    "for lr in learning_rates:\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation='relu'))\n",
    "    NN.add(Dense(100,activation='relu'))\n",
    "    NN.add(Dense(50, activation='relu'))\n",
    "    for j in range(5):\n",
    "        NN.add(Dense(50, activation='relu'))\n",
    "    opt = RMSprop(learning_rate= lr, momentum=0.9)    \n",
    "    NN.add(Dense(1,))\n",
    "    NN.compile(loss ='mean_squared_error', optimizer=opt,)\n",
    "\n",
    "    Models['learning_rate'][lr]=(NN)\n",
    "    \n",
    "counter = 0\n",
    "for i, model in Models['learning_rate'].items():\n",
    "    print('training model:')\n",
    "    \n",
    "    history = model.fit(x = x_train, y = y_train, epochs=60, verbose = 0)\n",
    "    plt.figure()\n",
    "    plt.title('learning rate: {}'.format(learning_rates[counter]))\n",
    "    plt.plot(history.history['loss'],)\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b00e2b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n",
      "training model:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loss function examination\n",
    "\n",
    "Models['loss_function'] = {}\n",
    "\n",
    "loss_functions = ['mean_squared_error','mean_absolute_error','mean_absolute_percentage_error',\n",
    "                  'mean_squared_logarithmic_error','cosine_similarity','huber','log_cosh']\n",
    "\n",
    "for func in loss_functions:\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation='relu'))\n",
    "    NN.add(Dense(100,activation='relu'))\n",
    "    NN.add(Dense(50, activation='relu'))\n",
    "    for j in range(5):\n",
    "        NN.add(Dense(50, activation='relu'))\n",
    "    opt = RMSprop(learning_rate= 0.001, momentum=0.9)    \n",
    "    NN.add(Dense(1,))\n",
    "    NN.compile(loss = func, optimizer=opt,)\n",
    "\n",
    "    Models['loss_function'][func]=NN\n",
    "    \n",
    "counter = 0\n",
    "for i, model in Models['loss_function'].items():\n",
    "    print('training model:')\n",
    "    \n",
    "    history = model.fit(x = x_train, y = y_train, epochs=60, verbose = 0)\n",
    "    plt.figure()\n",
    "    plt.title(loss_functions[counter])\n",
    "    plt.plot(history.history['loss'],)\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38bbb0df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "406/406 [==============================] - 1s 552us/step - loss: 0.4854\n",
      "Epoch 2/150\n",
      "406/406 [==============================] - 0s 485us/step - loss: 0.2446\n",
      "Epoch 3/150\n",
      "406/406 [==============================] - 0s 486us/step - loss: 0.2042\n",
      "Epoch 4/150\n",
      "406/406 [==============================] - 0s 543us/step - loss: 0.1851\n",
      "Epoch 5/150\n",
      "406/406 [==============================] - 0s 539us/step - loss: 0.1725\n",
      "Epoch 6/150\n",
      "406/406 [==============================] - 0s 525us/step - loss: 0.1639\n",
      "Epoch 7/150\n",
      "406/406 [==============================] - 0s 543us/step - loss: 0.1556\n",
      "Epoch 8/150\n",
      "406/406 [==============================] - 0s 531us/step - loss: 0.1511\n",
      "Epoch 9/150\n",
      "406/406 [==============================] - 0s 548us/step - loss: 0.1452\n",
      "Epoch 10/150\n",
      "406/406 [==============================] - 0s 548us/step - loss: 0.1408\n",
      "Epoch 11/150\n",
      "406/406 [==============================] - 0s 529us/step - loss: 0.1366\n",
      "Epoch 12/150\n",
      "406/406 [==============================] - 0s 525us/step - loss: 0.1339\n",
      "Epoch 13/150\n",
      "406/406 [==============================] - 0s 520us/step - loss: 0.1311\n",
      "Epoch 14/150\n",
      "406/406 [==============================] - 0s 527us/step - loss: 0.1300\n",
      "Epoch 15/150\n",
      "406/406 [==============================] - 0s 529us/step - loss: 0.1265\n",
      "Epoch 16/150\n",
      "406/406 [==============================] - 0s 519us/step - loss: 0.1247\n",
      "Epoch 17/150\n",
      "406/406 [==============================] - 0s 524us/step - loss: 0.1240\n",
      "Epoch 18/150\n",
      "406/406 [==============================] - 0s 539us/step - loss: 0.1216\n",
      "Epoch 19/150\n",
      "406/406 [==============================] - 0s 528us/step - loss: 0.1212\n",
      "Epoch 20/150\n",
      "406/406 [==============================] - 0s 526us/step - loss: 0.1167\n",
      "Epoch 21/150\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bdbcbe80>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = NN_model.fit(x = x_train, y = y_train, epochs=150,)\n",
    "\n",
    "plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e75c6d95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136/136 [==============================] - 0s 390us/step - loss: 0.1303\n"
     ]
    }
   ],
   "source": [
    "loss = NN_model.evaluate(x_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6981b60d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.7162762 ]\n",
      " [ 0.36186412]\n",
      " [-0.7473048 ]\n",
      " ...\n",
      " [ 0.10575387]\n",
      " [-0.3933204 ]\n",
      " [ 0.54295546]]\n"
     ]
    }
   ],
   "source": [
    "print(NN_model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3be1373e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(y_test).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3f44400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer-Dense50------------------------\n",
      "[1] R2: 88.51%, RMSE/mean: 2477.81%, avg abs % error: 297.15%\n",
      "[2] R2: 88.63%, RMSE/mean: 2464.61%, avg abs % error: 345.59%\n",
      "[3] R2: 86.45%, RMSE/mean: 2690.83%, avg abs % error: 401.46%\n",
      "[4] R2: 87.76%, RMSE/mean: 2557.51%, avg abs % error: 245.02%\n",
      "[5] R2: 85.68%, RMSE/mean: 2766.51%, avg abs % error: 315.33%\n",
      "activation------------------------\n",
      "[relu] R2: 87.73%, RMSE/mean: 2560.89%, avg abs % error: 298.93%\n",
      "[sigmoid] R2: 85.36%, RMSE/mean: 2796.81%, avg abs % error: 268.64%\n",
      "[tanh] R2: 84.22%, RMSE/mean: 2903.82%, avg abs % error: 286.88%\n",
      "[elu] R2: 86.71%, RMSE/mean: 2664.93%, avg abs % error: 271.72%\n",
      "[selu] R2: 85.01%, RMSE/mean: 2830.85%, avg abs % error: 262.11%\n",
      "[softmax] R2: -0.10%, RMSE/mean: 7314.27%, avg abs % error: 121.94%\n",
      "optimizer------------------------\n",
      "[SGD] R2: 87.58%, RMSE/mean: 2576.25%, avg abs % error: 375.10%\n",
      "[RMSprop] R2: 86.14%, RMSE/mean: 2721.99%, avg abs % error: 280.57%\n",
      "[Adam] R2: 87.24%, RMSE/mean: 2611.33%, avg abs % error: 335.60%\n",
      "[Adadelta] R2: 32.60%, RMSE/mean: 6001.88%, avg abs % error: 358.24%\n",
      "[Adagrad] R2: 77.55%, RMSE/mean: 3464.13%, avg abs % error: 336.81%\n",
      "[Adamax] R2: 86.80%, RMSE/mean: 2656.54%, avg abs % error: 266.41%\n",
      "[Nadam] R2: 86.77%, RMSE/mean: 2658.90%, avg abs % error: 295.80%\n",
      "[Ftrl] R2: -0.05%, RMSE/mean: 7312.57%, avg abs % error: 110.48%\n",
      "learning_rate------------------------\n",
      "[0.1] R2: -12.82%, RMSE/mean: 7765.17%, avg abs % error: 641.30%\n",
      "[0.01] R2: -0.00%, RMSE/mean: 7310.74%, avg abs % error: 125.55%\n",
      "[0.001] R2: 87.40%, RMSE/mean: 2594.54%, avg abs % error: 306.65%\n",
      "[0.0001] R2: 88.42%, RMSE/mean: 2488.17%, avg abs % error: 379.91%\n",
      "[1e-05] R2: 83.53%, RMSE/mean: 2966.88%, avg abs % error: 290.77%\n",
      "[1e-06] R2: 77.46%, RMSE/mean: 3470.94%, avg abs % error: 392.20%\n",
      "[1e-07] R2: 24.87%, RMSE/mean: 6336.92%, avg abs % error: 374.02%\n",
      "loss_function------------------------\n",
      "[mean_squared_error] R2: 87.87%, RMSE/mean: 2545.90%, avg abs % error: 285.61%\n",
      "[mean_absolute_error] R2: 88.64%, RMSE/mean: 2464.29%, avg abs % error: 280.23%\n",
      "[mean_absolute_percentage_error] R2: -0.02%, RMSE/mean: 7311.27%, avg abs % error: 101.18%\n",
      "[mean_squared_logarithmic_error] R2: 81.29%, RMSE/mean: 3162.23%, avg abs % error: 283.53%\n",
      "[cosine_similarity] R2: 1.25%, RMSE/mean: 7264.88%, avg abs % error: 283.63%\n",
      "[huber] R2: 86.50%, RMSE/mean: 2686.23%, avg abs % error: 270.99%\n",
      "[log_cosh] R2: 88.42%, RMSE/mean: 2487.28%, avg abs % error: 289.40%\n"
     ]
    }
   ],
   "source": [
    "def metrics(y_predict, y_true):  #given predicted and true values, return dictionary of R2, RMSE, RMSE/mean, average abs % error\n",
    "    metrics={}\n",
    "    residual = y_predict - y_true\n",
    "    average = np.average(y_true)\n",
    "    abs_percent_error = np.absolute(np.divide(residual,y_true))*100\n",
    "    metrics['R2'] = 1 - (np.sum(residual**2)/len(residual))/np.var(y_true)\n",
    "    metrics['RMSE']= (np.sum(residual**2)/len(residual))**0.5\n",
    "    metrics['RMSE/mean'] = metrics['RMSE'] / average\n",
    "    metrics['average absolute % error'] = np.average(abs_percent_error)\n",
    "    metrics['median absolute % error'] = np.median(abs_percent_error)\n",
    "    return metrics\n",
    "\n",
    "def metrics_unnorm(y_predict, y_true): #same to metrics() except un-normalizing y before calculation\n",
    "    m = metrics(y_unnorm(y_predict), y_unnorm(y_true))\n",
    "    return m\n",
    "\n",
    "def compare_model(Models, x_test, y_test, style='norm'): #given nested dictionary of models in the form Models[factor][parameter], print and store metrics\n",
    "    results={}\n",
    "    for factor, model_para in Models.items():\n",
    "        results[factor]={}\n",
    "        print(str(factor)+'------------------------')\n",
    "        for parameter, model in model_para.items():\n",
    "            if style=='norm':\n",
    "                m=metrics(np.squeeze(model.predict(x_test)), y_test)\n",
    "            if style=='unnorm':\n",
    "                m=metrics_unnorm(np.squeeze(model.predict(x_test)), y_test)\n",
    "            results[factor][parameter] = m\n",
    "            R2= \"{:.2f}\".format(m['R2']*100)\n",
    "            RMSE_div_mean = \"{:.2f}\".format(m['RMSE/mean']*100)\n",
    "            avg_abs_percent_error = \"{:.2f}\".format(m['average absolute % error'])\n",
    "            print('['+str(parameter)+'] R2: '+str(R2)+'%, RMSE/mean: '+str(RMSE_div_mean)+'%, avg abs % error: '+str(avg_abs_percent_error)+'%')\n",
    "    return results\n",
    "\n",
    "compare_results = compare_model(Models, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035d38d0",
   "metadata": {},
   "source": [
    "The above RMSE/mean and average absolute % error all look very terrible, all over 100%, because the mean of test data is nearly zero after normalization! \n",
    "\n",
    "Let's do an un-normalized version:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0991296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2': 0.8882650879692677,\n",
       " 'RMSE': 132499.71629584307,\n",
       " 'RMSE/mean': 0.24289342735422928,\n",
       " 'average absolute % error': 14.577723148373696}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_unnorm(np.squeeze(NN_model.predict(x_test)) , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "64891c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer-Dense50------------------------\n",
      "[1] R2: 88.51%, RMSE/mean: 24.63%, avg abs % error: 13.67%\n",
      "[2] R2: 88.63%, RMSE/mean: 24.50%, avg abs % error: 14.48%\n",
      "[3] R2: 86.45%, RMSE/mean: 26.75%, avg abs % error: 16.40%\n",
      "[4] R2: 87.76%, RMSE/mean: 25.42%, avg abs % error: 13.50%\n",
      "[5] R2: 85.68%, RMSE/mean: 27.50%, avg abs % error: 13.49%\n",
      "activation------------------------\n",
      "[relu] R2: 87.73%, RMSE/mean: 25.45%, avg abs % error: 15.53%\n",
      "[sigmoid] R2: 85.36%, RMSE/mean: 27.80%, avg abs % error: 15.41%\n",
      "[tanh] R2: 84.22%, RMSE/mean: 28.86%, avg abs % error: 14.84%\n",
      "[elu] R2: 86.71%, RMSE/mean: 26.49%, avg abs % error: 13.20%\n",
      "[selu] R2: 85.01%, RMSE/mean: 28.14%, avg abs % error: 19.19%\n",
      "[softmax] R2: -0.10%, RMSE/mean: 72.70%, avg abs % error: 53.02%\n",
      "optimizer------------------------\n",
      "[SGD] R2: 87.58%, RMSE/mean: 25.61%, avg abs % error: 15.94%\n",
      "[RMSprop] R2: 86.14%, RMSE/mean: 27.06%, avg abs % error: 16.55%\n",
      "[Adam] R2: 87.24%, RMSE/mean: 25.96%, avg abs % error: 13.48%\n",
      "[Adadelta] R2: 32.60%, RMSE/mean: 59.66%, avg abs % error: 43.54%\n",
      "[Adagrad] R2: 77.55%, RMSE/mean: 34.43%, avg abs % error: 20.16%\n",
      "[Adamax] R2: 86.80%, RMSE/mean: 26.40%, avg abs % error: 14.05%\n",
      "[Nadam] R2: 86.77%, RMSE/mean: 26.43%, avg abs % error: 13.51%\n",
      "[Ftrl] R2: -0.05%, RMSE/mean: 72.68%, avg abs % error: 53.52%\n",
      "learning_rate------------------------\n",
      "[0.1] R2: -12.82%, RMSE/mean: 77.18%, avg abs % error: 39.50%\n",
      "[0.01] R2: -0.00%, RMSE/mean: 72.66%, avg abs % error: 55.11%\n",
      "[0.001] R2: 87.40%, RMSE/mean: 25.79%, avg abs % error: 17.77%\n",
      "[0.0001] R2: 88.42%, RMSE/mean: 24.73%, avg abs % error: 13.84%\n",
      "[1e-05] R2: 83.53%, RMSE/mean: 29.49%, avg abs % error: 14.73%\n",
      "[1e-06] R2: 77.46%, RMSE/mean: 34.50%, avg abs % error: 19.27%\n",
      "[1e-07] R2: 24.87%, RMSE/mean: 62.99%, avg abs % error: 30.55%\n",
      "loss_function------------------------\n",
      "[mean_squared_error] R2: 87.87%, RMSE/mean: 25.30%, avg abs % error: 14.25%\n",
      "[mean_absolute_error] R2: 88.64%, RMSE/mean: 24.49%, avg abs % error: 13.32%\n",
      "[mean_absolute_percentage_error] R2: -0.02%, RMSE/mean: 72.67%, avg abs % error: 54.10%\n",
      "[mean_squared_logarithmic_error] R2: 81.29%, RMSE/mean: 31.43%, avg abs % error: 27.26%\n",
      "[cosine_similarity] R2: 1.25%, RMSE/mean: 72.21%, avg abs % error: 46.86%\n",
      "[huber] R2: 86.50%, RMSE/mean: 26.70%, avg abs % error: 16.10%\n",
      "[log_cosh] R2: 88.42%, RMSE/mean: 24.72%, avg abs % error: 19.04%\n"
     ]
    }
   ],
   "source": [
    "compare_results = compare_model(Models, x_test, y_test, style='unnorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ff02e8",
   "metadata": {},
   "source": [
    "# Summary of preliminary model comparisons\n",
    "\n",
    "\n",
    "\n",
    "For practical purpose, absolute percentage error would be the major concern for buyers and sellers for different price levels. Hence, we will evaluate model performance mainly based on this metrics - average absolute percentage error.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f799220",
   "metadata": {},
   "source": [
    "### Number of Layers\n",
    "\n",
    "Adding more layers of Dense(50) only generates similar or worse results. So for simplicity, we will choose to focus our work on fewer layers. The model with 1 added layer of Dense(50) is 17/17/100/50/1, with 3 hidden layers, which would be our base model for tunning in the next stage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9eafb0",
   "metadata": {},
   "source": [
    "### Activation Function\n",
    "\n",
    "Exponential Linear Unit (elu) is the best performer. It is a function that tend to converge cost to zero faster and produce more accurate results. It is similar to RELU, except that it can produce negative values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c57be6f",
   "metadata": {},
   "source": [
    "### Optimizer\n",
    "\n",
    "Adam, followed by Adam-like optimizers, performs the best."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93913e12",
   "metadata": {},
   "source": [
    "### Learning Rate\n",
    "\n",
    "Laerning rate of 0.0001 produces the best result."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3259cf3b",
   "metadata": {},
   "source": [
    "### Loss Function\n",
    "\n",
    "One intetesting result is that, measured by average absolute percentage error, the loss function of mean_absolute_percentage_error perfoms the worst instead of the best! One major reason is standard normalization would make measure based on percentage of predicted values very unstable, as the mean of predicted value is zero, the measure could approach infinity. The squaring of error may become smaller for error values below 1, so mean_squared_error may not be good choice. \n",
    "\n",
    "Let's choose the best performer from above results - the mean_absolute_error.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e40cfa6",
   "metadata": {},
   "source": [
    "# Further tuning with regularization\n",
    "\n",
    "From above, we will focus on neural network with structure 17/17/100/50/1, elu as activation function, Adam as optimizer, learning rate at 0.0001, loss function \"mean_absolute_error\".\n",
    "\n",
    "Note that previously we have not yet tuned with regularization, such as dropout, L2 regularization. One useful technique is to compare testing accuracy with training accuracy to detect under-fitting or over-fitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4612a235",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_50\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_402 (Dense)            (None, 17)                323       \n",
      "_________________________________________________________________\n",
      "dense_403 (Dense)            (None, 100)               1800      \n",
      "_________________________________________________________________\n",
      "dense_404 (Dense)            (None, 50)                5050      \n",
      "_________________________________________________________________\n",
      "dense_405 (Dense)            (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 7,224\n",
      "Trainable params: 7,224\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Base Structure v2\n",
    "\n",
    "NN_model2 = Sequential()\n",
    "NN_model2.add(Dense(17,input_dim = x_train.shape[1], activation='elu'))\n",
    "NN_model2.add(Dense(100,activation='elu'))\n",
    "NN_model2.add(Dense(50, activation='elu'))\n",
    "NN_model2.add(Dense(1,))\n",
    "\n",
    "opt = Adam(learning_rate = 0.0001)  \n",
    "# Compile the network :\n",
    "NN_model2.compile(loss ='mean_absolute_error', optimizer=opt,)\n",
    "NN_model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "25a3a2ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1708\n",
      "Epoch 2/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.1712\n",
      "Epoch 3/200\n",
      "406/406 [==============================] - 0s 542us/step - loss: 0.1711\n",
      "Epoch 4/200\n",
      "406/406 [==============================] - 0s 539us/step - loss: 0.1706\n",
      "Epoch 5/200\n",
      "406/406 [==============================] - 0s 550us/step - loss: 0.1711\n",
      "Epoch 6/200\n",
      "406/406 [==============================] - 0s 517us/step - loss: 0.1711\n",
      "Epoch 7/200\n",
      "406/406 [==============================] - 0s 542us/step - loss: 0.1710\n",
      "Epoch 8/200\n",
      "406/406 [==============================] - 0s 548us/step - loss: 0.1710\n",
      "Epoch 9/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1706\n",
      "Epoch 10/200\n",
      "406/406 [==============================] - 0s 536us/step - loss: 0.1711\n",
      "Epoch 11/200\n",
      "406/406 [==============================] - 0s 528us/step - loss: 0.1707\n",
      "Epoch 12/200\n",
      "406/406 [==============================] - 0s 496us/step - loss: 0.1707\n",
      "Epoch 13/200\n",
      "406/406 [==============================] - 0s 503us/step - loss: 0.1707\n",
      "Epoch 14/200\n",
      "406/406 [==============================] - 0s 549us/step - loss: 0.1707\n",
      "Epoch 15/200\n",
      "406/406 [==============================] - 0s 555us/step - loss: 0.1702\n",
      "Epoch 16/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1701\n",
      "Epoch 17/200\n",
      "406/406 [==============================] - 0s 539us/step - loss: 0.1703\n",
      "Epoch 18/200\n",
      "406/406 [==============================] - 0s 553us/step - loss: 0.1708\n",
      "Epoch 19/200\n",
      "406/406 [==============================] - 0s 557us/step - loss: 0.1706\n",
      "Epoch 20/200\n",
      "406/406 [==============================] - 0s 555us/step - loss: 0.1703\n",
      "Epoch 21/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1701\n",
      "Epoch 22/200\n",
      "406/406 [==============================] - 0s 547us/step - loss: 0.1703\n",
      "Epoch 23/200\n",
      "406/406 [==============================] - 0s 540us/step - loss: 0.1700\n",
      "Epoch 24/200\n",
      "406/406 [==============================] - 0s 581us/step - loss: 0.1700\n",
      "Epoch 25/200\n",
      "406/406 [==============================] - 0s 565us/step - loss: 0.1699\n",
      "Epoch 26/200\n",
      "406/406 [==============================] - 0s 565us/step - loss: 0.1702\n",
      "Epoch 27/200\n",
      "406/406 [==============================] - 0s 561us/step - loss: 0.1696\n",
      "Epoch 28/200\n",
      "406/406 [==============================] - 0s 529us/step - loss: 0.1702\n",
      "Epoch 29/200\n",
      "406/406 [==============================] - 0s 547us/step - loss: 0.1700\n",
      "Epoch 30/200\n",
      "406/406 [==============================] - 0s 550us/step - loss: 0.1700\n",
      "Epoch 31/200\n",
      "406/406 [==============================] - 0s 554us/step - loss: 0.1703\n",
      "Epoch 32/200\n",
      "406/406 [==============================] - 0s 572us/step - loss: 0.1695\n",
      "Epoch 33/200\n",
      "406/406 [==============================] - 0s 584us/step - loss: 0.1695\n",
      "Epoch 34/200\n",
      "406/406 [==============================] - 0s 580us/step - loss: 0.1698\n",
      "Epoch 35/200\n",
      "406/406 [==============================] - 0s 562us/step - loss: 0.1696\n",
      "Epoch 36/200\n",
      "406/406 [==============================] - 0s 567us/step - loss: 0.1696\n",
      "Epoch 37/200\n",
      "406/406 [==============================] - 0s 562us/step - loss: 0.1707\n",
      "Epoch 38/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1694\n",
      "Epoch 39/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1701\n",
      "Epoch 40/200\n",
      "406/406 [==============================] - 0s 567us/step - loss: 0.1697\n",
      "Epoch 41/200\n",
      "406/406 [==============================] - 0s 549us/step - loss: 0.1699\n",
      "Epoch 42/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1698\n",
      "Epoch 43/200\n",
      "406/406 [==============================] - 0s 545us/step - loss: 0.1692\n",
      "Epoch 44/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1691\n",
      "Epoch 45/200\n",
      "406/406 [==============================] - 0s 549us/step - loss: 0.1691\n",
      "Epoch 46/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1696\n",
      "Epoch 47/200\n",
      "406/406 [==============================] - 0s 584us/step - loss: 0.1692\n",
      "Epoch 48/200\n",
      "406/406 [==============================] - 0s 555us/step - loss: 0.1690\n",
      "Epoch 49/200\n",
      "406/406 [==============================] - 0s 557us/step - loss: 0.1691\n",
      "Epoch 50/200\n",
      "406/406 [==============================] - 0s 559us/step - loss: 0.1695\n",
      "Epoch 51/200\n",
      "406/406 [==============================] - 0s 545us/step - loss: 0.1691\n",
      "Epoch 52/200\n",
      "406/406 [==============================] - 0s 561us/step - loss: 0.1688\n",
      "Epoch 53/200\n",
      "406/406 [==============================] - 0s 568us/step - loss: 0.1690\n",
      "Epoch 54/200\n",
      "406/406 [==============================] - 0s 545us/step - loss: 0.1685\n",
      "Epoch 55/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1691\n",
      "Epoch 56/200\n",
      "406/406 [==============================] - 0s 537us/step - loss: 0.1690\n",
      "Epoch 57/200\n",
      "406/406 [==============================] - 0s 542us/step - loss: 0.1682\n",
      "Epoch 58/200\n",
      "406/406 [==============================] - 0s 550us/step - loss: 0.1683\n",
      "Epoch 59/200\n",
      "406/406 [==============================] - 0s 557us/step - loss: 0.1685\n",
      "Epoch 60/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1686\n",
      "Epoch 61/200\n",
      "406/406 [==============================] - 0s 571us/step - loss: 0.1689\n",
      "Epoch 62/200\n",
      "406/406 [==============================] - 0s 576us/step - loss: 0.1689\n",
      "Epoch 63/200\n",
      "406/406 [==============================] - 0s 582us/step - loss: 0.1689\n",
      "Epoch 64/200\n",
      "406/406 [==============================] - 0s 590us/step - loss: 0.1685\n",
      "Epoch 65/200\n",
      "406/406 [==============================] - 0s 564us/step - loss: 0.1690\n",
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     "text": [
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     ]
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     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bed6e190>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = NN_model2.fit(x = x_train, y = y_train, epochs=200,)\n",
    "\n",
    "plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2dbebdd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2': 0.9150037273690476,\n",
       " 'RMSE': 102893.62075553637,\n",
       " 'RMSE/mean': 0.1920174867998606,\n",
       " 'average absolute % error': 11.59537876597654}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_unnorm(np.squeeze(NN_model2.predict(x_train)) , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c3514bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2': 0.8886493570355943,\n",
       " 'RMSE': 132271.67927113103,\n",
       " 'RMSE/mean': 0.2424753985761729,\n",
       " 'average absolute % error': 12.7124393500427}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_unnorm(np.squeeze(NN_model2.predict(x_test)) , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb814d10",
   "metadata": {},
   "source": [
    "The testing error 12.7% is slightly higher than training error 11.6%, which may imply slight over-fitting and small room for improvement by regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a050296f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
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      "Epoch 97/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 98/200\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 33/200\n",
      "406/406 [==============================] - 0s 526us/step - loss: 0.2127\n",
      "Epoch 34/200\n",
      "406/406 [==============================] - 0s 523us/step - loss: 0.2125\n",
      "Epoch 35/200\n",
      "406/406 [==============================] - 0s 517us/step - loss: 0.2112\n",
      "Epoch 36/200\n",
      "406/406 [==============================] - 0s 521us/step - loss: 0.2104\n",
      "Epoch 37/200\n",
      "406/406 [==============================] - 0s 517us/step - loss: 0.2094\n",
      "Epoch 38/200\n",
      "406/406 [==============================] - 0s 520us/step - loss: 0.2092\n",
      "Epoch 39/200\n",
      "406/406 [==============================] - 0s 520us/step - loss: 0.2080\n",
      "Epoch 40/200\n",
      "406/406 [==============================] - 0s 518us/step - loss: 0.2079\n",
      "Epoch 41/200\n",
      "406/406 [==============================] - 0s 519us/step - loss: 0.2076\n",
      "Epoch 42/200\n",
      "406/406 [==============================] - 0s 514us/step - loss: 0.2067\n",
      "Epoch 43/200\n",
      "406/406 [==============================] - 0s 518us/step - loss: 0.2059\n",
      "Epoch 44/200\n",
      "406/406 [==============================] - 0s 515us/step - loss: 0.2055\n",
      "Epoch 45/200\n",
      "406/406 [==============================] - 0s 514us/step - loss: 0.2050\n",
      "Epoch 46/200\n",
      "406/406 [==============================] - 0s 514us/step - loss: 0.2044\n",
      "Epoch 47/200\n",
      "406/406 [==============================] - 0s 518us/step - loss: 0.2043\n",
      "Epoch 48/200\n",
      "406/406 [==============================] - 0s 523us/step - loss: 0.2036\n",
      "Epoch 49/200\n",
      "406/406 [==============================] - 0s 514us/step - loss: 0.2035\n",
      "Epoch 50/200\n",
      "406/406 [==============================] - 0s 531us/step - loss: 0.2027\n",
      "Epoch 51/200\n",
      "406/406 [==============================] - 0s 513us/step - loss: 0.2021\n",
      "Epoch 52/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "406/406 [==============================] - 0s 526us/step - loss: 0.2013\n",
      "Epoch 53/200\n",
      "406/406 [==============================] - 0s 503us/step - loss: 0.2016\n",
      "Epoch 54/200\n",
      "406/406 [==============================] - 0s 527us/step - loss: 0.2014\n",
      "Epoch 55/200\n",
      "406/406 [==============================] - 0s 572us/step - loss: 0.2006\n",
      "Epoch 56/200\n",
      "406/406 [==============================] - 0s 534us/step - loss: 0.2002\n",
      "Epoch 57/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1993\n",
      "Epoch 58/200\n",
      "406/406 [==============================] - 0s 546us/step - loss: 0.1996\n",
      "Epoch 59/200\n",
      "406/406 [==============================] - 0s 575us/step - loss: 0.1992\n",
      "Epoch 60/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1989\n",
      "Epoch 61/200\n",
      "406/406 [==============================] - 0s 531us/step - loss: 0.1989\n",
      "Epoch 62/200\n",
      "406/406 [==============================] - 0s 557us/step - loss: 0.1990\n",
      "Epoch 63/200\n",
      "406/406 [==============================] - 0s 558us/step - loss: 0.1978\n",
      "Epoch 64/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1972\n",
      "Epoch 65/200\n",
      "406/406 [==============================] - 0s 540us/step - loss: 0.1966\n",
      "Epoch 66/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1966\n",
      "Epoch 67/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1968\n",
      "Epoch 68/200\n",
      "406/406 [==============================] - 0s 547us/step - loss: 0.1967\n",
      "Epoch 69/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1949\n",
      "Epoch 70/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1958\n",
      "Epoch 71/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1945\n",
      "Epoch 72/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1946\n",
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      "Epoch 74/200\n",
      "406/406 [==============================] - 0s 551us/step - loss: 0.1946\n",
      "Epoch 75/200\n",
      "406/406 [==============================] - 0s 563us/step - loss: 0.1942\n",
      "Epoch 76/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1932\n",
      "Epoch 77/200\n",
      "406/406 [==============================] - 0s 537us/step - loss: 0.1935\n",
      "Epoch 78/200\n",
      "406/406 [==============================] - 0s 601us/step - loss: 0.1932\n",
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      "Epoch 81/200\n",
      "406/406 [==============================] - 0s 553us/step - loss: 0.1918\n",
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      "Epoch 83/200\n",
      "406/406 [==============================] - 0s 550us/step - loss: 0.1916\n",
      "Epoch 84/200\n",
      "406/406 [==============================] - 0s 546us/step - loss: 0.1917\n",
      "Epoch 85/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1914\n",
      "Epoch 86/200\n",
      "406/406 [==============================] - 0s 550us/step - loss: 0.1908\n",
      "Epoch 87/200\n",
      "406/406 [==============================] - 0s 567us/step - loss: 0.1909\n",
      "Epoch 88/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1906\n",
      "Epoch 89/200\n",
      "406/406 [==============================] - 0s 548us/step - loss: 0.1904\n",
      "Epoch 90/200\n",
      "406/406 [==============================] - 0s 558us/step - loss: 0.1903\n",
      "Epoch 91/200\n",
      "406/406 [==============================] - 0s 539us/step - loss: 0.1895\n",
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      "406/406 [==============================] - 0s 542us/step - loss: 0.1893\n",
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      "406/406 [==============================] - 0s 554us/step - loss: 0.1891\n",
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      "Epoch 97/200\n",
      "406/406 [==============================] - 0s 553us/step - loss: 0.1892\n",
      "Epoch 98/200\n",
      "406/406 [==============================] - 0s 540us/step - loss: 0.1878\n",
      "Epoch 99/200\n",
      "406/406 [==============================] - 0s 563us/step - loss: 0.1878\n",
      "Epoch 100/200\n",
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      "Epoch 101/200\n",
      "406/406 [==============================] - 0s 615us/step - loss: 0.1873\n",
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      "Epoch 103/200\n",
      "406/406 [==============================] - 0s 555us/step - loss: 0.1875\n",
      "Epoch 104/200\n",
      "406/406 [==============================] - 0s 567us/step - loss: 0.1865\n",
      "Epoch 105/200\n",
      "406/406 [==============================] - 0s 571us/step - loss: 0.1865\n",
      "Epoch 106/200\n",
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      "Epoch 107/200\n",
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      "Epoch 108/200\n",
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      "Epoch 109/200\n",
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      "Epoch 110/200\n",
      "406/406 [==============================] - 0s 555us/step - loss: 0.1864\n",
      "Epoch 111/200\n",
      "406/406 [==============================] - 0s 501us/step - loss: 0.1856\n",
      "Epoch 112/200\n",
      "406/406 [==============================] - 0s 504us/step - loss: 0.1856\n",
      "Epoch 113/200\n",
      "406/406 [==============================] - 0s 542us/step - loss: 0.1850\n",
      "Epoch 114/200\n",
      "406/406 [==============================] - 0s 536us/step - loss: 0.1854\n",
      "Epoch 115/200\n",
      "406/406 [==============================] - 0s 531us/step - loss: 0.1849\n",
      "Epoch 116/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1847\n",
      "Epoch 117/200\n",
      "406/406 [==============================] - 0s 529us/step - loss: 0.1846\n",
      "Epoch 118/200\n",
      "406/406 [==============================] - 0s 542us/step - loss: 0.1842\n",
      "Epoch 119/200\n",
      "406/406 [==============================] - 0s 533us/step - loss: 0.1841\n",
      "Epoch 120/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1840\n",
      "Epoch 121/200\n",
      "406/406 [==============================] - 0s 555us/step - loss: 0.1841\n",
      "Epoch 122/200\n",
      "406/406 [==============================] - 0s 554us/step - loss: 0.1844\n",
      "Epoch 123/200\n",
      "406/406 [==============================] - 0s 546us/step - loss: 0.1834\n",
      "Epoch 124/200\n",
      "406/406 [==============================] - 0s 567us/step - loss: 0.1840\n",
      "Epoch 125/200\n",
      "406/406 [==============================] - 0s 566us/step - loss: 0.1833\n",
      "Epoch 126/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1832\n",
      "Epoch 127/200\n",
      "406/406 [==============================] - 0s 543us/step - loss: 0.1827\n",
      "Epoch 128/200\n",
      "406/406 [==============================] - 0s 553us/step - loss: 0.1831\n",
      "Epoch 129/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1828\n",
      "Epoch 130/200\n",
      "406/406 [==============================] - 0s 530us/step - loss: 0.1822\n",
      "Epoch 131/200\n",
      "406/406 [==============================] - 0s 540us/step - loss: 0.1823\n",
      "Epoch 132/200\n",
      "406/406 [==============================] - 0s 534us/step - loss: 0.1824\n",
      "Epoch 133/200\n",
      "406/406 [==============================] - 0s 561us/step - loss: 0.1819\n",
      "Epoch 134/200\n",
      "406/406 [==============================] - 0s 576us/step - loss: 0.1820\n",
      "Epoch 135/200\n",
      "406/406 [==============================] - 0s 554us/step - loss: 0.1814\n",
      "Epoch 136/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1814\n",
      "Epoch 137/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1814\n",
      "Epoch 138/200\n",
      "406/406 [==============================] - 0s 554us/step - loss: 0.1815\n",
      "Epoch 139/200\n",
      "406/406 [==============================] - 0s 563us/step - loss: 0.1814\n",
      "Epoch 140/200\n",
      "406/406 [==============================] - 0s 552us/step - loss: 0.1815\n",
      "Epoch 141/200\n",
      "406/406 [==============================] - 0s 549us/step - loss: 0.1816\n",
      "Epoch 142/200\n",
      "406/406 [==============================] - 0s 546us/step - loss: 0.1807\n",
      "Epoch 143/200\n",
      "406/406 [==============================] - 0s 546us/step - loss: 0.1810\n",
      "Epoch 144/200\n",
      "406/406 [==============================] - 0s 558us/step - loss: 0.1809\n",
      "Epoch 145/200\n",
      "406/406 [==============================] - 0s 569us/step - loss: 0.1803\n",
      "Epoch 146/200\n",
      "406/406 [==============================] - 0s 556us/step - loss: 0.1804\n",
      "Epoch 147/200\n",
      "406/406 [==============================] - 0s 547us/step - loss: 0.1800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 148/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1802\n",
      "Epoch 149/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1804\n",
      "Epoch 150/200\n",
      "406/406 [==============================] - 0s 565us/step - loss: 0.1802\n",
      "Epoch 151/200\n",
      "406/406 [==============================] - 0s 527us/step - loss: 0.1797\n",
      "Epoch 152/200\n",
      "406/406 [==============================] - 0s 527us/step - loss: 0.1798\n",
      "Epoch 153/200\n",
      "406/406 [==============================] - 0s 526us/step - loss: 0.1798\n",
      "Epoch 154/200\n",
      "406/406 [==============================] - 0s 533us/step - loss: 0.1790\n",
      "Epoch 155/200\n",
      "406/406 [==============================] - 0s 553us/step - loss: 0.1794\n",
      "Epoch 156/200\n",
      "406/406 [==============================] - 0s 536us/step - loss: 0.1795\n",
      "Epoch 157/200\n",
      "406/406 [==============================] - 0s 543us/step - loss: 0.1792\n",
      "Epoch 158/200\n",
      "406/406 [==============================] - 0s 537us/step - loss: 0.1792\n",
      "Epoch 159/200\n",
      "406/406 [==============================] - 0s 540us/step - loss: 0.1787\n",
      "Epoch 160/200\n",
      "406/406 [==============================] - 0s 536us/step - loss: 0.1785\n",
      "Epoch 161/200\n",
      "406/406 [==============================] - 0s 536us/step - loss: 0.1790\n",
      "Epoch 162/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1787\n",
      "Epoch 163/200\n",
      "406/406 [==============================] - 0s 533us/step - loss: 0.1785\n",
      "Epoch 164/200\n",
      "406/406 [==============================] - 0s 510us/step - loss: 0.1784\n",
      "Epoch 165/200\n",
      "406/406 [==============================] - 0s 536us/step - loss: 0.1786\n",
      "Epoch 166/200\n",
      "406/406 [==============================] - 0s 533us/step - loss: 0.1778\n",
      "Epoch 167/200\n",
      "406/406 [==============================] - 0s 531us/step - loss: 0.1777\n",
      "Epoch 168/200\n",
      "406/406 [==============================] - 0s 550us/step - loss: 0.1780\n",
      "Epoch 169/200\n",
      "406/406 [==============================] - 0s 547us/step - loss: 0.1781\n",
      "Epoch 170/200\n",
      "406/406 [==============================] - 0s 532us/step - loss: 0.1776\n",
      "Epoch 171/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1778\n",
      "Epoch 172/200\n",
      "406/406 [==============================] - 0s 543us/step - loss: 0.1777\n",
      "Epoch 173/200\n",
      "406/406 [==============================] - 0s 530us/step - loss: 0.1775\n",
      "Epoch 174/200\n",
      "406/406 [==============================] - 0s 534us/step - loss: 0.1772\n",
      "Epoch 175/200\n",
      "406/406 [==============================] - 0s 525us/step - loss: 0.1775\n",
      "Epoch 176/200\n",
      "406/406 [==============================] - 0s 529us/step - loss: 0.1773\n",
      "Epoch 177/200\n",
      "406/406 [==============================] - 0s 531us/step - loss: 0.1771\n",
      "Epoch 178/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1777\n",
      "Epoch 179/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1766\n",
      "Epoch 180/200\n",
      "406/406 [==============================] - 0s 546us/step - loss: 0.1768\n",
      "Epoch 181/200\n",
      "406/406 [==============================] - 0s 537us/step - loss: 0.1770\n",
      "Epoch 182/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1767\n",
      "Epoch 183/200\n",
      "406/406 [==============================] - 0s 581us/step - loss: 0.1762\n",
      "Epoch 184/200\n",
      "406/406 [==============================] - 0s 534us/step - loss: 0.1760\n",
      "Epoch 185/200\n",
      "406/406 [==============================] - 0s 562us/step - loss: 0.1767\n",
      "Epoch 186/200\n",
      "406/406 [==============================] - 0s 544us/step - loss: 0.1759\n",
      "Epoch 187/200\n",
      "406/406 [==============================] - 0s 553us/step - loss: 0.1759\n",
      "Epoch 188/200\n",
      "406/406 [==============================] - 0s 535us/step - loss: 0.1758\n",
      "Epoch 189/200\n",
      "406/406 [==============================] - 0s 532us/step - loss: 0.1761\n",
      "Epoch 190/200\n",
      "406/406 [==============================] - 0s 547us/step - loss: 0.1760\n",
      "Epoch 191/200\n",
      "406/406 [==============================] - 0s 579us/step - loss: 0.1763\n",
      "Epoch 192/200\n",
      "406/406 [==============================] - 0s 564us/step - loss: 0.1755\n",
      "Epoch 193/200\n",
      "406/406 [==============================] - 0s 551us/step - loss: 0.1755\n",
      "Epoch 194/200\n",
      "406/406 [==============================] - 0s 541us/step - loss: 0.1757\n",
      "Epoch 195/200\n",
      "406/406 [==============================] - 0s 537us/step - loss: 0.1753\n",
      "Epoch 196/200\n",
      "406/406 [==============================] - 0s 538us/step - loss: 0.1760\n",
      "Epoch 197/200\n",
      "406/406 [==============================] - 0s 539us/step - loss: 0.1758\n",
      "Epoch 198/200\n",
      "406/406 [==============================] - 0s 731us/step - loss: 0.1750\n",
      "Epoch 199/200\n",
      "406/406 [==============================] - 0s 611us/step - loss: 0.1754\n",
      "Epoch 200/200\n",
      "406/406 [==============================] - 0s 567us/step - loss: 0.1749\n",
      "L2------------------------\n",
      "[0] R2: 88.51%, RMSE/mean: 24.63%, avg abs % error: 13.08%\n",
      "[0.0001] R2: 87.62%, RMSE/mean: 25.56%, avg abs % error: 12.77%\n",
      "[0.001] R2: 87.96%, RMSE/mean: 25.21%, avg abs % error: 13.75%\n",
      "[0.01] R2: 87.87%, RMSE/mean: 25.31%, avg abs % error: 13.14%\n",
      "[0.1] R2: 88.02%, RMSE/mean: 25.15%, avg abs % error: 13.12%\n",
      "[1] R2: 88.36%, RMSE/mean: 24.79%, avg abs % error: 13.06%\n"
     ]
    }
   ],
   "source": [
    "Models2 = {}\n",
    "\n",
    "Models2['L2'] = {}\n",
    "\n",
    "for r in [0,0.0001,0.001,0.01,0.1,1]:\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation='elu'))\n",
    "    NN.add(Dense(100,activation='elu'))\n",
    "    NN.add(Dense(50, activation='elu'))\n",
    "    NN.add(Dense(1,))\n",
    "    regularizers.l2(l2=r)\n",
    "    opt = Adam(learning_rate = 0.0001)  \n",
    "    # Compile the network :\n",
    "    NN.compile(loss ='mean_absolute_error', optimizer=opt,)\n",
    "    NN.fit(x = x_train, y = y_train, epochs=200, verbose=0)\n",
    "    Models2['L2'][r] = NN\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "92349918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L2------------------------\n",
      "[0] R2: 88.51%, RMSE/mean: 24.63%, avg abs % error: 13.08%\n",
      "[0.0001] R2: 87.62%, RMSE/mean: 25.56%, avg abs % error: 12.77%\n",
      "[0.001] R2: 87.96%, RMSE/mean: 25.21%, avg abs % error: 13.75%\n",
      "[0.01] R2: 87.87%, RMSE/mean: 25.31%, avg abs % error: 13.14%\n",
      "[0.1] R2: 88.02%, RMSE/mean: 25.15%, avg abs % error: 13.12%\n",
      "[1] R2: 88.36%, RMSE/mean: 24.79%, avg abs % error: 13.06%\n"
     ]
    }
   ],
   "source": [
    "compare_results = compare_model(Models2, x_test, y_test, style='unnorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29af27a0",
   "metadata": {},
   "source": [
    "From the above, L2 regularization at 0.0001 (the regularization lamda applied as penality to heavy weight) generates lowest percentage error.\n",
    "So we will adopt L2=0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2380ce1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "406/406 [==============================] - 0s 638us/step - loss: 0.3598\n",
      "Epoch 2/200\n",
      "406/406 [==============================] - 0s 589us/step - loss: 0.3192\n",
      "Epoch 3/200\n",
      "406/406 [==============================] - 0s 615us/step - loss: 0.3045\n",
      "Epoch 4/200\n",
      "406/406 [==============================] - 0s 626us/step - loss: 0.2941\n",
      "Epoch 5/200\n",
      "406/406 [==============================] - 0s 614us/step - loss: 0.2865\n",
      "Epoch 6/200\n",
      "406/406 [==============================] - 0s 617us/step - loss: 0.2803\n",
      "Epoch 7/200\n",
      "406/406 [==============================] - 0s 609us/step - loss: 0.2753\n",
      "Epoch 8/200\n",
      "406/406 [==============================] - 0s 616us/step - loss: 0.2702\n",
      "Epoch 9/200\n",
      "406/406 [==============================] - 0s 602us/step - loss: 0.2657\n",
      "Epoch 10/200\n",
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      "Epoch 11/200\n",
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     ]
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      "Epoch 80/200\n",
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      "Epoch 83/200\n",
      "406/406 [==============================] - 0s 598us/step - loss: 0.4853\n",
      "Epoch 84/200\n",
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      "Epoch 85/200\n",
      "406/406 [==============================] - 0s 596us/step - loss: 0.4874\n",
      "Epoch 86/200\n",
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      "Epoch 87/200\n",
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      "Epoch 88/200\n",
      "406/406 [==============================] - 0s 591us/step - loss: 0.4825\n",
      "Epoch 89/200\n",
      "406/406 [==============================] - 0s 605us/step - loss: 0.4799\n",
      "Epoch 90/200\n",
      "406/406 [==============================] - 0s 599us/step - loss: 0.4812\n",
      "Epoch 91/200\n",
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      "Epoch 92/200\n",
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      "Epoch 93/200\n",
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      "Epoch 94/200\n",
      "406/406 [==============================] - 0s 590us/step - loss: 0.4772\n",
      "Epoch 95/200\n",
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      "Epoch 96/200\n",
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      "Epoch 97/200\n",
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      "Epoch 98/200\n",
      "406/406 [==============================] - 0s 594us/step - loss: 0.4746\n",
      "Epoch 99/200\n",
      "406/406 [==============================] - 0s 602us/step - loss: 0.4767\n",
      "Epoch 100/200\n",
      "406/406 [==============================] - 0s 595us/step - loss: 0.4723\n",
      "Epoch 101/200\n",
      "406/406 [==============================] - 0s 599us/step - loss: 0.4745\n",
      "Epoch 102/200\n",
      "406/406 [==============================] - 0s 588us/step - loss: 0.4783\n",
      "Epoch 103/200\n",
      "406/406 [==============================] - 0s 643us/step - loss: 0.4759\n",
      "Epoch 104/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "406/406 [==============================] - 0s 600us/step - loss: 0.4747\n",
      "Epoch 105/200\n",
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      "406/406 [==============================] - 0s 588us/step - loss: 0.4730\n",
      "Epoch 107/200\n",
      "406/406 [==============================] - 0s 582us/step - loss: 0.4714\n",
      "Epoch 108/200\n",
      "406/406 [==============================] - 0s 581us/step - loss: 0.4682\n",
      "Epoch 109/200\n",
      "406/406 [==============================] - 0s 583us/step - loss: 0.4732\n",
      "Epoch 110/200\n",
      "406/406 [==============================] - 0s 591us/step - loss: 0.4735\n",
      "Epoch 111/200\n",
      "406/406 [==============================] - 0s 586us/step - loss: 0.4727\n",
      "Epoch 112/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.4690\n",
      "Epoch 113/200\n",
      "406/406 [==============================] - 0s 643us/step - loss: 0.4715\n",
      "Epoch 114/200\n",
      "406/406 [==============================] - 0s 578us/step - loss: 0.4708\n",
      "Epoch 115/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.4703\n",
      "Epoch 116/200\n",
      "406/406 [==============================] - 0s 594us/step - loss: 0.4720\n",
      "Epoch 117/200\n",
      "406/406 [==============================] - 0s 586us/step - loss: 0.4679\n",
      "Epoch 118/200\n",
      "406/406 [==============================] - 0s 589us/step - loss: 0.4694\n",
      "Epoch 119/200\n",
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      "Epoch 120/200\n",
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      "Epoch 121/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 128/200\n",
      "406/406 [==============================] - 0s 572us/step - loss: 0.4686\n",
      "Epoch 129/200\n",
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      "Epoch 134/200\n",
      "406/406 [==============================] - 0s 617us/step - loss: 0.4617\n",
      "Epoch 135/200\n",
      "406/406 [==============================] - 0s 593us/step - loss: 0.4656\n",
      "Epoch 136/200\n",
      "406/406 [==============================] - 0s 593us/step - loss: 0.4651\n",
      "Epoch 137/200\n",
      "406/406 [==============================] - 0s 608us/step - loss: 0.4574\n",
      "Epoch 138/200\n",
      "406/406 [==============================] - 0s 593us/step - loss: 0.4631\n",
      "Epoch 139/200\n",
      "406/406 [==============================] - 0s 603us/step - loss: 0.4620\n",
      "Epoch 140/200\n",
      "406/406 [==============================] - 0s 596us/step - loss: 0.4647\n",
      "Epoch 141/200\n",
      "406/406 [==============================] - 0s 586us/step - loss: 0.4601\n",
      "Epoch 142/200\n",
      "406/406 [==============================] - 0s 593us/step - loss: 0.4602\n",
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      "Epoch 147/200\n",
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      "Epoch 148/200\n",
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      "Epoch 152/200\n",
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      "Epoch 155/200\n",
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      "Epoch 156/200\n",
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      "Epoch 157/200\n",
      "406/406 [==============================] - 0s 587us/step - loss: 0.4589\n",
      "Epoch 158/200\n",
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      "Epoch 159/200\n",
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      "Epoch 160/200\n",
      "406/406 [==============================] - 0s 593us/step - loss: 0.4597\n",
      "Epoch 161/200\n",
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      "Epoch 162/200\n",
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      "Epoch 163/200\n",
      "406/406 [==============================] - 0s 594us/step - loss: 0.4527\n",
      "Epoch 164/200\n",
      "406/406 [==============================] - 0s 580us/step - loss: 0.4601\n",
      "Epoch 165/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.4571\n",
      "Epoch 166/200\n",
      "406/406 [==============================] - 0s 596us/step - loss: 0.4558\n",
      "Epoch 167/200\n",
      "406/406 [==============================] - 0s 578us/step - loss: 0.4586\n",
      "Epoch 168/200\n",
      "406/406 [==============================] - 0s 560us/step - loss: 0.4538\n",
      "Epoch 169/200\n",
      "406/406 [==============================] - 0s 587us/step - loss: 0.4578\n",
      "Epoch 170/200\n",
      "406/406 [==============================] - 0s 593us/step - loss: 0.4573\n",
      "Epoch 171/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.4568\n",
      "Epoch 172/200\n",
      "406/406 [==============================] - 0s 587us/step - loss: 0.4590\n",
      "Epoch 173/200\n",
      "406/406 [==============================] - 0s 585us/step - loss: 0.4544\n",
      "Epoch 174/200\n",
      "406/406 [==============================] - 0s 589us/step - loss: 0.4571\n",
      "Epoch 175/200\n",
      "406/406 [==============================] - 0s 617us/step - loss: 0.4612\n",
      "Epoch 176/200\n",
      "406/406 [==============================] - 0s 605us/step - loss: 0.4543\n",
      "Epoch 177/200\n",
      "406/406 [==============================] - 0s 590us/step - loss: 0.4569\n",
      "Epoch 178/200\n",
      "406/406 [==============================] - 0s 591us/step - loss: 0.4549\n",
      "Epoch 179/200\n",
      "406/406 [==============================] - 0s 616us/step - loss: 0.4538\n",
      "Epoch 180/200\n",
      "406/406 [==============================] - 0s 586us/step - loss: 0.4577\n",
      "Epoch 181/200\n",
      "406/406 [==============================] - 0s 590us/step - loss: 0.4544\n",
      "Epoch 182/200\n",
      "406/406 [==============================] - 0s 604us/step - loss: 0.4531\n",
      "Epoch 183/200\n",
      "406/406 [==============================] - 0s 577us/step - loss: 0.4595\n",
      "Epoch 184/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.4479\n",
      "Epoch 185/200\n",
      "406/406 [==============================] - 0s 595us/step - loss: 0.4531\n",
      "Epoch 186/200\n",
      "406/406 [==============================] - 0s 587us/step - loss: 0.4576\n",
      "Epoch 187/200\n",
      "406/406 [==============================] - 0s 582us/step - loss: 0.4607\n",
      "Epoch 188/200\n",
      "406/406 [==============================] - 0s 580us/step - loss: 0.4556\n",
      "Epoch 189/200\n",
      "406/406 [==============================] - 0s 587us/step - loss: 0.4519\n",
      "Epoch 190/200\n",
      "406/406 [==============================] - 0s 589us/step - loss: 0.4519\n",
      "Epoch 191/200\n",
      "406/406 [==============================] - 0s 579us/step - loss: 0.4534\n",
      "Epoch 192/200\n",
      "406/406 [==============================] - 0s 584us/step - loss: 0.4591\n",
      "Epoch 193/200\n",
      "406/406 [==============================] - 0s 597us/step - loss: 0.4532\n",
      "Epoch 194/200\n",
      "406/406 [==============================] - 0s 592us/step - loss: 0.4532\n",
      "Epoch 195/200\n",
      "406/406 [==============================] - 0s 578us/step - loss: 0.4505\n",
      "Epoch 196/200\n",
      "406/406 [==============================] - 0s 642us/step - loss: 0.4506\n",
      "Epoch 197/200\n",
      "406/406 [==============================] - 0s 652us/step - loss: 0.4532\n",
      "Epoch 198/200\n",
      "406/406 [==============================] - 0s 615us/step - loss: 0.4499\n",
      "Epoch 199/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "406/406 [==============================] - 0s 582us/step - loss: 0.4545\n",
      "Epoch 200/200\n",
      "406/406 [==============================] - 0s 585us/step - loss: 0.4493\n"
     ]
    }
   ],
   "source": [
    "Models2['Dropout']={}\n",
    "for drop in [0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:\n",
    "    NN = Sequential()\n",
    "    NN.add(Dense(17,input_dim = x_train.shape[1], activation='elu'))\n",
    "    NN.add(Dropout(drop))\n",
    "    NN.add(Dense(100,activation='elu'))\n",
    "    NN.add(Dropout(drop))\n",
    "    NN.add(Dense(50, activation='elu'))\n",
    "    NN.add(Dropout(drop))\n",
    "    NN.add(Dense(1,))\n",
    "    regularizers.l2(l2=0.0001)\n",
    "    opt = Adam(learning_rate = 0.0001)  \n",
    "    # Compile the network :\n",
    "    NN.compile(loss ='mean_absolute_error', optimizer=opt,)\n",
    "    NN.fit(x = x_train, y = y_train, epochs=200, )\n",
    "    Models2['Dropout'][drop] = NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "40debe51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L2------------------------\n",
      "[0] R2: 88.51%, RMSE/mean: 24.63%, avg abs % error: 13.08%\n",
      "[0.0001] R2: 87.62%, RMSE/mean: 25.56%, avg abs % error: 12.77%\n",
      "[0.001] R2: 87.96%, RMSE/mean: 25.21%, avg abs % error: 13.75%\n",
      "[0.01] R2: 87.87%, RMSE/mean: 25.31%, avg abs % error: 13.14%\n",
      "[0.1] R2: 88.02%, RMSE/mean: 25.15%, avg abs % error: 13.12%\n",
      "[1] R2: 45.33%, RMSE/mean: 53.73%, avg abs % error: 23.45%\n",
      "Dropout------------------------\n",
      "[0] R2: 88.37%, RMSE/mean: 24.78%, avg abs % error: 13.28%\n",
      "[0.05] R2: 84.75%, RMSE/mean: 28.38%, avg abs % error: 13.46%\n",
      "[0.1] R2: 83.21%, RMSE/mean: 29.77%, avg abs % error: 13.74%\n",
      "[0.2] R2: 80.83%, RMSE/mean: 31.81%, avg abs % error: 14.41%\n",
      "[0.3] R2: 77.78%, RMSE/mean: 34.25%, avg abs % error: 15.02%\n",
      "[0.4] R2: 75.10%, RMSE/mean: 36.26%, avg abs % error: 16.22%\n",
      "[0.5] R2: 71.72%, RMSE/mean: 38.64%, avg abs % error: 18.01%\n",
      "[0.6] R2: 67.10%, RMSE/mean: 41.68%, avg abs % error: 18.73%\n",
      "[0.7] R2: 62.06%, RMSE/mean: 44.76%, avg abs % error: 20.33%\n",
      "[0.8] R2: 55.60%, RMSE/mean: 48.42%, avg abs % error: 21.38%\n",
      "[0.9] R2: 45.36%, RMSE/mean: 53.71%, avg abs % error: 24.55%\n"
     ]
    }
   ],
   "source": [
    "compare_results = compare_model(Models2, x_test, y_test, style='unnorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30115bb4",
   "metadata": {},
   "source": [
    "From above result, dropout doesn't improve the performance and hence we will not use dropout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1a09d1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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