PAD_PROJEKT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4413e84f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LIB\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from xgboost import XGBRegressor\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06625b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Data\n",
    "df = pd.read_csv('BostonHousing.csv')\n",
    "df = df.drop('chas', axis = 'columns')\n",
    "features_names = df.columns.drop('medv')\n",
    "target_names = ['medv']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04dbc412",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data normalization\n",
    "\n",
    "x = df.drop('medv', axis = 'columns').values\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_normalized = pd.DataFrame(x_scaled)\n",
    "df_normalized.columns = features_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0064af36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train/Test split\n",
    "\n",
    "X = df_normalized\n",
    "y = df.medv\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1, test_size=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2209ef39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap\n",
    "\n",
    "corr = df.corr() #Corelation dataframe\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns,\n",
    "            yticklabels=corr.columns)\n",
    "plt.show() #Heatmap plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a24e5e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LinearRegressionModel\n",
    "LinearModel = LinearRegression()\n",
    "LinearModel.fit(train_X,train_y)\n",
    "prediction = LinearModel.predict(val_X)\n",
    "Coef_df = pd.DataFrame(columns=features_names)\n",
    "Coef_df.loc[0] = LinearModel.coef_\n",
    "Coef_df.loc[0] = Coef_df.loc[0].apply(lambda x: abs(x))\n",
    "Coef_df = Coef_df.sort_values(by=0, ascending=False, axis=1) #Od najbardziej waznych do najmniej waznych\n",
    "r2_score_linear = r2_score(val_y,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81325566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 607.3281 - mean_squared_error: 607.3281\n",
      "Epoch 2/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 606.2581 - mean_squared_error: 606.2581\n",
      "Epoch 3/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 605.1605 - mean_squared_error: 605.1605\n",
      "Epoch 4/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 603.9878 - mean_squared_error: 603.9878\n",
      "Epoch 5/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 602.8065 - mean_squared_error: 602.8065\n",
      "Epoch 6/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 601.6501 - mean_squared_error: 601.6501\n",
      "Epoch 7/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 600.3285 - mean_squared_error: 600.3284\n",
      "Epoch 8/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 599.1127 - mean_squared_error: 599.1127\n",
      "Epoch 9/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 597.7732 - mean_squared_error: 597.7732\n",
      "Epoch 10/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 596.3789 - mean_squared_error: 596.3789\n",
      "Epoch 11/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 594.9482 - mean_squared_error: 594.9482\n",
      "Epoch 12/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 593.4461 - mean_squared_error: 593.4461\n",
      "Epoch 13/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 591.8489 - mean_squared_error: 591.8489\n",
      "Epoch 14/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 590.1599 - mean_squared_error: 590.1599\n",
      "Epoch 15/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 588.3483 - mean_squared_error: 588.3483\n",
      "Epoch 16/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 586.4378 - mean_squared_error: 586.4378\n",
      "Epoch 17/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 584.3693 - mean_squared_error: 584.3693\n",
      "Epoch 18/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 582.2091 - mean_squared_error: 582.2091\n",
      "Epoch 19/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 579.7701 - mean_squared_error: 579.7701\n",
      "Epoch 20/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 577.1749 - mean_squared_error: 577.1749\n",
      "Epoch 21/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 574.5330 - mean_squared_error: 574.5330\n",
      "Epoch 22/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 571.7013 - mean_squared_error: 571.7013\n",
      "Epoch 23/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 568.8610 - mean_squared_error: 568.8610\n",
      "Epoch 24/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 565.8500 - mean_squared_error: 565.8500\n",
      "Epoch 25/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 562.7638 - mean_squared_error: 562.7638\n",
      "Epoch 26/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 559.6297 - mean_squared_error: 559.6297\n",
      "Epoch 27/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 556.4712 - mean_squared_error: 556.4712\n",
      "Epoch 28/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 553.1193 - mean_squared_error: 553.1193\n",
      "Epoch 29/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 549.7354 - mean_squared_error: 549.7354\n",
      "Epoch 30/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 546.2857 - mean_squared_error: 546.2857\n",
      "Epoch 31/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 542.6749 - mean_squared_error: 542.6749\n",
      "Epoch 32/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 538.9896 - mean_squared_error: 538.9896\n",
      "Epoch 33/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 535.2531 - mean_squared_error: 535.2531\n",
      "Epoch 34/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 531.4006 - mean_squared_error: 531.4006\n",
      "Epoch 35/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 527.4612 - mean_squared_error: 527.4612\n",
      "Epoch 36/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 523.4223 - mean_squared_error: 523.4223\n",
      "Epoch 37/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 519.1954 - mean_squared_error: 519.1954\n",
      "Epoch 38/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 514.9190 - mean_squared_error: 514.9190\n",
      "Epoch 39/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 510.3570 - mean_squared_error: 510.3570\n",
      "Epoch 40/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 505.7255 - mean_squared_error: 505.7255\n",
      "Epoch 41/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 500.9488 - mean_squared_error: 500.9488\n",
      "Epoch 42/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 496.2043 - mean_squared_error: 496.2043\n",
      "Epoch 43/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 491.1099 - mean_squared_error: 491.1099\n",
      "Epoch 44/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 485.9135 - mean_squared_error: 485.9135\n",
      "Epoch 45/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 480.5786 - mean_squared_error: 480.5786\n",
      "Epoch 46/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 474.8297 - mean_squared_error: 474.8297\n",
      "Epoch 47/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 469.2531 - mean_squared_error: 469.2531\n",
      "Epoch 48/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 463.0156 - mean_squared_error: 463.0156\n",
      "Epoch 49/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 456.8135 - mean_squared_error: 456.8135\n",
      "Epoch 50/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 450.1686 - mean_squared_error: 450.1686\n",
      "Epoch 51/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 443.5018 - mean_squared_error: 443.5018\n",
      "Epoch 52/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 436.4414 - mean_squared_error: 436.4414\n",
      "Epoch 53/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 429.4574 - mean_squared_error: 429.4574\n",
      "Epoch 54/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 421.7805 - mean_squared_error: 421.7805\n",
      "Epoch 55/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 414.3520 - mean_squared_error: 414.3520\n",
      "Epoch 56/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 406.7096 - mean_squared_error: 406.7096\n",
      "Epoch 57/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 398.9855 - mean_squared_error: 398.9855\n",
      "Epoch 58/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 390.9969 - mean_squared_error: 390.9969\n",
      "Epoch 59/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 383.0832 - mean_squared_error: 383.0832\n",
      "Epoch 60/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 375.5978 - mean_squared_error: 375.5978\n",
      "Epoch 61/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 367.7878 - mean_squared_error: 367.7878\n",
      "Epoch 62/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 359.8768 - mean_squared_error: 359.8768\n",
      "Epoch 63/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 352.1788 - mean_squared_error: 352.1788\n",
      "Epoch 64/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 344.5405 - mean_squared_error: 344.5405\n",
      "Epoch 65/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 336.9373 - mean_squared_error: 336.9373\n",
      "Epoch 66/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 329.6140 - mean_squared_error: 329.6140\n",
      "Epoch 67/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 321.9448 - mean_squared_error: 321.9448\n",
      "Epoch 68/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 314.8535 - mean_squared_error: 314.8535\n",
      "Epoch 69/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 307.3764 - mean_squared_error: 307.3764\n",
      "Epoch 70/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 300.5239 - mean_squared_error: 300.5239\n",
      "Epoch 71/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 293.1884 - mean_squared_error: 293.1884\n",
      "Epoch 72/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 286.3813 - mean_squared_error: 286.3813\n",
      "Epoch 73/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 279.4587 - mean_squared_error: 279.4587\n",
      "Epoch 74/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 272.7051 - mean_squared_error: 272.7051\n",
      "Epoch 75/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 266.2623 - mean_squared_error: 266.2623\n",
      "Epoch 76/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 259.6867 - mean_squared_error: 259.6867\n",
      "Epoch 77/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 253.1572 - mean_squared_error: 253.1572\n",
      "Epoch 78/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 246.9803 - mean_squared_error: 246.9803\n",
      "Epoch 79/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 240.8933 - mean_squared_error: 240.8933\n",
      "Epoch 80/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 234.8909 - mean_squared_error: 234.8909\n",
      "Epoch 81/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 229.1967 - mean_squared_error: 229.1967\n",
      "Epoch 82/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 223.1474 - mean_squared_error: 223.1474\n",
      "Epoch 83/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 217.4281 - mean_squared_error: 217.4281\n",
      "Epoch 84/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 212.2461 - mean_squared_error: 212.2461\n",
      "Epoch 85/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 206.9099 - mean_squared_error: 206.9099\n",
      "Epoch 86/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 201.6055 - mean_squared_error: 201.6055\n",
      "Epoch 87/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 196.7962 - mean_squared_error: 196.7962\n",
      "Epoch 88/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 191.3825 - mean_squared_error: 191.3825\n",
      "Epoch 89/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 186.7975 - mean_squared_error: 186.7975\n",
      "Epoch 90/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 182.3121 - mean_squared_error: 182.3121\n",
      "Epoch 91/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 177.6887 - mean_squared_error: 177.6887\n",
      "Epoch 92/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 173.5204 - mean_squared_error: 173.5204\n",
      "Epoch 93/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 169.2387 - mean_squared_error: 169.2387\n",
      "Epoch 94/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 165.2692 - mean_squared_error: 165.2692\n",
      "Epoch 95/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 161.3779 - mean_squared_error: 161.3779\n",
      "Epoch 96/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 157.6189 - mean_squared_error: 157.6189\n",
      "Epoch 97/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 153.9363 - mean_squared_error: 153.9363\n",
      "Epoch 98/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 150.4022 - mean_squared_error: 150.4022\n",
      "Epoch 99/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 147.2061 - mean_squared_error: 147.2061\n",
      "Epoch 100/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 143.9105 - mean_squared_error: 143.9105\n",
      "Epoch 101/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 140.8015 - mean_squared_error: 140.8015\n",
      "Epoch 102/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 137.9270 - mean_squared_error: 137.9270\n",
      "Epoch 103/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 135.1088 - mean_squared_error: 135.1088\n",
      "Epoch 104/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 132.5132 - mean_squared_error: 132.5132\n",
      "Epoch 105/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 129.9583 - mean_squared_error: 129.9583\n",
      "Epoch 106/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 127.6582 - mean_squared_error: 127.6582\n",
      "Epoch 107/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 125.2420 - mean_squared_error: 125.2420\n",
      "Epoch 108/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 123.2821 - mean_squared_error: 123.2821\n",
      "Epoch 109/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 120.9808 - mean_squared_error: 120.9808\n",
      "Epoch 110/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 119.2017 - mean_squared_error: 119.2017\n",
      "Epoch 111/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 117.3785 - mean_squared_error: 117.3785\n",
      "Epoch 112/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 115.5987 - mean_squared_error: 115.5987\n",
      "Epoch 113/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 114.0040 - mean_squared_error: 114.0040\n",
      "Epoch 114/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 112.5198 - mean_squared_error: 112.5198\n",
      "Epoch 115/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 111.3075 - mean_squared_error: 111.3075\n",
      "Epoch 116/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 109.7260 - mean_squared_error: 109.7260\n",
      "Epoch 117/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 108.4737 - mean_squared_error: 108.4737\n",
      "Epoch 118/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 107.3365 - mean_squared_error: 107.3365\n",
      "Epoch 119/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 106.2340 - mean_squared_error: 106.2340\n",
      "Epoch 120/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 105.3493 - mean_squared_error: 105.3493\n",
      "Epoch 121/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 104.2490 - mean_squared_error: 104.2490\n",
      "Epoch 122/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 103.6941 - mean_squared_error: 103.6941\n",
      "Epoch 123/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 102.7311 - mean_squared_error: 102.7311\n",
      "Epoch 124/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 101.9554 - mean_squared_error: 101.9554\n",
      "Epoch 125/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 101.2857 - mean_squared_error: 101.2857\n",
      "Epoch 126/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 100.6582 - mean_squared_error: 100.6582\n",
      "Epoch 127/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 100.0833 - mean_squared_error: 100.0833\n",
      "Epoch 128/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 99.5618 - mean_squared_error: 99.5618\n",
      "Epoch 129/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 99.0540 - mean_squared_error: 99.0540\n",
      "Epoch 130/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 98.6644 - mean_squared_error: 98.6644\n",
      "Epoch 131/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 98.1759 - mean_squared_error: 98.1759\n",
      "Epoch 132/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 97.8979 - mean_squared_error: 97.8979\n",
      "Epoch 133/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 97.4631 - mean_squared_error: 97.4631\n",
      "Epoch 134/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 97.1343 - mean_squared_error: 97.1343\n",
      "Epoch 135/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 96.9610 - mean_squared_error: 96.9610\n",
      "Epoch 136/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 96.5873 - mean_squared_error: 96.5873\n",
      "Epoch 137/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 96.3681 - mean_squared_error: 96.3681\n",
      "Epoch 138/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 96.2532 - mean_squared_error: 96.2532\n",
      "Epoch 139/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 95.9958 - mean_squared_error: 95.9958\n",
      "Epoch 140/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 95.8391 - mean_squared_error: 95.8391\n",
      "Epoch 141/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 95.6751 - mean_squared_error: 95.6751\n",
      "Epoch 142/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 95.5064 - mean_squared_error: 95.5064\n",
      "Epoch 143/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 95.3703 - mean_squared_error: 95.3703\n",
      "Epoch 144/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 95.2952 - mean_squared_error: 95.2952\n",
      "Epoch 145/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 95.1763 - mean_squared_error: 95.1763\n",
      "Epoch 146/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 95.0562 - mean_squared_error: 95.0562\n",
      "Epoch 147/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 95.0299 - mean_squared_error: 95.0299\n",
      "Epoch 148/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.8837 - mean_squared_error: 94.8837\n",
      "Epoch 149/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.8197 - mean_squared_error: 94.8197\n",
      "Epoch 150/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.7427 - mean_squared_error: 94.7427\n",
      "Epoch 151/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.7471 - mean_squared_error: 94.7471\n",
      "Epoch 152/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.6463 - mean_squared_error: 94.6463\n",
      "Epoch 153/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.6283 - mean_squared_error: 94.6283\n",
      "Epoch 154/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.5666 - mean_squared_error: 94.5666\n",
      "Epoch 155/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.5239 - mean_squared_error: 94.5239\n",
      "Epoch 156/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.4708 - mean_squared_error: 94.4708\n",
      "Epoch 157/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.4520 - mean_squared_error: 94.4520\n",
      "Epoch 158/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.4347 - mean_squared_error: 94.4347\n",
      "Epoch 159/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.3904 - mean_squared_error: 94.3904\n",
      "Epoch 160/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.3522 - mean_squared_error: 94.3522\n",
      "Epoch 161/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.3355 - mean_squared_error: 94.3355\n",
      "Epoch 162/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.3378 - mean_squared_error: 94.3378\n",
      "Epoch 163/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.3021 - mean_squared_error: 94.3021\n",
      "Epoch 164/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.2639 - mean_squared_error: 94.2639\n",
      "Epoch 165/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.2476 - mean_squared_error: 94.2476\n",
      "Epoch 166/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.2300 - mean_squared_error: 94.2300\n",
      "Epoch 167/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.2149 - mean_squared_error: 94.2149\n",
      "Epoch 168/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.1944 - mean_squared_error: 94.1944\n",
      "Epoch 169/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.1787 - mean_squared_error: 94.1787\n",
      "Epoch 170/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.1695 - mean_squared_error: 94.1695\n",
      "Epoch 171/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.1482 - mean_squared_error: 94.1482\n",
      "Epoch 172/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.1357 - mean_squared_error: 94.1357\n",
      "Epoch 173/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.1244 - mean_squared_error: 94.1244\n",
      "Epoch 174/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.1188 - mean_squared_error: 94.1188\n",
      "Epoch 175/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.0968 - mean_squared_error: 94.0968\n",
      "Epoch 176/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.0778 - mean_squared_error: 94.0778\n",
      "Epoch 177/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.0669 - mean_squared_error: 94.0669\n",
      "Epoch 178/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.0522 - mean_squared_error: 94.0522\n",
      "Epoch 179/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 94.0392 - mean_squared_error: 94.0392\n",
      "Epoch 180/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.0218 - mean_squared_error: 94.0218\n",
      "Epoch 181/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 94.0119 - mean_squared_error: 94.0119\n",
      "Epoch 182/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.9962 - mean_squared_error: 93.9962\n",
      "Epoch 183/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.9868 - mean_squared_error: 93.9868\n",
      "Epoch 184/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.9797 - mean_squared_error: 93.9797\n",
      "Epoch 185/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.9528 - mean_squared_error: 93.9528\n",
      "Epoch 186/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.9455 - mean_squared_error: 93.9455\n",
      "Epoch 187/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.9228 - mean_squared_error: 93.9228\n",
      "Epoch 188/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.9072 - mean_squared_error: 93.9072\n",
      "Epoch 189/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.8973 - mean_squared_error: 93.8973\n",
      "Epoch 190/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.8798 - mean_squared_error: 93.8798\n",
      "Epoch 191/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.8625 - mean_squared_error: 93.8625\n",
      "Epoch 192/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.8493 - mean_squared_error: 93.8493\n",
      "Epoch 193/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.8357 - mean_squared_error: 93.8357\n",
      "Epoch 194/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.8158 - mean_squared_error: 93.8158\n",
      "Epoch 195/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.8069 - mean_squared_error: 93.8069\n",
      "Epoch 196/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.7827 - mean_squared_error: 93.7827\n",
      "Epoch 197/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.7744 - mean_squared_error: 93.7744\n",
      "Epoch 198/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.7552 - mean_squared_error: 93.7552\n",
      "Epoch 199/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.7303 - mean_squared_error: 93.7303\n",
      "Epoch 200/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.7140 - mean_squared_error: 93.7140\n",
      "Epoch 201/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.6937 - mean_squared_error: 93.6937\n",
      "Epoch 202/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.6732 - mean_squared_error: 93.6732\n",
      "Epoch 203/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.6718 - mean_squared_error: 93.6718\n",
      "Epoch 204/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.6331 - mean_squared_error: 93.6331\n",
      "Epoch 205/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.6155 - mean_squared_error: 93.6155\n",
      "Epoch 206/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.5942 - mean_squared_error: 93.5942\n",
      "Epoch 207/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.5719 - mean_squared_error: 93.5719\n",
      "Epoch 208/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.5567 - mean_squared_error: 93.5567\n",
      "Epoch 209/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.5287 - mean_squared_error: 93.5287\n",
      "Epoch 210/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.5004 - mean_squared_error: 93.5004\n",
      "Epoch 211/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.4830 - mean_squared_error: 93.4830\n",
      "Epoch 212/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.4521 - mean_squared_error: 93.4521\n",
      "Epoch 213/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.4362 - mean_squared_error: 93.4362\n",
      "Epoch 214/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.4137 - mean_squared_error: 93.4137\n",
      "Epoch 215/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 93.3736 - mean_squared_error: 93.3736\n",
      "Epoch 216/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.3402 - mean_squared_error: 93.3402\n",
      "Epoch 217/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.3134 - mean_squared_error: 93.3134\n",
      "Epoch 218/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.2816 - mean_squared_error: 93.2816\n",
      "Epoch 219/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.2489 - mean_squared_error: 93.2489\n",
      "Epoch 220/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 93.2278 - mean_squared_error: 93.2278\n",
      "Epoch 221/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.1758 - mean_squared_error: 93.1758\n",
      "Epoch 222/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.1424 - mean_squared_error: 93.1424\n",
      "Epoch 223/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.1046 - mean_squared_error: 93.1046\n",
      "Epoch 224/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.0642 - mean_squared_error: 93.0642\n",
      "Epoch 225/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 93.0186 - mean_squared_error: 93.0186\n",
      "Epoch 226/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 92.9768 - mean_squared_error: 92.9768\n",
      "Epoch 227/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.9299 - mean_squared_error: 92.9299\n",
      "Epoch 228/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.8731 - mean_squared_error: 92.8731\n",
      "Epoch 229/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.8272 - mean_squared_error: 92.8272\n",
      "Epoch 230/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 92.7674 - mean_squared_error: 92.7674\n",
      "Epoch 231/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 92.7181 - mean_squared_error: 92.7181\n",
      "Epoch 232/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 92.6400 - mean_squared_error: 92.6400\n",
      "Epoch 233/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.5721 - mean_squared_error: 92.5721\n",
      "Epoch 234/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 92.4971 - mean_squared_error: 92.4971\n",
      "Epoch 235/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.4440 - mean_squared_error: 92.4440\n",
      "Epoch 236/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 92.3355 - mean_squared_error: 92.3355\n",
      "Epoch 237/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.2422 - mean_squared_error: 92.2422\n",
      "Epoch 238/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.1457 - mean_squared_error: 92.1457\n",
      "Epoch 239/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 92.0444 - mean_squared_error: 92.0444\n",
      "Epoch 240/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 91.9081 - mean_squared_error: 91.9081\n",
      "Epoch 241/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 91.7912 - mean_squared_error: 91.7912\n",
      "Epoch 242/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 91.6314 - mean_squared_error: 91.6314\n",
      "Epoch 243/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 91.4904 - mean_squared_error: 91.4904\n",
      "Epoch 244/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 91.3013 - mean_squared_error: 91.3013\n",
      "Epoch 245/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 91.0885 - mean_squared_error: 91.0885\n",
      "Epoch 246/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 90.8655 - mean_squared_error: 90.8655\n",
      "Epoch 247/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 90.5993 - mean_squared_error: 90.5993\n",
      "Epoch 248/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 90.3389 - mean_squared_error: 90.3389\n",
      "Epoch 249/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 90.0591 - mean_squared_error: 90.0591\n",
      "Epoch 250/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 89.6208 - mean_squared_error: 89.6208\n",
      "Epoch 251/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 89.3105 - mean_squared_error: 89.3105\n",
      "Epoch 252/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 88.7533 - mean_squared_error: 88.7533\n",
      "Epoch 253/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 88.3324 - mean_squared_error: 88.3324\n",
      "Epoch 254/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 87.7760 - mean_squared_error: 87.7760\n",
      "Epoch 255/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 87.1992 - mean_squared_error: 87.1992\n",
      "Epoch 256/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 86.5610 - mean_squared_error: 86.5610\n",
      "Epoch 257/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 85.7721 - mean_squared_error: 85.7721\n",
      "Epoch 258/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 85.1850 - mean_squared_error: 85.1850\n",
      "Epoch 259/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 84.3805 - mean_squared_error: 84.3805\n",
      "Epoch 260/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 83.5852 - mean_squared_error: 83.5852\n",
      "Epoch 261/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 82.7634 - mean_squared_error: 82.7634\n",
      "Epoch 262/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 82.0170 - mean_squared_error: 82.0170\n",
      "Epoch 263/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 81.2462 - mean_squared_error: 81.2462\n",
      "Epoch 264/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 80.5380 - mean_squared_error: 80.5380\n",
      "Epoch 265/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 79.7687 - mean_squared_error: 79.7687\n",
      "Epoch 266/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 79.0742 - mean_squared_error: 79.0742\n",
      "Epoch 267/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 78.4634 - mean_squared_error: 78.4634\n",
      "Epoch 268/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 77.8642 - mean_squared_error: 77.8642\n",
      "Epoch 269/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 77.2120 - mean_squared_error: 77.2120\n",
      "Epoch 270/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 76.7010 - mean_squared_error: 76.7010\n",
      "Epoch 271/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 76.1285 - mean_squared_error: 76.1285\n",
      "Epoch 272/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 75.5723 - mean_squared_error: 75.5723\n",
      "Epoch 273/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 75.0287 - mean_squared_error: 75.0287\n",
      "Epoch 274/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 74.6167 - mean_squared_error: 74.6167\n",
      "Epoch 275/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 74.0934 - mean_squared_error: 74.0934\n",
      "Epoch 276/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 73.6552 - mean_squared_error: 73.6552\n",
      "Epoch 277/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 73.1382 - mean_squared_error: 73.1382\n",
      "Epoch 278/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 72.7622 - mean_squared_error: 72.7622\n",
      "Epoch 279/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 72.3283 - mean_squared_error: 72.3283\n",
      "Epoch 280/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 71.8850 - mean_squared_error: 71.8850\n",
      "Epoch 281/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 71.4927 - mean_squared_error: 71.4927\n",
      "Epoch 282/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 71.1102 - mean_squared_error: 71.1102\n",
      "Epoch 283/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 70.7002 - mean_squared_error: 70.7002\n",
      "Epoch 284/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 70.3896 - mean_squared_error: 70.3896\n",
      "Epoch 285/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 70.0074 - mean_squared_error: 70.0074\n",
      "Epoch 286/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 69.6587 - mean_squared_error: 69.6587\n",
      "Epoch 287/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 69.2910 - mean_squared_error: 69.2910\n",
      "Epoch 288/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 68.9368 - mean_squared_error: 68.9368\n",
      "Epoch 289/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 68.6562 - mean_squared_error: 68.6562\n",
      "Epoch 290/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 68.3501 - mean_squared_error: 68.3501\n",
      "Epoch 291/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 68.0199 - mean_squared_error: 68.0199\n",
      "Epoch 292/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 67.7080 - mean_squared_error: 67.7080\n",
      "Epoch 293/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 67.3403 - mean_squared_error: 67.3403\n",
      "Epoch 294/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 67.0558 - mean_squared_error: 67.0558\n",
      "Epoch 295/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 66.7651 - mean_squared_error: 66.7651\n",
      "Epoch 296/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 66.4860 - mean_squared_error: 66.4860\n",
      "Epoch 297/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 66.1711 - mean_squared_error: 66.1711\n",
      "Epoch 298/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 65.9121 - mean_squared_error: 65.9121\n",
      "Epoch 299/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 65.6015 - mean_squared_error: 65.6015\n",
      "Epoch 300/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 65.3139 - mean_squared_error: 65.3139\n",
      "Epoch 301/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 65.0410 - mean_squared_error: 65.0410\n",
      "Epoch 302/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 64.7544 - mean_squared_error: 64.7544\n",
      "Epoch 303/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 64.4820 - mean_squared_error: 64.4820\n",
      "Epoch 304/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 64.1845 - mean_squared_error: 64.1845\n",
      "Epoch 305/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 63.9124 - mean_squared_error: 63.9124\n",
      "Epoch 306/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 63.6641 - mean_squared_error: 63.6641\n",
      "Epoch 307/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 63.3634 - mean_squared_error: 63.3634\n",
      "Epoch 308/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 63.0994 - mean_squared_error: 63.0994\n",
      "Epoch 309/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 62.8053 - mean_squared_error: 62.8053\n",
      "Epoch 310/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 62.5540 - mean_squared_error: 62.5540\n",
      "Epoch 311/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 62.2338 - mean_squared_error: 62.2338\n",
      "Epoch 312/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 61.9694 - mean_squared_error: 61.9694\n",
      "Epoch 313/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 61.6877 - mean_squared_error: 61.6877\n",
      "Epoch 314/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 61.4125 - mean_squared_error: 61.4125\n",
      "Epoch 315/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 61.1131 - mean_squared_error: 61.1131\n",
      "Epoch 316/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 60.8189 - mean_squared_error: 60.8189\n",
      "Epoch 317/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 60.5558 - mean_squared_error: 60.5558\n",
      "Epoch 318/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 60.2306 - mean_squared_error: 60.2306\n",
      "Epoch 319/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 59.9725 - mean_squared_error: 59.9725\n",
      "Epoch 320/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 59.6586 - mean_squared_error: 59.6586\n",
      "Epoch 321/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 59.3502 - mean_squared_error: 59.3502\n",
      "Epoch 322/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 59.0812 - mean_squared_error: 59.0812\n",
      "Epoch 323/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 58.7230 - mean_squared_error: 58.7230\n",
      "Epoch 324/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 58.4378 - mean_squared_error: 58.4378\n",
      "Epoch 325/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 58.1521 - mean_squared_error: 58.1521\n",
      "Epoch 326/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 57.8393 - mean_squared_error: 57.8393\n",
      "Epoch 327/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 57.5504 - mean_squared_error: 57.5504\n",
      "Epoch 328/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 57.2066 - mean_squared_error: 57.2066\n",
      "Epoch 329/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 56.8938 - mean_squared_error: 56.8938\n",
      "Epoch 330/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 56.5678 - mean_squared_error: 56.5678\n",
      "Epoch 331/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 56.3044 - mean_squared_error: 56.3044\n",
      "Epoch 332/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 56.0028 - mean_squared_error: 56.0028\n",
      "Epoch 333/700\n",
      "4/4 [==============================] - ETA: 0s - loss: 34.9119 - mean_squared_error: 34.91 - 0s 2ms/step - loss: 55.7100 - mean_squared_error: 55.7100\n",
      "Epoch 334/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 55.4826 - mean_squared_error: 55.4826\n",
      "Epoch 335/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 55.1623 - mean_squared_error: 55.1623\n",
      "Epoch 336/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 54.8691 - mean_squared_error: 54.8691\n",
      "Epoch 337/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 54.6427 - mean_squared_error: 54.6427\n",
      "Epoch 338/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 54.3425 - mean_squared_error: 54.3425\n",
      "Epoch 339/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 54.0728 - mean_squared_error: 54.0728\n",
      "Epoch 340/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 53.8624 - mean_squared_error: 53.8624\n",
      "Epoch 341/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 53.6471 - mean_squared_error: 53.6471\n",
      "Epoch 342/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 53.3697 - mean_squared_error: 53.3697\n",
      "Epoch 343/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 53.1743 - mean_squared_error: 53.1743\n",
      "Epoch 344/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 52.9590 - mean_squared_error: 52.9590\n",
      "Epoch 345/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 52.7612 - mean_squared_error: 52.7612\n",
      "Epoch 346/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 52.5721 - mean_squared_error: 52.5721\n",
      "Epoch 347/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 52.3174 - mean_squared_error: 52.3174\n",
      "Epoch 348/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 52.1315 - mean_squared_error: 52.1315\n",
      "Epoch 349/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 51.9497 - mean_squared_error: 51.9497\n",
      "Epoch 350/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 51.7485 - mean_squared_error: 51.7485\n",
      "Epoch 351/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 51.5901 - mean_squared_error: 51.5901\n",
      "Epoch 352/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 51.4178 - mean_squared_error: 51.4178\n",
      "Epoch 353/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 51.2329 - mean_squared_error: 51.2329\n",
      "Epoch 354/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 51.0473 - mean_squared_error: 51.0473\n",
      "Epoch 355/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 50.8877 - mean_squared_error: 50.8877\n",
      "Epoch 356/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 50.7009 - mean_squared_error: 50.7009\n",
      "Epoch 357/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 50.5243 - mean_squared_error: 50.5243\n",
      "Epoch 358/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 50.3714 - mean_squared_error: 50.3714\n",
      "Epoch 359/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 50.2933 - mean_squared_error: 50.2933\n",
      "Epoch 360/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 50.0295 - mean_squared_error: 50.0295\n",
      "Epoch 361/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 49.8981 - mean_squared_error: 49.8981\n",
      "Epoch 362/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 49.7146 - mean_squared_error: 49.7146\n",
      "Epoch 363/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 49.5964 - mean_squared_error: 49.5964\n",
      "Epoch 364/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 49.3954 - mean_squared_error: 49.3954\n",
      "Epoch 365/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 49.2780 - mean_squared_error: 49.2780\n",
      "Epoch 366/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 49.0742 - mean_squared_error: 49.0742\n",
      "Epoch 367/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 48.9294 - mean_squared_error: 48.9294\n",
      "Epoch 368/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 48.7746 - mean_squared_error: 48.7746\n",
      "Epoch 369/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 48.6043 - mean_squared_error: 48.6043\n",
      "Epoch 370/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 48.4665 - mean_squared_error: 48.4665\n",
      "Epoch 371/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 48.3209 - mean_squared_error: 48.3209\n",
      "Epoch 372/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 48.1393 - mean_squared_error: 48.1393\n",
      "Epoch 373/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 48.0058 - mean_squared_error: 48.0058\n",
      "Epoch 374/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 47.8603 - mean_squared_error: 47.8603\n",
      "Epoch 375/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 47.7037 - mean_squared_error: 47.7037\n",
      "Epoch 376/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 47.5411 - mean_squared_error: 47.5411\n",
      "Epoch 377/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 47.4143 - mean_squared_error: 47.4143\n",
      "Epoch 378/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 47.2811 - mean_squared_error: 47.2811\n",
      "Epoch 379/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 47.0720 - mean_squared_error: 47.0720\n",
      "Epoch 380/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 46.9785 - mean_squared_error: 46.9785\n",
      "Epoch 381/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 46.7855 - mean_squared_error: 46.7855\n",
      "Epoch 382/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 46.6633 - mean_squared_error: 46.6633\n",
      "Epoch 383/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 46.5067 - mean_squared_error: 46.5067\n",
      "Epoch 384/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 46.3343 - mean_squared_error: 46.3343\n",
      "Epoch 385/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 46.2450 - mean_squared_error: 46.2450\n",
      "Epoch 386/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 46.0492 - mean_squared_error: 46.0492\n",
      "Epoch 387/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 45.8961 - mean_squared_error: 45.8961\n",
      "Epoch 388/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 45.7373 - mean_squared_error: 45.7373\n",
      "Epoch 389/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 45.5761 - mean_squared_error: 45.5761\n",
      "Epoch 390/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 45.4739 - mean_squared_error: 45.4739\n",
      "Epoch 391/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 45.2801 - mean_squared_error: 45.2801\n",
      "Epoch 392/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 45.1237 - mean_squared_error: 45.1237\n",
      "Epoch 393/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 44.9918 - mean_squared_error: 44.9918\n",
      "Epoch 394/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 44.8363 - mean_squared_error: 44.8363\n",
      "Epoch 395/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 44.6789 - mean_squared_error: 44.6789\n",
      "Epoch 396/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 44.5422 - mean_squared_error: 44.5422\n",
      "Epoch 397/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 44.4019 - mean_squared_error: 44.4019\n",
      "Epoch 398/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 44.2495 - mean_squared_error: 44.2495\n",
      "Epoch 399/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 44.0682 - mean_squared_error: 44.0682\n",
      "Epoch 400/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 43.9479 - mean_squared_error: 43.9479\n",
      "Epoch 401/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 43.7828 - mean_squared_error: 43.7828\n",
      "Epoch 402/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 43.6565 - mean_squared_error: 43.6565\n",
      "Epoch 403/700\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 43.4620 - mean_squared_error: 43.4620\n",
      "Epoch 404/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 43.3284 - mean_squared_error: 43.3284\n",
      "Epoch 405/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 43.1662 - mean_squared_error: 43.1662\n",
      "Epoch 406/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 43.0240 - mean_squared_error: 43.0240\n",
      "Epoch 407/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 42.8652 - mean_squared_error: 42.8652\n",
      "Epoch 408/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 42.7257 - mean_squared_error: 42.7257\n",
      "Epoch 409/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 42.5785 - mean_squared_error: 42.5785\n",
      "Epoch 410/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 42.3981 - mean_squared_error: 42.3981\n",
      "Epoch 411/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 42.2997 - mean_squared_error: 42.2997\n",
      "Epoch 412/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 42.1207 - mean_squared_error: 42.1207\n",
      "Epoch 413/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 41.9128 - mean_squared_error: 41.9128\n",
      "Epoch 414/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 41.7851 - mean_squared_error: 41.7851\n",
      "Epoch 415/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 41.6248 - mean_squared_error: 41.6248\n",
      "Epoch 416/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 41.5017 - mean_squared_error: 41.5017\n",
      "Epoch 417/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 41.3059 - mean_squared_error: 41.3059\n",
      "Epoch 418/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 41.1744 - mean_squared_error: 41.1744\n",
      "Epoch 419/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 41.0060 - mean_squared_error: 41.0060\n",
      "Epoch 420/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 40.8381 - mean_squared_error: 40.8381\n",
      "Epoch 421/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 40.6823 - mean_squared_error: 40.6823\n",
      "Epoch 422/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 40.5540 - mean_squared_error: 40.5540\n",
      "Epoch 423/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 40.3542 - mean_squared_error: 40.3542\n",
      "Epoch 424/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 40.2124 - mean_squared_error: 40.2124\n",
      "Epoch 425/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 40.0416 - mean_squared_error: 40.0416\n",
      "Epoch 426/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 39.8909 - mean_squared_error: 39.8909\n",
      "Epoch 427/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 39.7300 - mean_squared_error: 39.7300\n",
      "Epoch 428/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 39.5662 - mean_squared_error: 39.5662\n",
      "Epoch 429/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 39.3751 - mean_squared_error: 39.3751\n",
      "Epoch 430/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 39.2506 - mean_squared_error: 39.2506\n",
      "Epoch 431/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 39.0519 - mean_squared_error: 39.0519\n",
      "Epoch 432/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 38.8968 - mean_squared_error: 38.8968\n",
      "Epoch 433/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 38.8266 - mean_squared_error: 38.8266\n",
      "Epoch 434/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 38.6751 - mean_squared_error: 38.6751\n",
      "Epoch 435/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 38.4072 - mean_squared_error: 38.4072\n",
      "Epoch 436/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 38.2143 - mean_squared_error: 38.2143\n",
      "Epoch 437/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 38.0759 - mean_squared_error: 38.0759\n",
      "Epoch 438/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 37.9181 - mean_squared_error: 37.9181\n",
      "Epoch 439/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 37.7626 - mean_squared_error: 37.7626\n",
      "Epoch 440/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 37.6314 - mean_squared_error: 37.6314\n",
      "Epoch 441/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 37.4116 - mean_squared_error: 37.4116\n",
      "Epoch 442/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 37.2474 - mean_squared_error: 37.2474\n",
      "Epoch 443/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 37.1012 - mean_squared_error: 37.1012\n",
      "Epoch 444/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 36.9617 - mean_squared_error: 36.9617\n",
      "Epoch 445/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 36.7546 - mean_squared_error: 36.7546\n",
      "Epoch 446/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 36.6061 - mean_squared_error: 36.6061\n",
      "Epoch 447/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 36.4580 - mean_squared_error: 36.4580\n",
      "Epoch 448/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 36.3002 - mean_squared_error: 36.3002\n",
      "Epoch 449/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 36.1236 - mean_squared_error: 36.1236\n",
      "Epoch 450/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 35.9608 - mean_squared_error: 35.9608\n",
      "Epoch 451/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 35.7817 - mean_squared_error: 35.7817\n",
      "Epoch 452/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 35.6481 - mean_squared_error: 35.6481\n",
      "Epoch 453/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 35.4521 - mean_squared_error: 35.4521\n",
      "Epoch 454/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 35.2682 - mean_squared_error: 35.2682\n",
      "Epoch 455/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 35.1186 - mean_squared_error: 35.1186\n",
      "Epoch 456/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 34.9889 - mean_squared_error: 34.9889\n",
      "Epoch 457/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 34.8239 - mean_squared_error: 34.8239\n",
      "Epoch 458/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 34.6553 - mean_squared_error: 34.6553\n",
      "Epoch 459/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 34.4378 - mean_squared_error: 34.4378\n",
      "Epoch 460/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 34.3525 - mean_squared_error: 34.3525\n",
      "Epoch 461/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 34.1760 - mean_squared_error: 34.1760\n",
      "Epoch 462/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 33.9808 - mean_squared_error: 33.9808\n",
      "Epoch 463/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 33.8583 - mean_squared_error: 33.8583\n",
      "Epoch 464/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 33.7042 - mean_squared_error: 33.7042\n",
      "Epoch 465/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 33.5201 - mean_squared_error: 33.5201\n",
      "Epoch 466/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 33.3729 - mean_squared_error: 33.3729\n",
      "Epoch 467/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 33.2402 - mean_squared_error: 33.2402\n",
      "Epoch 468/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 33.0640 - mean_squared_error: 33.0640\n",
      "Epoch 469/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 32.8940 - mean_squared_error: 32.8940\n",
      "Epoch 470/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 32.7748 - mean_squared_error: 32.7748\n",
      "Epoch 471/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 32.6259 - mean_squared_error: 32.6259\n",
      "Epoch 472/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 32.4498 - mean_squared_error: 32.4498\n",
      "Epoch 473/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 32.3231 - mean_squared_error: 32.3231\n",
      "Epoch 474/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 32.1653 - mean_squared_error: 32.1653\n",
      "Epoch 475/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 32.0254 - mean_squared_error: 32.0254\n",
      "Epoch 476/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 31.8842 - mean_squared_error: 31.8842\n",
      "Epoch 477/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 31.7324 - mean_squared_error: 31.7324\n",
      "Epoch 478/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 31.5990 - mean_squared_error: 31.5990\n",
      "Epoch 479/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 31.4546 - mean_squared_error: 31.4546\n",
      "Epoch 480/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 31.3138 - mean_squared_error: 31.3138\n",
      "Epoch 481/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 31.1645 - mean_squared_error: 31.1645\n",
      "Epoch 482/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 31.0538 - mean_squared_error: 31.0538\n",
      "Epoch 483/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.9112 - mean_squared_error: 30.9112\n",
      "Epoch 484/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.7770 - mean_squared_error: 30.7770\n",
      "Epoch 485/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.6570 - mean_squared_error: 30.6570\n",
      "Epoch 486/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.4967 - mean_squared_error: 30.4967\n",
      "Epoch 487/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.3971 - mean_squared_error: 30.3971\n",
      "Epoch 488/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.2735 - mean_squared_error: 30.2735\n",
      "Epoch 489/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.1263 - mean_squared_error: 30.1263\n",
      "Epoch 490/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 30.0192 - mean_squared_error: 30.0192\n",
      "Epoch 491/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 29.8900 - mean_squared_error: 29.8900\n",
      "Epoch 492/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 29.7584 - mean_squared_error: 29.7584\n",
      "Epoch 493/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 29.6303 - mean_squared_error: 29.6303\n",
      "Epoch 494/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 29.5135 - mean_squared_error: 29.5135\n",
      "Epoch 495/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 29.4124 - mean_squared_error: 29.4124\n",
      "Epoch 496/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 29.2967 - mean_squared_error: 29.2967\n",
      "Epoch 497/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 29.1618 - mean_squared_error: 29.1618\n",
      "Epoch 498/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 29.0419 - mean_squared_error: 29.0419\n",
      "Epoch 499/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.9307 - mean_squared_error: 28.9307\n",
      "Epoch 500/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.8381 - mean_squared_error: 28.8381\n",
      "Epoch 501/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.7233 - mean_squared_error: 28.7233\n",
      "Epoch 502/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.6243 - mean_squared_error: 28.6243\n",
      "Epoch 503/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.5015 - mean_squared_error: 28.5015\n",
      "Epoch 504/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.4244 - mean_squared_error: 28.4244\n",
      "Epoch 505/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 28.3220 - mean_squared_error: 28.3220\n",
      "Epoch 506/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.2284 - mean_squared_error: 28.2284\n",
      "Epoch 507/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 28.1061 - mean_squared_error: 28.1061\n",
      "Epoch 508/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 28.0266 - mean_squared_error: 28.0266\n",
      "Epoch 509/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.9097 - mean_squared_error: 27.9097\n",
      "Epoch 510/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.8439 - mean_squared_error: 27.8439\n",
      "Epoch 511/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.7119 - mean_squared_error: 27.7119\n",
      "Epoch 512/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 27.6293 - mean_squared_error: 27.6293\n",
      "Epoch 513/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 27.5382 - mean_squared_error: 27.5382\n",
      "Epoch 514/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.4770 - mean_squared_error: 27.4770\n",
      "Epoch 515/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.4503 - mean_squared_error: 27.4503\n",
      "Epoch 516/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.2943 - mean_squared_error: 27.2943\n",
      "Epoch 517/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.1979 - mean_squared_error: 27.1979\n",
      "Epoch 518/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 27.1019 - mean_squared_error: 27.1019\n",
      "Epoch 519/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 27.0442 - mean_squared_error: 27.0442\n",
      "Epoch 520/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 26.9413 - mean_squared_error: 26.9413\n",
      "Epoch 521/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.8655 - mean_squared_error: 26.8655\n",
      "Epoch 522/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 26.7861 - mean_squared_error: 26.7861\n",
      "Epoch 523/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.7057 - mean_squared_error: 26.7057\n",
      "Epoch 524/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 26.6478 - mean_squared_error: 26.6478\n",
      "Epoch 525/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.5576 - mean_squared_error: 26.5576\n",
      "Epoch 526/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 26.4960 - mean_squared_error: 26.4960\n",
      "Epoch 527/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.4289 - mean_squared_error: 26.4289\n",
      "Epoch 528/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.3278 - mean_squared_error: 26.3278\n",
      "Epoch 529/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.3039 - mean_squared_error: 26.3039\n",
      "Epoch 530/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 26.1763 - mean_squared_error: 26.1763\n",
      "Epoch 531/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 26.1104 - mean_squared_error: 26.1104\n",
      "Epoch 532/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 26.0542 - mean_squared_error: 26.0542\n",
      "Epoch 533/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.9884 - mean_squared_error: 25.9884\n",
      "Epoch 534/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 25.9166 - mean_squared_error: 25.9166\n",
      "Epoch 535/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.8477 - mean_squared_error: 25.8477\n",
      "Epoch 536/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 25.7722 - mean_squared_error: 25.7722\n",
      "Epoch 537/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.7157 - mean_squared_error: 25.7157\n",
      "Epoch 538/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 25.6731 - mean_squared_error: 25.6731\n",
      "Epoch 539/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.6202 - mean_squared_error: 25.6202\n",
      "Epoch 540/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.5433 - mean_squared_error: 25.5433\n",
      "Epoch 541/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.4804 - mean_squared_error: 25.4804\n",
      "Epoch 542/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 25.4451 - mean_squared_error: 25.4451\n",
      "Epoch 543/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 25.3603 - mean_squared_error: 25.3603\n",
      "Epoch 544/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.2828 - mean_squared_error: 25.2828\n",
      "Epoch 545/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.2457 - mean_squared_error: 25.2457\n",
      "Epoch 546/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 25.1836 - mean_squared_error: 25.1836\n",
      "Epoch 547/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 25.1281 - mean_squared_error: 25.1281\n",
      "Epoch 548/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.0840 - mean_squared_error: 25.0840\n",
      "Epoch 549/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 25.0589 - mean_squared_error: 25.0589\n",
      "Epoch 550/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.9621 - mean_squared_error: 24.9621\n",
      "Epoch 551/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.9345 - mean_squared_error: 24.9345\n",
      "Epoch 552/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 24.8420 - mean_squared_error: 24.8420\n",
      "Epoch 553/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.7938 - mean_squared_error: 24.7938\n",
      "Epoch 554/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.7577 - mean_squared_error: 24.7577\n",
      "Epoch 555/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.6999 - mean_squared_error: 24.6999\n",
      "Epoch 556/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 24.6402 - mean_squared_error: 24.6402\n",
      "Epoch 557/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.6109 - mean_squared_error: 24.6109\n",
      "Epoch 558/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.5669 - mean_squared_error: 24.5669\n",
      "Epoch 559/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.4958 - mean_squared_error: 24.4958\n",
      "Epoch 560/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.5298 - mean_squared_error: 24.5298\n",
      "Epoch 561/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.4033 - mean_squared_error: 24.4033\n",
      "Epoch 562/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.3677 - mean_squared_error: 24.3677\n",
      "Epoch 563/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 24.3399 - mean_squared_error: 24.3399\n",
      "Epoch 564/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.2740 - mean_squared_error: 24.2740\n",
      "Epoch 565/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.2652 - mean_squared_error: 24.2652\n",
      "Epoch 566/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.1984 - mean_squared_error: 24.1984\n",
      "Epoch 567/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.2201 - mean_squared_error: 24.2201\n",
      "Epoch 568/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 24.1260 - mean_squared_error: 24.1260\n",
      "Epoch 569/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.0578 - mean_squared_error: 24.0578\n",
      "Epoch 570/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.0702 - mean_squared_error: 24.0702\n",
      "Epoch 571/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.9745 - mean_squared_error: 23.9745\n",
      "Epoch 572/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.9296 - mean_squared_error: 23.9296\n",
      "Epoch 573/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.9101 - mean_squared_error: 23.9101\n",
      "Epoch 574/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.8503 - mean_squared_error: 23.8503\n",
      "Epoch 575/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.8453 - mean_squared_error: 23.8453\n",
      "Epoch 576/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.7944 - mean_squared_error: 23.7944\n",
      "Epoch 577/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 23.7392 - mean_squared_error: 23.7392\n",
      "Epoch 578/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.6995 - mean_squared_error: 23.6995\n",
      "Epoch 579/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.6663 - mean_squared_error: 23.6663\n",
      "Epoch 580/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.6581 - mean_squared_error: 23.6581\n",
      "Epoch 581/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.5804 - mean_squared_error: 23.5804\n",
      "Epoch 582/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.5652 - mean_squared_error: 23.5652\n",
      "Epoch 583/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 23.5598 - mean_squared_error: 23.5598\n",
      "Epoch 584/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.5060 - mean_squared_error: 23.5060\n",
      "Epoch 585/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 23.4528 - mean_squared_error: 23.4528\n",
      "Epoch 586/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.4006 - mean_squared_error: 23.4006\n",
      "Epoch 587/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.3723 - mean_squared_error: 23.3723\n",
      "Epoch 588/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.3536 - mean_squared_error: 23.3536\n",
      "Epoch 589/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 23.2939 - mean_squared_error: 23.2939\n",
      "Epoch 590/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.2818 - mean_squared_error: 23.2818\n",
      "Epoch 591/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.2721 - mean_squared_error: 23.2721\n",
      "Epoch 592/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 23.2100 - mean_squared_error: 23.2100\n",
      "Epoch 593/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.1682 - mean_squared_error: 23.1682\n",
      "Epoch 594/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 23.1401 - mean_squared_error: 23.1401\n",
      "Epoch 595/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.1263 - mean_squared_error: 23.1263\n",
      "Epoch 596/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 23.0582 - mean_squared_error: 23.0582\n",
      "Epoch 597/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 23.0491 - mean_squared_error: 23.0491\n",
      "Epoch 598/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 23.0013 - mean_squared_error: 23.0013\n",
      "Epoch 599/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.9834 - mean_squared_error: 22.9834\n",
      "Epoch 600/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.9523 - mean_squared_error: 22.9523\n",
      "Epoch 601/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.9325 - mean_squared_error: 22.9325\n",
      "Epoch 602/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.9069 - mean_squared_error: 22.9069\n",
      "Epoch 603/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.8515 - mean_squared_error: 22.8515\n",
      "Epoch 604/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.8201 - mean_squared_error: 22.8201\n",
      "Epoch 605/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.8040 - mean_squared_error: 22.8040\n",
      "Epoch 606/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.7678 - mean_squared_error: 22.7678\n",
      "Epoch 607/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.7420 - mean_squared_error: 22.7420\n",
      "Epoch 608/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.7078 - mean_squared_error: 22.7078\n",
      "Epoch 609/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.7060 - mean_squared_error: 22.7060\n",
      "Epoch 610/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.6509 - mean_squared_error: 22.6509\n",
      "Epoch 611/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.6390 - mean_squared_error: 22.6390\n",
      "Epoch 612/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.6017 - mean_squared_error: 22.6017\n",
      "Epoch 613/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.5745 - mean_squared_error: 22.5745\n",
      "Epoch 614/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.5476 - mean_squared_error: 22.5476\n",
      "Epoch 615/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.5790 - mean_squared_error: 22.5790\n",
      "Epoch 616/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.4905 - mean_squared_error: 22.4905\n",
      "Epoch 617/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.4527 - mean_squared_error: 22.4527\n",
      "Epoch 618/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.4273 - mean_squared_error: 22.4273\n",
      "Epoch 619/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.4340 - mean_squared_error: 22.4340\n",
      "Epoch 620/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.3788 - mean_squared_error: 22.3788\n",
      "Epoch 621/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.3462 - mean_squared_error: 22.3462\n",
      "Epoch 622/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.3190 - mean_squared_error: 22.3190\n",
      "Epoch 623/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.3132 - mean_squared_error: 22.3132\n",
      "Epoch 624/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.2799 - mean_squared_error: 22.2799\n",
      "Epoch 625/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.2608 - mean_squared_error: 22.2608\n",
      "Epoch 626/700\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 22.2327 - mean_squared_error: 22.2327\n",
      "Epoch 627/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.3453 - mean_squared_error: 22.3453\n",
      "Epoch 628/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.1807 - mean_squared_error: 22.1807\n",
      "Epoch 629/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.1447 - mean_squared_error: 22.1447\n",
      "Epoch 630/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.1592 - mean_squared_error: 22.1592\n",
      "Epoch 631/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 22.1016 - mean_squared_error: 22.1016\n",
      "Epoch 632/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.0705 - mean_squared_error: 22.0705\n",
      "Epoch 633/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.0528 - mean_squared_error: 22.0528\n",
      "Epoch 634/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.0371 - mean_squared_error: 22.0371\n",
      "Epoch 635/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.0109 - mean_squared_error: 22.0109\n",
      "Epoch 636/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.0971 - mean_squared_error: 22.0971\n",
      "Epoch 637/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.9809 - mean_squared_error: 21.9809\n",
      "Epoch 638/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.9369 - mean_squared_error: 21.9369\n",
      "Epoch 639/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.9055 - mean_squared_error: 21.9055\n",
      "Epoch 640/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.9052 - mean_squared_error: 21.9052\n",
      "Epoch 641/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.8793 - mean_squared_error: 21.8793\n",
      "Epoch 642/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.8522 - mean_squared_error: 21.8522\n",
      "Epoch 643/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.8154 - mean_squared_error: 21.8154\n",
      "Epoch 644/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.7992 - mean_squared_error: 21.7992\n",
      "Epoch 645/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.7701 - mean_squared_error: 21.7701\n",
      "Epoch 646/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.7564 - mean_squared_error: 21.7564\n",
      "Epoch 647/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.7414 - mean_squared_error: 21.7414\n",
      "Epoch 648/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.7235 - mean_squared_error: 21.7235\n",
      "Epoch 649/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.7211 - mean_squared_error: 21.7211\n",
      "Epoch 650/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.6688 - mean_squared_error: 21.6688\n",
      "Epoch 651/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.6447 - mean_squared_error: 21.6447\n",
      "Epoch 652/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.6873 - mean_squared_error: 21.6873\n",
      "Epoch 653/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.6405 - mean_squared_error: 21.6405\n",
      "Epoch 654/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.5846 - mean_squared_error: 21.5846\n",
      "Epoch 655/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.5617 - mean_squared_error: 21.5617\n",
      "Epoch 656/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.5380 - mean_squared_error: 21.5380\n",
      "Epoch 657/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.5215 - mean_squared_error: 21.5215\n",
      "Epoch 658/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 21.5136 - mean_squared_error: 21.5136\n",
      "Epoch 659/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.5126 - mean_squared_error: 21.5126\n",
      "Epoch 660/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.4944 - mean_squared_error: 21.4944\n",
      "Epoch 661/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.4595 - mean_squared_error: 21.4595\n",
      "Epoch 662/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.4453 - mean_squared_error: 21.4453\n",
      "Epoch 663/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.4302 - mean_squared_error: 21.4302\n",
      "Epoch 664/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.4046 - mean_squared_error: 21.4046\n",
      "Epoch 665/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.3623 - mean_squared_error: 21.3623\n",
      "Epoch 666/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.3670 - mean_squared_error: 21.3670\n",
      "Epoch 667/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.3421 - mean_squared_error: 21.3421\n",
      "Epoch 668/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.3262 - mean_squared_error: 21.3262\n",
      "Epoch 669/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.2794 - mean_squared_error: 21.2794\n",
      "Epoch 670/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.2970 - mean_squared_error: 21.2970\n",
      "Epoch 671/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.2581 - mean_squared_error: 21.2581\n",
      "Epoch 672/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.2345 - mean_squared_error: 21.2345\n",
      "Epoch 673/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.2012 - mean_squared_error: 21.2012\n",
      "Epoch 674/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.1934 - mean_squared_error: 21.1934\n",
      "Epoch 675/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.1769 - mean_squared_error: 21.1769\n",
      "Epoch 676/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.1612 - mean_squared_error: 21.1612\n",
      "Epoch 677/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.1428 - mean_squared_error: 21.1428\n",
      "Epoch 678/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 21.1179 - mean_squared_error: 21.1179\n",
      "Epoch 679/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0910 - mean_squared_error: 21.0910\n",
      "Epoch 680/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0791 - mean_squared_error: 21.0791\n",
      "Epoch 681/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0739 - mean_squared_error: 21.0739\n",
      "Epoch 682/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0443 - mean_squared_error: 21.0443\n",
      "Epoch 683/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0204 - mean_squared_error: 21.0204\n",
      "Epoch 684/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0274 - mean_squared_error: 21.0274\n",
      "Epoch 685/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 20.9877 - mean_squared_error: 20.9877\n",
      "Epoch 686/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0533 - mean_squared_error: 21.0533\n",
      "Epoch 687/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 20.9736 - mean_squared_error: 20.9736\n",
      "Epoch 688/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.9795 - mean_squared_error: 20.9795\n",
      "Epoch 689/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.9338 - mean_squared_error: 20.9338\n",
      "Epoch 690/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.9133 - mean_squared_error: 20.9133\n",
      "Epoch 691/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.8860 - mean_squared_error: 20.8860\n",
      "Epoch 692/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.8674 - mean_squared_error: 20.8674\n",
      "Epoch 693/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.8518 - mean_squared_error: 20.8518\n",
      "Epoch 694/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 20.8215 - mean_squared_error: 20.8215\n",
      "Epoch 695/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.8179 - mean_squared_error: 20.8179\n",
      "Epoch 696/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 20.8058 - mean_squared_error: 20.8058\n",
      "Epoch 697/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.8461 - mean_squared_error: 20.8461\n",
      "Epoch 698/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.8089 - mean_squared_error: 20.8089\n",
      "Epoch 699/700\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 20.7918 - mean_squared_error: 20.7918\n",
      "Epoch 700/700\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.7369 - mean_squared_error: 20.7369\n"
     ]
    }
   ],
   "source": [
    "#NN\n",
    "\n",
    "NN_model = Sequential()\n",
    "NN_model.add(Dense(3,input_dim=X.shape[1],activation = 'tanh'))\n",
    "NN_model.add(Dense(3, activation = 'relu'))\n",
    "NN_model.add(Dense(3, activation = 'relu'))\n",
    "NN_model.add(Dense(1, activation = 'linear'))\n",
    "NN_model.compile(loss='MeanSquaredError', optimizer='Adam', metrics=['MeanSquaredError'])\n",
    "NN_model.fit(train_X, train_y, epochs=700)\n",
    "prediction_NN = NN_model.predict(val_X)\n",
    "r2_score_NN = r2_score(val_y,prediction_NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "908af1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#XGBRegressor\n",
    "\n",
    "XGB_model = XGBRegressor()\n",
    "XGB_model.fit(train_X,train_y)\n",
    "prediction_XGB = XGB_model.predict(val_X)\n",
    "r2_score_XGB = r2_score(val_y,prediction_XGB)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9058c877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression R2score: 0.7048259296256516\n",
      "Small NN R2score: 0.7561564848311271\n",
      "XGB r2score: 0.7991583069832422\n"
     ]
    }
   ],
   "source": [
    "#R2SCORE\n",
    "\n",
    "print(\"Linear regression R2score:\", r2_score_linear)\n",
    "print(\"Small NN R2score:\", r2_score_NN)\n",
    "print(\"XGB r2score:\", r2_score_XGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3fb3b27",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Medv and Crim scatter\n",
    "\n",
    "sns.regplot(x='medv', y='crim', data=df, line_kws={'color': 'red'})\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4436303c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marcin\\AppData\\Local\\Temp/ipykernel_10392/4058280656.py:8: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dashboard\n",
    "\n",
    "dfm = Coef_df.melt(var_name='Column Names', value_name='Coefficient Value')\n",
    "fig, axs = plt.subplots(3,1)\n",
    "sns.barplot(x='Column Names', y='Coefficient Value', data=dfm, ax=axs[0])\n",
    "sns.histplot(data=df, x='nox', ax=axs[1])\n",
    "sns.regplot(x='rad', y='indus', data=df, ax=axs[2])\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "589b5149",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}