Notebook1B_Multivariate_regression_heating_cooling_load_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9e8a1f7",
   "metadata": {},
   "source": [
    "## Notebook 1B: Multivariate regression of energy consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ddb746",
   "metadata": {},
   "source": [
    "**1. Prepare Problem**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e693914",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "# a) Load libraries\n",
    "import pandas as pd\n",
    "from pandas import set_option\n",
    "from pandas import DataFrame\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tensorflow import keras\n",
    "from keras.callbacks import EarlyStopping\n",
    "from scikeras.wrappers import KerasRegressor\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.layers import BatchNormalization, Dropout, Dense \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from pickle import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f0544c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 10)\n",
      "   Relative Compactness  Surface Area  Wall Area  Roof Area  Overall Height  \\\n",
      "0                  0.98         514.5      294.0     110.25             7.0   \n",
      "1                  0.98         514.5      294.0     110.25             7.0   \n",
      "2                  0.98         514.5      294.0     110.25             7.0   \n",
      "3                  0.98         514.5      294.0     110.25             7.0   \n",
      "4                  0.90         563.5      318.5     122.50             7.0   \n",
      "5                  0.90         563.5      318.5     122.50             7.0   \n",
      "6                  0.90         563.5      318.5     122.50             7.0   \n",
      "7                  0.90         563.5      318.5     122.50             7.0   \n",
      "8                  0.86         588.0      294.0     147.00             7.0   \n",
      "9                  0.86         588.0      294.0     147.00             7.0   \n",
      "\n",
      "   Orientation  Glazing Area  Glazing Area Distribution  Heating Load  \\\n",
      "0            2           0.0                          0         15.55   \n",
      "1            3           0.0                          0         15.55   \n",
      "2            4           0.0                          0         15.55   \n",
      "3            5           0.0                          0         15.55   \n",
      "4            2           0.0                          0         20.84   \n",
      "5            3           0.0                          0         21.46   \n",
      "6            4           0.0                          0         20.71   \n",
      "7            5           0.0                          0         19.68   \n",
      "8            2           0.0                          0         19.50   \n",
      "9            3           0.0                          0         19.95   \n",
      "\n",
      "   Cooling Load  \n",
      "0         21.33  \n",
      "1         21.33  \n",
      "2         21.33  \n",
      "3         21.33  \n",
      "4         28.28  \n",
      "5         25.38  \n",
      "6         25.16  \n",
      "7         29.60  \n",
      "8         27.30  \n",
      "9         21.97  \n",
      "Relative Compactness         float64\n",
      "Surface Area                 float64\n",
      "Wall Area                    float64\n",
      "Roof Area                    float64\n",
      "Overall Height               float64\n",
      "Orientation                    int64\n",
      "Glazing Area                 float64\n",
      "Glazing Area Distribution      int64\n",
      "Heating Load                 float64\n",
      "Cooling Load                 float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# b) Load dataset\n",
    "# Load and view each file in the dataset\n",
    "data = pd.read_csv('EPB_data.csv', header=0)\n",
    "print(data.shape)\n",
    "print(data.head(10))\n",
    "print(data.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4183abc9",
   "metadata": {},
   "source": [
    "One of the approach in optimizing building energy efficiency is to minimize total energy consumption, which is the sum of heating and cooling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "848ccc10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 8) (768, 2)\n"
     ]
    }
   ],
   "source": [
    "# Define features and label\n",
    "X = data.drop(columns=['Heating Load', 'Cooling Load'], axis = 1)\n",
    "y = data[['Heating Load', 'Cooling Load']]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74102799",
   "metadata": {},
   "source": [
    "**2. Dataset summary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae17f8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Relative Compactness  Surface Area  Wall Area  Roof Area  \\\n",
      "count               768.000       768.000    768.000    768.000   \n",
      "mean                  0.764       671.708    318.500    176.604   \n",
      "std                   0.106        88.086     43.626     45.166   \n",
      "min                   0.620       514.500    245.000    110.250   \n",
      "25%                   0.682       606.375    294.000    140.875   \n",
      "50%                   0.750       673.750    318.500    183.750   \n",
      "75%                   0.830       741.125    343.000    220.500   \n",
      "max                   0.980       808.500    416.500    220.500   \n",
      "\n",
      "       Overall Height  Orientation  Glazing Area  Glazing Area Distribution  \\\n",
      "count         768.000      768.000       768.000                    768.000   \n",
      "mean            5.250        3.500         0.234                      2.812   \n",
      "std             1.751        1.119         0.133                      1.551   \n",
      "min             3.500        2.000         0.000                      0.000   \n",
      "25%             3.500        2.750         0.100                      1.750   \n",
      "50%             5.250        3.500         0.250                      3.000   \n",
      "75%             7.000        4.250         0.400                      4.000   \n",
      "max             7.000        5.000         0.400                      5.000   \n",
      "\n",
      "       Heating Load  Cooling Load  \n",
      "count       768.000       768.000  \n",
      "mean         22.307        24.588  \n",
      "std          10.090         9.513  \n",
      "min           6.010        10.900  \n",
      "25%          12.992        15.620  \n",
      "50%          18.950        22.080  \n",
      "75%          31.668        33.133  \n",
      "max          43.100        48.030  \n"
     ]
    }
   ],
   "source": [
    "# a) Descriptive statistics\n",
    "set_option(\"display.precision\", 3)\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9631265e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking categorical features\n",
    "cat_features = data.select_dtypes(include='O').keys()\n",
    "# display variabels\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fbe7e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b) Data visualizations\n",
    "# Box and Whisker Plots of a few features\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 6]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "raw_plot = pd.DataFrame(data)\n",
    "raw_plot.plot(kind='box', subplots=True, layout=(4,3), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f6966c",
   "metadata": {},
   "source": [
    "We can see the data skewed to high values in relative compactness, roof area, glazing area, and cooling load."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85db6941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the stack bar chart of Heating and Cooling Load\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(np.arange(len(data['Heating Load'])), data['Heating Load'], label='Heating Load')\n",
    "ax.bar(np.arange(len(data['Cooling Load'])), data['Cooling Load'], bottom=data['Heating Load'], label='Cooling Load')\n",
    "\n",
    "# add labels, title and legend\n",
    "ax.set_xlabel('Number of Samples')\n",
    "ax.set_ylabel('Energy Consumption (kWh)')\n",
    "ax.set_title('Heating and Cooling Load')\n",
    "ax.legend()\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000aa0da",
   "metadata": {},
   "source": [
    "The data displays a cyclical pattern that may have resulted from the measurements taken on weekdays (with longer hours) and weekends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf940bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with subplots\n",
    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(12, 8))\n",
    "\n",
    "# Flatten the axes array to simplify plotting\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Loop through each feature and create a density plot\n",
    "for i, feature in enumerate(raw_plot.columns):\n",
    "    sns.kdeplot(raw_plot[feature], ax=axes[i], color='steelblue', fill=True)\n",
    "    axes[i].set_title(feature)\n",
    "\n",
    "# Adjust the layout and spacing of the subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cfc153",
   "metadata": {},
   "source": [
    "The dataset seems to be complex as most of features exhibit non-Gaussian distributions, except for the Surface Area. There are several factors that could cause non-Gaussian distributions, including outliers, measurement errors, or the inherent characteristics of the building designs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1cbc8ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix between all pairs of attributes\n",
    "corr_matrix = data.corr(method='pearson')\n",
    "\n",
    "# Create a heatmap using seaborn\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, vmin=-1, vmax=1, annot=True, cmap=\"coolwarm\", ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d2fc79",
   "metadata": {},
   "source": [
    "According to the correlation matrix, the heating and cooling loads, as well as energy consumption, exhibit strong correlations with the overall height, orientation, and to some extent, the surface area. The orientation is considered to be an uncertain factor in building design due to limited choices compared to other variables and it plays a significant role in determining the energy efficiency of a building."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a8653",
   "metadata": {},
   "source": [
    "**3. Prepare Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b51f9418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Compactness          12\n",
      "Surface Area                  12\n",
      "Wall Area                      7\n",
      "Roof Area                      4\n",
      "Overall Height                 2\n",
      "Orientation                    4\n",
      "Glazing Area                   4\n",
      "Glazing Area Distribution      6\n",
      "Heating Load                 586\n",
      "Cooling Load                 636\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# a) Data Cleaning\n",
    "# summarize the number of unique values in each column\n",
    "print(data.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e029b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Compactness         0\n",
      "Surface Area                 0\n",
      "Wall Area                    0\n",
      "Roof Area                    0\n",
      "Overall Height               0\n",
      "Orientation                  0\n",
      "Glazing Area                 0\n",
      "Glazing Area Distribution    0\n",
      "Heating Load                 0\n",
      "Cooling Load                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify columns with missing values and count the number of missing values\n",
    "data.columns[data.isnull().any()]\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ad7798",
   "metadata": {},
   "source": [
    "It is necessary to eliminate outliers in numerical data columns to improve the accuracy of training and validation processes. This can be done by determining the correlation between these columns and the target variable, identifying columns with low correlation, and then filtering out values that fall outside of the lower and upper bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b86a7bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the correlation between numerical columns and target variables\n",
    "corr_matrix = data.select_dtypes(include=np.number).corrwith(data['Heating Load']).sort_values(ascending=False)\n",
    "\n",
    "# filter columns with low correlation \n",
    "corr_threshold = 0.05\n",
    "low_corr_columns = corr_matrix[corr_matrix.abs() < corr_threshold].index\n",
    "\n",
    "# remove outliers from numerical columns with low correlation\n",
    "for column in low_corr_columns:\n",
    "    lower = data[column].quantile(0.01)\n",
    "    upper = data[column].quantile(0.99)\n",
    "    data[column] = data[column].clip(lower, upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b86943d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the correlation between numerical columns and target variables\n",
    "corr_matrix = data.select_dtypes(include=np.number).corrwith(data['Cooling Load']).sort_values(ascending=False)\n",
    "\n",
    "# filter columns with low correlation \n",
    "corr_threshold = 0.05\n",
    "low_corr_columns = corr_matrix[corr_matrix.abs() < corr_threshold].index\n",
    "\n",
    "# remove outliers from numerical columns with low correlation\n",
    "for column in low_corr_columns:\n",
    "    lower = data[column].quantile(0.01)\n",
    "    upper = data[column].quantile(0.99)\n",
    "    data[column] = data[column].clip(lower, upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e41b92c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(576, 8) (576, 2) (192, 8) (192, 2)\n"
     ]
    }
   ],
   "source": [
    "# b) Split-out dataset into train and validation sets\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.25, random_state=1)\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f87f749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# c) Data Transforms\n",
    "# Standardize the dataset by rescaling the distribution of values \n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_val = scaler.fit_transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f676f9",
   "metadata": {},
   "source": [
    "**4. Evaluate Algorithms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69ee8103",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a) Spot check algorithms using cross-validation technique\n",
    "num_folds = 8\n",
    "seed = 8\n",
    "\n",
    "# Select 5 most popular linear and tree-based algorithms for evaluation\n",
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('EN', ElasticNet()))\n",
    "models.append(('RF', RandomForestRegressor()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('SVR', MultiOutputRegressor(SVR(gamma='auto'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "378a8c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neural Network algorithms\n",
    "# create keras Sequential model\n",
    "def baseline_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(300, input_shape = (8, ), activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(200, activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(100, activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(2))\n",
    "    # Compile model\n",
    "    model.compile(optimizer = 'adam',\n",
    "                loss = 'mean_squared_error',\n",
    "                metrics=['mse'])\n",
    "    return model\n",
    "\n",
    "# Build model\n",
    "model_NN = baseline_model()\n",
    "models.append(('NN', model_NN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "32711f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a callback for early stopping if the validation loss does not improve for 10 consecutive \n",
    "# epochs (patience=10).\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "099255c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -8.863599 (1.261067)\n",
      "EN: -44.681735 (8.746579)\n",
      "RF: -1.689117 (0.350230)\n",
      "KNN: -8.509963 (2.116446)\n",
      "SVR: -11.050826 (2.213697)\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpflal5gje\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 2s 137ms/step - loss: 608.9567 - mse: 608.9567 - val_loss: 693.9111 - val_mse: 693.9111\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 582.9422 - mse: 582.9422 - val_loss: 687.4919 - val_mse: 687.4919\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 566.7593 - mse: 566.7593 - val_loss: 679.9434 - val_mse: 679.9434\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 555.1415 - mse: 555.1415 - val_loss: 670.8430 - val_mse: 670.8430\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 544.6531 - mse: 544.6531 - val_loss: 660.1086 - val_mse: 660.1086\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 538.9494 - mse: 538.9494 - val_loss: 648.0949 - val_mse: 648.0949\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 536.0182 - mse: 536.0182 - val_loss: 635.0425 - val_mse: 635.0425\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 528.9432 - mse: 528.9432 - val_loss: 621.7921 - val_mse: 621.7921\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 525.8231 - mse: 525.8231 - val_loss: 608.4689 - val_mse: 608.4689\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 521.9846 - mse: 521.9846 - val_loss: 595.2154 - val_mse: 595.2154\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 515.2772 - mse: 515.2772 - val_loss: 582.9020 - val_mse: 582.9020\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 516.9443 - mse: 516.9443 - val_loss: 571.1492 - val_mse: 571.1492\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 510.4462 - mse: 510.4462 - val_loss: 560.2338 - val_mse: 560.2338\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 509.0158 - mse: 509.0158 - val_loss: 550.3760 - val_mse: 550.3760\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 502.5213 - mse: 502.5213 - val_loss: 539.9747 - val_mse: 539.9747\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 502.6202 - mse: 502.6202 - val_loss: 530.6984 - val_mse: 530.6984\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 499.6559 - mse: 499.6559 - val_loss: 522.0908 - val_mse: 522.0908\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 495.0463 - mse: 495.0463 - val_loss: 513.2878 - val_mse: 513.2878\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 493.8235 - mse: 493.8235 - val_loss: 503.6523 - val_mse: 503.6523\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 484.7426 - mse: 484.7426 - val_loss: 495.0192 - val_mse: 495.0192\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 486.0472 - mse: 486.0472 - val_loss: 486.4278 - val_mse: 486.4278\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 480.1405 - mse: 480.1405 - val_loss: 478.9135 - val_mse: 478.9135\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 478.5469 - mse: 478.5469 - val_loss: 472.4381 - val_mse: 472.4381\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 471.3323 - mse: 471.3323 - val_loss: 464.7325 - val_mse: 464.7325\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 471.1543 - mse: 471.1543 - val_loss: 457.6281 - val_mse: 457.6281\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 465.5358 - mse: 465.5358 - val_loss: 450.7124 - val_mse: 450.7124\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 460.1975 - mse: 460.1975 - val_loss: 443.0203 - val_mse: 443.0203\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 457.5621 - mse: 457.5621 - val_loss: 434.9380 - val_mse: 434.9380\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 449.6444 - mse: 449.6444 - val_loss: 427.5314 - val_mse: 427.5314\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 445.6173 - mse: 445.6173 - val_loss: 419.7243 - val_mse: 419.7243\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 446.4028 - mse: 446.4028 - val_loss: 412.7185 - val_mse: 412.7185\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 437.9324 - mse: 437.9324 - val_loss: 406.4333 - val_mse: 406.4333\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 431.8426 - mse: 431.8426 - val_loss: 399.3307 - val_mse: 399.3307\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 421.9176 - mse: 421.9176 - val_loss: 391.8739 - val_mse: 391.8739\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 420.8080 - mse: 420.8080 - val_loss: 385.4498 - val_mse: 385.4498\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 418.7507 - mse: 418.7507 - val_loss: 379.0998 - val_mse: 379.0998\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 407.9821 - mse: 407.9821 - val_loss: 371.6780 - val_mse: 371.6780\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 398.1877 - mse: 398.1877 - val_loss: 363.5609 - val_mse: 363.5609\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 393.4803 - mse: 393.4803 - val_loss: 354.1672 - val_mse: 354.1672\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 388.5112 - mse: 388.5112 - val_loss: 345.5069 - val_mse: 345.5069\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 381.4022 - mse: 381.4022 - val_loss: 336.3280 - val_mse: 336.3280\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 370.2383 - mse: 370.2383 - val_loss: 326.3176 - val_mse: 326.3176\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 368.5563 - mse: 368.5563 - val_loss: 319.4771 - val_mse: 319.4771\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 358.4044 - mse: 358.4044 - val_loss: 311.8654 - val_mse: 311.8654\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 346.9880 - mse: 346.9880 - val_loss: 302.6909 - val_mse: 302.6909\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 341.1111 - mse: 341.1111 - val_loss: 294.5817 - val_mse: 294.5817\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 335.0876 - mse: 335.0876 - val_loss: 287.0684 - val_mse: 287.0684\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 321.6856 - mse: 321.6856 - val_loss: 281.0138 - val_mse: 281.0138\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 316.5087 - mse: 316.5087 - val_loss: 274.1430 - val_mse: 274.1430\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 308.7094 - mse: 308.7094 - val_loss: 268.2396 - val_mse: 268.2396\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 298.6961 - mse: 298.6961 - val_loss: 262.9470 - val_mse: 262.9470\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 295.1852 - mse: 295.1852 - val_loss: 259.1947 - val_mse: 259.1947\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 281.1205 - mse: 281.1205 - val_loss: 255.0103 - val_mse: 255.0103\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 277.2402 - mse: 277.2402 - val_loss: 249.6306 - val_mse: 249.6306\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 265.4998 - mse: 265.4998 - val_loss: 241.2596 - val_mse: 241.2596\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 257.8835 - mse: 257.8835 - val_loss: 231.3529 - val_mse: 231.3529\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 253.1752 - mse: 253.1752 - val_loss: 223.2960 - val_mse: 223.2960\n",
      "Epoch 58/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 16ms/step - loss: 239.3645 - mse: 239.3645 - val_loss: 216.5052 - val_mse: 216.5052\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 234.7460 - mse: 234.7460 - val_loss: 209.9411 - val_mse: 209.9411\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 217.4332 - mse: 217.4332 - val_loss: 202.4181 - val_mse: 202.4181\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 211.9277 - mse: 211.9277 - val_loss: 196.4841 - val_mse: 196.4841\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 208.7944 - mse: 208.7944 - val_loss: 188.7025 - val_mse: 188.7025\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 200.7994 - mse: 200.7994 - val_loss: 180.3095 - val_mse: 180.3095\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 192.9283 - mse: 192.9283 - val_loss: 174.1897 - val_mse: 174.1897\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 184.9430 - mse: 184.9430 - val_loss: 169.5159 - val_mse: 169.5159\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 180.6762 - mse: 180.6762 - val_loss: 164.6440 - val_mse: 164.6440\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 166.0955 - mse: 166.0955 - val_loss: 157.9582 - val_mse: 157.9582\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 165.6133 - mse: 165.6133 - val_loss: 150.1099 - val_mse: 150.1099\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 157.3394 - mse: 157.3394 - val_loss: 144.5769 - val_mse: 144.5769\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 152.2669 - mse: 152.2669 - val_loss: 140.1544 - val_mse: 140.1544\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 145.9965 - mse: 145.9965 - val_loss: 135.2436 - val_mse: 135.2436\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 134.1350 - mse: 134.1350 - val_loss: 128.0397 - val_mse: 128.0397\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 123.1888 - mse: 123.1888 - val_loss: 119.0565 - val_mse: 119.0565\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 124.7940 - mse: 124.7940 - val_loss: 111.9993 - val_mse: 111.9993\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 114.6272 - mse: 114.6272 - val_loss: 105.3522 - val_mse: 105.3522\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 110.8515 - mse: 110.8515 - val_loss: 100.9480 - val_mse: 100.9480\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 108.0811 - mse: 108.0811 - val_loss: 96.5449 - val_mse: 96.5449\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 100.7930 - mse: 100.7930 - val_loss: 92.3228 - val_mse: 92.3228\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 90.9048 - mse: 90.9048 - val_loss: 88.4315 - val_mse: 88.4315\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 91.0975 - mse: 91.0975 - val_loss: 83.5958 - val_mse: 83.5958\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 83.8757 - mse: 83.8757 - val_loss: 77.7186 - val_mse: 77.7186\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 78.8550 - mse: 78.8550 - val_loss: 71.6910 - val_mse: 71.6910\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 75.3660 - mse: 75.3660 - val_loss: 68.4678 - val_mse: 68.4678\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 73.4247 - mse: 73.4247 - val_loss: 64.3867 - val_mse: 64.3867\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 69.5575 - mse: 69.5575 - val_loss: 61.4281 - val_mse: 61.4281\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 64.7756 - mse: 64.7756 - val_loss: 58.1986 - val_mse: 58.1986\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 62.4267 - mse: 62.4267 - val_loss: 52.3312 - val_mse: 52.3312\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 59.9913 - mse: 59.9913 - val_loss: 47.5497 - val_mse: 47.5497\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 57.5003 - mse: 57.5003 - val_loss: 45.5914 - val_mse: 45.5914\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 53.3801 - mse: 53.3801 - val_loss: 44.0528 - val_mse: 44.0528\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 53.1784 - mse: 53.1784 - val_loss: 43.4208 - val_mse: 43.4208\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 47.7645 - mse: 47.7645 - val_loss: 43.1126 - val_mse: 43.1126\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 42.6411 - mse: 42.6411 - val_loss: 43.9738 - val_mse: 43.9738\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 45.2152 - mse: 45.2152 - val_loss: 41.4733 - val_mse: 41.4733\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 38.8911 - mse: 38.8911 - val_loss: 37.2966 - val_mse: 37.2966\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 44.6490 - mse: 44.6490 - val_loss: 33.4138 - val_mse: 33.4138\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 38.4658 - mse: 38.4658 - val_loss: 31.0572 - val_mse: 31.0572\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 37.0487 - mse: 37.0487 - val_loss: 29.0411 - val_mse: 29.0411\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 35.8968 - mse: 35.8968 - val_loss: 28.0389 - val_mse: 28.0389\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 32.4682 - mse: 32.4682 - val_loss: 27.4468 - val_mse: 27.4468\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 35.4717 - mse: 35.4717 - val_loss: 26.6468 - val_mse: 26.6468\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 33.4859 - mse: 33.4859 - val_loss: 24.3476 - val_mse: 24.3476\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 31.8544 - mse: 31.8544 - val_loss: 22.6910 - val_mse: 22.6910\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 28.3536 - mse: 28.3536 - val_loss: 20.7128 - val_mse: 20.7128\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 32.2586 - mse: 32.2586 - val_loss: 19.0588 - val_mse: 19.0588\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.4879 - mse: 30.4879 - val_loss: 17.6440 - val_mse: 17.6440\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 28.5075 - mse: 28.5075 - val_loss: 17.4452 - val_mse: 17.4452\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 27.6956 - mse: 27.6956 - val_loss: 17.5013 - val_mse: 17.5013\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 27.9163 - mse: 27.9163 - val_loss: 17.4470 - val_mse: 17.4470\n",
      "Epoch 110/150\n",
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      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 26.7089 - mse: 26.7089 - val_loss: 16.6676 - val_mse: 16.6676\n",
      "Epoch 112/150\n",
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      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.6064 - mse: 25.6064 - val_loss: 16.9370 - val_mse: 16.9370\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 25.5029 - mse: 25.5029 - val_loss: 15.3486 - val_mse: 15.3486\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.4204 - mse: 24.4204 - val_loss: 13.1707 - val_mse: 13.1707\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.4187 - mse: 24.4187 - val_loss: 12.1568 - val_mse: 12.1568\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 23.8411 - mse: 23.8411 - val_loss: 11.9220 - val_mse: 11.9220\n",
      "Epoch 118/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 18ms/step - loss: 24.8573 - mse: 24.8573 - val_loss: 12.1252 - val_mse: 12.1252\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.3883 - mse: 24.3883 - val_loss: 11.9743 - val_mse: 11.9743\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.2342 - mse: 22.2342 - val_loss: 11.6232 - val_mse: 11.6232\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 21.9037 - mse: 21.9037 - val_loss: 11.2353 - val_mse: 11.2353\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.5786 - mse: 22.5786 - val_loss: 10.9788 - val_mse: 10.9788\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.9925 - mse: 21.9925 - val_loss: 10.6251 - val_mse: 10.6251\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.6155 - mse: 22.6155 - val_loss: 9.9478 - val_mse: 9.9478\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 23.2442 - mse: 23.2442 - val_loss: 9.5312 - val_mse: 9.5312\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.7116 - mse: 22.7116 - val_loss: 9.2653 - val_mse: 9.2653\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.1794 - mse: 22.1794 - val_loss: 9.3456 - val_mse: 9.3456\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.5753 - mse: 21.5753 - val_loss: 9.3686 - val_mse: 9.3686\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.7399 - mse: 20.7399 - val_loss: 9.6303 - val_mse: 9.6303\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.1909 - mse: 22.1909 - val_loss: 9.5577 - val_mse: 9.5577\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 23.6552 - mse: 23.6552 - val_loss: 9.2336 - val_mse: 9.2336\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.2977 - mse: 21.2977 - val_loss: 8.8848 - val_mse: 8.8848\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.7591 - mse: 20.7591 - val_loss: 8.7285 - val_mse: 8.7285\n",
      "Epoch 134/150\n",
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      "Epoch 135/150\n",
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      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.8193 - mse: 19.8193 - val_loss: 8.3972 - val_mse: 8.3972\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.4981 - mse: 21.4981 - val_loss: 8.4333 - val_mse: 8.4333\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.9682 - mse: 21.9682 - val_loss: 8.6605 - val_mse: 8.6605\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.8498 - mse: 22.8498 - val_loss: 8.4652 - val_mse: 8.4652\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.1210 - mse: 19.1210 - val_loss: 8.0678 - val_mse: 8.0678\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 19.8329 - mse: 19.8329 - val_loss: 8.0466 - val_mse: 8.0466\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.0722 - mse: 22.0722 - val_loss: 8.0058 - val_mse: 8.0058\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.5186 - mse: 20.5186 - val_loss: 7.8097 - val_mse: 7.8097\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.2157 - mse: 22.2157 - val_loss: 7.5706 - val_mse: 7.5706\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 17.9187 - mse: 17.9187 - val_loss: 7.4575 - val_mse: 7.4575\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.5003 - mse: 19.5003 - val_loss: 7.3825 - val_mse: 7.3825\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.6195 - mse: 22.6195 - val_loss: 7.4049 - val_mse: 7.4049\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.5702 - mse: 20.5702 - val_loss: 7.3940 - val_mse: 7.3940\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 24.4063 - mse: 24.4063 - val_loss: 7.2862 - val_mse: 7.2862\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.9189 - mse: 21.9189 - val_loss: 7.4267 - val_mse: 7.4267\n",
      "1/1 [==============================] - 0s 138ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmp510x4id4\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 75ms/step - loss: 629.3076 - mse: 629.3076 - val_loss: 693.4177 - val_mse: 693.4177\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 602.7726 - mse: 602.7726 - val_loss: 686.6501 - val_mse: 686.6501\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 586.4641 - mse: 586.4641 - val_loss: 679.1865 - val_mse: 679.1865\n",
      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 559.0601 - mse: 559.0601 - val_loss: 648.1650 - val_mse: 648.1650\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 554.2444 - mse: 554.2444 - val_loss: 635.5826 - val_mse: 635.5826\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 549.0417 - mse: 549.0417 - val_loss: 622.5663 - val_mse: 622.5663\n",
      "Epoch 9/150\n",
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      "Epoch 10/150\n",
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      "Epoch 11/150\n",
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      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 532.1010 - mse: 532.1010 - val_loss: 572.0682 - val_mse: 572.0682\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 530.7654 - mse: 530.7654 - val_loss: 560.5121 - val_mse: 560.5121\n",
      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 16/150\n",
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      "Epoch 17/150\n",
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      "Epoch 18/150\n",
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      "Epoch 20/150\n",
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      "Epoch 22/150\n",
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      "Epoch 23/150\n",
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      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 494.2181 - mse: 494.2181 - val_loss: 464.5383 - val_mse: 464.5383\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 490.3493 - mse: 490.3493 - val_loss: 458.0586 - val_mse: 458.0586\n",
      "Epoch 26/150\n",
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      "Epoch 27/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 11ms/step - loss: 485.4889 - mse: 485.4889 - val_loss: 444.0487 - val_mse: 444.0487\n",
      "Epoch 28/150\n",
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      "Epoch 29/150\n",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
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      "Epoch 32/150\n",
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      "Epoch 33/150\n",
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      "Epoch 34/150\n",
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      "Epoch 38/150\n",
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      "Epoch 39/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
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      "Epoch 42/150\n",
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      "Epoch 43/150\n",
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      "Epoch 44/150\n",
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      "Epoch 47/150\n",
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      "Epoch 48/150\n",
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      "Epoch 49/150\n",
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      "Epoch 50/150\n",
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      "Epoch 51/150\n",
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      "Epoch 52/150\n",
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      "Epoch 53/150\n",
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      "Epoch 54/150\n",
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      "Epoch 55/150\n",
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      "Epoch 56/150\n",
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      "Epoch 57/150\n",
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      "Epoch 58/150\n",
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      "Epoch 59/150\n",
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      "Epoch 60/150\n",
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      "Epoch 61/150\n",
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      "Epoch 62/150\n",
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      "Epoch 63/150\n",
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      "Epoch 64/150\n",
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      "Epoch 65/150\n",
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      "Epoch 66/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 161.6002 - mse: 161.6002 - val_loss: 137.0905 - val_mse: 137.0905\n",
      "Epoch 70/150\n",
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      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 146.0956 - mse: 146.0956 - val_loss: 126.0442 - val_mse: 126.0442\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 145.0930 - mse: 145.0930 - val_loss: 121.5560 - val_mse: 121.5560\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 138.7628 - mse: 138.7628 - val_loss: 113.9157 - val_mse: 113.9157\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 131.4503 - mse: 131.4503 - val_loss: 107.4232 - val_mse: 107.4232\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 127.5367 - mse: 127.5367 - val_loss: 100.4896 - val_mse: 100.4896\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 117.8554 - mse: 117.8554 - val_loss: 93.9897 - val_mse: 93.9897\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 112.9398 - mse: 112.9398 - val_loss: 90.5893 - val_mse: 90.5893\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 110.5281 - mse: 110.5281 - val_loss: 85.4574 - val_mse: 85.4574\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 101.2152 - mse: 101.2152 - val_loss: 82.1146 - val_mse: 82.1146\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 95.7489 - mse: 95.7489 - val_loss: 78.1756 - val_mse: 78.1756\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 89.0302 - mse: 89.0302 - val_loss: 73.2737 - val_mse: 73.2737\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 89.6339 - mse: 89.6339 - val_loss: 68.6779 - val_mse: 68.6779\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 80.5828 - mse: 80.5828 - val_loss: 65.7296 - val_mse: 65.7296\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 76.5922 - mse: 76.5922 - val_loss: 63.5477 - val_mse: 63.5477\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 77.5268 - mse: 77.5268 - val_loss: 61.4507 - val_mse: 61.4507\n",
      "Epoch 86/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 67.6309 - mse: 67.6309 - val_loss: 57.9942 - val_mse: 57.9942\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 71.1108 - mse: 71.1108 - val_loss: 55.3667 - val_mse: 55.3667\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 68.4023 - mse: 68.4023 - val_loss: 52.1653 - val_mse: 52.1653\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 56.6023 - mse: 56.6023 - val_loss: 49.0117 - val_mse: 49.0117\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 59.5645 - mse: 59.5645 - val_loss: 43.1717 - val_mse: 43.1717\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 54.0215 - mse: 54.0215 - val_loss: 37.9222 - val_mse: 37.9222\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 52.8544 - mse: 52.8544 - val_loss: 34.5321 - val_mse: 34.5321\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 49.7884 - mse: 49.7884 - val_loss: 32.1307 - val_mse: 32.1307\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 44.2157 - mse: 44.2157 - val_loss: 31.0573 - val_mse: 31.0573\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 42.4235 - mse: 42.4235 - val_loss: 29.8425 - val_mse: 29.8425\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 41.4248 - mse: 41.4248 - val_loss: 27.8658 - val_mse: 27.8658\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 42.6247 - mse: 42.6247 - val_loss: 26.4436 - val_mse: 26.4436\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 36.8355 - mse: 36.8355 - val_loss: 24.3002 - val_mse: 24.3002\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 35.6066 - mse: 35.6066 - val_loss: 22.8286 - val_mse: 22.8286\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 35.3073 - mse: 35.3073 - val_loss: 22.2971 - val_mse: 22.2971\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 34.2053 - mse: 34.2053 - val_loss: 21.8602 - val_mse: 21.8602\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 34.5735 - mse: 34.5735 - val_loss: 20.7970 - val_mse: 20.7970\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 33.1535 - mse: 33.1535 - val_loss: 19.5381 - val_mse: 19.5381\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 31.7218 - mse: 31.7218 - val_loss: 18.4202 - val_mse: 18.4202\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 32.6564 - mse: 32.6564 - val_loss: 16.6098 - val_mse: 16.6098\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 29.2089 - mse: 29.2089 - val_loss: 15.2258 - val_mse: 15.2258\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 28.3633 - mse: 28.3633 - val_loss: 15.2590 - val_mse: 15.2590\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 28.3036 - mse: 28.3036 - val_loss: 15.1977 - val_mse: 15.1977\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 28.7176 - mse: 28.7176 - val_loss: 15.2806 - val_mse: 15.2806\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 26.9505 - mse: 26.9505 - val_loss: 14.6951 - val_mse: 14.6951\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 28.4292 - mse: 28.4292 - val_loss: 13.4072 - val_mse: 13.4072\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.1125 - mse: 24.1125 - val_loss: 12.4769 - val_mse: 12.4769\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 25.6830 - mse: 25.6830 - val_loss: 12.2095 - val_mse: 12.2095\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 27.1150 - mse: 27.1150 - val_loss: 12.1820 - val_mse: 12.1820\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.8265 - mse: 23.8265 - val_loss: 11.9063 - val_mse: 11.9063\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 25.2221 - mse: 25.2221 - val_loss: 11.7348 - val_mse: 11.7348\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.0993 - mse: 24.0993 - val_loss: 11.3511 - val_mse: 11.3511\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 26.0573 - mse: 26.0573 - val_loss: 10.8788 - val_mse: 10.8788\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.9313 - mse: 24.9313 - val_loss: 10.5931 - val_mse: 10.5931\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 24.8720 - mse: 24.8720 - val_loss: 10.3626 - val_mse: 10.3626\n",
      "Epoch 121/150\n",
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      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.7693 - mse: 21.7693 - val_loss: 10.1813 - val_mse: 10.1813\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.8591 - mse: 22.8591 - val_loss: 10.0512 - val_mse: 10.0512\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.4127 - mse: 22.4127 - val_loss: 9.8730 - val_mse: 9.8730\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 23.2132 - mse: 23.2132 - val_loss: 9.7259 - val_mse: 9.7259\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.5286 - mse: 23.5286 - val_loss: 9.2917 - val_mse: 9.2917\n",
      "Epoch 127/150\n",
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      "Epoch 128/150\n",
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      "Epoch 129/150\n",
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      "Epoch 130/150\n",
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      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 21.4170 - mse: 21.4170 - val_loss: 9.4845 - val_mse: 9.4845\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.2369 - mse: 20.2369 - val_loss: 9.3398 - val_mse: 9.3398\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.4209 - mse: 19.4209 - val_loss: 9.2139 - val_mse: 9.2139\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.2859 - mse: 23.2859 - val_loss: 8.9943 - val_mse: 8.9943\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.8590 - mse: 21.8590 - val_loss: 8.8183 - val_mse: 8.8183\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.6216 - mse: 22.6216 - val_loss: 8.7558 - val_mse: 8.7558\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.6767 - mse: 20.6767 - val_loss: 8.7301 - val_mse: 8.7301\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.9035 - mse: 20.9035 - val_loss: 8.5686 - val_mse: 8.5686\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.9234 - mse: 21.9234 - val_loss: 8.3769 - val_mse: 8.3769\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.3756 - mse: 21.3756 - val_loss: 8.2095 - val_mse: 8.2095\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.7775 - mse: 19.7775 - val_loss: 8.0794 - val_mse: 8.0794\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.9274 - mse: 21.9274 - val_loss: 8.2539 - val_mse: 8.2539\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.7371 - mse: 21.7371 - val_loss: 8.4093 - val_mse: 8.4093\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.8704 - mse: 21.8704 - val_loss: 8.1318 - val_mse: 8.1318\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.4458 - mse: 20.4458 - val_loss: 7.9539 - val_mse: 7.9539\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.6416 - mse: 21.6416 - val_loss: 7.8620 - val_mse: 7.8620\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.5769 - mse: 19.5769 - val_loss: 7.7262 - val_mse: 7.7262\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 17.6383 - mse: 17.6383 - val_loss: 7.4977 - val_mse: 7.4977\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.4724 - mse: 20.4724 - val_loss: 7.4908 - val_mse: 7.4908\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.2181 - mse: 20.2181 - val_loss: 7.6304 - val_mse: 7.6304\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpw9a99b84\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 74ms/step - loss: 609.0414 - mse: 609.0414 - val_loss: 693.4536 - val_mse: 693.4536\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 584.1165 - mse: 584.1165 - val_loss: 687.5020 - val_mse: 687.5020\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 573.3819 - mse: 573.3818 - val_loss: 680.4978 - val_mse: 680.4978\n",
      "Epoch 4/150\n",
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      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 552.4930 - mse: 552.4930 - val_loss: 661.2146 - val_mse: 661.2146\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 546.2021 - mse: 546.2021 - val_loss: 649.6302 - val_mse: 649.6302\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 539.9702 - mse: 539.9702 - val_loss: 637.1813 - val_mse: 637.1813\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 535.3441 - mse: 535.3441 - val_loss: 624.4701 - val_mse: 624.4701\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 528.7903 - mse: 528.7904 - val_loss: 611.5972 - val_mse: 611.5972\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 530.0864 - mse: 530.0864 - val_loss: 599.2305 - val_mse: 599.2305\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 526.9279 - mse: 526.9279 - val_loss: 587.1705 - val_mse: 587.1705\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 521.3723 - mse: 521.3723 - val_loss: 575.8980 - val_mse: 575.8980\n",
      "Epoch 13/150\n",
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      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 515.1310 - mse: 515.1310 - val_loss: 556.2866 - val_mse: 556.2866\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 518.2778 - mse: 518.2778 - val_loss: 546.4614 - val_mse: 546.4614\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 510.4451 - mse: 510.4451 - val_loss: 537.1055 - val_mse: 537.1055\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 506.2784 - mse: 506.2784 - val_loss: 527.6868 - val_mse: 527.6868\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 502.0193 - mse: 502.0193 - val_loss: 519.2651 - val_mse: 519.2651\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 499.6329 - mse: 499.6329 - val_loss: 511.7227 - val_mse: 511.7227\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 495.7545 - mse: 495.7545 - val_loss: 504.6600 - val_mse: 504.6600\n",
      "Epoch 21/150\n",
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      "Epoch 22/150\n",
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      "Epoch 23/150\n",
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      "Epoch 24/150\n",
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      "Epoch 25/150\n",
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      "Epoch 26/150\n",
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      "Epoch 27/150\n",
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      "Epoch 28/150\n",
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      "Epoch 29/150\n",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 447.5164 - mse: 447.5164 - val_loss: 424.5107 - val_mse: 424.5107\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 447.5885 - mse: 447.5885 - val_loss: 417.7012 - val_mse: 417.7012\n",
      "Epoch 33/150\n",
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      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 437.3325 - mse: 437.3325 - val_loss: 405.6398 - val_mse: 405.6398\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 433.3424 - mse: 433.3424 - val_loss: 397.9148 - val_mse: 397.9148\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 425.4407 - mse: 425.4407 - val_loss: 389.5293 - val_mse: 389.5293\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 423.6893 - mse: 423.6893 - val_loss: 382.0290 - val_mse: 382.0290\n",
      "Epoch 38/150\n",
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      "Epoch 39/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 385.5795 - mse: 385.5795 - val_loss: 352.5284 - val_mse: 352.5284\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 379.6855 - mse: 379.6855 - val_loss: 345.5516 - val_mse: 345.5516\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 370.6324 - mse: 370.6324 - val_loss: 338.2379 - val_mse: 338.2379\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 364.7752 - mse: 364.7752 - val_loss: 330.7309 - val_mse: 330.7309\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 357.4244 - mse: 357.4244 - val_loss: 321.4703 - val_mse: 321.4703\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 351.3856 - mse: 351.3856 - val_loss: 313.4574 - val_mse: 313.4574\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 337.1549 - mse: 337.1549 - val_loss: 305.2635 - val_mse: 305.2635\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 327.0624 - mse: 327.0624 - val_loss: 295.8432 - val_mse: 295.8432\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 323.3300 - mse: 323.3300 - val_loss: 285.8496 - val_mse: 285.8496\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 313.2244 - mse: 313.2244 - val_loss: 276.2318 - val_mse: 276.2318\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 310.8954 - mse: 310.8954 - val_loss: 267.1182 - val_mse: 267.1182\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 295.9683 - mse: 295.9683 - val_loss: 260.3299 - val_mse: 260.3299\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 286.4282 - mse: 286.4282 - val_loss: 253.2952 - val_mse: 253.2952\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 279.2909 - mse: 279.2909 - val_loss: 245.4586 - val_mse: 245.4586\n",
      "Epoch 55/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 16ms/step - loss: 267.4891 - mse: 267.4891 - val_loss: 236.2043 - val_mse: 236.2043\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 263.5621 - mse: 263.5621 - val_loss: 228.6017 - val_mse: 228.6017\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 256.4250 - mse: 256.4250 - val_loss: 220.1445 - val_mse: 220.1445\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 247.9207 - mse: 247.9207 - val_loss: 209.2008 - val_mse: 209.2008\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 242.2670 - mse: 242.2670 - val_loss: 201.1747 - val_mse: 201.1747\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 230.5056 - mse: 230.5056 - val_loss: 194.7149 - val_mse: 194.7149\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 219.6988 - mse: 219.6988 - val_loss: 188.3090 - val_mse: 188.3090\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 211.4891 - mse: 211.4891 - val_loss: 181.9565 - val_mse: 181.9565\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 205.1295 - mse: 205.1295 - val_loss: 172.4177 - val_mse: 172.4177\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 195.5101 - mse: 195.5101 - val_loss: 161.6384 - val_mse: 161.6384\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 190.0269 - mse: 190.0269 - val_loss: 151.9711 - val_mse: 151.9711\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 179.8310 - mse: 179.8310 - val_loss: 144.6151 - val_mse: 144.6151\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 172.5041 - mse: 172.5041 - val_loss: 139.7004 - val_mse: 139.7004\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 166.9746 - mse: 166.9746 - val_loss: 134.7769 - val_mse: 134.7769\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 156.6217 - mse: 156.6217 - val_loss: 129.5592 - val_mse: 129.5592\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 150.9154 - mse: 150.9154 - val_loss: 122.3656 - val_mse: 122.3656\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 140.4096 - mse: 140.4096 - val_loss: 117.6556 - val_mse: 117.6556\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 134.7641 - mse: 134.7641 - val_loss: 114.6907 - val_mse: 114.6907\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 126.4687 - mse: 126.4687 - val_loss: 111.0387 - val_mse: 111.0387\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 128.4668 - mse: 128.4668 - val_loss: 106.0042 - val_mse: 106.0042\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 120.4293 - mse: 120.4293 - val_loss: 100.2752 - val_mse: 100.2752\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 114.2715 - mse: 114.2715 - val_loss: 95.8891 - val_mse: 95.8891\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 111.9827 - mse: 111.9827 - val_loss: 92.0535 - val_mse: 92.0535\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 107.5695 - mse: 107.5695 - val_loss: 85.9092 - val_mse: 85.9092\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 100.4971 - mse: 100.4971 - val_loss: 83.0394 - val_mse: 83.0394\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 93.6596 - mse: 93.6596 - val_loss: 79.4969 - val_mse: 79.4969\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 85.9283 - mse: 85.9283 - val_loss: 76.0869 - val_mse: 76.0869\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 85.9334 - mse: 85.9334 - val_loss: 72.4398 - val_mse: 72.4398\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 80.2676 - mse: 80.2676 - val_loss: 69.9747 - val_mse: 69.9747\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 73.3787 - mse: 73.3787 - val_loss: 66.3383 - val_mse: 66.3383\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 71.5298 - mse: 71.5298 - val_loss: 64.2875 - val_mse: 64.2875\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 67.4266 - mse: 67.4266 - val_loss: 59.2612 - val_mse: 59.2612\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 63.5782 - mse: 63.5782 - val_loss: 54.4202 - val_mse: 54.4202\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 63.0322 - mse: 63.0322 - val_loss: 50.1352 - val_mse: 50.1352\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 56.6193 - mse: 56.6193 - val_loss: 45.3790 - val_mse: 45.3790\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 56.7844 - mse: 56.7844 - val_loss: 42.0451 - val_mse: 42.0451\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 53.4082 - mse: 53.4082 - val_loss: 41.9373 - val_mse: 41.9373\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 49.2508 - mse: 49.2508 - val_loss: 42.5525 - val_mse: 42.5525\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 46.2926 - mse: 46.2926 - val_loss: 39.6085 - val_mse: 39.6085\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 48.9426 - mse: 48.9426 - val_loss: 35.6280 - val_mse: 35.6280\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 41.3077 - mse: 41.3077 - val_loss: 33.2982 - val_mse: 33.2982\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 39.0607 - mse: 39.0607 - val_loss: 32.5117 - val_mse: 32.5117\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 39.2555 - mse: 39.2555 - val_loss: 31.5009 - val_mse: 31.5009\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 36.8088 - mse: 36.8088 - val_loss: 30.1918 - val_mse: 30.1918\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 37.9469 - mse: 37.9469 - val_loss: 28.5055 - val_mse: 28.5055\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 36.5332 - mse: 36.5332 - val_loss: 26.6109 - val_mse: 26.6109\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 34.8897 - mse: 34.8897 - val_loss: 25.1128 - val_mse: 25.1128\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 37.1708 - mse: 37.1708 - val_loss: 22.2561 - val_mse: 22.2561\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 30.1086 - mse: 30.1086 - val_loss: 20.3101 - val_mse: 20.3101\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 29.5411 - mse: 29.5411 - val_loss: 19.0918 - val_mse: 19.0918\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 32.2071 - mse: 32.2071 - val_loss: 18.5342 - val_mse: 18.5342\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.7383 - mse: 30.7383 - val_loss: 18.4967 - val_mse: 18.4967\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 30.3575 - mse: 30.3575 - val_loss: 17.5843 - val_mse: 17.5843\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 28.7463 - mse: 28.7463 - val_loss: 15.8857 - val_mse: 15.8857\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.7823 - mse: 25.7823 - val_loss: 14.8661 - val_mse: 14.8661\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 26.6637 - mse: 26.6637 - val_loss: 14.3000 - val_mse: 14.3000\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 26.3397 - mse: 26.3397 - val_loss: 14.7793 - val_mse: 14.7793\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 26.0372 - mse: 26.0372 - val_loss: 14.6542 - val_mse: 14.6542\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 25.0320 - mse: 25.0320 - val_loss: 13.9259 - val_mse: 13.9259\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 27.9563 - mse: 27.9563 - val_loss: 13.6324 - val_mse: 13.6324\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.3006 - mse: 25.3006 - val_loss: 13.0499 - val_mse: 13.0499\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 23.8541 - mse: 23.8541 - val_loss: 12.4706 - val_mse: 12.4706\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.1921 - mse: 21.1921 - val_loss: 12.1856 - val_mse: 12.1856\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 24.0970 - mse: 24.0970 - val_loss: 11.5485 - val_mse: 11.5485\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.9742 - mse: 22.9742 - val_loss: 11.3158 - val_mse: 11.3158\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 25.2528 - mse: 25.2528 - val_loss: 10.7113 - val_mse: 10.7113\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.9778 - mse: 25.9778 - val_loss: 10.3325 - val_mse: 10.3325\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 22.8694 - mse: 22.8694 - val_loss: 9.8500 - val_mse: 9.8500\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.5231 - mse: 21.5231 - val_loss: 9.7595 - val_mse: 9.7595\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 25.0511 - mse: 25.0511 - val_loss: 9.8770 - val_mse: 9.8770\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.6174 - mse: 21.6174 - val_loss: 9.8683 - val_mse: 9.8683\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 24.6572 - mse: 24.6572 - val_loss: 9.6659 - val_mse: 9.6659\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.9581 - mse: 21.9581 - val_loss: 9.4471 - val_mse: 9.4471\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.8352 - mse: 21.8352 - val_loss: 9.0811 - val_mse: 9.0811\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.9625 - mse: 21.9625 - val_loss: 9.1609 - val_mse: 9.1609\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.3866 - mse: 20.3866 - val_loss: 8.9812 - val_mse: 8.9812\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.2431 - mse: 21.2431 - val_loss: 8.8155 - val_mse: 8.8155\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.8183 - mse: 21.8183 - val_loss: 8.8732 - val_mse: 8.8732\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 20.3276 - mse: 20.3276 - val_loss: 8.8845 - val_mse: 8.8845\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 21.1341 - mse: 21.1341 - val_loss: 8.6951 - val_mse: 8.6951\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.2055 - mse: 21.2055 - val_loss: 8.7475 - val_mse: 8.7475\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.9952 - mse: 21.9952 - val_loss: 8.3189 - val_mse: 8.3189\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.9143 - mse: 22.9143 - val_loss: 7.8997 - val_mse: 7.8997\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.1094 - mse: 21.1094 - val_loss: 7.6874 - val_mse: 7.6874\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.0785 - mse: 19.0785 - val_loss: 7.6268 - val_mse: 7.6268\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.0699 - mse: 21.0699 - val_loss: 7.8008 - val_mse: 7.8008\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.8373 - mse: 22.8373 - val_loss: 7.8449 - val_mse: 7.8449\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.4269 - mse: 19.4269 - val_loss: 7.8943 - val_mse: 7.8943\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.7005 - mse: 20.7005 - val_loss: 7.7028 - val_mse: 7.7028\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 18.9262 - mse: 18.9262 - val_loss: 7.4470 - val_mse: 7.4470\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.8358 - mse: 19.8358 - val_loss: 7.2895 - val_mse: 7.2895\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 18.9723 - mse: 18.9723 - val_loss: 7.2389 - val_mse: 7.2389\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 18.3508 - mse: 18.3508 - val_loss: 7.1929 - val_mse: 7.1929\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.7293 - mse: 20.7293 - val_loss: 7.0517 - val_mse: 7.0517\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.4403 - mse: 20.4403 - val_loss: 6.9201 - val_mse: 6.9201\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 22.7615 - mse: 22.7615 - val_loss: 7.0036 - val_mse: 7.0036\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpwwvn4iq9\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 72ms/step - loss: 619.9977 - mse: 619.9977 - val_loss: 693.9274 - val_mse: 693.9274\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 592.3241 - mse: 592.3241 - val_loss: 687.8721 - val_mse: 687.8721\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 577.5712 - mse: 577.5712 - val_loss: 680.6519 - val_mse: 680.6519\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 568.9721 - mse: 568.9721 - val_loss: 671.9899 - val_mse: 671.9899\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 565.1948 - mse: 565.1948 - val_loss: 661.5517 - val_mse: 661.5517\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 555.1207 - mse: 555.1207 - val_loss: 649.9610 - val_mse: 649.9610\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 544.3745 - mse: 544.3745 - val_loss: 637.7520 - val_mse: 637.7520\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 542.4281 - mse: 542.4281 - val_loss: 625.5068 - val_mse: 625.5068\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 538.2662 - mse: 538.2662 - val_loss: 613.2521 - val_mse: 613.2521\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 533.3378 - mse: 533.3378 - val_loss: 601.2211 - val_mse: 601.2211\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 530.8842 - mse: 530.8842 - val_loss: 589.8719 - val_mse: 589.8719\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 526.7352 - mse: 526.7352 - val_loss: 579.0067 - val_mse: 579.0067\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 523.2199 - mse: 523.2199 - val_loss: 568.4114 - val_mse: 568.4114\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 520.8688 - mse: 520.8688 - val_loss: 558.0119 - val_mse: 558.0119\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 519.4883 - mse: 519.4883 - val_loss: 547.9823 - val_mse: 547.9823\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 515.6761 - mse: 515.6761 - val_loss: 538.7839 - val_mse: 538.7839\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 509.1891 - mse: 509.1891 - val_loss: 530.6520 - val_mse: 530.6520\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 510.3528 - mse: 510.3528 - val_loss: 521.6175 - val_mse: 521.6175\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 503.6694 - mse: 503.6694 - val_loss: 512.4366 - val_mse: 512.4366\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 500.1332 - mse: 500.1332 - val_loss: 504.9300 - val_mse: 504.9300\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 499.1794 - mse: 499.1794 - val_loss: 498.2810 - val_mse: 498.2810\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 495.4149 - mse: 495.4149 - val_loss: 491.7031 - val_mse: 491.7031\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 488.1154 - mse: 488.1154 - val_loss: 484.9806 - val_mse: 484.9806\n",
      "Epoch 24/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 16ms/step - loss: 488.3729 - mse: 488.3729 - val_loss: 478.8286 - val_mse: 478.8286\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 482.8683 - mse: 482.8683 - val_loss: 473.4290 - val_mse: 473.4290\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 478.0202 - mse: 478.0202 - val_loss: 467.7828 - val_mse: 467.7828\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 471.0885 - mse: 471.0885 - val_loss: 461.6943 - val_mse: 461.6943\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 472.8013 - mse: 472.8013 - val_loss: 453.5567 - val_mse: 453.5567\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 464.7184 - mse: 464.7184 - val_loss: 445.6406 - val_mse: 445.6406\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 458.8749 - mse: 458.8749 - val_loss: 438.3525 - val_mse: 438.3525\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 461.1879 - mse: 461.1879 - val_loss: 430.6858 - val_mse: 430.6858\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 448.3608 - mse: 448.3608 - val_loss: 423.2251 - val_mse: 423.2251\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 450.3917 - mse: 450.3917 - val_loss: 415.3591 - val_mse: 415.3591\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 437.6041 - mse: 437.6041 - val_loss: 408.0817 - val_mse: 408.0817\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 439.0198 - mse: 439.0198 - val_loss: 401.6513 - val_mse: 401.6513\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 426.7445 - mse: 426.7445 - val_loss: 395.4355 - val_mse: 395.4355\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 424.3045 - mse: 424.3045 - val_loss: 390.1125 - val_mse: 390.1125\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 418.5978 - mse: 418.5978 - val_loss: 383.8616 - val_mse: 383.8616\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 407.7941 - mse: 407.7941 - val_loss: 377.9813 - val_mse: 377.9813\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 404.5872 - mse: 404.5872 - val_loss: 372.7915 - val_mse: 372.7915\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 394.1042 - mse: 394.1042 - val_loss: 367.5291 - val_mse: 367.5291\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 389.7517 - mse: 389.7517 - val_loss: 360.7831 - val_mse: 360.7831\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 379.5192 - mse: 379.5192 - val_loss: 352.0573 - val_mse: 352.0573\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 372.6153 - mse: 372.6153 - val_loss: 344.0291 - val_mse: 344.0291\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 364.8662 - mse: 364.8662 - val_loss: 335.6964 - val_mse: 335.6964\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 351.1546 - mse: 351.1546 - val_loss: 327.3714 - val_mse: 327.3714\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 345.4275 - mse: 345.4275 - val_loss: 321.0288 - val_mse: 321.0288\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 335.4473 - mse: 335.4473 - val_loss: 314.7283 - val_mse: 314.7283\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 329.8419 - mse: 329.8419 - val_loss: 308.7500 - val_mse: 308.7500\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 321.2559 - mse: 321.2559 - val_loss: 304.6314 - val_mse: 304.6314\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 315.5705 - mse: 315.5705 - val_loss: 301.4296 - val_mse: 301.4296\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 308.4597 - mse: 308.4597 - val_loss: 295.9343 - val_mse: 295.9343\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 297.8614 - mse: 297.8614 - val_loss: 288.5606 - val_mse: 288.5606\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 288.1435 - mse: 288.1435 - val_loss: 281.2801 - val_mse: 281.2801\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 283.2874 - mse: 283.2874 - val_loss: 272.3418 - val_mse: 272.3418\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 271.7379 - mse: 271.7379 - val_loss: 262.5590 - val_mse: 262.5590\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 261.3550 - mse: 261.3550 - val_loss: 254.9334 - val_mse: 254.9334\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 253.7709 - mse: 253.7709 - val_loss: 246.0664 - val_mse: 246.0664\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 248.0941 - mse: 248.0941 - val_loss: 237.0529 - val_mse: 237.0529\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 230.4745 - mse: 230.4745 - val_loss: 229.2131 - val_mse: 229.2131\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 226.8908 - mse: 226.8908 - val_loss: 221.1900 - val_mse: 221.1900\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 215.4174 - mse: 215.4174 - val_loss: 212.4015 - val_mse: 212.4015\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 211.9045 - mse: 211.9045 - val_loss: 204.3965 - val_mse: 204.3965\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 198.4845 - mse: 198.4845 - val_loss: 197.8559 - val_mse: 197.8559\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 195.7816 - mse: 195.7816 - val_loss: 191.8848 - val_mse: 191.8848\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 186.6228 - mse: 186.6228 - val_loss: 185.3542 - val_mse: 185.3542\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 173.2985 - mse: 173.2985 - val_loss: 179.2828 - val_mse: 179.2828\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 169.5191 - mse: 169.5191 - val_loss: 169.7965 - val_mse: 169.7965\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 165.3745 - mse: 165.3745 - val_loss: 161.0404 - val_mse: 161.0404\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 156.7654 - mse: 156.7654 - val_loss: 151.9439 - val_mse: 151.9439\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 152.9981 - mse: 152.9981 - val_loss: 144.2791 - val_mse: 144.2791\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 144.9086 - mse: 144.9086 - val_loss: 138.5629 - val_mse: 138.5629\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 136.7357 - mse: 136.7357 - val_loss: 132.3572 - val_mse: 132.3572\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 128.1673 - mse: 128.1673 - val_loss: 125.8215 - val_mse: 125.8215\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 124.1744 - mse: 124.1744 - val_loss: 118.6079 - val_mse: 118.6079\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 120.2881 - mse: 120.2881 - val_loss: 110.2104 - val_mse: 110.2104\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 109.9300 - mse: 109.9300 - val_loss: 101.6813 - val_mse: 101.6813\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 108.0456 - mse: 108.0456 - val_loss: 95.3024 - val_mse: 95.3024\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 94.4233 - mse: 94.4233 - val_loss: 89.4653 - val_mse: 89.4653\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 98.0774 - mse: 98.0774 - val_loss: 84.1179 - val_mse: 84.1179\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 89.8896 - mse: 89.8896 - val_loss: 78.2962 - val_mse: 78.2962\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 87.9139 - mse: 87.9139 - val_loss: 74.5997 - val_mse: 74.5997\n",
      "Epoch 83/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 15ms/step - loss: 82.3004 - mse: 82.3004 - val_loss: 70.8974 - val_mse: 70.8974\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 78.2432 - mse: 78.2432 - val_loss: 65.5524 - val_mse: 65.5524\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 77.8558 - mse: 77.8558 - val_loss: 61.8813 - val_mse: 61.8813\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 68.8806 - mse: 68.8806 - val_loss: 58.9308 - val_mse: 58.9308\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 64.1239 - mse: 64.1239 - val_loss: 55.4101 - val_mse: 55.4101\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 65.4189 - mse: 65.4189 - val_loss: 52.4642 - val_mse: 52.4642\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 58.1098 - mse: 58.1098 - val_loss: 49.3505 - val_mse: 49.3505\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 58.5414 - mse: 58.5414 - val_loss: 45.5233 - val_mse: 45.5233\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 59.7893 - mse: 59.7893 - val_loss: 39.3692 - val_mse: 39.3692\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 53.3996 - mse: 53.3996 - val_loss: 35.2522 - val_mse: 35.2522\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 50.6442 - mse: 50.6442 - val_loss: 33.6841 - val_mse: 33.6841\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 50.3253 - mse: 50.3253 - val_loss: 33.0900 - val_mse: 33.0900\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 45.8228 - mse: 45.8228 - val_loss: 31.9720 - val_mse: 31.9720\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 44.5586 - mse: 44.5586 - val_loss: 29.0921 - val_mse: 29.0921\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 40.5205 - mse: 40.5205 - val_loss: 27.2697 - val_mse: 27.2697\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 39.9853 - mse: 39.9853 - val_loss: 26.2331 - val_mse: 26.2331\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 38.4774 - mse: 38.4774 - val_loss: 25.7195 - val_mse: 25.7195\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 37.1729 - mse: 37.1729 - val_loss: 24.4239 - val_mse: 24.4239\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 35.7676 - mse: 35.7676 - val_loss: 23.6477 - val_mse: 23.6477\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 33.3118 - mse: 33.3118 - val_loss: 22.2061 - val_mse: 22.2061\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.0864 - mse: 30.0864 - val_loss: 20.5831 - val_mse: 20.5831\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 28.5150 - mse: 28.5150 - val_loss: 19.0812 - val_mse: 19.0812\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 30.6003 - mse: 30.6003 - val_loss: 18.0502 - val_mse: 18.0502\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 32.0654 - mse: 32.0654 - val_loss: 16.9575 - val_mse: 16.9575\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.1189 - mse: 30.1189 - val_loss: 16.1505 - val_mse: 16.1505\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 29.4156 - mse: 29.4156 - val_loss: 15.6654 - val_mse: 15.6654\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.2192 - mse: 30.2192 - val_loss: 14.7517 - val_mse: 14.7517\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 28.8307 - mse: 28.8307 - val_loss: 13.7350 - val_mse: 13.7350\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 26.4646 - mse: 26.4646 - val_loss: 13.5238 - val_mse: 13.5238\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.3877 - mse: 22.3877 - val_loss: 13.4724 - val_mse: 13.4724\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 26.3053 - mse: 26.3053 - val_loss: 13.2640 - val_mse: 13.2640\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 23.1137 - mse: 23.1137 - val_loss: 12.7128 - val_mse: 12.7128\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 28.9773 - mse: 28.9773 - val_loss: 11.9814 - val_mse: 11.9814\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 26.0131 - mse: 26.0131 - val_loss: 11.4606 - val_mse: 11.4606\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 23.4969 - mse: 23.4969 - val_loss: 11.1315 - val_mse: 11.1315\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.4903 - mse: 22.4903 - val_loss: 10.9482 - val_mse: 10.9482\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.4018 - mse: 24.4018 - val_loss: 10.8321 - val_mse: 10.8321\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 23.8838 - mse: 23.8838 - val_loss: 10.4262 - val_mse: 10.4262\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.9980 - mse: 19.9980 - val_loss: 10.0897 - val_mse: 10.0897\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.3210 - mse: 21.3210 - val_loss: 9.6652 - val_mse: 9.6652\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.0196 - mse: 23.0196 - val_loss: 9.3372 - val_mse: 9.3372\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.2768 - mse: 24.2768 - val_loss: 9.1797 - val_mse: 9.1797\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 25.1509 - mse: 25.1509 - val_loss: 9.3734 - val_mse: 9.3734\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.1808 - mse: 22.1808 - val_loss: 9.5132 - val_mse: 9.5132\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.3256 - mse: 23.3256 - val_loss: 9.3446 - val_mse: 9.3446\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.5954 - mse: 21.5954 - val_loss: 9.2548 - val_mse: 9.2548\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.8235 - mse: 25.8235 - val_loss: 8.9226 - val_mse: 8.9226\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.0419 - mse: 24.0419 - val_loss: 8.7301 - val_mse: 8.7301\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.1393 - mse: 23.1393 - val_loss: 8.7282 - val_mse: 8.7282\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.1281 - mse: 20.1281 - val_loss: 8.6200 - val_mse: 8.6200\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.3947 - mse: 22.3947 - val_loss: 8.5585 - val_mse: 8.5585\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.6320 - mse: 22.6320 - val_loss: 8.4795 - val_mse: 8.4795\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.3414 - mse: 21.3414 - val_loss: 8.4959 - val_mse: 8.4959\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.0842 - mse: 22.0842 - val_loss: 8.3509 - val_mse: 8.3509\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.4084 - mse: 21.4084 - val_loss: 8.0686 - val_mse: 8.0686\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.9563 - mse: 22.9563 - val_loss: 7.9766 - val_mse: 7.9766\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 22.7683 - mse: 22.7683 - val_loss: 8.0425 - val_mse: 8.0425\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.7940 - mse: 22.7940 - val_loss: 7.9262 - val_mse: 7.9262\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.6411 - mse: 21.6411 - val_loss: 7.8759 - val_mse: 7.8759\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.2966 - mse: 21.2966 - val_loss: 7.8108 - val_mse: 7.8108\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.6852 - mse: 21.6852 - val_loss: 7.7513 - val_mse: 7.7513\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.6593 - mse: 22.6593 - val_loss: 7.7364 - val_mse: 7.7364\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 20.0100 - mse: 20.0100 - val_loss: 7.5865 - val_mse: 7.5865\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.1690 - mse: 20.1690 - val_loss: 7.4655 - val_mse: 7.4655\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 19.8490 - mse: 19.8490 - val_loss: 7.3863 - val_mse: 7.3863\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.8611 - mse: 19.8611 - val_loss: 7.2702 - val_mse: 7.2702\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.2881 - mse: 21.2881 - val_loss: 7.0812 - val_mse: 7.0812\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 23.6502 - mse: 23.6502 - val_loss: 6.7898 - val_mse: 6.7898\n",
      "1/1 [==============================] - 0s 87ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpw9s7pdp4\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 74ms/step - loss: 630.7133 - mse: 630.7133 - val_loss: 693.7271 - val_mse: 693.7271\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 600.9106 - mse: 600.9106 - val_loss: 687.0672 - val_mse: 687.0672\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 586.2820 - mse: 586.2820 - val_loss: 679.0951 - val_mse: 679.0951\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 575.4022 - mse: 575.4022 - val_loss: 669.9610 - val_mse: 669.9610\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 564.3676 - mse: 564.3676 - val_loss: 659.1442 - val_mse: 659.1442\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 556.1967 - mse: 556.1967 - val_loss: 647.2442 - val_mse: 647.2442\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 552.9979 - mse: 552.9979 - val_loss: 634.3442 - val_mse: 634.3442\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 549.4597 - mse: 549.4597 - val_loss: 621.1542 - val_mse: 621.1542\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 548.8595 - mse: 548.8595 - val_loss: 608.1281 - val_mse: 608.1281\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 538.8081 - mse: 538.8081 - val_loss: 595.3580 - val_mse: 595.3580\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 537.3585 - mse: 537.3585 - val_loss: 583.2947 - val_mse: 583.2947\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 533.8961 - mse: 533.8961 - val_loss: 572.2154 - val_mse: 572.2154\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 533.3789 - mse: 533.3789 - val_loss: 561.0158 - val_mse: 561.0158\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 525.0478 - mse: 525.0478 - val_loss: 550.5938 - val_mse: 550.5938\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 528.6859 - mse: 528.6859 - val_loss: 540.5283 - val_mse: 540.5283\n",
      "Epoch 16/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 522.2812 - mse: 522.2812 - val_loss: 531.7296 - val_mse: 531.7296\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 518.9844 - mse: 518.9844 - val_loss: 523.3298 - val_mse: 523.3298\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 514.6180 - mse: 514.6180 - val_loss: 514.8047 - val_mse: 514.8047\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 514.5853 - mse: 514.5853 - val_loss: 506.8325 - val_mse: 506.8325\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 510.8138 - mse: 510.8138 - val_loss: 499.5822 - val_mse: 499.5822\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 504.2159 - mse: 504.2159 - val_loss: 491.3188 - val_mse: 491.3188\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 506.1797 - mse: 506.1797 - val_loss: 484.0143 - val_mse: 484.0143\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 499.0175 - mse: 499.0175 - val_loss: 475.7704 - val_mse: 475.7704\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 495.6657 - mse: 495.6657 - val_loss: 468.3551 - val_mse: 468.3551\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 495.2115 - mse: 495.2115 - val_loss: 461.7411 - val_mse: 461.7411\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 482.8271 - mse: 482.8271 - val_loss: 454.7856 - val_mse: 454.7856\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 478.3385 - mse: 478.3385 - val_loss: 448.5638 - val_mse: 448.5638\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 478.0328 - mse: 478.0328 - val_loss: 443.0915 - val_mse: 443.0915\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 472.9587 - mse: 472.9587 - val_loss: 438.4308 - val_mse: 438.4308\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 468.5676 - mse: 468.5676 - val_loss: 432.8375 - val_mse: 432.8375\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 459.6436 - mse: 459.6436 - val_loss: 426.8046 - val_mse: 426.8046\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 456.6069 - mse: 456.6069 - val_loss: 419.0706 - val_mse: 419.0706\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 455.2877 - mse: 455.2877 - val_loss: 411.2423 - val_mse: 411.2423\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 447.5965 - mse: 447.5965 - val_loss: 403.9005 - val_mse: 403.9005\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 435.8358 - mse: 435.8358 - val_loss: 396.3649 - val_mse: 396.3649\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 436.8818 - mse: 436.8818 - val_loss: 389.9278 - val_mse: 389.9278\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 424.2850 - mse: 424.2850 - val_loss: 383.2540 - val_mse: 383.2540\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 424.5954 - mse: 424.5954 - val_loss: 377.7031 - val_mse: 377.7031\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 413.4258 - mse: 413.4258 - val_loss: 372.9899 - val_mse: 372.9899\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 408.6645 - mse: 408.6645 - val_loss: 366.6941 - val_mse: 366.6941\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 403.9460 - mse: 403.9460 - val_loss: 359.9595 - val_mse: 359.9595\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 396.9582 - mse: 396.9582 - val_loss: 353.1244 - val_mse: 353.1244\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 388.7367 - mse: 388.7367 - val_loss: 347.0684 - val_mse: 347.0684\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 381.9458 - mse: 381.9458 - val_loss: 339.0947 - val_mse: 339.0947\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 373.9418 - mse: 373.9418 - val_loss: 331.3576 - val_mse: 331.3576\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 370.1892 - mse: 370.1892 - val_loss: 324.4851 - val_mse: 324.4851\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 353.0667 - mse: 353.0667 - val_loss: 318.1745 - val_mse: 318.1745\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 350.5754 - mse: 350.5754 - val_loss: 312.4951 - val_mse: 312.4951\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 336.1469 - mse: 336.1469 - val_loss: 305.6969 - val_mse: 305.6969\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 331.7983 - mse: 331.7983 - val_loss: 297.9676 - val_mse: 297.9676\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 320.9956 - mse: 320.9956 - val_loss: 288.8221 - val_mse: 288.8221\n",
      "Epoch 52/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 15ms/step - loss: 311.5542 - mse: 311.5542 - val_loss: 278.0880 - val_mse: 278.0880\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 301.9315 - mse: 301.9315 - val_loss: 266.5375 - val_mse: 266.5375\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 295.4984 - mse: 295.4984 - val_loss: 257.4763 - val_mse: 257.4763\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 283.1688 - mse: 283.1688 - val_loss: 250.7417 - val_mse: 250.7417\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 274.8406 - mse: 274.8406 - val_loss: 244.0003 - val_mse: 244.0003\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 270.8441 - mse: 270.8441 - val_loss: 237.6367 - val_mse: 237.6367\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 255.9369 - mse: 255.9369 - val_loss: 229.4095 - val_mse: 229.4095\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 250.3590 - mse: 250.3590 - val_loss: 221.4491 - val_mse: 221.4491\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 245.3381 - mse: 245.3381 - val_loss: 213.7774 - val_mse: 213.7774\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 230.7968 - mse: 230.7968 - val_loss: 205.5271 - val_mse: 205.5271\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 225.7555 - mse: 225.7555 - val_loss: 197.2293 - val_mse: 197.2293\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 217.9601 - mse: 217.9601 - val_loss: 189.7201 - val_mse: 189.7201\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 201.9137 - mse: 201.9137 - val_loss: 182.5476 - val_mse: 182.5476\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 202.4479 - mse: 202.4479 - val_loss: 176.0835 - val_mse: 176.0835\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 193.6511 - mse: 193.6511 - val_loss: 171.3067 - val_mse: 171.3067\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 185.5025 - mse: 185.5025 - val_loss: 168.6989 - val_mse: 168.6989\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 175.8555 - mse: 175.8555 - val_loss: 164.4777 - val_mse: 164.4777\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 167.1906 - mse: 167.1906 - val_loss: 158.7578 - val_mse: 158.7578\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 159.0257 - mse: 159.0257 - val_loss: 153.1439 - val_mse: 153.1439\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 148.4805 - mse: 148.4805 - val_loss: 147.4899 - val_mse: 147.4899\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 144.2647 - mse: 144.2647 - val_loss: 140.5486 - val_mse: 140.5486\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 145.7986 - mse: 145.7986 - val_loss: 132.5992 - val_mse: 132.5992\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 132.2553 - mse: 132.2553 - val_loss: 125.0635 - val_mse: 125.0635\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 124.4693 - mse: 124.4693 - val_loss: 118.8043 - val_mse: 118.8043\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 119.7735 - mse: 119.7735 - val_loss: 112.5704 - val_mse: 112.5704\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 113.0587 - mse: 113.0587 - val_loss: 105.8568 - val_mse: 105.8568\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 108.6188 - mse: 108.6188 - val_loss: 100.4678 - val_mse: 100.4678\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 103.6863 - mse: 103.6863 - val_loss: 96.2875 - val_mse: 96.2875\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 100.3787 - mse: 100.3787 - val_loss: 92.3712 - val_mse: 92.3712\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 94.1272 - mse: 94.1272 - val_loss: 88.6317 - val_mse: 88.6317\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 88.8201 - mse: 88.8201 - val_loss: 84.4981 - val_mse: 84.4981\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 79.6249 - mse: 79.6249 - val_loss: 79.8007 - val_mse: 79.8007\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 78.4915 - mse: 78.4915 - val_loss: 74.9871 - val_mse: 74.9871\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 73.8191 - mse: 73.8191 - val_loss: 70.4846 - val_mse: 70.4846\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 71.4193 - mse: 71.4193 - val_loss: 65.2851 - val_mse: 65.2851\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 68.1923 - mse: 68.1923 - val_loss: 60.3693 - val_mse: 60.3693\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 62.9468 - mse: 62.9468 - val_loss: 56.4237 - val_mse: 56.4237\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 59.3146 - mse: 59.3146 - val_loss: 53.7795 - val_mse: 53.7795\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 59.9903 - mse: 59.9903 - val_loss: 51.7283 - val_mse: 51.7283\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 56.2739 - mse: 56.2739 - val_loss: 50.1547 - val_mse: 50.1547\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 54.7249 - mse: 54.7249 - val_loss: 47.0740 - val_mse: 47.0740\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 50.4527 - mse: 50.4527 - val_loss: 44.2094 - val_mse: 44.2094\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 48.4563 - mse: 48.4563 - val_loss: 40.6500 - val_mse: 40.6500\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 46.3881 - mse: 46.3881 - val_loss: 37.6779 - val_mse: 37.6779\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 43.9317 - mse: 43.9317 - val_loss: 34.9695 - val_mse: 34.9695\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 40.3800 - mse: 40.3800 - val_loss: 32.8233 - val_mse: 32.8233\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 39.1159 - mse: 39.1159 - val_loss: 30.5558 - val_mse: 30.5558\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 35.3958 - mse: 35.3958 - val_loss: 28.2790 - val_mse: 28.2790\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 38.2941 - mse: 38.2941 - val_loss: 26.0858 - val_mse: 26.0858\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 34.8635 - mse: 34.8635 - val_loss: 25.0058 - val_mse: 25.0058\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 38.3702 - mse: 38.3702 - val_loss: 23.3906 - val_mse: 23.3906\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 35.1916 - mse: 35.1916 - val_loss: 22.0042 - val_mse: 22.0042\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 29.0658 - mse: 29.0658 - val_loss: 21.3936 - val_mse: 21.3936\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 28.2519 - mse: 28.2519 - val_loss: 20.3483 - val_mse: 20.3483\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 28.9833 - mse: 28.9833 - val_loss: 19.2445 - val_mse: 19.2445\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 31.2476 - mse: 31.2476 - val_loss: 17.8383 - val_mse: 17.8383\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 28.0344 - mse: 28.0344 - val_loss: 16.2677 - val_mse: 16.2677\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.5479 - mse: 27.5479 - val_loss: 15.5700 - val_mse: 15.5700\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.5997 - mse: 25.5997 - val_loss: 15.7388 - val_mse: 15.7388\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.8171 - mse: 27.8171 - val_loss: 16.6397 - val_mse: 16.6397\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.3613 - mse: 27.3613 - val_loss: 15.6393 - val_mse: 15.6393\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 25.3826 - mse: 25.3826 - val_loss: 14.8198 - val_mse: 14.8198\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 27.7678 - mse: 27.7678 - val_loss: 14.2418 - val_mse: 14.2418\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 26.7883 - mse: 26.7883 - val_loss: 13.9225 - val_mse: 13.9225\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.4874 - mse: 25.4874 - val_loss: 13.0506 - val_mse: 13.0506\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.5612 - mse: 24.5612 - val_loss: 11.5424 - val_mse: 11.5424\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.5258 - mse: 24.5258 - val_loss: 10.9773 - val_mse: 10.9773\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.7539 - mse: 24.7539 - val_loss: 10.3530 - val_mse: 10.3530\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 25.0772 - mse: 25.0772 - val_loss: 10.2989 - val_mse: 10.2989\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 24.3992 - mse: 24.3992 - val_loss: 10.4084 - val_mse: 10.4084\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.2144 - mse: 22.2144 - val_loss: 10.7614 - val_mse: 10.7614\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.3063 - mse: 22.3063 - val_loss: 10.6787 - val_mse: 10.6787\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 23.8188 - mse: 23.8188 - val_loss: 10.4773 - val_mse: 10.4773\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.1420 - mse: 21.1420 - val_loss: 10.3555 - val_mse: 10.3555\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.3168 - mse: 25.3168 - val_loss: 10.1212 - val_mse: 10.1212\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.2047 - mse: 22.2047 - val_loss: 9.8385 - val_mse: 9.8385\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.5089 - mse: 23.5089 - val_loss: 9.8575 - val_mse: 9.8575\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.3309 - mse: 20.3309 - val_loss: 9.8502 - val_mse: 9.8502\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 20.7544 - mse: 20.7544 - val_loss: 9.9391 - val_mse: 9.9391\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 21.2176 - mse: 21.2176 - val_loss: 9.6462 - val_mse: 9.6462\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.6808 - mse: 22.6808 - val_loss: 9.3492 - val_mse: 9.3492\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.9424 - mse: 22.9424 - val_loss: 9.0343 - val_mse: 9.0343\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.0197 - mse: 22.0197 - val_loss: 8.9131 - val_mse: 8.9131\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.0917 - mse: 23.0917 - val_loss: 8.4844 - val_mse: 8.4844\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 20.7975 - mse: 20.7975 - val_loss: 8.3875 - val_mse: 8.3875\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.3855 - mse: 20.3855 - val_loss: 8.2076 - val_mse: 8.2076\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.7881 - mse: 20.7881 - val_loss: 7.9505 - val_mse: 7.9505\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.8448 - mse: 21.8448 - val_loss: 7.8579 - val_mse: 7.8579\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.5159 - mse: 21.5159 - val_loss: 7.7216 - val_mse: 7.7216\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.9441 - mse: 20.9441 - val_loss: 7.7322 - val_mse: 7.7322\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.4564 - mse: 21.4564 - val_loss: 7.7513 - val_mse: 7.7513\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.1167 - mse: 23.1167 - val_loss: 7.6500 - val_mse: 7.6500\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.7307 - mse: 19.7307 - val_loss: 7.4520 - val_mse: 7.4520\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 20.8859 - mse: 20.8859 - val_loss: 7.2078 - val_mse: 7.2078\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.6654 - mse: 19.6654 - val_loss: 6.9126 - val_mse: 6.9126\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.9443 - mse: 19.9443 - val_loss: 6.6806 - val_mse: 6.6806\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 19.9531 - mse: 19.9531 - val_loss: 6.8435 - val_mse: 6.8435\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.8862 - mse: 20.8862 - val_loss: 6.6743 - val_mse: 6.6743\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 21.6271 - mse: 21.6271 - val_loss: 6.4077 - val_mse: 6.4077\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000172967B7EE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 90ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpz0nzy2y2\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 68ms/step - loss: 609.6445 - mse: 609.6445 - val_loss: 693.6579 - val_mse: 693.6579\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 582.8760 - mse: 582.8760 - val_loss: 687.4324 - val_mse: 687.4324\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 569.2878 - mse: 569.2878 - val_loss: 679.8377 - val_mse: 679.8377\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 557.3356 - mse: 557.3356 - val_loss: 670.4622 - val_mse: 670.4622\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 545.3667 - mse: 545.3667 - val_loss: 659.2599 - val_mse: 659.2599\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 542.0506 - mse: 542.0506 - val_loss: 647.3463 - val_mse: 647.3463\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 539.2632 - mse: 539.2631 - val_loss: 634.8737 - val_mse: 634.8737\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 529.6631 - mse: 529.6631 - val_loss: 622.3206 - val_mse: 622.3206\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 527.1434 - mse: 527.1434 - val_loss: 609.4689 - val_mse: 609.4689\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 526.7516 - mse: 526.7516 - val_loss: 596.8448 - val_mse: 596.8448\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 522.7141 - mse: 522.7141 - val_loss: 585.0196 - val_mse: 585.0196\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 515.2612 - mse: 515.2612 - val_loss: 573.8193 - val_mse: 573.8193\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 514.9134 - mse: 514.9134 - val_loss: 562.4783 - val_mse: 562.4783\n",
      "Epoch 14/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 512.9056 - mse: 512.9056 - val_loss: 552.4603 - val_mse: 552.4603\n",
      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 510.9132 - mse: 510.9132 - val_loss: 543.1864 - val_mse: 543.1864\n",
      "Epoch 16/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 16ms/step - loss: 506.2300 - mse: 506.2300 - val_loss: 535.1704 - val_mse: 535.1704\n",
      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 502.1083 - mse: 502.1083 - val_loss: 527.6756 - val_mse: 527.6756\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 501.3824 - mse: 501.3824 - val_loss: 519.9120 - val_mse: 519.9120\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 500.0100 - mse: 500.0100 - val_loss: 512.0648 - val_mse: 512.0648\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 490.4763 - mse: 490.4763 - val_loss: 503.5864 - val_mse: 503.5864\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 487.4706 - mse: 487.4706 - val_loss: 494.5955 - val_mse: 494.5955\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 486.6743 - mse: 486.6743 - val_loss: 485.3023 - val_mse: 485.3023\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 482.8417 - mse: 482.8417 - val_loss: 476.6346 - val_mse: 476.6346\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 482.0992 - mse: 482.0992 - val_loss: 467.8673 - val_mse: 467.8673\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 475.3147 - mse: 475.3147 - val_loss: 459.5574 - val_mse: 459.5574\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 470.3159 - mse: 470.3159 - val_loss: 451.7152 - val_mse: 451.7152\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 466.8756 - mse: 466.8756 - val_loss: 444.4770 - val_mse: 444.4770\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 465.8343 - mse: 465.8343 - val_loss: 437.5871 - val_mse: 437.5871\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 457.5081 - mse: 457.5081 - val_loss: 431.8605 - val_mse: 431.8605\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 453.2850 - mse: 453.2850 - val_loss: 425.5964 - val_mse: 425.5964\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 449.6554 - mse: 449.6554 - val_loss: 419.8381 - val_mse: 419.8381\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 444.9016 - mse: 444.9016 - val_loss: 414.5802 - val_mse: 414.5802\n",
      "Epoch 33/150\n",
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      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 430.6918 - mse: 430.6918 - val_loss: 403.9560 - val_mse: 403.9560\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 422.5302 - mse: 422.5302 - val_loss: 397.3528 - val_mse: 397.3528\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 421.3004 - mse: 421.3004 - val_loss: 389.3156 - val_mse: 389.3156\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 414.9792 - mse: 414.9792 - val_loss: 382.4803 - val_mse: 382.4803\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 408.1782 - mse: 408.1782 - val_loss: 377.0775 - val_mse: 377.0775\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 405.2562 - mse: 405.2562 - val_loss: 370.6621 - val_mse: 370.6621\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 392.6796 - mse: 392.6796 - val_loss: 363.8307 - val_mse: 363.8307\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 391.2126 - mse: 391.2126 - val_loss: 357.0842 - val_mse: 357.0842\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 374.3078 - mse: 374.3078 - val_loss: 349.1964 - val_mse: 349.1964\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 367.5135 - mse: 367.5135 - val_loss: 339.7354 - val_mse: 339.7354\n",
      "Epoch 44/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 350.6470 - mse: 350.6470 - val_loss: 309.1396 - val_mse: 309.1396\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 337.9266 - mse: 337.9266 - val_loss: 298.2418 - val_mse: 298.2418\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 332.5977 - mse: 332.5977 - val_loss: 289.8014 - val_mse: 289.8014\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 326.0353 - mse: 326.0353 - val_loss: 284.0523 - val_mse: 284.0523\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 308.5478 - mse: 308.5478 - val_loss: 277.3651 - val_mse: 277.3651\n",
      "Epoch 51/150\n",
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      "Epoch 52/150\n",
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      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 292.2320 - mse: 292.2320 - val_loss: 250.8666 - val_mse: 250.8666\n",
      "Epoch 54/150\n",
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      "Epoch 55/150\n",
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      "Epoch 56/150\n",
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      "Epoch 57/150\n",
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      "Epoch 59/150\n",
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      "Epoch 60/150\n",
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      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 215.3941 - mse: 215.3941 - val_loss: 196.4468 - val_mse: 196.4468\n",
      "Epoch 62/150\n",
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      "Epoch 63/150\n",
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      "Epoch 64/150\n",
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      "Epoch 65/150\n",
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      "Epoch 66/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
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      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 149.1573 - mse: 149.1573 - val_loss: 133.5791 - val_mse: 133.5791\n",
      "Epoch 71/150\n",
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      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 141.7401 - mse: 141.7401 - val_loss: 122.2579 - val_mse: 122.2579\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 131.4688 - mse: 131.4688 - val_loss: 118.5881 - val_mse: 118.5881\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 128.6466 - mse: 128.6466 - val_loss: 114.0713 - val_mse: 114.0713\n",
      "Epoch 75/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 16ms/step - loss: 114.9029 - mse: 114.9029 - val_loss: 108.4093 - val_mse: 108.4093\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 118.0319 - mse: 118.0319 - val_loss: 101.2495 - val_mse: 101.2495\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 111.2626 - mse: 111.2626 - val_loss: 92.5550 - val_mse: 92.5550\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 104.3356 - mse: 104.3356 - val_loss: 85.9925 - val_mse: 85.9925\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 97.7674 - mse: 97.7674 - val_loss: 82.6441 - val_mse: 82.6441\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 91.2478 - mse: 91.2478 - val_loss: 79.5565 - val_mse: 79.5565\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 84.8569 - mse: 84.8569 - val_loss: 75.6034 - val_mse: 75.6034\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 81.7439 - mse: 81.7439 - val_loss: 71.8941 - val_mse: 71.8941\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 78.5497 - mse: 78.5497 - val_loss: 68.8070 - val_mse: 68.8070\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 74.0971 - mse: 74.0971 - val_loss: 66.0263 - val_mse: 66.0263\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 66.8385 - mse: 66.8385 - val_loss: 63.0117 - val_mse: 63.0117\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 67.9940 - mse: 67.9940 - val_loss: 59.7597 - val_mse: 59.7597\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 66.2041 - mse: 66.2041 - val_loss: 56.2071 - val_mse: 56.2071\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 62.4672 - mse: 62.4672 - val_loss: 52.3923 - val_mse: 52.3923\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 58.2959 - mse: 58.2959 - val_loss: 48.1594 - val_mse: 48.1594\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 59.5115 - mse: 59.5115 - val_loss: 43.8046 - val_mse: 43.8046\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 54.7733 - mse: 54.7733 - val_loss: 40.8383 - val_mse: 40.8383\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 51.0275 - mse: 51.0275 - val_loss: 35.9569 - val_mse: 35.9569\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 44.5373 - mse: 44.5373 - val_loss: 33.8209 - val_mse: 33.8209\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 44.1901 - mse: 44.1901 - val_loss: 31.0217 - val_mse: 31.0217\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 41.2092 - mse: 41.2092 - val_loss: 29.5461 - val_mse: 29.5461\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 38.5709 - mse: 38.5709 - val_loss: 27.8205 - val_mse: 27.8205\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 38.5796 - mse: 38.5796 - val_loss: 26.9418 - val_mse: 26.9418\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 37.8250 - mse: 37.8250 - val_loss: 26.6235 - val_mse: 26.6235\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 38.7090 - mse: 38.7090 - val_loss: 25.5178 - val_mse: 25.5178\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 33.8790 - mse: 33.8790 - val_loss: 24.5918 - val_mse: 24.5918\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 35.9095 - mse: 35.9095 - val_loss: 23.8876 - val_mse: 23.8876\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 32.5615 - mse: 32.5615 - val_loss: 23.5587 - val_mse: 23.5587\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 33.5965 - mse: 33.5965 - val_loss: 22.3288 - val_mse: 22.3288\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.8126 - mse: 30.8126 - val_loss: 21.3053 - val_mse: 21.3053\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 29.9730 - mse: 29.9730 - val_loss: 19.8109 - val_mse: 19.8109\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.4333 - mse: 30.4333 - val_loss: 18.0220 - val_mse: 18.0220\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 31.5101 - mse: 31.5101 - val_loss: 16.5516 - val_mse: 16.5516\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.7927 - mse: 27.7927 - val_loss: 16.3406 - val_mse: 16.3406\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 27.5792 - mse: 27.5792 - val_loss: 16.2745 - val_mse: 16.2745\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 29.8794 - mse: 29.8794 - val_loss: 17.3364 - val_mse: 17.3364\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 28.3671 - mse: 28.3671 - val_loss: 17.2660 - val_mse: 17.2660\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 28.1327 - mse: 28.1327 - val_loss: 16.2981 - val_mse: 16.2981\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 26.7376 - mse: 26.7376 - val_loss: 14.5155 - val_mse: 14.5155\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 28.5238 - mse: 28.5238 - val_loss: 13.8403 - val_mse: 13.8403\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 25.6742 - mse: 25.6742 - val_loss: 13.5003 - val_mse: 13.5003\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.2305 - mse: 24.2305 - val_loss: 12.9969 - val_mse: 12.9969\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 26.1541 - mse: 26.1541 - val_loss: 12.5161 - val_mse: 12.5161\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 24.9621 - mse: 24.9621 - val_loss: 12.6679 - val_mse: 12.6679\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.4200 - mse: 22.4200 - val_loss: 13.8595 - val_mse: 13.8595\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.1995 - mse: 22.1995 - val_loss: 14.3795 - val_mse: 14.3795\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 23.3393 - mse: 23.3393 - val_loss: 12.8249 - val_mse: 12.8249\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.9785 - mse: 23.9785 - val_loss: 11.1093 - val_mse: 11.1093\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.0102 - mse: 22.0102 - val_loss: 11.1451 - val_mse: 11.1451\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 23.6299 - mse: 23.6299 - val_loss: 11.8496 - val_mse: 11.8496\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.4347 - mse: 22.4347 - val_loss: 11.6180 - val_mse: 11.6180\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.2182 - mse: 20.2182 - val_loss: 10.8305 - val_mse: 10.8305\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.9641 - mse: 23.9641 - val_loss: 10.1677 - val_mse: 10.1677\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.4919 - mse: 22.4919 - val_loss: 10.2134 - val_mse: 10.2134\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.9858 - mse: 20.9858 - val_loss: 10.6936 - val_mse: 10.6936\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.5783 - mse: 21.5783 - val_loss: 10.8249 - val_mse: 10.8249\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.0376 - mse: 24.0376 - val_loss: 10.4355 - val_mse: 10.4355\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.6057 - mse: 23.6057 - val_loss: 9.7907 - val_mse: 9.7907\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.1431 - mse: 22.1431 - val_loss: 9.3337 - val_mse: 9.3337\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.7480 - mse: 21.7480 - val_loss: 8.8557 - val_mse: 8.8557\n",
      "Epoch 135/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 15ms/step - loss: 19.9726 - mse: 19.9726 - val_loss: 8.5989 - val_mse: 8.5989\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.3223 - mse: 20.3223 - val_loss: 8.3930 - val_mse: 8.3930\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.5532 - mse: 22.5532 - val_loss: 8.3362 - val_mse: 8.3362\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 18.9502 - mse: 18.9502 - val_loss: 8.2071 - val_mse: 8.2071\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.6812 - mse: 22.6812 - val_loss: 8.1499 - val_mse: 8.1499\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.2406 - mse: 22.2406 - val_loss: 8.6411 - val_mse: 8.6411\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.6715 - mse: 22.6715 - val_loss: 8.3434 - val_mse: 8.3434\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.0769 - mse: 23.0769 - val_loss: 8.2043 - val_mse: 8.2043\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 20.3961 - mse: 20.3961 - val_loss: 8.2895 - val_mse: 8.2895\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.4004 - mse: 21.4004 - val_loss: 7.9493 - val_mse: 7.9493\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.8088 - mse: 21.8088 - val_loss: 7.5431 - val_mse: 7.5431\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.7599 - mse: 20.7599 - val_loss: 7.4217 - val_mse: 7.4217\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.1236 - mse: 20.1236 - val_loss: 7.6147 - val_mse: 7.6147\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.0714 - mse: 21.0714 - val_loss: 7.7986 - val_mse: 7.7986\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.2026 - mse: 20.2026 - val_loss: 7.5188 - val_mse: 7.5188\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.9367 - mse: 20.9367 - val_loss: 7.2185 - val_mse: 7.2185\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000017299CC71F0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmp7eo2ey3v\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 73ms/step - loss: 627.4960 - mse: 627.4960 - val_loss: 693.8286 - val_mse: 693.8286\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 596.6127 - mse: 596.6127 - val_loss: 687.1382 - val_mse: 687.1382\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 581.5087 - mse: 581.5087 - val_loss: 679.0357 - val_mse: 679.0357\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 573.6201 - mse: 573.6201 - val_loss: 669.6093 - val_mse: 669.6093\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 561.4081 - mse: 561.4081 - val_loss: 658.7309 - val_mse: 658.7309\n",
      "Epoch 6/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 554.2297 - mse: 554.2297 - val_loss: 646.5203 - val_mse: 646.5203\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 553.4547 - mse: 553.4547 - val_loss: 633.2772 - val_mse: 633.2772\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 545.2288 - mse: 545.2288 - val_loss: 619.6701 - val_mse: 619.6701\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 541.2916 - mse: 541.2916 - val_loss: 605.4694 - val_mse: 605.4694\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 534.9828 - mse: 534.9828 - val_loss: 591.5722 - val_mse: 591.5722\n",
      "Epoch 11/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 534.5391 - mse: 534.5391 - val_loss: 578.2330 - val_mse: 578.2330\n",
      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 531.7136 - mse: 531.7136 - val_loss: 565.7703 - val_mse: 565.7703\n",
      "Epoch 13/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 525.4008 - mse: 525.4008 - val_loss: 554.2531 - val_mse: 554.2531\n",
      "Epoch 14/150\n",
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      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 518.2496 - mse: 518.2496 - val_loss: 533.4089 - val_mse: 533.4089\n",
      "Epoch 16/150\n",
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      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 517.7291 - mse: 517.7291 - val_loss: 515.9331 - val_mse: 515.9331\n",
      "Epoch 18/150\n",
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      "Epoch 19/150\n",
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      "Epoch 20/150\n",
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      "Epoch 21/150\n",
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      "Epoch 22/150\n",
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      "Epoch 23/150\n",
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      "Epoch 24/150\n",
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      "Epoch 25/150\n",
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      "Epoch 26/150\n",
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      "Epoch 27/150\n",
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      "Epoch 28/150\n",
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      "Epoch 29/150\n",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
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      "Epoch 34/150\n",
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      "Epoch 37/150\n",
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      "Epoch 38/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 413.5227 - mse: 413.5227 - val_loss: 373.8431 - val_mse: 373.8431\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 408.8766 - mse: 408.8766 - val_loss: 365.9422 - val_mse: 365.9422\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 404.0817 - mse: 404.0817 - val_loss: 358.1078 - val_mse: 358.1078\n",
      "Epoch 41/150\n",
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      "Epoch 42/150\n",
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      "Epoch 43/150\n",
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      "Epoch 44/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
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      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 345.7192 - mse: 345.7192 - val_loss: 299.2179 - val_mse: 299.2179\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 339.5785 - mse: 339.5785 - val_loss: 291.3636 - val_mse: 291.3636\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 333.2816 - mse: 333.2816 - val_loss: 281.3228 - val_mse: 281.3228\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 326.8989 - mse: 326.8989 - val_loss: 272.7221 - val_mse: 272.7221\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 318.4942 - mse: 318.4942 - val_loss: 264.9879 - val_mse: 264.9879\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 301.4902 - mse: 301.4902 - val_loss: 258.0586 - val_mse: 258.0586\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 298.2742 - mse: 298.2742 - val_loss: 250.7297 - val_mse: 250.7297\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 293.0320 - mse: 293.0320 - val_loss: 243.0477 - val_mse: 243.0477\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 275.7557 - mse: 275.7557 - val_loss: 235.3119 - val_mse: 235.3119\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 272.9079 - mse: 272.9079 - val_loss: 227.9647 - val_mse: 227.9647\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 264.4397 - mse: 264.4397 - val_loss: 217.6345 - val_mse: 217.6345\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 261.3776 - mse: 261.3776 - val_loss: 208.8129 - val_mse: 208.8129\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 246.1871 - mse: 246.1871 - val_loss: 203.1110 - val_mse: 203.1110\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 229.2433 - mse: 229.2433 - val_loss: 197.8048 - val_mse: 197.8048\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 231.6143 - mse: 231.6143 - val_loss: 192.0398 - val_mse: 192.0398\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 224.3564 - mse: 224.3564 - val_loss: 185.3776 - val_mse: 185.3776\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 217.4772 - mse: 217.4772 - val_loss: 178.6812 - val_mse: 178.6812\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 201.7937 - mse: 201.7937 - val_loss: 169.4443 - val_mse: 169.4443\n",
      "Epoch 65/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 200.4181 - mse: 200.4181 - val_loss: 162.0364 - val_mse: 162.0364\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 186.9241 - mse: 186.9241 - val_loss: 155.7227 - val_mse: 155.7227\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 178.0938 - mse: 178.0938 - val_loss: 151.1481 - val_mse: 151.1481\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 167.6108 - mse: 167.6108 - val_loss: 144.9398 - val_mse: 144.9398\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 168.9772 - mse: 168.9772 - val_loss: 137.3360 - val_mse: 137.3360\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 157.5803 - mse: 157.5803 - val_loss: 130.9529 - val_mse: 130.9529\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 149.9868 - mse: 149.9868 - val_loss: 124.0975 - val_mse: 124.0975\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 140.9986 - mse: 140.9986 - val_loss: 117.6950 - val_mse: 117.6950\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 133.4206 - mse: 133.4206 - val_loss: 111.6172 - val_mse: 111.6172\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 129.8919 - mse: 129.8919 - val_loss: 108.0018 - val_mse: 108.0018\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 121.4624 - mse: 121.4624 - val_loss: 103.5056 - val_mse: 103.5056\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 117.3761 - mse: 117.3761 - val_loss: 98.2427 - val_mse: 98.2427\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 110.8776 - mse: 110.8776 - val_loss: 93.0142 - val_mse: 93.0142\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 107.6130 - mse: 107.6130 - val_loss: 88.0285 - val_mse: 88.0285\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 101.3267 - mse: 101.3267 - val_loss: 83.6751 - val_mse: 83.6751\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 98.5509 - mse: 98.5509 - val_loss: 78.0079 - val_mse: 78.0079\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 89.2102 - mse: 89.2102 - val_loss: 72.8706 - val_mse: 72.8706\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 86.3822 - mse: 86.3822 - val_loss: 68.5873 - val_mse: 68.5873\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 77.3944 - mse: 77.3944 - val_loss: 66.8163 - val_mse: 66.8163\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 75.8935 - mse: 75.8935 - val_loss: 64.7772 - val_mse: 64.7772\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 74.3829 - mse: 74.3829 - val_loss: 61.1073 - val_mse: 61.1073\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 73.1639 - mse: 73.1639 - val_loss: 57.6168 - val_mse: 57.6168\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 69.2915 - mse: 69.2915 - val_loss: 54.2271 - val_mse: 54.2271\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 67.3684 - mse: 67.3684 - val_loss: 47.9306 - val_mse: 47.9306\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 58.0085 - mse: 58.0085 - val_loss: 43.8087 - val_mse: 43.8087\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 60.2248 - mse: 60.2248 - val_loss: 42.2338 - val_mse: 42.2338\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 53.3473 - mse: 53.3473 - val_loss: 41.8483 - val_mse: 41.8483\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 53.7126 - mse: 53.7126 - val_loss: 39.4931 - val_mse: 39.4931\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 50.8066 - mse: 50.8066 - val_loss: 36.8862 - val_mse: 36.8862\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 46.9080 - mse: 46.9080 - val_loss: 32.9333 - val_mse: 32.9333\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 42.8674 - mse: 42.8674 - val_loss: 29.6992 - val_mse: 29.6992\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 42.1696 - mse: 42.1696 - val_loss: 28.0626 - val_mse: 28.0626\n",
      "Epoch 97/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 39.8582 - mse: 39.8582 - val_loss: 26.1715 - val_mse: 26.1715\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 36.2345 - mse: 36.2345 - val_loss: 25.9903 - val_mse: 25.9903\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 40.5623 - mse: 40.5623 - val_loss: 25.7921 - val_mse: 25.7921\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 32.8941 - mse: 32.8941 - val_loss: 24.6617 - val_mse: 24.6617\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 33.6976 - mse: 33.6976 - val_loss: 22.7857 - val_mse: 22.7857\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 31.6710 - mse: 31.6710 - val_loss: 21.2453 - val_mse: 21.2453\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 31.3126 - mse: 31.3126 - val_loss: 20.0128 - val_mse: 20.0128\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 32.9338 - mse: 32.9338 - val_loss: 19.7684 - val_mse: 19.7684\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 31.0185 - mse: 31.0185 - val_loss: 20.2176 - val_mse: 20.2176\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 29.3474 - mse: 29.3474 - val_loss: 19.0949 - val_mse: 19.0949\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 29.1812 - mse: 29.1812 - val_loss: 17.6102 - val_mse: 17.6102\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.8787 - mse: 27.8787 - val_loss: 16.6412 - val_mse: 16.6412\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 30.1547 - mse: 30.1547 - val_loss: 16.3665 - val_mse: 16.3665\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.4147 - mse: 27.4147 - val_loss: 15.6636 - val_mse: 15.6636\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.8598 - mse: 25.8598 - val_loss: 15.1150 - val_mse: 15.1150\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 25.0458 - mse: 25.0458 - val_loss: 14.4943 - val_mse: 14.4943\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.7197 - mse: 25.7197 - val_loss: 13.5821 - val_mse: 13.5821\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 26.1457 - mse: 26.1457 - val_loss: 13.8874 - val_mse: 13.8874\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 29.2393 - mse: 29.2393 - val_loss: 14.1958 - val_mse: 14.1958\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.5713 - mse: 22.5713 - val_loss: 14.1497 - val_mse: 14.1497\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.8709 - mse: 22.8709 - val_loss: 13.4746 - val_mse: 13.4746\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 22.2955 - mse: 22.2955 - val_loss: 13.1916 - val_mse: 13.1916\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.4126 - mse: 22.4126 - val_loss: 13.4965 - val_mse: 13.4965\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 23.8504 - mse: 23.8504 - val_loss: 13.2283 - val_mse: 13.2283\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.8400 - mse: 21.8400 - val_loss: 11.7742 - val_mse: 11.7742\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 23.0804 - mse: 23.0804 - val_loss: 10.3063 - val_mse: 10.3063\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 24.5817 - mse: 24.5817 - val_loss: 9.4637 - val_mse: 9.4637\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.0589 - mse: 22.0589 - val_loss: 9.3007 - val_mse: 9.3007\n",
      "Epoch 125/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.7875 - mse: 22.7875 - val_loss: 9.5083 - val_mse: 9.5083\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.9050 - mse: 23.9050 - val_loss: 9.7858 - val_mse: 9.7858\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.5201 - mse: 23.5201 - val_loss: 9.9957 - val_mse: 9.9957\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 20.1140 - mse: 20.1140 - val_loss: 9.8851 - val_mse: 9.8851\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.2616 - mse: 23.2616 - val_loss: 9.0958 - val_mse: 9.0958\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.4437 - mse: 21.4437 - val_loss: 8.8651 - val_mse: 8.8651\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.2399 - mse: 24.2399 - val_loss: 8.8607 - val_mse: 8.8607\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.8009 - mse: 20.8009 - val_loss: 8.8323 - val_mse: 8.8323\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.1379 - mse: 22.1379 - val_loss: 8.7322 - val_mse: 8.7322\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.7114 - mse: 19.7114 - val_loss: 8.6481 - val_mse: 8.6481\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.4511 - mse: 24.4511 - val_loss: 8.3955 - val_mse: 8.3955\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.5302 - mse: 19.5302 - val_loss: 8.0883 - val_mse: 8.0883\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.1208 - mse: 21.1208 - val_loss: 7.8421 - val_mse: 7.8421\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 19.1109 - mse: 19.1109 - val_loss: 7.6593 - val_mse: 7.6593\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.5889 - mse: 21.5889 - val_loss: 7.4840 - val_mse: 7.4840\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.9415 - mse: 19.9415 - val_loss: 7.4297 - val_mse: 7.4297\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.8710 - mse: 19.8710 - val_loss: 7.4090 - val_mse: 7.4090\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.0398 - mse: 19.0398 - val_loss: 7.4799 - val_mse: 7.4799\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 20.0967 - mse: 20.0967 - val_loss: 7.5998 - val_mse: 7.5998\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 20.8640 - mse: 20.8640 - val_loss: 7.7250 - val_mse: 7.7250\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.5426 - mse: 19.5426 - val_loss: 7.6811 - val_mse: 7.6811\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 20.6618 - mse: 20.6618 - val_loss: 7.7459 - val_mse: 7.7459\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.4139 - mse: 21.4139 - val_loss: 7.7941 - val_mse: 7.7941\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.5404 - mse: 21.5404 - val_loss: 7.3156 - val_mse: 7.3156\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 20.4381 - mse: 20.4381 - val_loss: 6.9496 - val_mse: 6.9496\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 17.9782 - mse: 17.9782 - val_loss: 6.8161 - val_mse: 6.8161\n",
      "1/1 [==============================] - 0s 99ms/step\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\huyha\\AppData\\Local\\Temp\\tmpcctdbe9n\\assets\n",
      "Epoch 1/150\n",
      "4/4 [==============================] - 1s 72ms/step - loss: 610.3788 - mse: 610.3788 - val_loss: 694.0345 - val_mse: 694.0345\n",
      "Epoch 2/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 583.6306 - mse: 583.6306 - val_loss: 687.7318 - val_mse: 687.7318\n",
      "Epoch 3/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 565.7877 - mse: 565.7877 - val_loss: 680.3913 - val_mse: 680.3913\n",
      "Epoch 4/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 553.9053 - mse: 553.9053 - val_loss: 671.3217 - val_mse: 671.3217\n",
      "Epoch 5/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 549.4086 - mse: 549.4086 - val_loss: 661.0498 - val_mse: 661.0498\n",
      "Epoch 6/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 544.1378 - mse: 544.1378 - val_loss: 649.8476 - val_mse: 649.8476\n",
      "Epoch 7/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 541.3997 - mse: 541.3997 - val_loss: 637.6099 - val_mse: 637.6099\n",
      "Epoch 8/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 533.5760 - mse: 533.5760 - val_loss: 624.7576 - val_mse: 624.7576\n",
      "Epoch 9/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 527.6010 - mse: 527.6010 - val_loss: 611.7079 - val_mse: 611.7079\n",
      "Epoch 10/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 521.3387 - mse: 521.3387 - val_loss: 598.7936 - val_mse: 598.7936\n",
      "Epoch 11/150\n",
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      "Epoch 12/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 517.0480 - mse: 517.0480 - val_loss: 574.5790 - val_mse: 574.5790\n",
      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 504.9073 - mse: 504.9073 - val_loss: 543.4783 - val_mse: 543.4783\n",
      "Epoch 16/150\n",
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      "Epoch 17/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 501.4696 - mse: 501.4696 - val_loss: 527.7319 - val_mse: 527.7319\n",
      "Epoch 18/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 498.0957 - mse: 498.0957 - val_loss: 520.4770 - val_mse: 520.4770\n",
      "Epoch 19/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 494.3987 - mse: 494.3987 - val_loss: 512.4396 - val_mse: 512.4396\n",
      "Epoch 20/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 487.9693 - mse: 487.9693 - val_loss: 503.9507 - val_mse: 503.9507\n",
      "Epoch 21/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 489.0199 - mse: 489.0199 - val_loss: 495.0771 - val_mse: 495.0771\n",
      "Epoch 22/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 483.0552 - mse: 483.0552 - val_loss: 485.9035 - val_mse: 485.9035\n",
      "Epoch 23/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 483.5176 - mse: 483.5176 - val_loss: 476.9560 - val_mse: 476.9560\n",
      "Epoch 24/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 478.6146 - mse: 478.6146 - val_loss: 469.7911 - val_mse: 469.7911\n",
      "Epoch 25/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 472.0415 - mse: 472.0415 - val_loss: 462.1256 - val_mse: 462.1256\n",
      "Epoch 26/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 476.5615 - mse: 476.5615 - val_loss: 454.6173 - val_mse: 454.6173\n",
      "Epoch 27/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 462.4835 - mse: 462.4835 - val_loss: 447.7821 - val_mse: 447.7821\n",
      "Epoch 28/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 462.8134 - mse: 462.8134 - val_loss: 441.4467 - val_mse: 441.4467\n",
      "Epoch 29/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 456.4069 - mse: 456.4069 - val_loss: 433.8289 - val_mse: 433.8289\n",
      "Epoch 30/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 453.7237 - mse: 453.7237 - val_loss: 426.8667 - val_mse: 426.8667\n",
      "Epoch 31/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 447.7798 - mse: 447.7798 - val_loss: 420.4717 - val_mse: 420.4717\n",
      "Epoch 32/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 440.5202 - mse: 440.5202 - val_loss: 414.2777 - val_mse: 414.2777\n",
      "Epoch 33/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 437.8255 - mse: 437.8255 - val_loss: 407.0558 - val_mse: 407.0558\n",
      "Epoch 34/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 425.4476 - mse: 425.4476 - val_loss: 398.3080 - val_mse: 398.3080\n",
      "Epoch 35/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 421.5112 - mse: 421.5112 - val_loss: 390.5796 - val_mse: 390.5796\n",
      "Epoch 36/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 421.0992 - mse: 421.0992 - val_loss: 384.3704 - val_mse: 384.3704\n",
      "Epoch 37/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 413.1535 - mse: 413.1535 - val_loss: 378.5922 - val_mse: 378.5922\n",
      "Epoch 38/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 400.6363 - mse: 400.6363 - val_loss: 370.9262 - val_mse: 370.9262\n",
      "Epoch 39/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 398.8539 - mse: 398.8539 - val_loss: 363.6814 - val_mse: 363.6814\n",
      "Epoch 40/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 393.5640 - mse: 393.5640 - val_loss: 358.8757 - val_mse: 358.8757\n",
      "Epoch 41/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 382.0463 - mse: 382.0463 - val_loss: 353.4968 - val_mse: 353.4968\n",
      "Epoch 42/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 373.6739 - mse: 373.6739 - val_loss: 346.3210 - val_mse: 346.3210\n",
      "Epoch 43/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 369.5516 - mse: 369.5516 - val_loss: 337.2148 - val_mse: 337.2148\n",
      "Epoch 44/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 362.5880 - mse: 362.5880 - val_loss: 328.2674 - val_mse: 328.2674\n",
      "Epoch 45/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 354.7043 - mse: 354.7043 - val_loss: 320.1591 - val_mse: 320.1591\n",
      "Epoch 46/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 347.1355 - mse: 347.1355 - val_loss: 311.9765 - val_mse: 311.9765\n",
      "Epoch 47/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 341.7193 - mse: 341.7193 - val_loss: 303.8789 - val_mse: 303.8789\n",
      "Epoch 48/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 325.5704 - mse: 325.5704 - val_loss: 297.8781 - val_mse: 297.8781\n",
      "Epoch 49/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 321.3247 - mse: 321.3247 - val_loss: 292.5729 - val_mse: 292.5729\n",
      "Epoch 50/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 315.9902 - mse: 315.9902 - val_loss: 285.9550 - val_mse: 285.9550\n",
      "Epoch 51/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 302.0822 - mse: 302.0822 - val_loss: 279.7588 - val_mse: 279.7588\n",
      "Epoch 52/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 297.3348 - mse: 297.3348 - val_loss: 270.4546 - val_mse: 270.4546\n",
      "Epoch 53/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 286.0065 - mse: 286.0065 - val_loss: 261.2403 - val_mse: 261.2403\n",
      "Epoch 54/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 284.5457 - mse: 284.5457 - val_loss: 252.8764 - val_mse: 252.8764\n",
      "Epoch 55/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 272.2625 - mse: 272.2625 - val_loss: 246.1011 - val_mse: 246.1011\n",
      "Epoch 56/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 262.9067 - mse: 262.9067 - val_loss: 240.5850 - val_mse: 240.5850\n",
      "Epoch 57/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 250.7572 - mse: 250.7572 - val_loss: 236.0517 - val_mse: 236.0517\n",
      "Epoch 58/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 244.1416 - mse: 244.1416 - val_loss: 230.1638 - val_mse: 230.1638\n",
      "Epoch 59/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 234.8573 - mse: 234.8573 - val_loss: 221.3738 - val_mse: 221.3738\n",
      "Epoch 60/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 231.0802 - mse: 231.0802 - val_loss: 211.3260 - val_mse: 211.3260\n",
      "Epoch 61/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 218.9970 - mse: 218.9970 - val_loss: 201.2504 - val_mse: 201.2504\n",
      "Epoch 62/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 212.6997 - mse: 212.6997 - val_loss: 193.0145 - val_mse: 193.0145\n",
      "Epoch 63/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 200.9080 - mse: 200.9080 - val_loss: 187.4940 - val_mse: 187.4940\n",
      "Epoch 64/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 197.3381 - mse: 197.3381 - val_loss: 184.7140 - val_mse: 184.7140\n",
      "Epoch 65/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 187.5902 - mse: 187.5902 - val_loss: 181.1713 - val_mse: 181.1713\n",
      "Epoch 66/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 181.0854 - mse: 181.0854 - val_loss: 172.2617 - val_mse: 172.2617\n",
      "Epoch 67/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 172.3915 - mse: 172.3915 - val_loss: 163.3068 - val_mse: 163.3068\n",
      "Epoch 68/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 169.0932 - mse: 169.0932 - val_loss: 154.9286 - val_mse: 154.9286\n",
      "Epoch 69/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 156.2485 - mse: 156.2485 - val_loss: 147.9371 - val_mse: 147.9371\n",
      "Epoch 70/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 156.5065 - mse: 156.5065 - val_loss: 141.8506 - val_mse: 141.8506\n",
      "Epoch 71/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 141.1177 - mse: 141.1177 - val_loss: 135.7118 - val_mse: 135.7118\n",
      "Epoch 72/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 142.1304 - mse: 142.1304 - val_loss: 128.3540 - val_mse: 128.3540\n",
      "Epoch 73/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 128.4853 - mse: 128.4853 - val_loss: 120.6938 - val_mse: 120.6938\n",
      "Epoch 74/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 128.0080 - mse: 128.0080 - val_loss: 112.7797 - val_mse: 112.7797\n",
      "Epoch 75/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 117.6623 - mse: 117.6623 - val_loss: 104.0901 - val_mse: 104.0901\n",
      "Epoch 76/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 112.5144 - mse: 112.5144 - val_loss: 95.8305 - val_mse: 95.8305\n",
      "Epoch 77/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 102.0387 - mse: 102.0387 - val_loss: 90.2083 - val_mse: 90.2083\n",
      "Epoch 78/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 103.8455 - mse: 103.8455 - val_loss: 87.2524 - val_mse: 87.2524\n",
      "Epoch 79/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 96.1289 - mse: 96.1289 - val_loss: 82.7031 - val_mse: 82.7031\n",
      "Epoch 80/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 93.5631 - mse: 93.5631 - val_loss: 76.4656 - val_mse: 76.4656\n",
      "Epoch 81/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 85.4384 - mse: 85.4384 - val_loss: 69.9453 - val_mse: 69.9453\n",
      "Epoch 82/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 80.2575 - mse: 80.2575 - val_loss: 64.6363 - val_mse: 64.6363\n",
      "Epoch 83/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 79.7275 - mse: 79.7275 - val_loss: 60.4447 - val_mse: 60.4447\n",
      "Epoch 84/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 77.1102 - mse: 77.1102 - val_loss: 57.4393 - val_mse: 57.4393\n",
      "Epoch 85/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 68.9341 - mse: 68.9341 - val_loss: 55.4418 - val_mse: 55.4418\n",
      "Epoch 86/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 64.4400 - mse: 64.4400 - val_loss: 54.2748 - val_mse: 54.2748\n",
      "Epoch 87/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 66.6824 - mse: 66.6824 - val_loss: 51.7646 - val_mse: 51.7646\n",
      "Epoch 88/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 62.1556 - mse: 62.1556 - val_loss: 46.7586 - val_mse: 46.7586\n",
      "Epoch 89/150\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 55.0761 - mse: 55.0761 - val_loss: 42.7805 - val_mse: 42.7805\n",
      "Epoch 90/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 56.5395 - mse: 56.5395 - val_loss: 39.4394 - val_mse: 39.4394\n",
      "Epoch 91/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 52.7151 - mse: 52.7151 - val_loss: 37.0786 - val_mse: 37.0786\n",
      "Epoch 92/150\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 51.4201 - mse: 51.4201 - val_loss: 35.1644 - val_mse: 35.1644\n",
      "Epoch 93/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 47.4822 - mse: 47.4822 - val_loss: 33.1632 - val_mse: 33.1632\n",
      "Epoch 94/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 44.5216 - mse: 44.5216 - val_loss: 30.8681 - val_mse: 30.8681\n",
      "Epoch 95/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 43.3719 - mse: 43.3719 - val_loss: 28.9610 - val_mse: 28.9610\n",
      "Epoch 96/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 43.0461 - mse: 43.0461 - val_loss: 27.7286 - val_mse: 27.7286\n",
      "Epoch 97/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 37.7133 - mse: 37.7133 - val_loss: 25.8734 - val_mse: 25.8734\n",
      "Epoch 98/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 37.3679 - mse: 37.3679 - val_loss: 25.0505 - val_mse: 25.0505\n",
      "Epoch 99/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 37.2181 - mse: 37.2181 - val_loss: 24.0151 - val_mse: 24.0151\n",
      "Epoch 100/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 31.8071 - mse: 31.8071 - val_loss: 23.0265 - val_mse: 23.0265\n",
      "Epoch 101/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 33.6996 - mse: 33.6996 - val_loss: 21.8041 - val_mse: 21.8041\n",
      "Epoch 102/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 30.3220 - mse: 30.3220 - val_loss: 20.8574 - val_mse: 20.8574\n",
      "Epoch 103/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 30.0817 - mse: 30.0817 - val_loss: 20.6174 - val_mse: 20.6174\n",
      "Epoch 104/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.6632 - mse: 27.6632 - val_loss: 19.3658 - val_mse: 19.3658\n",
      "Epoch 105/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 29.8824 - mse: 29.8824 - val_loss: 19.2553 - val_mse: 19.2553\n",
      "Epoch 106/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 28.6637 - mse: 28.6637 - val_loss: 18.6396 - val_mse: 18.6396\n",
      "Epoch 107/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 32.5500 - mse: 32.5500 - val_loss: 17.0621 - val_mse: 17.0621\n",
      "Epoch 108/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 27.8880 - mse: 27.8880 - val_loss: 15.4569 - val_mse: 15.4569\n",
      "Epoch 109/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 26.0991 - mse: 26.0991 - val_loss: 14.3324 - val_mse: 14.3324\n",
      "Epoch 110/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 24.7516 - mse: 24.7516 - val_loss: 14.2336 - val_mse: 14.2336\n",
      "Epoch 111/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.0414 - mse: 23.0414 - val_loss: 13.7067 - val_mse: 13.7067\n",
      "Epoch 112/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 24.1414 - mse: 24.1414 - val_loss: 13.3104 - val_mse: 13.3104\n",
      "Epoch 113/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 23.5953 - mse: 23.5953 - val_loss: 12.9883 - val_mse: 12.9883\n",
      "Epoch 114/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 25.3264 - mse: 25.3264 - val_loss: 12.6911 - val_mse: 12.6911\n",
      "Epoch 115/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.0752 - mse: 22.0752 - val_loss: 12.8620 - val_mse: 12.8620\n",
      "Epoch 116/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.8658 - mse: 23.8658 - val_loss: 12.5826 - val_mse: 12.5826\n",
      "Epoch 117/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 23.3813 - mse: 23.3813 - val_loss: 12.2230 - val_mse: 12.2230\n",
      "Epoch 118/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 20.9924 - mse: 20.9924 - val_loss: 12.0660 - val_mse: 12.0660\n",
      "Epoch 119/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.6214 - mse: 24.6214 - val_loss: 12.1463 - val_mse: 12.1463\n",
      "Epoch 120/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 24.9617 - mse: 24.9617 - val_loss: 12.0272 - val_mse: 12.0272\n",
      "Epoch 121/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.8384 - mse: 21.8384 - val_loss: 11.7574 - val_mse: 11.7574\n",
      "Epoch 122/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.8069 - mse: 22.8069 - val_loss: 11.6210 - val_mse: 11.6210\n",
      "Epoch 123/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 19.8941 - mse: 19.8941 - val_loss: 10.8720 - val_mse: 10.8720\n",
      "Epoch 124/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 24.4743 - mse: 24.4743 - val_loss: 10.2029 - val_mse: 10.2029\n",
      "Epoch 125/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 17ms/step - loss: 24.4045 - mse: 24.4045 - val_loss: 9.7884 - val_mse: 9.7884\n",
      "Epoch 126/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.9142 - mse: 22.9142 - val_loss: 9.4782 - val_mse: 9.4782\n",
      "Epoch 127/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.9883 - mse: 21.9883 - val_loss: 9.2361 - val_mse: 9.2361\n",
      "Epoch 128/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.2005 - mse: 22.2005 - val_loss: 9.0698 - val_mse: 9.0698\n",
      "Epoch 129/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 22.1466 - mse: 22.1466 - val_loss: 8.8922 - val_mse: 8.8922\n",
      "Epoch 130/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 19.3053 - mse: 19.3053 - val_loss: 8.9758 - val_mse: 8.9758\n",
      "Epoch 131/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.1683 - mse: 21.1683 - val_loss: 9.2687 - val_mse: 9.2687\n",
      "Epoch 132/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.5222 - mse: 21.5222 - val_loss: 9.4711 - val_mse: 9.4711\n",
      "Epoch 133/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 23.5636 - mse: 23.5636 - val_loss: 9.0286 - val_mse: 9.0286\n",
      "Epoch 134/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.4444 - mse: 20.4444 - val_loss: 8.6846 - val_mse: 8.6846\n",
      "Epoch 135/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 25.2865 - mse: 25.2865 - val_loss: 8.4105 - val_mse: 8.4105\n",
      "Epoch 136/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 22.1689 - mse: 22.1689 - val_loss: 8.4036 - val_mse: 8.4036\n",
      "Epoch 137/150\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 22.6206 - mse: 22.6206 - val_loss: 8.4334 - val_mse: 8.4334\n",
      "Epoch 138/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.7858 - mse: 19.7858 - val_loss: 8.4320 - val_mse: 8.4320\n",
      "Epoch 139/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.6179 - mse: 21.6179 - val_loss: 8.5096 - val_mse: 8.5096\n",
      "Epoch 140/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.5044 - mse: 21.5044 - val_loss: 8.7505 - val_mse: 8.7505\n",
      "Epoch 141/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.1933 - mse: 19.1933 - val_loss: 8.6992 - val_mse: 8.6992\n",
      "Epoch 142/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.6499 - mse: 20.6499 - val_loss: 8.3814 - val_mse: 8.3814\n",
      "Epoch 143/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 20.5318 - mse: 20.5318 - val_loss: 8.1714 - val_mse: 8.1714\n",
      "Epoch 144/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 21.6416 - mse: 21.6416 - val_loss: 8.0454 - val_mse: 8.0454\n",
      "Epoch 145/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 17.1077 - mse: 17.1077 - val_loss: 8.2056 - val_mse: 8.2056\n",
      "Epoch 146/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 21.5108 - mse: 21.5108 - val_loss: 8.4625 - val_mse: 8.4625\n",
      "Epoch 147/150\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 20.2511 - mse: 20.2511 - val_loss: 8.1915 - val_mse: 8.1915\n",
      "Epoch 148/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 20.1389 - mse: 20.1389 - val_loss: 7.5902 - val_mse: 7.5902\n",
      "Epoch 149/150\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 19.3756 - mse: 19.3756 - val_loss: 7.3760 - val_mse: 7.3760\n",
      "Epoch 150/150\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 20.4246 - mse: 20.4246 - val_loss: 7.2956 - val_mse: 7.2956\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "NN: -5.679715 (0.967159)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "alg_names = []\n",
    "\n",
    "# Initialize the KerasRegressor estimator with early stopping\n",
    "estimator = KerasRegressor(model=model_NN, batch_size=128, verbose=1, callbacks=[early_stopping])\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed, shuffle=True)\n",
    "    \n",
    "    # modify loop for NN\n",
    "    if name != 'NN':\n",
    "        cvs = cross_val_score(model, X_train, y_train, cv=kfold, scoring='neg_mean_squared_error')\n",
    "    else:    \n",
    "        validation_data = (X_val, y_val)\n",
    "        cvs = cross_val_score(estimator, X_train, y_train, cv=kfold, scoring='neg_mean_squared_error', fit_params={'validation_data': validation_data, 'epochs': 150})\n",
    "       \n",
    "    results.append(cvs)\n",
    "    alg_names.append(name)\n",
    "    output = \"%s: %f (%f)\" % (name, cvs.mean(), cvs.std())\n",
    "    print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ba4eeb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "5/5 [==============================] - 1s 59ms/step - loss: 616.5791 - mse: 616.5791 - val_loss: 692.3652 - val_mse: 692.3652\n",
      "Epoch 2/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 584.9875 - mse: 584.9875 - val_loss: 684.1837 - val_mse: 684.1837\n",
      "Epoch 3/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 567.0388 - mse: 567.0388 - val_loss: 673.6699 - val_mse: 673.6699\n",
      "Epoch 4/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 561.6024 - mse: 561.6024 - val_loss: 661.1407 - val_mse: 661.1407\n",
      "Epoch 5/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 548.2922 - mse: 548.2922 - val_loss: 646.8663 - val_mse: 646.8663\n",
      "Epoch 6/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 542.7639 - mse: 542.7639 - val_loss: 631.2789 - val_mse: 631.2789\n",
      "Epoch 7/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 535.9216 - mse: 535.9216 - val_loss: 615.9169 - val_mse: 615.9169\n",
      "Epoch 8/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 532.7103 - mse: 532.7103 - val_loss: 600.2155 - val_mse: 600.2155\n",
      "Epoch 9/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 530.8102 - mse: 530.8102 - val_loss: 585.7346 - val_mse: 585.7346\n",
      "Epoch 10/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 522.7648 - mse: 522.7648 - val_loss: 572.1830 - val_mse: 572.1830\n",
      "Epoch 11/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 516.6623 - mse: 516.6623 - val_loss: 559.0180 - val_mse: 559.0180\n",
      "Epoch 12/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 518.1570 - mse: 518.1570 - val_loss: 546.6819 - val_mse: 546.6819\n",
      "Epoch 13/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 509.6924 - mse: 509.6924 - val_loss: 535.3038 - val_mse: 535.3038\n",
      "Epoch 14/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 506.9359 - mse: 506.9359 - val_loss: 523.5010 - val_mse: 523.5010\n",
      "Epoch 15/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 500.0571 - mse: 500.0571 - val_loss: 511.7904 - val_mse: 511.7904\n",
      "Epoch 16/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 502.9256 - mse: 502.9256 - val_loss: 501.2018 - val_mse: 501.2018\n",
      "Epoch 17/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 495.1548 - mse: 495.1548 - val_loss: 492.3104 - val_mse: 492.3104\n",
      "Epoch 18/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 490.3869 - mse: 490.3869 - val_loss: 483.2831 - val_mse: 483.2831\n",
      "Epoch 19/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 487.1350 - mse: 487.1350 - val_loss: 474.3719 - val_mse: 474.3719\n",
      "Epoch 20/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 484.6595 - mse: 484.6595 - val_loss: 465.9610 - val_mse: 465.9610\n",
      "Epoch 21/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 476.6460 - mse: 476.6460 - val_loss: 457.3232 - val_mse: 457.3232\n",
      "Epoch 22/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 473.5692 - mse: 473.5692 - val_loss: 448.1599 - val_mse: 448.1599\n",
      "Epoch 23/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 466.9551 - mse: 466.9551 - val_loss: 440.9091 - val_mse: 440.9091\n",
      "Epoch 24/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 456.6537 - mse: 456.6537 - val_loss: 434.5473 - val_mse: 434.5473\n",
      "Epoch 25/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 453.4739 - mse: 453.4739 - val_loss: 425.7180 - val_mse: 425.7180\n",
      "Epoch 26/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 450.8221 - mse: 450.8221 - val_loss: 417.4975 - val_mse: 417.4975\n",
      "Epoch 27/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 442.2732 - mse: 442.2732 - val_loss: 406.6003 - val_mse: 406.6003\n",
      "Epoch 28/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 432.9490 - mse: 432.9490 - val_loss: 396.4057 - val_mse: 396.4057\n",
      "Epoch 29/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 428.0928 - mse: 428.0928 - val_loss: 386.8730 - val_mse: 386.8730\n",
      "Epoch 30/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 422.4142 - mse: 422.4142 - val_loss: 377.7092 - val_mse: 377.7092\n",
      "Epoch 31/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 412.9796 - mse: 412.9796 - val_loss: 369.3407 - val_mse: 369.3407\n",
      "Epoch 32/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 402.7037 - mse: 402.7037 - val_loss: 361.8048 - val_mse: 361.8048\n",
      "Epoch 33/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 394.1918 - mse: 394.1918 - val_loss: 352.9081 - val_mse: 352.9081\n",
      "Epoch 34/500\n",
      "5/5 [==============================] - 0s 16ms/step - loss: 385.1653 - mse: 385.1653 - val_loss: 341.1986 - val_mse: 341.1986\n",
      "Epoch 35/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 380.1561 - mse: 380.1561 - val_loss: 331.7398 - val_mse: 331.7398\n",
      "Epoch 36/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 370.1590 - mse: 370.1590 - val_loss: 319.2982 - val_mse: 319.2982\n",
      "Epoch 37/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 356.9142 - mse: 356.9142 - val_loss: 307.2201 - val_mse: 307.2201\n",
      "Epoch 38/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 351.2383 - mse: 351.2383 - val_loss: 297.7506 - val_mse: 297.7506\n",
      "Epoch 39/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 337.6215 - mse: 337.6215 - val_loss: 287.7682 - val_mse: 287.7682\n",
      "Epoch 40/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 330.6359 - mse: 330.6359 - val_loss: 281.2758 - val_mse: 281.2758\n",
      "Epoch 41/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 315.7326 - mse: 315.7326 - val_loss: 275.8190 - val_mse: 275.8190\n",
      "Epoch 42/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 303.8977 - mse: 303.8977 - val_loss: 267.8333 - val_mse: 267.8333\n",
      "Epoch 43/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 296.5256 - mse: 296.5256 - val_loss: 256.7587 - val_mse: 256.7587\n",
      "Epoch 44/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 287.1696 - mse: 287.1696 - val_loss: 246.2592 - val_mse: 246.2592\n",
      "Epoch 45/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 273.8888 - mse: 273.8888 - val_loss: 237.4281 - val_mse: 237.4281\n",
      "Epoch 46/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 258.2085 - mse: 258.2085 - val_loss: 228.2468 - val_mse: 228.2468\n",
      "Epoch 47/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 254.7982 - mse: 254.7982 - val_loss: 221.0000 - val_mse: 221.0000\n",
      "Epoch 48/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 243.0089 - mse: 243.0089 - val_loss: 207.7943 - val_mse: 207.7943\n",
      "Epoch 49/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 223.2250 - mse: 223.2250 - val_loss: 196.2795 - val_mse: 196.2795\n",
      "Epoch 50/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 219.9565 - mse: 219.9565 - val_loss: 184.8971 - val_mse: 184.8971\n",
      "Epoch 51/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 205.0349 - mse: 205.0349 - val_loss: 174.1051 - val_mse: 174.1051\n",
      "Epoch 52/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 203.1559 - mse: 203.1559 - val_loss: 167.4523 - val_mse: 167.4523\n",
      "Epoch 53/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 189.7110 - mse: 189.7110 - val_loss: 159.9305 - val_mse: 159.9305\n",
      "Epoch 54/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 175.2168 - mse: 175.2168 - val_loss: 151.5990 - val_mse: 151.5990\n",
      "Epoch 55/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 167.3579 - mse: 167.3579 - val_loss: 144.7739 - val_mse: 144.7739\n",
      "Epoch 56/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 160.8801 - mse: 160.8801 - val_loss: 136.1744 - val_mse: 136.1744\n",
      "Epoch 57/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 154.7324 - mse: 154.7324 - val_loss: 128.6035 - val_mse: 128.6035\n",
      "Epoch 58/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 141.3677 - mse: 141.3677 - val_loss: 122.5387 - val_mse: 122.5387\n",
      "Epoch 59/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 132.7061 - mse: 132.7061 - val_loss: 115.5373 - val_mse: 115.5373\n",
      "Epoch 60/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 12ms/step - loss: 133.1166 - mse: 133.1166 - val_loss: 105.7296 - val_mse: 105.7296\n",
      "Epoch 61/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 118.6626 - mse: 118.6626 - val_loss: 98.0777 - val_mse: 98.0777\n",
      "Epoch 62/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 116.2463 - mse: 116.2463 - val_loss: 91.6925 - val_mse: 91.6925\n",
      "Epoch 63/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 107.3364 - mse: 107.3364 - val_loss: 86.6353 - val_mse: 86.6353\n",
      "Epoch 64/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 96.7294 - mse: 96.7294 - val_loss: 78.5311 - val_mse: 78.5311\n",
      "Epoch 65/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 96.7533 - mse: 96.7533 - val_loss: 70.4140 - val_mse: 70.4140\n",
      "Epoch 66/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 87.2679 - mse: 87.2679 - val_loss: 64.5259 - val_mse: 64.5259\n",
      "Epoch 67/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 84.8976 - mse: 84.8976 - val_loss: 63.0463 - val_mse: 63.0463\n",
      "Epoch 68/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 79.0017 - mse: 79.0017 - val_loss: 59.2716 - val_mse: 59.2716\n",
      "Epoch 69/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 73.7434 - mse: 73.7434 - val_loss: 54.3451 - val_mse: 54.3451\n",
      "Epoch 70/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 65.1270 - mse: 65.1270 - val_loss: 48.5020 - val_mse: 48.5020\n",
      "Epoch 71/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 63.8651 - mse: 63.8651 - val_loss: 44.8877 - val_mse: 44.8877\n",
      "Epoch 72/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 60.2692 - mse: 60.2692 - val_loss: 41.2263 - val_mse: 41.2263\n",
      "Epoch 73/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 55.2969 - mse: 55.2969 - val_loss: 36.7089 - val_mse: 36.7089\n",
      "Epoch 74/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 55.6969 - mse: 55.6969 - val_loss: 32.8158 - val_mse: 32.8158\n",
      "Epoch 75/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 50.6131 - mse: 50.6131 - val_loss: 30.2560 - val_mse: 30.2560\n",
      "Epoch 76/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 47.6148 - mse: 47.6148 - val_loss: 29.8226 - val_mse: 29.8226\n",
      "Epoch 77/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 44.5515 - mse: 44.5515 - val_loss: 30.5388 - val_mse: 30.5388\n",
      "Epoch 78/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 38.1671 - mse: 38.1671 - val_loss: 28.8474 - val_mse: 28.8474\n",
      "Epoch 79/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 38.5733 - mse: 38.5733 - val_loss: 26.2581 - val_mse: 26.2581\n",
      "Epoch 80/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 35.4889 - mse: 35.4889 - val_loss: 23.4041 - val_mse: 23.4041\n",
      "Epoch 81/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 36.6011 - mse: 36.6011 - val_loss: 22.4325 - val_mse: 22.4325\n",
      "Epoch 82/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 32.5519 - mse: 32.5519 - val_loss: 21.4120 - val_mse: 21.4120\n",
      "Epoch 83/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 32.1377 - mse: 32.1377 - val_loss: 20.7188 - val_mse: 20.7188\n",
      "Epoch 84/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 31.6089 - mse: 31.6089 - val_loss: 18.7373 - val_mse: 18.7373\n",
      "Epoch 85/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 31.3195 - mse: 31.3195 - val_loss: 17.6003 - val_mse: 17.6003\n",
      "Epoch 86/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 29.5712 - mse: 29.5712 - val_loss: 16.0559 - val_mse: 16.0559\n",
      "Epoch 87/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 29.9238 - mse: 29.9238 - val_loss: 15.6449 - val_mse: 15.6449\n",
      "Epoch 88/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 27.0393 - mse: 27.0393 - val_loss: 14.8172 - val_mse: 14.8172\n",
      "Epoch 89/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 26.2152 - mse: 26.2152 - val_loss: 13.5813 - val_mse: 13.5813\n",
      "Epoch 90/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 29.5041 - mse: 29.5041 - val_loss: 13.1402 - val_mse: 13.1402\n",
      "Epoch 91/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 24.4738 - mse: 24.4738 - val_loss: 12.0527 - val_mse: 12.0527\n",
      "Epoch 92/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 26.2009 - mse: 26.2009 - val_loss: 12.1898 - val_mse: 12.1898\n",
      "Epoch 93/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 23.2142 - mse: 23.2142 - val_loss: 12.5146 - val_mse: 12.5146\n",
      "Epoch 94/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 29.0637 - mse: 29.0637 - val_loss: 12.9518 - val_mse: 12.9518\n",
      "Epoch 95/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.2934 - mse: 22.2934 - val_loss: 12.8435 - val_mse: 12.8435\n",
      "Epoch 96/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 24.1450 - mse: 24.1450 - val_loss: 12.2130 - val_mse: 12.2130\n",
      "Epoch 97/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.4771 - mse: 22.4771 - val_loss: 11.3064 - val_mse: 11.3064\n",
      "Epoch 98/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 23.1114 - mse: 23.1114 - val_loss: 10.8802 - val_mse: 10.8802\n",
      "Epoch 99/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 24.7208 - mse: 24.7208 - val_loss: 10.5779 - val_mse: 10.5779\n",
      "Epoch 100/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 22.6490 - mse: 22.6490 - val_loss: 9.7359 - val_mse: 9.7359\n",
      "Epoch 101/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 23.9637 - mse: 23.9637 - val_loss: 9.3333 - val_mse: 9.3333\n",
      "Epoch 102/500\n",
      "5/5 [==============================] - 0s 14ms/step - loss: 22.6831 - mse: 22.6831 - val_loss: 9.3004 - val_mse: 9.3004\n",
      "Epoch 103/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.7310 - mse: 22.7310 - val_loss: 9.9124 - val_mse: 9.9124\n",
      "Epoch 104/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 22.1452 - mse: 22.1452 - val_loss: 10.0134 - val_mse: 10.0134\n",
      "Epoch 105/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 23.6309 - mse: 23.6309 - val_loss: 9.5221 - val_mse: 9.5221\n",
      "Epoch 106/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.8395 - mse: 20.8395 - val_loss: 8.8755 - val_mse: 8.8755\n",
      "Epoch 107/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 23.3043 - mse: 23.3043 - val_loss: 8.5798 - val_mse: 8.5798\n",
      "Epoch 108/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.2841 - mse: 20.2841 - val_loss: 8.5860 - val_mse: 8.5860\n",
      "Epoch 109/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.8018 - mse: 20.8018 - val_loss: 8.5943 - val_mse: 8.5943\n",
      "Epoch 110/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.4679 - mse: 19.4679 - val_loss: 8.3735 - val_mse: 8.3735\n",
      "Epoch 111/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 22.5725 - mse: 22.5725 - val_loss: 7.9591 - val_mse: 7.9591\n",
      "Epoch 112/500\n",
      "5/5 [==============================] - 0s 15ms/step - loss: 21.8027 - mse: 21.8027 - val_loss: 7.8358 - val_mse: 7.8358\n",
      "Epoch 113/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 21.7398 - mse: 21.7398 - val_loss: 7.7351 - val_mse: 7.7351\n",
      "Epoch 114/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.6160 - mse: 19.6160 - val_loss: 7.6731 - val_mse: 7.6731\n",
      "Epoch 115/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.2280 - mse: 20.2280 - val_loss: 7.6403 - val_mse: 7.6403\n",
      "Epoch 116/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.5545 - mse: 22.5545 - val_loss: 7.3938 - val_mse: 7.3938\n",
      "Epoch 117/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.6031 - mse: 19.6031 - val_loss: 7.3913 - val_mse: 7.3913\n",
      "Epoch 118/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.0679 - mse: 19.0679 - val_loss: 7.2948 - val_mse: 7.2948\n",
      "Epoch 119/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.3146 - mse: 19.3146 - val_loss: 7.1291 - val_mse: 7.1291\n",
      "Epoch 120/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 21.4262 - mse: 21.4262 - val_loss: 7.0495 - val_mse: 7.0495\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 121/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 19.9059 - mse: 19.9059 - val_loss: 6.9000 - val_mse: 6.9000\n",
      "Epoch 122/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.0765 - mse: 22.0765 - val_loss: 6.7949 - val_mse: 6.7949\n",
      "Epoch 123/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.6036 - mse: 19.6036 - val_loss: 6.8704 - val_mse: 6.8704\n",
      "Epoch 124/500\n",
      "5/5 [==============================] - 0s 16ms/step - loss: 21.1786 - mse: 21.1786 - val_loss: 6.8985 - val_mse: 6.8985\n",
      "Epoch 125/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.4972 - mse: 22.4972 - val_loss: 6.7335 - val_mse: 6.7335\n",
      "Epoch 126/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 18.8568 - mse: 18.8568 - val_loss: 6.4185 - val_mse: 6.4185\n",
      "Epoch 127/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.3705 - mse: 20.3705 - val_loss: 6.4943 - val_mse: 6.4943\n",
      "Epoch 128/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 22.0952 - mse: 22.0952 - val_loss: 6.4132 - val_mse: 6.4132\n",
      "Epoch 129/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 16.3277 - mse: 16.3277 - val_loss: 7.0117 - val_mse: 7.0117\n",
      "Epoch 130/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.5719 - mse: 20.5719 - val_loss: 6.9247 - val_mse: 6.9247\n",
      "Epoch 131/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 18.0180 - mse: 18.0180 - val_loss: 6.4783 - val_mse: 6.4783\n",
      "Epoch 132/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 20.3847 - mse: 20.3847 - val_loss: 6.3017 - val_mse: 6.3017\n",
      "Epoch 133/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 19.2486 - mse: 19.2486 - val_loss: 6.1891 - val_mse: 6.1891\n",
      "Epoch 134/500\n",
      "5/5 [==============================] - 0s 9ms/step - loss: 18.0500 - mse: 18.0500 - val_loss: 6.1686 - val_mse: 6.1686\n",
      "Epoch 135/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 17.7361 - mse: 17.7361 - val_loss: 5.9113 - val_mse: 5.9113\n",
      "Epoch 136/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 20.7398 - mse: 20.7398 - val_loss: 5.8268 - val_mse: 5.8268\n",
      "Epoch 137/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 19.8358 - mse: 19.8358 - val_loss: 5.8136 - val_mse: 5.8136\n",
      "Epoch 138/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.0139 - mse: 19.0139 - val_loss: 5.7352 - val_mse: 5.7352\n",
      "Epoch 139/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.7218 - mse: 17.7218 - val_loss: 5.6590 - val_mse: 5.6590\n",
      "Epoch 140/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 19.7770 - mse: 19.7770 - val_loss: 5.5583 - val_mse: 5.5583\n",
      "Epoch 141/500\n",
      "5/5 [==============================] - 0s 17ms/step - loss: 18.7261 - mse: 18.7261 - val_loss: 5.4521 - val_mse: 5.4521\n",
      "Epoch 142/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.6870 - mse: 16.6870 - val_loss: 5.3850 - val_mse: 5.3850\n",
      "Epoch 143/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 17.7236 - mse: 17.7236 - val_loss: 5.6585 - val_mse: 5.6585\n",
      "Epoch 144/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 18.8136 - mse: 18.8136 - val_loss: 5.4001 - val_mse: 5.4001\n",
      "Epoch 145/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 18.1854 - mse: 18.1854 - val_loss: 5.3727 - val_mse: 5.3727\n",
      "Epoch 146/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.4432 - mse: 19.4432 - val_loss: 5.3603 - val_mse: 5.3603\n",
      "Epoch 147/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.1750 - mse: 17.1750 - val_loss: 5.5145 - val_mse: 5.5145\n",
      "Epoch 148/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 20.4510 - mse: 20.4510 - val_loss: 5.4382 - val_mse: 5.4382\n",
      "Epoch 149/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 21.0947 - mse: 21.0947 - val_loss: 5.3482 - val_mse: 5.3482\n",
      "Epoch 150/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.8440 - mse: 16.8440 - val_loss: 5.4650 - val_mse: 5.4650\n",
      "Epoch 151/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 17.2067 - mse: 17.2067 - val_loss: 5.4732 - val_mse: 5.4732\n",
      "Epoch 152/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 18.5405 - mse: 18.5405 - val_loss: 5.4793 - val_mse: 5.4793\n",
      "Epoch 153/500\n",
      "5/5 [==============================] - 0s 11ms/step - loss: 18.4851 - mse: 18.4851 - val_loss: 5.5986 - val_mse: 5.5986\n",
      "Epoch 154/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.4977 - mse: 17.4977 - val_loss: 5.5081 - val_mse: 5.5081\n",
      "Epoch 155/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.7996 - mse: 16.7996 - val_loss: 5.0904 - val_mse: 5.0904\n",
      "Epoch 156/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 18.7119 - mse: 18.7119 - val_loss: 4.8074 - val_mse: 4.8074\n",
      "Epoch 157/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.2796 - mse: 19.2796 - val_loss: 4.9450 - val_mse: 4.9450\n",
      "Epoch 158/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 17.1954 - mse: 17.1954 - val_loss: 4.8124 - val_mse: 4.8124\n",
      "Epoch 159/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.1144 - mse: 16.1144 - val_loss: 5.0923 - val_mse: 5.0923\n",
      "Epoch 160/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.0875 - mse: 16.0875 - val_loss: 4.8355 - val_mse: 4.8355\n",
      "Epoch 161/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.3115 - mse: 17.3115 - val_loss: 4.8831 - val_mse: 4.8831\n",
      "Epoch 162/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 19.0209 - mse: 19.0209 - val_loss: 4.7536 - val_mse: 4.7536\n",
      "Epoch 163/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.2609 - mse: 17.2609 - val_loss: 4.7939 - val_mse: 4.7939\n",
      "Epoch 164/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 18.9550 - mse: 18.9550 - val_loss: 4.7886 - val_mse: 4.7886\n",
      "Epoch 165/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.8390 - mse: 16.8390 - val_loss: 4.5417 - val_mse: 4.5417\n",
      "Epoch 166/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 17.6851 - mse: 17.6851 - val_loss: 4.4358 - val_mse: 4.4358\n",
      "Epoch 167/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 16.5046 - mse: 16.5046 - val_loss: 4.3156 - val_mse: 4.3156\n",
      "Epoch 168/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 18.1548 - mse: 18.1548 - val_loss: 4.2972 - val_mse: 4.2972\n",
      "Epoch 169/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.2256 - mse: 16.2256 - val_loss: 4.2414 - val_mse: 4.2414\n",
      "Epoch 170/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.9435 - mse: 16.9435 - val_loss: 4.1053 - val_mse: 4.1053\n",
      "Epoch 171/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 18.6688 - mse: 18.6688 - val_loss: 4.0060 - val_mse: 4.0060\n",
      "Epoch 172/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.7259 - mse: 16.7259 - val_loss: 4.0261 - val_mse: 4.0261\n",
      "Epoch 173/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.9361 - mse: 16.9361 - val_loss: 4.1651 - val_mse: 4.1651\n",
      "Epoch 174/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 18.8246 - mse: 18.8246 - val_loss: 4.5109 - val_mse: 4.5109\n",
      "Epoch 175/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 16.4379 - mse: 16.4379 - val_loss: 4.4101 - val_mse: 4.4101\n",
      "Epoch 176/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 15.6412 - mse: 15.6412 - val_loss: 4.4338 - val_mse: 4.4338\n",
      "Epoch 177/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 15.5898 - mse: 15.5898 - val_loss: 4.1636 - val_mse: 4.1636\n",
      "Epoch 178/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 15.8859 - mse: 15.8859 - val_loss: 4.1026 - val_mse: 4.1026\n",
      "Epoch 179/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.2699 - mse: 17.2699 - val_loss: 4.0474 - val_mse: 4.0474\n",
      "Epoch 180/500\n",
      "5/5 [==============================] - 0s 13ms/step - loss: 16.8264 - mse: 16.8264 - val_loss: 4.3185 - val_mse: 4.3185\n",
      "Epoch 181/500\n",
      "5/5 [==============================] - 0s 12ms/step - loss: 17.7765 - mse: 17.7765 - val_loss: 4.0432 - val_mse: 4.0432\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x172a36f2520>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve of NN model\n",
    "history = model_NN.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=500, batch_size=128, callbacks=[early_stopping])\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(history.history['loss'], label = 'training')\n",
    "plt.plot(history.history['val_loss'], label = 'testing')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title('Learning curve of the NN model')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9727c181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b) Compare algorithms and the NN model\n",
    "# Distribution of accuracy values calculated across 8 cross-validation folds.\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot()\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(alg_names)\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49996c8f",
   "metadata": {},
   "source": [
    "The Random Forest algorithm demonstrated superior performance compared to the other five machine learning algorithms and the neural network model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79de1ac",
   "metadata": {},
   "source": [
    "In a regression problem, it is common to use the mean error as a measure of validation. This is done by first calculating the mean absolute error (MAE) of the test data with the mean of dataset, and then calculating the MAE of the test data with the predicted data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d2e51ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of predicted data:  0.8418854166666667\n"
     ]
    }
   ],
   "source": [
    "# Evaluate performance of Random Forest algorithm on validation data\n",
    "model_RF = RandomForestRegressor()\n",
    "model_RF.fit(X_train, y_train)\n",
    "y_pred_RF = model_RF.predict(X_val)\n",
    "mae_pred_RF = mean_absolute_error(y_val, y_pred_RF)\n",
    "print(\"Mean Absolute Error of predicted data: \", mae_pred_RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce4349da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 0s 3ms/step - loss: 18.4048 - mse: 18.4048\n",
      "6/6 [==============================] - 0s 2ms/step\n",
      "(192, 2)\n",
      "Mean Absolute Error of predicted data:  1.5929388094941777\n"
     ]
    }
   ],
   "source": [
    "# Evaluate performance of NN algorithm on validation data\n",
    "model_NN.fit(X_train, y_train)\n",
    "y_pred_NN = model_NN.predict(X_val)\n",
    "print(y_pred_NN.shape)\n",
    "mae_pred_NN = mean_absolute_error(y_val, y_pred_NN)\n",
    "print(\"Mean Absolute Error of predicted data: \", mae_pred_NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d932a556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of original data:  8.900351223415804\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\Anaconda\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3472: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Define baseline mean_absolute_error of y_val in the data set\n",
    "y_mean = np.mean(y)\n",
    "y_mean = np.array([y_mean]*len(y_val))\n",
    "mae_ori = mean_absolute_error(y_val, y_mean)\n",
    "print(\"Mean Absolute Error of original data: \", mae_ori)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fc4aae",
   "metadata": {},
   "source": [
    "The resulting MAE values show that Random Forest looks better than neural network model in prediction performance. For the sake of simplicity, I selected Random Forest as the surrogate model for the multi-objective optimization process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d1925b",
   "metadata": {},
   "source": [
    "**5. Finalize Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4acd266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}\n"
     ]
    }
   ],
   "source": [
    "# a) Get best model parameters\n",
    "model_params = model_RF.get_params()\n",
    "\n",
    "# Print the model's parameters\n",
    "print(model_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e989a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
    "\n",
    "# Plot the actual and predicted heating load\n",
    "ax1.bar(np.arange(len(y_val)), y_val.iloc[:, 0], label='Actual heating load', color='orange')\n",
    "ax1.scatter(np.arange(len(y_pred_RF)), y_pred_RF[:, 0], label='Predicted data', color='red')\n",
    "ax1.set_ylabel('Heating Load')\n",
    "ax1.legend()\n",
    "\n",
    "# Plot the actual and predicted cooling load\n",
    "ax2.bar(np.arange(len(y_val)), y_val.iloc[:, 1], label='Actual cooling load')\n",
    "ax2.scatter(np.arange(len(y_pred_RF)), y_pred_RF[:, 1], label='Predicted data', color='blue')\n",
    "ax2.set_ylabel('Cooling Load')\n",
    "ax2.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea7bdb3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# b) Save model for later use\n",
    "# save the model to disk\n",
    "filename = 'finalized_model_multivariate.sav'\n",
    "dump(model_RF, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b4d9c4",
   "metadata": {},
   "source": [
    "**6. Conclusions**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d153e8",
   "metadata": {},
   "source": [
    "- Exploratory data analysis indicates that most input variables exhibit non-Gaussian distribution.\n",
    "\n",
    "- Based on our evaluation of five machine learning algorithms and a neural network model, we selected Random Forest as the surrogate model for multi-objective optimization due to its high score in cross validation and low Mean Squared Error values.\n",
    "\n",
    "- The Mean Absolute Error values suggest that the prediction accuracy is generally good, although the ElasticNet algorithm may require further refinement.\n",
    "\n",
    "- The results indicate that the performance of the Keras Sequential neural network model is comparable to that of other machine learning algorithms, and further hyperparameter tuning may improve its predictive accuracy."
   ]
  }
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