from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from tensorflow.python.keras.models import Sequential, Model
from tensorflow.python.keras.layers import Dense, Input
import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler   

#1. 데이터

datasets = load_boston()
x= datasets.data
y= datasets['target']

x_train, x_test, y_train, y_test = train_test_split (x,y,
                                                     train_size=0.8,
                                                     random_state=333,
                                                     )

scaler = StandardScaler()
# scaler.fit(x_train)   
# x_train = scaler.transform(x_train)

x_train= scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)            
print(np.min(x_test), np.max(x_test))      

#2. 모델

input1 = Input(shape=(13,))        
dense1 = Dense(30)(input1)
dense2 = Dense(20)(dense1)
dense3 = Dense(10)(dense2)
output1 = Dense(1)(dense3)
model = Model(inputs=input1, outputs=output1)

model.save('./_save/keras26_1_save_model.h5')    #모델을 저장하겠다.   모델 세이브의 확장자는 통상적으로 h5로한다.

# #3. 컴파일, 훈련

# model.compile(loss = 'mse', optimizer='adam',)
# model.fit(x_train,y_train,
#           epochs=10,
#           batch_size=32,
#           verbose=1,
#           )

# #4. 평가, 예측
# loss = model.evaluate(x_test, y_test)
# print('loss :', loss)