import numpy as np

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split as tts

#1. 데이터
datasets = fetch_california_housing()
x = datasets.data
y = datasets.target

x_train, x_test, y_train, y_test = tts(x,y,
                                       train_size=0.8,
                                       shuffle=True,
                                       random_state=337,
                                       )

#2. 모델
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(64, input_dim=8))
model.add(Dense(32))
model.add(Dense(32))
model.add(Dense(32))
model.add(Dense(1))

#3. 컴파일, 훈련

from tensorflow.keras.optimizers import Adam
learning_rate = 0.01
optimizer = Adam(learning_rate=learning_rate,)
model.compile(loss='mse', optimizer=optimizer)
model.fit(x_train, y_train, epochs = 100, batch_size = 32)

#4. 평가, 예측
results = model.evaluate(x_test, y_test)

print("loss :", results)