start = time()

from keras.models import Sequential
from keras.layers import Dense, Dropout,Input
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
from keras import layers
from keras.models import Model


# Parameters for denoising autoencoder
nb_visible = 120
nb_hidden = 64
batch_size = 16
# Build autoencoder model
input_img = Input(shape=(nb_visible,))

encoded = Dense(nb_hidden, activation='relu')(input_img)
decoded = Dense(nb_visible, activation='sigmoid')(encoded)

autoencoder = Model(input=input_img, output=decoded)
autoencoder.compile(loss='mean_squared_error',optimizer='adam',metrics=['mae'])
autoencoder.summary()

# Train
### 加一個early_stooping
import keras 

early_stopping = keras.callbacks.EarlyStopping(
    monitor='val_loss',
    min_delta=0.0001,
    patience=5, 
    verbose=0, 
    mode='auto'
)
autoencoder.fit(X_train_np, y_train_np, nb_epoch=50, batch_size=batch_size , shuffle=True,
                callbacks = [early_stopping],verbose = 1,validation_data=(X_test_np, y_test_np))
# Evaluate
evaluation = autoencoder.evaluate(X_test_np, y_test_np, batch_size=batch_size , verbose=1)
print('val_loss: %.6f, val_mean_absolute_error: %.6f' % (evaluation[0], evaluation[1]))

end = time()
print('耗時：'+str((end-start)/60))