# model.add(Dense(1))
# model.compile(loss='mean_squared_error', optimizer='adam')
# for i in range(nb_epoch):
# model.fit(X_train, y_train, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
# model.reset_states()
inputs_1_mae = Input(batch_shape=(batch_size, timesteps, 1))
lstm_1_mae = LSTM(10, stateful=True, return_sequences=True)(inputs_1_mae)
lstm_2_mae = LSTM(10, stateful=True, return_sequences=True)(lstm_1_mae)
output_1_mae = Dense(units=1)(lstm_2_mae)
regressor_mae = Model(inputs=inputs_1_mae, outputs=output_1_mae)
regressor_mae.compile(optimizer='adam', loss='mae')
regressor_mae.summary()
epochs = 2
for i in range(epochs):
print("Epoch: " + str(i))
regressor_mae.fit(X_train,
y_train,
shuffle=False,
epochs=1,
batch_size=batch_size)
regressor_mae.reset_states()
test_length = data_misc.get_test_length(dataset=raw_values,
batch_size=batch_size,
upper_train=upper_train,
timesteps=timesteps)
