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)
print(test_length)
upper_test = test_length + timesteps * 2
testset_length = test_length - upper_train
print(testset_length)
print(upper_train, upper_test, len(raw_values))
