# 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)