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)) # df_data_1_test = raw_values[upper_train - 1:upper_test] df_data_1_test = diff_values[upper_train:upper_test] test_set = df_data_1_test.values