# 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 # reshaping test_set = numpy.reshape(test_set, (test_set.shape[0], 1)) # scaling test_set = sc.fit_transform(numpy.float64(test_set)) x_test, y_test = data_misc.test_data_to_timesteps( test=test_set, testset_length=testset_length, timesteps=timesteps) rmse, predictions = compare(y_test, y_test) predicted_bcg_values_test_mae = regressor_mae.predict(x_test, batch_size=batch_size) regressor_mae.reset_states() print(predicted_bcg_values_test_mae.shape) # reshaping predicted_bcg_values_test_mae = numpy.reshape( predicted_bcg_values_test_mae, (predicted_bcg_values_test_mae.shape[0], predicted_bcg_values_test_mae.shape[1])) y_test = numpy.reshape(y_test, (y_test.shape[0], y_test.shape[1])) print(predicted_bcg_values_test_mae.shape) # inverse transform predicted_bcg_values_test_mae = sc.inverse_transform(