def predict(self, test_x, out_scaler, gen_test_file_list, sequential_training=False, stateful=False): #### compute predictions #### io_funcs = BinaryIOCollection() test_file_number = len(gen_test_file_list) print("generating features on held-out test data...") for utt_index in range(test_file_number): gen_test_file_name = gen_test_file_list[utt_index] test_id = os.path.splitext(os.path.basename(gen_test_file_name))[0] temp_test_x = test_x[test_id] num_of_rows = temp_test_x.shape[0] if stateful: temp_test_x = data_utils.get_stateful_input( temp_test_x, self.seq_length, self.batch_size) elif sequential_training: temp_test_x = np.reshape(temp_test_x, (1, num_of_rows, self.n_in)) predictions = self.model.predict(temp_test_x) if sequential_training: predictions = np.reshape(predictions, (num_of_rows, self.n_out)) data_utils.denorm_data(predictions, out_scaler) io_funcs.array_to_binary_file(predictions, gen_test_file_name) data_utils.drawProgressBar(utt_index + 1, test_file_number) sys.stdout.write("\n")
def predict(self, test_x, out_scaler, gen_test_file_list, sequential_training=False, stateful=False): #### compute predictions #### io_funcs = BinaryIOCollection() test_file_number = len(gen_test_file_list) print("generating features on held-out test data...") for utt_index in range(test_file_number): gen_test_file_name = gen_test_file_list[utt_index] test_id = os.path.splitext(os.path.basename(gen_test_file_name))[0] temp_test_x = test_x[test_id] num_of_rows = temp_test_x.shape[0] if stateful: temp_test_x = data_utils.get_stateful_input(temp_test_x, self.seq_length, self.batch_size) elif sequential_training: temp_test_x = np.reshape(temp_test_x, (1, num_of_rows, self.n_in)) predictions = self.model.predict(temp_test_x) if sequential_training: predictions = np.reshape(predictions, (num_of_rows, self.n_out)) data_utils.denorm_data(predictions, out_scaler) io_funcs.array_to_binary_file(predictions, gen_test_file_name) data_utils.drawProgressBar(utt_index+1, test_file_number) sys.stdout.write("\n")