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")