def save_preproccesing_parameters(dataset_dir):
y_dir = os.path.join(dataset_dir, 'train', 'y')
mean_path = os.path.join(dataset_dir, 'transform_0_mean.txt')
std_path = os.path.join(dataset_dir, 'transform_0_std.txt')
train_set = Datafolder_soundfiles(y_paths=walk_dir(y_dir))
training_data_loader = DataLoader(train_set, batch_size=1, num_workers=2)
scaler0 = StandardScaler()
t_0 = transform_tf(feature_transform_0())
for index, x, y in training_data_loader:
y = y[0].numpy()
data, Y_m, Y_a, length = t_0(y=y)
scaler0.partial_fit(data.numpy().T) # each time step is treated equally
np.savetxt(mean_path, scaler0.mean_)
np.savetxt(std_path, scaler0.scale_)
print(scaler0.mean_)
print(scaler0.scale_)
# training using a phase sesitive masks, ReLU activation on the output
p = {
'experiment_name':
'BLSTM_A11',
'model_class':
BLSTM_A,
'model_kwargs': {
'input_size': 100,
'output_size': 129,
'hidden_size': 384,
'LSTM_layers': 2,
'output_act_f': 'ReLU'
},
'input_transform':
transform_tf(
feature_transform_0(os.path.join('data', 'processed', 'dataset_5')),
ideal_phase_sensitive_target),
'output_transform':
apply_mask,
'training_set':
load_dataset_5_train,
'validation_set':
load_dataset_4_val,
'batch_size':
10,
'epochs_max':
57,
'samples_per_epoch':
21384, # length of dataset_3 and 4
'criterion':
nn.MSELoss(),
