Aesthetic fix.

experiments/_processes.py

@@ -102,8 +102,10 @@ def testing(model: Module,
  model.eval()
  with no_grad():
  _, _, true_values, hat_values = _sed_epoch(
- model=model, data_loader=data_loader,
- objective=None, optimizer=None,
+ model=model,
+ data_loader=data_loader,
+ objective=None,
+ optimizer=None,
  device=device)
 
  end_time = time() - start_time
@@ -166,9 +168,12 @@ def training(model:Module,
 
  model = model.train()
  model, epoch_tr_loss, true_training, hat_training = _sed_epoch(
- model=model, data_loader=data_loader_training,
- objective=objective, optimizer=optimizer,
- device=device, grad_norm=grad_norm)
+ model=model,
+ data_loader=data_loader_training,
+ objective=objective,
+ optimizer=optimizer,
+ device=device,
+ grad_norm=grad_norm)
 
  epoch_tr_loss = epoch_tr_loss.mean().item()
 
@@ -183,8 +188,10 @@ def training(model:Module,
  model = model.eval()
  with no_grad():
  model, epoch_va_loss, true_validation, hat_validation = _sed_epoch(
- model=model, data_loader=data_loader_validation,
- objective=objective, optimizer=None,
+ model=model,
+ data_loader=data_loader_validation,
+ objective=objective,
+ optimizer=None,
  device=device)
 
  epoch_va_loss = epoch_va_loss.mean().item()
@@ -208,7 +215,8 @@ def training(model:Module,
  end_time = time() - start_time
 
  results_training(
- epoch=epoch, training_loss=epoch_tr_loss,
+ epoch=epoch,
+ training_loss=epoch_tr_loss,
  validation_loss=epoch_va_loss,
  training_f1=f1_score_training,
  training_er=error_rate_training,
@@ -286,8 +294,10 @@ def experiment(settings: MutableMapping,
  cmd_msg('Starting training', start='\n\n-- ', end='\n\n')
 
  optimized_model = training(
- model=model, data_loader_training=training_data,
- optimizer=optimizer, objective=BCEWithLogitsLoss(),
+ model=model,
+ data_loader_training=training_data,
+ optimizer=optimizer,
+ objective=BCEWithLogitsLoss(),
  epochs=settings['training']['epochs'],
  data_loader_validation=validation_data,
  validation_patience=settings['training']['validation_patience'],