Added inline comments for explanation.

scripts/training.py

@@ -155,11 +155,14 @@ def _training_iteration(_m, _data, _device, _solver, _sep_l, _reg_twin,
 def training_process():
     """The training process.
     """
+    # Check what device we'll be using
     device = 'cuda' if not debug and cuda.is_available() else 'cpu'
 
+    # Inform about the device and time and date
     printing.print_intro_messages(device)
     printing.print_msg('Starting training process. Debug mode: {}'.format(debug))
 
+    # Set up MaD TwinNet
     with printing.InformAboutProcess('Setting up MaD TwinNet'):
         mad_twin_net = MaDTwinNet(
             rnn_enc_input_dim=hyper_parameters['reduced_dim'],
@@ -168,13 +171,14 @@ def training_process():
             context_length=hyper_parameters['context_length']
         ).to(device)
 
+    # Get the optimizer
     with printing.InformAboutProcess('Setting up optimizer'):
-        # Optimizer
         optimizer = optim.Adam(
             mad_twin_net.parameters(),
             lr=hyper_parameters['learning_rate']
         )
 
+    # Create the data feeder
     with printing.InformAboutProcess('Initializing data feeder'):
         epoch_it = data_feeder.data_feeder_training(
             window_size=hyper_parameters['window_size'],
@@ -187,6 +191,7 @@ def training_process():
             debug=debug
         )
 
+    # Inform about the future
     printing.print_msg('Training starts', end='\n\n')
 
     # Training loop
@@ -196,12 +201,14 @@ def training_process():
                 hyper_parameters['lambda_2'], hyper_parameters['max_grad_norm'])
      for e in range(training_constants['epochs'])]
 
-    # Kindly end and save the model
+    # Inform about the past
     printing.print_msg('Training done.', start='\n-- ')
 
+    # Save the model
     with printing.InformAboutProcess('Saving model.. '):
         save(mad_twin_net.mad.state_dict(), output_states_path['mad'])
 
+    # Say goodbye!
     printing.print_msg('That\'s all folks!')