Remove reconstruction examples after training

train.py

@@ -109,27 +109,6 @@ while total_change >= 1e-4:
 
     batch += 1
 
-print('\nFinished training\n')
+print('\nFinished training')
-
+print('Saving model as {}'.format(output_file))
-# Print some examples of reconstruction
-model.eval()
-print('Reconstruction examples:')
-print('Input vector\t\t\tOutput vector after softmax')
-
-with torch.no_grad():
-    onehot_example = util.messages_to_onehot(torch.arange(0, order), order)
-    raw_output = model(onehot_example)
-    raw_output.required_grad = False
-    reconstructed_example = torch.nn.functional.softmax(raw_output, dim=1)
-
-    for index in range(order):
-        print('{}\t\t{}'.format(
-            onehot_example[index].tolist(),
-            '[{}]'.format(', '.join(
-                '{:.5f}'.format(x)
-                for x in reconstructed_example[index].tolist()
-            ))
-        ))
-
-print('\nSaving model as {}'.format(output_file))
 torch.save(model.state_dict(), output_file)