sad

nnet/sad_rnn.py

@@ -63,6 +63,7 @@ class SAD_Dataset(Dataset):
                     start, stop = 0, len(features)
                 for i in range(start, min(stop, len(features)) - self.duration, self.step):
                     self.segments.append((show, i, i + self.duration))
+        self.input_size = features.shape[1]
 
         if shuffle:
             random.shuffle(self.segments)
@@ -242,11 +243,10 @@ class SAD_RNN():
             for batch_idx, (X, Y) in enumerate(training_set):
                 batch_loss = self._fit_batch(optimizer, criterion, X, Y)
                 losses[epoch].append(batch_loss)
-                sys.stdout.write("\rEpoch {}/{} ({}/{}), loss {:.5f}".format(
-                    epoch + 1, nb_epochs, it, est_it, numpy.mean(losses[epoch])))
+                sys.stdout.write("\rEpoch {}/{}, loss {:.5f}".format(
+                    epoch + 1, nb_epochs, numpy.mean(losses[epoch])))
                 sys.stdout.flush()
                 it += 1
-            #est_it = len(losses[epoch])
             torch.save(self.model.state_dict(), model_file_format.format(epoch+1))
 
     def get_labels(self, model_fn, show, features_server,