added comments in snli/train.py, no code changes (pytorch#177)

Author: andreh7

snli/train.py

@@ -38,6 +38,8 @@
 config.n_embed = len(inputs.vocab)
 config.d_out = len(answers.vocab)
 config.n_cells = config.n_layers
+
+# double the number of cells for bidirectional networks
 if config.birnn:
  config.n_cells *= 2
 
@@ -66,41 +68,71 @@
  train_iter.init_epoch()
  n_correct, n_total = 0, 0
  for batch_idx, batch in enumerate(train_iter):
+
+ # switch model to training mode, clear gradient accumulators
  model.train(); opt.zero_grad()
+
  iterations += 1
+
+ # forward pass
  answer = model(batch)
+
+ # calculate accuracy of predictions in the current batch
  n_correct += (torch.max(answer, 1)[1].view(batch.label.size()).data == batch.label.data).sum()
  n_total += batch.batch_size
  train_acc = 100. * n_correct/n_total
+
+ # calculate loss of the network output with respect to training labels
  loss = criterion(answer, batch.label)
+
+ # backpropagate and update optimizer learning rate
  loss.backward(); opt.step()
+
+ # checkpoint model periodically
  if iterations % args.save_every == 0:
  snapshot_prefix = os.path.join(args.save_path, 'snapshot')
  snapshot_path = snapshot_prefix + '_acc_{:.4f}_loss_{:.6f}_iter_{}_model.pt'.format(train_acc, loss.data[0], iterations)
  torch.save(model, snapshot_path)
  for f in glob.glob(snapshot_prefix + '*'):
  if f != snapshot_path:
  os.remove(f)
+
+ # evaluate performance on validation set periodically
  if iterations % args.dev_every == 0:
+
+ # switch model to evaluation mode
  model.eval(); dev_iter.init_epoch()
+
+ # calculate accuracy on validation set
  n_dev_correct, dev_loss = 0, 0
  for dev_batch_idx, dev_batch in enumerate(dev_iter):
  answer = model(dev_batch)
  n_dev_correct += (torch.max(answer, 1)[1].view(dev_batch.label.size()).data == dev_batch.label.data).sum()
  dev_loss = criterion(answer, dev_batch.label)
  dev_acc = 100. * n_dev_correct / len(dev)
+
  print(dev_log_template.format(time.time()-start,
  epoch, iterations, 1+batch_idx, len(train_iter),
  100. * (1+batch_idx) / len(train_iter), loss.data[0], dev_loss.data[0], train_acc, dev_acc))
+
+ # update best valiation set accuracy
  if dev_acc > best_dev_acc:
+
+ # found a model with better validation set accuracy
+
  best_dev_acc = dev_acc
  snapshot_prefix = os.path.join(args.save_path, 'best_snapshot')
  snapshot_path = snapshot_prefix + '_devacc_{}_devloss_{}__iter_{}_model.pt'.format(dev_acc, dev_loss.data[0], iterations)
+
+ # save model, delete previous 'best_snapshot' files
  torch.save(model, snapshot_path)
  for f in glob.glob(snapshot_prefix + '*'):
  if f != snapshot_path:
  os.remove(f)
+
  elif iterations % args.log_every == 0:
+
+ # print progress message
  print(log_template.format(time.time()-start,
  epoch, iterations, 1+batch_idx, len(train_iter),
  100. * (1+batch_idx) / len(train_iter), loss.data[0], ' '*8, n_correct/n_total*100, ' '*12))