accuracy now an average over log_interval batches

Author: bmccann

OpenNMT/train.py

@@ -186,9 +186,8 @@ def trainEpoch(epoch):
  # shuffle mini batch order
  batchOrder = torch.randperm(len(trainData))
 
- total_loss, report_loss = 0, 0
- total_words, report_tgt_words, report_src_words = 0, 0, 0
- total_num_correct = 0
+ total_loss, total_words, total_num_correct = 0
+ report_loss, report_tgt_words, report_src_words, report_num_correct = 0
  start = time.time()
  for i in range(len(trainData)):
 
@@ -206,23 +205,24 @@ def trainEpoch(epoch):
  # update the parameters
  optim.step()
 
- report_loss += loss
- total_num_correct += num_correct
- total_loss += loss
  num_words = targets.data.ne(onmt.Constants.PAD).sum()
- total_words += num_words
+ report_loss += loss
+ report_num_correct += num_correct
  report_tgt_words += num_words
  report_src_words += batch[0].data.ne(onmt.Constants.PAD).sum()
+ total_loss += loss
+ total_num_correct += num_correct
+ total_words += num_words
  if i % opt.log_interval == -1 % opt.log_interval:
  print("Epoch %2d, %5d/%5d; acc: %6.2f; ppl: %6.2f; %3.0f src tok/s; %3.0f tgt tok/s; %6.0f s elapsed" %
  (epoch, i, len(trainData),
- num_correct / num_words * 100,
+ report_num_correct / report_tgt_words * 100,
  math.exp(report_loss / report_tgt_words),
  report_src_words/(time.time()-start),
  report_tgt_words/(time.time()-start),
  time.time()-start_time))
 
- report_loss = report_tgt_words = report_src_words = 0
+ report_loss = report_tgt_words = report_src_words = report_num_correct = 0
  start = time.time()
 
  return total_loss / total_words, total_num_correct / total_words