accuracy now an average over log_interval batches

yuntai · Mar 14, 2017 · 0e77a0b · 0e77a0b
1 parent 39bb701
commit 0e77a0b
Showing 1 changed file with 9 additions and 9 deletions.
diff --git a/OpenNMT/train.py b/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