Quick fix metrics evaluation on run_classif_pytorch

run_classifier_pytorch.py

@@ -425,7 +425,7 @@ def input_fn_builder(features, seq_length, train_batch_size):
 
 def accuracy(out, labels):
     outputs = np.argmax(out, axis=1)
-    return np.sum(outputs==labels)/float(labels.size)
+    return np.sum(outputs==labels)
 
 def main():
     processors = {
@@ -491,6 +491,7 @@ def main():
                          t_total=num_train_steps)
 
     global_step = 0
+    total_tr_loss = 0
     if args.do_train:
         train_features = convert_examples_to_features(
             train_examples, label_list, args.max_seq_length, tokenizer)
@@ -512,6 +513,7 @@ def main():
         train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
 
         model.train()
+        nb_tr_examples = 0
         for epoch in range(int(args.num_train_epochs)):
             for input_ids, input_mask, segment_ids, label_ids in train_dataloader:
                 input_ids = input_ids.to(device)
@@ -520,6 +522,8 @@ def main():
                 label_ids = label_ids.to(device)
 
                 loss, _ = model(input_ids, segment_ids, input_mask, label_ids)
+                total_tr_loss += loss.item()
+                nb_tr_examples += input_ids.size(0)
                 loss.backward()
                 optimizer.step()
                 global_step += 1
@@ -572,7 +576,7 @@ def main():
         result = {'eval_loss': eval_loss,
                   'eval_accuracy': eval_accuracy,
                   'global_step': global_step,
-                  'loss': loss.item()}
+                  'loss': total_tr_loss/nb_tr_examples}#'loss': loss.item()}
 
         output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
         with open(output_eval_file, "w") as writer: