SUMIN/HuggingFace_transformer
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Fix tr_loss rescaling factor using global_step

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Mathieu Prouveur
2019-04-29 12:58:29 +02:00
parent ed8fad7390
commit 87b9ec3843
2 changed files with 5 additions and 5 deletions
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6
examples/run_classifier.py
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@@ -845,7 +845,7 @@ def main():
else:
loss.backward()
tr_loss += loss.item() * args.gradient_accumulation_steps
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
@@ -936,7 +936,7 @@ def main():
elif output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(task_name, preds, all_label_ids.numpy())
loss = tr_loss/nb_tr_steps if args.do_train else None
loss = tr_loss/global_step if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step
@@ -1004,7 +1004,7 @@ def main():
preds = preds[0]
preds = np.argmax(preds, axis=1)
result = compute_metrics(task_name, preds, all_label_ids.numpy())
loss = tr_loss/nb_tr_steps if args.do_train else None
loss = tr_loss/global_step if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step