SUMIN/HuggingFace_transformer
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Fix both loss and eval metrics -> more coherence on the loss (eval vs train and tf vs pt)

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VictorSanh
2018-11-03 16:48:24 -04:00
parent cd09cd5b40
commit 5858e8e4dd
1 changed files with 13 additions and 10 deletions
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23
run_classifier_pytorch.py
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@@ -507,7 +507,6 @@ 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)
@@ -529,8 +528,10 @@ 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 trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(train_dataloader, desc="Iteration"):
input_ids = input_ids.to(device)
input_mask = input_mask.float().to(device)
@@ -538,12 +539,14 @@ def main():
label_ids = label_ids.to(device)
loss, _ = model(input_ids, segment_ids, input_mask, label_ids)
total_tr_loss += loss.sum().item() # sum() is to account for multi-gpu support.
loss = loss.mean() # sum() is to account for multi-gpu support.
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
model.zero_grad()
loss.sum().backward() # sum() is to account for multi-gpu support.
loss.backward()
optimizer.step()
global_step += 1
nb_tr_steps += 1
if args.do_eval:
eval_examples = processor.get_dev_examples(args.data_dir)
@@ -567,9 +570,8 @@ def main():
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss = 0
eval_accuracy = 0
nb_eval_examples = 0
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.float().to(device)
@@ -582,18 +584,19 @@ def main():
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.sum().item()
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_examples #len(eval_dataloader)
eval_loss = eval_loss / nb_eval_steps #len(eval_dataloader)
eval_accuracy = eval_accuracy / nb_eval_examples #len(eval_dataloader)
result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': total_tr_loss/nb_tr_examples}#'loss': loss.item()}
'loss': tr_loss/nb_tr_steps}#'loss': loss.item()}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer: