SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

add best steps to train

Browse Source
This commit is contained in:
erenup
2019-08-20 19:06:41 +08:00
parent 97c30b73d5
commit fc74132598
1 changed files with 9 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
examples/single_model_scripts/run_multiple_choice.py
View File

@@ -127,6 +127,7 @@ def train(args, train_dataset, model, tokenizer):
global_step = 0 global_step = 0
tr_loss, logging_loss = 0.0, 0.0 tr_loss, logging_loss = 0.0, 0.0
best_dev_acc, best_dev_loss = 0.0, 99999999999.0 best_dev_acc, best_dev_loss = 0.0, 99999999999.0
best_steps = 0
model.zero_grad() model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproductibility (even between python 2 and 3) set_seed(args) # Added here for reproductibility (even between python 2 and 3)
@@ -171,6 +172,8 @@ def train(args, train_dataset, model, tokenizer):
if results["eval_loss"] < best_dev_loss: if results["eval_loss"] < best_dev_loss:
best_dev_acc = results["eval_acc"] best_dev_acc = results["eval_acc"]
best_dev_loss = results["eval_loss"] best_dev_loss = results["eval_loss"]
best_steps = global_step
if args.do_test:
results_test = evaluate(args, model, tokenizer, test=True) results_test = evaluate(args, model, tokenizer, test=True)
for key, value in results_test.items(): for key, value in results_test.items():
tb_writer.add_scalar('test_{}'.format(key), value, global_step) tb_writer.add_scalar('test_{}'.format(key), value, global_step)
@@ -201,7 +204,7 @@ def train(args, train_dataset, model, tokenizer):
if args.local_rank in [-1, 0]: if args.local_rank in [-1, 0]:
tb_writer.close() tb_writer.close()
return global_step, tr_loss / global_step return global_step, tr_loss / global_step, best_steps
def evaluate(args, model, tokenizer, prefix="", test=False): def evaluate(args, model, tokenizer, prefix="", test=False):
@@ -471,7 +474,7 @@ def main():
# Training # Training
if args.do_train: if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer) global_step, tr_loss, _ = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)