'weights': model.state_dict()
}
if valid_loss < best_loss:
filename = os.path.join(args.savedir, 'checkpoint_best.pt')
torch.save(save_vars, filename)
if epoch % args.save_epoch == 0:
filename = os.path.join(args.savedir, f'checkpoint_{epoch}.pt')
torch.save(save_vars, filename)
filename = os.path.join(args.savedir, 'checkpoint_last.pt')
torch.save(save_vars, filename)
# update
trainer.scheduler.step(valid_loss)
epoch += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser('''
An Implimentation of Transformer.
Attention is all you need.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017.
In Advances in Neural Information Processing Systems, pages 6000–6010.
''')
options.train_opts(parser)
options.model_opts(parser)
args = parser.parse_args()
main(args)
valid_loss /= len(valid_iter)
print(f"| epoch {str(epoch).zfill(3)} | valid ", end="")
print(f"| loss {valid_loss:.{4}} ", end="")
print(f"| ppl {math.exp(valid_loss):.{4}} ", end="")
print(f"| lr {trainer.get_lr():.1e} ", end="")
print(f"| clip {args.clip} ", end="")
print(f"| num_updates {trainer.n_updates} |")
# saving model
save_vars = {"train_args": args,
"state_dict": model.state_dict()}
if valid_loss < best_loss:
best_loss = valid_loss
save_model(save_vars, 'checkpoint_best.pt')
save_model(save_vars, "checkpoint_last.pt")
# update
trainer.scheduler.step(valid_loss)
epoch += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser('')
train_opts(parser)
model_opts(parser)
args = parser.parse_args()
main(args)
def make_options():
parser = argparse.ArgumentParser(description='train.py')
options.model_opts(parser)
options.train_opts(parser)
return parser.parse_args()