def build_optim(model, checkpoint):
if opt.train_from:
print('Loading optimizer from checkpoint.')
optim = checkpoint['optim']
optim.optimizer.load_state_dict(
checkpoint['optim'].optimizer.state_dict())
else:
# what members of opt does Optim need?
optim = table.Optim(
opt.optim, opt.learning_rate, opt.alpha, opt.max_grad_norm,
lr_decay=opt.learning_rate_decay,
start_decay_at=opt.start_decay_at,
opt=opt
)
optim.set_parameters(model.parameters())
return optim
def build_optimizer(model, checkpoint=None):
if args.train_from:
assert checkpoint is not None
logger.info(' * loading optimizer from checkpoint')
optim = checkpoint['optim']
optim.optimizer.load_state_dict(checkpoint['optim'].optimizer.state_dict())
else:
optim = table.Optim(
method=args.optim,
lr=args.learning_rate,
alpha=args.alpha,
max_grad_norm=args.max_grad_norm,
lr_decay=args.learning_rate_decay,
start_decay_at=args.start_decay_at,
opt=args
)
optim.set_parameters(model.parameters())
return optim