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[new] Add a training trick #131

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[new] Add a training trick #131

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0378545
Update trainer.py
LindaCY Jan 19, 2019
fa300ce
Merge branch 'master' into patch-1
FengZiYjun Feb 10, 2019
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35 changes: 33 additions & 2 deletions fastNLP/core/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,9 +32,10 @@

class Trainer(object):
def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch_size=32, print_every=50,
validate_every=-1, dev_data=None, save_path=None, optimizer=Adam(lr=0.01, weight_decay=0),
validate_every=-1, dev_data=None, save_path=None, optimizer=Adam(lr=0.01, weight_decay=0), halve_lr_epochs=-1,
check_code_level=0, metric_key=None, sampler=RandomSampler(), prefetch=False, use_tqdm=True,
use_cuda=False, callbacks=None):

"""
:param DataSet train_data: the training data
:param torch.nn.modules.module model: a PyTorch model
Expand All @@ -47,6 +48,7 @@ def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch
:param DataSet dev_data: the validation data
:param str save_path: file path to save models
:param Optimizer optimizer: an optimizer object
:param halve_lr_epochs: halve the learning rate if not imporving for [halve_lr_epochs] epochs. Default: -1 (never use it)
:param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict.\\
`ignore` will not check unused field; `warning` when warn if some field are not used; `strict` means
it will raise error if some field are not used. 检查的原理是通过使用很小的batch(默认两个sample)来检查代码是
Expand Down Expand Up @@ -108,6 +110,7 @@ def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch
self.dev_data = dev_data # If None, No validation.
self.model = model
self.losser = losser
self.halve_lr_epochs = halve_lr_epochs
self.metrics = metrics
self.n_epochs = int(n_epochs)
self.batch_size = int(batch_size)
Expand All @@ -130,6 +133,9 @@ def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch

self.use_tqdm = use_tqdm
self.print_every = abs(self.print_every)

for group in self.optimizer.param_groups:
self.lr = group['lr']

if self.dev_data is not None:
self.tester = Tester(model=self.model,
Expand Down Expand Up @@ -227,6 +233,7 @@ def _train(self):
else:
inner_tqdm = tqdm
self.step = 0
self.bad_valid = 0
start = time.time()
total_steps = (len(self.train_data) // self.batch_size + int(
len(self.train_data) % self.batch_size != 0)) * self.n_epochs
Expand Down Expand Up @@ -302,6 +309,24 @@ def _do_validation(self, epoch, step):
self.best_dev_perf = res
self.best_dev_epoch = epoch
self.best_dev_step = step

# halve the learning rate if not improving for [halve_lr_epochs] epochs, and restart training from the best model.
else:
self.bad_valid += 1
if self.halve_lr_epochs != -1:
if self.bad_valid >= self.halve_lr_epochs:
self.lr = self.lr / 2.0
print("halve learning rate to {}".format(self.lr))
model_name = "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time])
load_succeed = self._load_model(self.model, model_name)
if load_succeed:
print("Reloaded the best model.")
else:
print("Fail to reload best model.")
self._set_lr(self.optimizer, self.lr)
self.bad_valid = 0
print("bad valid: {}".format(self.bad_valid))

# get validation results; adjust optimizer
self.callback_manager.on_valid_end(res, self.metric_key, self.optimizer)
return res
Expand Down Expand Up @@ -406,7 +431,13 @@ def _better_eval_result(self, metrics):
else:
is_better = False
return is_better


def _set_lr(self, optimizer, lr):
# if self.optimizer == "YFOptimizer":
# optimizer.set_lr_factor(lr)
# else:
for group in optimizer.param_groups:
group['lr'] = lr

DEFAULT_CHECK_BATCH_SIZE = 2
DEFAULT_CHECK_NUM_BATCH = 2
Expand Down