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Add ability to turnoff weighted CrossEntropy

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Piotr Czapla
2019-05-14 13:26:57 +02:00
parent 6dc4a5f102
commit ad3c11edb8
1 changed files with 5 additions and 3 deletions
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ulmfit/train_clas.py
+5 -3
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@@ -122,7 +122,7 @@ class CLSHyperParams(LMHyperParams):
def train_cls(self, num_lm_epochs, unfreeze=True, num_cls_frozen_epochs=1, bs=40, drop_mul_lm=0.3, drop_mul_cls=0.5,
use_test_for_validation=False, num_cls_epochs=2, limit=None, noise=0.0, cls_max_len=20*70, lr_sched='layered',
label_smoothing_eps=0.0, random_init=False, dump_preds=None, early_stopping=True):
label_smoothing_eps=0.0, random_init=False, dump_preds=None, early_stopping=True, weighted_cross_entropy=True):
assert use_test_for_validation == False, "use_test_for_validation=True is not supported"
self.model_dir.mkdir(exist_ok=True, parents=True)
@@ -136,8 +136,10 @@ class CLSHyperParams(LMHyperParams):
self.train_lm(num_lm_epochs, data_lm=data_lm, drop_mult=drop_mul_lm, label_smoothing_eps=label_smoothing_eps)
else:
print("Language model already exist, skipping finetuning")
loss_func = CrossEntropyFlat(weight=torch.FloatTensor([0.5,30]).cuda())
if weighted_cross_entropy:
loss_func = CrossEntropyFlat(weight=torch.FloatTensor([0.5,30]).cuda())
else:
loss_func = CrossEntropyFlat()
self.set_seed(self.clsweightseed, "classifier weights")
learn = self.create_cls_learner(data_clas, drop_mult=drop_mul_cls, max_len=cls_max_len,