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Change the classfication training learning rate to the one that was working te best in my exp. on bidirectional clasification

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Piotr Czapla
2018-11-21 18:46:05 +01:00
parent 6e3ef21b1f
commit 979eb196d8
1 changed files with 7 additions and 6 deletions
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ulmfit/train_clas.py
+7 -6
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@@ -85,7 +85,7 @@ def new_train_clas(data_dir, lang='en', cuda_id=0, pretrain_name='wt103', model_
else:
emb_sz, nh, nl = 400, 1150, 3
lm_enc_finetuned = f"{lm_name}_{dataset}_{name}_enc"
lm_enc_finetuned = f"{lm_name}_{dataset}_{pretrain_name}_enc"
if fine_tune and not (model_dir/f"{lm_enc_finetuned}.pth").exists():
print('Fine-tuning the language model...')
learn = lm_learner(
@@ -120,22 +120,23 @@ def new_train_clas(data_dir, lang='en', cuda_id=0, pretrain_name='wt103', model_
train = True
if train:
learn.true_wd = False
print("Starting classifier training")
learn.fit_one_cycle(1, 2e-2, moms=(0.8, 0.7), wd=1e-7)
learn.fit_one_cycle(1, 5e-2, moms=(0.8, 0.7), wd=1e-7)
learn.freeze_to(-2)
learn.fit_one_cycle(1, slice(1e-2 / (2.6 ** 4), 1e-2), moms=(0.8, 0.7))
learn.fit_one_cycle(1, slice(5e-2 / (2.6 ** 4), 5e-2), moms=(0.8, 0.7), wd=1e-7)
learn.freeze_to(-3)
learn.fit_one_cycle(1, slice(5e-3 / (2.6 ** 4), 5e-3), moms=(0.8, 0.7))
learn.fit_one_cycle(1, slice(5e-4 / (2.6 ** 4), 5e-4), moms=(0.8, 0.7), wd=1e-7)
learn.unfreeze()
learn.fit_one_cycle(2, slice(1e-3 / (2.6 ** 4), 1e-3), moms=(0.8, 0.7))
learn.fit_one_cycle(2, slice(1e-2 / (2.6 ** 4), 1e-2), moms=(0.8, 0.7), wd=1e-7)
print(f"Saving models at {learn.path / learn.model_dir}")
learn.save(f'{model_name}_{name}')
results['accuracy'] = learn.validate()[1]
results['accuracy'] = learn.metrics[-1][0]
return results