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Classifier fails at fit_one_cycle

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Nirant K
2018-11-15 15:03:52 +00:00
parent 6622458fe7
commit 653f6e96a0
1 changed files with 20 additions and 15 deletions
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ulmfit/train_clas.py
+20 -15
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@@ -7,18 +7,20 @@ import pickle
from fastai.text import TextLMDataBunch, TextClasDataBunch, language_model_learner, text_classifier_learner
from fastai import fit_one_cycle
from fastai_contrib.utils import PAD, UNK, read_imdb, PAD_TOKEN_ID
from fastai_contrib.utils import PAD, UNK, read_imdb, prepare_imdb, PAD_TOKEN_ID
from sacremoses import MosesTokenizer
import fire
from collections import Counter
from pathlib import Path
CLASSES = ['neg', 'pos', 'unsup']
def new_train_clas(dir_path, lang='en', pretrain_name='wt-103', model_dir='models', qrnn=True,
fine_tune=True, clean=True, max_vocab=30000, bs=70, bptt=70):
def new_train_clas(dir_path, lang='en', pretrain_name='wt103', model_dir='models', qrnn=False,
fine_tune=False, clean=False, max_vocab=30000, bs=70, bptt=70):
dir_path = Path(dir_path)
model_dir = Path(model_dir)
model_dir = dir_path / model_dir
assert dir_path.exists(), f'Error: {dir_path} does not exist.'
assert model_dir.exists(), f'Error: {model_dir} does not exist.'
@@ -37,7 +39,7 @@ def new_train_clas(dir_path, lang='en', pretrain_name='wt-103', model_dir='model
trn_toks, trn_lbls = read_imdb(trn_path, MosesTokenizer(lang))
tst_toks, tst_lbls = read_imdb(tst_path, MosesTokenizer(lang))
# split off validation set if it does not exist
# split off validation set as 10% of training if it does not exist
val_path = dir_path / 'valid.csv'
if not val_path.exists():
trn_len = int(len(trn_toks) * 0.9)
@@ -68,18 +70,20 @@ def new_train_clas(dir_path, lang='en', pretrain_name='wt-103', model_dir='model
np.save(tmp_dir / f'{split}_ids.npy', ids)
np.save(tmp_dir / f'{split}_lbl.npy', lbl)
data_lm = TextLMDataBunch.from_id_files(tmp_dir, test='test')
data_clas = TextClasDataBunch.from_id_files(tmp_dir, test='test')
data_lm = TextLMDataBunch.from_csv(tmp_dir, '...')
data_clas = TextClasDataBunch.from_tokens(tmp_dir, '...')
else:
# use fastai peprocessing and tokenization
data_lm = TextLMDataBunch.from_csv(dir_path, bs=bs)
data_clas = TextClasDataBunch.from_csv(dir_path, vocab=data_lm.train_ds.vocab, bs=bs)
data_lm = TextLMDataBunch.from_csv(dir_path, csv_name='train.csv', test='test.csv', bs=bs,
classes=CLASSES)
data_clas = TextClasDataBunch.from_csv(dir_path, csv_name='train.csv',
vocab=data_lm.train_ds.vocab, bs=bs,classes=CLASSES)
# todo implemend save and load
#data_lm.save()
#data_clas.save()
#data_lm = TextLMDataBunch.load(path)
#data_clas = TextClasDataBunch.load(path, bs=bs)
# Todo implement save and load
# data_lm.save()
# data_clas.save()
# data_lm = TextLMDataBunch.load(path)
# data_clas = TextClasDataBunch.load(path, bs=bs)
if qrnn:
emb_sz, nh, nl = 400, 1550, 3
@@ -113,4 +117,5 @@ def new_train_clas(dir_path, lang='en', pretrain_name='wt-103', model_dir='model
fit_one_cycle(learn, 10, 5e-3, (0.8, 0.7), wd=1e-7)
if __name__ == '__main__': fire.Fire(new_train_clas)
if __name__ == '__main__':
fire.Fire(new_train_clas)