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Correct the label smoothing implementation

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Piotr Czapla committed 2019-02-22 16:55:35 +01:00
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results/MLDoc.md
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@@ -10,7 +10,9 @@
|ULMFiT L30k | | 95.4 | 95.15 | 93.67 | 88.42 | 89.20 | **87.27** | 90.20 |
|ULMFiT sp-fixed | | **95.6** | 94.80 | 94.20 | 88.52 | 88.72 | 86.85 | 90.47 |
|ULMFIT Q15k 1cyc| **94.62** | **95.65** | 95.15 | **94.42** | **89.92** | 89.60 | | 90.78/89.82 |
|ULMFIT L30k 1cyc| | **95.85** | **96.32** | **94.82** | 89.87 | **90.45** | **87.94** | **92.02/91.64** |
|ULMFIT Q15k 1c l| | | | | | | | **92.22** |
|ULMFIT L30k 1cyc| | **95.85** | **96.32** | **94.82** | 89.87 | **90.45** | **87.94** | 92.02/91.64 |
- L30k - LSTM sp30k trained using gradual unfreezing
- L30k-100 - --||-- **on 100 samples**
- ULMFiT sp-fixed - --||-- with fixed tokenization
results/logs/label_smoothing.md
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# MLDoc
## QRNN 15k
Exec 1
```
python -m ulmfit eval --glob="mldoc/zh-1/models/sp15k/qrnn_nl4.m" --name nl4-1cyc-sl --num-cls-epochs=8 --bs=18 --lr_sched=1cycle --label-smoothing-eps=0.1
Processing data/mldoc/zh-1/models/sp15k/qrnn_nl4.m
zh-1
Max vocab: 15000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/zh.dev.csv
Data lm, trn: 13500, val: 1500
Data cls, trn: 1000, val: 1000
Data tst, trn: 1000, val: 4000
Size of vocabulary: 15000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁,', '▁的', '▁。', '▁年', '▁、', '▁在', '▁一', '▁是', '▁中', '▁有']
Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15}
/home/pczapla/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/utils/cpp_extension.py:152: UserWarning:
Loading pretrained model
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl.m/info.json
Single training schedule
epoch train_loss valid_loss accuracy
1 0.859153 0.767961 0.864000
2 0.768888 0.775161 0.904000
3 0.658956 0.685653 0.902000
4 0.589073 0.618438 0.923000
5 0.540008 0.622157 0.915000
6 0.508080 0.606979 0.914000
7 0.487228 0.599491 0.918000
8 0.477516 0.602196 0.923000
Total time: 02:18
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl.m
Loss and accuracy using (cls_best): [0.2829206, tensor(0.9205)]
OrderedDict([('data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl.m',
0.9204999804496765)])
data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl.m: 0.9204999804496765
```
Exec 2
````python -m ulmfit eval --glob="mldoc/zh-1/models/sp15k/qrnn_nl4.m" --name nl4-1cyc-sl1 --num-cls-epochs=8 --bs=18 --lr_sched=1cycle --label-smoothing-eps=0.1
Processing data/mldoc/zh-1/models/sp15k/qrnn_nl4.m
zh-1
Max vocab: 15000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl1.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/zh.dev.csv
Data lm, trn: 13500, val: 1500
Data cls, trn: 1000, val: 1000
Data tst, trn: 1000, val: 4000
Size of vocabulary: 15000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁,', '▁的', '▁。', '▁年', '▁、', '▁在', '▁一', '▁是', '▁中', '▁有']
Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15}
Loading pretrained model
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl1.m/info.json
Single training schedule
epoch train_loss valid_loss accuracy
1 0.881513 0.712176 0.865000
2 0.743687 0.665091 0.906000
3 0.677436 0.687689 0.873000
4 0.595139 0.626483 0.920000
5 0.542732 0.600652 0.914000
6 0.512080 0.597546 0.916000
7 0.487021 0.597065 0.912000
8 0.476598 0.596792 0.914000
Total time: 02:20
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl1.m
Loss and accuracy using (cls_best): [0.29172945, tensor(0.9178)]
OrderedDict([('data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl1.m',
0.9177500009536743)])
data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl1.m: 0.9177500009536743
````
Exec 4
```bash
python -m ulmfit eval --glob="mldoc/zh-1/models/sp15k/qrnn_nl4.m" --name nl4-1cyc-sl-e4 --num-cls-epochs=4 --bs=18 --lr_sched=1cycle --label-smoothing-eps=0.1 ✘ 130
Processing data/mldoc/zh-1/models/sp15k/qrnn_nl4.m
zh-1
Max vocab: 15000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl-e4.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/zh.dev.csv
Data lm, trn: 13500, val: 1500
Data cls, trn: 1000, val: 1000
Data tst, trn: 1000, val: 4000
Size of vocabulary: 15000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁,', '▁的', '▁。', '▁年', '▁、', '▁在', '▁一', '▁是', '▁中', '▁有']
Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15}
Loading pretrained model
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl-e4.m/info.json
Single training schedule
epoch train_loss valid_loss accuracy
1 0.880415 0.677291 0.901000
2 0.729670 0.659975 0.911000
3 0.624817 0.603056 0.921000
4 0.542027 0.601961 0.921000
Total time: 01:08
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl-e4.m
Loss and accuracy using (cls_best): [0.28558904, tensor(0.9222)]
OrderedDict([('data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl-e4.m',
0.922249972820282)])
data/mldoc/zh-1/models/sp15k/qrnn_nl4-1cyc-sl-e4.m: 0.922249972820282
```
## LSTM sp30k
### 0.1
```bash
python -m ulmfit eval --glob="mldoc/zh-1/models/sp30k/lstm_nl4.m" --name nl4-1cyc-sl --num-cls-epochs=8 --bs=18 --lr_sched=1cycle --label-smoothing-eps=0.1
Processing data/mldoc/zh-1/models/sp30k/lstm_nl4.m
zh-1
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/zh.dev.csv
Data lm, trn: 13500, val: 1500
Data cls, trn: 1000, val: 1000
Data tst, trn: 1000, val: 4000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁,', '▁的', '▁。', '▁年', '▁、', '▁在', '▁一', '▁中', '▁人', '▁是']
Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15}
Loading pretrained model
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl.m/info.json
Single training schedule
epoch train_loss valid_loss accuracy
1 0.870432 0.670671 0.882000
2 0.754248 0.824157 0.895000
3 0.654601 0.727428 0.885000
4 0.602772 0.668668 0.901000
5 0.542110 0.625137 0.903000
6 0.506150 0.617842 0.913000
7 0.480944 0.616885 0.912000
8 0.472876 0.614381 0.911000
Total time: 06:38
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl.m
Loss and accuracy using (cls_best): [0.2977172, tensor(0.9233)]
OrderedDict([('data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl.m',
0.9232500195503235)])
data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl.m: 0.9232500195503235
```
### 0.2
```bash
python -m ulmfit eval --glob="mldoc/zh-1/models/sp30k/lstm_nl4.m" --name nl4-1cyc-sl2 --num-cls-epochs=8 --bs=18 --lr_sched=1cycle --label-smoothing-eps=0.2
Processing data/mldoc/zh-1/models/sp30k/lstm_nl4.m
zh-1
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl2.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/zh.dev.csv
Data lm, trn: 13500, val: 1500
Data cls, trn: 1000, val: 1000
Data tst, trn: 1000, val: 4000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁,', '▁的', '▁。', '▁年', '▁、', '▁在', '▁一', '▁中', '▁人', '▁是']
Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15}
Loading pretrained model
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl2.m/info.json
Single training schedule
epoch train_loss valid_loss accuracy
1 1.045619 0.908213 0.874000
2 0.957379 0.857977 0.921000
3 0.891791 0.852157 0.905000
4 0.845289 0.849923 0.914000
5 0.818228 0.848613 0.921000
6 0.787021 0.840483 0.920000
7 0.776123 0.844006 0.919000
8 0.762384 0.857240 0.916000
Total time: 06:33
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl2.m
Loss and accuracy using (cls_best): [0.40299156, tensor(0.9170)]
OrderedDict([('data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl2.m',
0.9169999957084656)])
data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl2.m: 0.9169999957084656
```
### 0.4
```bash
python -m ulmfit eval --glob="mldoc/zh-1/models/sp30k/lstm_nl4.m" --name nl4-1cyc-sl4 --num-cls-epochs=8 --bs=18 --lr_sched=1cycle --label-smoothing-eps=0.4
Processing data/mldoc/zh-1/models/sp30k/lstm_nl4.m
zh-1
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl4.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/zh.dev.csv
Data lm, trn: 13500, val: 1500
Data cls, trn: 1000, val: 1000
Data tst, trn: 1000, val: 4000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁,', '▁的', '▁。', '▁年', '▁、', '▁在', '▁一', '▁中', '▁人', '▁是']
Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15}
Loading pretrained model
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl4.m/info.json
Single training schedule
epoch train_loss valid_loss accuracy
1 1.251581 1.183341 0.898000
2 1.214358 1.201266 0.834000
3 1.190343 1.165525 0.919000
4 1.168018 1.172510 0.903000
5 1.149965 1.161660 0.914000
6 1.140140 1.161689 0.915000
7 1.135877 1.159853 0.912000
8 1.134425 1.160039 0.911000
Total time: 06:34
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl4.m
Loss and accuracy using (cls_best): [0.64041936, tensor(0.9195)]
OrderedDict([('data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl4.m',
0.9194999933242798)])
data/mldoc/zh-1/models/sp30k/lstm_nl4-1cyc-sl4.m: 0.9194999933242798
```
results/logs/qrnn-ru.md
+12
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@@ -1,4 +1,16 @@
# QRNN RU
## SP15k nl8
data/wiki/ru-100/models/sp15k/qrnn_nl8.m
export CUDA_VISIBLE_DEVICES=0
LANG=ru
python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --base-lm-path data/wiki/${LANG}-100/models/sp15k/qrnn_nl8.m --lang=${LANG} --name 'nl8' - train 20 --bs 20 --num-cls-epochs=8 --lr_sched=1cycle
export CUDA_VISIBLE_DEVICES=0
LANG=de
python -m ulmfit cls --dataset-path data/mldoc-m/${LANG}-1 --base-lm-path data/wiki-m/${LANG}-100/models/sp15k/lstm_nl4.m --lang=${LANG} --name 'nl4' - train 20 --bs 20 --num-cls-epochs=8 --lr_sched=1cycle
## SP30k nl4
### LM
```
tests/test_end_to_end.py
+3 -1
View File
@@ -39,7 +39,8 @@ def get_test_data():
copy_head(wt / 'en.wiki.train.tokens', test_wt / 'en.wiki.valid.tokens', n=600*sz)
copy_head(wt / 'en.wiki.train.tokens', test_wt / 'en.wiki.test.tokens', n=600*sz)
copy_head(imdb / 'train.csv', test_imdb / 'train.csv', n=10*sz)
copy_head(imdb / 'train.csv', test_imdb / 'test.csv', n=6*sz)
copy_head(imdb / 'train.csv', test_imdb / 'test.csv', n=6 * sz)
copy_head(imdb / 'train.csv', test_imdb / 'dev.csv', n=6 * sz)
copy_head(imdb / 'train.csv', test_imdb / 'unsup.csv', n=1*sz)
return test_data, test_wt
@@ -109,6 +110,7 @@ def test_ulmfit_fastai_end_to_end():
qrnn=False,
tokenizer='f',
max_vocab=100,
nl=1,
name=lm_name,
)
exp.train_lm(num_epochs=1, bs=2)
ulmfit/__main__.py
+1 -1
View File
@@ -90,7 +90,7 @@ class ULMFiT:
try:
params = CLSHyperParams.from_lm(dataset_path, base_model, lang=lang, name=name, cuda_id=cuda_id)
key = str(params.model_dir.relative_to(Path.cwd()))
if (params.model_dir/"cls_last.pth").exists():
if (params.model_dir/"cls_best.pth").exists():
print("Evaluating previously trained model")
results[key] = params.validate_cls()[1]
else:
ulmfit/pretrain_lm.py
+1 -1
View File
@@ -231,7 +231,7 @@ class LMHyperParams:
# partial(SaveModelCallback, every='improvement', name='lm') disabled due to Memory issues
]
if label_smoothing_eps > 0.0:
learn.loss_func = LabelSmoothingCrossEntropy(eps=label_smoothing_eps)
learn.loss_func = FlattenedLoss(LabelSmoothingCrossEntropy, eps=label_smoothing_eps)
return learn
def load_train_text(self):
ulmfit/train_clas.py
+3 -3
View File
@@ -77,7 +77,7 @@ class CLSHyperParams(LMHyperParams):
if self.need_fine_tune_lm: self.train_lm(num_lm_epochs, data_lm=data_lm, drop_mult=drop_mul_lm, label_smoothing_eps=label_smoothing_eps)
learn = self.create_cls_learner(data_clas, drop_mult=drop_mul_cls, max_len=cls_max_len, label_smoothing_eps=label_smoothing_eps)
try:
learn.load('cls_last')
learn.load('cls_best')
print("Loading last classifier")
except FileNotFoundError:
learn.load_encoder(ENC_BEST)
@@ -94,7 +94,7 @@ class CLSHyperParams(LMHyperParams):
del learn
return self.validate_cls('cls_best', bs=bs, data_tst=data_tst, learn=None)
def validate_cls(self, save_name='cls_last', bs=40, data_tst=None, learn=None):
def validate_cls(self, save_name='cls_best', bs=40, data_tst=None, learn=None):
if data_tst is None:
_, _, data_tst = self.load_cls_data(bs)
if learn is None:
@@ -125,7 +125,7 @@ class CLSHyperParams(LMHyperParams):
#partial(SaveModelCallback, every='improvement', name='cls_best') disabled due to memory issues
]
if label_smoothing_eps > 0.0:
learn.loss_func = LabelSmoothingCrossEntropy(eps=label_smoothing_eps)
learn.loss_func = FlattenedLoss(LabelSmoothingCrossEntropy, eps=label_smoothing_eps)
return learn
def load_cls_data(self, bs, **kwargs):