1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
|
from __future__ import print_function
from thinc.linear.linear import LinearModel
from thinc.neural._classes.model import Model
from thinc.extra import datasets
from thinc.neural.util import to_categorical
import spacy
from spacy.attrs import ORTH
def preprocess(ops, keys):
lengths = ops.asarray([arr.shape[0] for arr in keys])
keys = ops.xp.concatenate(keys)
vals = ops.allocate(keys.shape[0]) + 1
return keys, vals, lengths
def main():
train, dev = datasets.imdb()
train_X, train_y = zip(*train)
dev_X, dev_y = zip(*dev)
model = LinearModel(2)
train_y = to_categorical(train_y, nb_classes=2)
dev_y = to_categorical(dev_y, nb_classes=2)
nlp = spacy.load('en')
train_X = [model.ops.asarray([tok.orth for tok in doc], dtype='uint64')
for doc in nlp.pipe(train_X)]
dev_X = [model.ops.asarray([tok.orth for tok in doc], dtype='uint64')
for doc in nlp.pipe(dev_X)]
dev_X = preprocess(model.ops, dev_X)
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
trainer.dropout = 0.0
trainer.batch_size = 512
trainer.nb_epoch = 3
trainer.each_epoch.append(lambda: print(model.evaluate(dev_X, dev_y)))
for X, y in trainer.iterate(train_X, train_y):
keys_vals_lens = preprocess(model.ops, X)
scores, backprop = model.begin_update(keys_vals_lens,
drop=trainer.dropout)
backprop(scores-y, optimizer)
with model.use_params(optimizer.averages):
print(model.evaluate(dev_X, dev_y))
if __name__ == '__main__':
main()
|
