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Pass embed size correctly in tagger, and cache embeddings for efficiency

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This commit is contained in:
Matthew Honnibal 2017-08-12 05:45:20 -05:00
parent 1a59db1c86
commit ebe0f7f641
1 changed files with 22 additions and 8 deletions
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30
spacy/_ml.py
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@ -23,8 +23,10 @@ from thinc.neural._classes.attention import ParametricAttention
from thinc.linear.linear import LinearModel
from thinc.api import uniqued, wrap, flatten_add_lengths
from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP
from .tokens.doc import Doc
from . import util
import numpy
import io
@ -208,6 +210,17 @@ class PrecomputableMaxouts(Model):
return Yfp, backward
def drop_layer(layer, factor=1.0):
def drop_layer_fwd(X, drop=0.):
drop *= factor
mask = layer.ops.get_dropout_mask((1,), drop)
if mask is not None and mask[0] == 0.:
return X, lambda dX, sgd=None: dX
else:
return layer.begin_update(X, drop=drop)
return wrap(drop_layer_fwd, layer)
def Tok2Vec(width, embed_size, preprocess=None):
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
@ -220,13 +233,13 @@ def Tok2Vec(width, embed_size, preprocess=None):
tok2vec = (
with_flatten(
asarray(Model.ops, dtype='uint64')
>> embed
>> Maxout(width, width*4, pieces=3)
>> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
>> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
>> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
>> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3)),
pad=4)
>> uniqued(embed >> Maxout(width, width*4, pieces=3), column=5)
>> Residual(
(ExtractWindow(nW=1) >> ReLu(width, width*3))
>> (ExtractWindow(nW=1) >> ReLu(width, width*3))
>> (ExtractWindow(nW=1) >> ReLu(width, width*3))
>> (ExtractWindow(nW=1) >> ReLu(width, width*3))
), pad=4)
)
if preprocess not in (False, None):
tok2vec = preprocess >> tok2vec
@ -430,9 +443,10 @@ def getitem(i):
return layerize(getitem_fwd)
def build_tagger_model(nr_class, token_vector_width, **cfg):
embed_size = util.env_opt('embed_size', 7500)
with Model.define_operators({'>>': chain, '+': add}):
# Input: (doc, tensor) tuples
private_tok2vec = Tok2Vec(token_vector_width, 7500, preprocess=doc2feats())
private_tok2vec = Tok2Vec(token_vector_width, embed_size, preprocess=doc2feats())
model = (
fine_tune(private_tok2vec)