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spaCy
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Use LayerNorm and SELU in Tok2Vec

This commit is contained in:
Matthew Honnibal 2017-08-06 18:33:18 +02:00
parent 78498a072d
commit 3ed203de25
1 changed files with 6 additions and 5 deletions
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11
spacy/_ml.py
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@ -10,6 +10,7 @@ import cytoolz
from thinc.neural._classes.convolution import ExtractWindow from thinc.neural._classes.convolution import ExtractWindow
from thinc.neural._classes.static_vectors import StaticVectors from thinc.neural._classes.static_vectors import StaticVectors
from thinc.neural._classes.batchnorm import BatchNorm from thinc.neural._classes.batchnorm import BatchNorm
from thinc.neural._classes.layernorm import LayerNorm as LN
from thinc.neural._classes.resnet import Residual from thinc.neural._classes.resnet import Residual
from thinc.neural import ReLu from thinc.neural import ReLu
from thinc.neural._classes.selu import SELU from thinc.neural._classes.selu import SELU
@ -220,11 +221,11 @@ def Tok2Vec(width, embed_size, preprocess=None):
with_flatten( with_flatten(
asarray(Model.ops, dtype='uint64') asarray(Model.ops, dtype='uint64')
>> uniqued(embed, column=5) >> uniqued(embed, column=5)
>> Maxout(width, width*4, pieces=3) >> LN(Maxout(width, width*4, pieces=3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)) >> Residual(ExtractWindow(nW=1) >> SELU(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)) >> Residual(ExtractWindow(nW=1) >> SELU(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)) >> Residual(ExtractWindow(nW=1) >> SELU(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)), >> Residual(ExtractWindow(nW=1) >> SELU(width, width*3)),
pad=4) pad=4)
) )
if preprocess not in (False, None): if preprocess not in (False, None):