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Pass embed size for parser fine-tune. Use SELU

This commit is contained in:
Matthew Honnibal 2017-08-09 17:52:53 -05:00
parent f93f2bed58
commit f37528ef58
1 changed files with 6 additions and 4 deletions
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10
spacy/syntax/nn_parser.pyx
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@ -37,7 +37,8 @@ from preshed.maps cimport MapStruct
from preshed.maps cimport map_get from preshed.maps cimport map_get
from thinc.api import layerize, chain, noop, clone from thinc.api import layerize, chain, noop, clone
from thinc.neural import Model, Affine, ELU, ReLu, Maxout from thinc.neural import Model, Affine, ReLu, Maxout
from thinc.neural._classes.selu import SELU
from thinc.neural.ops import NumpyOps, CupyOps from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module from thinc.neural.util import get_array_module
@ -238,8 +239,9 @@ cdef class Parser:
token_vector_width = util.env_opt('token_vector_width', token_vector_width) token_vector_width = util.env_opt('token_vector_width', token_vector_width)
hidden_width = util.env_opt('hidden_width', hidden_width) hidden_width = util.env_opt('hidden_width', hidden_width)
parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2) parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
tensors = fine_tune(Tok2Vec(token_vector_width, 7500, embed_size = util.env_opt('embed_size', 7500)
preprocess=doc2feats(cols=[ID, NORM, PREFIX, SUFFIX, TAG]))) tensors = fine_tune(Tok2Vec(token_vector_width, embed_size,
preprocess=doc2feats(cols=[ID, NORM, PREFIX, SUFFIX, SHAPE])))
if parser_maxout_pieces == 1: if parser_maxout_pieces == 1:
lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class, lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature, nF=cls.nr_feature,
@ -252,7 +254,7 @@ cdef class Parser:
with Model.use_device('cpu'): with Model.use_device('cpu'):
upper = chain( upper = chain(
clone(Maxout(hidden_width), (depth-1)), clone(SELU(hidden_width), (depth-1)),
zero_init(Affine(nr_class, drop_factor=0.0)) zero_init(Affine(nr_class, drop_factor=0.0))
) )
# TODO: This is an unfortunate hack atm! # TODO: This is an unfortunate hack atm!