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Add hidden layers for tagger

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This commit is contained in:
Matthew Honnibal 2017-05-24 20:09:51 -05:00
parent 620df0414f
commit 467bbeadb8
1 changed files with 6 additions and 4 deletions
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spacy/pipeline.pyx
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@ -119,7 +119,7 @@ class TokenVectorEncoder(object):
assert tokvecs.shape[0] == len(doc) assert tokvecs.shape[0] == len(doc)
doc.tensor = tokvecs doc.tensor = tokvecs
def update(self, docs, golds, state=None, drop=0., sgd=None): def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
"""Update the model. """Update the model.
docs (iterable): A batch of `Doc` objects. docs (iterable): A batch of `Doc` objects.
@ -199,7 +199,7 @@ class NeuralTagger(object):
vocab.morphology.assign_tag_id(&doc.c[j], tag_id) vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
idx += 1 idx += 1
def update(self, docs_tokvecs, golds, drop=0., sgd=None): def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
docs, tokvecs = docs_tokvecs docs, tokvecs = docs_tokvecs
if self.model.nI is None: if self.model.nI is None:
@ -248,7 +248,8 @@ class NeuralTagger(object):
vocab.morphology.lemmatizer) vocab.morphology.lemmatizer)
token_vector_width = pipeline[0].model.nO token_vector_width = pipeline[0].model.nO
self.model = with_flatten( self.model = with_flatten(
Softmax(self.vocab.morphology.n_tags, token_vector_width)) chain(Maxout(token_vector_width, token_vector_width),
Softmax(self.vocab.morphology.n_tags, token_vector_width)))
def use_params(self, params): def use_params(self, params):
with self.model.use_params(params): with self.model.use_params(params):
@ -274,7 +275,8 @@ class NeuralLabeller(NeuralTagger):
self.labels[dep] = len(self.labels) self.labels[dep] = len(self.labels)
token_vector_width = pipeline[0].model.nO token_vector_width = pipeline[0].model.nO
self.model = with_flatten( self.model = with_flatten(
Softmax(len(self.labels), token_vector_width)) chain(Maxout(token_vector_width, token_vector_width),
Softmax(len(self.labels), token_vector_width)))
def get_loss(self, docs, golds, scores): def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores) scores = self.model.ops.flatten(scores)