explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-01-12 10:16:27 +03:00
Code Issues Packages Projects Releases Wiki Activity

Fix shape inference in begin_training

Browse Source
This commit is contained in:
Matthw Honnibal 2020-05-21 19:26:29 +02:00
parent 1729165e90
commit f075655deb
5 changed files with 14 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
spacy/ml/models/parser.py
View File

@ -15,10 +15,9 @@ def build_tb_parser_model(
use_upper=True, use_upper=True,
nO=None, nO=None,
): ):
token_vector_width = tok2vec.get_dim("nO")
tok2vec = chain( tok2vec = chain(
tok2vec, tok2vec,
with_array(Linear(hidden_width, token_vector_width)), with_array(Linear(hidden_width)),
list2array(), list2array(),
) )
tok2vec.set_dim("nO", hidden_width) tok2vec.set_dim("nO", hidden_width)

3
spacy/ml/models/tagger.py
View File

@ -6,9 +6,8 @@ from ...util import registry
@registry.architectures.register("spacy.Tagger.v1") @registry.architectures.register("spacy.Tagger.v1")
def build_tagger_model(tok2vec, nO=None) -> Model: def build_tagger_model(tok2vec, nO=None) -> Model:
token_vector_width = tok2vec.get_dim("nO")
# TODO: glorot_uniform_init seems to work a bit better than zero_init here?! # TODO: glorot_uniform_init seems to work a bit better than zero_init here?!
output_layer = Softmax(nO, nI=token_vector_width, init_W=zero_init) output_layer = Softmax(nO, init_W=zero_init)
softmax = with_array(output_layer) softmax = with_array(output_layer)
model = chain(tok2vec, softmax) model = chain(tok2vec, softmax)
model.set_ref("tok2vec", tok2vec) model.set_ref("tok2vec", tok2vec)

4
spacy/ml/tb_framework.py
View File

@ -38,8 +38,8 @@ def forward(model, X, is_train):
def init(model, X=None, Y=None): def init(model, X=None, Y=None):
tok2vec = model.get_ref("tok2vec").initialize() tok2vec = model.get_ref("tok2vec").initialize(X=X)
lower = model.get_ref("lower").initialize(X=X) lower = model.get_ref("lower").initialize()
if model.attrs["has_upper"]: if model.attrs["has_upper"]:
statevecs = model.ops.alloc2f(2, lower.get_dim("nO")) statevecs = model.ops.alloc2f(2, lower.get_dim("nO"))
model.get_ref("upper").initialize(X=statevecs) model.get_ref("upper").initialize(X=statevecs)

7
spacy/pipeline/pipes.pyx
View File

@ -531,7 +531,12 @@ class Tagger(Pipe):
vocab.morphology.lemmatizer, vocab.morphology.lemmatizer,
exc=vocab.morphology.exc) exc=vocab.morphology.exc)
self.set_output(len(self.labels)) self.set_output(len(self.labels))
self.model.initialize() doc_sample = [Doc(self.vocab, words=["hello", "world"])]
for name, component in pipeline:
if component is self:
break
doc_sample = list(component.pipe(doc_sample))
self.model.initialize(X=doc_sample)
# Get batch of example docs, example outputs to call begin_training(). # Get batch of example docs, example outputs to call begin_training().
# This lets the model infer shapes. # This lets the model infer shapes.
link_vectors_to_models(self.vocab) link_vectors_to_models(self.vocab)

4
spacy/syntax/nn_parser.pyx
View File

@ -629,6 +629,10 @@ cdef class Parser:
for doc, gold in parses: for doc, gold in parses:
doc_sample.append(doc) doc_sample.append(doc)
gold_sample.append(gold) gold_sample.append(gold)
for name, component in pipeline:
if component is self:
break
doc_sample = list(component.pipe(doc_sample))
self.model.initialize(doc_sample, gold_sample) self.model.initialize(doc_sample, gold_sample)
if pipeline is not None: if pipeline is not None:
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg) self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)