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Don't randomise pipeline for training, and don't update if no gradient

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This commit is contained in:
Matthew Honnibal 2017-05-27 08:20:13 -05:00
parent 3d22fcaf0b
commit 73a643d32a
1 changed files with 3 additions and 4 deletions
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5
spacy/language.py
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@ -212,17 +212,16 @@ class Language(object):
""" """
tok2vec = self.pipeline[0] tok2vec = self.pipeline[0]
feats = tok2vec.doc2feats(docs) feats = tok2vec.doc2feats(docs)
procs = list(self.pipeline[1:])
random.shuffle(procs)
grads = {} grads = {}
def get_grads(W, dW, key=None): def get_grads(W, dW, key=None):
grads[key] = (W, dW) grads[key] = (W, dW)
for proc in procs: for proc in self.pipeline[1:]:
if not hasattr(proc, 'update'): if not hasattr(proc, 'update'):
continue continue
tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop) tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop)
d_tokvecses = proc.update((docs, tokvecses), golds, d_tokvecses = proc.update((docs, tokvecses), golds,
drop=drop, sgd=get_grads, losses=losses) drop=drop, sgd=get_grads, losses=losses)
if d_tokvecses is not None:
bp_tokvecses(d_tokvecses, sgd=sgd) bp_tokvecses(d_tokvecses, sgd=sgd)
for key, (W, dW) in grads.items(): for key, (W, dW) in grads.items():
sgd(W, dW, key=key) sgd(W, dW, key=key)