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Rearrange multi-task learning

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This commit is contained in:
Matthew Honnibal 2017-05-24 20:10:54 -05:00
parent 135a13790c
commit e6cc927ab1
1 changed files with 13 additions and 4 deletions
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17
spacy/language.py
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@ -6,7 +6,8 @@ import dill
import numpy import numpy
from thinc.neural import Model from thinc.neural import Model
from thinc.neural.ops import NumpyOps, CupyOps from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.optimizers import Adam from thinc.neural.optimizers import Adam, SGD
import random
from .tokenizer import Tokenizer from .tokenizer import Tokenizer
from .vocab import Vocab from .vocab import Vocab
@ -194,7 +195,7 @@ class Language(object):
proc(doc) proc(doc)
return doc return doc
def update(self, docs, golds, drop=0., sgd=None): def update(self, docs, golds, drop=0., sgd=None, losses=None):
"""Update the models in the pipeline. """Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects. docs (iterable): A batch of `Doc` objects.
@ -211,12 +212,20 @@ class Language(object):
""" """
tok2vec = self.pipeline[0] tok2vec = self.pipeline[0]
feats = tok2vec.doc2feats(docs) feats = tok2vec.doc2feats(docs)
for proc in self.pipeline[1:]: procs = list(self.pipeline[1:])
random.shuffle(procs)
grads = {}
def get_grads(W, dW, key=None):
grads[key] = (W, dW)
for proc in procs:
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, sgd=sgd, drop=drop) d_tokvecses = proc.update((docs, tokvecses), golds,
drop=drop, sgd=sgd, losses=losses)
bp_tokvecses(d_tokvecses, sgd=sgd) bp_tokvecses(d_tokvecses, sgd=sgd)
for key, (W, dW) in grads.items():
sgd(W, dW, key=key)
# Clear the tensor variable, to free GPU memory. # Clear the tensor variable, to free GPU memory.
# If we don't do this, the memory leak gets pretty # If we don't do this, the memory leak gets pretty
# bad, because we may be holding part of a batch. # bad, because we may be holding part of a batch.