Don't randomise pipeline for training, and don't update if no gradient
@@ -212,17 +212,16 @@ class Language(object):
"""
tok2vec = self.pipeline[0]
feats = tok2vec.doc2feats(docs)
- procs = list(self.pipeline[1:])
- random.shuffle(procs)
grads = {}
def get_grads(W, dW, key=None):
grads[key] = (W, dW)
- for proc in procs:
+ for proc in self.pipeline[1:]:
if not hasattr(proc, 'update'):
continue
tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop)
d_tokvecses = proc.update((docs, tokvecses), golds,
drop=drop, sgd=get_grads, losses=losses)
+ if d_tokvecses is not None:
bp_tokvecses(d_tokvecses, sgd=sgd)
for key, (W, dW) in grads.items():
sgd(W, dW, key=key)
