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Don't call begin_training if updating new model (see #3059) [ci skip]

This commit is contained in:
Ines Montani 2018-12-17 13:45:49 +01:00
parent ab4b61fb6e
commit e3405f8af3
1 changed files with 4 additions and 2 deletions
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6
examples/training/train_ner.py
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@ -56,7 +56,10 @@ def main(model=None, output_dir=None, n_iter=100):
# get names of other pipes to disable them during training # get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"] other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
with nlp.disable_pipes(*other_pipes): # only train NER with nlp.disable_pipes(*other_pipes): # only train NER
optimizer = nlp.begin_training() # reset and initialize the weights randomly – but only if we're
# training a new model
if model is None:
optimizer = nlp.begin_training()
for itn in range(n_iter): for itn in range(n_iter):
random.shuffle(TRAIN_DATA) random.shuffle(TRAIN_DATA)
losses = {} losses = {}
@ -68,7 +71,6 @@ def main(model=None, output_dir=None, n_iter=100):
texts, # batch of texts texts, # batch of texts
annotations, # batch of annotations annotations, # batch of annotations
drop=0.5, # dropout - make it harder to memorise data drop=0.5, # dropout - make it harder to memorise data
sgd=optimizer, # callable to update weights
losses=losses, losses=losses,
) )
print("Losses", losses) print("Losses", losses)