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Update rehearsal example

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This commit is contained in:
Matthew Honnibal 2019-02-24 16:17:41 +01:00
parent 3ef4da3503
commit 7ac0f9626c
1 changed files with 21 additions and 4 deletions
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examples/training/rehearsal.py
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@ -4,7 +4,7 @@ import random
import srsly import srsly
import spacy import spacy
from spacy.gold import GoldParse from spacy.gold import GoldParse
from spacy.util import minibatch from spacy.util import minibatch, compounding
LABEL = "ANIMAL" LABEL = "ANIMAL"
@ -54,9 +54,17 @@ def main(model_name, unlabelled_loc):
nlp.get_pipe("ner").add_label(LABEL) nlp.get_pipe("ner").add_label(LABEL)
raw_docs = list(read_raw_data(nlp, unlabelled_loc)) raw_docs = list(read_raw_data(nlp, unlabelled_loc))
optimizer = nlp.resume_training() optimizer = nlp.resume_training()
# Avoid use of Adam when resuming training. I don't understand this well
# yet, but I'm getting weird results from Adam. Try commenting out the
# nlp.update(), and using Adam -- you'll find the models drift apart.
# I guess Adam is losing precision, introducing gradient noise?
optimizer.alpha = 0.1
optimizer.b1 = 0.0
optimizer.b2 = 0.0
# 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"]
sizes = compounding(1.0, 4.0, 1.001)
with nlp.disable_pipes(*other_pipes): with nlp.disable_pipes(*other_pipes):
for itn in range(n_iter): for itn in range(n_iter):
random.shuffle(TRAIN_DATA) random.shuffle(TRAIN_DATA)
@ -64,13 +72,22 @@ def main(model_name, unlabelled_loc):
losses = {} losses = {}
r_losses = {} r_losses = {}
# batch up the examples using spaCy's minibatch # batch up the examples using spaCy's minibatch
raw_batches = minibatch(raw_docs, size=batch_size) raw_batches = minibatch(raw_docs, size=4)
for doc, gold in TRAIN_DATA: for batch in minibatch(TRAIN_DATA, size=sizes):
nlp.update([doc], [gold], sgd=optimizer, drop=dropout, losses=losses) docs, golds = zip(*batch)
nlp.update(docs, golds, sgd=optimizer, drop=dropout, losses=losses)
raw_batch = list(next(raw_batches)) raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses) nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses)
print("Losses", losses) print("Losses", losses)
print("R. Losses", r_losses) print("R. Losses", r_losses)
print(nlp.get_pipe('ner').model.unseen_classes)
test_text = "Do you like horses?"
doc = nlp(test_text)
print("Entities in '%s'" % test_text)
for ent in doc.ents:
print(ent.label_, ent.text)
if __name__ == "__main__": if __name__ == "__main__":