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Fix dropout and batch-size defaults

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This commit is contained in:
Matthew Honnibal 2018-12-01 13:42:35 +00:00
parent 9536ee787c
commit d9d339186b
1 changed files with 5 additions and 4 deletions
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9
spacy/cli/train.py
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@ -22,13 +22,13 @@ from .. import about
# Batch size starts at 1 and grows, so that we make updates quickly # Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training. # at the beginning of training.
dropout_rates = util.decaying( dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.2), util.env_opt("dropout_from", 0.1),
util.env_opt("dropout_to", 0.2), util.env_opt("dropout_to", 0.1),
util.env_opt("dropout_decay", 0.0), util.env_opt("dropout_decay", 0.0),
) )
batch_sizes = util.compounding( batch_sizes = util.compounding(
util.env_opt("batch_from", 1000), util.env_opt("batch_from", 750),
util.env_opt("batch_to", 1000), util.env_opt("batch_to", 750),
util.env_opt("batch_compound", 1.001), util.env_opt("batch_compound", 1.001),
) )
@ -169,6 +169,7 @@ def train(
else: else:
# Start with a blank model, call begin_training # Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu) optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
nlp._optimizer = None nlp._optimizer = None
# Load in pre-trained weights # Load in pre-trained weights