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Tweak training params

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This commit is contained in:
Matthew Honnibal 2018-12-09 17:08:58 +00:00
parent 40e0da9cc1
commit 24f2e9bc07
1 changed files with 7 additions and 4 deletions
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11
spacy/cli/train.py
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@ -9,6 +9,7 @@ from timeit import default_timer as timer
import shutil
import srsly
from wasabi import Printer
from thinc.rates import slanted_triangular
from ._messages import Messages
from .._ml import create_default_optimizer
@ -23,13 +24,13 @@ from .. import about
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.1),
util.env_opt("dropout_to", 0.1),
util.env_opt("dropout_from", 0.2),
util.env_opt("dropout_to", 0.2),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 750),
util.env_opt("batch_to", 750),
util.env_opt("batch_from", 100),
util.env_opt("batch_to", 1000),
util.env_opt("batch_compound", 1.001),
)
@ -171,6 +172,8 @@ def train(
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
optimizer.b1_decay = 0.0001
optimizer.b2_decay = 0.0001
nlp._optimizer = None
# Load in pre-trained weights