Set dropout rate to 0.2
@@ -112,8 +112,8 @@ def train(
# 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(
