Tweaks to train script
@@ -57,9 +57,9 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
# starts high and decays sharply, to force the optimizer to explore.
# 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', 0.0),
- util.env_opt('dropout_to', 0.0),
- util.env_opt('dropout_decay', 0.0))
+ dropout_rates = util.decaying(util.env_opt('dropout_from', 0.5),
+ util.env_opt('dropout_to', 0.2),
+ util.env_opt('dropout_decay', 1e-4))
batch_sizes = util.compounding(util.env_opt('batch_from', 1),
util.env_opt('batch_to', 64),
util.env_opt('batch_compound', 1.001))
@@ -72,7 +72,7 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
print("Itn.\tDep. Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
for i in range(n_iter):
- with tqdm.tqdm(total=corpus.count_train()) as pbar:
+ with tqdm.tqdm(total=corpus.count_train(), leave=False) as pbar:
train_docs = corpus.train_docs(nlp, projectivize=True,
gold_preproc=False, shuffle=i)
losses = {}
