Add env opts for optimizer
@@ -276,7 +276,15 @@ class Language(object):
context = proc.begin_training(get_gold_tuples(),
pipeline=self.pipeline)
contexts.append(context)
- optimizer = Adam(Model.ops, 0.001)
+ learn_rate = util.env_opt('learn_rate', 0.001)
+ beta1 = util.env_opt('optimizer_B1', 0.9)
+ beta2 = util.env_opt('optimizer_B2', 0.999)
+ eps = util.env_opt('optimizer_eps', 1e-08)
+ L2 = util.env_opt('L2_penalty', 1e-6)
+ max_grad_norm = util.env_opt('grad_norm_clip', 1.)
+ optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
+ beta2=beta2, eps=eps)
+ optimizer.max_grad_norm = max_grad_norm
return optimizer
def evaluate(self, docs_golds):
