def build_loss(self, *args, **kargs):
if self.config.loss == "softmax_loss":
self.loss, _ = point_wise_loss.softmax_loss(self.logits, self.gold_label,
*args, **kargs)
elif self.config.loss == "sparse_amsoftmax_loss":
self.loss, _ = point_wise_loss.sparse_amsoftmax_loss(self.logits, self.gold_label,
self.config, *args, **kargs)
elif self.config.loss == "focal_loss_multi_v1":
self.loss, _ = point_wise_loss.focal_loss_multi_v1(self.logits, self.gold_label,
self.config, *args, **kargs)
if self.config.with_center_loss:
self.center_loss, _ = point_wise_loss.center_loss_v2(self.sent_repres,
self.gold_label, centers=self.memory,
config=self.config,
*args, **kargs)
self.loss = self.loss + self.config.center_gamma * self.center_loss
if self.config.get("mode", "train") == "train":
if self.config.with_label_regularization:
print("===with class regularization===")
self.class_loss, _ = point_wise_loss.focal_loss_multi_v1(
self.class_logits, self.gold_label,
self.config, *args, **kargs)
self.loss += self.config.class_penalty * self.class_loss
trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)
print("List of Variables:")
for v in trainable_vars:
print(v.name)
def build_loss(self, *args, **kargs):
if self.config.loss == "softmax_loss":
self.loss, _ = point_wise_loss.softmax_loss(
self.logits, self.gold_label, *args, **kargs)
elif self.config.loss == "sparse_amsoftmax_loss":
self.loss, _ = point_wise_loss.sparse_amsoftmax_loss(
self.logits, self.gold_label, self.config, *args, **kargs)
elif self.config.loss == "focal_loss_multi_v1":
self.loss, _ = point_wise_loss.focal_loss_multi_v1(
self.logits, self.gold_label, self.config, *args, **kargs)
if self.config.with_center_loss:
self.center_loss, _ = point_wise_loss.center_loss_v2(
self.sent_repres, self.gold_label, self.config, *args, **kargs)
self.loss = self.loss + self.config.center_gamma * self.center_loss
