improve logging for vat loss

bob/learn/tensorflow/loss/vat.py

@@ -108,20 +108,20 @@ class VATLoss:
         architecture = partial(architecture, reuse=True)
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):
             vat_loss = self.virtual_adversarial_loss(features, logits, architecture, mode)
-            tf.summary.scalar("vat_loss", vat_loss)
+            tf.summary.scalar("loss_VAT", vat_loss)
             tf.add_to_collection(tf.GraphKeys.LOSSES, vat_loss)
             if self.method == 'vat':
                 loss = vat_loss
             elif self.method == 'vatent':
                 ent_loss = entropy_y_x(logits)
-                tf.summary.scalar("entropy_loss", ent_loss)
+                tf.summary.scalar("loss_entropy", ent_loss)
                 tf.add_to_collection(tf.GraphKeys.LOSSES, ent_loss)
                 loss = vat_loss + ent_loss
             else:
                 raise ValueError
             return loss
 
-    def virtual_adversarial_loss(self, features, logits, architecture, mode, name="vat_loss"):
+    def virtual_adversarial_loss(self, features, logits, architecture, mode, name="vat_loss_op"):
         r_vadv = self.generate_virtual_adversarial_perturbation(features, logits, architecture, mode)
         logit_p = tf.stop_gradient(logits)
         adversarial_input = features + r_vadv