improve logging in losses

bob/learn/tensorflow/loss/StyleLoss.py

@@ -4,8 +4,8 @@
 
 import logging
 import tensorflow as tf
-logger = logging.getLogger("bob.learn.tensorflow")
 import functools
+logger = logging.getLogger("bob.learn.tensorflow")
 
 
 def content_loss(noises, content_features):
@@ -24,7 +24,7 @@ def content_loss(noises, content_features):
     ----------
 
      noises: :any:`list`
-        A list of tf.Tensor containing all the noises convolved        
+        A list of tf.Tensor containing all the noises convolved
 
      content_features: :any:`list`
         A list of numpy.array containing all the content_features convolved
@@ -36,7 +36,7 @@ def content_loss(noises, content_features):
         content_losses.append((2 * tf.nn.l2_loss(n - c) / c.size))
     return functools.reduce(tf.add, content_losses)
 
-     
+
 def linear_gram_style_loss(noises, gram_style_features):
     """
 
@@ -89,7 +89,7 @@ def denoising_loss(noise):
 
     noise_y_size = _tensor_size(noise[:,1:,:,:])
     noise_x_size = _tensor_size(noise[:,:,1:,:])
-    denoise_loss = 2 * ( (tf.nn.l2_loss(noise[:,1:,:,:] - noise[:,:shape[1]-1,:,:]) / noise_y_size) + 
+    denoise_loss = 2 * ( (tf.nn.l2_loss(noise[:,1:,:,:] - noise[:,:shape[1]-1,:,:]) / noise_y_size) +
                     (tf.nn.l2_loss(noise[:,:,1:,:] - noise[:,:,:shape[2]-1,:]) / noise_x_size))
 
     return denoise_loss

bob/learn/tensorflow/loss/TripletLoss.py

@@ -57,24 +57,19 @@ def triplet_loss(anchor_embedding,
         with tf.name_scope("TripletLoss"):
             # Between
             between_class_loss = tf.reduce_mean(d_negative)
-            tf.summary.scalar('between_class', between_class_loss)
+            tf.summary.scalar('loss_between_class', between_class_loss)
             tf.add_to_collection(tf.GraphKeys.LOSSES, between_class_loss)
 
             # Within
             within_class_loss = tf.reduce_mean(d_positive)
-            tf.summary.scalar('within_class', within_class_loss)
+            tf.summary.scalar('loss_within_class', within_class_loss)
             tf.add_to_collection(tf.GraphKeys.LOSSES, within_class_loss)
 
             # Total loss
             loss = tf.reduce_mean(
                 tf.maximum(basic_loss, 0.0), 0, name="total_loss")
             tf.add_to_collection(tf.GraphKeys.LOSSES, loss)
-            tf.summary.scalar('loss_raw', loss)
-
-            # Appending the regularization loss
-            #regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
-            #loss = tf.add_n([loss] + regularization_losses, name="total_loss")
-            #tf.summary.scalar('loss', loss)
+            tf.summary.scalar('loss_triplet', loss)
 
         return loss