improve logging

bob/learn/tensorflow/loss/BaseLoss.py

@@ -13,12 +13,12 @@ def mean_cross_entropy_loss(logits, labels, add_regularization_losses=True):
     """
     Simple CrossEntropy loss.
     Basically it wrapps the function tf.nn.sparse_softmax_cross_entropy_with_logits.
-    
+
     **Parameters**
       logits:
       labels:
       add_regularization_losses: Regulize the loss???
-    
+
     """
 
     with tf.variable_scope('cross_entropy_loss'):
@@ -50,7 +50,7 @@ def mean_cross_entropy_center_loss(logits,
     """
     Implementation of the CrossEntropy + Center Loss from the paper
     "A Discriminative Feature Learning Approach for Deep Face Recognition"(http://ydwen.github.io/papers/WenECCV16.pdf)
-    
+
     **Parameters**
       logits:
       prelogits:
@@ -67,7 +67,7 @@ def mean_cross_entropy_center_loss(logits,
                 logits=logits, labels=labels),
             name="cross_entropy_loss")
         tf.add_to_collection(tf.GraphKeys.LOSSES, cross_loss)
-        tf.summary.scalar('cross_entropy_loss', cross_loss)
+        tf.summary.scalar('loss_cross_entropy', cross_loss)
 
     # Appending center loss
     with tf.variable_scope('center_loss'):
@@ -79,14 +79,14 @@ def mean_cross_entropy_center_loss(logits,
             initializer=tf.constant_initializer(0),
             trainable=False)
 
-        #label = tf.reshape(labels, [-1])
+        # label = tf.reshape(labels, [-1])
         centers_batch = tf.gather(centers, labels)
         diff = (1 - alpha) * (centers_batch - prelogits)
         centers = tf.scatter_sub(centers, labels, diff)
         center_loss = tf.reduce_mean(tf.square(prelogits - centers_batch))
         tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
                              center_loss * factor)
-        tf.summary.scalar('center_loss', center_loss)
+        tf.summary.scalar('loss_center', center_loss)
 
     # Adding the regularizers in the loss
     with tf.variable_scope('total_loss'):
@@ -95,7 +95,7 @@ def mean_cross_entropy_center_loss(logits,
         total_loss = tf.add_n(
             [cross_loss] + regularization_losses, name="total_loss")
         tf.add_to_collection(tf.GraphKeys.LOSSES, total_loss)
-        tf.summary.scalar('total_loss', total_loss)
+        tf.summary.scalar('loss_total', total_loss)
 
     loss = dict()
     loss['loss'] = total_loss

bob/learn/tensorflow/loss/ContrastiveLoss.py

@@ -3,16 +3,15 @@
 # @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
 
 import logging
-logger = logging.getLogger("bob.learn.tensorflow")
 import tensorflow as tf
+from bob.learn.tensorflow.utils import (
+    compute_euclidean_distance,
+)
 
-from bob.learn.tensorflow.utils import compute_euclidean_distance
+logger = logging.getLogger(__name__)
 
 
-def contrastive_loss(left_embedding,
-                     right_embedding,
-                     labels,
-                     contrastive_margin=2.0):
+def contrastive_loss(left_embedding, right_embedding, labels, contrastive_margin=2.0):
     """
     Compute the contrastive loss as in
 
@@ -49,18 +48,16 @@ def contrastive_loss(left_embedding,
 
         with tf.name_scope("within_class"):
             one = tf.constant(1.0)
-            within_class = tf.multiply(one - labels,
-                                       tf.square(d))  # (1-Y)*(d^2)
-            within_class_loss = tf.reduce_mean(
-                within_class, name="within_class")
+            within_class = tf.multiply(one - labels, tf.square(d))  # (1-Y)*(d^2)
+            within_class_loss = tf.reduce_mean(within_class, name="within_class")
             tf.add_to_collection(tf.GraphKeys.LOSSES, within_class_loss)
 
         with tf.name_scope("between_class"):
             max_part = tf.square(tf.maximum(contrastive_margin - d, 0))
             between_class = tf.multiply(
-                labels, max_part)  # (Y) * max((margin - d)^2, 0)
-            between_class_loss = tf.reduce_mean(
-                between_class, name="between_class")
+                labels, max_part
+            )  # (Y) * max((margin - d)^2, 0)
+            between_class_loss = tf.reduce_mean(between_class, name="between_class")
             tf.add_to_collection(tf.GraphKeys.LOSSES, between_class_loss)
 
         with tf.name_scope("total_loss"):
@@ -68,8 +65,8 @@ def contrastive_loss(left_embedding,
             loss = tf.reduce_mean(loss, name="contrastive_loss")
             tf.add_to_collection(tf.GraphKeys.LOSSES, loss)
 
-        tf.summary.scalar('contrastive_loss', loss)
-        tf.summary.scalar('between_class', between_class_loss)
-        tf.summary.scalar('within_class', within_class_loss)
+        tf.summary.scalar("contrastive_loss", loss)
+        tf.summary.scalar("between_class", between_class_loss)
+        tf.summary.scalar("within_class", within_class_loss)
 
         return loss