Updates

• config file from train.py accepts tensors as input
• TFRecord, add keyword argument to flag the shuffling mechanism
• Added InceptionResnetV1 in our basket of networks
• Added an option to sum the regularization losses in the CrossEntropy loss
• Implemented validation with embeddings (officially appending to the tensorboard)
• Created method Trainer.load_variables_from_external_model that load variables from another model in the current graph
• Implemented MeanSoftMaxLossCenterLoss (http://ydwen.github.io/papers/WenECCV16.pdf)
• Patched InceptionV2 (Added the flag trainable for each variable in the model)
• Implemented TFRecordImage (read, data augmentation, scaling and casting to float32)
• Added data type as input in the TFRecord datashufflers

bob/learn/tensorflow/loss/BaseLoss.py

@@ -61,7 +61,7 @@ class MeanSoftMaxLossCenterLoss(object):
     Mean softmax loss. Basically it wrapps the function tf.nn.sparse_softmax_cross_entropy_with_logits.
     """
 
-    def __init__(self, name="loss", add_regularization_losses=True, alpha=0.9, factor=0.01, n_classes=10):
+    def __init__(self, name="loss", alpha=0.9, factor=0.01, n_classes=10):
         """
         Constructor
         
@@ -73,46 +73,36 @@ class MeanSoftMaxLossCenterLoss(object):
         """
     
         self.name = name
-        self.add_regularization_losses = add_regularization_losses
 
         self.n_classes = n_classes
         self.alpha = alpha
         self.factor = factor
 
 
-    def append_center_loss(self, features, label):
-        nrof_features = features.get_shape()[1]
-        
-        centers = tf.get_variable('centers', [self.n_classes, nrof_features], dtype=tf.float32,
-            initializer=tf.constant_initializer(0), trainable=False)
-            
-        label = tf.reshape(label, [-1])
-        centers_batch = tf.gather(centers, label)
-        diff = (1 - self.alpha) * (centers_batch - features)
-        centers = tf.scatter_sub(centers, label, diff)
-        loss = tf.reduce_mean(tf.square(features - centers_batch))
-        
-        return loss
-
-
-    def __call__(self, logits_prelogits, label):
-    
-        #TODO: Test the dictionary
-    
-        logits = logits_prelogits['logits']
-    
+    def __call__(self, logits, prelogits, label):           
         # Cross entropy
-        loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
-                                          logits=logits, labels=label), name=self.name)
+        with tf.variable_scope('cross_entropy_loss'):
+            loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
+                                              logits=logits, labels=label), name=self.name)
 
-        # Appending center loss
-        prelogits = logits_prelogits['prelogits']
-        center_loss = self.append_center_loss(prelogits, label)
-        tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, center_loss * self.factor)
+        # Appending center loss        
+        with tf.variable_scope('center_loss'):
+            n_features = prelogits.get_shape()[1]
+            
+            centers = tf.get_variable('centers', [self.n_classes, n_features], dtype=tf.float32,
+                initializer=tf.constant_initializer(0), trainable=False)
+                
+            label = tf.reshape(label, [-1])
+            centers_batch = tf.gather(centers, label)
+            diff = (1 - self.alpha) * (centers_batch - prelogits)
+            centers = tf.scatter_sub(centers, label, diff)
+            center_loss = tf.reduce_mean(tf.square(prelogits - centers_batch))       
+            tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, center_loss * self.factor)
     
         # Adding the regularizers in the loss
-        if self.add_regularization_losses:
+        with tf.variable_scope('total_loss'):
             regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
-            loss =  tf.add_n([loss] + regularization_losses, name='total_loss')
+            total_loss =  tf.add_n([loss] + regularization_losses, name='total_loss')
             
-        return loss            
+        return total_loss, centers
+