Pass logits and labels to losses using kwargs

bob/learn/tensorflow/estimators/Logits.py

@@ -80,12 +80,12 @@ class Logits(estimator.Estimator):
             "scopes": dict({"<SOURCE_SCOPE>/": "<TARGET_SCOPE>/"}),
             "trainable_variables": [<LIST OF VARIABLES OR SCOPES THAT YOU WANT TO RETRAIN>]
         }
-        
+
       apply_moving_averages: bool
         Apply exponential moving average in the training variables and in the loss.
         https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
         By default the decay for the variable averages is 0.9999 and for the loss is 0.9
-        
+
     """
 
     def __init__(self,
@@ -144,7 +144,7 @@ class Logits(estimator.Estimator):
                 with tf.control_dependencies([variable_averages_op]):
 
                     # Compute Loss (for both TRAIN and EVAL modes)
-                    self.loss = self.loss_op(logits, labels)
+                    self.loss = self.loss_op(logits=logits, labels=labels)
 
                     # Compute the moving average of all individual losses and the total loss.
                     loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
@@ -187,7 +187,7 @@ class Logits(estimator.Estimator):
                     mode=mode, predictions=predictions)
 
             # IF Validation
-            self.loss = self.loss_op(logits, labels)
+            self.loss = self.loss_op(logits=logits, labels=labels)
 
             if self.embedding_validation:
                 predictions_op = predict_using_tensors(
@@ -266,7 +266,7 @@ class LogitsCenterLoss(estimator.Estimator):
       params:
         Extra params for the model function (please see
         https://www.tensorflow.org/extend/estimators for more info)
-        
+
       extra_checkpoint: dict
         In case you want to use other model to initialize some variables.
         This argument should be in the following format
@@ -275,13 +275,13 @@ class LogitsCenterLoss(estimator.Estimator):
             "scopes": dict({"<SOURCE_SCOPE>/": "<TARGET_SCOPE>/"}),
             "trainable_variables": [<LIST OF VARIABLES OR SCOPES THAT YOU WANT TO TRAIN>]
         }
-        
+
       apply_moving_averages: bool
         Apply exponential moving average in the training variables and in the loss.
         https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
         By default the decay for the variable averages is 0.9999 and for the loss is 0.9
-        
-      
+
+
 
     """
 
@@ -365,7 +365,7 @@ class LogitsCenterLoss(estimator.Estimator):
                     # Compute the moving average of all individual losses and the total loss.
                     loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
                     loss_averages_op = loss_averages.apply(tf.get_collection(tf.GraphKeys.LOSSES))
-                    
+
                     for l in tf.get_collection(tf.GraphKeys.LOSSES):
                         tf.summary.scalar(l.op.name, loss_averages.average(l))