Merge pull request #217 from tensorflow/sguada-patch-2

Update slim README.md

inception/inception/slim/README.md

@@ -383,7 +383,7 @@ images, labels = ...
 predictions =  ...
 
 # Define the loss functions and get the total loss.
-loss = losses.ClassificationLoss(predictions, labels)
+loss = losses.cross_entropy_loss(predictions, labels)
 ```
 
 In this example, we start by creating the model (using TF-Slim's VGG
@@ -398,8 +398,8 @@ images, scene_labels, depth_labels = ...
 scene_predictions, depth_predictions = CreateMultiTaskModel(images)
 
 # Define the loss functions and get the total loss.
-classification_loss = slim.losses.ClassificationLoss(scene_predictions, scene_labels)
-sum_of_squares_loss = slim.losses.SumOfSquaresLoss(depth_predictions, depth_labels)
+classification_loss = slim.losses.cross_entropy_loss(scene_predictions, scene_labels)
+sum_of_squares_loss = slim.losses.l2loss(depth_predictions - depth_labels)
 
 # The following two lines have the same effect:
 total_loss1 = classification_loss + sum_of_squares_loss
@@ -407,7 +407,7 @@ total_loss2 = tf.get_collection(slim.losses.LOSSES_COLLECTION)
 ```
 
 In this example, we have two losses which we add by calling
-`losses.ClassificationLoss` and `losses.SumOfSquaresLoss`. We can obtain the
+`losses.cross_entropy_loss` and `losses.l2loss`. We can obtain the
 total loss by adding them together (`total_loss1`) or by calling
 `losses.GetTotalLoss()`. How did this work? When you create a loss function via
 TF-Slim, TF-Slim adds the loss to a special TensorFlow collection of loss
@@ -426,8 +426,8 @@ images, scene_labels, depth_labels, pose_labels = ...
 scene_predictions, depth_predictions, pose_predictions = CreateMultiTaskModel(images)
 
 # Define the loss functions and get the total loss.
-classification_loss = slim.losses.ClassificationLoss(scene_predictions, scene_labels)
-sum_of_squares_loss = slim.losses.SumOfSquaresLoss(depth_predictions, depth_labels)
+classification_loss = slim.losses.cross_entropy_loss(scene_predictions, scene_labels)
+sum_of_squares_loss = slim.losses.l2loss(depth_predictions - depth_labels)
 pose_loss = MyCustomLossFunction(pose_predictions, pose_labels)
 tf.add_to_collection(slim.losses.LOSSES_COLLECTION, pose_loss) # Letting TF-Slim know about the additional loss.