add cost_type constraint to weighted_cost interface (#206)

python/paddle/trainer/config_parser.py

@@ -1715,7 +1715,6 @@ def define_cost(class_name, cost_type):
     g_cost_map[cost_type] = cls
 
 define_cost('MultiClassCrossEntropy', 'multi-class-cross-entropy')
-define_cost('ClassificationErrorLayer', 'classification_error')
 define_cost('RankingCost', 'rank-cost')
 define_cost('AucValidation', 'auc-validation')
 define_cost('PnpairValidation', 'pnpair-validation')

python/paddle/trainer_config_helpers/layers.py

@@ -2799,7 +2799,7 @@ def __cost_input__(input, label, weight=None):
     
 
 @wrap_name_default()
-def regression_cost(input, label, weight=None, cost='square_error', name=None):
+def regression_cost(input, label, weight=None, name=None):
     """
     Regression Layer.
 
@@ -2814,21 +2814,18 @@ def regression_cost(input, label, weight=None, cost='square_error', name=None):
     :param weight: The weight affects the cost, namely the scale of cost.
                    It is an optional argument.
     :type weight: LayerOutput
-    :param cost: Cost method.
-    :type cost: basestring
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
     ipts, parents = __cost_input__(input, label, weight)
 
-    Layer(inputs=ipts, type=cost, name=name)
+    Layer(inputs=ipts, type="square_error", name=name)
     return LayerOutput(name, LayerType.COST, parents=parents)
 
 
 @wrap_name_default("cost")
 @layer_support()
 def classification_cost(input, label, weight=None, name=None,
-                        cost="multi-class-cross-entropy",
                         evaluator=classification_error_evaluator,
                         layer_attr=None):
     """
@@ -2843,8 +2840,6 @@ def classification_cost(input, label, weight=None, name=None,
     :param weight: The weight affects the cost, namely the scale of cost.
                    It is an optional argument.
     :type weight: LayerOutput
-    :param cost: cost method.
-    :type cost: basestring
     :param evaluator: Evaluator method.
     :param layer_attr: layer's extra attribute.
     :type layer_attr: ExtraLayerAttribute
@@ -2857,7 +2852,7 @@ def classification_cost(input, label, weight=None, name=None,
 
     ipts, parents = __cost_input__(input, label, weight)
 
-    Layer(name=name, type=cost, inputs=ipts,
+    Layer(name=name, type="multi-class-cross-entropy", inputs=ipts,
           **ExtraLayerAttribute.to_kwargs(layer_attr))
 
     def __add_evaluator__(e):
@@ -3819,8 +3814,8 @@ def multi_binary_label_cross_entropy(input, label, name=None, coeff=1.0):
     if input.activation is None or \
             not isinstance(input.activation, SigmoidActivation):
         logger.log(logging.WARN,
-                   "%s is not recommend for batch normalization's activation, "
-                   "maybe the relu is better" % repr(input.activation))
+                   "%s is not recommend for multi_binary_label_cross_entropy's activation, "
+                   "maybe the sigmoid is better" % repr(input.activation))
 
     Layer(name=name,
           type=LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,