Add sum_cost to document

And rebase

Change-Id: I7ea234b3aa8fc70675af15d91db08242c43fb5ff

doc/source/gserver/layers/layer.rst

@@ -465,6 +465,11 @@ SumOfSquaresCostLayer
 ..  doxygenclass:: paddle::SumOfSquaresCostLayer
     :members:
 
+SumCostLayer
+`````````````````````
+..  doxygenclass:: paddle::SumCostLayer
+    :members:
+
 CosSimLayer
 -----------
 ..  doxygenclass:: paddle::CosSimLayer

doc/ui/api/trainer_config_helpers/layers.rst

@@ -395,6 +395,12 @@ hsigmoid
     :members: hsigmoid
     :noindex:
 
+sum_cost
+---------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: sum_cost
+    :noindex:
+
 Check Layer 
 ============
 

paddle/gserver/layers/CostLayer.cpp

@@ -562,6 +562,12 @@ void HuberTwoClass::backwardImpIn(
   }
 }
 
+/**
+ * This cost layer compute the sum of its input as loss.
+ * \f[
+ * o(i) = \sum_{j=1}^D y_{ij}
+ * \f]
+ */
 class SumCostLayer : public Layer {
 public:
   explicit SumCostLayer(const LayerConfig& config) : Layer(config) {}

paddle/gserver/layers/CostLayer.h

@@ -129,7 +129,7 @@ protected:
  * This cost layer compute Euclidean (L2) loss for real-valued regression
  * tasks.
  * \f[
- * L = \frac{1}{2N} \sum_{i=1}^N {|| \hat{y}_i - y_i||_2^2}
+ * L = \sum_{i=1}^N {|| \hat{y}_i - y_i||_2^2}
  * \f]
  */
 class SumOfSquaresCostLayer : public CostLayer {

python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr

@@ -23,6 +23,17 @@ layers {
   size: 10
   active_type: ""
 }
+layers {
+  name: "__fc_layer_0__"
+  type: "fc"
+  size: 4
+  active_type: "tanh"
+  inputs {
+    input_layer_name: "input"
+    input_parameter_name: "___fc_layer_0__.w0"
+  }
+  bias_parameter_name: "___fc_layer_0__.wbias"
+}
 layers {
   name: "__ctc_layer_0__"
   type: "ctc"
@@ -36,17 +47,6 @@ layers {
   }
   norm_by_times: false
 }
-layers {
-  name: "__fc_layer_0__"
-  type: "fc"
-  size: 4
-  active_type: "tanh"
-  inputs {
-    input_layer_name: "input"
-    input_parameter_name: "___fc_layer_0__.w0"
-  }
-  bias_parameter_name: "___fc_layer_0__.wbias"
-}
 layers {
   name: "crf_label"
   type: "data"
@@ -191,6 +191,16 @@ layers {
   }
   coeff: 1.0
 }
+layers {
+  name: "__sum_cost_0__"
+  type: "sum_cost"
+  size: 1
+  active_type: ""
+  inputs {
+    input_layer_name: "__fc_layer_0__"
+  }
+  coeff: 1.0
+}
 parameters {
   name: "___fc_layer_0__.w0"
   size: 800
@@ -241,14 +251,15 @@ output_layer_names: "__cross_entropy_0__"
 output_layer_names: "__cross_entropy_with_selfnorm_0__"
 output_layer_names: "__huber_cost_0__"
 output_layer_names: "__multi_binary_label_cross_entropy_0__"
+output_layer_names: "__sum_cost_0__"
 sub_models {
   name: "root"
   layer_names: "input"
   layer_names: "labels"
   layer_names: "probs"
   layer_names: "xe-label"
-  layer_names: "__ctc_layer_0__"
   layer_names: "__fc_layer_0__"
+  layer_names: "__ctc_layer_0__"
   layer_names: "crf_label"
   layer_names: "__crf_layer_0__"
   layer_names: "left"
@@ -264,6 +275,7 @@ sub_models {
   layer_names: "huber_label"
   layer_names: "__huber_cost_0__"
   layer_names: "__multi_binary_label_cross_entropy_0__"
+  layer_names: "__sum_cost_0__"
   input_layer_names: "input"
   input_layer_names: "labels"
   input_layer_names: "crf_label"
@@ -284,6 +296,7 @@ sub_models {
   output_layer_names: "__cross_entropy_with_selfnorm_0__"
   output_layer_names: "__huber_cost_0__"
   output_layer_names: "__multi_binary_label_cross_entropy_0__"
+  output_layer_names: "__sum_cost_0__"
   is_recurrent_layer_group: false
 }