Update cross entropy operator by following reviewer's comments.

paddle/operators/cross_entropy_op.cc

@@ -54,6 +54,9 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input(X) of CrossEntropyOp must not be null.");
+
     auto dx = ctx.Output<LoDTensor>(framework::GradVarName("X"));
     auto x = ctx.Input<Tensor>("X");
 
@@ -74,11 +77,14 @@ CrossEntropy Operator.
 
 The second input (Label tensor) supports two kinds of shapes:
 1) Rank(Label) = 1, Label[i] indicates the class index for sample i:
+
                 Y[i] = -log(X[i, Label[i]])
 
 2) Rank(Label) = 2, Label[i, j] indicates the soft label of class j
    for sample i:
+
                 Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
+
    Please make sure that in this case the summuation of each row of Label
    equals one. If each row of Label has only one non-zero element (equals 1),
    it degenerates to a standard one-hot representation.

paddle/operators/cross_entropy_op.cu

@@ -14,6 +14,7 @@
 
 #include "paddle/framework/op_registry.h"
 #include "paddle/platform/assert.h"
+#include "paddle/platform/hostdevice.h"
 
 namespace paddle {
 namespace operators {
@@ -21,7 +22,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename T>
-__host__ __device__ T tolerable_value(const T x) {
+HOSTDEVICE T tolerable_value(const T x) {
   PADDLE_ASSERT(std::is_floating_point<T>::value);
   const T kApproInf = 1e20;
   if (x == INFINITY) {

python/paddle/v2/framework/tests/test_cross_entropy_op.py

@@ -45,7 +45,7 @@ class TestCrossEntropySoftLabel(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Y', max_relative_error=0.05)
+        self.check_grad(['X'], 'Y')
 
 
 if __name__ == "__main__":