diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc
index 40c51a64c49bc064f55975ef6ced1d54070f1291..7d062ad67c048bc6bef68121f86334eb3f1efe92 100644
--- a/paddle/operators/softmax_op.cc
+++ b/paddle/operators/softmax_op.cc
@@ -24,7 +24,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
-                   "The input of softmax op must be matrix");
+                   "The input of softmax op must be a matrix.");
     ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
@@ -34,9 +34,27 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
   SoftmaxOpMaker(framework::OpProto *proto,
                  framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "input of softmax");
-    AddOutput("Y", "output of softmax");
-    AddComment("Softmax Op");
+    AddInput("X",
+             "The input tensor of softmax. "
+             "2-D with shape [batch_size, input_feature_dimensions].");
+    AddOutput("Y", "The normalized values with the same shape as X.");
+    AddComment(R"DOC(
+The input of softmax operator is a 2-D tensor with shape N x K (N is the
+batch_size, K is the dimension of input feature). The output tensor has the
+same shape as the input tensor.
+
+For each row of the input tensor, the softmax operator squashes the
+K-dimensional vector of arbitrary real values to a K-dimensional vector of real
+values in the range [0, 1] that add up to 1. Specifically, it computes the
+exponential of the given dimension and the sum of exponential values of all
+the other dimensions in the K-dimensional vector input. Then the ratio of the
+exponential of the given dimension and the sum of exponential values of all
+the other dimensions is the output of the softmax operator.
+
+For each row `i` and each column `j` in X, we have:
+    Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
+
+)DOC");
   }
 };