diff --git a/paddle/operators/softmax_with_cross_entropy_op.cc b/paddle/operators/softmax_with_cross_entropy_op.cc
index cb2aa30055de5dbeffc008d2d488027cc85fa13d..b6f33ad9e030bc69184de2fb5b88c9dcb07e71a9 100644
--- a/paddle/operators/softmax_with_cross_entropy_op.cc
+++ b/paddle/operators/softmax_with_cross_entropy_op.cc
@@ -23,11 +23,6 @@ class SoftmaxWithCrossEntropyOpMaker
   SoftmaxWithCrossEntropyOpMaker(framework::OpProto* proto,
                                  framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddAttr<bool>(
-        "softLabel",
-        "(bool, default: false), A flag to indicate whether to interpretate "
-        "the given labels as soft labels.")
-        .SetDefault(false);
     AddInput("Logits",
              "(Tensor, default: Tensor<float>), The unscaled log probabilities "
              "which is a 2-D tensor with shape [N x K]. N is the batch_size, "
@@ -49,6 +44,11 @@ class SoftmaxWithCrossEntropyOpMaker
     AddOutput("Loss",
               "(Tensor, default: Tensor<float>), A 2-D tensor. The cross "
               "entropy loss with shape [N x 1].");
+    AddAttr<bool>(
+        "softLabel",
+        "(bool, default: false), A flag to indicate whether to interpretate "
+        "the given labels as soft labels.")
+        .SetDefault(false);
     AddComment(R"DOC(
 Cross entropy loss with softmax are used as the output layer extensively. This
 operator computes the softmax normalized values for each row of the input
@@ -95,18 +95,18 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
 
     const Tensor* logits = ctx.Input<Tensor>("Logits");
     const Tensor* labels = ctx.Input<Tensor>("Label");
-    PADDLE_ENFORCE(
-        logits->dims().size() == 2UL,
+    PADDLE_ENFORCE_EQ(
+        logits->dims().size(), 2UL,
         "The input of softmax_with_cross_entropy should be a 2-D tensor.");
-    PADDLE_ENFORCE(ctx.Input<Tensor>("Label")->dims().size() == 2UL,
-                   "The labels should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Label")->dims().size(), 2UL,
+                      "The labels should be a 2-D tensor.");
 
     if (ctx.Attr<bool>("softLabel")) {
       PADDLE_ENFORCE_EQ(logits->dims()[1], labels->dims()[1],
                         "If Attr(softLabel) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
-      PADDLE_ENFORCE_EQ(labels->dims()[1], 1,
+      PADDLE_ENFORCE_EQ(labels->dims()[1], 1UL,
                         "If Attr(softLabel) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }
@@ -130,21 +130,21 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Softmax"),
                             "Input(Softmax) should be not null.");
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
-                            "Input(Lable) should be not null.");
+                            "Input(Label) should be not null.");
     PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(framework::GradVarName("Logits")),
                             "Output(Logits@Grad) should be not null.");
 
     const Tensor* softmax = ctx.Input<Tensor>("Softmax");
     const Tensor* labels = ctx.Input<Tensor>("Label");
-    PADDLE_ENFORCE(ctx.Input<Tensor>("Label")->dims().size() == 2UL,
-                   "The labels should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Label")->dims().size(), 2UL,
+                      "The labels should be a 2-D tensor.");
 
     if (ctx.Attr<bool>("softLabel")) {
       PADDLE_ENFORCE_EQ(softmax->dims()[1], labels->dims()[1],
                         "When Attr(softLabel) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
-      PADDLE_ENFORCE_EQ(labels->dims()[1], 1,
+      PADDLE_ENFORCE_EQ(labels->dims()[1], 1UL,
                         "When Attr(softLabel) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }