diff --git a/paddle/operators/batch_norm_op.cc b/paddle/operators/batch_norm_op.cc
index 49cb0fa4d9d8ada649105a9b14ad960f6cda1a7e..98db28ddee7c6cdb37fe7732649d4fc38de7b873 100644
--- a/paddle/operators/batch_norm_op.cc
+++ b/paddle/operators/batch_norm_op.cc
@@ -50,10 +50,6 @@ class BatchNormOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("SavedMean"), "");
     PADDLE_ENFORCE(ctx->HasOutput("SavedVariance"), "");
 
-    const float epsilon = ctx->Attrs().Get<float>("epsilon");
-    PADDLE_ENFORCE_GE(epsilon, 0.0, "epsilon should be larger than 0");
-    PADDLE_ENFORCE_LE(epsilon, 0.001, "epsilon should not be too large");
-
     // make sure Mean/MeanOut and Variance/VarianceOut share memory in Python
     PADDLE_ENFORCE_EQ(ctx->Inputs("Mean")[0], ctx->Outputs("MeanOut")[0],
                       "Mean and MeanOut should share the same memory");
@@ -91,7 +87,12 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddAttr<bool>("is_test", "").SetDefault(false);
     AddAttr<float>("momentum", "").SetDefault(0.9);
-    AddAttr<float>("epsilon", "").SetDefault(1e-5);
+    AddAttr<float>("epsilon", "")
+        .SetDefault(1e-5)
+        .AddCustomChecker([](const float &epsilon) {
+          PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
+                         "'epsilon' should be between 0.0 and 0.001.");
+        });
     AddAttr<std::string>("data_layout", "").SetDefault("NCHW");
     AddInput("X", "The input tensor");
     AddInput("Scale",
diff --git a/paddle/operators/dropout_op.cc b/paddle/operators/dropout_op.cc
index d3130c1a7e7cd913dd2d79665df8fef82de83cd4..35cb18797ff66cb87a6658e73ce02b0bfae29baa 100644
--- a/paddle/operators/dropout_op.cc
+++ b/paddle/operators/dropout_op.cc
@@ -25,8 +25,6 @@ class DropoutOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
-    PADDLE_ENFORCE_GE(ctx->Attrs().Get<float>("dropout_prob"), 0);
-    PADDLE_ENFORCE_LE(ctx->Attrs().Get<float>("dropout_prob"), 1);
 
     auto x_dims = ctx->GetInputDim("X");
     ctx->SetOutputDim("Out", x_dims);
@@ -47,7 +45,11 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Mask", "The random sampled dropout mask.").AsIntermediate();
 
     AddAttr<float>("dropout_prob", "Probability of setting units to zero.")
-        .SetDefault(.5f);
+        .SetDefault(.5f)
+        .AddCustomChecker([](const float& drop_p) {
+          PADDLE_ENFORCE(drop_p >= 0.0f && drop_p <= 1.0f,
+                         "'dropout_prob' must be between 0.0 and 1.0.");
+        });
     AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
     AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
 
@@ -78,8 +80,6 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) must not be null.");
 
-    PADDLE_ENFORCE_GE(ctx->Attrs().Get<float>("dropout_prob"), 0);
-    PADDLE_ENFORCE_LE(ctx->Attrs().Get<float>("dropout_prob"), 1);
     auto x_dims = ctx->GetInputDim("X");
     auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
     PADDLE_ENFORCE_EQ(x_dims, out_dims,
diff --git a/paddle/operators/dropout_op.cu b/paddle/operators/dropout_op.cu
index c0b4aaa9196eb23871e2d39cea4f33f53562ffe4..c56930336e865079f1b96df0f35b0a051fe63a27 100644
--- a/paddle/operators/dropout_op.cu
+++ b/paddle/operators/dropout_op.cu
@@ -30,16 +30,15 @@ struct MaskGenerator {
   __host__ __device__ MaskGenerator(AttrType dropout_prob, int seed)
       : dropout_prob(dropout_prob), seed(seed) {}
 
-  __host__ __device__ T operator()(const unsigned int n) const {
+  inline __host__ __device__ T operator()(const unsigned int n) const {
     thrust::minstd_rand rng;
     rng.seed(seed);
     thrust::uniform_real_distribution<AttrType> dist(0, 1);
     rng.discard(n);
     if (dist(rng) < dropout_prob) {
       return static_cast<T>(0);
-    } else {
-      return static_cast<T>(1);
     }
+    return static_cast<T>(1);
   }
 };