diff --git a/src/operator/nn/batch_norm-inl.h b/src/operator/nn/batch_norm-inl.h
index 4838570bda97..1220156f1056 100644
--- a/src/operator/nn/batch_norm-inl.h
+++ b/src/operator/nn/batch_norm-inl.h
@@ -227,8 +227,10 @@ void BatchNormCompute(const nnvm::NodeAttrs& attrs,
                       const std::vector<TBlob>& outputs) {
   const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
   CHECK_EQ(inputs.size(), 5U);
-  std::vector<TBlob> in_data(inputs.begin(), inputs.begin() + 3);
-  std::vector<TBlob> aux_states(inputs.begin() + 3, inputs.end());
+  std::vector<TBlob> in_data(inputs.begin(),
+                             inputs.begin() + (int) batchnorm::kInMovingMean);
+  std::vector<TBlob> aux_states(inputs.begin() + (int) batchnorm::kInMovingMean,
+                                inputs.end());
   MSHADOW_REAL_TYPE_SWITCH_EX(inputs[0].type_flag_, DType, AccReal, {
     GetBatchNormOp<xpu, DType, AccReal>(param).Forward(ctx, in_data,
         req, outputs, aux_states);
@@ -242,11 +244,16 @@ void BatchNormGradCompute(const nnvm::NodeAttrs& attrs,
                           const std::vector<TBlob>& outputs) {
   CHECK_EQ(inputs.size(), 11U);
   const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
-  std::vector<TBlob> out_grad(inputs.begin(),
-      inputs.begin() + (param.output_mean_var ? 3U : 1U));
-  std::vector<TBlob> in_data(inputs.begin() + 3, inputs.begin() + 6);
-  std::vector<TBlob> aux_states(inputs.begin() + 6, inputs.begin() + 8);
-  std::vector<TBlob> out_data(inputs.begin() + 8, inputs.end());
+  int num_out_grads = param.output_mean_var ? 3U : 1U;
+  int in_data_start = 3;
+  int aux_states_start = in_data_start + (int) batchnorm::kInMovingMean;
+  int out_data_start = in_data_start + (int) batchnorm::kInMovingVar + 1;
+  std::vector<TBlob> out_grad(inputs.begin(), inputs.begin() + num_out_grads);
+  std::vector<TBlob> in_data(inputs.begin() + in_data_start,
+                             inputs.begin() + aux_states_start);
+  std::vector<TBlob> aux_states(inputs.begin() + aux_states_start,
+                                inputs.begin() + out_data_start);
+  std::vector<TBlob> out_data(inputs.begin() + out_data_start, inputs.end());
   std::vector<TBlob> in_grad(outputs.begin(), outputs.begin() + 3);
 
   MSHADOW_REAL_TYPE_SWITCH_EX(out_grad[0].type_flag_, DType, AccReal, {
diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc
index 298de204a53f..bbf4da9874c4 100644
--- a/src/operator/nn/batch_norm.cc
+++ b/src/operator/nn/batch_norm.cc
@@ -323,7 +323,7 @@ static bool BatchNormShape(const nnvm::NodeAttrs& attrs,
   const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
   using namespace mshadow;
   CHECK_EQ(in_shape->size(), 5U) << "Input:[data, gamma, beta, MovingMean, MovingVar]";
-  const TShape &dshape = in_shape->at(0);
+  const TShape &dshape = in_shape->at(batchnorm::kData);
 
   const size_t channelAxis = static_cast<size_t>(param.axis < 0
       ? static_cast<int>(dshape.ndim()) + param.axis
@@ -336,10 +336,10 @@ static bool BatchNormShape(const nnvm::NodeAttrs& attrs,
     return false;
   }
 
-  in_shape->at(1) = TShape(Shape1(channelCount));
-  in_shape->at(2) = TShape(Shape1(channelCount));
-  in_shape->at(3) = TShape(Shape1(channelCount));  // kMovingMean
-  in_shape->at(4) = TShape(Shape1(channelCount));  // kMovingVar
+  in_shape->at(batchnorm::kGamma) = TShape(Shape1(channelCount));
+  in_shape->at(batchnorm::kBeta) = TShape(Shape1(channelCount));
+  in_shape->at(batchnorm::kInMovingMean) = TShape(Shape1(channelCount));  // kMovingMean
+  in_shape->at(batchnorm::kInMovingVar) = TShape(Shape1(channelCount));  // kMovingVar
 
   out_shape->clear();
   out_shape->push_back(dshape);                // kOut