diff --git a/Source/CNTKv2LibraryDll/proto/onnx/CNTKToONNX.cpp b/Source/CNTKv2LibraryDll/proto/onnx/CNTKToONNX.cpp
index f3f5623ee..a5a94901f 100644
--- a/Source/CNTKv2LibraryDll/proto/onnx/CNTKToONNX.cpp
+++ b/Source/CNTKv2LibraryDll/proto/onnx/CNTKToONNX.cpp
@@ -5637,7 +5637,7 @@ onnxruntime::Node* CNTKToONNXHelper::CreateNode(const FunctionPtr& src,
     }
     else if (src->OpName() == L"Pooling" && src->Inputs()[0].HasBatchAxis() && src->Inputs()[0].HasSequenceAxis())
     {
-        // in case a Pooling op is created with bother batch and sequence axes, we need to reshape its input and output to match 
+        // in case a Pooling op is created with both batch and sequence axes, we need to reshape its input and output to match 
         // ONNX spec of [N, C, H, W] shape requirement.
         return CreatePoolingNode(src, graph, functionNodes, variableNodes, scanLoops, createLoopIndex);
     }
@@ -7109,6 +7109,14 @@ void CNTKToONNXHelper::CopyAttributes(const FunctionPtr& src, onnxruntime::Node*
             }
             else
             {
+                if (src->Inputs()[0].HasBatchAxis() && src->Inputs()[0].HasSequenceAxis())
+                {
+                    if (!std::all_of(lowerPad.begin(), lowerPad.end(), [](int64_t pad) {return pad == 0; }) ||
+                        !std::all_of(upperPad.begin(), upperPad.end(), [](int64_t pad) {return pad == 0; }))
+                    {
+                        fprintf(stderr, "Warning: Cannot set upperPad and lowerPad with pooling ops. Padding values will be computed according to kernel and input shapes.");
+                    }
+                }
                 if (isPooling)
                     PutPadAttrInNode(node, autoPadding, kernelShape, inputShape, strides, /*dilation=*/std::vector<size_t>(kernelShape.Rank(), 1),
                                      ceilOutDim, /*transpose=*/!isPooling);
@@ -8661,7 +8669,8 @@ onnxruntime::Node* CNTKToONNXHelper::CreatePoolingNode(const FunctionPtr& src,
     vector<int64_t> newDimOutputFromPooling = ToINTS(*outputs[0]->TypeAsProto());
     onnxruntime::Node* postReshape = AddReshapeNode(*poolingOutputArg, newDimOutputFromPooling, outputs[0]->Name(), graph);
 
-    return poolingNode;
+    functionNodes.emplace(src, poolingNode);
+    return postReshape;
 }
 
 onnxruntime::Node* CNTKToONNXHelper::CreateConvolutionNode(const FunctionPtr& src,