diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 73ca07be76ee..65ca2c25b2f2 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -232,6 +232,17 @@ def convert_fully_connected(node, **kwargs):
 
     fcnode = []
 
+    op_name = "flatten_" + str(kwargs["idx"])
+    flatten_node = onnx.helper.make_node(
+        'Flatten',
+        inputs=[input_nodes[0]],
+        outputs=[op_name],
+        name=op_name
+    )
+
+    input_nodes[0] = op_name
+    fcnode.append(flatten_node)
+
     if no_bias:
         data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
         bias_name = "bias" + str(kwargs["idx"])
diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py b/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
index b02d970f9c2d..c1f327e20ba2 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
@@ -231,6 +231,10 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
         # Determine output shape
         output_shape = MXNetGraph.infer_output_shape(sym, params, in_shape, output_label)
 
+        output_suffix = '_output'
+        output_names = [
+            o[:-len(output_suffix)] for o in sym.list_outputs() if o.endswith(output_suffix)]
+
         weights = MXNetGraph.convert_weights_to_numpy(params)
 
         mx_graph = json.loads(sym.tojson())["nodes"]
@@ -294,26 +298,25 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
                     # If converted node is NodeProto, add it in processed nodes list
                     elif isinstance(converted_node, NodeProto):
                         onnx_processed_nodes.append(converted_node)
-                        if idx == (len(mx_graph) - 1):
-                            # If converted node doesnt have name, use it from output field
-                            if not converted_node.name:
-                                onnx_processed_outputs.append(
-                                    make_tensor_value_info(
-                                        name=converted_node.output[0],
-                                        elem_type=in_type,
-                                        shape=output_shape
-                                    )
+                        # If converted node doesnt have name, use it from output field
+                        if not converted_node.name and idx == (len(mx_graph) - 1):
+                            onnx_processed_outputs.append(
+                                make_tensor_value_info(
+                                    name=converted_node.output[0],
+                                    elem_type=in_type,
+                                    shape=output_shape
                                 )
-                            else:
-                                onnx_processed_outputs.append(
-                                    make_tensor_value_info(
-                                        name=converted_node.name,
-                                        elem_type=in_type,
-                                        shape=output_shape
-                                    )
+                            )
+                        elif converted_node.name in output_names:
+                            onnx_processed_outputs.append(
+                                make_tensor_value_info(
+                                    name=converted_node.name,
+                                    elem_type=in_type,
+                                    shape=output_shape
                                 )
-                            if verbose:
-                                logging.info("Output node is: %s", converted_node.name)
+                            )
+                        if verbose:
+                            logging.info("Output node is: %s", converted_node.name)
                     elif isinstance(converted_node, TensorProto):
                         raise ValueError("Did not expect TensorProto")
                     else: