diff --git a/nnvm/python/nnvm/frontend/tensorflow.py b/nnvm/python/nnvm/frontend/tensorflow.py
index 73b291e486af..636f55adb863 100644
--- a/nnvm/python/nnvm/frontend/tensorflow.py
+++ b/nnvm/python/nnvm/frontend/tensorflow.py
@@ -880,6 +880,28 @@ def _get_abs_layer_name(node):
                                                      params, num_layers)
         return sym
 
+
+def _parse_import_prerequisites(graph):
+    """ Calculate the named preconditions from TensorFlow `graph`.
+    Return prerequisites for parsing:
+     a. Set of operator names which don't have their mapping in TVM, i.e.
+        which are not supported
+    """
+    missing_operators = set()
+    for node in graph.node:
+        if node.op == "Placeholder":
+            pass
+        elif node.op == "Const":
+            pass
+        else:
+            if any([node.op in t for t in [_identity_list, _convert_map, _convert_map_rnn]]):
+                pass
+            else:
+                missing_operators.add(node.op)
+
+    return missing_operators
+
+
 class GraphProto(object):
     """ A helper class for handling nnvm graph copying from Tensorflow GraphDef.
     Definition:
@@ -902,7 +924,7 @@ def from_tensorflow(self, graph):
         Follow the tensorflow graph definition to parse and convert it to NNVM.
         Some of the assumptions listed below.
 
-            -> First Const or Placeholder node will be considered as graph input.
+            -> First Placeholder or Const node will be considered as graph input.
             -> Rest all Const nodes are params.
             -> Last node is assumed as graph output.
             -> _output_shapes : Attribute should present in the tenserflow forzen graph.
@@ -911,6 +933,7 @@ def from_tensorflow(self, graph):
             -> CheckNumerics: No implementation as of now for this.
                               Just copies input to output.
 
+        TODO: Change algorithm to stop treating first 'Const' in a special way.
 
         Parameters
         ----------
@@ -924,10 +947,6 @@ def from_tensorflow(self, graph):
         params : dict
             A dict of name: tvm.nd.array pairs, used as pretrained weights
         """
-        # Parse throught all nodes and start extracting
-        # params aka Const nodes
-        # input nodes  : First const node
-        # normal nodes : other normal nodes
 
         try:
             from tensorflow.python.framework import tensor_util
@@ -935,12 +954,18 @@ def from_tensorflow(self, graph):
             raise ImportError(
                 "Unable to import tensorflow which is required {}".format(e))
 
+        missing_operators = _parse_import_prerequisites(graph)
+
+        if missing_operators:
+            raise NotImplementedError( \
+                "The following operators are not implemented: {}".format(missing_operators))
+
+        # Parse the nodes to re-create TF graph using Symbol API of NNVM
         for node in graph.node:
             # Tensorflow doesn't have seperate list for params extraction.
             # Operator name 'Const' is treated as a parameter to build NNVM params dict.
             input_shapes = {}
             if node.op == "Placeholder":
-                # Assuming only one input graph with type 'Placeholder'
                 self._input_node = node.name
                 self._num_input += 1
 
@@ -955,7 +980,6 @@ def from_tensorflow(self, graph):
                     raise NotImplementedError( \
                         "Please freeze the graph with add_shapes=True")
             elif node.op == "Const":
-                # Assuming first Const node as Graph Input node
                 if self._input_node == '':
                     self._input_node = node.name
                     self._num_input += 1
@@ -998,7 +1022,7 @@ def from_tensorflow(self, graph):
                 # Pass the node name too in attr
                 attr["_node_name"] = node.name
 
-                #ToDo: Some of the tensorflow operators maintain internaly maintain
+                #ToDo: Some of the tensorflow operators internaly maintain
                 #execution layers and its output name will the layer number along with
                 #graph node name.eg: Node name:- 'Model/RNN/cell_0/RnnCell', but the
                 #output name will be 'Model/RNN/cell_0/RnnCell:0'. In this case,