diff --git a/mace/python/tools/converter_tool/tensorflow_converter.py b/mace/python/tools/converter_tool/tensorflow_converter.py
index 81bf2027eb4e382df13146d3c8a102c67705cf8e..1d9619386338426a2ebc00f266ee8d7ef45a2c47 100644
--- a/mace/python/tools/converter_tool/tensorflow_converter.py
+++ b/mace/python/tools/converter_tool/tensorflow_converter.py
@@ -101,6 +101,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
             'AvgPool': self.convert_pooling,
             'MaxPool': self.convert_pooling,
             'Squeeze': self.convert_identity,
+            'Identity': self.convert_identity,
             'Reshape': self.convert_reshape,
             'Shape': self.convert_nop,
             'Softmax': self.convert_softmax,
@@ -153,12 +154,14 @@ class TensorflowConverter(base_converter.ConverterInterface):
 
     def add_shape_info(self, tf_graph_def):
         for node in tf_graph_def.node:
-            if node.name in self._option.input_nodes:
-                del node.attr['shape'].shape.dim[:]
-                node.attr['shape'].shape.dim.extend([
-                    tensor_shape_pb2.TensorShapeProto.Dim(size=i) for i in
-                    self._option.input_nodes[node.name].shape
-                ])
+            for input_node in self._option.input_nodes.values():
+                if node.name == input_node.name \
+                        or node.name + ':0' == input_node.name:
+                    del node.attr['shape'].shape.dim[:]
+                    node.attr['shape'].shape.dim.extend([
+                        tensor_shape_pb2.TensorShapeProto.Dim(size=i) for i in
+                        input_node.shape
+                    ])
 
     @staticmethod
     def get_scope(tensor_name):
diff --git a/mace/python/tools/tf_ops_stats.py b/mace/python/tools/tf_ops_stats.py
index 21d2db80a9b7fd60a72dee059e3a4f02bfeec198..2d9152392cfd3a0802fc32d7433098979bc56115 100644
--- a/mace/python/tools/tf_ops_stats.py
+++ b/mace/python/tools/tf_ops_stats.py
@@ -45,8 +45,11 @@ def to_int_list(long_list):
 def add_shape_info(input_graph_def, input_nodes, input_shapes):
     inputs_replaced_graph = graph_pb2.GraphDef()
     for node in input_graph_def.node:
-        if node.name in input_nodes:
-            idx = input_nodes.index(node.name)
+        if node.name in input_nodes or node.name + ':0' in input_nodes:
+            if node.name in input_nodes:
+                idx = input_nodes.index(node.name)
+            else:
+                idx = input_nodes.index(node.name + ':0')
             input_shape = input_shapes[idx]
             print input_shape
             placeholder_node = copy.deepcopy(node)
diff --git a/tools/validate.py b/tools/validate.py
index dba6a3e2db2b2b38b53d4f1c925645f246ebd3a2..b98fd0894adff8f44bb9109231274c147d14c372 100644
--- a/tools/validate.py
+++ b/tools/validate.py
@@ -65,6 +65,13 @@ def compare_output(platform, device_type, output_name, mace_out_value,
         sys.exit(-1)
 
 
+def normalize_tf_tensor_name(name):
+    if name.find(':') == -1:
+        return name + ':0'
+    else:
+        return name
+
+
 def validate_tf_model(platform, device_type, model_file, input_file,
                       mace_out_file, input_names, input_shapes, output_names):
     import tensorflow as tf
@@ -88,13 +95,14 @@ def validate_tf_model(platform, device_type, model_file, input_file,
                         common.formatted_file_name(input_file, input_names[i]))
                     input_value = input_value.reshape(input_shapes[i])
                     input_node = graph.get_tensor_by_name(
-                        input_names[i] + ':0')
+                        normalize_tf_tensor_name(input_names[i]))
                     input_dict[input_node] = input_value
 
                 output_nodes = []
                 for name in output_names:
                     output_nodes.extend(
-                        [graph.get_tensor_by_name(name + ':0')])
+                        [graph.get_tensor_by_name(
+                            normalize_tf_tensor_name(name))])
                 output_values = session.run(output_nodes, feed_dict=input_dict)
                 for i in range(len(output_names)):
                     output_file_name = common.formatted_file_name(