diff --git a/mace/python/tools/tf_converter_lib.py b/mace/python/tools/tf_converter_lib.py
index 27df84accf8859a20454f4c512ce688ccea8081a..823090d3c8720256db09bef36cbb8ca3759c0c4d 100644
--- a/mace/python/tools/tf_converter_lib.py
+++ b/mace/python/tools/tf_converter_lib.py
@@ -30,14 +30,13 @@ def convert_tensor(op, tensor):
   else:
     raise Exception("Not supported tensor type: " + tf_dt.name)
 
-
 def get_input_tensor(op, index):
   input_tensor = op.inputs[index]
   if input_tensor.op.type == 'Reshape':
     input_tensor = get_input_tensor(input_tensor.op, 0)
   return input_tensor
 
-def convert_ops(unresolved_ops, net_def):
+def convert_ops(ops_map, unresolved_ops, net_def):
   ops_count = len(unresolved_ops)
   resolved_count = 1
 
@@ -77,6 +76,28 @@ def convert_ops(unresolved_ops, net_def):
       bias_add_op = unresolved_ops[1]
       op_def.input.extend([bias_add_op.inputs[1].name])
       resolved_count = 2
+
+    if ops_count >= 3 and unresolved_ops[1].type == 'Relu':
+      op_def.name = "FusedConv2D"
+      resolved_count = 3
+  elif first_op.type == 'FusedBatchNorm':
+    op_def = net_def.op.add()
+    op_def.name = first_op.name
+    first_op.type = 'BatchNorm'
+    op_def.input.extend([input.name for input in first_op.inputs])
+    op_def.output.extend([output.name for output in first_op.outputs])
+    output_shapes = []
+    for output in first_op.outputs:
+      output_shape = mace_pb2.OutputShape()
+      output_shape.dims.extend(output.shape.as_list())
+      output_shapes.append(output_shape)
+    op_def.output_shape.extend(output_shapes)
+    epsilon_arg = op_def.arg.add()
+    epsilon_arg.name = 'epsilon'
+    epsilon_arg.f = first_op.get_attr('epsilon')
+    data_format_arg = op_def.arg.add()
+    data_format_arg.name = 'data_format'
+    data_format_arg.s = 'NHWC'
   elif first_op.type == 'Add' and first_op.name.endswith(
       'batchnorm/add') and ops_count > 7:
     add_op = first_op
@@ -90,6 +111,7 @@ def convert_ops(unresolved_ops, net_def):
                     mul_1_op.type != 'Mul' or sub_op.type != 'Sub' or add_1_op.type != 'Add':
       raise Exception('Invalid BatchNorm Op')
 
+    get_input_tensor(mul_1_op, 0)
     input_name = get_input_tensor(mul_1_op, 0).name
     gamma = get_input_tensor(mul_op, 1).name
     beta = get_input_tensor(sub_op, 0).name
@@ -168,15 +190,33 @@ def convert_ops(unresolved_ops, net_def):
     op_def = net_def.op.add()
     op_def.name = first_op.name
     op_def.type = "Concat"
-    op_def.input.extend([input.name for input in first_op.inputs])
+    op_def.input.extend([first_op.inputs[i] for i in xrange(2)])
     op_def.output.extend([output.name for output in first_op.outputs])
+    axis_arg = op_def.arg.add()
+    axis_arg.name = 'axis'
+    axis_arg.i = get_input_tensor(first_op, 2).eval().astype(np.int32)
     output_shapes = []
     for output in first_op.outputs:
       output_shape = mace_pb2.OutputShape()
       output_shape.dims.extend(output.shape.as_list())
       output_shapes.append(output_shape)
     op_def.output_shape.extend(output_shapes)
-  elif first_op.type in ['Relu', 'ResizeBilinear', 'SpaceToBatchND',
+  elif first_op.type == 'ResizeBilinear':
+    op_def = net_def.op.add()
+    op_def.name = first_op.name
+    op_def.type = "ResizeBilinear"
+    op_def.input.extend(first_op.inputs[0])
+    op_def.output.extend([output.name for output in first_op.outputs])
+    size_arg = op_def.arg.add()
+    size_arg.name = 'size'
+    size_arg.ints.extend(get_input_tensor(first_op, 1).eval().astype(np.int32).flat)
+    output_shapes = []
+    for output in first_op.outputs:
+      output_shape = mace_pb2.OutputShape()
+      output_shape.dims.extend(output.shape.as_list())
+      output_shapes.append(output_shape)
+    op_def.output_shape.extend(output_shapes)
+  elif first_op.type in ['Relu', 'SpaceToBatchND',
                          'BatchToSpaceND', 'BiasAdd', 'FusedBatchNorm']:
     op_def = net_def.op.add()
     op_def.name = first_op.name
@@ -204,9 +244,16 @@ def convert_to_mace_pb(input_graph_def):
     with session.graph.as_default() as graph:
       tf.import_graph_def(input_graph_def, name="")
       ops = graph.get_operations()
+      ops_map = {}
+      for op in ops:
+        if op.name not in ops_map.keys():
+          ops_map[op.name] = op
+        else:
+          raise ValueError("Duplicate op names detected for ", op.name)
+
       unresolved_ops = ops
       while len(unresolved_ops) > 0:
-        convert_ops(unresolved_ops, net_def)
+        convert_ops(ops_map, unresolved_ops, net_def)
 
   print "Done."