diff --git a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py
index 4d1cd6a6..e5255d4e 100644
--- a/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py
+++ b/utensor_cgen/backend/utensor/_graph_lower/_op_lower.py
@@ -40,6 +40,8 @@ class uTensorRearchGraphLower(uTensorGraphLowerBase):
   class OptypeRenameManager(object):
     NAME_MAP = {
       'Add': 'AddOperator',
+      'Mul': 'MulOperator',
+      'Sub': 'SubOperator',
       'Conv2D': 'ConvOperator',
       'MatMul': 'MatrixMultOperator'
     }
@@ -89,6 +91,8 @@ def apply(cls, ugraph):
       for op_info in ugraph.get_ops_by_type('FullyConnectedOperator'):
         if cls._check_quantized(op_info):
           op_info.code_gen_attributes['namespaces'] = ('TflmSymQuantOps',)
+        else:
+          op_info.code_gen_attributes['namespaces'] = ('ReferenceOperators',)
 
     @classmethod
     def _check_quantized(cls, op_info):
diff --git a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py
index b07552f7..ace26096 100644
--- a/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py
+++ b/utensor_cgen/backend/utensor/code_generator/rearch/_operators/_impls.py
@@ -478,6 +478,69 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
       nested_namespaces=type(self).namespaces,
     )
 
+@OperatorFactory.register
+class _ConvOperator(_CommonParams):
+  op_type = "ConvOperator"
+
+  @classmethod
+  @must_return_type(Hashable)
+  def get_constructor_parameters(cls, op_info):
+
+    strides = [
+        1,
+        op_info.op_attr['StrideW'],
+        op_info.op_attr['StrideH'],
+        1,
+      ]
+    padding = cls._PADDING_MAP[op_info.op_attr['Padding']]
+    strides_str = ','.join(map(str, strides))
+    return ("{{ {} }}".format(strides_str), padding)
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_var_name=op_var_name,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return ConvOpEvalSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+    )
+
+@OperatorFactory.register
+class _FullyConnectedOperator(_CommonParams):
+  namespaces = ('ReferenceOperators',)
+  op_type = "FullyConnectedOperator"
+
+  @classmethod
+  @must_return_type(Hashable)
+  def get_constructor_parameters(cls, op_info):
+    activation_idx = cls._ACTIVATION_STR_PATTERN.match(
+      op_info.op_attr['FusedActivationFunction']
+    ).group(1)
+    activation = cls._ACTIVATION_MAP[activation_idx]
+    return (activation,)
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_var_name=op_var_name,
+      nested_namespaces=type(self).namespaces,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return FullyConnectedSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+      nested_namespaces=type(self).namespaces,
+    )
 
 @OperatorFactory.register
 class _QuantizedFullyConnectedOperator(_CommonParams):
@@ -521,3 +584,158 @@ def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
     return MissingOpEvalSnippet(op_info, tensor_var_map)
 
 OperatorFactory._operators[_MissingOperator.op_type] = _MissingOperator
+
+@OperatorFactory.register
+class _BatchNormOperator(_Operator):
+  namespaces = ('ReferenceOperators',)
+  op_type = "BatchNormOperator"
+
+  @classmethod
+  @must_return_type(Hashable)
+  def get_constructor_parameters(cls, op_info):
+    strides = [
+        1,
+        op_info.op_attr['StrideW'],
+        op_info.op_attr['StrideH'],
+        1,
+      ]
+    padding = cls._PADDING_MAP[op_info.op_attr['Padding']]
+    strides_str = ','.join(map(str, strides))
+    return ("{{ {} }}".format(strides_str), padding)
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_var_name=op_var_name,
+      nested_namespaces=type(self).namespaces,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return BatchNormSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+      nested_namespaces=type(self).namespaces,
+    )
+
+@OperatorFactory.register
+class _MeanOperator(_Operator):
+  namespaces = ('ReferenceOperators',)
+  op_type = "MeanOperator"
+
+  @classmethod
+  @must_return_type(Hashable)
+  def get_constructor_parameters(cls, op_info):
+    keep_dims = str(op_info.op_attr["keep_dims"])
+    return (" {} ".format(keep_dims), )
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_var_name=op_var_name,
+      nested_namespaces=type(self).namespaces,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return BatchNormSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+      nested_namespaces=type(self).namespaces,
+    )
+
+@OperatorFactory.register
+class _SoftmaxOperator(_CommonParams):
+  namespaces = ('ReferenceOperators',)
+  op_type = "SoftmaxOperator"
+
+  @classmethod
+  @must_return_type(Hashable)
+  def get_constructor_parameters(cls, op_info):
+    Beta = op_info.op_attr["Beta"]
+    return (" %f " % Beta,)
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_var_name=op_var_name,
+      nested_namespaces=type(self).namespaces,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return BatchNormSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.out_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+      nested_namespaces=type(self).namespaces,
+    )
+
+@OperatorFactory.register
+class _MulOperator(_Operator):
+  namespaces = ('ReferenceOperators',)
+  op_type = 'MulOperator'
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.in_dtypes[0]],
+      op_var_name=op_var_name,
+      nested_namespaces=type(self).namespaces,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return MulOpEvalSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.in_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+      nested_namespaces=type(self).namespaces,
+    )
+
+@OperatorFactory.register
+class _SubOperator(_Operator):
+  namespaces = ('ReferenceOperators',)
+  op_type = 'SubOperator'
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.in_dtypes[0]],
+      op_var_name=op_var_name,
+      nested_namespaces=type(self).namespaces,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return SubOpEvalSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.in_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+      nested_namespaces=type(self).namespaces,
+    )
+
+@OperatorFactory.register
+class _SigmoidOperator(_Operator):
+  namespaces = ('ReferenceOperators',)
+  op_type = 'SigmoidOperator'
+
+  def get_declare_snippet(self, op_var_name, tensor_var_map):
+    return DeclareOpSnippet(
+      op=self,
+      templ_dtypes=[self.in_dtypes[0]],
+      op_var_name=op_var_name,
+    )
+
+  def get_eval_snippet(self, op_var_name, op_info, tensor_var_map):
+    return SigmoidOpEvalSnippet(
+      op_info=op_info,
+      templ_dtypes=[self.in_dtypes[0]],
+      op_name=op_var_name,
+      tensor_var_map=tensor_var_map,
+    )
diff --git a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py
index a184420d..4ded93d4 100644
--- a/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py
+++ b/utensor_cgen/backend/utensor/snippets/rearch/_snippets.py
@@ -28,8 +28,16 @@
   "MinPoolEvalSnippet",
   "MaxPoolEvalSnippet",
   "QuantizedFullyConnectedSnippet",
+  "FullyConnectedSnippet",
   "MissingOpEvalSnippet",
+  "BatchNormSnippet",
   "TimeSlotContainer",
+  "MulOpEvalSnippet",
+  "SubOpEvalSnippet",
+  "ConvOpEvalSnippet",
+  "MeanOpEvalSnippet",
+  "SoftmaxOpEvalSnippet",
+  "SigmoidOpEvalSnippet",
   "SimpleContainer",
 ]
 
@@ -156,6 +164,9 @@ class DepthwiseSeperateConvOpEvalSnippet(OpEvalSnippet):
   __inputs__ = ["in", "depthwise_filter", "pointwise_filter"]
   __outputs__ = ["out"]
 
+class ConvOpEvalSnippet(OpEvalSnippet):
+  __inputs__ = ["in", "filter"]
+  __outputs__ = ["out"]
 
 class QuantDepthwiseSeperateConvOpEvalSnippet(OpEvalSnippet):
   __inputs__ = ["in", "filter", "bias"]
@@ -226,11 +237,31 @@ class MaxPoolEvalSnippet(OpEvalSnippet):
   __inputs__ = ["in"]
   __outputs__ = ["out"]
 
+class FullyConnectedSnippet(OpEvalSnippet):
+  __inputs__ = ["input", "filter", "bias"]
+  __outputs__ = ["output"]
 
 class QuantizedFullyConnectedSnippet(OpEvalSnippet):
   __inputs__ = ["input", "filter", "bias"]
   __outputs__ = ["output"]
 
+class BatchNormSnippet(OpEvalSnippet):
+  __inputs__ = ["x", "mean", "variance", "offset", "scale"]
+  __outputs__ = ["output"]
+
+class MulOpEvalSnippet(OpEvalSnippet):
+  __inputs__ = ['a', 'b']
+  __outputs__ = ['c']
+class SubOpEvalSnippet(OpEvalSnippet):
+  __inputs__ = ['a', 'b']
+  __outputs__ = ['c']
+class MeanOpEvalSnippet(OpEvalSnippet):
+  __inputs__ = ['input', 'axis']
+  __outputs__ = ['output']
+class SoftmaxOpEvalSnippet(OpEvalSnippet):
+  __inputs__ = ['input']
+  __outputs__ = ['output']
+
 
 class MissingOpEvalSnippet(OpEvalSnippet):
   __template_name__ = "snippets/rearch/op_missing.cpp"
@@ -252,6 +283,9 @@ def __init__(self, op_info, tensor_var_map):
     ]
     self.template_vars['output_tensors'] = op_info.output_tensors[:]
     self.template_vars['quant_params_map'] = quant_params_map
+class SigmoidOpEvalSnippet(OpEvalSnippet):
+  __inputs__ = ['in']
+  __outputs__ = ['out']
 
 
 class TimeSlotContainer(SnippetBase):
diff --git a/utensor_cgen/frontend/tflite.py b/utensor_cgen/frontend/tflite.py
index d7229a35..9897af2f 100644
--- a/utensor_cgen/frontend/tflite.py
+++ b/utensor_cgen/frontend/tflite.py
@@ -82,10 +82,18 @@ def _build_tensor_map(self, fb_model, ugraph):
       tensor_name = tensor.Name().decode('utf8')
       if tensor_name is "" or None:
         tensor_name = "tensor_" + str(idx)
-
+      
       dtype = self._TENSOR_NP_TYPE[tensor.Type()]
       attributes = dict()
       quant_params = tensor.Quantization()
+#      if quant_params is not None and quant_params.ZeroPointAsNumpy() and quant_params.ScaleAsNumpy():
+#        zp = quant_params.ZeroPointAsNumpy()
+#        if zp.dtype == np.dtype('<i8'):
+#          zp = zp.astype('int8')
+#        else:
+#          zp = zp.astype('uint8')
+#        attributes["quantization_zeros"] = zp
+#        attributes["quantization_scales"] = quant_params.ScaleAsNumpy()
       if quant_params is not None and \
         quant_params.ZeroPointLength() and \
         quant_params.ScaleLength():
@@ -427,20 +435,161 @@ def argmax_op_data(op, fb_mdel):
 
   return option_dict
 
+def reducer_op_data(op, fb_mdel):
+  option_dict = {}
+  if op.CustomOptionsLength() < 1:
+    from .tflite_flatbuffer.ReducerOptions import ReducerOptions
+
+    option = ReducerOptions()
+    builtin_data = op.BuiltinOptions()
+    option.Init(builtin_data.Bytes, builtin_data.Pos)
+    option_dict["keep_dims"] = option.KeepDims()
+  else:
+    option_dict[
+      _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()]
+    ] = op.CustomOptionsAsNumpy()
+
+  return option_dict
+def softmax_op_data(op, fb_mdel):
+  option_dict = {}
+  if op.CustomOptionsLength() < 1:
+    from .tflite_flatbuffer.SoftmaxOptions import SoftmaxOptions
+
+    option = SoftmaxOptions()
+    builtin_data = op.BuiltinOptions()
+    option.Init(builtin_data.Bytes, builtin_data.Pos)
+    option_dict["Beta"] = option.Beta()
+    #option_dict["OutputType"] = option.OutputType()
+  else:
+    option_dict[
+      _CUSTOM_OPTION_FORMAT_MAP[op.CustomOptionsFormat()]
+    ] = op.CustomOptionsAsNumpy()
+
+  return option_dict
+
+
 def default_op_data(op, fb_mdel):
   op_type = _get_op_type(op, fb_mdel)
   logger.warning('the op data parser is missing for %s', op_type)
   return {}
 
 _OP_DATA_FUNC_MAP = defaultdict(lambda: default_op_data)
-_OP_DATA_FUNC_MAP["QUANTIZE"] = quantize_op_data
-_OP_DATA_FUNC_MAP["DEPTHWISE_CONV_2D"] = depthwise_conv2d_op_data
-_OP_DATA_FUNC_MAP["CONV_2D"] = conv_2d_op_data
-_OP_DATA_FUNC_MAP["MAX_POOL_2D"] = pool2d_op_data
-_OP_DATA_FUNC_MAP["RESHAPE"] = reshape_op_data
-_OP_DATA_FUNC_MAP["FULLY_CONNECTED"] = fully_connected_op_data
-_OP_DATA_FUNC_MAP["DEQUANTIZE"] = dequantize_op_data
-_OP_DATA_FUNC_MAP["ARG_MAX"] = argmax_op_data
+#_OP_DATA_FUNC_MAP["ADD"]                          = None
+#_OP_DATA_FUNC_MAP["AVERAGE_POOL_2D"]              = None
+#_OP_DATA_FUNC_MAP["CONCATENATION"]                = None
+_OP_DATA_FUNC_MAP["CONV_2D"]                      = conv_2d_op_data
+_OP_DATA_FUNC_MAP["DEPTHWISE_CONV_2D"]            = depthwise_conv2d_op_data
+_OP_DATA_FUNC_MAP["DEQUANTIZE"]                   = dequantize_op_data
+#_OP_DATA_FUNC_MAP["EMBEDDING_LOOKUP"]             = None
+#_OP_DATA_FUNC_MAP["FLOOR"]                        = None
+_OP_DATA_FUNC_MAP["FULLY_CONNECTED"]              = fully_connected_op_data
+#_OP_DATA_FUNC_MAP["HASHTABLE_LOOKUP"]             = None
+#_OP_DATA_FUNC_MAP["L2_NORMALIZATION"]             = None
+#_OP_DATA_FUNC_MAP["L2_POOL_2D"]                   = None
+#_OP_DATA_FUNC_MAP["LOCAL_RESPONSE_NORMALIZATION"] = None
+#_OP_DATA_FUNC_MAP["LOGISTIC"]                     = None
+#_OP_DATA_FUNC_MAP["LSH_PROJECTION"]               = None
+#_OP_DATA_FUNC_MAP["LSTM"]                         = None
+_OP_DATA_FUNC_MAP["MAX_POOL_2D"]                  = pool2d_op_data
+#_OP_DATA_FUNC_MAP["MUL"]                          = None
+#_OP_DATA_FUNC_MAP["RELU"]                         = None
+#_OP_DATA_FUNC_MAP["RELU_N1_TO_1"]                 = None
+#_OP_DATA_FUNC_MAP["RELU6"]                        = None
+_OP_DATA_FUNC_MAP["RESHAPE"]                      = reshape_op_data
+#_OP_DATA_FUNC_MAP["RESIZE_BILINEAR"]              = None
+#_OP_DATA_FUNC_MAP["RNN"]                          = None
+_OP_DATA_FUNC_MAP["SOFTMAX"]                      = softmax_op_data
+#_OP_DATA_FUNC_MAP["SPACE_TO_DEPTH"]               = None
+#_OP_DATA_FUNC_MAP["SVDF"]                         = None
+#_OP_DATA_FUNC_MAP["TANH"]                         = None
+#_OP_DATA_FUNC_MAP["CONCAT_EMBEDDINGS"]            = None
+#_OP_DATA_FUNC_MAP["SKIP_GRAM"]                    = None
+#_OP_DATA_FUNC_MAP["CALL"]                         = None
+#_OP_DATA_FUNC_MAP["CUSTOM"]                       = None
+#_OP_DATA_FUNC_MAP["EMBEDDING_LOOKUP_SPARSE"]      = None
+#_OP_DATA_FUNC_MAP["PAD"]                          = None
+#_OP_DATA_FUNC_MAP["UNIDIRECTIONAL_SEQUENCE_RNN"]  = None
+#_OP_DATA_FUNC_MAP["GATHER"]                       = None
+#_OP_DATA_FUNC_MAP["BATCH_TO_SPACE_ND"]            = None
+#_OP_DATA_FUNC_MAP["SPACE_TO_BATCH_ND"]            = None
+#_OP_DATA_FUNC_MAP["TRANSPOSE"]                    = None
+_OP_DATA_FUNC_MAP["MEAN"]                         = reducer_op_data
+#_OP_DATA_FUNC_MAP["SUB"]                          = None
+#_OP_DATA_FUNC_MAP["DIV"]                          = None
+#_OP_DATA_FUNC_MAP["SQUEEZE"]                      = None
+#_OP_DATA_FUNC_MAP["UNIDIRECTIONAL_SEQUENCE_LSTM"] = None
+#_OP_DATA_FUNC_MAP["STRIDED_SLICE"]                = None
+#_OP_DATA_FUNC_MAP["BIDIRECTIONAL_SEQUENCE_RNN"]   = None
+#_OP_DATA_FUNC_MAP["EXP"]                          = None
+#_OP_DATA_FUNC_MAP["TOPK_V2"]                      = None
+#_OP_DATA_FUNC_MAP["SPLIT"]                        = None
+#_OP_DATA_FUNC_MAP["LOG_SOFTMAX"]                  = None
+#_OP_DATA_FUNC_MAP["DELEGATE"]                     = None
+#_OP_DATA_FUNC_MAP["BIDIRECTIONAL_SEQUENCE_LSTM"]  = None
+#_OP_DATA_FUNC_MAP["CAST"]                         = None
+#_OP_DATA_FUNC_MAP["PRELU"]                        = None
+#_OP_DATA_FUNC_MAP["MAXIMUM"]                      = None
+_OP_DATA_FUNC_MAP["ARG_MAX"]                      = argmax_op_data
+#_OP_DATA_FUNC_MAP["MINIMUM"]                      = None
+#_OP_DATA_FUNC_MAP["LESS"]                         = None
+#_OP_DATA_FUNC_MAP["NEG"]                          = None
+#_OP_DATA_FUNC_MAP["PADV2"]                        = None
+#_OP_DATA_FUNC_MAP["GREATER"]                      = None
+#_OP_DATA_FUNC_MAP["GREATER_EQUAL"]                = None
+#_OP_DATA_FUNC_MAP["LESS_EQUAL"]                   = None
+#_OP_DATA_FUNC_MAP["SELECT"]                       = None
+#_OP_DATA_FUNC_MAP["SLICE"]                        = None
+#_OP_DATA_FUNC_MAP["SIN"]                          = None
+#_OP_DATA_FUNC_MAP["TRANSPOSE_CONV"]               = None
+#_OP_DATA_FUNC_MAP["SPARSE_TO_DENSE"]              = None
+#_OP_DATA_FUNC_MAP["TILE"]                         = None
+#_OP_DATA_FUNC_MAP["EXPAND_DIMS"]                  = None
+#_OP_DATA_FUNC_MAP["EQUAL"]                        = None
+#_OP_DATA_FUNC_MAP["NOT_EQUAL"]                    = None
+#_OP_DATA_FUNC_MAP["LOG"]                          = None
+#_OP_DATA_FUNC_MAP["SUM"]                          = None
+#_OP_DATA_FUNC_MAP["SQRT"]                         = None
+#_OP_DATA_FUNC_MAP["RSQRT"]                        = None
+#_OP_DATA_FUNC_MAP["SHAPE"]                        = None
+#_OP_DATA_FUNC_MAP["POW"]                          = None
+#_OP_DATA_FUNC_MAP["ARG_MIN"]                      = None
+#_OP_DATA_FUNC_MAP["FAKE_QUANT"]                   = None
+#_OP_DATA_FUNC_MAP["REDUCE_PROD"]                  = None
+#_OP_DATA_FUNC_MAP["REDUCE_MAX"]                   = None
+#_OP_DATA_FUNC_MAP["PACK"]                         = None
+#_OP_DATA_FUNC_MAP["LOGICAL_OR"]                   = None
+#_OP_DATA_FUNC_MAP["ONE_HOT"]                      = None
+#_OP_DATA_FUNC_MAP["LOGICAL_AND"]                  = None
+#_OP_DATA_FUNC_MAP["LOGICAL_NOT"]                  = None
+#_OP_DATA_FUNC_MAP["UNPACK"]                       = None
+#_OP_DATA_FUNC_MAP["REDUCE_MIN"]                   = None
+#_OP_DATA_FUNC_MAP["FLOOR_DIV"]                    = None
+#_OP_DATA_FUNC_MAP["REDUCE_ANY"]                   = None
+#_OP_DATA_FUNC_MAP["SQUARE"]                       = None
+#_OP_DATA_FUNC_MAP["ZEROS_LIKE"]                   = None
+#_OP_DATA_FUNC_MAP["FILL"]                         = None
+#_OP_DATA_FUNC_MAP["FLOOR_MOD"]                    = None
+#_OP_DATA_FUNC_MAP["RANGE"]                        = None
+#_OP_DATA_FUNC_MAP["RESIZE_NEAREST_NEIGHBOR"]      = None
+#_OP_DATA_FUNC_MAP["LEAKY_RELU"]                   = None
+#_OP_DATA_FUNC_MAP["SQUARED_DIFFERENCE"]           = None
+#_OP_DATA_FUNC_MAP["MIRROR_PAD"]                   = None
+#_OP_DATA_FUNC_MAP["ABS"]                          = None
+#_OP_DATA_FUNC_MAP["SPLIT_V"]                      = None
+#_OP_DATA_FUNC_MAP["UNIQUE"]                       = None
+#_OP_DATA_FUNC_MAP["CEIL"]                         = None
+#_OP_DATA_FUNC_MAP["REVERSE_V2"]                   = None
+#_OP_DATA_FUNC_MAP["ADD_N"]                        = None
+#_OP_DATA_FUNC_MAP["GATHER_ND"]                    = None
+#_OP_DATA_FUNC_MAP["COS"]                          = None
+#_OP_DATA_FUNC_MAP["WHERE"]                        = None
+#_OP_DATA_FUNC_MAP["RANK"]                         = None
+#_OP_DATA_FUNC_MAP["ELU"]                          = None
+#_OP_DATA_FUNC_MAP["REVERSE_SEQUENCE"]             = None
+#_OP_DATA_FUNC_MAP["MATRIX_DIAG"]                  = None
+_OP_DATA_FUNC_MAP["QUANTIZE"]                     = quantize_op_data
+#_OP_DATA_FUNC_MAP["MATRIX_SET_DIAG"]              = None
+
 
 def _get_op_type(op, fb_model):
   local_op_code = op.OpcodeIndex()
diff --git a/utensor_cgen/legalizer/tflite.py b/utensor_cgen/legalizer/tflite.py
index 200ecbeb..8eec014a 100644
--- a/utensor_cgen/legalizer/tflite.py
+++ b/utensor_cgen/legalizer/tflite.py
@@ -31,6 +31,14 @@ class _OpTypeRename(object):
     "Dequantize": "DequantizeOperator",
     "Reshape": "ReshapeOperator",
     "Conv2d": "Conv2dOperator",
+    "Mul": "MulOperator",
+    "Add": "AddOperator",
+    "Sub": "SubOperator",
+    "Conv2d": "ConvOperator",
+    "Mean": "MeanOperator",
+    "Softmax": "SoftmaxOperator",
+    "Sigmoid": "SigmoidOperator",
+    "Logistic": "SigmoidOperator",
   }
   
   @classmethod