diff --git a/x2paddle/decoder/tf_decoder.py b/x2paddle/decoder/tf_decoder.py
index b00c8a71218d4abc7df93f8dc43ba7550d487b4c..dc64b43691f300c1b22e8af448cf1601b5c9058e 100644
--- a/x2paddle/decoder/tf_decoder.py
+++ b/x2paddle/decoder/tf_decoder.py
@@ -58,7 +58,7 @@ class TFGraphNode(GraphNode):
 
     @property
     def dtype(self):
-        keys = ['dtype', 'T', 'DstT']
+        keys = ['dtype', 'T', 'DstT', 'Tparams']
         for k in keys:
             dtype = self.layer.attr[k].type
             if dtype > 0:
@@ -109,6 +109,8 @@ class TFGraphNode(GraphNode):
         attr = self.layer.attr[name]
         field = attr.WhichOneof('value')
         value = getattr(attr, field) if field else None
+        if name == "squeeze_dims" and not value.ListFields():
+            return None
 
         if isinstance(value, attr_value_pb2.AttrValue.ListValue):
             result = list(value.ListFields()[0][1])
@@ -466,7 +468,10 @@ class TFDecoder(object):
                                                                   ":0")
                 if shape.count(-1) > 0:
                     shape[shape.index(-1)] = b
-                feed[input_tensor] = numpy.random.random_sample(shape)
+                if dtype == 3:
+                    feed[input_tensor] = numpy.random.randint(1, 10, size=shape)
+                else:
+                    feed[input_tensor] = numpy.random.random_sample(shape)
             output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
             if use_diff_inputs:
                 results.append(
diff --git a/x2paddle/op_mapper/tf2paddle/tf_op_mapper.py b/x2paddle/op_mapper/tf2paddle/tf_op_mapper.py
index f89c2201c33e41d2326b7d82fe750f1d7c5cc1c9..819c5e5457e93f9daf0807823a48cf7666077962 100644
--- a/x2paddle/op_mapper/tf2paddle/tf_op_mapper.py
+++ b/x2paddle/op_mapper/tf2paddle/tf_op_mapper.py
@@ -680,11 +680,14 @@ class TFOpMapper():
     def Squeeze(self, node):
         input = self.graph.get_input_node(node, 0)
         squeeze_dims = node.get_attr('squeeze_dims')
+        axis = node.get_attr('axis')
+        if squeeze_dims != None and axis == None:
+            axis = squeeze_dims
         self.paddle_graph.add_layer(
             kernel="paddle.squeeze",
             inputs={"x": input.name},
             outputs=[node.name],
-            axis=squeeze_dims)
+            axis=axis)
 
     def Shape(self, node):
         input = self.graph.get_input_node(node, 0)
@@ -1519,8 +1522,18 @@ class TFOpMapper():
             attr['axis'] = dim
         else:
             inputs['axis'] = y.name
-        self.paddle_graph.add_layer(
-            "paddle.unsqueeze", inputs=inputs, outputs=[node.name], **attr)
+        if len(x.out_shapes[0]) == 0:
+            value = self.decoder.infer_tensor(x, use_diff_inputs=False).tolist()
+            self.paddle_graph.add_layer(
+                "paddle.full",
+                inputs={},
+                outputs=[node.name],
+                dtype=string(x.dtype),
+                shape=[1],
+                fill_value=value)
+        else:
+            self.paddle_graph.add_layer(
+                "paddle.unsqueeze", inputs=inputs, outputs=[node.name], **attr)
 
     def ReverseV2(self, node):
         x = self.graph.get_input_node(node, 0)
@@ -1646,3 +1659,26 @@ class TFOpMapper():
             inputs={"x": transpose_name},
             outputs=[node.name],
             shape=shape)
+
+    def Sign(self, node):
+        x = self.graph.get_input_node(node, 0)
+        support_list = ["float16", "float32", "float64"]
+        if x.dtype not in support_list:
+            self.paddle_graph.add_layer(
+                "paddle.cast",
+                inputs={"x": x.name},
+                outputs=[node.name],
+                dtype=string("float32"))
+            self.paddle_graph.add_layer(
+                kernel="paddle.sign",
+                inputs={"x": node.name},
+                outputs=[node.name])
+            self.paddle_graph.add_layer(
+                "paddle.cast",
+                inputs={"x": node.name},
+                outputs=[node.name],
+                dtype=string(x.dtype))
+        else:
+            self.paddle_graph.add_layer(
+                kernel="paddle.sign", inputs={"x": x.name},
+                outputs=[node.name])