diff --git a/tf2onnx/onnx_opset/generator.py b/tf2onnx/onnx_opset/generator.py
index 0b59dca6b..f6e0409cb 100644
--- a/tf2onnx/onnx_opset/generator.py
+++ b/tf2onnx/onnx_opset/generator.py
@@ -60,8 +60,8 @@ def version_1(cls, ctx, node, **kwargs):
         # in the rewriter does not trigger. grappler will send the random uniform
         # with shape as input so we need to pickup the input here and if the shape is
         # const we make it an attribute.
-        seed = node.get_attr("seed")
-        node.set_attr("seed", float(seed.f))
+        seed = node.get_attr("seed2")
+        node.set_attr("seed", float(seed.i))
         utils.make_sure(node.inputs[0].is_const(), "%s node with non-const shape requires opset >= 9", node.type)
         shape = node.inputs[0].get_tensor_value()
         ctx.remove_input(node, node.input[0], 0)
@@ -88,8 +88,8 @@ def version_9(cls, ctx, node, **kwargs):
         if node.inputs[0].is_const():
             cls.version_1(ctx, node, **kwargs)
         else:
-            seed = node.get_attr("seed")
-            node.set_attr("seed", float(seed.f))
+            seed = node.get_attr("seed2")
+            node.set_attr("seed", float(seed.i))
             cast_node = ctx.make_node("Cast", [node.input[0]], attr={'to': onnx_pb.TensorProto.INT64})
             const_node = ctx.make_node("ConstantOfShape", cast_node.output)
             inputs = node.input.copy()