diff --git a/docs/Graph Data Format.md b/docs/Graph Data Format.md
deleted file mode 100644
index 8c8c029fd237922e4c2e325b2b71bb2776207ebf..0000000000000000000000000000000000000000
--- a/docs/Graph Data Format.md	
+++ /dev/null
@@ -1,85 +0,0 @@
-## VisualDL Graph data format design
-
-## Backgroud
-Neural network have a concept of `Model`. VisualDL has a very import feature that it can visualize the structure of deep learning `Model`. As an visualize tool that want to support many different deeplearning framework, we should use a generic Model data format that is supported by most of frameworks.
-
-Facebook opensourced a [ONNX](http://onnx.ai/)(Open Neural Network Exchange). ONNX is a open format to represent deep learning models. It now officially supported by world's most popular framework like Tensorflow, Caffe2, MxNet, PaddlePaddle Fluid, so we decided to choose ONNX as the standard Model format and support the visualization of ONNX model.
-
-## IR of ONNX
-The dscription of ONNX IR can be found [here](https://github.com/onnx/onnx/blob/master/docs/IR.md). The most important part is the defination of [Graph](https://github.com/onnx/onnx/blob/master/docs/IR.md#graphs).
-
-Each computation dataflow graph is structured as a list of nodes that form a graph. Each node is a call to an operator. Nodes have zero or more inputs, one or more outputs, and zero or more attribute-value pairs. 
-
-## Rest API data format
-Frontend use rest api to get data from server, the data format will be json, the data structure of a Graph is as below. Each Graph will have three vector inside:
-
-- `node` represent Operator. It have type, input and output.
-- `input` represent input datas or parameters, it have shape info.
-- `output` represent output data of the graph, it have shape info.
-
-```json
-{
-    "graph": {
-        "nodes": [
-            {
-                "name": "op_1",
-                "type": "mul_op",
-                "input": [
-                    "in1",
-                    "w1"
-                ],
-                "output": [
-                    "out1",
-                    "out2"
-                ]
-            },
-            {
-                "name": "op_2",
-                "type": "add_op",
-                "input": [
-                    "out1",
-                    "b1"
-                ],
-                "output": [
-                    "out3"
-                ]
-            }
-        ],
-        "inputs": [
-            {
-                "name": "in1",
-                "shape": [
-                    -1,
-                    2
-                ]
-            },
-            {
-                "name": "b1",
-                "shape": [
-                    1,
-                    3
-                ]
-            },
-            {
-                "name": "w1",
-                "shape": [
-                    2,
-                    3
-                ]
-            }
-        ],
-        "outputs": [
-            {
-                "name": "out3",
-                "shape": [
-                    -1,
-                    3
-                ]
-            }
-        ]
-    }
-}
-```
-
-
-
diff --git a/docs/graph_data_format.md b/docs/graph_data_format.md
new file mode 100644
index 0000000000000000000000000000000000000000..ef6e1b9a990c3e81a2acee695d84751b26817fa6
--- /dev/null
+++ b/docs/graph_data_format.md
@@ -0,0 +1,85 @@
+## VisualDL Graph data format design
+
+## Background
+Neural network has a concept of `Model`. VisualDL has a very import feature that it can visualize the structure of deep learning `Model`. As a visualization tool that wants to support many different deep learning frameworks, we should use a generic Model data format that is supported by most of the frameworks.
+
+Facebook open-sourced an [ONNX](http://onnx.ai/)(Open Neural Network Exchange). ONNX is an open format to represent deep learning models. It now officially supported by world's most popular framework like Tensorflow, Caffe2, MxNet, PaddlePaddle Fluid, so we decided to choose ONNX as the standard Model format and support the visualization of ONNX model.
+
+## IR of ONNX
+The description of ONNX IR can be found [here](https://github.com/onnx/onnx/blob/master/docs/IR.md). The most important part is the definition of [Graph](https://github.com/onnx/onnx/blob/master/docs/IR.md#graphs).
+
+Each computation data flow graph is structured as a list of nodes that form a graph. Each node is a call to an operator. Nodes have zero or more inputs, one or more outputs, and zero or more attribute-value pairs. 
+
+## Rest API data format
+Frontend use rest API to get data from the server, the data format will be JSON, the data structure of a Graph is as below. Each Graph will have three vectors inside:
+
+- `node` represent Operator. It has type, input, and output.
+- `input` represent input data or parameters, it has shape info.
+- `output` represent output data of the graph, it has shape info.
+
+```json
+{
+    "graph": {
+        "nodes": [
+            {
+                "name": "op_1",
+                "type": "mul_op",
+                "input": [
+                    "in1",
+                    "w1"
+                ],
+                "output": [
+                    "out1",
+                    "out2"
+                ]
+            },
+            {
+                "name": "op_2",
+                "type": "add_op",
+                "input": [
+                    "out1",
+                    "b1"
+                ],
+                "output": [
+                    "out3"
+                ]
+            }
+        ],
+        "inputs": [
+            {
+                "name": "in1",
+                "shape": [
+                    -1,
+                    2
+                ]
+            },
+            {
+                "name": "b1",
+                "shape": [
+                    1,
+                    3
+                ]
+            },
+            {
+                "name": "w1",
+                "shape": [
+                    2,
+                    3
+                ]
+            }
+        ],
+        "outputs": [
+            {
+                "name": "out3",
+                "shape": [
+                    -1,
+                    3
+                ]
+            }
+        ]
+    }
+}
+```
+
+
+