Merge pull request #10518 from weixing02/v2_deadlinks

V2 deadlinks

doc/v2/design/mkl/mkldnn.md

@@ -5,7 +5,7 @@
 充分展现英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。
 
 <div align="center">
-<img src="image/overview.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/overview.png"><br/>
 Figure 1. PaddlePaddle on IA
 </div>
 
@@ -42,16 +42,43 @@ Figure 1. PaddlePaddle on IA
 
 MKL，MKLML以及MKL-DNN三者关系如下表：
 
-| Name        |  Open Source     | License     | Descriptions  |
-| :---------- | :--------------- | :---------- | :------------ |
-|   MKL       |     No           | Proprietary | Accelerate math processing routines |
-|   MKLML     |     No           | Proprietary | Small package of MKL, especially for Machine Learning |
-|   MKL-DNN   |     Yes          | Apache 2.0  | Accelerate primitives processing routines especially for Deep Neural Networks  |
+<table>
+<thead>
+<tr>
+<th>Name</th>
+<th>Open Source</th>
+<th>License</th>
+<th>Descriptions</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>MKL</td>
+<td>No</td>
+<td>Proprietary</td>
+<td>Accelerate math processing routines</td>
+</tr>
+<tr>
+<td>MKLML</td>
+<td>No</td>
+<td>Proprietary</td>
+<td>Small package of MKL, especially for Machine Learning</td>
+</tr>
+
+<tr>
+<td>MKL-DNN</td>
+<td>Yes</td>
+<td>Apache 2.0</td>
+<td>Accelerate primitives processing routines especially for Deep Neural Networks</td>
+</tr>
+
+</tbody>
+</table>
 
 MKLML可以与MKL-DNN共同使用，以此达到最好的性能。
 
 <div align="center">
-<img src="image/engine.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/engine.png"><br/>
 Figure 2. PaddlePaddle with MKL Engines
 </div>
 
@@ -103,7 +130,7 @@ MKL-DNN的库目前只有动态库`libmkldnn.so`。
 所以我们定义了一个`MKLDNNMatrix`用于管理MKL-DNN数据的不同格式以及相互之间的转换。
 
 <div align="center">
-<img src="image/matrix.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/matrix.png"><br/>
 Figure 3. MKLDNNMatrix
 </div>
 
@@ -113,7 +140,7 @@ Figure 3. MKLDNNMatrix
 子类只需要使用定义好的接口，实现具体的函数功能即可。
 
 <div align="center">
-<img src="image/layers.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/layers.png"><br/>
 Figure 4. MKLDNNLayer
 </div>
 
@@ -150,7 +177,7 @@ Figure 4. MKLDNNLayer
 所以整体上，在实现每个子类的时候就不需要关心分支的事情了。
 
 <div align="center">
-<img src="image/gradients.png"><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/v2/images/gradients.png"><br/>
 Figure 5. Merge Gradients
 </div>
 

doc/v2/images/glossary_rnn.dot

@@ -0,0 +1,42 @@
+digraph G{
+	subgraph cluster_timestep0 {
+		label="recurrent timestep i-1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc0_0 [label="fc 0"]
+		fc0_1 [label="fc 1"]
+		fc0_2 [label="fc 2"]
+
+		fc0_0 -> fc0_1
+		fc0_1 -> fc0_2
+	}
+
+	subgraph cluster_timestep1 {
+		label="recurrent timestep i"
+		node [style=filled];
+		fc1_0 [label="fc 0"]
+		fc1_1 [label="fc 1"]
+		fc1_2 [label="fc 2"]
+		color=blue
+
+		fc1_0 -> fc1_1
+		fc1_1 -> fc1_2
+	}
+
+	subgraph cluster_timestep2 {
+		label="recurrent timestep i+1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc2_0 [label="fc 0"]
+		fc2_1 [label="fc 1"]
+		fc2_2 [label="fc 2"]
+
+		fc2_0 -> fc2_1
+		fc2_1 -> fc2_2
+	}
+	
+	
+	fc0_1 -> fc1_1 [style="dotted" constraint=false]
+	fc1_1 -> fc2_1 [style="dotted" constraint=false]
+
+}

doc/v2/images/glossary_rnn_with_memory.dot

@@ -0,0 +1,48 @@
+digraph G{
+	subgraph cluster_timestep0 {
+		label="recurrent timestep i-1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc0_0 [label="fc 0"]
+		fc0_1 [label="fc 1"]
+		fc0_2 [label="fc 2"]
+		m0 [label="memory"]
+		fc0_0 -> fc0_1
+		fc0_1 -> fc0_2
+		fc0_1 -> m0
+		m0 -> fc0_1
+	}
+
+	subgraph cluster_timestep1 {
+		label="recurrent timestep i"
+		node [style=filled];
+		fc1_0 [label="fc 0"]
+		fc1_1 [label="fc 1"]
+		fc1_2 [label="fc 2"]
+		m1 [label="memory"]
+		color=blue
+		fc1_0 -> fc1_1
+		fc1_1 -> fc1_2
+		fc1_1 -> m1
+		m1 -> fc1_1
+	}
+
+	subgraph cluster_timestep2 {
+		label="recurrent timestep i+1"
+		bgcolor=lightgray
+		node [style=filled,color=white]
+		fc2_0 [label="fc 0"]
+		fc2_1 [label="fc 1"]
+		fc2_2 [label="fc 2"]
+		m2 [label="memory"]
+		fc2_0 -> fc2_1
+		fc2_1 -> fc2_2
+		fc2_1 -> m2
+		m2 -> fc2_1
+	}
+	
+	
+	m0 -> m1 [style="dotted" constraint=false]
+	m1 -> m2 [style="dotted" constraint=false]
+
+}