Merge pull request #9641 from weixing02/img

Fix links error for github images

Author: luotao1

doc/fluid/design/algorithm/parameter_average.md

@@ -5,10 +5,10 @@ In a large scale machine learning setup where the size of the training data is h
 
 Polyak and Juditsky (1992) showed that the test performance of simple average of parameters obtained by Stochastic Gradient Descent (SGD) is as good as that of parameter values that are obtained by training the model over and over again, over the training dataset.
 
-Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="./images/theta_star.gif"/><br/> . The averaging is done as follows:
+Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/theta_star.gif"/><br/> . The averaging is done as follows:
 
 <p align="center">
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/asgd.gif"><br />
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/asgd.gif"><br />
 </p>
 
 We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above.

doc/fluid/design/concurrent/channel.md

@@ -114,13 +114,13 @@ current thread under two conditions:
 #### Channel Send
 
 <p align="center">
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/channel_send.png"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/channel_send.png"/><br/>
 </p>
 
 #### Channel Receive
 
 <p align="center">
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/channel_recv.png"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/channel_recv.png"/><br/>
 </p>
 
 ## Limitations and Considerations

doc/fluid/design/concurrent/concurrent_programming.md

@@ -23,21 +23,25 @@ The following table compares concepts in Fluid and Go
 <td>user-defined functions </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/fluid">layers</a></td>
+<td></td>
 </tr>
 <tr>
 <td>control-flow and built-in functions </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">intrinsics/operators</a></td>
+<td></td>
 </tr>
 <tr>
 <td>goroutines, channels </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h">class ThreadPool</a></td>
+<td></td>
 </tr>
 <tr>
 <td>runtime </td>
 <td>
 <a href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h">class Executor</a></td>
+<td></td>
 </tr>
 </tbody>
 </table>

doc/fluid/design/concurrent/select_op.md

@@ -254,7 +254,7 @@ only one case will be executed.
 ### select_op flow
 
 <p align="center">
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/select_op_workflow.png"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/select_op_workflow.png"/><br/>
 </p>
 
 The select algorithm is inspired by golang's select routine.  Please refer to

doc/fluid/design/dist_train/distributed_architecture.md

@@ -40,11 +40,11 @@ computation is only specified in Python code which sits outside of PaddlePaddle,
 
 Similar to how a compiler uses an intermediate representation (IR) so that the programmer does not need to manually optimize their code for most of the cases, we can have an intermediate representation in PaddlePaddle as well. The compiler optimizes the IR as follows:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/compiler.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/compiler.png"/>
 
 PaddlePaddle can support model parallelism by converting the IR so that the user no longer needs to manually perform the computation and operations in the Python component:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/paddle-compile.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/paddle-compile.png"/>
 
 The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the computation dependency graph and the variables used in the computation.
 
@@ -60,7 +60,7 @@ For a detailed explanation, refer to this document -
 
 The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/distributed_architecture.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/distributed_architecture.png"/>
 
 The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*.
 
@@ -152,7 +152,7 @@ for data in train_reader():
 `JobDesc` object describe the distributed job resource specification to run on
 Cluster environment.
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/remote_executor.png" width="500" align="center" />
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/remote_executor.png" width="500" align="center" />
 
 `RemoteExecutor.run` sends the `ProgramDesc` and
 [TrainingJob](https://github.com/PaddlePaddle/cloud/blob/unreleased-tpr/doc/autoscale/README.md#training-job-resource)
@@ -171,7 +171,7 @@ In the future, a more general placement algorithm should be implemented, which m
 
 The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/local_architecture.png"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/local_architecture.png"/>
 
 
 ### Training Data

doc/fluid/design/dist_train/multi_cpu.md

@@ -8,11 +8,11 @@ Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph.
 
 ## Transpiler
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/[email protected]" width="300">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/[email protected]" width="300">
 
 After converted:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/[email protected]" width="1000">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/[email protected]" width="1000">
 
 ## Implement
 

doc/fluid/design/dist_train/parameter_server.md

@@ -41,11 +41,11 @@ We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
 Below is an example of converting the user defined graph to the
 subgraphs for the trainer and the parameter server:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/local-graph.png" width="300"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/local-graph.png" width="300"/>
 
 After converting:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/dist-graph.png" width="700"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/dist-graph.png" width="700"/>
 
 1. The parameter variable W and its optimizer program are placed on the parameter server.
 1. Operators are added to the program.
@@ -69,8 +69,7 @@ In Fluid, we introduce [SelectedRows](../selected_rows.md) to represent a list o
 non-zero gradient data. So when we do parameter optimization both locally and remotely,
 we only need to send those non-zero rows to the optimizer operators:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/sparse_update.png" width="700" />
-
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/sparse_update.png" width="700" />
 ### Benefits
 
 - Model parallelism becomes easier to implement: it is an extension to

doc/fluid/design/dynamic_rnn/rnn.md

@@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i
 ## RNN Algorithm Implementation
 
 <p align="center">
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/rnn.jpg"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/rnn.jpg"/>
 </p>
 
 The above diagram shows an RNN unrolled into a full network.

doc/fluid/design/modules/batch_norm_op.md

@@ -66,7 +66,7 @@ As most C++ operators do, `batch_norm_op` is defined by inputs, outputs, attribu
 
 The following graph showes the training computational process of `batch_norm_op`:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/batch_norm_op_kernel.png" width="800"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/batch_norm_op_kernel.png" width="800"/>
 
 cudnn provides APIs to finish the whole series of computation, we can use them in our GPU kernel.
 
@@ -124,7 +124,7 @@ for pass_id in range(PASS_NUM):
 `is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables, but be placed in two different branches. That is to say, if a network contains a `batch_norm_op`, it will fork into two branches, one go through `train_batch_norm_op` and the other one go through `infer_batch_norm_op`:
 
 <div align=center>
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/batch_norm_fork.png" width="500"/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/batch_norm_fork.png" width="500"/>
 </div>
 
 Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate.

doc/fluid/design/modules/regularization.md

@@ -6,17 +6,17 @@ A central problem in machine learning is how to design an algorithm that will pe
 ### Parameter Norm Penalties
 Most common regularization approaches in deep learning are based on limiting the capacity of the models by adding a parameter norm penalty to the objective function `J`. This is given as follows:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/loss_equation.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/loss_equation.png" align="center"/><br/>
 
 The parameter `alpha` is a hyperparameter that weights the relative contribution of the norm penalty term, `omega`, relative to the standard objective function `J`.
 
 The most commonly used norm penalties are the L2 norm penalty and the L1 norm penalty. These are given as follows:
 
 ##### L2 Regularization:
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/l2_regularization.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/l2_regularization.png" align="center"/><br/>
 
 ##### L1 Regularization
-<img src=".https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/l1_regularization.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/l1_regularization.png" align="center"/><br/>
 
 A much more detailed mathematical background of regularization can be found [here](http://www.deeplearningbook.org/contents/regularization.html).
 
@@ -40,11 +40,11 @@ The idea of building ops for regularization is in sync with the refactored Paddl
 
 Below is an example of a really simple feed forward neural network.
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/feed_forward.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/feed_forward.png" align="center"/><br/>
 
 The Python API will modify this computation graph to add regularization operators. The modified computation graph will look as follows:
 
-<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/feed_forward_regularized.png" align="center"/><br/>
+<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/feed_forward_regularized.png" align="center"/><br/>
 
 ### Python API implementation for Regularization