Skip to content

Fix links error for github images #9641

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Apr 4, 2018
Merged

Fix links error for github images #9641

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4d1fe5c
Fix github links error
Apr 4, 2018
fcb4844
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
Apr 4, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions doc/fluid/design/algorithm/parameter_average.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand Down
4 changes: 2 additions & 2 deletions doc/fluid/design/concurrent/channel.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down
4 changes: 4 additions & 0 deletions doc/fluid/design/concurrent/concurrent_programming.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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>
Expand Down
2 changes: 1 addition & 1 deletion doc/fluid/design/concurrent/select_op.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down
10 changes: 5 additions & 5 deletions doc/fluid/design/dist_train/distributed_architecture.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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.

Expand All @@ -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*.

Expand Down Expand Up @@ -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)
Expand All @@ -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
Expand Down
4 changes: 2 additions & 2 deletions doc/fluid/design/dist_train/multi_cpu.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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/single-thread@3x.png" width="300">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/single-thread@3x.png" width="300">

After converted:

<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/multi-threads@3x.png" width="1000">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/multi-threads@3x.png" width="1000">

## Implement

Expand Down
7 changes: 3 additions & 4 deletions doc/fluid/design/dist_train/parameter_server.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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
Expand Down
2 changes: 1 addition & 1 deletion doc/fluid/design/dynamic_rnn/rnn.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand Down
4 changes: 2 additions & 2 deletions doc/fluid/design/modules/batch_norm_op.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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.

Expand Down Expand Up @@ -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.
Expand Down
10 changes: 5 additions & 5 deletions doc/fluid/design/modules/regularization.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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).

Expand All @@ -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

Expand Down
4 changes: 2 additions & 2 deletions doc/fluid/design/network/deep_speech_2.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -116,7 +116,7 @@ The classical DS2 network contains 15 layers (from bottom to top):
- **One** CTC-loss layer

<div align="center">
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/ds2_network.png" width=350><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ds2_network.png" width=350><br/>
Figure 1. Archetecture of Deep Speech 2 Network.
</div>

Expand Down Expand Up @@ -208,7 +208,7 @@ TODO by Assignees
### Beam Search with CTC and LM

<div align="center">
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/beam_search.png" width=600><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/beam_search.png" width=600><br/>
Figure 2. Algorithm for CTC Beam Search Decoder.
</div>

Expand Down
2 changes: 1 addition & 1 deletion doc/fluid/design/network/sequence_decoder.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -199,7 +199,7 @@ Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail i

## LoD and shape changes during decoding
<p align="center">
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/LOD-and-shape-changes-during-decoding.jpg"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/LOD-and-shape-changes-during-decoding.jpg"/>
</p>

According to the image above, the only phase that changes the LoD is beam search.
Expand Down
4 changes: 2 additions & 2 deletions doc/fluid/design/others/gan_api.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,14 +7,14 @@ It applies several important concepts in machine learning system design, includi
In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation.

<p align="center">
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/test.dot.png" width = "35%" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/test.dot.png" width = "35%" align="center"/><br/>
Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run.
</p>

The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563.

<p align="center">
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/dcgan.png" width = "90%" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/dcgan.png" width = "90%" align="center"/><br/>
Figure 2. Photo borrowed from the original DC-GAN paper.
</p>

Expand Down
2 changes: 1 addition & 1 deletion doc/fluid/dev/releasing_process.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ PaddlePaddle每次发新的版本，遵循以下流程:
可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。然后按照上述的方法
使用`twine`工具上传即可。

<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/ci_build_whl.png">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ci_build_whl.png">

* 注：CI环境使用 https://github.com/PaddlePaddle/buildtools 这里的DockerImage作为编译环境以支持更多的Linux
发型版，如果需要手动编译，也可以使用这些镜像。这些镜像也可以从 https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/ 下载得到。
Expand Down
2 changes: 1 addition & 1 deletion doc/fluid/howto/performance/profiler.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ But how to record the time for the mixed C++ and CUDA program? There many C++ A

The overall flow is shown as the following figure.

<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/profiler.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/profiler.png" align="center"/><br/>

### Event

Expand Down