diff --git a/doc/howto/deep_model/rnn/rnn_config_cn.rst b/doc/howto/deep_model/rnn/rnn_config_cn.rst index 8d65b3512d0d99438898ec555a57f904691247f2..ac2bd0775f4ab2e0a0c37462e2c23001123b152b 100644 --- a/doc/howto/deep_model/rnn/rnn_config_cn.rst +++ b/doc/howto/deep_model/rnn/rnn_config_cn.rst @@ -33,8 +33,7 @@ PaddlePaddle yield src_ids, trg_ids, trg_ids_next -有关如何编写数据提供程序的更多细节描述，请参考 -`PyDataProvider2 <../../ui/data_provider/index.html>`__\ 。完整的数据提供文件在 +有关如何编写数据提供程序的更多细节描述，请参考 :ref:`api_pydataprovider2` 。完整的数据提供文件在 ``demo/seqToseq/dataprovider.py``\ 。 配置循环神经网络架构 @@ -132,9 +131,7 @@ Sequence to Sequence Model with Attention 模型的编码器部分如下所示。它叫做\ ``grumemory``\ 来表示门控循环神经网络。如果网络架构简单，那么推荐使用循环神经网络的方法，因为它比 ``recurrent_group`` -更快。我们已经实现了大多数常用的循环神经网络架构，可以参考 -`Layers <../../ui/api/trainer_config_helpers/layers_index.html>`__ -了解更多细节。 +更快。我们已经实现了大多数常用的循环神经网络架构，可以参考 :ref:`api_trainer_config_helpers_layers` 了解更多细节。 我们还将编码向量投射到 ``decoder_size`` 维空间。这通过获得反向循环网络的第一个实例，并将其投射到 @@ -276,9 +273,6 @@ attention，门控循环单元单步函数和输出函数： result_file=gen_trans_file) outputs(beam_gen) -注意，这种生成技术只用于类似解码器的生成过程。如果你正在处理序列标记任务，请参阅 -`Semantic Role Labeling -Demo <../../demo/semantic_role_labeling/index.html>`__ -了解更多详细信息。 +注意，这种生成技术只用于类似解码器的生成过程。如果你正在处理序列标记任务，请参阅 :ref:`semantic_role_labeling` 了解更多详细信息。 完整的配置文件在\ ``demo/seqToseq/seqToseq_net.py``\ 。 diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index 201bcae48df29eecca175a63fb2723ad687e7f69..422dc3bd811ae8b31dbdd6fa8637d6e44b29ac76 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -331,15 +331,15 @@ For sharing the training data across all the Kubernetes nodes, we use EFS (Elast 1. Make sure you added AmazonElasticFileSystemFullAccess policy in your group. 1. Create the Elastic File System in AWS console, and attach the new VPC with it. - +
![](src/create_efs.png)
1. Modify the Kubernetes security group under ec2/Security Groups, add additional inbound policy "All TCP TCP 0 - 65535 0.0.0.0/0" for Kubernetes default VPC security group. - +
![](src/add_security_group.png)
1. Follow the EC2 mount instruction to mount the disk onto all the Kubernetes nodes, we recommend to mount EFS disk onto ~/efs. - +
![](src/efs_mount.png)
Before starting the training, you should place your user config and divided training data onto EFS. When the training start, each task will copy related files from EFS into container, and it will also write the training results back onto EFS, we will show you how to place the data later in this article. diff --git a/doc/tutorials/gan/gan.png b/doc/tutorials/gan/gan.png index 001ed6cc19e8911f9b10f63211c9658160b3a06e..0eafd7cb49b545f412f8e775804bcd0b22c42454 100644 Binary files a/doc/tutorials/gan/gan.png and b/doc/tutorials/gan/gan.png differ diff --git a/doc/tutorials/gan/index_en.md b/doc/tutorials/gan/index_en.md index 99c8d730117a469c89abb218eeacf66103c0cbed..ac9ed37b2264778869f92c0910b1cb946fb4427f 100644 --- a/doc/tutorials/gan/index_en.md +++ b/doc/tutorials/gan/index_en.md @@ -4,9 +4,7 @@ This demo implements GAN training described in the original [GAN paper](https:// The high-level structure of GAN is shown in Figure. 1 below. It is composed of two major parts: a generator and a discriminator, both of which are based on neural networks. The generator takes in some kind of noise with a known distribution and transforms it into an image. The discriminator takes in an image and determines whether it is artificially generated by the generator or a real image. So the generator and the discriminator are in a competitive game in which generator is trying to generate image to look as real as possible to fool the discriminator, while the discriminator is trying to distinguish between real and fake images. -

- -

+
![](./gan.png)

Figure 1. GAN-Model-Structure figure credit @@ -111,9 +109,7 @@ $python gan_trainer.py -d uniform --useGpu 1 ``` The generated samples can be found in ./uniform_samples/ and one example is shown below as Figure 2. One can see that it roughly recovers the 2D uniform distribution. -

- -

+
![](./uniform_sample.png)

Figure 2. Uniform Sample

@@ -135,9 +131,7 @@ To train the GAN model on mnist data, one can use the following command: $python gan_trainer.py -d mnist --useGpu 1 ``` The generated sample images can be found at ./mnist_samples/ and one example is shown below as Figure 3. -

- -

+
![](./mnist_sample.png)

Figure 3. MNIST Sample

diff --git a/doc/tutorials/gan/uniform_sample.png b/doc/tutorials/gan/uniform_sample.png index 4a96c45cae82673f5a1df986f2643a8026da7937..e716c48e782019a757bed0cb443f2ed97386cbe2 100644 Binary files a/doc/tutorials/gan/uniform_sample.png and b/doc/tutorials/gan/uniform_sample.png differ