From e31cfcd2715feb16a5961020b9ae19c3e3013123 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Tue, 10 Oct 2017 14:06:29 -0700 Subject: [PATCH] gan --- doc/design/gan_api.md | 22 +--------------------- 1 file changed, 1 insertion(+), 21 deletions(-) diff --git a/doc/design/gan_api.md b/doc/design/gan_api.md index f9bf5939f4a..5764112f3c8 100644 --- a/doc/design/gan_api.md +++ b/doc/design/gan_api.md @@ -6,32 +6,12 @@ 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. -| important building blocks | People in Charge | Required | -|---------------------------|-------------------|----------| -| convolution 2d (done) | Chengduo | Y | -| cudnn conv 2d (missing) | Chengduo | N | -| deconv 2d (missing) | Zhuoyuan, Zhihong | Y | -| cudnn deconv 2d (missing) | Zhuoyuan, Zhihong | N | -| batch norm (missing) | Zhuoyuan, Jiayi | Y | -| cudnn batch norm (missing)| Zhuoyuan, Jiayi | N | -| max-pooling (done) | ? | Y | -| cudnn-max-pool (missing) | Chengduo | Y | -| fc (done) | ? | Y | -| softmax loss (done) | ? | Y | -| reshape op (done) | ? | Y | -| Dependency Engine (done) | Jiayi | Y * | -| Python API (done) | Longfei, Jiayi | Y * | -| Executor (done) | Tony | Y * | -| Multi optimizer (woking) | Longfei | Y * | -| Optimizer with any para | ? | Y * | -| Concat op (done) | ? | N (Cond) | -| Repmat op (done) | ? | N (Cond) | -


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.

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


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