From 4238b9b95cda29618828a9a477afecb3bbed984e Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Mon, 9 Oct 2017 14:35:45 -0700 Subject: [PATCH] gan_api --- doc/design/gan_api.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/design/gan_api.md b/doc/design/gan_api.md index 0db18f92a74..4fcff8b70a3 100644 --- a/doc/design/gan_api.md +++ b/doc/design/gan_api.md @@ -1,10 +1,10 @@ # Design for GAN -GAN (General Adversarial Net) is an important model for unsupervised learning and widely used in many areas. +GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas. -It contains several important machine learning concepts, including building and running subgraphs, dependency tracing, different optimizers in one executor and so forth. +It applies several important concepts in machine learning system design, including building and running subgraphs, dependency tracing, different optimizers in one executor and so forth. -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 as an example due to its good performance on image generation. +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 | |---------------------------|-------------------|----------| @@ -51,7 +51,7 @@ Returns a 0/1 binary label. build the whole GAN model, define training loss for both generator and discrimator. ## Discussion on Engine Functions required to build GAN -- Trace the ternsor and variable dependency in the engine executor. (Very critical, otherwise GAN can'be be trained correctly) +- Trace the tensor and variable dependency in the engine executor. (Very critical, otherwise GAN can'be be trained correctly) - Different optimizers responsible for optimizing different loss. To be more detailed, we introduce our design of DCGAN as following: -- GitLab