李宏毅GAN学习笔记（2）

2019-03-30 本文已影响1人 SmallRookie

GAN Lecture 2

In each traing iteration:

Sample m positive examples $\{(c^1, x^1), (c^2, x^2), \dots, (c^m, x^m)\}$ from database
Sample m noise samples $\{z^1, z^2, \dots, z^m\}$ from a distribution
Obtaining generated data $\{\tilde{x}^1, \tilde{x}^2, \dots, \tilde{x}^m\}$ , $\tilde{x}^i=G(c^i, z^i)$
Sample m objects $\{\hat{x}^1, \hat{x}^2, \dots, \hat{x}^m\}$ from database
Update discriminator parameters to maximize
- $\tilde{V}=\frac{1}{m}\sum_{i=1}^mlogD(c^i, x^i)+\frac{1}{m}\sum_{i=1}^mlog(1-D(c^i, \tilde{x}^i))+\frac{1}{m}_{i=1}^mlog(1-D(c^i, \hat{x}^i))$
- $\theta_d \leftarrow \theta_d+\eta\bigtriangledown\tilde{V}(\theta_d)$

Learning D

Sample m noise samples $\{z^1,z^2,\dots,z^m\}$ from a distribution
Sample m conditions $\{c^1,c^2,\dots,c^m\}$ from a database
Update generator parameters to maximize
- $\tilde{V}=\frac{1}{m}\sum_{i=1}^mlog(D(G(c^i, z^i)))$ , $\theta_g \leftarrow\eta\bigtriangledown\tilde{V}(\theta_g)$