Skip to content

Simple Tensorflow Implementation of "Spectral Normalization for Generative Adversarial Networks" (ICLR 2018)

License

Notifications You must be signed in to change notification settings

taki0112/Spectral_Normalization-Tensorflow

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

29 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Spectral_Normalization-Tensorflow

Simple Tensorflow Implementation of Spectral Normalization for Generative Adversarial Networks (ICLR 2018)

Usage

> python main.py --dataset mnist --sn True

Summary

sn

Simple Code

def spectral_norm(w, iteration=1):
   w_shape = w.shape.as_list()
   w = tf.reshape(w, [-1, w_shape[-1]])

   u = tf.get_variable("u", [1, w_shape[-1]], initializer=tf.random_normal_initializer(), trainable=False)

   u_hat = u
   v_hat = None
   for i in range(iteration):
       """
       power iteration
       Usually iteration = 1 will be enough
       """
       v_ = tf.matmul(u_hat, tf.transpose(w))
       v_hat = tf.nn.l2_normalize(v_)

       u_ = tf.matmul(v_hat, w)
       u_hat = tf.nn.l2_normalize(u_)

   u_hat = tf.stop_gradient(u_hat)
   v_hat = tf.stop_gradient(v_hat)

   sigma = tf.matmul(tf.matmul(v_hat, w), tf.transpose(u_hat))

   with tf.control_dependencies([u.assign(u_hat)]):
       w_norm = w / sigma
       w_norm = tf.reshape(w_norm, w_shape)


   return w_norm

How to use

   w = tf.get_variable("kernel", shape=[kernel, kernel, x.get_shape()[-1], channels])
   b = tf.get_variable("bias", [channels], initializer=tf.constant_initializer(0.0))

   x = tf.nn.conv2d(input=x, filter=spectral_norm(w), strides=[1, stride, stride, 1]) + b

Related works

Author

Junho Kim

About

Simple Tensorflow Implementation of "Spectral Normalization for Generative Adversarial Networks" (ICLR 2018)

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages