Thanks for your code sharing.
I have a question regarding a computation of gradient penalty in your code.
In gaan.py,
epsilon = tf.random_uniform(shape=tf.shape(self.X), minval=0., maxval=1.)
I think for the convex combination for each sample (same epsilon should be applied to all dims in each sample),
epsilon = tf.random_uniform(shape=[tf.shape(self.X)[0],1], minval=0., maxval=1.)
Hi, Ngoc-Trung,
Thanks for your code sharing. I have a question regarding a computation of gradient penalty in your code.
In gaan.py, epsilon = tf.random_uniform(shape=tf.shape(self.X), minval=0., maxval=1.)
I think for the convex combination for each sample (same epsilon should be applied to all dims in each sample), epsilon = tf.random_uniform(shape=[tf.shape(self.X)[0],1], minval=0., maxval=1.)
Is it correct?
Thanks, Sungwoong.