wgan gp loss definition

wgan_gp_loss.py

import tensorflow as tf
def gradient_panalty(real, fake, discriminator, alpha, gp_lambda=10):
    # alpha = tf.placeholder(shape=[None, 1, 1, 1], dtype=tf.float32)
    # alpha = tf.random_uniform(shape=[batch_size, 1, 1, 1], minval=0., maxval=1.)
    interpolated = real + alpha * (fake - real)
    logit = discriminator(interpolated)
    grad = tf.gradients(logit, interpolated)[0]  # gradient of D(interpolated)
    grad_norm = tf.norm(tf.layers.flatten(grad), axis=1)  # l2 norm
    gp = gp_lambda * tf.reduce_mean(tf.square(grad_norm - 1.))
    return gp