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tensorflow implementation of Wasserstein distance with gradient penalty
wGAN implemented on top of tensorflow as described in: [Wasserstein GAN](
with improvements as described in: [Improved Training of Wasserstein GANs](
import tensorflow as tf
# define earth mover distance (wasserstein loss)
def em_loss(y_coefficients, y_pred):
return tf.reduce_mean(tf.multiply(y_coefficients, y_pred))
# construct computation graph for calculating the gradient penalty (improved wGAN) and training the discriminator
# sample a batch of noise (generator input)
_z = tf.placeholder(tf.float32, shape=(batch_size, rand_dim))
# sample a batch of real images
_x = tf.placeholder(tf.float32, shape=(batch_size, img_height, img_width, img_channels))
# generate a batch of images with the current generator
_g_z = generator_model(_z)
# calculate `x_hat`
epsilon = tf.placeholder(tf.float32, shape=(batch_size, 1, 1, 1))
x_hat = epsilon * _x + (1.0 - epsilon) * _g_z
# gradient penalty
gradients = tf.gradients(discriminator_model(x_hat), [x_hat])
_gradient_penalty = 10.0 * tf.square(tf.norm(gradients[0], ord=2) - 1.0)
# calculate discriminator's loss
_disc_loss = em_loss(tf.ones(batch_size), discriminator_model(_g_z)) - \
em_loss(tf.ones(batch_size), discriminator_model(_x)) + \
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shamanez commented Jul 5, 2018

Can you please explain about line 32. In some implementations, I have seen like the following,

grad= tf.sqrt(tf.reduce_sum(tf.square(grad_D_X_hat), reduction_indices=red_idx))

gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)

Is this the same?

Also, should we take reduce some before taking the reduce mean?

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