def virtual_adversarial_loss(self, x, adj, logit):
adv_x = x + self.r_vadv
logit_p = tf.stop_gradient(logit)
logit_m = self.forward(adv_x, adj)
loss = kl_divergence_with_logit(logit_p, logit_m)
return loss
def virtual_adversarial_loss(self, x, adj, logit, epsilon):
d = tf.random.normal(shape=tf.shape(x), dtype=self.floatx)
r_vadv = get_normalized_vector(d) * epsilon
logit_p = tf.stop_gradient(logit)
logit_m = self.forward(x + r_vadv, adj)
loss = kl_divergence_with_logit(logit_p, logit_m)
return loss
def virtual_adversarial_loss(self, x, adj, logit, adv_mask):
d = tf.random.normal(shape=tf.shape(x), dtype=self.floatx)
for _ in range(self.n_power_iterations):
d = get_normalized_vector(d) * self.xi
logit_p = logit
with tf.GradientTape() as tape:
tape.watch(d)
logit_m = self.forward(x + d, adj)
dist = kl_divergence_with_logit(logit_p, logit_m, adv_mask)
grad = tape.gradient(dist, d)
d = tf.stop_gradient(grad)
r_vadv = get_normalized_vector(d) * self.epsilon
logit_p = tf.stop_gradient(logit)
logit_m = self.forward(x + r_vadv, adj)
loss = kl_divergence_with_logit(logit_p, logit_m, adv_mask)
return tf.identity(loss)
def virtual_adversarial_loss(self, x, adj, logit, adv_mask):
d = tf.random.normal(shape=tf.shape(x), dtype=self.floatx)
model = self.model
for _ in range(self.n_power_iterations):
d = get_normalized_vector(d) * self.xi
logit_p = logit
with tf.GradientTape() as tape:
tape.watch(d)
logit_m = model([x + d, adj, self.index_all], training=True)
dist = kl_divergence_with_logit(logit_p, logit_m, adv_mask)
grad = tape.gradient(dist, d)
d = tf.stop_gradient(grad)
r_vadv = get_normalized_vector(d) * self.epsilon
logit_p = tf.stop_gradient(logit)
logit_m = model([x + r_vadv, adj, self.index_all])
loss = kl_divergence_with_logit(logit_p, logit_m, adv_mask)
return loss
