def divergence_loss(self, parameters):
return normal_kl_loss(*parameters)
def loss(self, source_parameters, total_parameters, reconstruction,
target):
loss_val = self.rec_loss(reconstruction, target)
kld = vl.normal_kl_loss(total_parameters, source_parameters)
return loss_val + kld
def loss(self, parameters, approximation, target):
loss_val = func.binary_cross_entropy_with_logits(
approximation, target, reduction="sum") / target.size(0)
for mean, logvar in parameters:
loss_val += vl.normal_kl_loss(mean, logvar)
return loss_val
def loss(self, parameters, prior_parameters, approximation, target):
loss_val = func.binary_cross_entropy_with_logits(
approximation, target, reduction="sum") / target.size(0)
for p, p_r in zip(parameters, prior_parameters):
loss_val += vl.normal_kl_loss(*p, *p_r)
return loss_val
def divergence_loss(self, parameters, prior_parameters):
mu, lv = parameters
mu_r, lv_r = prior_parameters
result = vl.normal_kl_loss(mu, lv, mu_r, lv_r)
return result
def divergence_loss(self, normal_parameters, decoder_parameters):
tc_loss = vl.tc_encoder_loss(*decoder_parameters)
div_loss = vl.normal_kl_loss(*normal_parameters)
result = div_loss + self.gamma * tc_loss
return result
