def evaluate_IWAE(self,K, inputs,x_tilda, mu, log_variance):
z_samples, mu_h1, sigma, eps_samples = self.reparametrize_iwae(K, mu , log_variance)
log_Qz_x = torch.sum(-0.5*(eps_samples)**2 - torch.log(sigma), -1)
#K,128. first dimension basically contains how many samples taken
p_set=[]
for z in z_samples:
p = self.decoder(z)
p_set.append(p)
p_x_given_z=torch.stack(p_set)
log_P_z = torch.sum(-0.5*z_samples**2, -1)
#K, 128
cross_entropy = F.binary_cross_entropy(x_tilda, inputs, reduction='none')
cross_entropy= cross_entropy.sum(-1).sum(-1).sum(-1)
#doing normalisation
log_weight = log_P_z - cross_entropy - log_Qz_x
log_weight = log_weight - torch.max(log_weight, 0)[0]
weight = torch.exp(log_weight)
weight = weight / torch.sum(weight, 0)
weight = Variable(weight.data, requires_grad = False)
loss = torch.sum(torch.sum(weight * (log_P_z - cross_entropy - log_Qz_x) , 0))
return loss
def loss_function(x_tilda, x, mu, log_variance):
# print('size of x tilda:',x_tilda.size(), ':x size: ', x.size() )
cross_entropy = F.binary_cross_entropy(x_tilda, x, reduction='sum')
#print('cross entropy: ', cross_entropy)
#cross_entropy/= BATCH_SIZE
Kl_divergence = -0.5 * torch.sum(1 + log_variance - mu.pow(2) - log_variance.exp())
#Kl_divergence/= BATCH_SIZE
#print('cross entropy: ', cross_entropy,' : KL_divergence: ',Kl_divergence )
return cross_entropy + Kl_divergence
def Loss(x,rec_x,mean,logvar):
bce_loss=F.binary_cross_entropy(input=rec_x,target=x,size_average=False)
bld_loss=0.5*torch.sum(mean.pow(2)+logvar.exp()-logvar-1)
return(bce_loss+bld_loss)
def loss_function(x_tild, x, mu, log_sigma):
x = x.view(-1, 784)
x_tild = x_tild.view(-1, 784)
Bce = F.binary_cross_entropy(x_tild, x, reduction='sum')
KL_D = 0.5 * torch.sum(1 + log_sigma - mu**2 - log_sigma.exp())
return Bce - KL_D
