def cost(X, Z_prior_mean, Z_prior_logvar,
Z_mean, Z_logvar, X_mean, X_logvar,
lengths):
mask = T.arange(X.shape[0]).dimshuffle(0,'x')\
< lengths.dimshuffle('x',0)
encoding_cost = mask * vae.kl_divergence(
mean_1=Z_prior_mean, logvar_1=Z_prior_logvar,
mean_2=Z_mean, logvar_2=Z_logvar
)
reconstruction_cost = mask * vae.gaussian_nll(X, X_mean, X_logvar)
return -T.sum(encoding_cost + reconstruction_cost)/T.sum(mask)
def cost(X, Z_prior_mean, Z_prior_std, Z_mean, Z_std, X_mean, X_std, lengths):
mask = T.arange(X.shape[0]).dimshuffle(0,'x')\
< lengths.dimshuffle('x',0)
encoding_cost = T.switch(
mask,
vae.kl_divergence(
mean_1=Z_mean,
std_1=Z_std,
mean_2=Z_prior_mean,
std_2=Z_prior_std,
), 0)
reconstruction_cost = T.switch(mask, vae.gaussian_nll(X, X_mean, X_std), 0)
return -T.sum(encoding_cost + reconstruction_cost) / T.sum(mask)
def cost(X,
Z_prior_mean, Z_prior_std,
Z_mean, Z_std,
X_mean, X_std,
lengths):
mask = T.arange(X.shape[0]).dimshuffle(0,'x')\
< lengths.dimshuffle('x',0)
encoding_cost = T.switch(mask,
vae.kl_divergence(
mean_1=Z_mean, std_1=Z_std,
mean_2=Z_prior_mean, std_2=Z_prior_std,
),
0
)
reconstruction_cost = T.switch(mask,
vae.gaussian_nll(X, X_mean, X_std),
0
)
return -T.sum(encoding_cost + reconstruction_cost)/T.sum(mask)
