def get_loss(self, image): recon_mean, latent_mean, latent_log_std, latent_samples = \ self.forward(image) # kl term kl_q = modeling_lib.get_kl_q_standard_normal(latent_mean, latent_log_std) # bernoulli likelihood loglik = modeling_lib.get_bernoulli_loglik(recon_mean, image) return -loglik + kl_q
def get_loss_cond_pixel_1d(self, image, pixel_1d): # forward recon_mean, latent_mean, latent_log_std, latent_samples, pixel_2d = \ self.forward_cond_pixel_1d(image, pixel_1d) # kl term kl_latent = \ modeling_lib.get_kl_q_standard_normal(latent_mean, latent_log_std) # bernoulli likelihood loglik = modeling_lib.get_bernoulli_loglik(recon_mean, image) return -loglik + kl_latent
def get_loss_cond_pixel_1d(self, one_hot_pixel, image, \ use_cached_image = False): # forward if use_cached_image: image_ = None else: image_ = image recon_mean, latent_mean, latent_log_std, latent_samples = \ self.forward_cond_pixel_1d(one_hot_pixel, image_) # kl term kl_latent = \ modeling_lib.get_kl_q_standard_normal(latent_mean, latent_log_std) # bernoulli likelihood loglik = modeling_lib.get_bernoulli_loglik(recon_mean, image) return -loglik + kl_latent