(self, hr_images, lr_images)
Args: [0, 255]
Args: [0, 255]
Args : [ 0 255 ]
def construct_net(self, hr_images, lr_images):
    """
    Args: [0, 255]
    """
    #labels
    labels = hr_images
    #normalization images [-0.5, 0.5]
    hr_images = hr_images / 255.0 - 0.5
    lr_images = lr_images / 255.0 - 0.5
    self.prior_logits = self.prior_network(hr_images)
    self.conditioning_logits = self.conditioning_network(lr_images)

    loss1 = self.softmax_loss(self.prior_logits + self.conditioning_logits, labels)
    loss2 = self.softmax_loss(self.conditioning_logits, labels)
    loss3 = self.softmax_loss(self.prior_logits, labels)

    self.loss = loss1 + loss2
    tf.summary.scalar('loss', self.loss)
    tf.summary.scalar('loss_prior', loss3)
def construct_net ( self , hr_images , lr_images ) : #labels labels = hr_images #normalization images [-0.5, 0.5] hr_images = hr_images / 255.0 - 0.5 lr_images = lr_images / 255.0 - 0.5 self . prior_logits = self . prior_network ( hr_images ) self . conditioning_logits = self . conditioning_network ( lr_images ) loss1 = self . softmax_loss ( self . prior_logits + self . conditioning_logits , labels ) loss2 = self . softmax_loss ( self . conditioning_logits , labels ) loss3 = self . softmax_loss ( self . prior_logits , labels ) self . loss = loss1 + loss2 tf . summary . scalar ( 'loss' , self . loss ) tf . summary . scalar ( 'loss_prior' , loss3 )