Loss Functions

[nagaswethar]

self.g_loss_a2b = self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake))\

• self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
• self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) \
• self.Lg_lambda * gradloss_criterion(self.real_A, self.fake_B, self.weighted_seg_A) \
• self.Lg_lambda * gradloss_criterion(self.real_B, self.fake_A, self.weighted_seg_B)
  self.g_loss_b2a = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake))\
• self.L1_lambda * abs_criterion(self.real_A, self.fake_A_)\
• self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) \
• self.Lg_lambda * gradloss_criterion(self.real_A, self.fake_B, self.weighted_seg_A)\
• self.Lg_lambda * gradloss_criterion(self.real_B, self.fake_A, self.weighted_seg_B)\

can you explain this loss function and what \ indicates in code