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I don't understand how exactly the loss function in line 5 of algorithm 1 in the original WGAN paper is implemented here. In your code you minimise
self.discriminator_loss = discriminator_loss_fake + discriminator_loss_real
However, according to the paper shouldn't it be maximising:
self.discriminator_loss = discriminator_loss_real - discriminator_loss_fake
or alternatively minimising:
self.discriminator_loss = discriminator_loss_fake - discriminator_loss_real
That is, should this be a minus in your total loss?
The text was updated successfully, but these errors were encountered:
I thought the first algorithm is implemented for cross entropy, which discriminator_loss_fake + discriminator_loss_real
discriminator_loss_fake + discriminator_loss_real
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I don't understand how exactly the loss function in line 5 of algorithm 1 in the original WGAN paper is implemented here. In your code you minimise
self.discriminator_loss = discriminator_loss_fake + discriminator_loss_real
However, according to the paper shouldn't it be maximising:
self.discriminator_loss = discriminator_loss_real - discriminator_loss_fake
or alternatively minimising:
self.discriminator_loss = discriminator_loss_fake - discriminator_loss_real
That is, should this be a minus in your total loss?
The text was updated successfully, but these errors were encountered: