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I'm doubt that why generator loss is defined as gen_loss = K.mean(fake_output), maybe should it be gen_loss = K.mean(real_output) to confuse discriminator?
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The generator can't influence the results of the real outputs (they come from the dataset itself), so it wouldn't make sense to use gen_loss = K.mean(real_output) as the gradients wouldn't flow through the generator. Generator losses are typically formulated as a function of D(G(z)) i.e. in terms of fake_output, such as -D(G(z)) in hinge loss or -log(D(G(z)) in non-saturating loss.
I'm doubt that why generator loss is defined as
gen_loss = K.mean(fake_output)
, maybe should it begen_loss = K.mean(real_output)
to confuse discriminator?The text was updated successfully, but these errors were encountered: