Generative Adversarial Networks

The PyTorch adaptation of this section was initially contributed by @StevenJokess, as reviewed and revised by @anirudh. The PR may not show up on Git due to the suspended account of the former.

Still suck at GAN to tensorflow…
Anyone helps?

TF finished!

loss = nn.BCEWithLogitsLoss(reduction='sum')

With this loss where reduction is ‘sum’, I think the model does not consider the data size(batch size) in gradient descent.

Isn’t it better to use
loss = nn.BCEWithLogitsLoss(reduction='mean') with
metric.add(update_D(X, Z, net_D, net_G, loss, trainer_D)* batch_size ,update_G(Z, net_D, net_G, loss, trainer_G)* batch_size, batch_size)

If it is right, Could I commit this change?


@goldpiggy @astonzhang Thanks in advance :slight_smile:

If the generator does a perfect job, then 𝐷(𝐱′)≈1D(x′)≈1 so the above loss near 0, which results the gradients are too small to make a good progress for the discriminator. So commonly we minimize the following loss:

which is just feed 𝐱′=𝐺(𝐳)x′=G(z) into the discriminator but giving label 𝑦=1y=1.

The sentences above look quite confusing to me… There might be some grammatical errors in there…
Could you please rewrite these so they look coherent and natural?

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This sentence also confuses me a lot. Authors, please, correct this. Or at least specify the source of this sentence?

Same to me, I don’t understand why the above loss is near 0 and the gradients become small?