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Fix #208, optimize DCGAN #216

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Fix #208, optimize DCGAN #216

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32 changes: 19 additions & 13 deletions vision/dcgan_mnist/dcgan_mnist.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@ using Statistics
using Parameters: @with_kw
using Printf
using Random
using Zygote: @nograd

@with_kw struct HyperParams
batch_size::Int = 128
Expand Down Expand Up @@ -38,29 +39,35 @@ end

generator_loss(fake_output) = mean(logitbinarycrossentropy.(fake_output, 1f0))

function train_discriminator!(gen, dscr, x, opt_dscr, hparams)
noise = randn!(similar(x, (hparams.latent_dim, hparams.batch_size)))
fake_input = gen(noise)
function train_discriminator!(dscr, fake, x, opt_dscr, hparams)
ps = Flux.params(dscr)
# Taking gradient
loss, back = Flux.pullback(ps) do
discriminator_loss(dscr(x), dscr(fake_input))
discriminator_loss(dscr(x), dscr(fake))
end
grad = back(1f0)
update!(opt_dscr, ps, grad)
return loss
end

function train_generator!(gen, dscr, x, opt_gen, hparams)
@nograd train_discriminator!

function train_gen_dscr!(gen, dscr, x, opt_gen, opt_dscr, hparams)
noise = randn!(similar(x, (hparams.latent_dim, hparams.batch_size)))
ps = Flux.params(gen)
ps_gen = Flux.params(gen)
# Taking gradient
loss, back = Flux.pullback(ps) do
generator_loss(dscr(gen(noise)))

loss_dscr = nothing
loss_gen, back_gen = Flux.pullback(ps_gen) do
fake = gen(noise)
loss_dscr = train_discriminator!(dscr, fake, x, opt_dscr, hparams)
generator_loss(dscr(fake))
end
grad = back(1f0)
update!(opt_gen, ps, grad)
return loss

grad_gen = back_gen(1f0)
update!(opt_gen, ps_gen, grad_gen)

return loss_gen, loss_dscr
end

function train(; kws...)
Expand Down Expand Up @@ -109,8 +116,7 @@ function train(; kws...)
@info "Epoch $ep"
for x in data
# Update discriminator and generator
loss_dscr = train_discriminator!(gen, dscr, x, opt_dscr, hparams)
loss_gen = train_generator!(gen, dscr, x, opt_gen, hparams)
loss_gen, loss_dscr = train_gen_dscr!(gen, dscr, x, opt_gen, opt_dscr, hparams)

if train_steps % hparams.verbose_freq == 0
@info("Train step $(train_steps), Discriminator loss = $(loss_dscr), Generator loss = $(loss_gen)")
Expand Down