Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Fix #208, optimize DCGAN #216

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Fix #208, optimize DCGAN #216

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 19 additions & 13 deletions vision/dcgan_mnist/dcgan_mnist.jl
View file
Open in desktop
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