Hi all,
My output images are not quite right when I run wgan.ipynb from Lesson 12.
I have download the 20% sample and unzipped it under fastai/courses/dl2/ so that i have data/lsun/bedroom below dl2.
I run all the code, no error messages. The output of the first train(1, False) line is:
Loss_D [-1.53975]; Loss_G [0.77398]; D_real [-0.76954]; Loss_D_fake [0.77021]
and the second train(1, False) is:
Loss_D [-1.53432]; Loss_G [0.76891]; D_real [-0.77165]; Loss_D_fake [0.76267]
Both run in about 6 seconds. I thought that code took time to train. Do I have to run them multiple times ? If so, how many times ? And both train(1, False) ?
But my output images look like this:
Some progress, but some questions below.
After a few runs, i managed to get to that output, which starts to look like bedrooms:
I can continue to train my model, but I want to be sure that I’m running the right code in the right sequence at each loop.
So in dl2/wgan.ipynb, in the last cells, we see:
train(1, False)
then
fixed_noise = create_noise(bs)
then
set_trainable(netD, True)
set_trainable(netG, True)
optimizerD = optim.RMSprop(netD.parameters(), lr = 1e-5)
optimizerG = optim.RMSprop(netG.parameters(), lr = 1e-5)
then
train(1, False)
then
netD.eval(); netG.eval();
fake = netG(fixed_noise).data.cpu()
faked = np.clip(md.trn_ds.denorm(fake),0,1)
plt.figure(figsize=(9,9))
plt.imshow(gallery(faked, 8));
then
torch.save(netG.state_dict(), TMP_PATH/'netG_2.h5')
torch.save(netD.state_dict(), TMP_PATH/'netD_2.h5')
Is this correct that I need to run these cells in sequence, many times until i reach a good enough output ?
Can I change the 2
train(1, False)
for
train(20, False)
for example ? to avoid attending the code too often while it runs ? Or each train cell needs to run only once and in sequence ?
It’s not very clear from the video as Jeremy is not running the code.
thanks
