cifar10 result not good as expect ! about wassersteingan HOT 7 CLOSED

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The running of the code has finished !

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@martinarjovsky

fake_samples_25000.png

loss log

[167/1000][555/782][24987] Loss_D: -0.772816 Loss_G: -0.022373 Loss_D_real: -0.193230 Loss_D_fake 0.579586
[167/1000][560/782][24988] Loss_D: -0.800285 Loss_G: 0.586990 Loss_D_real: -0.633111 Loss_D_fake 0.167174
[167/1000][565/782][24989] Loss_D: -0.585662 Loss_G: 0.091040 Loss_D_real: -0.018860 Loss_D_fake 0.566802
[167/1000][570/782][24990] Loss_D: -0.930666 Loss_G: 0.580418 Loss_D_real: -0.650177 Loss_D_fake 0.280490
[167/1000][575/782][24991] Loss_D: -0.745919 Loss_G: 0.111690 Loss_D_real: -0.156507 Loss_D_fake 0.589412
[167/1000][580/782][24992] Loss_D: -0.981289 Loss_G: 0.589674 Loss_D_real: -0.631602 Loss_D_fake 0.349687
[167/1000][585/782][24993] Loss_D: -0.933379 Loss_G: 0.301309 Loss_D_real: -0.388805 Loss_D_fake 0.544573
[167/1000][590/782][24994] Loss_D: -1.077024 Loss_G: 0.548679 Loss_D_real: -0.589278 Loss_D_fake 0.487745
[167/1000][595/782][24995] Loss_D: -0.914252 Loss_G: 0.511773 Loss_D_real: -0.556782 Loss_D_fake 0.357470
[167/1000][600/782][24996] Loss_D: -1.090694 Loss_G: 0.532181 Loss_D_real: -0.572747 Loss_D_fake 0.517948
[167/1000][605/782][24997] Loss_D: -0.898265 Loss_G: 0.501241 Loss_D_real: -0.532952 Loss_D_fake 0.365313
[167/1000][610/782][24998] Loss_D: -0.943638 Loss_G: 0.485056 Loss_D_real: -0.502485 Loss_D_fake 0.441153
[167/1000][615/782][24999] Loss_D: -0.991872 Loss_G: 0.501545 Loss_D_real: -0.539097 Loss_D_fake 0.452775
[167/1000][620/782][25000] Loss_D: -1.001911 Loss_G: 0.499365 Loss_D_real: -0.527643 Loss_D_fake 0.474269

I train it 25000 iters, but the result seems still not right.
Could you help me find out what's wrong with it?

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I have change the model into 256 image size(Input image size 64 to 256 size).
then run this code 41600 iteration (800 epochs and 270 iterations at Batchsize 32).
I have use 9000 face image data set.
but over result is not generated good.
/home/mnit/PycharmProjects/ICB2019/WGAN_Pytorch_Clf256/samples/fake_samples_41600.png

Random Seed: 6408
G True
G True
DCGAN_G_nobn(
(main): Sequential(
(initial.100-512.convt): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
(initial.512.relu): ReLU(inplace)
(pyramid.512-256.convt): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.256.relu): ReLU(inplace)
(pyramid.256-128.convt): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.128.relu): ReLU(inplace)
(pyramid.128-64.convt): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.64.relu): ReLU(inplace)
(pyramid.64-32.convt): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.32.relu): ReLU(inplace)
(pyramid.32-16.convt): ConvTranspose2d(32, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.16.relu): ReLU(inplace)
(final.16-3.convt): ConvTranspose2d(16, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(final.3.tanh): Tanh()
)
)
D True
('initial WGAN Dis: ndf csize ndf', 16, 128, 16)
('Input Feature', 16, 'output feature', 32)
('WGAN Dis: size csize ndf', 256, 64, 32)
('Input Feature', 32, 'output feature', 64)
('WGAN Dis: size csize ndf', 256, 32, 64)
('Input Feature', 64, 'output feature', 128)
('WGAN Dis: size csize ndf', 256, 16, 128)
('Input Feature', 128, 'output feature', 256)
('WGAN Dis: size csize ndf', 256, 8, 256)
('Input Feature', 256, 'output feature', 512)
('WGAN Dis: size csize ndf', 256, 4, 512)
DCGAN_D(
(main): Sequential(
(initial.conv.3-16): Conv2d(3, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(initial.relu.16): LeakyReLU(0.2, inplace)
(pyramid.16-32.conv): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.32.batchnorm): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
(pyramid.32.relu): LeakyReLU(0.2, inplace)
(pyramid.32-64.conv): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.64.batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
(pyramid.64.relu): LeakyReLU(0.2, inplace)
(pyramid.64-128.conv): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.128.batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(pyramid.128.relu): LeakyReLU(0.2, inplace)
(pyramid.128-256.conv): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.256.batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
(pyramid.256.relu): LeakyReLU(0.2, inplace)
(pyramid.256-512.conv): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid.512.batchnorm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(pyramid.512.relu): LeakyReLU(0.2, inplace)
(final.512-1.conv): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
)
)

Loss_D: -1.515402 Loss_G: 0.700609 Loss_D_real: -0.823006 Loss_D_fake 0.692396
Loss_D: -1.515402 Loss_G: 0.700609 Loss_D_real: -0.823006 Loss_D_fake 0.692396
Loss_D: -1.515402 Loss_G: 0.700609 Loss_D_real: -0.823006 Loss_D_fake 0.692396
loss fuction is not change at 41600 iteration

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@praveenkumarchandaliya

I think you can try small images such as 3232 or 6464. The method work well in all dataset with small image size in my experiment.

good luck.

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@zyoohv Have you got good results for CIFAR10 data with default parameter settings? How many epochs have you run? Thanks!

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I haven't run the code in cifar 10. You may want to take a look at https://github.com/igul222/improved_wgan_training where we provide a very good cifar10 model.

Cheers :)
Martin

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