about lesson8:DCGAN

[backkon]

Why do I get the log as shown below:

[0/20][0/32]	Loss_D: 1.8010	Loss_G: 8.0963	D(x): 0.4317	D(G(z)): 0.3617 / 0.0005
[0/20][10/32]	Loss_D: 7.5290	Loss_G: 38.1183	D(x): 0.9269	D(G(z)): 0.9958 / 0.0000
[0/20][20/32]	Loss_D: 0.0425	Loss_G: 58.4481	D(x): 0.9731	D(G(z)): 0.0000 / 0.0000
[0/20][30/32]	Loss_D: 0.2492	Loss_G: 57.3842	D(x): 0.9787	D(G(z)): 0.0000 / 0.0000
[1/20][0/32]	Loss_D: 0.0000	Loss_G: 56.7433	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000
[1/20][10/32]	Loss_D: 0.0000	Loss_G: 56.9849	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000
[1/20][20/32]	Loss_D: 0.0001	Loss_G: 56.8024	D(x): 0.9999	D(G(z)): 0.0000 / 0.0000
[1/20][30/32]	Loss_D: 0.0000	Loss_G: 57.0740	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000

The outputs have always been noise, I am running according to the source code, where is the problem?

[zhangxiann]

I haven`t encountered this problem.
here is my log:

[0/20][0/32]	Loss_D: 2.2277	Loss_G: 15.5083	D(x): 0.5273	D(G(z)): 0.6209 / 0.0000
[0/20][10/32]	Loss_D: 0.2962	Loss_G: 46.8450	D(x): 0.9128	D(G(z)): 0.0000 / 0.0000
[0/20][20/32]	Loss_D: 0.0020	Loss_G: 52.9141	D(x): 0.9982	D(G(z)): 0.0000 / 0.0000
[0/20][30/32]	Loss_D: 0.2574	Loss_G: 51.1907	D(x): 0.9840	D(G(z)): 0.0000 / 0.0000
[1/20][0/32]	Loss_D: 0.0000	Loss_G: 51.1631	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000
[1/20][10/32]	Loss_D: 0.0005	Loss_G: 51.2719	D(x): 0.9995	D(G(z)): 0.0000 / 0.0000
[1/20][20/32]	Loss_D: 0.0001	Loss_G: 50.5107	D(x): 0.9999	D(G(z)): 0.0000 / 0.0000
[1/20][30/32]	Loss_D: 0.0000	Loss_G: 50.6817	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000
[2/20][0/32]	Loss_D: 0.0000	Loss_G: 50.4814	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000
[2/20][10/32]	Loss_D: 0.0000	Loss_G: 50.3472	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000
[2/20][20/32]	Loss_D: 0.0000	Loss_G: 50.2126	D(x): 1.0000	D(G(z)): 0.0000 / 0.0000

Perhaps you can try to run more epochs.

[backkon]

Your log is about the same as mine. Why is D(G(z)) 0/0? The second term of D(G(z)) should be getting closer and closer to 1?

[zhangxiann]

• D(G(z)) denotes the predicted probability of generate(fake) images.
• D(x) denotes the predicted probability of real images.
  I think, for the discriminator，it should try to distinguish the real and fake images.
  So the D(x) is getting closer to 1, the D(G(z)) is getting closer to 0.

[backkon]

I got results as shown below:

[zhangxiann]

Wait, I will try to find out waht`s happening.