Questions in the Learning Process

[ChiwoongLEE]

Hello.

I'm trying to learn an image with an image size (720, 1280, 3), and I've confirmed that the initial ether is 1,000,000.

Do we usually proceed with the learning until the iter reaches 1,000,000?
Currently (mine)
iter=180000,
d_loss=1.0000
g_loss=-0.2722
ae_loss=0.0576
ae_loss1=0.0150
ae_loss2=0.0425.

I wonder if it is right to converge to 1 for d_loss.

[nipponjo]

Hello, it is hard to know exactly when to stop, so I would mostly look at the results. In the paper, the learning rate is not lowered and I think it is difficult to predict if and when it would make sense.
The the d_loss (hinge loss) is defined as:

hinge_pos = torch.mean(torch.relu(1-pos))
hinge_neg = torch.mean(torch.relu(1+neg))
d_loss = 0.5*hinge_pos + 0.5*hinge_neg 

A value of 1 usually means, that the discriminator returns a value around 0 for the original and inpainted image.