Problem about training the model
aliinassiri opened this issue · 10 comments
Hello, many thanks for putting these information about the model. I have problem while running the DCGAN_Train.py file. When the training loop starts after second batch ,the values of Discriminator and Generator loss don't change and D(x) ,G(D(Z)) will be 1. I don't have any idea about this problem. I run the file on windows 10, NVIDIA GEFORCE GTX 1650
.
i met the same problem, did u solve it?
thank u. i gonna try it. i tried to use different random seed, but the GAN was collapse and i got the follow loss image...... So are u GAN work properly?
sorry for replying late. I Train the model with learning rate that I have mentioned before for 500 epoch. The loss of the model is in the below image. I think because of changing learning rate for better result I have to train for more than 500 epoch. And other problem I have is that when I want to validate the result with the Lumerical, the scripts that the author provided doesn't work, Do you have similar problem?
.
I doubt that the picture is not aligned with the data in excel, because the code does not consider whether the picture is aligned with the data in excel.
I doubt that the picture is not aligned with the data in excel, because the code does not consider whether the picture is aligned with the data in excel.
I think there might be an issue with the 'importbinary(files{i}, 'microns');' command for loading the file. It's possible that the older version of Lumerical and the newer version have slightly different ways of importing images.
I'm going to manually input the image instead of using the command for this line. Currently, the generated absorption spectrum by Gnet doesn't differ significantly from the spectral characteristics used for training.
thank u. i gonna try it. i tried to use different random seed, but the GAN was collapse and i got the follow loss image...... So are u GAN work properly?
sorry for replying late. I Train the model with learning rate that I have mentioned before for 500 epoch. The loss of the model is in the below image. I think because of changing learning rate for better result I have to train for more than 500 epoch. And other problem I have is that when I want to validate the result with the Lumerical, the scripts that the author provided doesn't work, Do you have similar problem? .
Hello, when I adjust the learning rate to 0.00001 as you said, the value of D(x) becomes zero all the time, and the discriminator can't make a judgment on the real and the output picture, do you know how to solve it?