Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548
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
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