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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

Python 100.00%
dcgan-pytorch generative-adversarial-network nanophotonics

multiclass_metasurface_inversedesign's Issues

Problem about training the model

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
bandicam 2022-06-25 14-13-41-830
.

Problem with DCGAN_Predict.py

I am not able to run DCGAN_Predict

The problem is in line 174:
props = compare(i, 1, 0, results_folder, netGDir, spectra_path)

The error is:
Screenshot 2023-02-21 120629

Can anyone tell me what seems to be the problem?

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