dcgan_code
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ImageNet pretrained model layer dimensions
I'm studying this code and would like to know if my understanding is correct.
Generator:
layer | gifn | gain_ifn | bias_ifn |
---|---|---|---|
1 | (100, 128 * 8 * 4 * 4) | 128 * 8 * 4 * 4 | 128 * 8 * 4 * 4 |
2 | (1024, 512, 5, 5) | 512 | 512 |
3 | (512, 256, 5, 5) | 256 | 256 |
4 | (256, 128, 5, 5) | 128 | 128 |
5 | (128, 64, 5, 5) | 64 | 64 |
6 | (64, 32, 5, 5) | 32 | 32 |
output | (32, 3, 5, 5) | ---- | ---- |
Discriminator:
layer | gifn | gain_ifn | bias_ifn |
---|---|---|---|
1 | (32, 3, 5, 5) | ---- | ---- |
2 | (64, 32, 5, 5) | 64 | 64 |
3 | (128, 64, 5, 5) | 128 | 128 |
4 | (256, 128, 5, 5) | 256 | 256 |
5 | (512, 256, 5, 5) | 512 | 512 |
6 | (1024, 512, 5, 5) | 1024 | 1024 |
output | (128 * 8 * 4 * 4, 1) | ---- | ---- |
Are these dimensions correct for the ImageNet model?
Also could you please upload the training code for ImageNet?