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This doesn't match the ResNet paper, which defines ResNet-32 with CIFAR-10 as having an input layer of 16 filters followed by stacks of layers with 16, 32, and 64 filters:
Also, the pruning code reports a total of 1 857 498 parameters, much more than the 0.46M reported in the paper. Changing hps.residual_filters to [16, 16, 32, 64] gives me a much closer value of 468 890 parameters.
The text was updated successfully, but these errors were encountered:
The hparams for ResNet-32 seem to define an input layer with 16 filters followed by stacks of layers with 32, 64, and then 128 filters:
https://github.com/for-ai/TD/blob/master/hparams/resnet.py#L12
This doesn't match the ResNet paper, which defines ResNet-32 with CIFAR-10 as having an input layer of 16 filters followed by stacks of layers with 16, 32, and 64 filters:
Also, the pruning code reports a total of 1 857 498 parameters, much more than the 0.46M reported in the paper. Changing
hps.residual_filters
to[16, 16, 32, 64]
gives me a much closer value of 468 890 parameters.The text was updated successfully, but these errors were encountered: