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add pruner unit test #1771
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add pruner unit test #1771
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QuanluZhang
approved these changes
Nov 22, 2019
chicm-ms
approved these changes
Nov 25, 2019
xuehui1991
reviewed
Nov 25, 2019
| @@ -34,6 +34,6 @@ We implemented one of the experiments in ['Learning Efficient Convolutional Netw | |||
| | Model | Error(paper/ours) | Parameters | Pruned | | |||
| | ------------- | ----------------- | ---------- | --------- | | |||
| | VGGNet | 6.34/6.40 | 20.04M | | | |||
| | Pruned-VGGNet | 6.20/6.39 | 2.03M | 88.5% | | |||
| | Pruned-VGGNet | 6.20/6.26 | 2.03M | 88.5% | | |||
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could you provide some settings in those experiments? like hyper-parameters.
| @@ -169,7 +169,7 @@ def main(): | |||
| new_model.to(device) | |||
| new_model.load_state_dict(torch.load('pruned_vgg19_cifar10.pth')) | |||
| test(new_model, device, test_loader) | |||
| # top1 = 93.61% | |||
| # top1 = 93.74% | |||
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does this value will change by some random seed? If will, we should not assume the top1 value is a specific value.
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add l1filter_pruner and slim_pruner unit test