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| author | YurenHao0426 <Blackhao0426@gmail.com> | 2026-04-26 09:31:30 -0500 |
|---|---|---|
| committer | YurenHao0426 <Blackhao0426@gmail.com> | 2026-04-26 09:31:30 -0500 |
| commit | a501c1c84b6ac4ff7dbf2e4b92cebd3122eb7abe (patch) | |
| tree | 25a83479302e211359bd4f49df44b2bf69d0aaee /experiments/cifar_resmlp.py | |
| parent | 9751e97dd190b8667c337215dcb70e0cab8f92ff (diff) | |
BP+EP audit for d=512 L=2 qualifying seeds + CIFAR-100 support
BP results for qualifying seeds (1, 2, 5) on d=512 L=2:
BP s1: 0.606, s2: 0.608, s5: 0.607 (all above frozen 0.349)
FA s1: 0.347, s2: 0.346, s5: 0.341 (all below frozen, cos +0.47-0.49)
DFA s1: 0.298, s2: 0.297, s5: 0.296 (all below frozen, cos +0.18-0.21)
EP did not save (likely architecture compatibility issue at d=512 L=2).
Also: added CIFAR-100 dataset support to both cifar_resmlp.py and
resmlp_frozen_blocks_baseline.py for the harder-task scan.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Diffstat (limited to 'experiments/cifar_resmlp.py')
| -rw-r--r-- | experiments/cifar_resmlp.py | 15 |
1 files changed, 15 insertions, 0 deletions
diff --git a/experiments/cifar_resmlp.py b/experiments/cifar_resmlp.py index 05a355d..435b484 100644 --- a/experiments/cifar_resmlp.py +++ b/experiments/cifar_resmlp.py @@ -47,6 +47,21 @@ def get_data(dataset='cifar10', batch_size=128): testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) input_dim = 32 * 32 * 3 num_classes = 10 + elif dataset == 'cifar100': + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)), + ]) + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)), + ]) + trainset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform_train) + testset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform_test) + input_dim = 32 * 32 * 3 + num_classes = 100 elif dataset == 'fashionmnist': transform_train = transforms.Compose([ transforms.RandomCrop(28, padding=2), |