From a501c1c84b6ac4ff7dbf2e4b92cebd3122eb7abe Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Sun, 26 Apr 2026 09:31:30 -0500 Subject: 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) --- experiments/cifar_resmlp.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) (limited to 'experiments/cifar_resmlp.py') 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), -- cgit v1.2.3