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"""Quick FA-only snapshot evolution. Reuses the full script's train_fa + diagnose."""
import os, sys, json, argparse
import numpy as np
import torch
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from experiments.snapshot_evolution_residual_explosion import (
get_cifar10, fixed_eval_buffer, train_fa
)
from models.residual_mlp import ResidualMLP
def main():
p = argparse.ArgumentParser()
p.add_argument('--output', type=str, required=True)
p.add_argument('--epochs', type=int, default=100)
p.add_argument('--seed', type=int, default=42)
p.add_argument('--depth', type=int, default=4)
p.add_argument('--d_hidden', type=int, default=256)
args = p.parse_args()
device = torch.device('cuda:0')
train_loader, test_loader = get_cifar10(batch_size=128)
x_eval, y_eval = fixed_eval_buffer(test_loader, device, n_samples=1024)
L, d, C = args.depth, args.d_hidden, 10
print(f"FA snapshot: depth={L}, d={d}, seed={args.seed}, epochs={args.epochs}", flush=True)
torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
model = ResidualMLP(3072, d, C, L).to(device)
fa_log = train_fa(model, train_loader, x_eval, y_eval, device,
args.epochs, 1e-3, 0.01, log_every=1)
with open(args.output, 'w') as f:
json.dump({'fa_log': fa_log, 'seed': args.seed, 'depth': L, 'd_hidden': d}, f, indent=2)
print(f"Saved: {args.output}", flush=True)
if __name__ == '__main__':
main()
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