1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
|
"""Train the local-learning baseline 'zoo' and report test accuracy, for the
comparison table alongside SDIL/DFA/BP. FA and EP are the working ones; PEPITA
and FF are included but currently undertuned.
Usage: python experiments/zoo.py --dataset mnist --methods fa,ep --epochs 10
"""
import argparse
import json
import os
import sys
import time
import torch
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from sdil.local_baselines import FANet, PEPITANet, FFNet, EPNet
from sdil.baselines import evaluate
from sdil.data import get_dataset, onehot
def run_method(m, args, device):
tr, te, n_in, n_out = get_dataset(args.dataset, args.batch_size, device=device)
sizes = [n_in] + [args.width] * args.depth + [10]
t0 = time.time()
if m == "fa":
net = FANet(sizes, act="tanh", device=device, seed=args.seed)
for ep in range(args.epochs):
for x, y in tr:
net.fa_step(x, y, onehot(y, 10, device=device), args.eta, 0.9)
acc = evaluate(net, te)[0]
elif m == "pepita":
net = PEPITANet(sizes, act="tanh", device=device, seed=args.seed, f_scale=0.5)
for ep in range(args.epochs):
for x, y in tr:
net.pepita_step(x, y, onehot(y, 10, device=device), args.eta, 0.9)
acc = evaluate(net, te)[0]
elif m == "ff":
net = FFNet(sizes, act="relu", device=device, seed=args.seed, threshold=2.0, overlay_val=10.0)
for ep in range(args.epochs):
for x, y in tr:
net.train_step(x, y, args.eta)
acc = net.evaluate(te)[0]
elif m == "ep":
net = EPNet([n_in, args.width, 10], device=device, seed=args.seed,
beta=0.5, dt=0.5, T_free=20, T_nudge=8)
for ep in range(args.epochs):
for x, y in tr:
net.train_step(x, y, onehot(y, 10, device=device), args.eta)
acc = net.evaluate(te)[0]
else:
raise ValueError(m)
return acc, time.time() - t0
def main():
p = argparse.ArgumentParser()
p.add_argument("--dataset", default="mnist")
p.add_argument("--methods", default="fa,ep,pepita,ff")
p.add_argument("--depth", type=int, default=2)
p.add_argument("--width", type=int, default=500)
p.add_argument("--epochs", type=int, default=10)
p.add_argument("--batch_size", type=int, default=64)
p.add_argument("--eta", type=float, default=0.1)
p.add_argument("--seed", type=int, default=0)
p.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
p.add_argument("--outdir", default="results")
p.add_argument("--tag", default="zoo")
args = p.parse_args()
# sensible per-method default LRs if the shared one is off
lrs = {"fa": 0.05, "ep": 0.1, "pepita": 0.05, "ff": 0.03}
out = {"args": vars(args), "acc": {}}
for m in args.methods.split(","):
args.eta = lrs.get(m, args.eta)
acc, secs = run_method(m, args, args.device)
out["acc"][m] = acc
print(f"[{args.dataset}] {m}: test_acc {acc:.4f} ({secs:.0f}s)", flush=True)
os.makedirs(args.outdir, exist_ok=True)
with open(os.path.join(args.outdir, f"{args.tag}.json"), "w") as f:
json.dump(out, f)
print(f"saved -> {args.outdir}/{args.tag}.json", flush=True)
if __name__ == "__main__":
main()
|
