diff options
Diffstat (limited to 'experiments')
| -rw-r--r-- | experiments/run.py | 7 | ||||
| -rw-r--r-- | experiments/smoke.py | 24 |
2 files changed, 30 insertions, 1 deletions
diff --git a/experiments/run.py b/experiments/run.py index 93c1862..3b0f961 100644 --- a/experiments/run.py +++ b/experiments/run.py @@ -190,7 +190,8 @@ def build(args, device): if args.mode == "bp": net = BPNet(sizes, act=args.act, device=device, seed=args.seed, w_scale=args.w_scale, nuis_rho=0.0, residual=bool(args.residual), - predictor_mode=args.predictor_mode) + predictor_mode=args.predictor_mode, + vectorizer_mode=args.vectorizer_mode) cfg = SDILConfig(eta=args.eta, momentum=args.momentum) return net, cfg if args.mode == "fa": @@ -198,12 +199,14 @@ def build(args, device): w_scale=args.w_scale, nuis_rho=0.0, residual=bool(args.residual), predictor_mode=args.predictor_mode, + vectorizer_mode=args.vectorizer_mode, b_scale=args.feedback_scale) return net, SDILConfig(eta=args.eta, momentum=args.momentum) net = SDILNet(sizes, act=args.act, device=device, seed=args.seed, w_scale=args.w_scale, a_scale=args.a_scale, nuis_rho=args.nuis_rho, feedback=args.feedback, residual=bool(args.residual), predictor_mode=args.predictor_mode, + vectorizer_mode=args.vectorizer_mode, traffic_mode=args.traffic_mode, nuis_seed=args.traffic_seed) if args.mode == "dfa": cfg = dfa_config(eta=args.eta, momentum=args.momentum) @@ -583,6 +586,8 @@ def get_args(): p.add_argument("--traffic_mode", default="soma", choices=["none", "soma", "topdown", "mixed"]) p.add_argument("--predictor_mode", default="diagonal", choices=["diagonal", "full"]) + p.add_argument("--vectorizer_mode", default="linear", + choices=["linear", "soma_gated", "context_gated"]) p.add_argument("--normalize_delta", type=int, default=0) p.add_argument("--settle_steps", type=int, default=0) p.add_argument("--kappa", type=float, default=0.0) diff --git a/experiments/smoke.py b/experiments/smoke.py index 025d3cc..fcc0e9c 100644 --- a/experiments/smoke.py +++ b/experiments/smoke.py @@ -132,6 +132,29 @@ def check_traffic_seed_isolation(): print("CHECK0d traffic seed changes feedback only: passed") +def check_state_conditioned_vectorizers(): + """Gated apical features must start as linear feedback and learn locally.""" + torch.manual_seed(17) + x = torch.randn(48, 6) + y = torch.randint(0, 2, (48,)) + yoh = onehot(y, 2) + for mode in ("soma_gated", "context_gated"): + net = SDILNet([6, 8, 8, 2], act="relu", device="cpu", seed=9, + residual=True, vectorizer_mode=mode) + fwd = net.forward(x) + c = net.output_error(fwd["h"][-1], yoh) + context = fwd["h"][-2] + before = net.vectorizer(0, c, fwd["h"][1], context) + assert torch.allclose(before, c @ net.A[0].t()) + gates_before = [gate.clone() for gate in net.A_gate] + cfg = SDILConfig(eta=0.01, eta_A=0.02, pert_every=1, + pert_ndirs=2, pert_mode="simultaneous") + sdil_step(net, x, y, yoh, cfg, step=0) + assert any(not torch.equal(old, new) + for old, new in zip(gates_before, net.A_gate)) + print("CHECK0e state-conditioned vectorizers: zero-init and local calibration passed") + + def main(): torch.manual_seed(0) dev = "cpu" @@ -139,6 +162,7 @@ def main(): check_neutral_predictor() check_topdown_predictor() check_traffic_seed_isolation() + check_state_conditioned_vectorizers() print("loading MNIST subset...") tr, te, n_in, n_out = get_dataset("mnist", batch_size=128, device=dev) xb, yb = next(iter(tr)) |