File size: 2,158 Bytes
032a961 | 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 | #!/usr/bin/env python3
"""A/B test: ORIGINAL reservoir params (rich recurrent states) with honest
vocab-masked metrics, with/without trace centering, various eta_r."""
import sys
import numpy as np
sys.path.insert(0, "/home/z/my-project/download/spira")
from spira import SPIRA, SPIRAConfig
from spira.spiking import SpikingCore
data = open("/home/z/my-project/data/train.bin", "rb").read()
cfg = SPIRAConfig(snn_tokens=60_000, am_tokens=1000, epochs=1)
model = SPIRA.build(data, cfg, log=lambda m: None)
ids = model._ids_train
N = 60_000
neurons = SPIRA.input_neurons_stream(ids[:N], cfg.d_in, cfg.k_in)
cols = model.cols_of(ids[: N + 1])
cov = (cols[1:] != cfg.vocab_size)[: N - 1].mean()
print(f"coverage: {cov:.3f}")
# ORIGINAL params that reached EMA 0.52-0.82 (with the UNK shortcut metric)
variants = [
("sharp+delta e=.70", dict(w_in_scale=3.0, in_fanout=8, w_rec_scale=0.25, rec_fanout=24, inh_frac=0.5, eta_delta=0.70), True),
("sharp+delta e=1.0", dict(w_in_scale=3.0, in_fanout=8, w_rec_scale=0.25, rec_fanout=24, inh_frac=0.5, eta_delta=1.0), True),
("sharp+delta e=1.5", dict(w_in_scale=3.0, in_fanout=8, w_rec_scale=0.25, rec_fanout=24, inh_frac=0.5, eta_delta=1.5), True),
("sharp+delta e=1.0 tau.3", dict(w_in_scale=3.0, in_fanout=8, w_rec_scale=0.25, rec_fanout=24, inh_frac=0.5, eta_delta=1.0, tau_trace=0.35), True),
]
for name, kw, center in variants:
core = SpikingCore(n_out=cfg.vocab_size + 1, seed=cfg.seed, **kw)
if not center:
# disable centering by pinning x_mean to zero
class NoCenter:
pass
core._center = False
orig_step = core.step
import types
# monkeypatch: after step, reset x_mean to 0
def step_noc(self, input_idx, _orig=orig_step):
x = _orig(input_idx)
self.x_mean = np.zeros_like(self.x_mean)
return x
core.step = types.MethodType(step_noc, core)
res = core.train_pass(neurons[: N - 1], cols[1:N], log_every=30_000, log_fn=lambda d: None)
print(f"{name:28s} overall={res['accuracy']:.4f} in-vocab~={res['accuracy']/cov:.4f} "
f"ema={res['acc_ema_final']:.4f}")
|