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| author | YurenHao0426 <Blackhao0426@gmail.com> | 2026-04-02 23:34:12 -0500 |
|---|---|---|
| committer | YurenHao0426 <Blackhao0426@gmail.com> | 2026-04-02 23:34:12 -0500 |
| commit | 2aaabd9a95386bf9f274cb9907ac6a5306171759 (patch) | |
| tree | fbc7f8e5f59ded66489b2141f6e1d41ded0dc43e /experiments | |
| parent | 142bf87309ad8180770238ce097b0b1c9d33f5d7 (diff) | |
Fix EP credit sign: negate (h_nudge - h_free)/β to align with BP grad direction
EP nudge moves h toward lower loss (opposite to BP grad which points toward loss increase).
Without negation, Gamma is negative and rho is -0.25.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Diffstat (limited to 'experiments')
| -rw-r--r-- | experiments/ep_baseline.py | 3 | ||||
| -rw-r--r-- | experiments/ep_synthetic.py | 4 |
2 files changed, 5 insertions, 2 deletions
diff --git a/experiments/ep_baseline.py b/experiments/ep_baseline.py index de7d853..7f3d004 100644 --- a/experiments/ep_baseline.py +++ b/experiments/ep_baseline.py @@ -206,7 +206,8 @@ def ep_credit_signals(model, x, y, beta, T_nudge, alpha_nudge): _, h_free = model(x, return_hidden=True) h_nudged = ep_nudged_phase(model, x, y, h_free, beta, T_nudge, alpha_nudge) L = model.num_blocks - credits = [(h_nudged[l] - h_free[l]) / beta for l in range(L)] + # Negate: EP nudge moves h toward lower loss, opposite to BP grad direction + credits = [-(h_nudged[l] - h_free[l]) / beta for l in range(L)] return credits, h_free, h_nudged diff --git a/experiments/ep_synthetic.py b/experiments/ep_synthetic.py index 7daecde..a2f24df 100644 --- a/experiments/ep_synthetic.py +++ b/experiments/ep_synthetic.py @@ -115,7 +115,9 @@ def compute_diagnostics(model, teacher, dev, d, C, L, beta=0.5, T_nudge=20, alph gammas,rhos=[],[] with torch.no_grad():_,hi=model(x,return_hidden=True) for l in range(L): - a_ep=(h_nudge[l+1].detach()-h_free[l+1])/beta + # EP nudge moves h toward lower loss, so (h_nudge - h_free) points opposite to BP grad. + # Negate to align with BP gradient convention (pointing toward loss increase). + a_ep=-(h_nudge[l+1].detach()-h_free[l+1])/beta gammas.append(cosine_similarity_batch(a_ep,bp[l+1])) def mk(sl): def f(h): |