method: condition apical vectorizers on neural state

1 files changed, 3 insertions, 1 deletions

diff --git a/sdil/probes.py b/sdil/probes.py
index 1698d75..0075bca 100644
--- a/sdil/probes.py
+++ b/sdil/probes.py
@@ -74,7 +74,7 @@ def alignment_report(net, x, y, y_onehot, cfg):
         negg = -g
         teaching, a, innovation = core.teaching_signal(
             net, l, c, h[l + 1], cfg, context)
-        Ac = c @ net.A[l].t()                           # pure vectorizer output
+        Ac = net.vectorizer(l, c, h[l + 1], context)    # pure vectorizer output
         traffic = net.apical_traffic(l, h[l + 1], context)
         unexplained = traffic - net.baseline(l, h[l + 1])
         # include the phi' gain that the plasticity rule actually applies,
@@ -145,6 +145,8 @@ def _clone(net):
     n.W = [w.clone() for w in net.W]
     n.b = [b.clone() for b in net.b]
     n.A = [a.clone() for a in net.A]
+    n.A_gate = ([a.clone() for a in net.A_gate]
+                if getattr(net, "A_gate", None) is not None else None)
     n.P = [p.clone() for p in net.P]
     n.P_bias = ([p.clone() for p in net.P_bias]
                 if getattr(net, "P_bias", None) is not None else None)