diff options
Diffstat (limited to 'experiments/conv_local_smoke.py')
| -rw-r--r-- | experiments/conv_local_smoke.py | 52 |
1 files changed, 52 insertions, 0 deletions
diff --git a/experiments/conv_local_smoke.py b/experiments/conv_local_smoke.py index cdf1ad0..1b18048 100644 --- a/experiments/conv_local_smoke.py +++ b/experiments/conv_local_smoke.py @@ -1023,6 +1023,51 @@ def kp_mixed_traffic_checks(): assert all(torch.equal(before, after) for before, after in zip( frozen_before_projection, net.P_traffic + net.P_traffic_bias)) + # Raw and matched controls pay for the identical neutral projection but do + # not apply its subtractive direction. Raw must remain exactly the mixed + # apical vector. Matched may borrow only projected innovation's norm. + projected_raw = net.mixed_apical_components( + instruction, forward["hiddens"], "raw", + neutral_projection=True) + projected_matched = net.mixed_apical_components( + instruction, forward["hiddens"], "matched", + neutral_projection=True) + sham_raw_error = max(float((left - right).abs().max()) + for left, right in zip( + projected_raw["used"], projected_raw["raw"])) + sham_projection_report_error = max( + abs(projected_raw["neutral_projection"][key] + - projected_matched["neutral_projection"][key]) + for key in ( + "pre_projection_traffic_rms_ratio", + "post_projection_traffic_rms_ratio", + "max_absolute_pre_projection_soma_slope", + "max_absolute_post_projection_soma_slope", + "max_positive_post_projection_soma_slope", + "min_post_projection_soma_slope", + "max_absolute_correction_slope", + )) + sham_matched_norm_errors = [] + sham_matched_direction_errors = [] + for raw, innovation, matched in zip( + projected_matched["raw"], projected_matched["innovation"], + projected_matched["matched"]): + raw_flat = raw.flatten(1) + innovation_flat = innovation.flatten(1) + matched_flat = matched.flatten(1) + sham_matched_norm_errors.append(float(( + (matched_flat.norm(dim=1) - innovation_flat.norm(dim=1)).abs() + / innovation_flat.norm(dim=1).clamp_min(1e-30)).max())) + sham_matched_direction_errors.append(float((F.cosine_similarity( + raw_flat, matched_flat, dim=1) - 1.0).abs().max())) + assert sham_raw_error == 0.0 + assert sham_projection_report_error == 0.0 + assert max(sham_matched_norm_errors) < 1e-12 + assert max(sham_matched_direction_errors) < 1e-12 + assert projected_raw["neutral_projection"]["instruction_observations"] == 0 + assert all(torch.equal(before, after) for before, after in zip( + frozen_before_projection, net.P_traffic + net.P_traffic_bias)) + for slope, bias in zip(net.P_traffic, net.P_traffic_bias): slope.zero_() bias.zero_() @@ -1142,6 +1187,13 @@ def kp_mixed_traffic_checks(): "post_projection_traffic_rms_ratio"], "kp_traffic_projected_residual_slope": projection[ "max_absolute_post_projection_soma_slope"], + "kp_traffic_sham_raw_error": sham_raw_error, + "kp_traffic_sham_projection_report_error": ( + sham_projection_report_error), + "kp_traffic_sham_matched_norm_error": max( + sham_matched_norm_errors), + "kp_traffic_sham_matched_direction_error": max( + sham_matched_direction_errors), "kp_traffic_matched_norm_error": max(norm_errors), "kp_traffic_matched_direction_error": max(direction_errors), "kp_traffic_reciprocal_correlation_error": max(correlation_errors), |