results: reject one-epoch affine advantage across seeds

10 files changed, 1001 insertions, 0 deletions

diff --git a/RAIN_EP_RELEASED_PROFILE.md b/RAIN_EP_RELEASED_PROFILE.md
index 796f36e..eaefe87 100644
--- a/RAIN_EP_RELEASED_PROFILE.md
+++ b/RAIN_EP_RELEASED_PROFILE.md
@@ -105,3 +105,22 @@ R1 setting: affine SDIL is 1.50 percentage points below the matched local
 intercept and 3.65 points below the oracle. The state-dependent result must
 therefore be treated as unconfirmed until a paired multi-seed accuracy study
 shows otherwise. No residual-bias diagnostic can override this endpoint.
+
+## R2 paired-seed accuracy check
+
+R2 repeated the state-dependent cadence-10 comparison for four network
+initializations while keeping the data split and every other R1 setting fixed.
+The paired holdout accuracies were:
+
+| seed | intercept-only | affine SDIL | SDIL minus intercept |
+|---:|---:|---:|---:|
+| 1988 | 43.85% | 42.35% | -1.50 points |
+| 1989 | 42.45% | 38.60% | -3.85 points |
+| 1990 | 36.65% | 36.10% | -0.55 points |
+| 1991 | 48.30% | 47.75% | -0.55 points |
+| mean | 42.81% | 41.20% | -1.61 points |
+
+Affine SDIL lost all four paired comparisons. The one-epoch state-dependent
+claim is therefore negative. The remaining accuracy question is whether the
+affine predictor needs a longer trajectory to learn the state relation; R3
+tests that question on full-data ten-epoch trajectories.
diff --git a/results/ep_bias/centered_r2/seed_1988_intercept.json b/results/ep_bias/centered_r2/seed_1988_intercept.json
new file mode 100644
index 0000000..71090fc
--- /dev/null
+++ b/results/ep_bias/centered_r2/seed_1988_intercept.json
@@ -0,0 +1,116 @@
+{
+  "schema": "rain_ep_structured_bias_screen_v1",
+  "sdil": {
+    "revision": "81838bf82efa1027b263ff0af7917ae367d35bd7"
+  },
+  "author": {
+    "repository": "https://github.com/rain-neuromorphics/energy-based-learning",
+    "revision": "6b253fd8a5d267535f58ab79992256ef10031ceb"
+  },
+  "protocol": {
+    "dataset": "FashionMNIST",
+    "network": "author comparative-study ConvHopfieldEnergy32",
+    "network_protocol": "comparative32",
+    "algorithm": "equilibrium propagation",
+    "beta_policy": "centered",
+    "beta_seed": 7100,
+    "beta_value": 0.25,
+    "adapter": "dillavou",
+    "mode": "constant",
+    "bias_ratio": 1.0,
+    "dillavou_drift_ratio": 0.0,
+    "dillavou_calibration_steps": 1,
+    "dillavou_profile": {
+      "normalized_offsets": [
+        0.9356214982615908,
+        1.6020613079954447,
+        -0.10013991355431538,
+        0.7402593971899275
+      ],
+      "normalized_state_variations": [
+        0.5259526502913017,
+        0.31316961348123956,
+        0.3831972460091073,
+        -0.04782433762159926
+      ],
+      "source": "/home/yurenh2/sdil/results/physical_bias/p0_state_dependence.json"
+    },
+    "predictor_rate": 1.0,
+    "neutral_cadence": 10,
+    "layer_calibration_steps": 1,
+    "layer_bias_normalization": "clean_difference",
+    "calibration_batches": 0,
+    "calibration_observations": 0,
+    "calibration_seconds": 9.5367431640625e-07,
+    "extra_equilibrium_phases_for_predictor": 0,
+    "predictor_neutral_source": "instruction_off_local_update_probe",
+    "bias_ratio_normalization": "initial_clean_local_update_rms_for_simulation_only",
+    "bias_ratio_normalization_visible_to_predictor": false,
+    "epochs": 1,
+    "train_limit": 10000,
+    "test_limit": 2000,
+    "evaluation_split": "train_holdout",
+    "data_seed": 6200,
+    "batch_size": 128,
+    "training_iterations": 15,
+    "inference_iterations": 60,
+    "schedule_epochs": 100,
+    "seed": 1988,
+    "device": "cuda",
+    "determinism": "best_effort_warn_only",
+    "autodiff_used_for_learning": false
+  },
+  "hardware": {
+    "torch_version": "2.3.1+cu118",
+    "torch_cuda_version": "11.8",
+    "cuda_visible_devices": "0",
+    "device_name": "NVIDIA GeForce GTX 1080"
+  },
+  "metrics": [
+    {
+      "epoch": 1,
+      "train_accuracy": 0.249,
+      "train_cost": 0.4324162456512451,
+      "test_accuracy": 0.4385,
+      "test_cost": 0.3619317226409912,
+      "finite": true,
+      "corrector": {
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+        "bias_model": "dillavou_released_affine_update",
+        "bias_profile_source": "/home/yurenh2/sdil/results/physical_bias/p0_state_dependence.json",
+        "clean_update_rms": 0.0004936866395159558,
+        "bias_update_rms": 0.00025171011857831057,
+        "residual_update_rms": 1.413279982759424e-06,
+        "bias_to_clean_update_rms": 0.5098580727748768,
+        "residual_to_clean_update_rms": 0.002862706562497013,
+        "neutral_observations": 9
+      },
+      "wall_seconds": 137.64498448371887
+    }
+  ],
+  "epochs_completed": 1,
+  "beta_sign_counts": {
+    "positive": 79,
+    "negative": 79
+  },
+  "final": {
+    "epoch": 1,
+    "train_accuracy": 0.249,
+    "train_cost": 0.4324162456512451,
+    "test_accuracy": 0.4385,
+    "test_cost": 0.3619317226409912,
+    "finite": true,
+    "corrector": {
+      "step": 78,
+      "bias_model": "dillavou_released_affine_update",
+      "bias_profile_source": "/home/yurenh2/sdil/results/physical_bias/p0_state_dependence.json",
+      "clean_update_rms": 0.0004936866395159558,
+      "bias_update_rms": 0.00025171011857831057,
+      "residual_update_rms": 1.413279982759424e-06,
+      "bias_to_clean_update_rms": 0.5098580727748768,
+      "residual_to_clean_update_rms": 0.002862706562497013,
+      "neutral_observations": 9
+    },
+    "wall_seconds": 137.64498448371887
+  }
+}
diff --git a/results/ep_bias/centered_r2/seed_1988_sdil.json b/results/ep_bias/centered_r2/seed_1988_sdil.json
new file mode 100644
index 0000000..03fd47a
--- /dev/null
+++ b/results/ep_bias/centered_r2/seed_1988_sdil.json
@@ -0,0 +1,116 @@
+{
+  "schema": "rain_ep_structured_bias_screen_v1",
+  "sdil": {
+    "revision": "81838bf82efa1027b263ff0af7917ae367d35bd7"
+  },
+  "author": {
+    "repository": "https://github.com/rain-neuromorphics/energy-based-learning",
+    "revision": "6b253fd8a5d267535f58ab79992256ef10031ceb"
+  },
+  "protocol": {
+    "dataset": "FashionMNIST",
+    "network": "author comparative-study ConvHopfieldEnergy32",
+    "network_protocol": "comparative32",
+    "algorithm": "equilibrium propagation",
+    "beta_policy": "centered",
+    "beta_seed": 7100,
+    "beta_value": 0.25,
+    "adapter": "dillavou",
+    "mode": "innovation",
+    "bias_ratio": 1.0,
+    "dillavou_drift_ratio": 0.0,
+    "dillavou_calibration_steps": 1,
+    "dillavou_profile": {
+      "normalized_offsets": [
+        0.9356214982615908,
+        1.6020613079954447,
+        -0.10013991355431538,
+        0.7402593971899275
+      ],
+      "normalized_state_variations": [
+        0.5259526502913017,
+        0.31316961348123956,
+        0.3831972460091073,
+        -0.04782433762159926
+      ],
+      "source": "/home/yurenh2/sdil/results/physical_bias/p0_state_dependence.json"
+    },
+    "predictor_rate": 1.0,
+    "neutral_cadence": 10,
+    "layer_calibration_steps": 1,
+    "layer_bias_normalization": "clean_difference",
+    "calibration_batches": 0,
+    "calibration_observations": 0,
+    "calibration_seconds": 7.152557373046875e-07,
+    "extra_equilibrium_phases_for_predictor": 0,
+    "predictor_neutral_source": "instruction_off_local_update_probe",
+    "bias_ratio_normalization": "initial_clean_local_update_rms_for_simulation_only",
+    "bias_ratio_normalization_visible_to_predictor": false,
+    "epochs": 1,
+    "train_limit": 10000,
+    "test_limit": 2000,
+    "evaluation_split": "train_holdout",
+    "data_seed": 6200,
+    "batch_size": 128,
+    "training_iterations": 15,
+    "inference_iterations": 60,
+    "schedule_epochs": 100,
+    "seed": 1988,
+    "device": "cuda",
+    "determinism": "best_effort_warn_only",
+    "autodiff_used_for_learning": false
+  },
+  "hardware": {
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+    "torch_cuda_version": "11.8",
+    "cuda_visible_devices": "1",
+    "device_name": "NVIDIA GeForce GTX 1080"
+  },
+  "metrics": [
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+        "bias_model": "dillavou_released_affine_update",
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+        "neutral_observations": 9
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+      "wall_seconds": 140.6538872718811
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+  ],
+  "epochs_completed": 1,
+  "beta_sign_counts": {
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+    "negative": 79
+  },
+  "final": {
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+    "train_cost": 0.4320692925453186,
+    "test_accuracy": 0.4235,
+    "test_cost": 0.3628904857635498,
+    "finite": true,
+    "corrector": {
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+      "bias_model": "dillavou_released_affine_update",
+      "bias_profile_source": "/home/yurenh2/sdil/results/physical_bias/p0_state_dependence.json",
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+}
diff --git a/results/ep_bias/centered_r2/seed_1989_intercept.json b/results/ep_bias/centered_r2/seed_1989_intercept.json
new file mode 100644
index 0000000..044381f
--- /dev/null
+++ b/results/ep_bias/centered_r2/seed_1989_intercept.json
@@ -0,0 +1,116 @@
+{
+  "schema": "rain_ep_structured_bias_screen_v1",
+  "sdil": {
+    "revision": "81838bf82efa1027b263ff0af7917ae367d35bd7"
+  },
+  "author": {
+    "repository": "https://github.com/rain-neuromorphics/energy-based-learning",
+    "revision": "6b253fd8a5d267535f58ab79992256ef10031ceb"
+  },
+  "protocol": {
+    "dataset": "FashionMNIST",
+    "network": "author comparative-study ConvHopfieldEnergy32",
+    "network_protocol": "comparative32",
+    "algorithm": "equilibrium propagation",
+    "beta_policy": "centered",
+    "beta_seed": 7100,
+    "beta_value": 0.25,
+    "adapter": "dillavou",
+    "mode": "constant",
+    "bias_ratio": 1.0,
+    "dillavou_drift_ratio": 0.0,
+    "dillavou_calibration_steps": 1,
+    "dillavou_profile": {
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+        1.6020613079954447,
+        -0.10013991355431538,
+        0.7402593971899275
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+        0.31316961348123956,
+        0.3831972460091073,
+        -0.04782433762159926
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+    "test_limit": 2000,
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+    "seed": 1989,
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+    "autodiff_used_for_learning": false
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+    "torch_cuda_version": "11.8",
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diff --git a/results/ep_bias/centered_r2/seed_1989_sdil.json b/results/ep_bias/centered_r2/seed_1989_sdil.json
new file mode 100644
index 0000000..385967d
--- /dev/null
+++ b/results/ep_bias/centered_r2/seed_1989_sdil.json
@@ -0,0 +1,116 @@
+{
+  "schema": "rain_ep_structured_bias_screen_v1",
+  "sdil": {
+    "revision": "81838bf82efa1027b263ff0af7917ae367d35bd7"
+  },
+  "author": {
+    "repository": "https://github.com/rain-neuromorphics/energy-based-learning",
+    "revision": "6b253fd8a5d267535f58ab79992256ef10031ceb"
+  },
+  "protocol": {
+    "dataset": "FashionMNIST",
+    "network": "author comparative-study ConvHopfieldEnergy32",
+    "network_protocol": "comparative32",
+    "algorithm": "equilibrium propagation",
+    "beta_policy": "centered",
+    "beta_seed": 7100,
+    "beta_value": 0.25,
+    "adapter": "dillavou",
+    "mode": "innovation",
+    "bias_ratio": 1.0,
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+    "seed": 1989,
+    "device": "cuda",
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+    "autodiff_used_for_learning": false
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+    "torch_cuda_version": "11.8",
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+  }
+}
diff --git a/results/ep_bias/centered_r2/seed_1990_intercept.json b/results/ep_bias/centered_r2/seed_1990_intercept.json
new file mode 100644
index 0000000..af517f5
--- /dev/null
+++ b/results/ep_bias/centered_r2/seed_1990_intercept.json
@@ -0,0 +1,116 @@
+{
+  "schema": "rain_ep_structured_bias_screen_v1",
+  "sdil": {
+    "revision": "81838bf82efa1027b263ff0af7917ae367d35bd7"
+  },
+  "author": {
+    "repository": "https://github.com/rain-neuromorphics/energy-based-learning",
+    "revision": "6b253fd8a5d267535f58ab79992256ef10031ceb"
+  },
+  "protocol": {
+    "dataset": "FashionMNIST",
+    "network": "author comparative-study ConvHopfieldEnergy32",
+    "network_protocol": "comparative32",
+    "algorithm": "equilibrium propagation",
+    "beta_policy": "centered",
+    "beta_seed": 7100,
+    "beta_value": 0.25,
+    "adapter": "dillavou",
+    "mode": "constant",
+    "bias_ratio": 1.0,
+    "dillavou_drift_ratio": 0.0,
+    "dillavou_calibration_steps": 1,
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new file mode 100644
index 0000000..5bae336
--- /dev/null
+++ b/results/ep_bias/centered_r2/seed_1990_sdil.json
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new file mode 100644
index 0000000..26ca16b
--- /dev/null
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new file mode 100644
index 0000000..bd5852e
--- /dev/null
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new file mode 100644
index 0000000..f1b4864
--- /dev/null
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