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#!/usr/bin/env bash
set -euo pipefail
ROOT=/home/yurenh2/sdil
AUTHOR=/scratch/yurenh2/energy-based-learning
PYTHON=/scratch/yurenh2/venvs/burstccn/bin/python
PROFILE="$ROOT/results/physical_bias/p0_state_dependence.json"
OUT="$ROOT/results/ep_bias/centered_r3"
mkdir -p "$OUT"
cd "$AUTHOR"
run_cell() {
local gpu=$1
local seed=$2
local mode=$3
local tag=$4
CUBLAS_WORKSPACE_CONFIG=:4096:8 CUDA_VISIBLE_DEVICES="$gpu" "$PYTHON" \
"$ROOT/experiments/rain_ep_bias_train.py" \
--author-root "$AUTHOR" --device cuda \
--adapter dillavou --network-protocol comparative32 \
--beta-policy centered --beta-value 0.25 \
--mode "$mode" --bias-ratio 1 --dillavou-drift-ratio 0 \
--dillavou-profile-json "$PROFILE" --predictor-rate 1 \
--dillavou-calibration-steps 1 --neutral-cadence 10 \
--epochs 10 --schedule-epochs 100 \
--train-limit 60000 --test-limit 10000 --batch-size 128 \
--training-iterations 15 --inference-iterations 60 \
--evaluation-split test --data-seed 6200 \
--seed "$seed" --beta-seed 7100 --deterministic \
--output "$OUT/$tag.json" > "$OUT/$tag.log" 2>&1
}
seeds=(1988 1989)
methods=(clean raw constant innovation)
tags=(clean raw intercept sdil)
gpu=0
for seed in "${seeds[@]}"; do
for index in "${!methods[@]}"; do
run_cell "$gpu" "$seed" "${methods[$index]}" \
"seed_${seed}_${tags[$index]}" &
gpu=$((gpu + 1))
done
done
wait
"$PYTHON" - "$OUT" <<'PY'
import json
from pathlib import Path
import statistics
import sys
root = Path(sys.argv[1])
by_seed = {}
for path in sorted(root.glob("seed_*.json")):
_, seed, method = path.stem.split("_", 2)
report = json.loads(path.read_text())
trajectory = [epoch["test_accuracy"] for epoch in report["metrics"]]
by_seed.setdefault(seed, {})[method] = {
"trajectory": trajectory,
"best_accuracy": max(trajectory),
"final_accuracy": trajectory[-1],
"epochs_completed": len(trajectory),
"finite": report["final"]["finite"],
}
summary = {"by_seed": by_seed, "final_accuracy": {}}
for method in ("clean", "raw", "intercept", "sdil"):
values = [methods[method]["final_accuracy"] for methods in by_seed.values()]
summary["final_accuracy"][method] = {
"mean": statistics.mean(values),
"values": values,
}
(root / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
print(json.dumps(summary, indent=2))
PY
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