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+#!/bin/bash
+#SBATCH --job-name=snn_posthoc
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA40x4
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus-per-node=1
+#SBATCH --mem=64G
+#SBATCH --time=48:00:00
+#SBATCH --output=runs/slurm_logs/%j_posthoc.out
+#SBATCH --error=runs/slurm_logs/%j_posthoc.err
+
+# ============================================================
+# Experiment: Post-hoc Lyapunov Fine-tuning
+# ============================================================
+# Strategy:
+# 1. Train vanilla network for 100 epochs (learn features)
+# 2. Fine-tune with Lyapunov regularization for 50 epochs
+#
+# This allows the network to learn first, then we stabilize
+# the dynamics without fighting chaotic initialization.
+# ============================================================
+
+set -e
+
+PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
+cd "$PROJECT_DIR"
+
+mkdir -p runs/slurm_logs data runs/posthoc_finetune
+
+echo "============================================================"
+echo "POST-HOC FINE-TUNING Experiment"
+echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
+echo "Start: $(date)"
+echo "============================================================"
+nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+echo "============================================================"
+
+python files/experiments/posthoc_finetune.py \
+    --dataset cifar100 \
+    --depths 4 8 12 16 \
+    --T 4 \
+    --pretrain_epochs 100 \
+    --finetune_epochs 50 \
+    --batch_size 128 \
+    --lr 0.001 \
+    --finetune_lr 0.0001 \
+    --lambda_reg 0.1 \
+    --lambda_target -0.1 \
+    --reg_type extreme \
+    --lyap_threshold 2.0 \
+    --data_dir ./data \
+    --out_dir runs/posthoc_finetune \
+    --device cuda
+
+echo "============================================================"
+echo "Finished: $(date)"
+echo "============================================================"