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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 "============================================================"
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