#!/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 "============================================================"