#!/bin/bash
#SBATCH --job-name=snn_wkext
#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_weak_extreme.out
#SBATCH --error=runs/slurm_logs/%j_weak_extreme.err

# ============================================================
# Experiment: Weak Reg + Extreme Threshold
# ============================================================
# Combine both approaches:
# - Very weak regularization (lambda_reg=0.01)
# - Only penalize extreme chaos (lambda > 3.0)
#
# This should give the network maximum freedom to learn while
# providing a safety net against explosive dynamics.
# ============================================================

set -e

PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
cd "$PROJECT_DIR"

mkdir -p runs/slurm_logs data

echo "============================================================"
echo "WEAK REG + EXTREME 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/depth_scaling_benchmark.py \
    --dataset cifar100 \
    --depths 4 8 12 16 \
    --T 4 \
    --epochs 150 \
    --batch_size 128 \
    --lr 0.001 \
    --lambda_reg 0.01 \
    --lambda_target -0.1 \
    --reg_type extreme \
    --lyap_threshold 3.0 \
    --warmup_epochs 20 \
    --data_dir ./data \
    --out_dir runs/depth_scaling_weak_extreme \
    --device cuda

echo "============================================================"
echo "Finished: $(date)"
echo "============================================================"