#!/bin/bash
#SBATCH --job-name=snn_weak_reg
#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_reg.out
#SBATCH --error=runs/slurm_logs/%j_weak_reg.err

# ============================================================
# Experiment 1: Weaker Regularization (lambda_reg=0.01)
# ============================================================
# Hypothesis: The current lambda_reg=0.3 is too strong, causing
# the Lyapunov penalty to dominate the CE loss and prevent learning.
#
# With lambda_reg=0.01:
# - Penalty contribution: 0.01 * 4 = 0.04 (vs CE ~4.6)
# - Network can still learn while gently being regularized
# ============================================================

set -e

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

mkdir -p runs/slurm_logs data

echo "============================================================"
echo "WEAK REGULARIZATION Experiment (lambda_reg=0.01)"
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 squared \
    --warmup_epochs 20 \
    --data_dir ./data \
    --out_dir runs/depth_scaling_weak_reg \
    --device cuda

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