#!/bin/bash
#SBATCH --job-name=snn_target1
#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_target1.out
#SBATCH --error=runs/slurm_logs/%j_target1.err

# ============================================================
# Experiment 2: Smarter Target (lambda_target=1.0)
# ============================================================
# Hypothesis: Targeting lambda=-0.1 (edge of chaos) is too aggressive
# when the network naturally starts at lambda~2-3 (chaotic).
#
# With lambda_target=1.0:
# - We aim to REDUCE chaos, not eliminate it
# - Penalty: (lambda - 1.0)^2 is smaller when lambda~2
# - Allows some chaos while preventing extreme instability
# ============================================================

set -e

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

mkdir -p runs/slurm_logs data

echo "============================================================"
echo "SMARTER TARGET Experiment (lambda_target=1.0)"
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 0.1 \
    --lambda_target 1.0 \
    --reg_type squared \
    --warmup_epochs 20 \
    --data_dir ./data \
    --out_dir runs/depth_scaling_target1 \
    --device cuda

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