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#!/bin/bash
#SBATCH --job-name=snn_cifar100_depth
#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_cifar100_depth.out
#SBATCH --error=runs/slurm_logs/%j_cifar100_depth.err
# ============================================================
# CIFAR-100 Depth Scaling Benchmark
# ============================================================
# KEY EXPERIMENT: Show that deep SNNs outperform shallow ones
# when trained with Lyapunov regularization.
#
# CIFAR-100 (100 classes) is ideal because:
# - Complex enough that shallow networks plateau
# - Deep networks can learn richer representations
# - Standard benchmark with known baselines
#
# Expected results:
# - Shallow (4 layers): Similar for both methods
# - Deep (16 layers): Vanilla fails/plateaus, Lyapunov succeeds
# ============================================================
set -e
PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
cd "$PROJECT_DIR"
mkdir -p runs/slurm_logs data
echo "============================================================"
echo "CIFAR-100 Depth Scaling Benchmark"
echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
echo "Start: $(date)"
echo "============================================================"
nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
echo "============================================================"
# Test depths: 4, 8, 12, 16, 20 conv layers
python files/experiments/depth_scaling_benchmark.py \
--dataset cifar100 \
--depths 4 8 12 16 20 \
--T 4 \
--epochs 150 \
--batch_size 128 \
--lr 0.001 \
--lambda_reg 0.3 \
--lambda_target -0.1 \
--data_dir ./data \
--out_dir runs/depth_scaling \
--device cuda
echo "============================================================"
echo "Finished: $(date)"
echo "============================================================"
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