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#!/bin/bash
#SBATCH --job-name=snn_asymm
#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_asymm.out
#SBATCH --error=runs/slurm_logs/%j_asymm.err
# ============================================================
# Asymmetric Lyapunov Regularization Experiment
# ============================================================
# Hypothesis: Using asymmetric penalty will balance between
# preventing chaos and allowing learning.
#
# Asymmetric loss:
# - Strong penalty for chaos (lambda > 0): relu(lambda)^2
# - Weak penalty for collapse (lambda < -1): 0.1 * relu(-lambda-1)^2
#
# This allows dynamics in the "sweet spot" of slightly negative
# Lyapunov exponents (stable but not dead).
# ============================================================
set -e
PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
cd "$PROJECT_DIR"
mkdir -p runs/slurm_logs data
echo "============================================================"
echo "ASYMMETRIC Lyapunov Regularization"
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 conv layers
# Using asymmetric loss + 20 epoch warmup
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 0.3 \
--lambda_target -0.1 \
--reg_type asymmetric \
--warmup_epochs 20 \
--data_dir ./data \
--out_dir runs/depth_scaling_asymm \
--device cuda
echo "============================================================"
echo "Finished: $(date)"
echo "============================================================"
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