# python train_model.py \
#         name=cifar10_dt_net_attention_2d_alpha_0.5_ssh \
#         problem=cifar10 \
#         problem/model=dt_net_2d_custom \
#         problem.model.num_class=10 \
#         problem.hyp.alpha=0.5 \
#         problem.hyp.beta=0.8 \
#         problem.hyp.epochs=200 \
#         problem.model.max_iters=30 \
#         problem.model.model_path="/home/tranhieu/workdir/research/deep-thinking/outputs/cifar10_dt_net_attention_2d_alpha_0.5_ssh/training-2024-12-02 13:10:05.654452/model_.pth"

# python test_model.py \
#         name=cifar10_dt_net_attention_2d_alpha_0.5_ssh \
#         problem=cifar10 \
#         problem/model=dt_net_2d_custom \
#         problem.model.num_class=10 \
#         problem.hyp.alpha=0.5 \
#         problem.hyp.epochs=130 \
#         problem.model.model_path="/home/tranhieu/workdir/research/deep-thinking/outputs/cifar10_dt_net_attention_2d_alpha_0.5_ssh/training-2024-12-02 13:10:05.654452"

python test_per_sample.py \
        name=cifar10_dt_net_attention_2d_alpha_0.5_ssh \
        problem=cifar10 \
        problem/model=dt_net_2d_custom \
        problem.model.num_class=10 \
        problem.hyp.alpha=0.5 \
        problem.hyp.epochs=130 \
        problem.model.model_path="/home/tranhieu/workdir/research/deep-thinking/outputs/cifar10_dt_net_attention_2d_alpha_0.5_ssh/training-2024-12-03 21:50:30.121632/"