# python train_model.py \
#         name=tiny_imagenet_c_cnn_ligru_tiny_imagenet_alpha_0.0 \
#         problem=tiny_imagenet_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_ligru_tiny_imagenet \
#         problem.model.num_class=200 \
#         problem.model.width=128 \
#         problem.hyp.epochs=200 \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.act=0.0 \
#         problem.hyp.train_batch_size=128 \
#         problem.hyp.noise_problem='gaussian_noise' \

# python train_model.py \
#         name=tiny_imagenet_c_cnn_gru_gn_tiny_imagenet_alpha_0.0 \
#         problem=tiny_imagenet_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_gru_gn_tiny_imagenet \
#         problem.model.num_class=200 \
#         problem.model.width=64 \
#         problem.hyp.epochs=150 \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.act=0.0 \
#         problem.hyp.train_batch_size=64 \
#         problem.hyp.noise_problem='gaussian_noise' \

# python train_model.py \
#         name=tiny_imagenet_c_cnn_gru_gn_tiny_imagenet_act_0.0002 \
#         problem=tiny_imagenet_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_gru_gn_tiny_imagenet \
#         problem.model.num_class=200 \
#         problem.model.width=128 \
#         problem.hyp.epochs=200 \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.act=0.0002 \
#         problem.hyp.train_batch_size=128 \
#         problem.hyp.noise_problem='gaussian_noise' \


# python train_model.py \
#         name=tiny_imagenet_c_cnn_ligru_tiny_imagenet_act_0.0002 \
#         problem=tiny_imagenet_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_ligru_tiny_imagenet \
#         problem.model.num_class=200 \
#         problem.model.width=128 \
#         problem.hyp.epochs=200 \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.act=0.0002 \
#         problem.hyp.train_batch_size=128 \
#         problem.hyp.noise_problem='gaussian_noise' \

python train_model.py \
        name=tiny_imagenet_c_resnet_tiny_imagenet \
        problem=tiny_imagenet_c \
        problem/model=dt_net_2d_custom \
        problem.model.model=resnet_tiny_imagenet \
        problem.model.num_class=200 \
        problem.model.width=64 \
        problem.model.max_iters=30 \
        problem.hyp.epochs=150 \
        problem.hyp.alpha=0.0 \
        problem.hyp.beta=0.5 \
        problem.hyp.act=0.0 \
        problem.hyp.train_batch_size=128 \
        problem.hyp.noise_problem='gaussian_noise' \
