conv_split_cub.py [753:783]:
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                learning_rate_list = [0.03]
        elif imp_method == 'MAS':
            if args.online_cross_val or args.cross_validate_mode:
                synap_stgth_list = [0.1, 1, 10, 100]
            else:
                synap_stgth_list = [0.1]
                learning_rate_list = [0.03]
        elif imp_method == 'RWALK':
            if args.online_cross_val or args.cross_validate_mode:
                synap_stgth_list = [0.1, 1, 10, 100]
            else:
                synap_stgth_list = [1]
                learning_rate_list = [0.03]
        elif imp_method == 'S-GEM':
            synap_stgth_list = [0]
            if args.online_cross_val:
                pass
            else:
                learning_rate_list = [args.learning_rate]
        elif imp_method == 'A-GEM':
            synap_stgth_list = [0]
            if args.online_cross_val or args.cross_validate_mode:
                pass
            else:
                learning_rate_list = [0.03]

        for synap_stgth in synap_stgth_list:
            for lr in learning_rate_list:
                # Generate the experiment key and store the meta data in a file
                exper_meta_data = {'ARCH': args.arch,
                        'DATASET': 'SPLIT_CUB',
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conv_split_cub_hybrid.py [783:813]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                learning_rate_list = [0.03]
        elif imp_method == 'MAS':
            if args.online_cross_val or args.cross_validate_mode:
                synap_stgth_list = [0.1, 1, 10, 100]
            else:
                synap_stgth_list = [0.1]
                learning_rate_list = [0.03]
        elif imp_method == 'RWALK':
            if args.online_cross_val or args.cross_validate_mode:
                synap_stgth_list = [0.1, 1, 10, 100]
            else:
                synap_stgth_list = [1]
                learning_rate_list = [0.03]
        elif imp_method == 'S-GEM':
            synap_stgth_list = [0]
            if args.online_cross_val:
                pass
            else:
                learning_rate_list = [args.learning_rate]
        elif imp_method == 'A-GEM':
            synap_stgth_list = [0]
            if args.online_cross_val or args.cross_validate_mode:
                pass
            else:
                learning_rate_list = [0.03]

        for synap_stgth in synap_stgth_list:
            for lr in learning_rate_list:
                # Generate the experiment key and store the meta data in a file
                exper_meta_data = {'ARCH': args.arch,
                    'DATASET': 'SPLIT_CUB',
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