experiment_configs/tinyimagenet/one_dimensional_subspaces/eval_curves.py [43:96]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            f"+beta={args.beta}"
            f"+num_samples={args.num_samples}"
            f"+seed={args.seed}"
        )

        # Now, analyze.

        args.resume = [
            f"learning-subspaces-results/tinyimagenet/one-dimesnional-subspaces/{name_string}+try=0/"
            f"epoch_{args.epochs}_iter_{args.epochs * 782}.pt"
        ] * 2

        args.num_models = 2
        args.save = False
        args.save_data = True
        args.pretrained = True
        args.epochs = 0
        args.trainer = "eval_one_dim_subspaces"
        args.update_bn = True

        acc_data = {}
        for i, alpha0 in enumerate(
            [
                0.0,
                0.05,
                0.1,
                0.15,
                0.2,
                0.25,
                0.3,
                0.35,
                0.4,
                0.45,
                0.5,
                0.5,
                0.55,
                0.6,
                0.65,
                0.7,
                0.75,
                0.8,
                0.85,
                0.9,
                0.95,
                1.0,
            ]
        ):
            args.alpha0 = alpha0
            args.alpha1 = 1.0 - alpha0
            args.name = (
                f"{name_string}+alpha0={args.alpha0}+alpha1={args.alpha1}"
            )
            args.save_epochs = []
            run()
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



experiment_configs/tinyimagenet/one_dimensional_subspaces/eval_lines_layerwise.py [43:96]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            f"+beta={args.beta}"
            f"+num_samples={args.num_samples}"
            f"+seed={args.seed}"
        )

        # Now, analyze.

        args.resume = [
            f"learning-subspaces-results/tinyimagenet/one-dimesnional-subspaces/{name_string}+try=0/"
            f"epoch_{args.epochs}_iter_{args.epochs * 782}.pt"
        ] * 2

        args.num_models = 2
        args.save = False
        args.save_data = True
        args.pretrained = True
        args.epochs = 0
        args.trainer = "eval_one_dim_subspaces"
        args.update_bn = True

        acc_data = {}
        for i, alpha0 in enumerate(
            [
                0.0,
                0.05,
                0.1,
                0.15,
                0.2,
                0.25,
                0.3,
                0.35,
                0.4,
                0.45,
                0.5,
                0.5,
                0.55,
                0.6,
                0.65,
                0.7,
                0.75,
                0.8,
                0.85,
                0.9,
                0.95,
                1.0,
            ]
        ):
            args.alpha0 = alpha0
            args.alpha1 = 1.0 - alpha0
            args.name = (
                f"{name_string}+alpha0={args.alpha0}+alpha1={args.alpha1}"
            )
            args.save_epochs = []
            run()
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -