diff --git a/Experiments/plot_real_diagonal.py b/Experiments/plot_diagonal_uncertainties.py similarity index 89% rename from Experiments/plot_real_diagonal.py rename to Experiments/plot_diagonal_uncertainties.py index d2abda6db2c527edd0731c0a544d1771b425f271..5401a44a1e7665d65e2176ecd1f196ffd52a1c05 100644 --- a/Experiments/plot_real_diagonal.py +++ b/Experiments/plot_diagonal_uncertainties.py @@ -62,19 +62,7 @@ train_data, test_data = load_data(normalize=normalize) input_dim = train_data[0][0].numel() output_dim = train_data[0][1].numel() -# try: -# gpu_number = eiv_conf_dict["gpu_number"] -# device = torch.device(f'cuda:{gpu_number}') -# try: -# torch.tensor([0.0]).to(device) -# except RuntimeError: -# if torch.cuda.is_available(): -# print('Switched to GPU 0') -# device = torch.device('cuda:0') -# else: -# print('No cuda available, using CPU') -# device = torch.device('cpu') -# except KeyError: +# do computations on cpu device = torch.device('cpu') @@ -164,19 +152,19 @@ seed_list = range(noneiv_conf_dict["seed_range"][0], noneiv_uncertainties = 0 eiv_uncertainties = 0 number_of_seeds = len(seed_list) -out_dim = 0 number_of_steps = 100 for seed in tqdm(seed_list): x_diagonal = create_diagonal(train=train_data, number_of_steps=number_of_steps) - results = collect_predictions(x_diagonal, - seed=seed) - noneiv_uncertainties += 1/number_of_seeds * results['noneiv']['uncertainties'][..., out_dim] - eiv_uncertainties += 1/number_of_seeds * results['eiv']['uncertainties'][..., out_dim] + results = collect_predictions(x_diagonal, seed=seed) + noneiv_uncertainties += 1/number_of_seeds * results['noneiv']['uncertainties'].mean(dim=-1) + eiv_uncertainties += 1/number_of_seeds * results['eiv']['uncertainties'].mean(dim=-1) plt.figure(1) plt.clf() -plot_x = torch.linspace(0,1, steps=number_of_steps) +plot_x = torch.linspace(0, 1, steps=number_of_steps) plt.fill_between(plot_x, noneiv_uncertainties, color='b', alpha=0.5) plt.fill_between(plot_x, eiv_uncertainties, color='r', alpha=0.5) -plt.savefig('results/figures/intersection') +plt.xlabel(r'$\lambda$') +plt.ylabel(r'$u$') +plt.savefig(f'results/figures/diagonal_uncertainties_{data}.pdf')