def dataset_from_numpy(X, Y, classes=None): targets = torch.LongTensor(list(Y)) ds = TensorDataset(torch.from_numpy(X).type(torch.FloatTensor), targets) ds.targets = targets ds.classes = classes if classes is not None else [ i for i in range(len(np.unique(Y))) ] return ds
std=1, lambbda=0.005, batchsize=128): train_x, train_y = synthesizeData(case, input_d, 50000, mu, std, lambbda) test_x, test_y = synthesizeData(case, input_d, 10000, mu, std, lambbda) if case in [7, 8, 9, 10, 11, 12, 13]: train_dataset = TensorDataset(train_x, train_y) test_dataset = TensorDataset(test_x, test_y) else: train_dataset = TensorDataset(train_x.t(), train_y.t()) test_dataset = TensorDataset(test_x.t(), test_y.t()) train_dataset.classes = lambda: None test_dataset.classes = lambda: None if case in [1, 2, 3, 7, 8, 9, 10, 11, 12]: class_holder = ['0 - zero', '1 - one'] elif case in [4, 5, 6, 13]: class_holder = [ '0 - zero', '1 - one', '2 - two', '3 - three', '4 - four', '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine' ] else: raise ValueError("Check class holder!") train_loader = DataLoader(train_dataset, batch_size=batchsize, pin_memory=True,