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,
