How Can I Use I In Loop To Make Variance?

How can I change this code train_0.append(0) train_1.append(1) train_2.append(2) train_3.append(3) using loop like under? for i in range(4): train_i.append(i) My code occurs

Solution 1:

There is some methods how to do this, but usually such as problems defines bad code... Better to do something with code and covert big amount of variables to something iterable.

There is some methods:

for i inrange(4):
    train = globals().get("train_{}".format(i), None)
    if train:
        train.append(i)

for i inrange(4):
    try:
        eval("train_{0}.append({0})".format(i))
    except:
        pass

Solution 2:

In the class, to define self.variance, how I can adjust your solution?

for i in range(4):
        globals()["test_{}".format(i)].append(ToTensor(vectors[i]))

Becuase upper code works with your help.

But under case (in class) it doesn't work.

classMyDataset():
    def__init__(self, cropped_img_vectors, targets):
        self.data_0 = cropped_img_vectors[0]
        self.data_1 = cropped_img_vectors[1]
        self.data_2 = cropped_img_vectors[2]
        self.data_3 = cropped_img_vectors[3]
        self.targets = targets

    def__getitem__(self, index):
        data_0 = self.data_0[index]
        data_1 = self.data_1[index]
        data_2 = self.data_2[index]
        data_3 = self.data_3[index]
        y = self.targets[index]
        dataset = []
        for i inrange(4):
            dataset.append(["data_{}".format(i)])
        return dataset, y

    def__len__(self):
        returnlen(self.data_0)

I changed uppder to under.

classMyDataset():
    def__init__(self, cropped_1pixel_dataset, targets):
        for i inrange(4):
            globals()["self.data_{}".format(i)] = cropped_1pixel_dataset[i]
        self.targets = targets

    def__getitem__(self, index):
        for i inrange(4):
            globals()["data_{}".format(i)] = cropped_1pixel_dataset[i][index]
        y = self.targets[index]
        return [globals()["data_{}".format(i)] for i inrange(4)], y

    def__len__(self):
        returnlen(self.data_0)

And after run this cell,

MyDataset(train_cropped_1pixel_dataset, train_dataset.targets)

it occur this error.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-11-960ee70394c1> in <module>
      3 train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
      4                                            batch_size = batch_size,
----> 5                                            shuffle = True)

~/.local/lib/python3.5/site-packages/torch/utils/data/dataloader.py in __init__(self, dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn)
    800if sampler is None:
    801if shuffle:
--> 802                     sampler = RandomSampler(dataset)803else:
    804                     sampler = SequentialSampler(dataset)

~/.local/lib/python3.5/site-packages/torch/utils/data/sampler.py in __init__(self, data_source, replacement, num_samples)
     5859ifself.num_samples is None:
---> 60             self.num_samples = len(self.data_source)6162ifnot isinstance(self.num_samples, int) orself.num_samples <= 0:

<ipython-input-10-293dc919d173> in __len__(self)
     1213     def __len__(self):
---> 14         return len(self.data_0)

AttributeError: 'MyDataset' object has no attribute 'data_0'

I really need helps.. Thank you.

Solution 3:

Provided that you have all train_<i> variables defined in global scope you could access them via globals(). Demo

train_0 = []
train_1 = []
train_2 = []
train_3 = []


for i inrange(4):
    globals()[f'train_{i}'].append(i)

print(train_0, train_1, train_2, train_3)