Torch helps you organize your data through DataLoader. We can use it to package the data through batch training. We can have our own data format (NumPy array, for example, or any other) loaded into Tensor, along with a wrapper.

The following is an example of a dataset where random numbers are taken into the dataset in batches and trained:

import torch
import torch.utils.data as Data

torch.manual_seed(1) # reproducible

BATCH_SIZE = 5

x = torch.linspace(1, 10, 10) # this is x data (torch tensor)
y = torch.linspace(10, 1, 10) # this is y data (torch tensor)

torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
 dataset=torch_dataset, # torch TensorDataset format
 batch_size=BATCH_SIZE, # mini batch size
 shuffle=True, # random shuffle for training
 num_workers=2, # subprocesses for loading data
)

def show_batch():
 for epoch in range(3): # train entire dataset 3 times
 for step, (batch_x, batch_y) in enumerate(loader): # for each training step
 # train your data...
 print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',
 batch_x.numpy(), '| batch y: ', batch_y.numpy())

if __name__ == '__main__':
 show_batch()

The output of the code is as follows:

Epoch:  0 | Step:  0 | batch x:  [ 5.  7. 10.  3.  4.] | batch y:  [6. 4. 1. 8. 7.]
Epoch:  0 | Step:  1 | batch x:  [2. 1. 8. 9. 6.] | batch y:  [ 9. 10.  3.  2.  5.]
Epoch:  1 | Step:  0 | batch x:  [ 4.  6.  7. 10.  8.] | batch y:  [7. 5. 4. 1. 3.]
Epoch:  1 | Step:  1 | batch x:  [5. 3. 2. 1. 9.] | batch y:  [ 6.  8.  9. 10.  2.]
Epoch:  2 | Step:  0 | batch x:  [ 4.  2.  5.  6. 10.] | batch y:  [7. 9. 6. 5. 1.]
Epoch:  2 | Step:  1 | batch x:  [3. 9. 1. 8. 7.] | batch y:  [ 8.  2. 10.  3.  4.]