import torch.utils.data as data train_set = data.TensorDataset(train_data, train_labels) test_set = data.TensorDataset(test_data, test_labels) combined_set = data.ConcatDataset([train_set, test_set])
from torchvision.datasets import MNIST train_set = MNIST('./data', train=True, download=True) test_set = MNIST('./data', train=False, download=True) combined_set = data.ConcatDataset([train_set, test_set])In this example, the MNIST dataset is used to create two datasets (train_set and test_set) for training and testing purposes. The ConcatDataset is used to create a single dataset (combined_set) by concatenating these two datasets. Overall, the ConcatDataset class is a useful tool for combining multiple datasets into a single dataset, which can be useful for various machine learning tasks.