Passing 3 dimensional and one dimensional features to neural network with PyTorch Dataloader
I have examples with size 2x8x8 as tensor and I'm using PyTorch Dataloader for them. But now I want to add an additional 1 dim tensor with size 1 (a single number) as input too.
So I have two input parameters for the neural network, a multidimensional for convolutional layers and an additional one that I will concatenate later.
Probably I could use two dataloader, for every tensor shape one, but than I could not shuffle them.
How can I use a single PyTorch Dataloader for this two different input tensors?

This is not about the dataloader, this should be done in the your dataset. Implement your own dataset by making it inherit from torch.util.data.Dataset (you need to implement __len__ and __getitem__). Make your __getitem__method return both your tensors, and you should be fine.
You can follow this tutorial if  you need.
