Hey torch-expert :),

I need to create my trainloader using Dataloader class from pytorch library.

The problem is that I’ve 1 tensor of feature extracted, 1 tensor for labels and 1 tensor 28x28 extracted by fashion_mnist dataset. How can I combine different tensor into one, such that I can send into my model? (resnet18). These 3 tensors are separated at this moment due by necessity. In the specific, I have (128,2), (128) and (28x28) tensors.

Thanks in advance

How would you like to combine these tensors?

Resnet expects an image tensor of `[batch_size, 3, 224, 224]`

as its input.

I’m currently unsure, how to construct such a tensor using your input values.

Exactly. I have to construct a tensor with that shape. How can I do? Thanks

i need to replicate the train loader using these information. The main reason that i’m working only on features. After that, i need to compact these tensor into one, in order to be accepted by resnet18. These information are saved into

**class_list[y].append([instance, single_instance[-1], labels])** where,

each object is a tensor (features, 28x28 tensor, 28 tensor label).

When i try to convert this into Tensor, i obtain an error of shape caused by 28x28 tensor…

I’m really not sure how you would like to create a tensor of `[3, 224, 224]`

using your data.

Your raw data has `128*2+128+28*28 = 1168`

values, while the expected image tensor has `150528`

values.

Would you like to upsample the features somehow?

While this could make sense for the MNIST data, I’m not sure, how you would like to treat your features.