[FIXED] I want to change my VIT from single-label multi-class classification to multi-label. How should I rewrite the evaluation and loss sections?

Issue

The new labels look like: [0, 1, 1, 0, 0, 1, 0]. The original loss function: torch.nn.CrossEntropyLoss() The calculation segment： pred = model(images.to(device)) loss = loss_function(pred, labels.to(device)) (How to use torch.nn.BCEWithLogitsLoss to replace that?

I have gotten some answers from GPTs and Google without executable details.

Solution

If you have your labels in that format already, you can just swap the loss function.

import torch
import torch.nn as nn

loss_fn = nn. BCEWithLogitsLoss()

logits = torch.randn(3)
labels = torch.tensor([1, 0, 1]).float()

loss = loss_fn(logits, labels)

Answered By - Karl