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Hello, thanks for your implementation. However, I found that the "probs" for binary classification doesn't sum up to be 1.0.
prediction = model.forward(image.float()) loss = torch.nn.BCEWithLogitsLoss(weight=weight)(prediction, label) loss.backward() optimizer.step() loss_value = loss.item() losses.append(loss_value) probas = torch.sigmoid(prediction) y_trues.append(int(label[0][1])) y_preds.append(probas[0][1].item())
The issue mentioned is located in "https://github.com/ahmedbesbes/mrnet/blob/master/train.py"
The text was updated successfully, but these errors were encountered:
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Hello, thanks for your implementation. However, I found that the "probs" for binary classification doesn't sum up to be 1.0.
The issue mentioned is located in "https://github.com/ahmedbesbes/mrnet/blob/master/train.py"
The text was updated successfully, but these errors were encountered: