| incorrect_examples = [] | |
| incorrect_labels = [] | |
| incorrect_pred = [] | |
| model.eval() | |
| for data,target in test_loader: | |
| data , target = data.to(device), target.to(device) | |
| output = model(data) # shape = torch.Size([batch_size, 10]) | |
| pred = output.argmax(dim=1, keepdim=True) #pred will be a 2d tensor of shape [batch_size,1] | |
| idxs_mask = ((pred == target.view_as(pred))==False).view(-1) | |
| if idxs_mask.numel(): #if index masks is non-empty append the correspoding data value in incorrect examples | |
| incorrect_examples.append(data[idxs_mask].squeeze().cpu().numpy()) | |
| incorrect_labels.append(target[idxs_mask].cpu().numpy()) #the corresponding target to the misclassified image | |
| incorrect_pred.append(pred[idxs_mask].squeeze().cpu().numpy()) #the corresponiding predicted class of the misclassified image |