modified loocv tl res

Using sample 48 as test data
Resetting model
Epoch 0/9
----------
Loss: 0.7175 Acc: 0.5000
Epoch 1/9
----------
Loss: 0.7165 Acc: 0.5000
Epoch 2/9
----------
Loss: 0.7154 Acc: 0.5000
Epoch 3/9
----------
Loss: 0.7156 Acc: 0.5000
Epoch 4/9
----------
Loss: 0.7180 Acc: 0.5000
Epoch 5/9
----------
Loss: 0.7164 Acc: 0.5000
Epoch 6/9
----------
Loss: 0.7166 Acc: 0.5000
Epoch 7/9
----------
Loss: 0.7163 Acc: 0.5000
Epoch 8/9
----------
Loss: 0.7172 Acc: 0.5000
Epoch 9/9
----------
Loss: 0.7165 Acc: 0.5000
Training complete in 0m 21s
<class 'torch.Tensor'>
torch.Size([1, 3, 224, 224])
Using sample 49 as test data
Resetting model
Epoch 0/9
----------
Loss: 0.7190 Acc: 0.5000
Epoch 1/9
----------
Loss: 0.7185 Acc: 0.5000
Epoch 2/9
----------
Loss: 0.7134 Acc: 0.5000
Epoch 3/9
----------
Loss: 0.7175 Acc: 0.5000
Epoch 4/9
----------
Loss: 0.7204 Acc: 0.5000
Epoch 5/9
----------
Loss: 0.7188 Acc: 0.5000
Epoch 6/9
----------
Loss: 0.7172 Acc: 0.5000
Epoch 7/9
----------
Loss: 0.7183 Acc: 0.5000
Epoch 8/9
----------
Loss: 0.7139 Acc: 0.5000
Epoch 9/9
----------
Loss: 0.7160 Acc: 0.5000
Training complete in 0m 22s
<class 'torch.Tensor'>
torch.Size([1, 3, 224, 224])
loocv preds:  [tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0')]
loocv targets:  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
acc score:  0.5
confusion matrix:
 [[25  0]
 [25  0]]
confidence score for each image:  tensor([[0.7054, 0.2946]], device='cuda:0', grad_fn=<SoftmaxBackward>)