PyTorch [1 if x > 0.5 else 0 for x in outputs ] with tensors
Question:
I have a list outputs from a sigmoid function as a tensor in PyTorch
E.g
output (type) = torch.Size([4]) tensor([0.4481, 0.4014, 0.5820, 0.2877], device='cuda:0',
As I’m doing binary classification I want to turn all values bellow 0.5 to 0 and above 0.5 to 1.
Traditionally with a NumPy array you can use list iterators:
output_prediction = [1 if x > 0.5 else 0 for x in outputs ]
This would work, however I have to later convert output_prediction back to a tensor to use
torch.sum(ouput_prediction == labels.data)
Where labels.data is a binary tensor of labels.
Is there a way to use list iterators with tensors?
Answers:
prob = torch.tensor([0.3,0.4,0.6,0.7])
out = (prob>0.5).float()
# tensor([0.,0.,1.,1.])
Explanation: In pytorch, you can directly use prob>0.5 to get a torch.bool type tensor. Then you can convert to float type via .float().
Why not consider using a loopless solution? Maybe something like below would suffice:
In [34]: output = torch.tensor([0.4481, 0.4014, 0.5820, 0.2877])
# subtract off the threshold value (0.5), create a boolean mask,
# and then cast the resultant tensor to an `int` type
In [35]: result = torch.as_tensor((output - 0.5) > 0, dtype=torch.int32)
In [36]: result
Out[36]: tensor([0, 0, 1, 0], dtype=torch.int32)
result = torch.as_tensor((output - 0.5) > 0, dtype=torch.int32), turns the require_grad to False.
To train your model use this code:
<p>>m = torch.nn.Sigmoid()</p>
>loss = criterion(m(output),target)
review above code.
I have a list outputs from a sigmoid function as a tensor in PyTorch
E.g
output (type) = torch.Size([4]) tensor([0.4481, 0.4014, 0.5820, 0.2877], device='cuda:0',
As I’m doing binary classification I want to turn all values bellow 0.5 to 0 and above 0.5 to 1.
Traditionally with a NumPy array you can use list iterators:
output_prediction = [1 if x > 0.5 else 0 for x in outputs ]
This would work, however I have to later convert output_prediction back to a tensor to use
torch.sum(ouput_prediction == labels.data)
Where labels.data is a binary tensor of labels.
Is there a way to use list iterators with tensors?
prob = torch.tensor([0.3,0.4,0.6,0.7])
out = (prob>0.5).float()
# tensor([0.,0.,1.,1.])
Explanation: In pytorch, you can directly use prob>0.5 to get a torch.bool type tensor. Then you can convert to float type via .float().
Why not consider using a loopless solution? Maybe something like below would suffice:
In [34]: output = torch.tensor([0.4481, 0.4014, 0.5820, 0.2877])
# subtract off the threshold value (0.5), create a boolean mask,
# and then cast the resultant tensor to an `int` type
In [35]: result = torch.as_tensor((output - 0.5) > 0, dtype=torch.int32)
In [36]: result
Out[36]: tensor([0, 0, 1, 0], dtype=torch.int32)
result = torch.as_tensor((output - 0.5) > 0, dtype=torch.int32), turns the require_grad to False.
To train your model use this code:
<p>>m = torch.nn.Sigmoid()</p>
>loss = criterion(m(output),target)
review above code.