Menu
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?
424
To compare two tensors element-wise in PyTorch, we use the torch. eq() method. It compares the corresponding elements and returns "True" if the two elements are same, else it returns "False".
Define a PyTorch tensor. Access the value of a single element at particular index using indexing or access the values of sequence of elements using slicing. Modify the accessed values with new values using the assignment operator. Finally, print the tensor to check if the tensor is modified with the new values.
. item() ensures that you append only the float values to the list rather the tensor itself. You are basically converting a single element tensor value to a python number. This should not affect the performance in any way.
A torch. Tensor is a multi-dimensional matrix containing elements of a single data type.
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().
67
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
