condor_pytorch version: 1.0.0

ordinal_softmax

ordinal_softmax(x, device='cpu')

Convert the ordinal logit output to label probabilities.

Parameters

x: torch.Tensor, shape=(num_samples,num_classes-1) Logit output of the final Dense(num_classes-1) layer.

device: 'cpu', 'cuda', or None (default='cpu')
If GPUs are utilized, then the device should be passed accordingly.

Returns

probs_tensor: torch.Tensor, shape=(num_samples, num_classes) Probabilities of each class (columns) for each sample (rows).

Examples

    >>> ordinal_softmax(torch.tensor([[-1.,1],[-2,2]]))
    tensor([[0.7311, 0.0723, 0.1966],
    [0.8808, 0.0142, 0.1050]])