fmin(input, other, *, out=None) → Tensor¶
Computes the element-wise minimum of
This is like
torch.minimum()except it handles NaNs differently: if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the minimum. Only if both elements are NaN is NaN propagated.
This function is a wrapper around C++’s
std::fminand is similar to NumPy’s
Supports broadcasting to a common shape, type promotion, and integer and floating-point inputs.
- Keyword Arguments
out (Tensor, optional) – the output tensor.
>>> a = torch.tensor([2.2, float('nan'), 2.1, float('nan')]) >>> b = torch.tensor([-9.3, 0.1, float('nan'), float('nan')]) >>> torch.fmin(a, b) tensor([-9.3000, 0.1000, 2.1000, nan])