torch.fmin(input, other, *, out=None) → Tensor

Computes the element-wise minimum of input and other.

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::fmin and is similar to NumPy’s fmin function.

Supports broadcasting to a common shape, type promotion, and integer and floating-point inputs.

  • input (Tensor) – the input tensor.

  • other (Tensor) – the second input tensor

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])


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