Table of Contents
Shortcuts

# torch.fmin

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.

  Parameters:

    - input (Tensor) – the input tensor.

    - other (Tensor) – the second input tensor

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> 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])

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.