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# torch.min

torch.min(input) → Tensor

  Returns the minimum value of all elements in the `input` tensor.

  Warning

  This function produces deterministic (sub)gradients unlike `min(dim=0)`

  Parameters:

    input (Tensor) – the input tensor.

  Example:

      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[ 0.6750,  1.0857,  1.7197]])
      >>> torch.min(a)
      tensor(0.6750)

  torch.min(input, dim, keepdim=False, *, out=None)

  Returns a namedtuple `(values, indices)` where `values` is the minimum value of each row of the `input` tensor in the given dimension `dim`. And `indices` is the index location of each minimum value found (argmin).

  If `keepdim` is `True`, the output tensors are of the same size as `input` except in the dimension `dim` where they are of size 1. Otherwise, `dim` is squeezed (see `torch.squeeze()`), resulting in the output tensors having 1 fewer dimension than `input`.

  Note

  If there are multiple minimal values in a reduced row then the indices of the first minimal value are returned.

  Parameters:

    - input (Tensor) – the input tensor.

    - dim (int) – the dimension to reduce.

    - keepdim (bool) – whether the output tensor has `dim` retained or not.

  Keyword Arguments:

    out (tuple, optional) – the tuple of two output tensors (min, min_indices)

  Example:

      >>> a = torch.randn(4, 4)
      >>> a
      tensor([[-0.6248,  1.1334, -1.1899, -0.2803],
              [-1.4644, -0.2635, -0.3651,  0.6134],
              [ 0.2457,  0.0384,  1.0128,  0.7015],
              [-0.1153,  2.9849,  2.1458,  0.5788]])
      >>> torch.min(a, 1)
      torch.return_types.min(values=tensor([-1.1899, -1.4644,  0.0384, -0.1153]), indices=tensor([2, 0, 1, 0]))

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

  See `torch.minimum()`.

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