Function max

imperative/python/megengine/functional/math.py:440–477

r"""Calculates the maximum of tensor elements over a given axis (or axes). Args: inp: input tensor. Should have a numeric data type. axis: axis or axes along which maximums must be computed. By default, the maximum must be computed over the entire tensor.

(
    inp: Tensor,
    axis: Optional[Union[int, Sequence[int]]] = None,
    keepdims: bool = False,
)

def max(
    inp: Tensor,
    axis: Optional[Union[int, Sequence[int]]] = None,
    keepdims: bool = False,
) -> Tensor:
    r"""Calculates the maximum of tensor elements over a given axis (or axes).

    Args:
        inp: input tensor. Should have a numeric data type.
        axis: axis or axes along which maximums must be computed.
            By default, the maximum must be computed over the entire tensor.
            If a sequence of integers, maximums must be computed over multiple axes.
        keepdims: if ``True``, the reduced axes (dimensions) must be included in the result as singleton dimensions,
            and, accordingly, the result must be compatible with the input tensor (see :ref:`broadcasting-rule`).
            Otherwise, if ``False``, the reduced axes (dimensions) must not be included in the result.

    Returns:
        if the maximum was computed over the entire tensor, a zero-dimensional tensor containing the maximum;
        otherwise, a non-zero-dimensional tensor containing the maximums.

    .. admonition:: Special Cases

       If :math:`x_i` is ``NaN``, the maximum is ``NaN`` (i.e., ``NaN`` values propagate).

    Examples:

        >>> x = Tensor([[1, 2], [3, 4]])
        >>> F.max(x)
        Tensor(4, dtype=int32, device=xpux:0)

        Along an axis:

        >>> F.max(x, axis=0)
        Tensor([3 4], dtype=int32, device=xpux:0)
        >>> F.max(x, axis=1)
        Tensor([2 4], dtype=int32, device=xpux:0)
    """
    return inp.max(axis=axis, keepdims=keepdims)


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