Max

self | (Tensor) the input tensor. |
---|---|

dim | (int) the dimension to reduce. |

keepdim | (bool) whether the output tensor has |

out | (tuple, optional) the result tuple of two output tensors (max, max_indices) |

other | (Tensor) the second input tensor |

When the shapes do not match, the shape of the returned output tensor follows the broadcasting rules .

Returns the maximum value of all elements in the `input`

tensor.

Returns a namedtuple `(values, indices)`

where `values`

is the maximum
value of each row of the `input`

tensor in the given dimension
`dim`

. And `indices`

is the index location of each maximum value found
(argmax).

`indices`

does not necessarily contain the first occurrence of each
maximal value found, unless it is unique.
The exact implementation details are device-specific.
Do not expect the same result when run on CPU and GPU in general.

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`

.

Each element of the tensor `input`

is compared with the corresponding
element of the tensor `other`

and an element-wise maximum is taken.

The shapes of `input`

and `other`

don't need to match,
but they must be broadcastable .

$$ \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i) $$

if (torch_is_installed()) { a = torch_randn(c(1, 3)) a torch_max(a) a = torch_randn(c(4, 4)) a torch_max(a, dim = 1) a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_max(a, other = b) }#> torch_tensor #> -0.0928 #> -0.3423 #> -0.4207 #> 2.3147 #> [ CPUFloatType{4} ]