Min

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

dim | (int) the dimension to reduce. |

keepdim | (bool) whether the output tensor has |

out | (tuple, optional) the tuple of two output tensors (min, min_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 minimum value of all elements in the `input`

tensor.

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).

`indices`

does not necessarily contain the first occurrence of each
minimal 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 minimum is taken.
The resulting tensor is returned.

The shapes of `input`

and `other`

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

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

if (torch_is_installed()) { a = torch_randn(c(1, 3)) a torch_min(a) a = torch_randn(c(4, 4)) a torch_min(a, dim = 1) a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_min(a, other = b) }#> torch_tensor #> 0.01 * #> -2.0323 #> -12.8656 #> -200.6399 #> -114.5560 #> [ CPUFloatType{4} ]