Div
Arguments
- self
(Tensor) the input tensor.
- other
(Number) the number to be divided to each element of
input- rounding_mode
(str, optional) – Type of rounding applied to the result:
NULL- default behavior. Performs no rounding and, if both input and other are integer types, promotes the inputs to the default scalar type. Equivalent to true division in Python (the / operator) and NumPy’snp.true_divide."trunc" - rounds the results of the division towards zero. Equivalent to C-style integer division.
"floor" - rounds the results of the division down. Equivalent to floor division in Python (the // operator) and NumPy’s
np.floor_divide.
div(input, other, out=NULL) -> Tensor
Divides each element of the input input with the scalar other and
returns a new resulting tensor.
Each element of the tensor input is divided by each element of the tensor
other. The resulting tensor is returned.
$$
\mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
$$
The shapes of input and other must be broadcastable
. If the torch_dtype of input and
other differ, the torch_dtype of the result tensor is determined
following rules described in the type promotion documentation
. If out is specified, the result must be
castable to the torch_dtype of the
specified output tensor. Integral division by zero leads to undefined behavior.
Warning
Integer division using div is deprecated, and in a future release div will
perform true division like torch_true_divide().
Use torch_floor_divide() to perform integer division,
instead.
$$
\mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
$$
If the torch_dtype of input and other differ, the
torch_dtype of the result tensor is determined following rules
described in the type promotion documentation . If
out is specified, the result must be castable
to the torch_dtype of the specified output tensor. Integral division
by zero leads to undefined behavior.
Examples
if (torch_is_installed()) {
a = torch_randn(c(5))
a
torch_div(a, 0.5)
a = torch_randn(c(4, 4))
a
b = torch_randn(c(4))
b
torch_div(a, b)
}
#> torch_tensor
#> -6.5519 1.2453 -0.1096 -0.8693
#> -0.9804 -0.9979 -0.1613 -0.8451
#> -3.1065 -1.1321 0.2044 -0.4906
#> -2.2265 -0.2979 0.2346 -1.1609
#> [ CPUFloatType{4,4} ]