Div

Usage

torch_div(self, other, rounding_mode)

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’s np.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.

$${\text{out}}_i=\frac{{\text{input}}_i}{{\text{other}}_i}$$ 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.

$${\text{out}}_i=\frac{{\text{input}}_i}{\text{other}}$$ 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
#>  0.4066  0.8488  0.8874 -0.1661
#>  0.0266 -0.3959 -1.7568 -0.8114
#>  0.0829  0.5218 -0.6705  0.0329
#> -1.4102  0.1434 -1.1056  4.4986
#> [ CPUFloatType{4,4} ]