mindspore.ops.div
- mindspore.ops.div(input, other, *, rounding_mode=None)[source]
Divides the first input tensor by the second input tensor in floating-point type element-wise.
\[out_{i} = input_{i} / other_{i}\]Note
When the two inputs have different shapes, they must be able to broadcast to a common shape.
The two inputs can not be bool type at the same time, [True, Tensor(True, bool_), Tensor(np.array([True]), bool_)] are all considered bool type.
The two inputs comply with the implicit type conversion rules to make the data types consistent.
- Parameters
- Keyword Arguments
rounding_mode (str, optional) –
Type of rounding applied to the result. Default:
None
. Three types are defined as,None: Default behavior, which is the same as true division in Python or true_divide in NumPy.
”floor”: Rounds the division of the inputs down, which is the same as floor division in Python or floor_divide in NumPy.
”trunc”: Rounds the division of the inputs towards zero, which is the same as C-style integer division.
- Returns
Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.
- Raises
TypeError – If input and other is not one of the following: Tensor, Number, bool.
ValueError – If rounding_mode value is not None, “floor” or “trunc”.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32) >>> output = ops.div(x, y) >>> print(output) [0.25 0.4 0.5]