mindspore.ops.Div
- class mindspore.ops.Div[source]
Computes the quotient of dividing the first input tensor by the second input tensor element-wise.
Refer to
mindspore.ops.div()
for more details.Note
One of the two inputs must be a Tensor, 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.
- Inputs:
- Outputs:
Tensor, the shape is the same as the one of the input x , y after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> # case 1 :has same data type and shape of the two inputs >>> x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) >>> y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) >>> div = ops.Div() >>> output = div(x, y) >>> print(output) [-1.3333334 2.5 2. ] >>> # case 2 : different data type and shape of the two inputs >>> x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) >>> y = Tensor(2, mindspore.int32) >>> output = div(x, y) >>> print(output) [-2. 2.5 3.] >>> print(output.dtype) Float32