mindspore.ops.floor_divide

mindspore.ops.floor_divide(input, other)

Compute element-wise division of input by other and floor the result.

If input and other have different data types, the implicit type conversion rules are followed. Inputs must be two tensors or one tensor and one scalar. When the inputs are two tensors, their shapes must be broadcastable, and their data types cannot both be bool simultaneously.

$$ou{t}_i=\text{floor}(\frac{inpu{t}_i}{othe{r}_i})$$

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• input (Union[Tensor, Number, bool]) – The first input tensor.

• other (Union[Tensor, Number, bool]) – The second input tensor.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> # case 1: Two tensors with boolean and integer data type.
>>> input = mindspore.tensor([True, True, False])
>>> other = mindspore.tensor([1, 2, 4])
>>> output = mindspore.ops.floor_divide(input, other)
>>> print(output)
[1 0 0]
>>>
>>> # case 2: One tensor and one scalar.
>>> input = mindspore.tensor([1, 2, 4])
>>> other = mindspore.tensor(1.5)
>>> output = mindspore.ops.floor_divide(input, other)
>>> print(output)
[0. 1. 2.]
>>>
>>> # case 3: When inputs have different data types, type promotion rules are followed.
>>> input = mindspore.tensor([1, 2, 4], mindspore.int32)
>>> other = mindspore.tensor([1.1, 2.5, -1.5], mindspore.float32)
>>> output = mindspore.ops.floor_divide(input, other)
>>> print(output)
[ 0.  0. -3.]