mindspore.ops.Div

class mindspore.ops.Div[source]

Computes the quotient of dividing the first input tensor by the second input tensor element-wise.

\[out_{i} = \frac{x_i}{y_i}\]

Note

Inputs of x and y comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one scalar.
When the inputs are two tensors, dtypes of them cannot be bool at the same time, and the shapes of them can be broadcast.
When the inputs are one tensor and one scalar, the scalar could only be a constant.

Inputs:

x (Union[Tensor, number.Number, bool]) - The first input is a number.Number or a bool or a tensor whose data type is number or bool_.
y (Union[Tensor, number.Number, bool]) - The second input, when the first input is a Tensor, the second input should be a number.Number or bool value, or a Tensor whose data type is number or bool_. When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.

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.

Raises

TypeError – If neither x nor y is a Tensor.
TypeError – If data types of x and y are both Tensor with bool_.

Supported Platforms:: Ascend GPU CPU

Examples

>>> # 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