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mindspore.ops.Div

class mindspore.ops.Div

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

$$ou{t}_i=\frac{x_i}{y_i}$$

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