# mindspore.ops.Equal

class mindspore.ops.Equal[source]

Computes the equivalence between two tensors element-wise.

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, the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

$\begin{split}out_{i} =\begin{cases} & \text{True, if } x_{i} = y_{i} \\ & \text{False, if } x_{i} \ne y_{i} \end{cases}\end{split}$
Inputs:
• x (Union[Tensor, Number]) - The first input is a number or a tensor whose data type is number.

• y (Union[Tensor, Number]) - The second input is a number when the first input is a tensor or a tensor whose data type is number. The data type is the same as the first input.

Outputs:

Tensor, the shape is the same as the one after broadcasting,and the data type is bool.

Raises

TypeError – If neither x nor y is a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # case 1: The shape of two inputs are different
>>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> equal = ops.Equal()
>>> output = equal(x, 2.0)
>>> print(output)
[False True False]
>>> # case 2: The shape of two inputs are the same
>>> x = Tensor(np.array([1, 2, 3]), mindspore.int32)
>>> y = Tensor(np.array([1, 2, 4]), mindspore.int32)
>>> equal = ops.Equal()
>>> output = equal(x, y)
>>> print(output)
[ True  True False]