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mindspore.ops.Add

class mindspore.ops.Add

Adds two input 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, dtypes of them cannot be both bool, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

$$ou{t}_i=x_i+y_i$$

Inputs:

• x (Union[Tensor, Number, bool]) - The first input is a number, or a bool, or a tensor whose data type is number or bool.

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

Outputs:

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises

TypeError – If x and y is not one of the following: Tensor, Number, bool.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # case 1: x and y are both Tensor.
>>> add = ops.Add()
>>> x = Tensor(np.array([1, 2, 3]).astype(np.float32))
>>> y = Tensor(np.array([4, 5, 6]).astype(np.float32))
>>> output = add(x, y)
>>> print(output)
[5. 7. 9.]
>>> # case 2: x is a scalar and y is a Tensor
>>> add = ops.Add()
>>> x = Tensor(1, mindspore.int32)
>>> y = Tensor(np.array([4, 5, 6]).astype(np.float32))
>>> output = add(x, y)
>>> print(output)
[5. 6. 7.]
>>> # the data type of x is int32, the data type of y is float32,
>>> # and the output is the data format of higher precision flost32.
>>> print(output.dtype)
Float32