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mindspore.ops.fmax

mindspore.ops.fmax(input, other)

Computes the maximum of input tensors element-wise.

$$outpu{t}_i=max(x{1}_i,x{2}_i)$$

Note

• Inputs of input and other comply with the implicit type conversion rules to make the data types consistent.

• Shapes of input and other should be able to broadcast.

• If one of the elements to be compared is NaN, another element is returned.

Parameters

• input (Tensor) – The first tensor. The supported dtypes are: float16, float32, float64, int32, int64.

• other (Tensor) – The second tensor. The supported dtypes are: float16, float32, float64, int32, int64.

Returns

A 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 input or other is not Tensor.

• TypeError – If dtype of input or other is not one of: float16, float32, float64, int32, int64.

• ValueError – If the shape of input and other can not broadcast.

Supported Platforms:

CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
>>> x2 = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
>>> output = ops.fmax(x1, x2)
>>> print(output)
[4. 5. 6.]