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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

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mindspore.ops.nextafter

mindspore.ops.nextafter(input, other)

Returns the next representable floating-point value after input towards other element-wise.

Say there are two float32 numbers $a$, $b$, and let the representable delta of float32 datatype is $eps$. If $a<b$, then the next representable of $a$ towards $b$ is $a+eps$, the next representable of $b$ towards $a$ is $b-eps$.

$$ou{t}_i=nextafter(inpu{t}_i,othe{r}_i)$$

For more detailed information, refer to A Self Regularized Non-Monotonic Neural Activation Function.

Parameters

• input (Tensor) – The first input tensor. The shape of tensor is $(N,\ast )$ where $\ast$ means, any number of additional dimensions. Must be one of the following types: float32, float64.

• other (Tensor) – The second input tensor. The shape of tensor is $(N,\ast )$ where $\ast$ means, any number of additional dimensions. Must be one of the following types: float32, float64.

Returns

Tensor, has the same shape and data type as input.

Raises

• TypeError – If neither input nor other is a Tensor.

• TypeError – If the dtype of input and other is not one of: float32, float64.

• TypeError – If the dtypes of input and other are not same.

• ValueError – If input's shape is not the same as other.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_ = Tensor(np.asarray([0.0]), mindspore.float32)
>>> other_ = Tensor(np.asarray([0.1]), mindspore.float32)
>>> output_ = ops.nextafter(input_, other_)
>>> print(output_)
[1.e-45]