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mindspore.train.MAE

class mindspore.train.MAE

Calculates the mean absolute error(MAE).

Creates a criterion that measures the MAE between each element in the input: $x$ and the target: $y$.

$$\text{MAE}=\frac{\sum _{i=1}^n\Vert {y\mathrm{\_}pred}_i-y_i\Vert }{n}$$

where $n$ is batch size.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore.train import MAE
>>>
>>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
>>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
>>> error = MAE()
>>> error.clear()
>>> error.update(x, y)
>>> result = error.eval()
>>> print(result)
0.037499990314245224

clear()

Clears the internal evaluation result.

eval()

Computes the mean absolute error(MAE).

Returns

numpy.float64. The computed result.

Raises

RuntimeError – If the total number of samples is 0.

update(*inputs)

Updates the internal evaluation result $y_{pred}$ and $y$.

Parameters

inputs – Input y_pred and y for calculating MAE where the shape of y_pred and y are both N-D and the shape should be the same.

Raises

ValueError – If the number of the input is not 2.