mindspore.nn.MAE

class mindspore.nn.MAE

Calculates the mean absolute error.

Creates a criterion that measures the mean absolute error (MAE) between each element in the input: \(x\) and the target: \(y\).

\[\text{MAE} = \frac{\sum_{i=1}^n \|y_i - x_i\|}{n}\]

Here \(y_i\) is the prediction and \(x_i\) is the true value.

Note

The method update must be called with the form update(y_pred, y).

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> 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 = nn.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.

Returns

Float, the computed result.

Raises

RuntimeError – If the number of the total samples is 0.

update(*inputs)

Updates the internal evaluation result \(y_{pred}\) and \(y\).

Parameters

inputs – Input y_pred and y for calculating mean absolute error where the shape of y_pred and y are both N-D and the shape are the same.

Raises

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