mindspore.nn.MAE
- class mindspore.nn.MAE[source]
Calculates the mean absolute error(MAE).
Creates a criterion that measures the MAE between each element in the input: \(x\) and the target: \(y\).
\[\text{MAE} = \frac{\sum_{i=1}^n \|{y\_pred}_i - y_i\|}{n}\]where \(n\) is batch size.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> import mindspore >>> 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
- eval()[source]
Computes the mean absolute error(MAE).
- Returns
numpy.float64. The computed result.
- Raises
RuntimeError – If the total number of samples is 0.
- update(*inputs)[source]
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.