mindspore.nn.MAE
- class mindspore.nn.MAE[source]
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
>>> 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.
- Returns
Float, the computed result.
- Raises
RuntimeError – If the number of the total samples is 0.
- update(*inputs)[source]
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.