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Source code for ignite.contrib.metrics.regression.mean_error

from __future__ import division

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric


[docs]class MeanError(Metric): r""" Calculates the Mean Error: :math:`\text{ME} = \frac{1}{n}\sum_{j=1}^n (A_j - P_j)`, where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in the reference `Botchkarev 2018`__. - `update` must receive output of the form `(y_pred, y)`. - `y` and `y_pred` must be of same shape. __ https://arxiv.org/abs/1809.03006 """
[docs] def reset(self): self._sum_of_errors = 0.0 self._num_examples = 0
[docs] def update(self, output): y_pred, y = output errors = (y.view_as(y_pred) - y_pred) self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0]
[docs] def compute(self): if self._num_examples == 0: raise NotComputableError('MeanError must have at least one example before it can be computed') return self._sum_of_errors / self._num_examples

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