mxnet.metric.MSE¶
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class
mxnet.metric.
MSE
(name='mse', output_names=None, label_names=None)[source]¶ Computes Mean Squared Error (MSE) loss.
The mean squared error is given by
\[\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}\]Parameters: - name (str) – Name of this metric instance for display.
- output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
- label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))] >>> labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))] >>> mean_squared_error = mx.metric.MSE() >>> mean_squared_error.update(labels = labels, preds = predicts) >>> print mean_squared_error.get() ('mse', 0.375)
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__init__
(name='mse', output_names=None, label_names=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([name, output_names, label_names])Initialize self. get
()Gets the current evaluation result. get_config
()Save configurations of metric. get_global
()Gets the current global evaluation result. get_global_name_value
()Returns zipped name and value pairs for global results. get_name_value
()Returns zipped name and value pairs. reset
()Resets the internal evaluation result to initial state. reset_local
()Resets the local portion of the internal evaluation results to initial state. update
(labels, preds)Updates the internal evaluation result. update_dict
(label, pred)Update the internal evaluation with named label and pred