mxnet.metric.MSE¶

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)

__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