mxnet.metric.RMSE¶

class mxnet.metric.RMSE(name='rmse', output_names=None, label_names=None)[source]¶

Computes Root Mean Squred Error (RMSE) loss.

The root mean squared error is given by

\[\sqrt{\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}}\]

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))]
>>> root_mean_squared_error = mx.metric.RMSE()
>>> root_mean_squared_error.update(labels = labels, preds = predicts)
>>> print root_mean_squared_error.get()
('rmse', 0.612372457981)

__init__(name='rmse', 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