Table Of Contents
Table Of Contents

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_name_value() Returns zipped name and value pairs.
reset() Resets the internal evaluation result to initial state.
update(labels, preds) Updates the internal evaluation result.
update_dict(label, pred) Update the internal evaluation with named label and pred