mxnet.metric.Accuracy¶

class mxnet.metric.Accuracy(axis=1, name='accuracy', output_names=None, label_names=None)[source]¶

Computes accuracy classification score.

The accuracy score is defined as

\[\text{accuracy}(y, \hat{y}) = \frac{1}{n} \sum_{i=0}^{n-1} \text{1}(\hat{y_i} == y_i)\]

Parameters

axis (int, default=1) – The axis that represents classes
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([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> labels   = [mx.nd.array([0, 1, 1])]
>>> acc = mx.metric.Accuracy()
>>> acc.update(preds = predicts, labels = labels)
>>> print acc.get()
('accuracy', 0.6666666666666666)

__init__(axis=1, name='accuracy', output_names=None, label_names=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([axis, 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