mxnet.metric.CrossEntropy¶

class mxnet.metric.CrossEntropy(eps=1e-12, name='cross-entropy', output_names=None, label_names=None)[source]¶

Computes Cross Entropy loss.

The cross entropy over a batch of sample size \(N\) is given by

\[-\sum_{n=1}^{N}\sum_{k=1}^{K}t_{nk}\log (y_{nk}),\]

where \(t_{nk}=1\) if and only if sample \(n\) belongs to class \(k\). \(y_{nk}\) denotes the probability of sample \(n\) belonging to class \(k\).

Parameters

eps (float) – Cross Entropy loss is undefined for predicted value is 0 or 1, so predicted values are added with the small constant.
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])]
>>> ce = mx.metric.CrossEntropy()
>>> ce.update(labels, predicts)
>>> print ce.get()
('cross-entropy', 0.57159948348999023)

__init__(eps=1e-12, name='cross-entropy', output_names=None, label_names=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([eps, 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