NAME

AI::MXNet::Metric - Online evaluation metric module.

DESCRIPTION

Base class of all evaluation metrics.

NAME

AI::MXNet::Perplexity

DESCRIPTION

Calculate perplexity.

Parameters
----------
ignore_label : int or undef
    index of invalid label to ignore when
    counting. usually should be -1. Include
    all entries if undef.
axis : int (default -1)
    The axis from prediction that was used to
    compute softmax. By default uses the last
    axis.

NAME

AI::MXNet::PearsonCorrelation

DESCRIPTION

Computes Pearson correlation.

Parameters
----------
name : str
    Name of this metric instance for display.

Examples
--------
>>> $predicts = [mx->nd->array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> $labels   = [mx->nd->array([[1, 0], [0, 1], [0, 1]])]
>>> $pr = mx->metric->PearsonCorrelation()
>>> $pr->update($labels, $predicts)
>>> print pr->get()
('pearson-correlation', '0.421637061887229')

DESCRIPTION

Custom evaluation metric that takes a sub ref.

Parameters
----------
eval_function : subref
    Customized evaluation function.
name : str, optional
    The name of the metric
allow_extra_outputs : bool
    If true, the prediction outputs can have extra outputs.
    This is useful in RNN, where the states are also produced
    in outputs for forwarding.

create

Create an evaluation metric.

Parameters
----------
metric : str or sub ref
    The name of the metric, or a function
    providing statistics given pred, label NDArray.