/ 
AI-MXNet-1.31
River stage one • 2 direct dependents • 2 total dependents
/ AI::MXNet::Metric

NAME

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

DESCRIPTION

Base class of all evaluation metrics.

get_config

Save configurations of metric. Can be recreated
    from configs with mx->metric->create(%{ $config })

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')

NAME

AI::MXNet::Loss

DESCRIPTION

Dummy metric for directly printing loss.

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

NAME

AI::MXNet::Confidence

DESCRIPTION

Accuracy by confidence buckets.

Parameters
----------
name : str
    Name of this metric instance for display.
num_classes: Int
    number of classes
confidence_thresholds: ArrayRef[Num]
    confidence buckets
For example
my $composite_metric  = AI::MXNet::CompositeEvalMetric->new;
$composite_metric->add(mx->metric->create('acc'));
$composite_metric->add(
    AI::MXNet::Confidence->new(
        num_classes => 2,
        confidence_thresholds => [ 0.5, 0.7, 0.8, 0.9 ],
    )
);

NAME

AI::MXNet::CustomMetric

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.