AI::MXNet::Metric - Online evaluation metric module.
Base class of all evaluation metrics.
AI::MXNet::Perplexity
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.
AI::MXNet::PearsonCorrelation
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')
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 an evaluation metric. Parameters ---------- metric : str or sub ref The name of the metric, or a function providing statistics given pred, label NDArray.
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.