Table Of Contents
Table Of Contents

mxnet.metric

Online evaluation metric module.

Metrics

Accuracy([axis, name, output_names, label_names]) Computes accuracy classification score.
Caffe([name, output_names, label_names]) Dummy metric for caffe criterions.
CompositeEvalMetric([metrics, name, …]) Manages multiple evaluation metrics.
CrossEntropy([eps, name, output_names, …]) Computes Cross Entropy loss.
CustomMetric(feval[, name, …]) Computes a customized evaluation metric.
EvalMetric(name[, output_names, label_names]) Base class for all evaluation metrics.
F1([name, output_names, label_names, average]) Computes the F1 score of a binary classification problem.
Loss([name, output_names, label_names]) Dummy metric for directly printing loss.
MAE([name, output_names, label_names]) Computes Mean Absolute Error (MAE) loss.
MCC([name, output_names, label_names, average]) Computes the Matthews Correlation Coefficient of a binary classification problem.
MSE([name, output_names, label_names]) Computes Mean Squared Error (MSE) loss.
NegativeLogLikelihood([eps, name, …]) Computes the negative log-likelihood loss.
PearsonCorrelation([name, output_names, …]) Computes Pearson correlation.
Perplexity(ignore_label[, axis, name, …]) Computes perplexity.
RMSE([name, output_names, label_names]) Computes Root Mean Squred Error (RMSE) loss.
TopKAccuracy([top_k, name, output_names, …]) Computes top k predictions accuracy.
Torch([name, output_names, label_names]) Dummy metric for torch criterions.

Helper functions

check_label_shapes(labels, preds[, wrap, shape]) Helper function for checking shape of label and prediction
create(metric, *args, **kwargs) Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function.
np(numpy_feval[, name, allow_extra_outputs]) Creates a custom evaluation metric that receives its inputs as numpy arrays.