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Additional metrics integrated with the keras NN library, taken directly from tensorflow.

Project description

travis sonar_quality sonar_maintainability sonar_coverage Maintainability pip

Additional metrics integrated with the keras NN library, taken directly from Tensorflow

How do I get this package?

As usual, just install it with pip:

pip install extra_keras_metrics

How do I use this package?

Just by importing it you will be able to access all the non-parametric metrics, such as “auprc” and “auroc”:

import extra_keras_metrics

model = my_keras_model()
    metrics=["auroc", "auprc"]

For the parametric metrics, such as “average_precision_at_k”, you will need to import them, such as:

from extra_keras_metrics import average_precision_at_k

model = my_keras_model()
    metrics=[average_precision_at_k(1), average_precision_at_k(2)]

This way in the history of the model you will find both the metrics indexed as “average_precision_at_k_1” and “average_precision_at_k_2” respectively.

Which metrics do I get?

You will get all the metrics from Tensorflow. At the time of writing, the ones available are the following:

The non-parametric ones are:

  • auprc

  • auroc

  • false_negatives

  • false_positives

  • mean_absolute_error

  • mean_squared_error

  • precision

  • recall

  • root_mean_squared_error

  • true_negatives

  • true_positives

The parametric ones are:

  • average_precision_at_k

  • false_negatives_at_thresholds

  • false_positives_at_thresholds

  • mean_cosine_distance

  • mean_iou

  • mean_per_class_accuracy

  • mean_relative_error

  • precision_at_k

  • precision_at_thresholds

  • recall_at_k

  • recall_at_thresholds

  • sensitivity_at_specificity

  • specificity_at_sensitivity

  • true_negatives_at_thresholds

  • true_positives_at_thresholds

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