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Additional metrics integrated with the keras NN library, taken directly from `Tensorflow <https://www.tensorflow.org/api_docs/python/tf/metrics/>`_

Project description

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

How do I install this package?

As usual, just download it using pip:

pip install extra_keras_metrics

Tests Coverage

Since some software handling coverages sometimes get slightly different results, here’s three of them:

Coveralls Coverage SonarCloud Coverage Code Climate Coverate

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()
model.compile(
    optimizer="sgd",
    loss="binary_crossentropy",
    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()
model.compile(
    optimizer="sgd",
    loss="binary_crossentropy",
    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 following metrics taken directly from Tensorflow. At the time of writing, the ones available are the following:

The non-parametric ones are (tested against their conterpart from sklearn):

The parametric ones are (only execution is tested, no baseline in sklearn was available):

Extras

I’ve created also another couple packages you might enjoy: one, called extra_keras_utils that contains some commonly used code for Keras projects and plot_keras_history which automatically plots a keras training history.

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