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Multilayer Feed-Forward Neural Network (MuFFNN) models with TensorFlow and scikit-learn

Project description


Build status Latest version on PyPI

scikit-learn-compatible neural network models implemented in TensorFlow


This package currently supports Python 3.6 and 3.7.

Installation with pip is recommended:

pip install muffnn

You can install the dependencies via:

pip install -r requirements.txt

If you have trouble installing TensorFlow, see this page for more details.

For development, a few additional dependencies are needed:

pip install -r dev-requirements.txt


Each estimator in the code follows the scikit-learn API. Thus usage follows the scikit-learn conventions:

from muffnn import MLPClassifier

X, y = load_some_data()

mlp = MLPClassifier(), y)

X_new = load_some_unlabeled_data()
y_pred = mlp.predict(X_new)

Further, serialization of the TensorFlow graph and data is handled automatically when the object is pickled:

import pickle

with open('est.pkl', 'wb') as fp:
    pickle.dump(est, fp)


See for information about contributing to this project.



See LICENSE.txt for details.

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