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()
mlp.fit(X, 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 CONTIBUTING.md
for information about contributing to this
project.
BSD-3
See LICENSE.txt
for details.