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Deep neural networks without the learning cliff! A wrapper library compatible with scikit-learn.

Project description



Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons as a wrapper for the powerful Lasagne library that’s compatible with scikit-learn for a more user-friendly and Pythonic interface.

NOTE: This project is possible thanks to the Conference on July 18-20. Join us in Vienna!

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Thanks to the underlying Lasagne implementation, this library supports the following neural network features, which are exposed in an intuitive and well documented API:

  • Activation Functions —
    • Nonlinear: Sigmoid, Tanh, Rectifier.
    • Linear: Linear, Gaussian, Softmax.
  • Layer Types — Convolution (greyscale and color, 2D), Dense (standard, 1D).
  • Learning Rules — sgd, momentum, nesterov, adadelta, adagrad, rmsprop.
  • Regularization — L1, L2 and dropout.
  • Dataset Formats — numpy.ndarray, scipy.sparse, coming soon: iterators.

If a feature you need is missing, consider opening a GitHub Issue with a detailed explanation about the use case and we’ll see what we can do.


To download and setup the latest official release, you can do so from PYPI directly:

> pip install scikit-neuralnetwork

This will install a copy of Lasagne too as a dependency. We recommend you use a virtual environment for Python.

Then, you can run the tests using nosetests -v sknn, and other samples or benchmarks are available in the examples/ folder.

Getting Started

The library supports both regressors (to estimate continuous outputs from inputs) and classifiers (to predict labels from features). This is the sklearn-compatible API:

from sknn.mlp import Classifier, Layer

nn = Classifier(
        Layer("Rectifier", units=100),
    n_iter=10), y_train)

y_valid = nn.predict(X_valid)

score = nn.score(X_test, y_test)

The generated documentation as a standalone page where you can find more information about parameters, as well as examples in the User Guide.

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