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Neural Network wrapper for pylearn2 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 pylearn2 library that’s compatible with scikit-learn for a more user-friendly and Pythonic interface.

NOTE: This project is possible thanks to the nucl.ai Conference on July 20-22. Join us in Vienna!

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Features

Thanks to the underlying pylearn2 implementation, this library supports the following neural network features, which are exposed in an intuitive and well documented API:

  • Activation Types —
    • Nonlinear: Sigmoid, Tanh, Rectifier, Maxout.
    • 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 Types — 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.

Installation & Testing

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

> pip install scikit-neuralnetwork

This contains its own packaged version of pylearn2 from the date of the release (and tag) but will use any globally installed version if available.

Then, you can run the samples and benchmarks 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(
    layers=[
        Layer("Rectifier", units=100),
        Layer("Linear")],
    learning_rate=0.02,
    n_iter=10)
nn.fit(X_train, 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.


Documentation Status Code Coverage License Type Project Stars

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