scikit-neuralnetwork 0.4

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 Functions —

  • 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 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.

Installation

To download and setup the latest official release, you can 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 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(
    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.