scikit-neuralnetwork

Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons, auto-encoders and (soon) recurrent neural networks with a stable Future Proof™ interface that's compatible with scikit-learn for a more user-friendly and Pythonic interface. It's a wrapper for powerful existing libraries such as lasagne currently, with plans for blocks.

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

Features

By importing the sknn package provided by this library, you can easily train deep neural networks as regressors (to estimate continuous outputs from inputs) and classifiers (to predict discrete labels from features).

Thanks to the underlying Lasagne implementation, the code supports the following neural network features — exposed in an intuitive and well documented API:

Activation Functions — Sigmoid, Tanh, Rectifier, Softmax, Linear.
Layer Types — Convolution (greyscale and color, 2D), Dense (standard, 1D).
Learning Rules — sgd, momentum, nesterov, adadelta, adagrad, rmsprop, adam.
Regularization — L1, L2, dropout, and batch normalization.
Dataset Formats — numpy.ndarray, scipy.sparse, pandas.DataFrame and iterators (via callback).

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

A) Download Latest Release [Recommended]

If you want to use the latest official release, you can do so from PYPI directly:

> pip install scikit-neuralnetwork

This will install the latest official Lasagne and Theano as well as other minor packages too as a dependency. We strongly suggest you use a virtualenv for Python.

B) Pulling Repositories [Optional]

If you want to use the more advanced features like convolution, pooling or upscaling, these depend on the latest code from Lasagne and Theano master branches. You can install them manually as follows:

> pip install -r https://raw.githubusercontent.com/aigamedev/scikit-neuralnetwork/master/requirements.txt

Once that's done, you can grab this repository and install from setup.py in the exact same way:

> git clone https://github.com/aigamedev/scikit-neuralnetwork.git
> cd scikit-neuralnetwork; python setup.py develop

This will make the sknn package globally available within Python as a reference to the current directory.

Running Automated Tests

Then, you can run the samples and benchmarks available in the examples/ folder, or launch the tests to check everything is working:

> pip install nose
> nosetests -v sknn.tests

We strive to maintain 100% test coverage for all code-paths, to ensure that rapid changes in the underlying backend libraries are caught automatically.

Getting Started

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

from sknn.mlp import Classifier, Layer

nn = Classifier(
    layers=[
        Layer("Rectifier", units=100),
        Layer("Softmax")],
    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.

Demonstration

To run the example that generates the visualization above using our sknn.mlp.Classifier, just run the following command in the project's root folder:

> python examples/plot_mlp.py --params activation

There are multiple parameters you can plot as well, for example iterations, rules or units. The datasets are randomized each time, but the output should be an image that looks like this...

Links & References

Lasagne by benanne — The amazing neural network library that powers sknn.
Theano by LISA Lab — Underlying array/math library for efficient computation.
scikit-learn by INRIA — Machine learning library with an elegant Pythonic interface.

carlkentor/scikit-neuralnetwork