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. Oh, and it runs on your GPU by default.

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

You'll need to first install some dependencies manually. Unfortunately, pylearn2 isn't yet installable via PyPI and recommends an editable (pip -e) installation:

> pip install numpy scipy theano
> pip install -e git+https://github.com/lisa-lab/pylearn2.git#egg=Package

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

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

We strive to maintain 100% test coverage for all code-paths, to ensure that rapid changes in the underlying pylearn2 library are caught automatically.

To run a visualization that uses the sknn.mlp.MultiLayerPerceptronClassifier 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...

Here are the results of testing 10 epochs of training for two-thirds of the original MNIST data, on Ubuntu 14.04 and a GeForce GTX 650 (Memory: 1024Mb, Cores: 384). You can run the following command:

> python examples/bench_mnist.py (sknn|lasagne)

... to generate the results below.

All the networks have a single hidden layer with 300 hidden units of the default type, and were given the same data with validation and monitoring disabled. The remaining third of the MNIST dataset was only used to test the score once training terminated.

WARNING: For the theano powered libraries, these numbers are somewhat sensitive to parameter changes so please do not consider them definitive! It's likely tweaking parameters in both libraries would make training times very similar...

The library supports both regressors (to estimate continuous outputs) and classifiers (to predict classes). 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.