neuralnetworks: Deep Neural Networks with GPU support

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Github Link:

Description

This is a Java implementation of some of the algorithms for training deep neural networks. GPU support is provided via the OpenCL and Aparapi. The architecture is designed with modularity, extensibility and pluggability in mind.

Git structure

I’m using the git-flow model. The most stable (but older) sources are available in the master branch, while the latest ones are in the develop branch.

If you want to use the previous Java 7 compatible version you can check out this release.

Neural network types

Multilayer perceptron
Restricted Boltzmann Machine
Autoencoder
Deep belief network
Stacked autoencodeer
Convolutional networks with max pooling, average poolng and stochastic pooling.
Maxout networks (work-in-progress)
Training algorithms

Backpropagation – supports multilayer perceptrons, convolutional networks and dropout.
Contrastive divergence and persistent contrastive divergence implemented using these and these guidelines.
Greedy layer-wise training for deep networks – works for stacked autoencoders and DBNs, but supports any kind of training.
All the algorithms support GPU execution.

Out of the box supported datasets are MNIST, CIFAR-10/CIFAR-100 (experimental, not much testing), IRIS and XOR, but you can easily implement your own.

Experimental support of RGB image preprocessing operations – affine transformations, cropping, and color scaling (see Generaltest.java -> testImageInputProvider).

Activation functions

Logistic
Tanh
Rectifiers
Softplus
Softmax
Weighted sum
All the functions support GPU execution. They can be applied to all types of networks and all training algorithms. You can also implement new activations.