This project is a work-in-progress, which provides a flexible framework to experiment and run neural network of heterogeneous topologies. A preliminary release will be visible soon. neuron is written in Scala which adopts the so-called "define-by-run" scheme.

• template vs. module
• neural network operators
• autoencoders (w or w/o tiled weight)
• activation functions: logistic, tanh, ReLU, softplus
• metrics (L1, L2, Mahalanobis, Softmax)
• regularization: weight decay, activation sparsity, dropout, maxout
• data parallel framework: atomic parameters + distributed states
• optimization: LBFGS, SGD, SAGD, SGD with momentum
• recursive neural network

The simplest example to train a regularized multi-layer perceptron to predict handwritten digits from MNIST dataset. It takes around ten minutes.

package neuron.examples

import neuron.core._
import neuron.math._

object MLP_MNIST extends Workspace with Optimizable {
    def main(args: Array[String]): Unit = {
      // set @MLP=784-200-10, @weight_decay=1E-4
      nn = (new RegularizedLinearNN(200, 10, 1E-4) **
            new SingleLayerNeuralNetwork(200) **
            new RegularizedLinearNN(784, 200, 1E-4)).create() // nn is declared in trait @Optimizable
            
      // load standard MNIST training data
      val (xData, yData) = LoadData.mnistDataM("std", "train")
      
      // generate random weight and initialize
      val theta0 = nn.getRandomWeights("get random weights").toWeightVector()
      nn.setWeights("set weight", theta0);
      
      // full-batch training (@maxIter=200, @distance=SoftMaxDistance)
      val (_, theta) = trainx(xData, yData, theta0, 200, SoftMaxDistance)
      
      // load standard MNIST testing data
      val (xDataTest, yDataTest) = LoadData.mnistDataM("std", "t10k")
      
      // estimate accuracy
      val accuracy = (yDataTest.argmaxCol() countEquals nn(xDataTest, null).argmaxCol()) / xDataTest.cols.toDouble
      println(accuracy)
    }
}
/* Accuracy: 0.9806 */

Also have a look at

• Basics: explains the most fundamental ideas for the use of neuron, and why they featured neuron as a good choice for prototyping neural networks that leverage flexibility, simplicity and efficiency.
• Auto-Encoder: a special family of unsupervised neural network.
• Examples: we have more examples under folder src/main/scala/neuron/tutorials/
• Scaladoc: TBA

• How is neuron different from other deep learning libraries (such as theano, torch7, etc), besides it is Scala based?

  We argue that not only the number of parameters contributes to the representation ability of neural network, but also its infrastructure (network topology, train strategy, etc.) Neuron focuses on fast prototyping novel network architecture. Using Scala, we attempt to make the implementation of neural network in a mixed functional and imperative way ... though neuron is not at the mature shape to be industrial proven.

• How is the speed of neruon?

  Neuron is currently backed by breeze for numerical computation, which should be fast. And the extra cost for data control flow is minimized. Neuron provides convenient data parallelization.

• Breeze and Nak: a set of libraries for machine learning and numerical computing
• UFLDL Tutorial: a Stanford course, find solutions at Github
• ScalaSTM: a lightweight software transactional memory for Scala

The MIT License (MIT)

Copyright (c) 2014 - 2015 Jianbo Ye

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