SimpleDNN is a machine learning lightweight open-source library written in Kotlin designed to support relevant neural network architectures in natural language processing tasks
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README.md

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SimpleDNN is a machine learning lightweight open-source library written in Kotlin whose purpose is to support the development of feed-forward and recurrent Artificial Neural Networks.

SimpleDNN is part of KotlinNLP and has been designed to support relevant neural network architectures in natural language processing tasks such as pos-tagging, syntactic parsing and named-entity recognition.

As it is written in Kotlin it is interoperable with other JVM languages (e.g. Java and Scala). Mathematical operations within the library are performed with jblas. Different libraries can be used instead, although during our experiments it proved to be the fastest. The effort required is minimum as the essential mathematical functions are wrapped in a single package and fully tested — saving you valuable time.

Note

SimpleDNN does not use the computational graph model and does not perform automatic differentiation of functions.

In case you are looking for state-of-the-art technology to create sophisticated flexible network architectures, you should consider the following libraries: Deeplearning4j, mxnet, DyNet, Torch, Keras, Theano and TensorFlow.

If instead a simpler yet well structured neural network almost ready to use is what you need, then you are in the right place!

Introduction

Building a basic Neural Network with SimpleDNN does not require much more effort than just configuring a stack of neural network layers, with one input layer, any number of hidden layers, and one output layer:

import com.kotlinnlp.simplednn.core.neuralnetwork.NeuralNetwork
import com.kotlinnlp.simplednn.core.functionalities.activations.ELU
import com.kotlinnlp.simplednn.core.functionalities.activations.Softmax
import com.kotlinnlp.simplednn.core.layers.LayerType

/**
  * Create a fully connected neural network with an input layer with dropout,
  * two hidden layers with ELU activation function
  * and an output one with Softmax activation for classification purpose.
  */
val neuralNetwork = NeuralNetwork(
    LayerConfiguration( // input layer
        size = 784, 
        dropout = 0.25),
    LayerConfiguration( // first hidden layer
        size = 100,
        activationFunction = ELU(),
        connectionType = LayerType.Connection.Feedforward),
    LayerConfiguration( // second hidden layer
        size = 100, 
        ctivationFunction = ELU(),
        connectionType = LayerType.Connection.Feedforward),
    LayerConfiguration( // output layer
        size = 10,
        activationFunction = Softmax(),
        connectionType = LayerType.Connection.Feedforward))
    
neuralNetwork.initialize() // initialize the parameters to random values

This library consists of three main building blocks:

  • NeuralNetwork: the core module which contains the architectural model.
  • NeuralProcessor: it acts (reading) on a NeuralNetwork to compute its training and predictions.
  • Optimizer: it optimizes (writing) a NeuralNetwork applying the knowledge given from the errors accumulated during the training process.

Getting Started

Import with Maven

<dependency>
    <groupId>com.kotlinnlp</groupId>
    <artifactId>simplednn</artifactId>
    <version>0.11.0</version>
</dependency>

Examples

Try some examples of usage of SimpleDNN running the files in the examples folder.

To make the examples working download the datasets here, then set their paths copying the file example/config/configuration.yml.example to example/config/configuration.yml and editing it properly.

Model Serialization

A NeuralNetwork object contains the model as parameters (i.e. weights and bias) which can be optimized after a training process. It provides simple dump() and load() methods to serialize and afterwards read its model.

import java.io.FileInputStream
import java.io.FileOutputStream

fun main(args: Array<String>) {
    
    // Load the network from file
    val inputFilePath = "/path/to/input_net.serialized"
    val fileInputStream = FileInputStream(inputFilePath)
    val neuralNetwork = NeuralNetwork.load(fileInputStream)
    
    // Save the network to file    
    val outputFilePath = "/path/to/output_net.serialized"
    val fileOutputStream = FileOutputStream(outputFilePath)
    neuralNetwork.dump(fileOutputStream)
}

License

This software is released under the terms of the Mozilla Public License, v. 2.0

Contributions

We greatly appreciate any bug reports and contributions, which can be made by filing an issue or making a pull request through the github page.