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# VS Code files for those working on multiple tools
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||||
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||||
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|
||||
|
||||
# JetBrains Rider
|
||||
*.sln.iml
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
{
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
// Use IntelliSense to find out which attributes exist for C# debugging
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||||
// Use hover for the description of the existing attributes
|
||||
// For further information visit https://github.com/OmniSharp/omnisharp-vscode/blob/master/debugger-launchjson.md
|
||||
"name": ".NET Core Launch (console)",
|
||||
"type": "coreclr",
|
||||
"request": "launch",
|
||||
"preLaunchTask": "build",
|
||||
// If you have changed target frameworks, make sure to update the program path.
|
||||
"program": "${workspaceFolder}/bin/Debug/net5.0/NeuralNetwork2021.dll",
|
||||
"args": [],
|
||||
"cwd": "${workspaceFolder}",
|
||||
// For more information about the 'console' field, see https://aka.ms/VSCode-CS-LaunchJson-Console
|
||||
"console": "internalConsole",
|
||||
"stopAtEntry": false
|
||||
},
|
||||
{
|
||||
"name": ".NET Core Attach",
|
||||
"type": "coreclr",
|
||||
"request": "attach"
|
||||
}
|
||||
]
|
||||
}
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||||
|
|
@ -0,0 +1,42 @@
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|||
{
|
||||
"version": "2.0.0",
|
||||
"tasks": [
|
||||
{
|
||||
"label": "build",
|
||||
"command": "dotnet",
|
||||
"type": "process",
|
||||
"args": [
|
||||
"build",
|
||||
"${workspaceFolder}/NeuralNetwork2021.csproj",
|
||||
"/property:GenerateFullPaths=true",
|
||||
"/consoleloggerparameters:NoSummary"
|
||||
],
|
||||
"problemMatcher": "$msCompile"
|
||||
},
|
||||
{
|
||||
"label": "publish",
|
||||
"command": "dotnet",
|
||||
"type": "process",
|
||||
"args": [
|
||||
"publish",
|
||||
"${workspaceFolder}/NeuralNetwork2021.csproj",
|
||||
"/property:GenerateFullPaths=true",
|
||||
"/consoleloggerparameters:NoSummary"
|
||||
],
|
||||
"problemMatcher": "$msCompile"
|
||||
},
|
||||
{
|
||||
"label": "watch",
|
||||
"command": "dotnet",
|
||||
"type": "process",
|
||||
"args": [
|
||||
"watch",
|
||||
"run",
|
||||
"${workspaceFolder}/NeuralNetwork2021.csproj",
|
||||
"/property:GenerateFullPaths=true",
|
||||
"/consoleloggerparameters:NoSummary"
|
||||
],
|
||||
"problemMatcher": "$msCompile"
|
||||
}
|
||||
]
|
||||
}
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||||
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|
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<Project Sdk="Microsoft.NET.Sdk">
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||||
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||||
<PropertyGroup>
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||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net5.0</TargetFramework>
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||||
</PropertyGroup>
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||||
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||||
</Project>
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using System;
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||||
using System.Linq;
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||||
using Syntriax.NeuralNetwork;
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using Syntriax.NeuralNetwork.Misc;
|
||||
using Syntriax.NeuralNetwork.NeuronActivations;
|
||||
|
||||
namespace NeuralNetwork2021
|
||||
{
|
||||
class Program
|
||||
{
|
||||
static void Main(string[] args)
|
||||
{
|
||||
const int epochCount = 5000;
|
||||
const int epochPrintInterval = 1;
|
||||
|
||||
const int dataSeed = 10;
|
||||
const int weightSeed = 0;
|
||||
const int dropoutSeed = 0;
|
||||
DropoutNeuronDecorator.Random = new Random(dropoutSeed);
|
||||
|
||||
double learningRate = 0.001;
|
||||
|
||||
Data data = new Data(DataTest.LoadData().ToList(), 4, seed: dataSeed);
|
||||
|
||||
NeuralNetwork neuralNetwork = new NeuralNetwork
|
||||
(
|
||||
data.InputCount,
|
||||
new int[] { 10 },
|
||||
data.OutputCount
|
||||
);
|
||||
|
||||
foreach (LayerBase layer in neuralNetwork.GetLayerList())
|
||||
layer.SetActivation(StaticActivation<Relu>.Instance);
|
||||
// neuralNetwork.outputLayer.SetActivation(StaticActivation<Relu>.Instance);
|
||||
|
||||
neuralNetwork.Randomize(weightSeed);
|
||||
|
||||
for (int k = 0; k < epochCount; k++)
|
||||
{
|
||||
if (k % epochPrintInterval == 0)
|
||||
Console.WriteLine($"Epoch: {k}\tHata: { neuralNetwork.GetTotalError(data) }");
|
||||
|
||||
neuralNetwork.Train(data, learningRate);
|
||||
}
|
||||
Console.WriteLine($"Hata: { neuralNetwork.GetTotalError(data) }");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
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|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class InputNeuron : Neuron
|
||||
{
|
||||
public double Value = 0.0;
|
||||
|
||||
public override double Output => Value;
|
||||
}
|
||||
}
|
||||
|
|
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|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class HiddenLayer : LayerBase
|
||||
{
|
||||
public HiddenLayer(int neuronCount, LayerBase from = null) : base(neuronCount, from) { }
|
||||
|
||||
protected override void SetLayer(int neuronCount, LayerBase from)
|
||||
{
|
||||
neurons = new INeuron[neuronCount];
|
||||
|
||||
for (int i = 0; i < neuronCount; i++)
|
||||
{
|
||||
neurons[i] = new Neuron(from == null ? null : from.neurons);
|
||||
// neurons[i] = new DropoutNeuronDecorator(neurons[i], 0.2); // TODO
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class InputLayer : LayerBase
|
||||
{
|
||||
public InputLayer(int neuronCount, LayerBase from = null) : base(neuronCount, from) { }
|
||||
|
||||
protected override void SetLayer(int neuronCount, LayerBase from)
|
||||
{
|
||||
neurons = new Neuron[neuronCount];
|
||||
|
||||
for (int i = 0; i < neuronCount; i++)
|
||||
neurons[i] = new InputNeuron();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
using Syntriax.NeuralNetwork.NeuronActivations;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public abstract class LayerBase
|
||||
{
|
||||
public INeuron[] neurons { get; protected set; } = null;
|
||||
|
||||
public LayerBase(int neuronCount, LayerBase from = null) => SetLayer(neuronCount, from);
|
||||
|
||||
protected abstract void SetLayer(int neuronCount, LayerBase from);
|
||||
|
||||
public void FireLayer()
|
||||
{
|
||||
foreach (INeuron neuron in neurons)
|
||||
neuron.Calculate();
|
||||
}
|
||||
|
||||
public void SetActivation(INeuronActivation neuronActivation)
|
||||
{
|
||||
foreach (INeuron neuron in neurons)
|
||||
neuron.NeuronActivation = neuronActivation;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class OutputLayer : HiddenLayer
|
||||
{
|
||||
public OutputLayer(int neuronCount, LayerBase from = null) : base(neuronCount, from) { }
|
||||
|
||||
protected override void SetLayer(int neuronCount, LayerBase from)
|
||||
{
|
||||
neurons = new INeuron[neuronCount];
|
||||
|
||||
for (int i = 0; i < neuronCount; i++)
|
||||
neurons[i] = new Neuron(from.neurons);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace Syntriax.NeuralNetwork.Misc
|
||||
{
|
||||
public class Data
|
||||
{
|
||||
public int InputCount => trainInput[0].Length;
|
||||
public int OutputCount => trainOutput[0].Length;
|
||||
public List<double[]> trainInput { get; private set; } = null;
|
||||
public List<double[]> trainOutput { get; private set; } = null;
|
||||
public List<double[]> testInput { get; private set; } = null;
|
||||
public List<double[]> testOutput { get; private set; } = null;
|
||||
|
||||
public Data(List<double[]> data, int inputCount, double trainRatio = 0.2, int? seed = null)
|
||||
{
|
||||
int indexToSwap = 0;
|
||||
int count = data.Count;
|
||||
int testCount = (int)(count * trainRatio);
|
||||
Random random = new Random(seed ?? 0);
|
||||
|
||||
double[] inputArray = null;
|
||||
double[] outputArray = null;
|
||||
|
||||
trainInput = new List<double[]>(count - testCount);
|
||||
trainOutput = new List<double[]>(count - testCount);
|
||||
testInput = new List<double[]>(testCount);
|
||||
testOutput = new List<double[]>(testCount);
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
indexToSwap = random.Next(i, count);
|
||||
(data[i], data[indexToSwap]) = (data[indexToSwap], data[i]);
|
||||
}
|
||||
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
(inputArray, outputArray) = SplitData(data[i], inputCount);
|
||||
if (i < testCount)
|
||||
{
|
||||
testInput.Add(inputArray);
|
||||
testOutput.Add(outputArray);
|
||||
}
|
||||
else
|
||||
{
|
||||
trainInput.Add(inputArray);
|
||||
trainOutput.Add(outputArray);
|
||||
}
|
||||
}
|
||||
}
|
||||
public Data(List<double[]> train, List<double[]> test, int inputCount, double trainRatio = 0.2)
|
||||
{
|
||||
double[] inputArray = null;
|
||||
double[] outputArray = null;
|
||||
|
||||
trainInput = new List<double[]>(train.Count);
|
||||
trainOutput = new List<double[]>(train.Count);
|
||||
testInput = new List<double[]>(test.Count);
|
||||
testOutput = new List<double[]>(test.Count);
|
||||
|
||||
for (int i = 0; i < train.Count; i++)
|
||||
{
|
||||
(inputArray, outputArray) = SplitData(train[i], inputCount);
|
||||
trainInput.Add(inputArray);
|
||||
trainOutput.Add(outputArray);
|
||||
}
|
||||
|
||||
for (int i = 0; i < test.Count; i++)
|
||||
{
|
||||
(inputArray, outputArray) = SplitData(test[i], inputCount);
|
||||
testInput.Add(inputArray);
|
||||
testOutput.Add(outputArray);
|
||||
}
|
||||
}
|
||||
|
||||
private (double[] inputArray, double[] outputArray) SplitData(double[] array, int inputCount)
|
||||
{
|
||||
int outputCount = array.Length - inputCount + 1;
|
||||
int i = 0;
|
||||
double[] input = new double[inputCount];
|
||||
double[] output = new double[outputCount];
|
||||
|
||||
for (i = 0; i < array.Length; i++)
|
||||
if (i >= inputCount)
|
||||
output[i - inputCount] = array[i];
|
||||
else
|
||||
input[i] = array[i];
|
||||
|
||||
return (input, output);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
// using System;
|
||||
|
||||
// namespace Syntriax.NeuralNetwork.Misc
|
||||
// {
|
||||
// public class DataNew
|
||||
// {
|
||||
// public int InputCount => trainInput.GetLength(0);
|
||||
// public int OutputCount => trainOutput.GetLength(0);
|
||||
// public double[,] trainInput { get; private set; } = null;
|
||||
// public double[,] trainOutput { get; private set; } = null;
|
||||
// public double[,] testInput { get; private set; } = null;
|
||||
// public double[,] testOutput { get; private set; } = null;
|
||||
|
||||
// public DataNew(double[,] data, int inputCount, double testRatio = 0.2, int? seed = null)
|
||||
// {
|
||||
// int attributeCount = data.GetLength(1);
|
||||
// int dataCount = data.GetLength(0);
|
||||
// int testCount = (int)(dataCount * testRatio);
|
||||
// int trainCount = dataCount - testCount;
|
||||
|
||||
// double[,] trainData = new double[trainCount, attributeCount];
|
||||
// double[,] testData = new double[testCount, attributeCount];
|
||||
|
||||
// if (seed != null)
|
||||
// Shuffle(data, attributeCount, new Random(seed.Value));
|
||||
|
||||
// int i = 0;
|
||||
// int a = 0;
|
||||
// for (i = 0; i < dataCount; i++)
|
||||
// for (a = 0; a < attributeCount; a++)
|
||||
|
||||
|
||||
// }
|
||||
|
||||
// private void Shuffle(double[,] data, int attributeCount, Random random)
|
||||
// {
|
||||
// int indexToSwap = 0;
|
||||
// int length = data.Length;
|
||||
// int i = 0;
|
||||
// int a = 0;
|
||||
|
||||
// for (i = 0; i < length; i++)
|
||||
// {
|
||||
// indexToSwap = random.Next(i, length);
|
||||
// for (a = 0; a < attributeCount; a++)
|
||||
// (data[i, a], data[indexToSwap, a]) = (data[indexToSwap, a], data[i, a]);
|
||||
// }
|
||||
// }
|
||||
|
||||
// public DataNew(double[,] train, double[,] test, int inputCount)
|
||||
// {
|
||||
// int attributeCount = train.GetLength(1);
|
||||
// int attributeInputCount = inputCount;
|
||||
// int attributeOutputCount = attributeCount - attributeInputCount;
|
||||
|
||||
// int trainCount = train.GetLength(0);
|
||||
// int testCount = test.GetLength(0);
|
||||
|
||||
// trainInput = new double[train.GetLength(0), attributeInputCount];
|
||||
// trainOutput = new double[train.GetLength(0), attributeOutputCount];
|
||||
// testInput = new double[test.GetLength(0), attributeInputCount];
|
||||
// testOutput = new double[test.GetLength(0), attributeOutputCount];
|
||||
|
||||
// SplitIntoAttributeArrays(train, trainInput, trainOutput, trainCount, attributeInputCount, attributeOutputCount);
|
||||
// SplitIntoAttributeArrays(test, testInput, testOutput, testCount, attributeInputCount, attributeOutputCount);
|
||||
// }
|
||||
|
||||
// private void SplitIntoAttributeArrays(double[,] source, double[,] inputDestination, double[,] outputDestination,
|
||||
// int count, int inputCount, int outputCount)
|
||||
// {
|
||||
// int i = 0;
|
||||
// int j = 0;
|
||||
|
||||
// for (i = 0; i < count; i++)
|
||||
// {
|
||||
// for (j = 0; j < inputCount; j++)
|
||||
// inputDestination[i, j] = source[i, j];
|
||||
// for (j = 0; j < outputCount; j++)
|
||||
// outputDestination[i, j] = source[i, j + inputCount];
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
|
|
@ -0,0 +1,163 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.IO;
|
||||
|
||||
namespace Syntriax.NeuralNetwork.Misc
|
||||
{
|
||||
public class DataTest
|
||||
{
|
||||
public static double[][] LoadData() => new double[][]
|
||||
{
|
||||
new double[] {5.1,3.5,1.4,0.2,0,0,1},
|
||||
new double[] {4.9,3.0,1.4,0.2,0,0,1},
|
||||
new double[] {4.7,3.2,1.3,0.2,0,0,1},
|
||||
new double[] {4.6,3.1,1.5,0.2,0,0,1},
|
||||
new double[] {5.0,3.6,1.4,0.2,0,0,1},
|
||||
new double[] {5.4,3.9,1.7,0.4,0,0,1},
|
||||
new double[] {4.6,3.4,1.4,0.3,0,0,1},
|
||||
new double[] {5.0,3.4,1.5,0.2,0,0,1},
|
||||
new double[] {4.4,2.9,1.4,0.2,0,0,1},
|
||||
new double[] {4.9,3.1,1.5,0.1,0,0,1},
|
||||
new double[] {5.4,3.7,1.5,0.2,0,0,1},
|
||||
new double[] {4.8,3.4,1.6,0.2,0,0,1},
|
||||
new double[] {4.8,3.0,1.4,0.1,0,0,1},
|
||||
new double[] {4.3,3.0,1.1,0.1,0,0,1},
|
||||
new double[] {5.8,4.0,1.2,0.2,0,0,1},
|
||||
new double[] {5.7,4.4,1.5,0.4,0,0,1},
|
||||
new double[] {5.4,3.9,1.3,0.4,0,0,1},
|
||||
new double[] {5.1,3.5,1.4,0.3,0,0,1},
|
||||
new double[] {5.7,3.8,1.7,0.3,0,0,1},
|
||||
new double[] {5.1,3.8,1.5,0.3,0,0,1},
|
||||
new double[] {5.4,3.4,1.7,0.2,0,0,1},
|
||||
new double[] {5.1,3.7,1.5,0.4,0,0,1},
|
||||
new double[] {4.6,3.6,1.0,0.2,0,0,1},
|
||||
new double[] {5.1,3.3,1.7,0.5,0,0,1},
|
||||
new double[] {4.8,3.4,1.9,0.2,0,0,1},
|
||||
new double[] {5.0,3.0,1.6,0.2,0,0,1},
|
||||
new double[] {5.0,3.4,1.6,0.4,0,0,1},
|
||||
new double[] {5.2,3.5,1.5,0.2,0,0,1},
|
||||
new double[] {5.2,3.4,1.4,0.2,0,0,1},
|
||||
new double[] {4.7,3.2,1.6,0.2,0,0,1},
|
||||
new double[] {4.8,3.1,1.6,0.2,0,0,1},
|
||||
new double[] {5.4,3.4,1.5,0.4,0,0,1},
|
||||
new double[] {5.2,4.1,1.5,0.1,0,0,1},
|
||||
new double[] {5.5,4.2,1.4,0.2,0,0,1},
|
||||
new double[] {4.9,3.1,1.5,0.1,0,0,1},
|
||||
new double[] {5.0,3.2,1.2,0.2,0,0,1},
|
||||
new double[] {5.5,3.5,1.3,0.2,0,0,1},
|
||||
new double[] {4.9,3.1,1.5,0.1,0,0,1},
|
||||
new double[] {4.4,3.0,1.3,0.2,0,0,1},
|
||||
new double[] {5.1,3.4,1.5,0.2,0,0,1},
|
||||
new double[] {5.0,3.5,1.3,0.3,0,0,1},
|
||||
new double[] {4.5,2.3,1.3,0.3,0,0,1},
|
||||
new double[] {4.4,3.2,1.3,0.2,0,0,1},
|
||||
new double[] {5.0,3.5,1.6,0.6,0,0,1},
|
||||
new double[] {5.1,3.8,1.9,0.4,0,0,1},
|
||||
new double[] {4.8,3.0,1.4,0.3,0,0,1},
|
||||
new double[] {5.1,3.8,1.6,0.2,0,0,1},
|
||||
new double[] {4.6,3.2,1.4,0.2,0,0,1},
|
||||
new double[] {5.3,3.7,1.5,0.2,0,0,1},
|
||||
new double[] {5.0,3.3,1.4,0.2,0,0,1},
|
||||
new double[] {7.0,3.2,4.7,1.4,0,1,0},
|
||||
new double[] {6.4,3.2,4.5,1.5,0,1,0},
|
||||
new double[] {6.9,3.1,4.9,1.5,0,1,0},
|
||||
new double[] {5.5,2.3,4.0,1.3,0,1,0},
|
||||
new double[] {6.5,2.8,4.6,1.5,0,1,0},
|
||||
new double[] {5.7,2.8,4.5,1.3,0,1,0},
|
||||
new double[] {6.3,3.3,4.7,1.6,0,1,0},
|
||||
new double[] {4.9,2.4,3.3,1.0,0,1,0},
|
||||
new double[] {6.6,2.9,4.6,1.3,0,1,0},
|
||||
new double[] {5.2,2.7,3.9,1.4,0,1,0},
|
||||
new double[] {5.0,2.0,3.5,1.0,0,1,0},
|
||||
new double[] {5.9,3.0,4.2,1.5,0,1,0},
|
||||
new double[] {6.0,2.2,4.0,1.0,0,1,0},
|
||||
new double[] {6.1,2.9,4.7,1.4,0,1,0},
|
||||
new double[] {5.6,2.9,3.6,1.3,0,1,0},
|
||||
new double[] {6.7,3.1,4.4,1.4,0,1,0},
|
||||
new double[] {5.6,3.0,4.5,1.5,0,1,0},
|
||||
new double[] {5.8,2.7,4.1,1.0,0,1,0},
|
||||
new double[] {6.2,2.2,4.5,1.5,0,1,0},
|
||||
new double[] {5.6,2.5,3.9,1.1,0,1,0},
|
||||
new double[] {5.9,3.2,4.8,1.8,0,1,0},
|
||||
new double[] {6.1,2.8,4.0,1.3,0,1,0},
|
||||
new double[] {6.3,2.5,4.9,1.5,0,1,0},
|
||||
new double[] {6.1,2.8,4.7,1.2,0,1,0},
|
||||
new double[] {6.4,2.9,4.3,1.3,0,1,0},
|
||||
new double[] {6.6,3.0,4.4,1.4,0,1,0},
|
||||
new double[] {6.8,2.8,4.8,1.4,0,1,0},
|
||||
new double[] {6.7,3.0,5.0,1.7,0,1,0},
|
||||
new double[] {6.0,2.9,4.5,1.5,0,1,0},
|
||||
new double[] {5.7,2.6,3.5,1.0,0,1,0},
|
||||
new double[] {5.5,2.4,3.8,1.1,0,1,0},
|
||||
new double[] {5.5,2.4,3.7,1.0,0,1,0},
|
||||
new double[] {5.8,2.7,3.9,1.2,0,1,0},
|
||||
new double[] {6.0,2.7,5.1,1.6,0,1,0},
|
||||
new double[] {5.4,3.0,4.5,1.5,0,1,0},
|
||||
new double[] {6.0,3.4,4.5,1.6,0,1,0},
|
||||
new double[] {6.7,3.1,4.7,1.5,0,1,0},
|
||||
new double[] {6.3,2.3,4.4,1.3,0,1,0},
|
||||
new double[] {5.6,3.0,4.1,1.3,0,1,0},
|
||||
new double[] {5.5,2.5,4.0,1.3,0,1,0},
|
||||
new double[] {5.5,2.6,4.4,1.2,0,1,0},
|
||||
new double[] {6.1,3.0,4.6,1.4,0,1,0},
|
||||
new double[] {5.8,2.6,4.0,1.2,0,1,0},
|
||||
new double[] {5.0,2.3,3.3,1.0,0,1,0},
|
||||
new double[] {5.6,2.7,4.2,1.3,0,1,0},
|
||||
new double[] {5.7,3.0,4.2,1.2,0,1,0},
|
||||
new double[] {5.7,2.9,4.2,1.3,0,1,0},
|
||||
new double[] {6.2,2.9,4.3,1.3,0,1,0},
|
||||
new double[] {5.1,2.5,3.0,1.1,0,1,0},
|
||||
new double[] {5.7,2.8,4.1,1.3,0,1,0},
|
||||
new double[] {6.3,3.3,6.0,2.5,1,0,0},
|
||||
new double[] {5.8,2.7,5.1,1.9,1,0,0},
|
||||
new double[] {7.1,3.0,5.9,2.1,1,0,0},
|
||||
new double[] {6.3,2.9,5.6,1.8,1,0,0},
|
||||
new double[] {6.5,3.0,5.8,2.2,1,0,0},
|
||||
new double[] {7.6,3.0,6.6,2.1,1,0,0},
|
||||
new double[] {4.9,2.5,4.5,1.7,1,0,0},
|
||||
new double[] {7.3,2.9,6.3,1.8,1,0,0},
|
||||
new double[] {6.7,2.5,5.8,1.8,1,0,0},
|
||||
new double[] {7.2,3.6,6.1,2.5,1,0,0},
|
||||
new double[] {6.5,3.2,5.1,2.0,1,0,0},
|
||||
new double[] {6.4,2.7,5.3,1.9,1,0,0},
|
||||
new double[] {6.8,3.0,5.5,2.1,1,0,0},
|
||||
new double[] {5.7,2.5,5.0,2.0,1,0,0},
|
||||
new double[] {5.8,2.8,5.1,2.4,1,0,0},
|
||||
new double[] {6.4,3.2,5.3,2.3,1,0,0},
|
||||
new double[] {6.5,3.0,5.5,1.8,1,0,0},
|
||||
new double[] {7.7,3.8,6.7,2.2,1,0,0},
|
||||
new double[] {7.7,2.6,6.9,2.3,1,0,0},
|
||||
new double[] {6.0,2.2,5.0,1.5,1,0,0},
|
||||
new double[] {6.9,3.2,5.7,2.3,1,0,0},
|
||||
new double[] {5.6,2.8,4.9,2.0,1,0,0},
|
||||
new double[] {7.7,2.8,6.7,2.0,1,0,0},
|
||||
new double[] {6.3,2.7,4.9,1.8,1,0,0},
|
||||
new double[] {6.7,3.3,5.7,2.1,1,0,0},
|
||||
new double[] {7.2,3.2,6.0,1.8,1,0,0},
|
||||
new double[] {6.2,2.8,4.8,1.8,1,0,0},
|
||||
new double[] {6.1,3.0,4.9,1.8,1,0,0},
|
||||
new double[] {6.4,2.8,5.6,2.1,1,0,0},
|
||||
new double[] {7.2,3.0,5.8,1.6,1,0,0},
|
||||
new double[] {7.4,2.8,6.1,1.9,1,0,0},
|
||||
new double[] {7.9,3.8,6.4,2.0,1,0,0},
|
||||
new double[] {6.4,2.8,5.6,2.2,1,0,0},
|
||||
new double[] {6.3,2.8,5.1,1.5,1,0,0},
|
||||
new double[] {6.1,2.6,5.6,1.4,1,0,0},
|
||||
new double[] {7.7,3.0,6.1,2.3,1,0,0},
|
||||
new double[] {6.3,3.4,5.6,2.4,1,0,0},
|
||||
new double[] {6.4,3.1,5.5,1.8,1,0,0},
|
||||
new double[] {6.0,3.0,4.8,1.8,1,0,0},
|
||||
new double[] {6.9,3.1,5.4,2.1,1,0,0},
|
||||
new double[] {6.7,3.1,5.6,2.4,1,0,0},
|
||||
new double[] {6.9,3.1,5.1,2.3,1,0,0},
|
||||
new double[] {5.8,2.7,5.1,1.9,1,0,0},
|
||||
new double[] {6.8,3.2,5.9,2.3,1,0,0},
|
||||
new double[] {6.7,3.3,5.7,2.5,1,0,0},
|
||||
new double[] {6.7,3.0,5.2,2.3,1,0,0},
|
||||
new double[] {6.3,2.5,5.0,1.9,1,0,0},
|
||||
new double[] {6.5,3.0,5.2,2.0,1,0,0},
|
||||
new double[] {6.2,3.4,5.4,2.3,1,0,0},
|
||||
new double[] {5.9,3.0,5.1,1.8,1,0,0}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class NeuralNetwork
|
||||
{
|
||||
public LayerBase inputLayer { get; private set; } = null;
|
||||
public LayerBase[] hiddenLayers { get; private set; } = null;
|
||||
public LayerBase outputLayer { get; private set; } = null;
|
||||
|
||||
public NeuralNetwork(int inputCount, int[] hiddenCounts = null, int outputCount = 1)
|
||||
{
|
||||
inputLayer = new InputLayer(inputCount);
|
||||
|
||||
if (hiddenCounts != null && hiddenCounts.Length > 0)
|
||||
{
|
||||
int hiddenCount = hiddenCounts.Length;
|
||||
|
||||
hiddenLayers = new HiddenLayer[hiddenCount];
|
||||
hiddenLayers[0] = new HiddenLayer(hiddenCounts[0], inputLayer);
|
||||
|
||||
for (int i = 1; i < hiddenCount; i++)
|
||||
hiddenLayers[i] = new HiddenLayer(hiddenCounts[i], hiddenLayers[i - 1]);
|
||||
|
||||
outputLayer = new HiddenLayer(outputCount, hiddenLayers[hiddenLayers.Length - 1]);
|
||||
return;
|
||||
}
|
||||
|
||||
outputLayer = new OutputLayer(outputCount, inputLayer);
|
||||
}
|
||||
|
||||
public void FireNetwork()
|
||||
{
|
||||
if (hiddenLayers != null)
|
||||
foreach (LayerBase layer in hiddenLayers)
|
||||
layer.FireLayer();
|
||||
|
||||
outputLayer.FireLayer();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,184 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using Syntriax.NeuralNetwork.Misc;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public static class NeuralNetworkExtensions
|
||||
{
|
||||
public static List<LayerBase> GetLayerList(this NeuralNetwork neuralNetwork)
|
||||
{
|
||||
List<LayerBase> layers = new List<LayerBase>(2 + neuralNetwork.hiddenLayers.Length);
|
||||
|
||||
layers.Add(neuralNetwork.inputLayer);
|
||||
layers.AddRange(neuralNetwork.hiddenLayers);
|
||||
layers.Add(neuralNetwork.outputLayer);
|
||||
|
||||
return layers;
|
||||
}
|
||||
|
||||
public static List<INeuron> GetNeuronList(this NeuralNetwork neuralNetwork)
|
||||
{
|
||||
int neuronCount = 0;
|
||||
List<LayerBase> layers = neuralNetwork.GetLayerList();
|
||||
|
||||
foreach (LayerBase layer in layers)
|
||||
neuronCount += layer.neurons.Length;
|
||||
|
||||
List<INeuron> neurons = new List<INeuron>(neuronCount);
|
||||
|
||||
foreach (LayerBase layer in layers)
|
||||
neurons.AddRange(layer.neurons);
|
||||
|
||||
return neurons;
|
||||
}
|
||||
|
||||
public static List<ISynapse> GetSynapseList(this NeuralNetwork neuralNetwork)
|
||||
{
|
||||
int synapseCount = 0;
|
||||
List<INeuron> neurons = neuralNetwork.GetNeuronList();
|
||||
|
||||
foreach (INeuron neuron in neurons)
|
||||
synapseCount += neuron.Synapses.Length + 1; // + 1 for the bias
|
||||
|
||||
List<ISynapse> synapses = new List<ISynapse>(synapseCount);
|
||||
|
||||
foreach (INeuron neuron in neurons)
|
||||
{
|
||||
synapses.AddRange(neuron.Synapses);
|
||||
synapses.Add(neuron.Bias);
|
||||
}
|
||||
|
||||
return synapses;
|
||||
}
|
||||
|
||||
/* ------------------------------------------------------------- */
|
||||
|
||||
public static void Randomize(this NeuralNetwork neuralNetwork, int? seed = null)
|
||||
{
|
||||
Random random = new Random(seed ?? 0);
|
||||
|
||||
foreach (ISynapse synapse in neuralNetwork.GetSynapseList())
|
||||
synapse.Weight = random.NextDouble();
|
||||
}
|
||||
|
||||
/* ------------------------------------------------------------- */
|
||||
|
||||
public static void SetInput(this NeuralNetwork neuralNetwork, int neuronIndex, double value)
|
||||
{
|
||||
InputNeuron inputNeuron = (InputNeuron)neuralNetwork.inputLayer.neurons[neuronIndex];
|
||||
inputNeuron.Value = value;
|
||||
}
|
||||
public static void SetInputs(this NeuralNetwork neuralNetwork, double[] inputs)
|
||||
{
|
||||
for (int i = 0; i < inputs.Length; i++)
|
||||
neuralNetwork.SetInput(i, inputs[i]);
|
||||
}
|
||||
|
||||
public static double GetOutput(this NeuralNetwork neuralNetwork, int neuronIndex) =>
|
||||
neuralNetwork.outputLayer.neurons[neuronIndex].Output;
|
||||
|
||||
|
||||
/* ------------------------------------------------------------- */
|
||||
|
||||
public static double GetErrorOfNeuron(NeuralNetwork neuralNetwork, double[] output, int i) =>
|
||||
output[i] - neuralNetwork.GetOutput(i);
|
||||
|
||||
public static double[] GetErrors(this NeuralNetwork neuralNetwork, double[] outputs)
|
||||
{
|
||||
int length = neuralNetwork.outputLayer.neurons.Length;
|
||||
double[] result = new double[length];
|
||||
for (int i = 0; i < length; i++)
|
||||
result[i] = GetErrorOfNeuron(neuralNetwork, outputs, i);
|
||||
return result;
|
||||
}
|
||||
|
||||
public static double GetTotalError(this NeuralNetwork neuralNetwork, Data data)
|
||||
{
|
||||
double totalError = 0.0f;
|
||||
double[] errors = null;
|
||||
|
||||
for (int i = 0; i < data.testInput.Count; i++)
|
||||
{
|
||||
neuralNetwork.SetInputs(data.testInput[i]);
|
||||
neuralNetwork.FireNetwork();
|
||||
|
||||
errors = neuralNetwork.GetErrors(data.testOutput[i]);
|
||||
|
||||
foreach (double error in errors)
|
||||
totalError += error * error;
|
||||
}
|
||||
|
||||
return totalError;
|
||||
}
|
||||
|
||||
/* ------------------------------------------------------------- */
|
||||
|
||||
public static double[] GetSoftMax(this NeuralNetwork neuralNetwork)
|
||||
{
|
||||
// TODO
|
||||
return null;
|
||||
}
|
||||
|
||||
public static int GetMaxIndex(this NeuralNetwork neuralNetwork)
|
||||
{
|
||||
double maxValue = double.MinValue;
|
||||
int maxIndex = 0;
|
||||
|
||||
int count = neuralNetwork.outputLayer.neurons.Length;
|
||||
for (int i = 0; i < count; i++)
|
||||
{
|
||||
double output = neuralNetwork.GetOutput(i);
|
||||
if (output > maxValue)
|
||||
{
|
||||
maxValue = output;
|
||||
maxIndex = i;
|
||||
}
|
||||
}
|
||||
|
||||
return maxIndex;
|
||||
}
|
||||
|
||||
/* ------------------------------------------------------------- */
|
||||
|
||||
public static void Train(this NeuralNetwork neuralNetwork, Data data, double learningRate)
|
||||
{
|
||||
double[] errors = null;
|
||||
for (int i = 0; i < data.trainInput.Count; i++)
|
||||
{
|
||||
neuralNetwork.SetInputs(data.trainInput[i]);
|
||||
neuralNetwork.FireNetwork();
|
||||
|
||||
errors = neuralNetwork.GetErrors(data.trainOutput[i]);
|
||||
|
||||
neuralNetwork.BackPropagate(errors, learningRate);
|
||||
}
|
||||
}
|
||||
|
||||
public static void BackPropagate(this NeuralNetwork neuralNetwork, double[] errors, double learningRate)
|
||||
{
|
||||
for (int i = 0; i < errors.Length; i++)
|
||||
Correct(
|
||||
neuralNetwork.outputLayer.neurons[i],
|
||||
errors[i],
|
||||
learningRate
|
||||
);
|
||||
}
|
||||
|
||||
public static void Correct(this INeuron neuron, double error, double learningRate)
|
||||
{
|
||||
if (neuron.Synapses == null)
|
||||
return;
|
||||
|
||||
double incoming = neuron.NeuronActivation.Derivative(neuron.Output) * error;
|
||||
foreach (ISynapse synapse in neuron.Synapses)
|
||||
Correct(synapse.From, incoming * synapse.Weight, learningRate);
|
||||
|
||||
double delta = incoming * learningRate;
|
||||
foreach (ISynapse synapse in neuron.Synapses)
|
||||
synapse.Weight += delta * synapse.From.Output;
|
||||
|
||||
neuron.Bias.Weight += delta;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
using System;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class DropoutNeuronDecorator : NeuronDecorator
|
||||
{
|
||||
private double dropoutRate = 0;
|
||||
public bool IsActive = false;
|
||||
public static Random Random = new Random(0);
|
||||
|
||||
public DropoutNeuronDecorator(INeuron neuron, double dropoutRate) : base(neuron) => this.dropoutRate = dropoutRate;
|
||||
|
||||
public override double Calculate()
|
||||
{
|
||||
base.Calculate();
|
||||
|
||||
if (Random.NextDouble() < dropoutRate)
|
||||
Output = 0.0;
|
||||
|
||||
return Output;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
using Syntriax.NeuralNetwork.NeuronActivations;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public interface INeuron
|
||||
{
|
||||
ISynapse[] Synapses { get; }
|
||||
ISynapse Bias { get; }
|
||||
INeuronActivation NeuronActivation { get; set; }
|
||||
double Output { get; }
|
||||
|
||||
double Calculate();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,49 @@
|
|||
using Syntriax.NeuralNetwork.NeuronActivations;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class Neuron : INeuron
|
||||
{
|
||||
public ISynapse[] Synapses { get; private set; } = null;
|
||||
public ISynapse Bias { get; private set; } = null;
|
||||
|
||||
public INeuronActivation NeuronActivation { get; set; } = new Default();
|
||||
|
||||
public virtual double Output { get; private set; } = 0.0;
|
||||
|
||||
public Neuron(INeuron[] neurons = null)
|
||||
{
|
||||
if (neurons == null)
|
||||
{
|
||||
Synapses = new Synapse[0];
|
||||
Bias = new Synapse();
|
||||
return;
|
||||
}
|
||||
|
||||
Synapses = new ISynapse[neurons.Length];
|
||||
Bias = new Synapse();
|
||||
|
||||
int length = neurons.Length;
|
||||
for (int i = 0; i < length; i++)
|
||||
{
|
||||
Synapses[i] = new Synapse();
|
||||
Synapses[i].From = neurons[i];
|
||||
// Synapses[i] = new MomentumSynapseDecorator(Synapses[i]); // TODO
|
||||
}
|
||||
}
|
||||
|
||||
public double Calculate()
|
||||
{
|
||||
Output = 0.0;
|
||||
|
||||
foreach (ISynapse synapse in Synapses)
|
||||
Output += synapse.Output;
|
||||
|
||||
Output += Bias.Output;
|
||||
|
||||
Output = NeuronActivation.Activation(Output);
|
||||
|
||||
return Output;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
using Syntriax.NeuralNetwork.NeuronActivations;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public abstract class NeuronDecorator : INeuron
|
||||
{
|
||||
protected INeuron _neuron = null;
|
||||
|
||||
public ISynapse[] Synapses => _neuron.Synapses;
|
||||
public ISynapse Bias => _neuron.Bias;
|
||||
|
||||
public INeuronActivation NeuronActivation
|
||||
{
|
||||
get => _neuron.NeuronActivation;
|
||||
set => _neuron.NeuronActivation = value;
|
||||
}
|
||||
|
||||
public virtual double Output { get; protected set; } = 0.0;
|
||||
|
||||
protected NeuronDecorator(INeuron neuron) => _neuron = neuron;
|
||||
|
||||
public virtual double Calculate()
|
||||
{
|
||||
Output = 0.0;
|
||||
|
||||
foreach (ISynapse synapse in Synapses)
|
||||
Output += synapse.Output;
|
||||
|
||||
Output += Bias.Output;
|
||||
|
||||
Output = NeuronActivation.Activation(Output);
|
||||
|
||||
return Output;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
namespace Syntriax.NeuralNetwork.NeuronActivations
|
||||
{
|
||||
public class Default : INeuronActivation
|
||||
{
|
||||
public double Activation(double value) => value;
|
||||
|
||||
public double Derivative(double value) => value;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
namespace Syntriax.NeuralNetwork.NeuronActivations
|
||||
{
|
||||
public interface INeuronActivation
|
||||
{
|
||||
double Activation(double value);
|
||||
double Derivative(double value);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
namespace Syntriax.NeuralNetwork.NeuronActivations
|
||||
{
|
||||
public class Relu : INeuronActivation
|
||||
{
|
||||
public double Activation(double value)
|
||||
{
|
||||
if (value >= 0.0)
|
||||
return value;
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
public double Derivative(double value)
|
||||
{
|
||||
if (value >= 0.0)
|
||||
return 1.0;
|
||||
return 0.001;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
using System;
|
||||
|
||||
namespace Syntriax.NeuralNetwork.NeuronActivations
|
||||
{
|
||||
public class Sigmoid : INeuronActivation
|
||||
{
|
||||
public double Activation(double value) => 1.0 / (1.0 + Math.Exp(-value));
|
||||
|
||||
public double Derivative(double value) => value * (1.0 - value);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
using System;
|
||||
|
||||
namespace Syntriax.NeuralNetwork.NeuronActivations
|
||||
{
|
||||
public class StaticActivation<T> where T : INeuronActivation, new()
|
||||
{
|
||||
private static readonly Lazy<T> instance = new Lazy<T>(() => new T());
|
||||
|
||||
public static T Instance => instance.Value;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
using System;
|
||||
|
||||
namespace Syntriax.NeuralNetwork.NeuronActivations
|
||||
{
|
||||
public class TanH : INeuronActivation
|
||||
{
|
||||
public double Activation(double value) => (Math.Exp(value) - Math.Exp(-value)) / (Math.Exp(value) + Math.Exp(-value));
|
||||
|
||||
public double Derivative(double value) => 1.0 - value * value;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
using System;
|
||||
|
||||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class DropoutSynapseDecorator : SynapseDecorator
|
||||
{
|
||||
public bool IsActive = false;
|
||||
public Random random = null;
|
||||
|
||||
public override double Weight
|
||||
{
|
||||
get => base.Weight;
|
||||
set
|
||||
{
|
||||
if (random.Next() % 2 == 0)
|
||||
base.Weight = 0;
|
||||
else
|
||||
base.Weight = value;
|
||||
}
|
||||
}
|
||||
|
||||
public DropoutSynapseDecorator(ISynapse synapse, Random random = null) : base(synapse)
|
||||
=> SetRandom(random);
|
||||
|
||||
public void SetRandom(Random random) => this.random = random;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public interface ISynapse
|
||||
{
|
||||
INeuron From { get; set; }
|
||||
double Output { get; }
|
||||
double Weight { get; set; }
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public class MomentumSynapseDecorator : SynapseDecorator
|
||||
{
|
||||
private double _momentum = 0.0;
|
||||
private const double Beta = 0.9;
|
||||
|
||||
public override double Weight
|
||||
{
|
||||
get => base.Weight;
|
||||
set
|
||||
{
|
||||
double difference = value - base.Weight;
|
||||
|
||||
// TODO Might be an error here
|
||||
if (difference * _momentum >= 0.0 == _momentum >= 0.0)
|
||||
base.Weight = value + _momentum * Beta;
|
||||
else
|
||||
base.Weight = value;
|
||||
|
||||
_momentum = difference;
|
||||
}
|
||||
}
|
||||
|
||||
public MomentumSynapseDecorator(ISynapse synapse) : base(synapse) { }
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
|
||||
public class Synapse : ISynapse
|
||||
{
|
||||
private INeuron from = null;
|
||||
public INeuron From
|
||||
{
|
||||
get => from;
|
||||
set
|
||||
{
|
||||
if (from == null)
|
||||
from = value;
|
||||
}
|
||||
}
|
||||
|
||||
public double Output => Weight * (from == null ? 1.0 : from.Output);
|
||||
|
||||
|
||||
public double Weight { get; set; } = 0.0;
|
||||
|
||||
public Synapse() { }
|
||||
public Synapse(INeuron from) => this.from = from;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
namespace Syntriax.NeuralNetwork
|
||||
{
|
||||
public abstract class SynapseDecorator : ISynapse
|
||||
{
|
||||
protected ISynapse _synapse = null;
|
||||
|
||||
public INeuron From { get => _synapse.From; set => _synapse.From = value; }
|
||||
|
||||
public virtual double Output => _synapse.Output;
|
||||
|
||||
public virtual double Weight { get => _synapse.Weight; set => _synapse.Weight = value; }
|
||||
|
||||
protected SynapseDecorator(ISynapse synapse) => SetSynapse(synapse);
|
||||
public virtual void SetSynapse(ISynapse synapse) => _synapse = synapse;
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue