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SimpleNeuralNet.cs
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SimpleNeuralNet.cs
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using System;
using System.Collections.Generic;
using System.Data;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using ConsoleTableExt;
namespace StandardExamples
{
class SimpleNeuralNet
{
public void Run()
{
NetworkModel model = new NetworkModel();
model.Layers.Add(new NeuralLayer(2, 0.1, "INPUT"));
model.Layers.Add(new NeuralLayer(1, 0.1, "OUTPUT"));
model.Build();
Console.WriteLine("----Before Training------------");
model.Print();
Console.WriteLine();
NeuralData X = new NeuralData(4);
X.Add(0, 0);
X.Add(0, 1);
X.Add(1, 0);
X.Add(1, 1);
NeuralData Y = new NeuralData(4);
Y.Add(0);
Y.Add(0);
Y.Add(0);
Y.Add(1);
model.Train(X, Y, iterations: 10, learningRate: 0.1);
Console.WriteLine();
Console.WriteLine("----After Training------------");
model.Print();
}
}
class Pulse
{
public double Value { get; set; }
}
class Dendrite
{
public Dendrite()
{
InputPulse = new Pulse();
}
public Pulse InputPulse { get; set; }
public double SynapticWeight { get; set; }
public bool Learnable { get; set; } = true;
}
class Neuron
{
public List<Dendrite> Dendrites { get; set; }
public Pulse OutputPulse { get; set; }
public Neuron()
{
Dendrites = new List<Dendrite>();
OutputPulse = new Pulse();
}
public void Fire()
{
OutputPulse.Value = Sum();
OutputPulse.Value = Activation(OutputPulse.Value);
}
public void UpdateWeights(double new_weights)
{
foreach (var terminal in Dendrites)
{
terminal.SynapticWeight = new_weights;
}
}
private double Sum()
{
double computeValue = 0.0f;
foreach (var d in Dendrites)
{
computeValue += d.InputPulse.Value * d.SynapticWeight;
}
return computeValue;
}
private double Activation(double input)
{
double threshold = 1;
return input <= threshold ? 0 : threshold;
}
}
class NeuralLayer
{
public List<Neuron> Neurons { get; set; }
public string Name { get; set; }
public double Weight { get; set; }
public NeuralLayer(int count, double initialWeight, string name = "")
{
Neurons = new List<Neuron>();
for (int i = 0; i < count; i++)
{
Neurons.Add(new Neuron());
}
Weight = initialWeight;
Name = name;
}
public void Optimize(double learningRate, double delta)
{
Weight += learningRate * delta;
foreach (var neuron in Neurons)
{
neuron.UpdateWeights(Weight);
}
}
public void Forward()
{
foreach (var neuron in Neurons)
{
neuron.Fire();
}
}
public void Log()
{
Console.WriteLine("{0}, Weight: {1}", Name, Weight);
}
}
class NetworkModel
{
public List<NeuralLayer> Layers { get; set; }
public NetworkModel()
{
Layers = new List<NeuralLayer>();
}
public void AddLayer(NeuralLayer layer)
{
int dendriteCount = 1;
if (Layers.Count > 0)
{
dendriteCount = Layers.Last().Neurons.Count;
}
foreach (var element in layer.Neurons)
{
for (int i = 0; i < dendriteCount; i++)
{
element.Dendrites.Add(new Dendrite());
}
}
}
public void Build()
{
int i = 0;
foreach (var layer in Layers)
{
if (i >= Layers.Count - 1)
{
break;
}
var nextLayer = Layers[i + 1];
CreateNetwork(layer, nextLayer);
i++;
}
}
public void Train(NeuralData X, NeuralData Y, int iterations, double learningRate = 0.1)
{
int epoch = 1;
//Loop till the number of iterations
while (iterations >= epoch)
{
//Get the input layers
var inputLayer = Layers[0];
List<double> outputs = new List<double>();
//Loop through the record
for (int i = 0; i < X.Data.Length; i++)
{
//Set the input data into the first layer
for (int j = 0; j < X.Data[i].Length; j++)
{
inputLayer.Neurons[j].OutputPulse.Value = X.Data[i][j];
}
//Fire all the neurons and collect the output
ComputeOutput();
outputs.Add(Layers.Last().Neurons.First().OutputPulse.Value);
}
//Check the accuracy score against Y with the actual output
double accuracySum = 0;
int y_counter = 0;
outputs.ForEach((x) => {
if (x == Y.Data[y_counter].First())
{
accuracySum++;
}
y_counter++;
});
//Optimize the synaptic weights
OptimizeWeights(accuracySum / y_counter);
Console.WriteLine("Epoch: {0}, Accuracy: {1} %", epoch, (accuracySum / y_counter) * 100);
epoch++;
}
}
public void Print()
{
DataTable dt = new DataTable();
dt.Columns.Add("Name");
dt.Columns.Add("Neurons");
dt.Columns.Add("Weight");
foreach (var element in Layers)
{
DataRow row = dt.NewRow();
row[0] = element.Name;
row[1] = element.Neurons.Count;
row[2] = element.Weight;
dt.Rows.Add(row);
}
ConsoleTableBuilder builder = ConsoleTableBuilder.From(dt);
builder.ExportAndWrite();
}
private void ComputeOutput()
{
bool first = true;
foreach (var layer in Layers)
{
//Skip first layer as it is input
if (first)
{
first = false;
continue;
}
layer.Forward();
}
}
private void OptimizeWeights(double accuracy)
{
float lr = 0.1f;
//Skip if the accuracy reached 100%
if (accuracy == 1)
{
return;
}
if (accuracy > 1)
{
lr = -lr;
}
//Update the weights for all the layers
foreach (var layer in Layers)
{
layer.Optimize(lr, 1);
}
}
private void CreateNetwork(NeuralLayer connectingFrom, NeuralLayer connectingTo)
{
foreach (var from in connectingFrom.Neurons)
{
from.Dendrites = new List<Dendrite>();
from.Dendrites.Add(new Dendrite());
}
foreach (var to in connectingTo.Neurons)
{
to.Dendrites = new List<Dendrite>();
foreach (var from in connectingFrom.Neurons)
{
to.Dendrites.Add(new Dendrite() { InputPulse = from.OutputPulse, SynapticWeight = connectingTo.Weight });
}
}
}
}
class NeuralData
{
public double[][] Data { get; set; }
int counter = 0;
public NeuralData(int rows)
{
Data = new double[rows][];
}
public void Add(params double[] rec)
{
Data[counter] = rec;
counter++;
}
}
}