private static void Main()
{
List <string> inputList = new List <string> {
"input1", "input2"
};
List <string> outputList = new List <string> {
"output1", "output2"
};
Console.WriteLine("Basic test");
Console.WriteLine();
NeuralNetwork network1 = new NeuralNetwork(inputList, outputList, 2, 2);
network1.HiddenNeurons[0].InputLinks[0].Weight = 1;
network1.HiddenNeurons[2].InputLinks[0].Weight = 1;
network1.OutputNeurons[0].InputLinks[0].Weight = 1;
network1.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }
});
Console.WriteLine(network1);
Console.WriteLine();
Console.WriteLine("Copying test");
Console.WriteLine();
NeuralNetwork network2 = network1.Copy();
network2.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }
});
Console.WriteLine(network2);
Console.WriteLine();
Console.WriteLine("Random weights test");
Console.WriteLine();
NeuralNetwork network3 = network2.Copy();
network3.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }
});
Console.WriteLine(network3);
Console.WriteLine();
network3.HiddenNeurons[0].InputLinks[0].Weight = -0.083920758234613382;
network3.HiddenNeurons[0].InputLinks[1].Weight = -0.10348547168394251;
network3.HiddenNeurons[1].InputLinks[0].Weight = 0.9864023685598281;
network3.HiddenNeurons[1].InputLinks[1].Weight = 0.15295621398799475;
network3.HiddenNeurons[2].InputLinks[0].Weight = -0.070737256110288152;
network3.HiddenNeurons[2].InputLinks[1].Weight = -1.0365333983959844;
network3.HiddenNeurons[3].InputLinks[0].Weight = 0.46109638325388069;
network3.HiddenNeurons[3].InputLinks[1].Weight = -0.25149451732354522;
network3.OutputNeurons[0].InputLinks[0].Weight = 0.50648030282771972;
network3.OutputNeurons[0].InputLinks[1].Weight = 0.51670017869582552;
network3.OutputNeurons[1].InputLinks[0].Weight = 0.23281424843657786;
network3.OutputNeurons[1].InputLinks[1].Weight = -0.60550942224139681;
network3.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }
});
Console.WriteLine(network3);
Console.WriteLine();
Console.WriteLine("xml serialization test");
Console.WriteLine();
network3.Serialize("network.xml");
NeuralNetwork network4 = NeuralNetwork.Deserialize("network.xml");
network4.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }
});
Console.WriteLine(network4);
Console.WriteLine();
Console.WriteLine("file save/load test");
Console.WriteLine();
network4.ExtraProperties = new Dictionary <string, string>
{
{ "propDouble", 5.3.ToString() },
{ "propFloat", 1.1f.ToString() },
{ "propString", "abcd" },
};
network4.SaveToFile("network.txt");
NeuralNetwork network5 = NeuralNetwork.LoadFromFile("network.txt");
foreach (string propName in network5.ExtraProperties.Keys)
{
Console.WriteLine($"{propName} {network5.ExtraProperties[propName]}");
}
network5.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }
});
Console.WriteLine(network5);
Console.WriteLine();
Console.WriteLine("Adding input neuron test");
Console.WriteLine();
network5.AddInputNeuron("newInput", true);
network5.HiddenNeurons[0].SetWeight("newInput", 0.333);
network5.HiddenNeurons[1].SetWeight("newInput", -0.333);
network5.Feedforward(new Dictionary <string, double> {
{ "input1", 2 }, { "input2", 3 }, { "newInput", 5.5 }
});
Console.WriteLine(network5);
Console.WriteLine();
Console.WriteLine("Removing input neuron test");
Console.WriteLine();
network5.RemoveInputNeuron("input1");
network5.Feedforward(new Dictionary <string, double> {
{ "input2", 2 }, { "newInput", 3 }
});
Console.WriteLine(network5);
Console.WriteLine();
Console.WriteLine("Adding output neuron test");
Console.WriteLine();
Neuron newOutputNeuron = network5.AddOutputNeuron("newOutput", true);
newOutputNeuron.SetWeights(new List <double> {
0.555, -0.555
});
network5.Feedforward(new Dictionary <string, double> {
{ "input2", 2 }, { "newInput", 3 }
});
Console.WriteLine(network5);
Console.WriteLine();
Console.WriteLine("Removing output neuron test");
Console.WriteLine();
network5.RemoveOutputNeuron("output1");
network5.Feedforward(new Dictionary <string, double> {
{ "input2", 2 }, { "newInput", 3 }
});
Console.WriteLine(network5);
Console.WriteLine();
Console.WriteLine("Diff test");
Console.WriteLine();
NeuralNetwork network6 = network5.Copy();
network6.Mutate(1, 1);
network6.Diff(network5);
foreach (Neuron neuron in network6.AllNeurons)
{
Console.WriteLine($"{neuron.Name}");
foreach (double weightDelta in neuron.WeightDeltas)
{
Console.WriteLine($" {weightDelta}");
}
}
Console.WriteLine(network6);
network6.Push(0.1);
network6.Feedforward(new Dictionary <string, double> {
{ "input2", 2 }, { "newInput", 3 }
});
Console.WriteLine(network6);
}
/// <summary>
/// Loads neural network from txt file.
/// </summary>
/// <param name="filename">Name of the file.</param>
/// <returns>Neural network loaded from the file.</returns>
#pragma warning disable SA1204 // Static elements should appear before instance elements
public static NeuralNetwork LoadFromFile(string filename)
#pragma warning restore SA1204 // Static elements should appear before instance elements
{
// object which will be returned
NeuralNetwork loadedNetwork = new NeuralNetwork();
using (StreamReader reader = new StreamReader(filename))
{
// loading network properties
loadedNetwork.Id = int.Parse(reader.ReadLine().Split(' ')[1]);
// loading extra properties
while (true)
{
string nextLine = reader.ReadLine();
// list of properties ends with empty line
if (nextLine == null || nextLine == string.Empty)
{
break;
}
loadedNetwork.ExtraProperties.Add(nextLine.Split(' ')[0], nextLine.Split(' ')[1]);
}
// first pass, loading list of neurons and creating them
while (true)
{
string nextLine = reader.ReadLine();
// checking if list has ended
if (nextLine == null || nextLine == string.Empty)
{
break;
}
// skipping lines with weights
if (nextLine.StartsWith(" "))
{
continue;
}
// creating new neuron
Neuron newNeuron = null;
string[] neuronHeader = nextLine.Split(' ');
switch (neuronHeader[0])
{
case "Input": newNeuron = loadedNetwork.AddInputNeuron(string.Empty); break;
case "Hidden": newNeuron = loadedNetwork.AddHiddenNeuron(string.Empty); break;
case "Output": newNeuron = loadedNetwork.AddOutputNeuron(string.Empty); break;
}
newNeuron.Id = int.Parse(neuronHeader[1]);
newNeuron.Name = neuronHeader[2];
}
// reset position to the beginning of the file
reader.DiscardBufferedData();
reader.BaseStream.Seek(0, SeekOrigin.Begin);
// skipping network parameters and empty line in the beginning
while (true)
{
string nextLine = reader.ReadLine();
if (nextLine == string.Empty)
{
break;
}
}
// second pass, loading neurons
while (true)
{
// cheking if list has ended
string nextLine = reader.ReadLine();
if (nextLine == null || nextLine == string.Empty)
{
break;
}
// finding neuron
string[] neuronHeader = nextLine.Split(' ');
int neuronId = int.Parse(neuronHeader[1]);
Neuron neuron = loadedNetwork.GetNeuronById(neuronId);
// loading weights and bias
while (true)
{
string[] weightOrBiasLine = reader.ReadLine().Trim().Split(' ');
string label = weightOrBiasLine[0];
double value = double.Parse(weightOrBiasLine[1]);
if (label == "w")
{
int connection = int.Parse(weightOrBiasLine[2]);
Neuron anotherNeuron = loadedNetwork.GetNeuronById(connection);
Neuron.InputLink newInputLink = loadedNetwork.Connect(anotherNeuron, neuron);
newInputLink.Weight = value;
}
else if (label == "b")
{
neuron.Bias = value;
break; // line with bias is the last line of the neuron
}
}
}
}
return(loadedNetwork);
}
