static void Step6()
{
Console.WriteLine("STEP 6: Evaluate network...");
NetworkEvaluator evaluator = new NetworkEvaluator();
evaluator.Evaluate(DataFilesInfoGetter.NetworkFile, DataFilesInfoGetter.EncogAnalystFile, DataFilesInfoGetter.NormalizedEvaluateFile);
}
static void Main(string[] args)
{
NetworkConfiguration configuration = new NetworkConfiguration()
{
DetectionValue = 1.0,
Epochs = 100000,
LearningCoefficient = 0.00001,
Momentum = 0.9,
Name = "",
NoDetectionValue = 0.0,
Seed = 0,
WeightInitialisationMethod = RandomType.Linear,
WeightInitialisationSize = 1.0
};
NetworkEvaluator evaluator = new NetworkEvaluator();
evaluator.Evaluate(configuration);
//tests = new Queue<NetworkConfiguration>(NetworkConfiguration.Read());
//new Thread(TestConfiguration).Start();
//new Thread(TestConfiguration).Start();
//new Thread(TestConfiguration).Start();
//new Thread(TestConfiguration).Start();
Console.ReadLine();
}
static void TestConfiguration()
{
NetworkEvaluator evaluator = new NetworkEvaluator();
while (true)
{
NetworkConfiguration configuration;
lock (tests)
{
if (tests.Count == 0)
{
break;
}
configuration = tests.Dequeue();
}
Console.WriteLine("[{0}] Starting test of {1}", DateTime.Now, configuration.Name);
evaluator.Evaluate(configuration);
}
}
private void TestInitialAccuracy(NetworkEvaluator networkEvaluator, NativeSlice2D <float> tempResult)
{
float totalError = 0.0f;
int totalCorrect = 0;
for (int i = 0; i < _dataset.TestingSetSize; i++)
{
_dataset.GetTestCase(i, out var trainingInput, out var trainingResult);
networkEvaluator.Evaluate(trainingInput, tempResult, 1).Complete();
//var error = Math.Abs(tempResult[0] - trainingResult[0, 0]);
var error = CrossEntropyCost(tempResult[0, 0], trainingResult[0, 0]);
bool wasCorrect = error < 0.5f;
totalCorrect += wasCorrect ? 1 : 0;
totalError += error;
}
float averageError = totalError / (float)_dataset.TestingSetSize;
float accuracy = (float)totalCorrect / (float)_dataset.TestingSetSize;
//Forward test
Debug.Log($"Initial: Accuracy:{accuracy:P2} Average Error:{averageError:F4}");
}
private IEnumerator TrainingProcess()
{
var neuralNetworkConfig = new NetworkDescription()
{
Layers = new List <LayerParamaters>()
{
new LayerParamaters()
{
LayerType = ELayerType.Input,
NeuronCount = InputAttributeCount,
NeuronType = ENeruonType.Input
},
new LayerParamaters()
{
LayerType = ELayerType.Hidden,
NeuronCount = 64,
NeuronType = ENeruonType.Sigmoid
},
new LayerParamaters()
{
LayerType = ELayerType.Hidden,
NeuronCount = 16,
NeuronType = ENeruonType.Sigmoid
},
new LayerParamaters()
{
LayerType = ELayerType.Output,
NeuronCount = 1,
NeuronType = ENeruonType.Sigmoid
},
}
};
var neuralNetwork = new NeuralNetwork(neuralNetworkConfig);
neuralNetwork.InitNetworkWithRandomValues(-0.05f, 0.05f);
var networkEvaluator = new NetworkEvaluator(neuralNetwork);
var tempResult = new NativeArray2D <float>(1, 1);
//Test inital accuracy
TestInitialAccuracy(networkEvaluator, tempResult.Slice());
//Learn over a number of iterations
for (int epoch = 0; epoch < Epochs; epoch++)
{
//For each epoch, perform training
for (int tc = 0; tc < _dataset.TrainingSetSize; tc++)
{
_dataset.GetTrainingCase(tc, out var trainingInput, out var trainingResult);
//Evolve network
var jobHandle = networkEvaluator.GradientDescentBackpropigate(trainingInput, trainingResult, 1, out _);
jobHandle.Complete();
}
float totalError = 0.0f;
int totalCorrect = 0;
for (int i = 0; i < _dataset.TestingSetSize; i++)
{
_dataset.GetTestCase(i, out var trainingInput, out var trainingResult);
networkEvaluator.Evaluate(trainingInput, tempResult.Slice(), 1).Complete();
var error = Math.Abs(tempResult[0, 0] - trainingResult[0, 0]);
bool wasCorrect = error < 0.5f;
totalCorrect += wasCorrect ? 1 : 0;
totalError += error;
}
float averageError = totalError / (float)_dataset.TestingSetSize;
float accuracy = (float)totalCorrect / (float)_dataset.TestingSetSize;
//Forward test
Debug.Log($"Epoch {epoch}: Accuracy:{accuracy:P2} Average Error:{averageError:F4}");
yield return(null);
}
tempResult.BackingStore.Dispose();
}
static void Main(string[] args)
{
//Set the path of the file containing the data set
//string dataFilePath = @"C:\Users\kevin\Desktop\squaredtest.csv"; NutrioxDataset
string dataFilePath = @"C:\Users\Bruker\Desktop\NutrioxDataset.csv";
//string dataFilePath = @"C:\Users\Bruker\Desktop\-5to5-200Rows.csv";
//Create a new data set
DataSet.DataSet dataSet = new DataSet.DataSet(dataFilePath, true);
//Apply desired data preprocessing to the data set
dataSet.PreProcessDataSet(NormalizationType.MinMax, 2, EncodingType.None, null);
//Create a model hyperparameter layer structure
LayerStructure layerStructure = new LayerStructure()
{
numberOfInputNodes = 2, HiddenLayerList = new List <int> {
5, 5
}, numberOfOutputNodes = 1
};
//Create an instance of the desired optimalization strategy to use
var regularizationStrategyFactory = new RegularizationStrategyFactory();
StochasticGradientDescent SGD = new StochasticGradientDescent(new SigmoidFunction(), new IdentityFunction(), new MeanSquaredError(), RegularizationType.None, regularizationStrategyFactory);
//Create training hyperparameters
TrainingParameters trainingParams = new TrainingParameters()
{
epochs = 500, learningRate = 0.01, momentum = 0.01, RegularizationLambda = 0.00
};
//Create an instance of a neural network
//ArtificialNeuralNetwork ann = new ArtificialNeuralNetwork(layerStructure, trainingParams, dataSet, SGD, new GaussianDistribution());
//Or Load a Network from XML
XML xml = new XML();
ArtificialNeuralNetwork ann = xml.LoadNetwork(@"C:\Users\Bruker\Desktop\BestNet.xml", dataSet) as ArtificialNeuralNetwork;
//Apply the desired training/test data set split ratios.
ann.SplitDataSetIntoTrainAndTestSets(0.7);
//Initiate network training
//ann.TrainNetwork();
var crossValidationStrategyFactory = new CrossValidationStrategyFactory();
NetworkEvaluator evaluator = new NetworkEvaluator(ann);
CrossValidator crossValidator = new CrossValidator(ann, evaluator, crossValidationStrategyFactory);
//Cross-validate the fitted model
//crossValidator.KFold(10, 0.007);
//Evaluate the fitted model on the test set
evaluator.EvaluateNetwork(0.007);
//--Optional--//
//Serialize and save the fitted model
//XML xml = new XML();
//xml.SaveNetwork(dataFilePath, ann);
//Extract model information
//ann.SaveListOfErrors();
//ann.GetApproximatedFunction(ann.SavePath + "/Function.txt");
Console.ReadLine();
}
