private void buttonGenerate_Click(object sender, EventArgs e)
{
if (_data == null)
{
XtraMessageBox.Show("There is no data create model!", "Uh Oh!", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
var model = new PerceptronModel <Student>();
_classifier = model.Generate(_data);
XtraMessageBox.Show("Done!", "Success!", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
public void Run(int firstHiddenLayerSize, int secondHiddenLayerSize, int iterations, float learningRate, int batchSize)
{
var digitReader = new HandwrittenDigitReader();
var trainDigits = digitReader.Iterate(
MnistFiles.FindByType(MNISTFileType.TrainImages).FileName,
MnistFiles.FindByType(MNISTFileType.TrainLabels).FileName);
var testDigits = digitReader.Iterate(
MnistFiles.FindByType(MNISTFileType.TestImages).FileName,
MnistFiles.FindByType(MNISTFileType.TestLabels).FileName);
var trainingData = trainDigits.Select(im => im.ToDataPoint());
var testData = testDigits.Select(im => im.ToDataPoint());
var cancellationToken = new CancellationTokenSource();
_model = new PerceptronModel(
HandwrittenDigit.ImageSize * HandwrittenDigit.ImageSize,
firstHiddenLayerSize,
secondHiddenLayerSize,
10);
_classifier = new PerceptronClassifier(
_model,
learningRate,
batchSize);
var watch = Stopwatch.StartNew();
_classifier.IterationComplete += j =>
{
//Console.WriteLine($"Epoch {j}: {MNISTEvaulate(net, testData)} / {testData.Count}");
OnLog(
$"Iteration #{j} has completed: {EvaluateClassifier(_classifier, testData)} / {testData.Count()} [{watch.ElapsedMilliseconds} ms]");
watch = Stopwatch.StartNew();
if (j == iterations)
{
cancellationToken.Cancel();
}
EpochComplete?.Invoke(j);
};
_classifier.Train(trainingData, cancellationToken.Token);
}
public DemoRunner()
{
MnistFiles = new MNISTDatabase();
var defaultFile = new FileInfo("network.xml");
if (defaultFile.Exists)
{
var xml = defaultFile.OpenRead();
using (var textReader = new StreamReader(xml))
{
var serializer = new PerceptronModelSerializer();
_model = serializer.Deserialize(textReader);
}
}
else
{
_model = new PerceptronModel(30);
}
_classifier = new PerceptronClassifier(_model, 2, 50);
}
private Guid ProcessSave(CNN network, string name, NetworkContext context)
{
var cnnModel = new CnnModel
{
CnnModelId = Guid.NewGuid()
};
var layers = new List <CnnLayer>();
var cnnWeights = new List <CnnWeights>();
for (int i = 0; i < network.LayersCount; i++)
{
if (network.Layers[i] is ConvolutionalLayer)
{
var cnnLayer = network.Layers[i] as ConvolutionalLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
KernelsCount = cnnLayer.KernelsCount,
FeatureMapsCountIn = cnnLayer.KernelDepth,
LayerType = (byte)LayerType.CovolutionalLayer,
Model = cnnModel
};
var weights = new CnnWeights
{
CnnWeightsId = Guid.NewGuid(),
Layer = layer,
LayerId = layer.CnnLayerId
};
var w = string.Empty;
var builder = new StringBuilder(String.Empty);
for (int j = 0; j < cnnLayer.KernelsCount; ++j)
{
for (int k = 0; k < cnnLayer.KernelDepth; ++k)
{
for (int a = 0; a < cnnLayer.Kernels[j][k].Length; ++a)
{
for (int b = 0; b < cnnLayer.Kernels[j][k][a].Length; ++b)
{
builder.Append(cnnLayer.Kernels[j][k][a][b] + ";");
}
}
}
}
w = builder.ToString();
weights.Weights = w;
cnnWeights.Add(weights);
layer.Weights = weights;
layers.Add(layer);
}
else if (network.Layers[i] is PollingLayer)
{
var cnnLayer = network.Layers[i] as PollingLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
LayerType = (byte)LayerType.PoolingLayer,
Model = cnnModel
};
layers.Add(layer);
}
else
{
var cnnLayer = network.Layers[i] as ReLuLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
LayerType = (byte)LayerType.ReluLayer,
Model = cnnModel
};
layers.Add(layer);
}
}
var l = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
KernelHeight = network.FlattenLayer.KernelSize,
KernelWidth = network.FlattenLayer.KernelSize,
LayerType = (byte)LayerType.FlattenLayer,
Model = cnnModel
};
layers.Add(l);
cnnModel.Layers = layers;
var perceptronModel = new PerceptronModel
{
PerceptronModelId = Guid.NewGuid()
};
var percLayers = new List <PerceptronLayer>();
var percWeights = new List <PerceptronWeights>();
for (var i = 0; i < network.Perceptron.LayersCount; ++i)
{
var layer = network.Perceptron.Layers[i];
var perLayer = new PerceptronLayer
{
PerceptronLayerId = Guid.NewGuid(),
NeuronsCount = layer.NeuronsCount,
PositionIn = i,
Perceptron = perceptronModel
};
var weights = new PerceptronWeights
{
PerceptronWeightsId = perLayer.PerceptronLayerId,
Height = layer.WeightRowsCount,
Width = layer.WeightColumnsCount
};
string w = String.Empty;
var builder = new StringBuilder(String.Empty);
for (int a = 0; a < layer.Weights.Length; ++a)
{
for (int b = 0; b < layer.Weights[a].Length; ++b)
{
builder.Append(layer.Weights[a][b] + ";");
}
}
w = builder.ToString();
weights.Weights = w;
percWeights.Add(weights);
percLayers.Add(perLayer);
}
perceptronModel.Layers = percLayers;
//save
var networkModel = new NetworkModel
{
NetworkModelId = Guid.NewGuid(),
Perceptron = perceptronModel,
PerceptronId = perceptronModel.PerceptronModelId,
Cnn = cnnModel,
CnnId = cnnModel.CnnModelId,
Name = name
};
cnnModel.NetworkModel = networkModel;
cnnModel.NetworkModelId = networkModel.NetworkModelId;
perceptronModel.NetworkModel = networkModel;
perceptronModel.NetworkModelId = networkModel.NetworkModelId;
context.NetworkModels.Add(networkModel);
context.CnnLayers.AddRange(layers);
context.CnnWeightsSet.AddRange(cnnWeights);
context.CnnModels.Add(cnnModel);
context.PerceptronLayers.AddRange(percLayers);
context.PerceptronWeights.AddRange(percWeights);
context.PerceptronModels.Add(perceptronModel);
context.SaveChanges();
return(networkModel.NetworkModelId);
}
