public void TestDeserialize()
{
var path = "D:\\3.xml";
var layOutput = (LayerOutput)LayerOutput.Deserialize(path);
Assert.AreEqual(layOutput.Perceptrons[0].WeightArray.Length, 5);
}
/// <summary>
/// </summary>
/// <param name="nm">Режима работы сети (Train, Test, Demo).</param>
/// <param name="LayerInput">Входной слой.</param>
public Network(NetworkMode nm, LayerInput LayerInput)
{
int[] numbNeuronLayers = { 1800, 120, 84, 2 };
input_layer = LayerInput;
Console.WriteLine($"Слой свертки...");
first_convolution = new ConvolutionFirst();
Console.WriteLine($"Слой субдескритизации...");
first_subsamping = new SubsampingFirst();
Console.WriteLine($"Слой свертки...");
second_convolution = new ConvolutionSecond();
Console.WriteLine($"Слой субдескритизации...");
second_subsamping = new SubsampingSecond();
int numbLayerHiddens = 2;
hidden_layer = new LayerHidden[numbLayerHiddens];
for (int i = 0; i < numbLayerHiddens; i++)
{
Console.WriteLine($"Полносвязный слой {i}...");
hidden_layer[i] = new LayerHidden(numbNeuronLayers[i + 1], numbNeuronLayers[i],
NeuronType.Hidden, $"LayerHiddenConvolution{i}");
}
Console.WriteLine($"Полносвязный слой output...");
output_layer = new LayerOutput(numbNeuronLayers[numbNeuronLayers.Length - 1], numbNeuronLayers[numbNeuronLayers.Length - 2],
NeuronType.Output, "LayerOutputConvolution");
fact = new double[numbNeuronLayers[numbNeuronLayers.Length - 1]];
}
public void TestSerializer()
{
var path = "D:\\3.xml";
var outputLayer = new LayerOutput(new DataShape(5, 1), 1);
outputLayer.Serializer(path);
Assert.IsTrue(File.Exists(path));
}
/// <summary>
/// </summary>
/// <param name="nm">Режима работы сети (Train, Test, Demo).</param>
/// <param name="numbLayerHiddens">Количество слоев сети.</param>
/// <param name="numbNeuronLayers">Массив из количества нейронов в слоях.</param>
public Network(NetworkMode nm, int numbLayerHiddens, int[] numbNeuronLayers, LayerInput LayerInput)
{
input_layer = LayerInput;
hidden_layer = new LayerHidden[numbLayerHiddens];
for (int i = 0; i < numbLayerHiddens; i++)
{
Console.WriteLine($"Слой персептрона {i}...");
hidden_layer[i] = new LayerHidden(numbNeuronLayers[i + 1], numbNeuronLayers[i],
NeuronType.Hidden, $"LayerHidden{i}");
}
Console.WriteLine($"Слой персептрона output...");
output_layer = new LayerOutput(numbNeuronLayers[numbNeuronLayers.Length - 1], numbNeuronLayers[numbNeuronLayers.Length - 2],
NeuronType.Output, nameof(output_layer));
fact = new double[numbNeuronLayers[numbNeuronLayers.Length - 1]];
}
private void AddLayerOutput(int neurons)
{
var layer = new LayerOutput
{
Neurons = new List <Neuron>(),
};
for (var i = 1; i <= neurons; i++)
{
var neuron = new NeuronOutput
{
Connections1 = new List <Connection>(),
Connections2 = new List <Connection>(),
};
neuron.Bias = Math2.Range(-1f, 1);
layer.Neurons.Add(neuron);
}
this.Layers.Add(layer);
}
public void CreateLayNet(TrainParameters layNetParameter)
{
LayerNet = new List <Layer>();
var inputLayer = new LayerInput(InputDataShape)
{
LayerIndex = 0
};
LayerNet.Add(inputLayer);
layNetParameter.InterLayerStructs.ToList().ForEach(a =>
{
var dataShape = LayerNet.Last().ShapeOut;
switch (a.LayerType)
{
case LayerType.FullConnectLayer:
var fullConnectedLayer = new LayerFullConnected(dataShape, a.NeureCount)
{
LayerIndex = LayerNet.Count
};
LayerNet.Add(fullConnectedLayer);
break;
case LayerType.PoolingLayer:
var poolingLayer = new LayerPooling(dataShape)
{
LayerIndex = LayerNet.Count
};
LayerNet.Add(poolingLayer);
break;
}
});
var outputLayer = new LayerOutput(LayerNet.Last().ShapeOut, OutputDataShape)
{
LayerIndex = LayerNet.Count
};
LayerNet.Add(outputLayer);
}
public void TestInitial()
{
var outputLayer = new LayerOutput(new DataShape(5, 1), 1);
Assert.AreNotEqual(outputLayer, null);
}
