/// <summary>
/// Инициализация реальной схемы.
/// </summary>
/// <param name="filterMatrixSize">Размер матриц фильтра.</param>
/// <param name="poolingMatrixSize">Размер пуллинговых матриц.</param>
private Dictionary <int, List <Layer> > InitializeRealScheme(int filterMatrixSize, int poolingMatrixSize)
{
var realScheme = new Dictionary <int, List <Layer> >();
var virtualScheme = _topology.GetScheme();
var firstValue = _dataSet.GetDataSetForNumber().First().Value.First();
foreach (var pair in virtualScheme)
{
var virtualElements = pair.Value;
var realElements = new List <Layer>();
var index = 0;
foreach (var layerType in virtualElements)
{
Layer element = null;
switch (layerType)
{
case LayerType.Input:
element = new InputLayer(firstValue);
element.Initialize(NetworkModeType.Learning);
break;
case LayerType.Convolution:
var previousKey = pair.Key - 1;
var previousElements = realScheme[previousKey];
var previousType = virtualScheme[previousKey].First();
if (previousElements.Count != virtualElements.Count &&
previousType.Equals(LayerType.Input))
{
var previousElement = previousElements.FirstOrDefault() as InputLayer;
if (previousElement is null)
{
throw new Exception("Предыдущий слой оказался Null!");
}
var map = previousElement.GetData(LayerReturnType.Map);
element = new ConvolutionLayer(map, filterMatrixSize);
element.Initialize(NetworkModeType.Learning);
}
else
{
throw new NotImplementedException();
}
break;
case LayerType.Subsampling:
previousKey = pair.Key - 1;
previousElements = realScheme[previousKey];
previousType = virtualScheme[previousKey][index];
if (previousElements.Count == virtualElements.Count &&
previousType.Equals(LayerType.Convolution))
{
var previousElement = previousElements[index] as ConvolutionLayer;
if (previousElement is null)
{
throw new Exception("Предыдущий слой оказался Null!");
}
var map = previousElement.GetData(LayerReturnType.Map);
element = new SubsamplingLayer(map, poolingMatrixSize);
element.Initialize(NetworkModeType.Learning);
}
else
{
throw new NotImplementedException();
}
break;
case LayerType.Hidden:
previousKey = pair.Key - 1;
previousElements = realScheme[previousKey];
previousType = virtualScheme[previousKey][index];
if (previousElements.Count == virtualElements.Count &&
previousType.Equals(LayerType.Subsampling))
{
var previousElement = previousElements[index] as SubsamplingLayer;
if (previousElement is null)
{
throw new Exception("Предыдущий слой оказался Null!");
}
var neurons = previousElement.GetData(LayerReturnType.Neurons) as List <NeuronFromMap>;
element = new HiddenLayer(neurons);
element.Initialize(NetworkModeType.Learning);
}
else
{
throw new NotImplementedException();
}
break;
case LayerType.Output:
previousKey = pair.Key - 1;
previousElements = realScheme[previousKey];
previousType = virtualScheme[previousKey][index];
if (previousElements.Count > virtualElements.Count &&
previousType.Equals(LayerType.Hidden))
{
var neurons = new List <Neuron>();
foreach (var elementInLastLayer in previousElements)
{
var previousElement = elementInLastLayer as HiddenLayer;
if (previousElement is null)
{
throw new Exception("Предыдущий слой оказался Null!");
}
var data = previousElement.GetData(LayerReturnType.Neurons) as List <NeuronFromMap>;
neurons.AddRange(data);
}
var outputs = neurons.Select(neuron => neuron.Output).ToList();
element = new OutputLayer(outputs);
element.Initialize(NetworkModeType.Learning);
}
else
{
throw new NotImplementedException();
}
break;
default:
throw new Exception("Неизвестный тип слоя!");
}
realElements.Add(element);
++index;
}
realScheme.Add(pair.Key, realElements);
}
return(realScheme);
}
