/// <summary>
/// 增加全连接层,仅用于BP网络
/// </summary>
/// <param name="cnnFullLayer"></param>
public void AddCnnFullLayer(int inputCount, int outputCount, CnnNode.ActivationFunctionTypes activationFunctionType, bool standardization)
{
CnnFullLayer cnnFullLayer = new CnnFullLayer(inputCount,
outputCount, activationFunctionType, standardization);
CnnFullLayerList.Add(cnnFullLayer);
}
/// <summary>
/// 增加全连接层,仅用于BP网络
/// </summary>
/// <param name="cnnFullLayer"></param>
public void AddCnnFullLayer(int inputCount, int outputCount, CnnNode.ActivationFunctionTypes activationFunctionType, bool standardization)
{
foreach (Cnn cnn in cnnList)
{
cnn.AddCnnFullLayer(inputCount, outputCount, activationFunctionType, standardization);
}
}
/// <summary>
/// 创建卷积层
/// </summary>
/// <param name="convolutionKernelCount"></param>
/// <param name="inputWidth"></param>
/// <param name="inputHeight"></param>
/// <param name="receptiveFieldWidth"></param>
/// <param name="receptiveFieldHeight"></param>
/// <param name="offsetWidth"></param>
/// <param name="offsetHeight"></param>
/// <param name="activationFunctionType"></param>
public void CreateCnnKernel(int convolutionKernelCount, int inputWidth, int inputHeight,
int receptiveFieldWidth, int receptiveFieldHeight, int offsetWidth,
int offsetHeight, CnnNode.ActivationFunctionTypes activationFunctionType, int LastLayerCount, bool standardization, bool[,] layerLinks)
{
this.ConvolutionKernelCount = convolutionKernelCount;
CnnKernelList = new List <CnnKernel>();
for (int i = 0; i < ConvolutionKernelCount; i++)
{
int inputCount = 0;//单个卷积神经元输入数量
for (int j = 0; j < LastLayerCount; j++)
{
if (layerLinks == null)
{
inputCount = 1;
break;
}
if (layerLinks[i, j])
{
inputCount++;
}
}
CnnKernelList.Add(new CnnKernel(inputWidth, inputHeight,
receptiveFieldWidth, receptiveFieldHeight,
offsetWidth, offsetHeight, activationFunctionType, inputCount, ConvolutionKernelCount, standardization));
}
}
/// <summary>
/// 增加后续卷积层（最后一层一定为单个输出，代表输出中的其中一个值）
/// </summary>
/// <param name="cnnConvolutionLayer"></param>
public void AddCnnConvolutionLayer(int convolutionKernelCount,
int receptiveFieldWidth, int receptiveFieldHeight,
int offsetWidth, int offsetHeight, CnnNode.ActivationFunctionTypes activationFunctionType,
int poolingReceptiveFieldWidth, int poolingReceptiveFieldHeight, CnnPooling.PoolingTypes poolingType,
bool standardization, bool isFullLayerLinks)
{
foreach (Cnn cnn in cnnList)
{
cnn.AddCnnConvolutionLayer(convolutionKernelCount,
receptiveFieldWidth, receptiveFieldHeight,
offsetWidth, offsetHeight, activationFunctionType,
poolingReceptiveFieldWidth, poolingReceptiveFieldHeight, poolingType,
standardization, isFullLayerLinks);
}
}
/// <summary>
/// 增加首个卷积层
/// </summary>
/// <param name="cnnConvolutionLayer"></param>
public void AddCnnConvolutionLayer(int convolutionKernelCount,
int inputWidth, int inputHeight, int receptiveFieldWidth, int receptiveFieldHeight,
int offsetWidth, int offsetHeight, CnnNode.ActivationFunctionTypes activationFunctionType,
int poolingReceptiveFieldWidth, int poolingReceptiveFieldHeight, CnnPooling.PoolingTypes poolingType,
bool standardization)
{
CnnConvolutionLayer cnnConvolutionLayer = new CnnConvolutionLayer();
//创建卷积层
cnnConvolutionLayer.CreateCnnKernel(convolutionKernelCount, inputWidth, inputHeight, receptiveFieldWidth, receptiveFieldHeight, offsetWidth, offsetHeight, activationFunctionType, 1, standardization, null);
if (poolingType != 0)
{
//创建池化层
cnnConvolutionLayer.CreateCnnPooling(poolingReceptiveFieldWidth, poolingReceptiveFieldHeight, activationFunctionType, poolingType);
}
CnnConvolutionLayerList.Add(cnnConvolutionLayer);
}
/// <summary>
/// 增加全连接层，在卷积层后，要先创建完卷积层
/// </summary>
/// <param name="cnnFullLayer"></param>
public void AddCnnFullLayer(int outputCount, CnnNode.ActivationFunctionTypes activationFunctionType, bool standardization)
{
if (CnnFullLayerList.Count == 0)
{
//连接卷积层
var cnnConvolutionLayerLast = CnnConvolutionLayerList[CnnConvolutionLayerList.Count - 1];//最后的卷积层
CnnFullLayer cnnFullLayer = new CnnFullLayer(cnnConvolutionLayerLast.ConvolutionKernelCount
* cnnConvolutionLayerLast.OutputWidth
* cnnConvolutionLayerLast.OutputHeight,
outputCount, activationFunctionType, standardization);
CnnFullLayerList.Add(cnnFullLayer);
}
else
{
var cnnFullLayerLast = CnnFullLayerList[CnnFullLayerList.Count - 1];//最后的卷积层
CnnFullLayer cnnFullLayer = new CnnFullLayer(cnnFullLayerLast.OutputCount,
outputCount, activationFunctionType, standardization);
CnnFullLayerList.Add(cnnFullLayer);
}
}
/// <summary>
/// 增加后续卷积层
/// </summary>
/// <param name="cnnConvolutionLayer"></param>
public void AddCnnConvolutionLayer(int convolutionKernelCount,
int receptiveFieldWidth, int receptiveFieldHeight,
int offsetWidth, int offsetHeight, CnnNode.ActivationFunctionTypes activationFunctionType,
int poolingReceptiveFieldWidth, int poolingReceptiveFieldHeight, CnnPooling.PoolingTypes poolingType,
bool standardization, bool isFullLayerLinks)
{
var cnnConvolutionLayerLast = CnnConvolutionLayerList[CnnConvolutionLayerList.Count - 1];//最后的卷积层
bool[,] layerLinks = new bool[convolutionKernelCount, cnnConvolutionLayerLast.ConvolutionKernelCount];
if (!isFullLayerLinks)
{
//随机创建卷积层间连接
Random random = new Random();
for (int i = 0; i < convolutionKernelCount; i++)
{
int linkCount = 0; //每层链接数
while (linkCount < 2) //确保最低链接数
{
for (int j = 0; j < cnnConvolutionLayerLast.ConvolutionKernelCount; j++)
{
if (random.NextDouble() < 0.5)
{
layerLinks[i, j] = false;
}
else
{
layerLinks[i, j] = true;
linkCount++;
}
}
}
//排除相同链接
for (int j = 0; j < i; j++)
{
linkCount = 0;//相同链接数
for (int k = 0; k < cnnConvolutionLayerLast.ConvolutionKernelCount; k++)
{
if (layerLinks[i, k] == layerLinks[j, k])
{
linkCount++;
}
}
if (linkCount == cnnConvolutionLayerLast.ConvolutionKernelCount)
{
i--;
break;
}
}
}
}
else
{
//全链接
for (int i = 0; i < convolutionKernelCount; i++)
{
for (int j = 0; j < cnnConvolutionLayerLast.ConvolutionKernelCount; j++)
{
layerLinks[i, j] = true;
}
}
}
convolutionLinkList.Add(layerLinks);
CnnConvolutionLayer cnnConvolutionLayer = new CnnConvolutionLayer();
//创建卷积层
cnnConvolutionLayer.CreateCnnKernel(convolutionKernelCount, cnnConvolutionLayerLast.OutputWidth, cnnConvolutionLayerLast.OutputHeight,
receptiveFieldWidth, receptiveFieldHeight, offsetWidth, offsetHeight, activationFunctionType, cnnConvolutionLayerLast.ConvolutionKernelCount, standardization, layerLinks);
if (poolingType != 0)
{
//创建池化层
cnnConvolutionLayer.CreateCnnPooling(poolingReceptiveFieldWidth, poolingReceptiveFieldHeight, activationFunctionType, poolingType);
}
CnnConvolutionLayerList.Add(cnnConvolutionLayer);
}
/// <summary>
/// 创建池化层
/// </summary>
/// <param name="receptiveFieldWidth"></param>
/// <param name="receptiveFieldHeight"></param>
/// <param name="activationFunctionType"></param>
public void CreateCnnPooling(int receptiveFieldWidth, int receptiveFieldHeight, CnnNode.ActivationFunctionTypes activationFunctionType, CnnPooling.PoolingTypes poolingType)
{
if (CnnKernelList == null || CnnKernelList.Count == 0)
{
throw new Exception("需先创建卷积层");
}
CnnPoolingList = new List <CnnPooling>();
for (int i = 0; i < ConvolutionKernelCount; i++)
{
CnnPoolingList.Add(new CnnPooling(CnnKernelList[0].ConvolutionKernelWidth, CnnKernelList[0].ConvolutionKernelHeight,
receptiveFieldWidth, receptiveFieldHeight, activationFunctionType, poolingType, CnnKernelList[0].InputCount, CnnKernelList[0].OutputCount));
}
}
