public double[,,,] forward(double[,,,] input)
{
//获取样本的总数
int sampleCount = input.GetLength(0);
//获取单个样本的深度
int sampleSingleDepth = input.GetLength(1);
//获取特征图的行数（高）
int inputRow = input.GetLength(2);
//获取特征图的列数（宽）
int inputColumn = input.GetLength(3);
//创建最大值的
SingleMaxIndex = new int[sampleCount][][];
int row = (inputRow - PadRow) / Stride + 1;
int column = (inputColumn - PadColumn) / Stride + 1;
var result = new double[sampleCount, sampleSingleDepth, row, column];
for (int sampleIndex = 0; sampleIndex < sampleCount; sampleIndex++)
{
//初始化特征图最大索引对象
SingleMaxIndex[sampleIndex] = new int[sampleSingleDepth][];
//记录特征图的行和列信息
InputShape = new MatrixShape(inputRow, inputColumn);
for (int depth = 0; depth < sampleSingleDepth; depth++)
{
LMatrix pad = im2col(input.GetNextDimVal(sampleIndex, depth, inputRow, inputColumn), row, column, PadRow, PadColumn, Stride);
SingleMaxIndex[sampleIndex][depth] = pad.MaxIndex();
//LMatrix data = pad.Matrix.Select(m => m.Max()).ToArray();
LMatrix data = pad.Max(1);
result.SetDimVal(data.ReShape(row, column), sampleIndex, depth, row, column);
}
}
return(result);
}
public double Accuracy(LMatrix lable)
{
var y = Predict();
int len = y.GetLength(0);
LMatrix maxtrx = y;
var res = maxtrx.MaxIndex();
var lab = lable.MaxIndex();
int sum = 0;
for (int i = 0; i < len; i++)
{
sum += (res[i] == lab[i]) ? 1 : 0;
}
return(sum * 1.0d / len);
}