private TestDataSet GetRandomData(int batchCount, Func <int> getIndexFunc)
{
TestDataSet result = new TestDataSet(new NdArray(Shape, batchCount), new NdArray(new[] { 1 }, batchCount));
for (int i = 0; i < batchCount; i++)
{
int index = getIndexFunc();
Real[] labeledData = Get(index);
Array.Copy(labeledData, 0, result.Data.Data, i * result.Data.Length, result.Data.Length);
result.Label.Data[i] = DataLabel[index];
}
return(result);
}
//全データを読み込む
public TestDataSet <T> GetAllDataSet()
{
int batchCount = Data.Length;
T[] data = new T[NdArray.ShapeToLength(Shape) * Data.Length];
for (int i = 0; i < batchCount; i++)
{
T[] labeledData = Get(i);
Array.Copy(labeledData, 0, data, i * labeledData.Length, labeledData.Length);
}
TestDataSet <T> result = new TestDataSet <T>(NdArray.Convert(data, Shape, batchCount), NdArray.Convert(DataLabel, new[] { 1 }, batchCount));
return(result);
}
private TestDataSet <T> GetRandomData(int batchCount, Func <int> getIndexFunc)
{
T[] data = new T[NdArray.ShapeToLength(Shape) * batchCount];
int[] label = new int[batchCount];
for (int i = 0; i < batchCount; i++)
{
int index = getIndexFunc();
T[] labeledData = Get(index);
Array.Copy(labeledData, 0, data, i * labeledData.Length, labeledData.Length);
label[i] = DataLabel[index];
}
TestDataSet <T> result = new TestDataSet <T>(NdArray.Convert(data, Shape, batchCount), NdArray.Convert(label, new[] { 1 }, batchCount));
return(result);
}
public static double Accuracy(FunctionStack functionStack, TestDataSet dataSet)
{
return(Accuracy(functionStack, dataSet.Data, dataSet.Label));
}
public static Real Train(FunctionStack functionStack, TestDataSet dataSet, LossFunction lossFunction, bool isUpdate = true)
{
return(Train(functionStack, dataSet.Data, dataSet.Label, lossFunction, isUpdate));
}
public static T Accuracy <T>(FunctionStack <T> functionStack, TestDataSet <T> dataSet, LossFunction <T, int> lossFunction, out T loss) where T : unmanaged, IComparable <T>
{
return(Accuracy(functionStack, dataSet.Data, dataSet.Label, lossFunction, out loss));
}
public static T Accuracy <T>(FunctionStack <T> functionStack, TestDataSet <T> dataSet) where T : unmanaged, IComparable <T>
{
return(Accuracy(functionStack, dataSet.Data, dataSet.Label));
}
public static T Train <T>(FunctionStack <T> functionStack, TestDataSet <T> dataSet, LossFunction <T, int> lossFunction, Optimizer <T> optimizer = null) where T : unmanaged, IComparable <T>
{
return(Train(functionStack, dataSet.Data, dataSet.Label, lossFunction, optimizer));
}
