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));
}
public static T Train <T, LabelType>(FunctionStack <T> functionStack, NdArray <T> input, NdArray <LabelType> teach, LossFunction <T, LabelType> lossFunction, Optimizer <T> optimizer = null) where T : unmanaged, IComparable <T> where LabelType : unmanaged, IComparable <LabelType>
{
optimizer?.SetUp(functionStack);
//結果の誤差保存用
NdArray <T> result = functionStack.Forward(input)[0];
T loss = lossFunction.Evaluate(result, teach);
//Backwardのバッチを実行
functionStack.Backward(result);
//更新
optimizer?.Update();
return(loss);
}
//バッチで学習処理を行う
public static T Train <T, LabelType>(FunctionStack <T> functionStack, T[][] input, LabelType[][] teach, LossFunction <T, LabelType> lossFunction, Optimizer <T> optimizer = null) where T : unmanaged, IComparable <T> where LabelType : unmanaged, IComparable <LabelType>
{
return(Train(functionStack, NdArray.FromArrays(input), NdArray.FromArrays(teach), lossFunction, optimizer));
}
