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Removed ADA_GRAD_UPDATE macro.

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nodchip
2020-09-09 00:01:53 +09:00
parent 05d26499b4
commit 0271d70775
2 changed files with 2 additions and 62 deletions
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10
src/learn/learn.h
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@@ -9,9 +9,6 @@
// update formula
// ----------------------
// Ada Grad. Recommended because it is stable.
// #define ADA_GRAD_UPDATE
// SGD looking only at the sign of the gradient. It requires less memory, but the accuracy is...
// #define SGD_UPDATE
@@ -136,13 +133,8 @@ typedef float LearnFloatType;
// ----------------------
#define LOSS_FUNCTION_IS_ELMO_METHOD
#define ADA_GRAD_UPDATE
// Character string according to update formula. (Output for debugging.)
// Implemented various update expressions, but concluded that AdaGrad is the best in terms of speed and memory.
#if defined(ADA_GRAD_UPDATE)
#define LEARN_UPDATE "AdaGrad"
#elif defined(SGD_UPDATE)
#if defined(SGD_UPDATE)
#define LEARN_UPDATE "SGD"
#endif

54
src/learn/learning_tools.h
View File

@@ -76,59 +76,7 @@ namespace EvalLearningTools
template <typename T> void updateFV(T& v) { updateFV(v, 1.0); }
#if defined (ADA_GRAD_UPDATE)
// Since the maximum value that can be accurately calculated with float is INT16_MAX*256-1
// Keep the small value as a marker.
const LearnFloatType V0_NOT_INIT = (INT16_MAX * 128);
// What holds v internally. The previous implementation kept a fixed decimal with only a fractional part to save memory,
// Since it is doubtful in accuracy and the visibility is bad, it was abolished.
LearnFloatType v0 = LearnFloatType(V0_NOT_INIT);
// AdaGrad g2
LearnFloatType g2 = LearnFloatType(0);
// update with AdaGrad
// When executing this function, the value of g and the member do not change
// Guaranteed by the caller. It does not have to be an atomic operation.
// k is a coefficient for eta. 1.0 is usually sufficient. If you want to lower eta for your turn item, set this to 1/8.0 etc.
template <typename T>
void updateFV(T& v,double k)
{
// AdaGrad update formula
// Gradient vector is g, vector to be updated is v, η(eta) is a constant,
// g2 = g2 + g^2
// v = v - ηg/sqrt(g2)
constexpr double epsilon = 0.000001;
if (g == LearnFloatType(0))
return;
g2 += g * g;
// If v0 is V0_NOT_INIT, it means that the value is not initialized with the value of KK/KKP/KPP array,
// In this case, read the value of v from the one passed in the argument.
double V = (v0 == V0_NOT_INIT) ? v : v0;
V -= k * eta * (double)g / sqrt((double)g2 + epsilon);
// Limit the value of V to be within the range of types.
// By the way, windows.h defines the min and max macros, so to avoid it,
// Here, it is enclosed in parentheses so that it is not treated as a function-like macro.
V = (std::min)((double)(std::numeric_limits<T>::max)() , V);
V = (std::max)((double)(std::numeric_limits<T>::min)() , V);
v0 = (LearnFloatType)V;
v = (T)round(V);
// Clear g because one update of mini-batch for this element is over
// g[i] = 0;
// → There is a problem of dimension reduction, so this will be done by the caller.
}
#elif defined(SGD_UPDATE)
#if defined(SGD_UPDATE)
// See only the sign of the gradient Update with SGD
// When executing this function, the value of g and the member do not change