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Removed LOSS_FUNCTION_IS_WINNING_PERCENTAGE macro.

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nodchip
2020-09-09 00:08:56 +09:00
parent f3a158725d
commit d37eb63581
2 changed files with 1 additions and 44 deletions
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9
src/learn/learn.h
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@@ -9,11 +9,6 @@
// Select the objective function
// ----------------------
// The objective function is the sum of squares of the difference in winning percentage
// See learner.cpp for more information.
//#define LOSS_FUNCTION_IS_WINNING_PERCENTAGE
// Objective function is cross entropy
// See learner.cpp for more information.
// So-called ordinary "rag cloth squeezer"
@@ -126,9 +121,7 @@ typedef float LearnFloatType;
#define LOSS_FUNCTION_IS_ELMO_METHOD
#if defined(LOSS_FUNCTION_IS_WINNING_PERCENTAGE)
#define LOSS_FUNCTION "WINNING_PERCENTAGE"
#elif defined(LOSS_FUNCTION_IS_CROSS_ENTOROPY)
#if defined(LOSS_FUNCTION_IS_CROSS_ENTOROPY)
#define LOSS_FUNCTION "CROSS_ENTOROPY"
#elif defined(LOSS_FUNCTION_IS_CROSS_ENTOROPY_FOR_VALUE)
#define LOSS_FUNCTION "CROSS_ENTOROPY_FOR_VALUE"

36
src/learn/learner.cpp
View File

@@ -163,42 +163,6 @@ namespace Learner
return ((y2 - y1) / epsilon) / winning_probability_coefficient;
}
// When the objective function is the sum of squares of the difference in winning percentage
#if defined (LOSS_FUNCTION_IS_WINNING_PERCENTAGE)
// function to calculate the gradient
double calc_grad(Value deep, Value shallow, PackedSfenValue& psv)
{
// The square of the win rate difference minimizes it in the objective function.
// Objective function J = 1/2m Σ (win_rate(shallow)-win_rate(deep) )^2
// However, σ is a sigmoid function that converts the
// evaluation value into the difference in the winning percentage.
// m is the number of samples. shallow is the evaluation value
// for a shallow search (qsearch()). deep is the evaluation value for deep search.
// If W is the feature vector (parameter of the evaluation function)
// and Xi and Yi are teachers
// shallow = W*Xi // * is the Hadamard product, transposing W and meaning X
// f(Xi) = win_rate(W*Xi)
// If σ(i th deep) = Yi,
// J = m/2 Σ (f(Xi)-Yi )^2
// becomes a common expression.
// W is a vector, and if we write the jth element as Wj, from the chain rule
// ∂J/∂Wj = ∂J/∂f ・∂f/∂W ・∂W/∂Wj
// = 1/m Σ (f(Xi)-y) ・f'(Xi) ・ 1
// 1/m will be multiplied later, but the contents of Σ can
// be retained in the array as the value of the gradient.
// f'(Xi) = win_rate'(shallow) = sigmoid'(shallow/600) = dsigmoid(shallow / 600) / 600
// This /600 at the end is adjusted by the learning rate, so do not write it..
// Also, the coefficient of 1/m is unnecessary if you use the update
// formula that has the automatic gradient adjustment function like Adam and AdaGrad.
// Therefore, it is not necessary to save it in memory.
const double p = winning_percentage(deep, psv.gamePly);
const double q = winning_percentage(shallow, psv.gamePly);
return (q - p) * Math::dsigmoid(double(shallow) / 600.0);
}
#endif
#if defined (LOSS_FUNCTION_IS_CROSS_ENTOROPY)
double calc_grad(Value deep, Value shallow, const PackedSfenValue& psv)
{