HCha/Stockfish
1
0
Fork 0
mirror of https://github.com/HChaZZY/Stockfish.git synced 2025-12-25 03:26:24 +08:00
Code Issues Packages Projects Releases Wiki Activity

Removed LOSS_FUNCTION_IS_CROSS_ENTOROPY macro.

Browse Source
This commit is contained in:
nodchip
2020-09-09 00:10:04 +09:00
parent d37eb63581
commit f52fbf8006
2 changed files with 1 additions and 37 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
src/learn/learn.h
View File

@@ -9,11 +9,6 @@
// Select the objective function
// ----------------------
// Objective function is cross entropy
// See learner.cpp for more information.
// So-called ordinary "rag cloth squeezer"
//#define LOSS_FUNCTION_IS_CROSS_ENTOROPY
// A version in which the objective function is cross entropy, but the win rate function is not passed
// #define LOSS_FUNCTION_IS_CROSS_ENTOROPY_FOR_VALUE
@@ -121,9 +116,7 @@ typedef float LearnFloatType;
#define LOSS_FUNCTION_IS_ELMO_METHOD
#if defined(LOSS_FUNCTION_IS_CROSS_ENTOROPY)
#define LOSS_FUNCTION "CROSS_ENTOROPY"
#elif defined(LOSS_FUNCTION_IS_CROSS_ENTOROPY_FOR_VALUE)
#if defined(LOSS_FUNCTION_IS_CROSS_ENTOROPY_FOR_VALUE)
#define LOSS_FUNCTION "CROSS_ENTOROPY_FOR_VALUE"
#elif defined(LOSS_FUNCTION_IS_ELMO_METHOD)
#define LOSS_FUNCTION "ELMO_METHOD(WCSC27)"

29
src/learn/learner.cpp
View File

@@ -163,35 +163,6 @@ namespace Learner
return ((y2 - y1) / epsilon) / winning_probability_coefficient;
}
#if defined (LOSS_FUNCTION_IS_CROSS_ENTOROPY)
double calc_grad(Value deep, Value shallow, const PackedSfenValue& psv)
{
// Objective function with cross entropy
// For the concept and nature of cross entropy,
// http://nnadl-ja.github.io/nnadl_site_ja/chap3.html#the_cross-entropy_cost_function
// http://postd.cc/visual-information-theory-3/
// Refer to etc.
// Objective function design)
// We want to make the distribution of p closer to the distribution of q
// → Think of it as the problem of minimizing the cross entropy
// between the probability distributions of p and q.
// J = H(p,q) =-Σ p(x) log(q(x)) = -p log q-(1-p) log(1-q)
// x
// p is a constant and q is a Wi function (q = σ(W・Xi) ).
// ∂J/∂Wi = -p・q'/q-(1-p)(1-q)'/(1-q)
// = ...
// = q-p.
const double p = winning_percentage(deep, psv.gamePly);
const double q = winning_percentage(shallow, psv.gamePly);
return q - p;
}
#endif
#if defined ( LOSS_FUNCTION_IS_CROSS_ENTOROPY_FOR_VALUE )
double calc_grad(Value deep, Value shallow, const PackedSfenValue& psv)
{