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Cross entropy loss.

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This commit is contained in:
Tomasz Sobczyk
2020-11-29 17:08:22 +01:00
committed by nodchip
parent 539bd2d1c8
commit aa55692b97
2 changed files with 51 additions and 2 deletions
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36
src/learn/autograd.h
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@@ -282,6 +282,38 @@ namespace Learner::Autograd::UnivariateStatic
return Product(Constant(lhs), std::move(rhs));
}
template <typename ArgT, typename T = typename ArgT::ValueType>
struct Negation : Evaluable<Negation<ArgT, T>>
{
using ValueType = T;
explicit Negation(ArgT x) :
m_x(std::move(x))
{
}
template <typename... ArgsTs>
T value(const std::tuple<ArgsTs...>& args) const
{
return -m_x.value(args);
}
template <typename... ArgsTs>
T grad(const std::tuple<ArgsTs...>& args) const
{
return -m_x.grad(args);
}
private:
ArgT m_x;
};
template <typename ArgT, typename T = typename ArgT::ValueType>
auto operator-(ArgT x)
{
return Negation(std::move(x));
}
template <typename ArgT, typename T = typename ArgT::ValueType>
struct Sigmoid : Evaluable<Sigmoid<ArgT, T>>
{
@@ -318,7 +350,7 @@ namespace Learner::Autograd::UnivariateStatic
}
};
template <typename ArgT>
template <typename ArgT, typename T = typename ArgT::ValueType>
auto sigmoid(ArgT x)
{
return Sigmoid(std::move(x));
@@ -394,7 +426,7 @@ namespace Learner::Autograd::UnivariateStatic
}
};
template <typename ArgT>
template <typename ArgT, typename T = typename ArgT::ValueType>
auto log(ArgT x)
{
return Log(std::move(x));

17
src/learn/learn.cpp
View File

@@ -200,11 +200,14 @@ namespace Learner
static ValueWithGrad<double> get_loss(Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
/*
auto q_ = sigmoid(VariableParameter<double, 0>{} * winning_probability_coefficient);
auto p_ = sigmoid(ConstantParameter<double, 1>{} * winning_probability_coefficient);
auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
auto lambda_ = ConstantParameter<double, 3>{};
auto loss_ = pow(lambda_ * (q_ - p_) + (1.0 - lambda_) * (q_ - t_), 2.0);
*/
/*
auto q_ = VariableParameter<double, 0>{};
@@ -212,6 +215,20 @@ namespace Learner
auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
*/
const double epsilon = 1e-12;
auto q_ = sigmoid(VariableParameter<double, 0>{} * winning_probability_coefficient);
auto p_ = sigmoid(ConstantParameter<double, 1>{} * winning_probability_coefficient);
auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
auto lambda_ = ConstantParameter<double, 3>{};
auto teacher_entropy_ = -(p_ * log(p_ + epsilon) + (1.0 - p_) * log(1.0 - p_ + epsilon));
auto outcome_entropy_ = -(t_ * log(t_ + epsilon) + (1.0 - t_) * log(1.0 - t_ + epsilon));
auto teacher_loss_ = -(p_ * log(q_) + (1.0 - p_) * log(1.0 - q_));
auto outcome_loss_ = -(t_ * log(q_) + (1.0 - t_) * log(1.0 - q_));
auto result_ = lambda_ * teacher_loss_ + (1.0 - lambda_) * outcome_loss_;
auto entropy_ = lambda_ * teacher_entropy_ + (1.0 - lambda_) * outcome_entropy_;
auto loss_ = result_ - entropy_;
auto args = std::tuple((double)shallow, (double)teacher_signal, (double)result, calculate_lambda(teacher_signal));
return loss_.eval(args);
}