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Make the autograd loss expression chain thread_local.

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This commit is contained in:
Tomasz Sobczyk
2020-11-30 15:14:26 +01:00
committed by nodchip
parent 8adf00ae6e
commit 891abf5511
1 changed files with 20 additions and 13 deletions
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33
src/learn/learn.cpp
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@@ -215,21 +215,28 @@ namespace Learner
auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
*/
const double epsilon = 1e-12;
constexpr 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_;
static thread_local auto q_ = sigmoid(VariableParameter<double, 0>{} * ConstantParameter<double, 4>{});
static thread_local auto p_ = sigmoid(ConstantParameter<double, 1>{} * ConstantParameter<double, 4>{});
static thread_local auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
static thread_local auto lambda_ = ConstantParameter<double, 3>{};
static thread_local auto teacher_entropy_ = -(p_ * log(p_ + epsilon) + (1.0 - p_) * log(1.0 - p_ + epsilon));
static thread_local auto outcome_entropy_ = -(t_ * log(t_ + epsilon) + (1.0 - t_) * log(1.0 - t_ + epsilon));
static thread_local auto teacher_loss_ = -(p_ * log(q_) + (1.0 - p_) * log(1.0 - q_));
static thread_local auto outcome_loss_ = -(t_ * log(q_) + (1.0 - t_) * log(1.0 - q_));
static thread_local auto result_ = lambda_ * teacher_loss_ + (1.0 - lambda_) * outcome_loss_;
static thread_local auto entropy_ = lambda_ * teacher_entropy_ + (1.0 - lambda_) * outcome_entropy_;
static thread_local auto loss_ = result_ - entropy_;
auto args = std::tuple(
(double)shallow,
(double)teacher_signal,
(double)result,
calculate_lambda(teacher_signal),
winning_probability_coefficient
);
auto args = std::tuple((double)shallow, (double)teacher_signal, (double)result, calculate_lambda(teacher_signal));
return loss_.eval(args).clamp_grad(max_grad);
}