class_QPanda_Variational_AdaGradOptimizer.rst

pair: class; QPanda::Variational::AdaGradOptimizer

class QPanda::Variational::AdaGradOptimizer

cpp

#include <Optimizer.h>

class AdaGradOptimizer: public QPanda::Variational::Optimizer<doxid-class_q_panda_1_1_variational_1_1_optimizer> { public: // construction

AdaGradOptimizer<doxid-class_q_panda_1_1_variational_1_1_ada_grad_optimizer_1a8787f200f71b79d42d81cb8db846ddb4>(

var<doxid-class_q_panda_1_1_variational_1_1var> lost, double learning_rate = 0.01, double initial_accumulator_value = 0.0, double epsilon = 1e-10 );

// methods

virtual std::unordered_map<var<doxid-class_q_panda_1_1_variational_1_1var>, MatrixXd> compute_gradients<doxid-class_q_panda_1_1_variational_1_1_ada_grad_optimizer_1ac442e9a6624501638ff77242f2746c31>(std::unordered_set<var<doxid-class_q_panda_1_1_variational_1_1var>>& var_set); virtual std::unordered_set<var<doxid-class_q_panda_1_1_variational_1_1var>> get_variables<doxid-class_q_panda_1_1_variational_1_1_ada_grad_optimizer_1a3c79e0481e05831bdad3ba627b3a0bc3>(); virtual double get_loss<doxid-class_q_panda_1_1_variational_1_1_ada_grad_optimizer_1a69ed7cc6897cff1f5fc25449b43dca86>();

virtual bool run<doxid-class_q_panda_1_1_variational_1_1_ada_grad_optimizer_1a96b1a883e835bb47da379222e5f442b9>(

std::unordered_set<var<doxid-class_q_panda_1_1_variational_1_1var>>& leaves, size_t t = 0 );

static std::shared_ptr<Optimizer<doxid-class_q_panda_1_1_variational_1_1_optimizer>> minimize<doxid-class_q_panda_1_1_variational_1_1_ada_grad_optimizer_1a6ebdf9d2919db29749a4c9d91947e47d>(

var<doxid-class_q_panda_1_1_variational_1_1var>& lost, double learning_rate = 0.01, double initial_accumulator_value = 0.0, double epsilon = 1e-10 );

};

Inherited Members

cpp

public:

// methods

virtual std::unordered_set<var<doxid-class_q_panda_1_1_variational_1_1var>> get_variables<doxid-class_q_panda_1_1_variational_1_1_optimizer_1a2a86c7101b183404f8507119954b8142>() = 0; virtual std::unordered_map<var<doxid-class_q_panda_1_1_variational_1_1var>, MatrixXd> compute_gradients<doxid-class_q_panda_1_1_variational_1_1_optimizer_1a956b08145e8efa6beb20eb6d6c062c07>(std::unordered_set<var<doxid-class_q_panda_1_1_variational_1_1var>>& var_set) = 0; virtual double get_loss<doxid-class_q_panda_1_1_variational_1_1_optimizer_1a6717bbf6bfeda77780e08cd8cfef64a8>() = 0; virtual bool run<doxid-class_q_panda_1_1_variational_1_1_optimizer_1aa697607b501c4106c53c3fe9d0a8c3dc>(std::unordered_set<var<doxid-class_q_panda_1_1_variational_1_1var>>& leaves, size_t t = 0) = 0;