wehub-resource-sync
56 lines
1.6 KiB
Markdown
56 lines
1.6 KiB
Markdown
# 优化器使用
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## SGD with momentum
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使用示例
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```cpp
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// 新建SGD优化器
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std::shared_ptr<SGD> solver(new SGD);
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// 设置模型中需要优化的参数
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solver->append(model->parameters());
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// 设置momentum和weight decay
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solver->setMomentum(0.9f);
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solver->setWeightDecay(0.0005f);
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// 设置正则化方法，默认L2
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solver->setRegularizationMethod(RegularizationMethod::L2);
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// 设置学习率
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solver->setLearningRate(0.001);
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// 根据loss计算梯度，并更新参数
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solver->step(loss);
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```
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## ADAM
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使用示例
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```cpp
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// 新建ADAM优化器
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std::shared_ptr<SGD> solver(new ADAM);
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// 设置模型中需要优化的参数
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solver->append(model->parameters());
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// 设置ADAM的两个momentum，设置weight decay
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solver->setMomentum(0.9f);
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solver->setMomentum2(0.99f);
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solver->setWeightDecay(0.0005f);
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// 设置正则化方法，默认L2
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solver->setRegularizationMethod(RegularizationMethod::L2);
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// 设置学习率
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solver->setLearningRate(0.001);
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// 根据loss计算梯度，并更新参数
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solver->step(loss);
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```
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## Loss
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目前支持的Loss，也可自行设计
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```cpp
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VARP _CrossEntropy(Express::VARP predicts, Express::VARP oneHotTargets);
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VARP _KLDivergence(Express::VARP predicts, Express::VARP oneHotTargets);
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VARP _MSE(Express::VARP predicts, Express::VARP oneHotTargets);
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VARP _MAE(Express::VARP predicts, Express::VARP oneHotTargets);
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VARP _Hinge(Express::VARP predicts, Express::VARP oneHotTargets);
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VARP _DistillLoss(Express::VARP studentLogits, Express::VARP teacherLogits, Express::VARP oneHotTargets,
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const float temperature, const float alpha);
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``` |