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Common regularization methods in deep learning (Regularization) and detailed explanation of WeightDecay parameters in optimizers

2022-08-08 14:31:00 【And the princess】

Common regularization methods in deep learning (Regularization) and a detailed explanation of the WeightDecay parameter in the optimizer - Programmer SoughtWeight DecayL1 RegularizationL2 RegularizationElastic Network RegularizationRandom Dropout(dropout)Regularization MethodsUse of pytorchUsing Weight DecayAppendix What is RegularizationThe word "regularization" sounds very tall, but we can simply understand it as "constraint".Regularization is a constraint on parameters. In the process of training, especially for deep neural networks, there are many parameters in the network layer. If the value range of each parameter is also large, it will cause some training problems.Trouble (after training for a long time, it may enter a state of overfitting), so we explicitly control the model's class by constraining the parameters.https://blog.csdn.net/qq_41881259/article/details/106419489

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