A comprehensive introduction to hyperparameters and their meaning

Hyperparameters are tuning parameters in machine learning algorithms, used to improve algorithm performance and training process. They are set before training, and the weights and biases are optimized through training. By adjusting the hyperparameters, the accuracy and generalization ability of the model can be improved.

How to set hyperparameters

When initially setting hyperparameters, you can refer to hyperparameter values ​​used in other similar machine learning problems, or Find the optimal hyperparameters through repeated training.

What are the hyperparameters

Hyperparameters related to the network structure

• Dropout: Dropout is a regularization technique used to prevent overfitting and improve accuracy.
• Network weight initialization: It is useful to use different weight initialization schemes depending on the activation function used on the neural network layer. In most cases, use a uniform distribution.
• Activation function: The activation function is used to introduce nonlinearity into the algorithm model. This enables deep learning algorithms to predict boundaries non-linearly.

Hyperparameters related to training algorithms

• Learning rate: The learning rate defines how quickly the network updates parameters. When the learning rate is low, the algorithm learning process will slow down, but it will converge smoothly; a higher learning rate will speed up the learning speed, but it is not conducive to convergence.
• epoch: The number of times the entire training data is presented to the network during training.
• Batch size: refers to the number of subsamples provided to the network after a parameter update occurs.
• Momentum: Helps avoid oscillations, typically use a momentum between 0.5 and 0.9.

The difference between hyperparameters and parameters

Hyperparameters, also called model hyperparameters, are outside the model and cannot be determined from the data estimate its value.

Parameters, also called model parameters, are configuration variables inside the model. Its value can be estimated from the data. Models require parameters to make predictions.

Parameters are usually learned from data and are not set manually by developers; hyperparameters are usually set manually by developers.

Hyperparameter tuning

Hyperparameter tuning is to find the optimal combination of hyperparameters. Hyperparameters essentially control the machine learning model. The overall behavior of the algorithm, so finding the optimal values ​​of the hyperparameters is crucial for the algorithm model. If hyperparameter tuning fails, the model will fail to converge and effectively minimize the loss function. This will cause the model results to no longer be accurate.

Common hyperparameter tuning methods include grid search, random search, and Bayesian optimization.

Grid search is the most basic hyperparameter tuning method, which will traverse all possible hyperparameter combinations.

Random search is to randomly sample within a preset range to find a better combination of hyperparameters.

Bayesian optimization is a sequence model-based optimization (SMBO) algorithm that uses previous hyperparameter values ​​to improve the next hyperparameter. This method iterates until the best hyperparameter is found. parameter.

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