There are many hyperparameters that need to be set in deep learning, and they can have a significant impact on the performance of the model and the training process. Here are some common hyperparameters and what they do:
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Learning Rate: Controls the step size of parameter updates. A smaller learning rate can make the model converge more stably, but may require more training time; a larger learning rate can speed up the convergence, but may cause instability or miss the optimal solution.
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Batch Size: The number of samples input to the model in each iteration. A larger batch size can improve training efficiency, but it may also cause the model to fall into a local minimum or miss the optimal solution; a smaller batch size can help the model generalize better, but may increase training time.
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Number of iterations (Epochs): The number of times the training data set is completely traversed. More iterations can make the model learn more fully, but if too many, it may lead to overfitting.
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Regularization parameter (Regularization): Used to control the complexity of the model. Regularization helps reduce overfitting by introducing a penalty on the complexity of the model. Common regularization methods include L1 regularization, L2 regularization, etc.
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Network structure-related hyperparameters: such as the number of layers, the number of neurons in each layer, the selection of activation functions, etc. These hyperparameters directly affect the expressive power and complexity of the model.
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Optimizer parameters: including momentum, weight decay, etc. These parameters affect how and how fast the parameters are updated, thereby affecting the training process of the model.
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Dropout parameter: used to control the proportion of random inactivation (dropout). Dropout is a regularization technique that helps reduce overfitting.
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Kernel size, stride size and padding method in convolutional neural network (CNN).
The above are just some common hyperparameters in deep learning, which may be adjusted according to specific problems and models in practical applications. Tuning hyperparameters requires repeated experimentation and evaluation to find the best combination to improve model performance.