How to address the issue of overfitting in PyTorch?

In PyTorch, you can address the problem of overfitting using the following methods:

1. Data augmentation refers to performing a series of random transformations on training data, such as rotation, translation, and scaling, in order to increase the diversity of the data and reduce the likelihood of overfitting.
2. Regularization: Adding a regularization term such as L1 or L2 to the loss function of a model can reduce its complexity and prevent overfitting.
3. During training, dropout randomly sets the output of some neurons to zero, reducing dependencies between neurons and preventing overfitting.
4. Early stopping: Monitoring the model’s performance on the validation set and stopping training when the validation set’s performance starts to decline can prevent overfitting.
5. Cross validation involves dividing the training set into multiple subsets and alternately using one subset as the validation set, in order to more accurately evaluate the performance of the model and avoid overfitting.
6. Streamlining the model structure, by reducing the number of parameters or layers, can decrease the complexity of the model and mitigate the risk of overfitting.
7. Utilizing pre-trained models: by using models pre-trained on large-scale datasets as initial parameters, it can enhance the model’s generalization ability and decrease the likelihood of overfitting.