Hello everyone! Today we are going to talk about a major trouble in deep learning - overfitting. This guy made us memorize the model very well, but he was like a wooden head when applying it. Don’t worry, I’ll teach you a few tricks to easily resolve the problem of “overfitting”.
1. Data enhancement
A common cause of overfitting is insufficient data and the training set is too small. At this time we can use data augmentation.
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Image data enhancement: Rotate, flip, crop and other operations on images to create more diverse data samples.
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Text data enhancement: Perform operations such as word replacement, deletion, and insertion on text to expand data samples.
2. Regularization
Regularization is a powerful weapon that can effectively suppress model overfitting.
- L1 and L2 regularization: Add a penalty term to the loss function to force the weight of the model to remain small to avoid overfitting caused by excessive weight.
3. Dropout
Hey, don’t let the model get too narcissistic! Discarding can break the model's narcissistic behavior.
- Random discarding: randomly discarding a part of neurons during training so that the model does not rely on specific neurons and increases robustness.
4. Early Stopping
When training the model, sometimes we will "stop it when it's good" to prevent it from over-learning.
- Monitor the verification loss: observe the performance of the model on the verification set, and stop training in time when the verification loss no longer decreases.
5. Data set partitioning
Partitioning the data set is also an important means to deal with overfitting.
- Training set, validation set and test set: divide the data set reasonably to prevent the model from peeking at the answers during the "interview".
6. Transfer learning
If you have too little data, no problem, transfer learning can help.
- Utilize pre-trained models: first train the model on other data sets, and then fine-tune it on your data set. Save time and achieve good results.
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Okay, now you should know how to deal with the "overfitting" problem in deep learning. Data augmentation, regularization, dropout, early stopping, dataset partitioning, and transfer learning are your weapons to make your model smarter and more flexible. Believe me, you can easily solve the "over-fitting" problem! Come on, you are the best!
