Hi Claudia,
I have interpreted from your question that you are getting a very low error rate on your training samples but a high error rate on your testing samples, while the validation error rate remains the same.
This looks like a classic example of overfitting where the model performs extremely well on the training set but fails to perform on the testing data. You can use the following ways to remedy this:
- Regularization: You can use L2 regularization in your loss function to reduce the overfitting in your model. This promotes generalization in the model.
- Early stopping: Monitor the performance of the model on a validation set and stop training when performance begins to degrade, indicating overfitting. This can be specified in the training options in MATLAB.
- Data Augmentation : You can perform data augmentation on your training dataset to create new samples from the existing data samples by adding noise, applying temporal distortions, or using techniques like back-translation for text data. This causes the model to generalize well. You can find data augmentation options in MATLAB datastores.
- Reduce model complexity: Try reducing model complexity so that the overfitting is remedied.
- Weight initialization: Initialize weights using an initialization scheme like Xavier or He initialization.
- Hyperparameter optimization: Find better values of hyperparameters in your model by performing grid search or random search on the hyperparameters.
For more information about data augmentation and early stopping, refer to the below documentation:
- https://www.mathworks.com/help/deeplearning/ref/trainingoptions.html
- https://www.mathworks.com/help/deeplearning/ref/imagedataaugmenter.html
Hope this helps.
