Sorry about that, let me try to explain in simpler terms:

It sounds like your model is overfitting to the training data. This means that your model performs really well on the training data (hence the high training accuracy), but does not generalize well to new data (hence the lower test accuracy).

Some potential reasons for overfitting are:

- Your model is too complex for the amount of data you have. Try simplifying your model architecture (fewer layers or neurons).
- You are training for too many epochs. This allows your model to memorize patterns in the training data that don't appear in the test data. Try training for fewer epochs.
- You are using too much regularization. Regularization helps prevent overfitting, but too much can hurt performance. Try reducing the regularization strength.

To evaluate model performance, it's important to check both training and test accuracy. Training accuracy alone is not sufficient, since that just shows how well your model fits the training data. The test accuracy is a better measure of how well your model will perform on new data.

I hope this helps! Let me know if you have any other questions.
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