Tensorflow Model Accuracy

My model which I have trained on a set of 29K images for 36 classes and validated on 7K images. The model has a training accuracy of 94.59% and validation accuracy of 95.72% It has

Solution 1:

1. 95% is rather good for 36 classes. If your validation accuracy is higher than training accuracy, you are underfitting. You can run some more epochs, until your training accuracy is a bit higher than validation accuracy.
2. Exactly, if training accuracy is much higher, you are overfitting.

Solution 2:

1. The training accuracy should always be higher than the testing accuracy/ validation accuracy. This is because your model has to be good on the data that it is provided to be able to predict unknown datas. However, it also happens sometimes and the reason could be a. The test test wasn't randomly selected or was randomly selected but turned out to be favourable one (a coincidence). b. Your Model is very generalized and in combination with the first problem.

2. Check the learning curve first, Your case is rare in which the training accuracy is smaller. Solution may be more data or More complex models or more epochs (Solution to underfitting)