Keras deep learning why validation accuracy stuck in a value every time?
Given just this information it is hard to tell what might be the underlying problem. In general, the machine learning engineer is always working with a direct trade-off between overfitting and model complexity. If the model isn't complex enough, it may not be powerful enough to capture all of the useful information necessary to solve a problem. However, if our model is very complex (especially if we have a limited amount of data at our disposal), we run the risk of overfitting. Deep learning takes the approach of solving very complex problems with complex models and taking additional countermeasures to prevent overfitting.
Three of the most common ways to do that are

Regularization
Dropout
Data augmentation

If your model is not complex enough:

Make it bigger (easy)
Make it smarter (hard)
I am trying to train InceptionV3 network with my custom dataset (36 classes 130 samples per each). And some parameters fro my network: