Keras Tensoflow model optimization based on test runs possible?
You are the only one how could answer this question. Let me see if I can explain my answer. You have 2 factors to take in consideration. The complexity of your model and the diversity of your pictures and those go hand in hand. It is usually not required to train your model on all data because there is a certain degree of "repetition". That is basically the reason why Stochastic Gradient Descendent works.
You could try to find an optimal dataset size by doing the following.

Select a random number of images ( let's say, 500).
Pick a configuration for our model, epochs, etc
Train your model and record the accuracy
Increase the number of images by 10% and repeat step 1-4 a few times.
Plot the accuracy of your model.

If you notice that the accuracy does not increase, it could be because you either reached the "optimal dataset size" for your model or your model is not capable of learn anymore.
At this point, you could start reducing the complexity of your model, number of epochs, etc and again plot the accuracy. If that does not decreased , your model was capable of learning but there was nothing more to learn from your images.
Just an idea.
i'm kind of new to the Keras/Tensorflow field and am currently trying to learn by using existing tutorial models for keras and trying to modify them afterwards. I chose an image classification task as this is what i will need it for if i ever get far enough to be able to handle the whole stuff ;)
Situation: I gathered 20k indoor and outdoor pictures of apartements and houses to sort them into indoor/outdoor pictures. The model I use and modified step by step has an accuracy of 95.2% right now but i would like to try reaching an even higher accuracy.
Problem: On my PC, is takes about 24h to run 50 epochs with training and testing and a batch size of 128 with pictures of 256x256. This means, it takes endless to check if a modification to the model does result in an reasonable improvement of the result.
For example, lower batch size, smaller pictures or less epochs result in lower accuracy of the model. Reducing epochs has a smaller impact on the divergence between the test run and an full run, but in the end, it does not make a difference if the model needs 12 or 24 hours for training, if i would like to check, if a modification does have a positive effect.
My question: How do you check if changes to a model tend to result in a higher accuracy? Test runs with only a few pics? Reducing the epochs to a very low number? I tested some ways but every time a reduce the complexity in terms of pictures number, picture resolution, epochs etc., a "test run" does most of the time not indicate the final accuracy with a full run. E.g. test run performs worse while full run does better or vice versa.
Any hints? Would be much appreciated!