If I'm performing a text classification task using a model built in Keras, and, for example, I am attempting to predict the appropriate tag for a given Stack Overflow question:
How do I subtract 1 from an integer?
And the ground-truth tag for this question is:
But my model is predicting:
If I were to retrain my model, but this time add the above question and tag in both the training and testing data, would the model be guaranteed to predict the correct tag for this question in the test data?
I suppose the tl;dr is: Are neural networks deterministic if they encounter identical data during training and testing?
I'm aware it's not a good idea to use the same data in both training and testing, but I'm interested from a hypothetical perspective, and for gaining more insight into how neural networks actually learn. My intuition for this question is "no", but I'd really be interested in being pointed to some relevant literature that expands/explains that intuition.