Machine Learning: Sudoku Benchmark - Batch size

Context

In the process of machine learning, the model is fed with a dataset for training. While the entire dataset is used for training and/or validation, it is previously splitted into several chunk called batchs. There isn't a simple and optimized for all model, it mosty depends of its design and the underlying hardware.

The hyperparameter batch size has two main implicatons:

A large chunk allows faster computations, as data transfer are less involved
An adpated size improve the quality of the learning

Observaton: What is the optimal batch size ?

We iterate over different batch sizes and we find the best one by crossing train speed, loss and inference score

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Assertions

Loss and inference score are inversely correlated. A loss greater than 0.35 reduces the performance
The train speed doesn't increase after 1024.
The loss is too high after 1024