I just started NN few months ago , now playing with data using Pytorch. I learnt how we use embedding for high cardinal data and reduce it to low dimensions. There is one thumb of role i saw that for reducing high dimensional categorical data in the form of embedding you use following formula
embedding_sizes = [(n_categories, min(50, (n_categories+1)//2)) for _,n_categories in embedded_cols.items()]
embedding_sizes
[(69, 35), (11, 6)]

After creating this embedding layer how do we know these embedding layers are appropriate for MLP ? Do we check score with different size of embedding layer or do we visualise this layer? If we visualise then what are the ways to visualise , in short how can i validate that my embedding layers are good in terms of its reduced size from 69 to 35 and 11 to 6?