 Solution

The usual Akaike information criterion correction assumes a regular maximum-likelihood fit with a meaningful fixed parameter dimension. Here stochastic optimization stopped after five epochs need not attain the maximum likelihood estimator, and neural-network symmetries, inactive units, and heavy overparameterization make the raw count $2502$ a poor effective dimension. Either failure invalidates a direct AIC comparison with an ordinary logistic regression.