Can we cross-validate the neuron no. and hidden layer no. of a feed-forward neural network? Confused about tons of relevant info on the web, so I appreciate if anyone of you can clarify for me. I will use the model to binary classify for your reference.
As for the problem, let's say in a feed-forward network with hidden layer no. to be decided, I want to know how many neurons I should put in (each) hidden layer(s), so

What are the most commonly referred metrics to decide the no. of the neurons (in each hidden layer)? Recall, accuracy, precision or...?
Can we use some kind of cross validations (e.g. Stratified K-Fold) to testify the performance of the network with different no. of hidden neurons and different no. of hidden layers in terms of the above metrics?

Thanks a lot in advance.
Yes. validation is the best way to choose model hyperparameters such as neurons number.
Validation with neural net is not substantial different from validation with other models.
The metric should be chosen according to the task the network performs. The validation method should be chosen according to the amount of data, training time, certainty level you want to achieve and compute and time resources you have.