# If adding 1 hidden layer to a neural network does not improve accuracy, can I exclude that adding >1 improves it?

Suppose I have trained a neural net to solve a classification problem with $m$ hidden layers, each having $n_i$ neurons: $$n_1,\dots,n_m$$ and let's also assume $$n_1>n_2>\dots>n_m ,$$ and that I keep the number of connections $N_c$ constant.

Now if I have found that networks with $m=M$ generally perform worse than a network with $m=M-1$ (i.e. I made a few attempts), does it make sense to try a network with $m=M+1$ or will it generally perform even worse?

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