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Model/network design has multiple guidelines, a basic one is: The solving capacity of the network should be larger than the possibility space of the problem to be solved. Solving capacity (learning capacity) of a network (dense usually) can be calculated as the product of number of neurons in all layers, for example: Input shape: 10 values Network shape: [...


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The most obvious way more classes increase the network size it the output layer, but I don't believe there is a rule of thumb for the size of the entire network. As I understand it, there is no clear answer how big a network needs to be to achieve a certain performance with regard to the number of layers compared to the number of classes. This is a very ...


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