'Why does my neural network perform better with more input neurons than features/variables?
Why does my neural network perform better with more input neurons than features/variables? Now if I use the exact input neurons to features/variables the neural network performs much worse. For example, I have 6 dimensions of data that are 200 in length (or 200 samples). Within that data there are groups of 6 data sets - is this why my NN with 36 neuron inputs performs better than 6 neuron inputs? The dimensions should suggest I just need a NN with 6 input neurons. The hidden layers for the 6 inputs are 12 and 6 and for the 36 inputs, are 72 and 36 respectively. It's been a long time since I last used NNs and so many thanks for answers or pointers on this?
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